Snow Country GX Platform and Program, Road Management Methods

Abstract

In snowy and cold regions, rapid and planned snow removal is essential for maintaining urban functions and ensuring safe and smooth road traffic during the winter. Traditionally, snow removal has been largely reliant on individual efforts, resulting in fragmented understanding of road conditions, traffic conditions, and weather conditions. This has led to challenges such as uneven distribution of information, inconsistent decision-making, inefficient work, and uncertainty regarding cost-effectiveness, sustainability, and environmental considerations. [Solution] The present invention provides a snow country GX platform that derives evaluation information for each road or management unit and can associate KPIs, achievement indicators, and GX indicators with respect to improving the efficiency of snow removal work, enhancing the automation or autonomy of snow removal work, maintaining sidewalks or living spaces through collaborative snow removal activities, optimizing sustainable urban structures, mobility, or urban development, promoting the utilization of snow as a resource through a carbon cycle model, establishing a GX economic cycle or generating financial resources, and strengthening regional resilience through the linkage of infrastructure maintenance and disaster resilience.

Description

Technical Field

[0001] The present invention relates to a snow country GX platform for integrally managing the planning, implementation, evaluation, and improvement of snow removal and snow clearance on snow-covered roads. More specifically, it relates to a basic technology for integrally operating by integrating and analyzing a plurality of external and internal information to determine the necessity and priority of snow removal and snow clearance, formulating and updating the snow removal and snow clearance implementation plans for each road or management unit, managing work execution, visualizing results, and continuously improving. The present invention is also applicable to a program for realizing the snow country GX platform by a computer and a road management method. The snow country GX platform encompasses conventional snow removal judgment support systems and snow removal operation platforms, and also includes upper-level functions such as management of operation-level KPIs and GX indicators, financial, accounting, and revenue distribution management, environmental value evaluation, calculation of CO2 reduction amounts, carbon credit and GX economic cycle management, contract and incentive matching, audit and accountability management, cooperation among related organizations, and information disclosure.

Background Art

[0002] In snowy and cold regions, rapid and planned snow removal and snow clearance are essential for maintaining urban functions and ensuring the safety and smoothness of road traffic in winter. Conventionally, the operation has mainly relied on personal inspections by staff and reports from residents, making it easy for the grasp of road conditions, traffic conditions, weather conditions, and road facility conditions to be fragmented, and there have been problems such as information dispersion, variation in judgment, inefficiency of work, cost-effectiveness, sustainability, and lack of consideration for the environment. In this specification, in accordance with the actual situation of operation, the operation status of public transportation, the compatibility with emergency transportation and road opening, the operation status of snow disposal sites, environmental loads such as CO2 emissions, the status of equipment and personnel deployment, the execution status of budgets and accounting, etc. can also be targets for evaluating KPIs or GX indicators related to snow removal and snow clearance operations. A mechanism for objectively evaluating and optimizing these in terms of time and space is required.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

[0004] In recent years, technologies have been proposed that use AI to analyze patrol vehicle camera images to understand snow accumulation and snow dam conditions (e.g., Patent Document 1), and technologies that use vibration sensing of optical fibers laid along routes to estimate snow accumulation, freezing conditions, and traffic conditions (e.g., Patent Document 2). Furthermore, interactive information provision systems that use AI to analyze the content of SNS and chat messages and present relevant information (e.g., Patent Document 3) are also known. However, these are all limited to specific information sources and individual functions, and do not necessarily provide end-to-end support for "operations," from overall decision-making and prioritization of snow removal and snow removal implementation plans to performance evaluation and improvement for the next period. Therefore, the challenge to be addressed is not merely decision-making support, but providing an operational platform that comprehensively integrates planning, execution, evaluation, and improvement based on KPIs and GX indicators, ensuring explainability and cost-effectiveness. In view of the above-mentioned problems, the present invention aims to provide a snow country GX platform that goes beyond decision support (determining necessity and priority) to integrate governance functions including the formulation, updating, and execution management of plans based on KPIs and GX indicators, quantitative evaluation of results, financial, settlement, and revenue distribution management, environmental value assessment and CO2 reduction calculation, GX economic cycle management including carbon credits, contract and incentive matching, audit and accountability management, and continuous improvement. [Means for solving the problem]

[0005] The Snow Country GX Platform consists of an information infrastructure responsible for acquiring, accumulating, and normalizing diverse information; analysis that estimates and integrates necessity, urgency, and impact; execution that formulates and updates snow removal plans for each road or management unit, and carries out operations including dispatch orders, equipment allocation, and time zone switching; and improvement and governance that records and visualizes results and reflects them in the next term's policies. The analysis derives evaluation information for each road or management unit based on acquired information, and this evaluation information includes achievement indicators that show the degree of achievement or deviation from KPIs and GX indicators related to the operational requirements of snow removal work efficiency, advancement of automation or autonomy of snow removal work, maintenance of sidewalks or living spaces through community-collaborative snow removal activities, optimization of sustainable urban structure or mobility or urban development, promotion of snow resource utilization through carbon cycle models, establishment of GX economic circulation or financial resource creation, and regional resilience through the linkage of infrastructure maintenance and disaster resilience. In road snow removal operations, a pre-formulated snow removal plan (including at least the route or work section, the snow removal contractor in charge, and the snow removal implementation method) is registered, which includes at least one road that contributes to ensuring the passage of emergency vehicles. Based on the evaluation information and the plan, a snow removal implementation plan is formulated or updated by optimization based on the evaluation information, targeting at least one road that may be designated as a road clearing route or emergency transport route, or at least one road leading to such a road clearing route or emergency transport route. The Snow Country GX Platform optimizes the plan, adjusts priorities, and performs post-implementation evaluations based on the KPI or GX indicators. An example of the configuration of the present invention is shown below in accordance with the claims. In this invention, CO2 is a notation for carbon dioxide. [1] A snow country GX platform based on KPIs and GX indicators, comprising an information acquisition unit, an analysis unit, and a road snow removal unit, wherein the road snow removal unit registers a pre-formulated snow removal plan (including at least the route or work section, the snow removal contractor in charge, and the snow removal implementation method) that includes at least one road that contributes to ensuring the passage of emergency vehicles, the analysis unit derives evaluation information for each road or management unit from the information acquired by the information acquisition unit, and the evaluation information includes an achievement index that represents the degree of achievement or deviation from the KPI or GX indicator with respect to at least one of the following operational requirements: (1) the efficiency of snow removal work, and (2) the degree of automation or autonomy of snow removal work. Snow Country GX Platform, characterized in that (3) maintaining sidewalks or living spaces through collaborative snow removal activities in the community, (4) optimizing sustainable urban structures, mobility, or urban development, (5) promoting the utilization of snow as a resource through a carbon cycle model, (6) establishing a GX economic cycle or generating financial resources, and (7) strengthening the region through the coordination of infrastructure maintenance and disaster resilience, the Road Snow Removal Department formulates or updates a snow removal implementation plan for at least one road that can be designated as a road clearing route or an emergency transport route, or a road leading to such a road clearing route or an emergency transport route, based on the evaluation information and the registered snow removal plan, through optimization based on the evaluation information. [2][1] Snow Country GX Platform, wherein the road snow removal unit generates a timeline (progress plan including milestones) corresponding to the road traffic environment based on at least one of the following acquired by the information acquisition unit: road surface conditions, traffic conditions, road space conditions, weather conditions, vehicle driving conditions, resident notification conditions, or snow removal conditions; sets and outputs at least one of the snow removal standards, priorities, or snow removal implementation methods in the timeline; sets and outputs the milestones (including at least one start time and end time) for each route or work section; and can associate the output with the evaluation information, KPIs, and achievement indicators. The Snow Country GX platform as described in [3][1] or [2], wherein the Snow Country GX platform comprises a robotics unit, the road snow removal unit performs a process to generate commands, monitor progress, or replan snow removal work based on the evaluation information derived by the analysis unit, the robotics unit performs a process to generate start commands, stop commands, or work switching commands for snow removal work corresponding to a timeline (progress plan including milestones), with at least one of the following as the control target: an autonomous snow removal vehicle, a snow removal drone, or robotics equipment (including at least a snow removal robot), based on at least one of the commands provided by the road snow removal unit, the evaluation information provided by the analysis unit, or information on the road traffic environment acquired by the information acquisition unit, and the Snow Country GX platform is capable of associating the process with the evaluation information, the KPI, and the achievement indicator. [4][1] Snow Country GX Platform, characterized in that the information acquisition unit acquires at least one piece of information on school routes (including at least school route information, pedestrian traffic information, attributes of target students, vehicle traffic information, or snow-related hazards) from administrative bodies, schools, boards of education, PTAs, or local organizations (including at least neighborhood associations), the information on school routes including snow-related hazards; the analysis unit scores the degree of danger on the school routes based on the information on school routes acquired by the information acquisition unit; the road snow removal unit processes to determine the priority of community-based snow removal activities based on the score of the degree of danger and the degree of community cooperation; and the processing can be associated with the KPI and the achievement indicator. The Snow Country GX platform described in [5][1], wherein the information acquisition unit acquires information on snow accumulation around a house (including at least snow depth) provided by residents, and the analysis unit generates a snow accumulation score that scores the snow accumulation situation for each region or area, taking into account the time-series changes around the house, based on the information on snow accumulation around the house acquired by the information acquisition unit, and performs processing to derive the evaluation information that reflects the snow accumulation score. The Snow Country GX platform described in [6][5], wherein the information acquisition unit acquires information about a house provided by a resident (including at least the shape and structure of the roof) and information about weather conditions (including at least the amount of snowfall, temperature, or solar radiation), and the analysis unit calculates a roof snow risk level indicating the degree of risk of roof snow for each house based on the information about the house, information about the snow accumulation around the house or the snow accumulation score, and the information about weather conditions, and performs processing to derive the evaluation information that reflects the roof snow risk level. The Snow Country GX platform described in [7][1], wherein the Snow Country GX platform is capable of registering or referencing at least one piece of urban planning information, and further comprises a town development section, wherein the town development section evaluates the maintenance level or priority of the road management length in each region based on the demographic information and snow removal cost information acquired or referenced by the information acquisition section, and based on the evaluation results, classifies the area into one of the following: an area where snow removal maintenance is required intensively, an area where maintenance is possible through community cooperation, or an area where maintenance is to be carried out in stages, and the Snow Country GX platform performs a process to optimize the road management length and evaluate the urban structure in relation to the current plan based on the classification results and the urban planning information, and can associate the process with the KPI, the achievement indicator, and the GX indicator. The Snow Country GX Platform described in [8][7], wherein the urban development department calculates a snow removal score that quantifies at least one of the following indices: priority of snow removal, snow removal efficiency, maintenance cost, or degree of regional cooperation in each region or living area, based on the classification results and the evaluation results of the urban structure; and processes information to support at least one of the following indices: relocation of residents or businesses, reallocation of regional resources, development plan for snow melting ditches, or regional redevelopment, based on the snow removal score and at least one of the transportation convenience or lifestyle-related indicators calculated by the urban development department; and the Snow Country GX Platform is capable of associating the processing with the KPI, the achievement index, and the GX index. The Snow Country GX Platform described in [9][7] or [8], wherein the information acquisition unit acquires or refers to fixed asset information or usage status information, including location information (including latitude and longitude), as information concerning vacant land or vacant houses; the urban development unit extracts land from the vacant land or vacant houses that can be used as snow storage sites based on the information concerning vacant land or vacant houses acquired or referred to by the information acquisition unit, weather conditions, traffic conditions, and snow removal status; evaluates at least one of the following for the candidate sites: road structure, snow removal conditions, snow removal routes, location conditions, traffic safety, and impact on the surrounding environment; and performs processing to generate a snow storage site layout plan that contributes to improving snow removal efficiency or reducing the burden on the local environment; and the Snow Country GX Platform is capable of associating the processing with the KPI, the achievement indicator, and the GX indicator. A Snow Country GX platform as described in any one of paragraphs

[10] , [7], or [8], wherein the urban development section acquires, references, or inputs information on vacant houses at risk of collapse, evaluates the urgency and impact of the collapse of the vacant houses based on the snow removal score or the evaluation results of the urban structure, performs a process to select the site of the vacant house or the site after demolition as a candidate snow storage site for each work section based on the evaluation results and the snow removal status (including at least the snow removal status of surrounding roads) obtained by the information acquisition section, and the Snow Country GX platform is capable of associating the process with the KPI, the achievement indicator, and the GX indicator.

[11] [1] Snow Country GX Platform, wherein the Snow Country GX Platform comprises a town development unit and an environment unit, the information acquisition unit acquires or refers to information regarding mobility demand and mobility supply status, the environment unit performs processing to calculate at least one traffic congestion index or driving efficiency index based on vehicle driving status acquired by the information acquisition unit (including at least one of bus operation information, connected car probe information, or ETC2.0 probe information), the town development unit performs processing to integrally evaluate regional traffic flow, mobility demand, and snow removal response status based on at least two different pieces of information including information regarding mobility demand, information regarding mobility supply status, traffic congestion index, driving efficiency index, urban structure evaluation results, snow removal score, snow removal implementation plan, or urban plan, and derives basic information for constructing a MaaS platform targeting means of transportation throughout the region, including private cars, and the Snow Country GX Platform is capable of associating the processing with the KPI, achievement index, and GX index. The Snow Country GX platform described in

[12]

[11] , wherein the Snow Country GX platform comprises an information provision unit, the analysis unit comprehensively analyzes information (including at least weather forecasts) regarding snow removal status, road conditions, traffic conditions, the mobility supply status, and weather conditions based on the basic information for constructing the MaaS platform (e.g., mobility demand model, road drivability model, mode of transport selection model, snow removal optimization model, environmental load model), generates mobility proposal information that dynamically optimizes at least one of departure time, travel route, or travel schedule for mode of transport from the results of the analysis, the information provision unit processes to output the mobility proposal information regarding areas where a decrease in safety due to poor roads or an increase in environmental load due to traffic congestion is predicted, and the Snow Country GX platform is capable of associating the said process with the KPI, the achievement indicator, and the GX indicator.

[13] [1] Snow Country GX Platform, wherein the Snow Country GX Platform comprises an environmental unit, the information acquisition unit acquires or references information on forest conditions, information on snow cooling utilization or snow-derived energy utilization, and information on electricity consumption or heat demand in the region, the environmental unit constructs a forest absorption model that estimates the amount of CO2 absorbed by forest absorption in the region based on the information on forest conditions, the snow resource energy model that estimates the amount of energy consumption replaced by snow cooling utilization or snow-derived energy utilization and the amount of CO2 emission reduction associated with said replacement based on the information on snow cooling utilization or snow-derived energy utilization and the electricity consumption or heat demand in the region, the Snow Country GX Platform further integrates the results of the forest absorption model and the snow resource energy model to construct a carbon cycle model that represents the carbon cycle on a regional scale based on forest resources and snow resource energy utilization, outputs the estimated results of the carbon cycle based on the carbon cycle model, and the Snow Country GX Platform is capable of associating the estimated results with the KPI, the achievement index, and the GX index. The Snow Country GX platform described in

[14]

[11] and

[13] , wherein the Snow Country GX platform includes a financial settlement management unit, and the financial settlement management unit normalizes the amount of CO2 reduction from each source to a common unit based on at least one of the following: the amount of CO2 reduction from the environment based on the estimation results of the carbon cycle model, the amount of CO2 reduction from traffic estimated based on the traffic congestion index or the driving efficiency index, or the amount of CO2 reduction from snow removal calculated based on the snow removal implementation plan and the snow removal response status, and converts the normalized result into a monetary value as environmental value data, The Snow Country GX Platform further performs a process to generate GX economic circulation information at the municipal or regional level corresponding to the scope of the calculation of the amount of CO2 reduction, and based on the GX economic circulation information, performs at least one of the following processes: application for issuance of carbon credits, cooperation with registration institutions, calculation of estimated sales amount, redistribution of profits, or fundraising through community contribution type funding means (e.g., hometown tax donation, crowdfunding), and the Snow Country GX Platform is characterized in that it can associate the said process with the KPI, the achievement indicator, and the GX indicator.

[15] [1] Snow Country GX Platform, wherein the Snow Country GX Platform comprises an infrastructure maintenance unit, the information acquisition unit acquires multiple types of information, including information on road damage in snowy regions (including damage caused by freeze-thaw cycles or snow removal operations that occur during the winter), information on snow removal conditions, information on weather conditions, and information on the traffic conditions of large vehicles (including at least ETC information, traffic volume survey data, camera-derived data, or weight estimation sensor-derived data), the infrastructure maintenance unit estimates a contribution rate model of road damage factors, including factors specific to snowy regions, based on the multiple types of information, and performs processing to calculate a preventive maintenance index that quantifies at least one of the following: signs of road damage, risk of road damage progression, or probability of serious road damage occurring. The Snow Country GX platform described in

[16]

[15] , wherein the Snow Country GX platform further comprises a road clearing unit, the Snow Country GX platform integrates and manages in a common format performance information of snow removal contractors and road maintenance contractors, availability information of equipment and personnel owned by both contractors, and information on the types of contractors and the scope of work they can handle, and based on this integrated management, it performs processing to propose and configure a year-round comprehensive management system based on the management system for snow removal, the infrastructure maintenance unit generates a road maintenance plan based on the preventive maintenance indicators and performs processing to support the coordination of operations and the allocation of equipment for implementing preventive maintenance for road maintenance based on the comprehensive management system, and the road clearing unit performs processing to dynamically optimize the road clearing plan in the event of a disaster in cooperation with the preventive maintenance indicators and the comprehensive management system. The Snow Country GX Platform described in

[17]

[16] , wherein the Snow Country GX Platform integrates multiple types of road maintenance plans, snow removal plans, and road clearing plans, generates regional resilience information for integrated management of road maintenance, snow removal operations, and road clearing in snow country regions, and can associate the regional resilience information with the KPIs, achievement indicators, and GX indicators. The Snow Country GX platform described in

[18]

[17] , wherein the Snow Country GX platform performs a process to calculate at least one of the following for adaptation funds related to adaptation measures to prepare for disasters caused by climate change, based on the regional resilience information: the required amount, the allocation policy, or the procurement plan. A snow country GX platform characterized in that the process can be associated with the KPI, the achievement indicator, and the GX indicator.

[19] [1] The Snow Country GX platform described in

[19] [1] comprises a town planning department, an environmental department, an infrastructure maintenance department, and a road clearing department, and further comprises an AI management department that oversees the AI ​​of each of the said departments, wherein the AI ​​management department outputs the evaluation information, each plan information (including at least one of the snow removal plan, road maintenance plan, and road clearing plan), or each indicator information (the GX indicator, the achievement indicator, traffic congestion indicator, driving efficiency indicator, lifestyle-related indicator, preventive maintenance indicator, and estimation of the carbon cycle model) output by at least one of the said departments, the analysis department, the road snow removal department, the town planning department, the environmental department, the infrastructure maintenance department, and the road clearing department. A snow country GX platform characterized by taking at least one of the following as input (including at least one of the results): performing inference or learning (including online learning or additional learning) using an AI method (including at least one of machine learning models, rule-based inference, optimization or search algorithms, probabilistic models, and heuristic processing); and outputting at least one of the following to at least one of the analysis unit, the road snow removal unit, the urban development unit, the environment unit, the infrastructure maintenance unit, or the road clearing unit.

[20] A program for causing a computer to function as the Snow Country GX platform described in [1], characterized in that the computer functions as at least one of the following: an information acquisition unit, an analysis unit, a road snow removal unit, a robotics unit, a town planning unit, an environmental unit, a financial settlement management unit, an infrastructure maintenance unit, a road clearing unit, or an information provision unit; it performs inference or learning (including online learning or additional learning) using an AI method (including at least one of a machine learning model, a rule-based inference, an optimization or search algorithm, a probabilistic model, and a heuristic process); and it generates at least one piece of information to be output in at least one of the following: an information acquisition unit, an analysis unit, a road snow removal unit, a robotics unit, a town planning unit, an environmental unit, a financial settlement management unit, an infrastructure maintenance unit, a road clearing unit, or an information provision unit, using at least a portion of the data acquired by the information acquisition unit or the preprocessing results of such data as input.

[21] A program comprising a sequence of instructions for causing a computer to perform at least one of the following (A) or (B): (A) a function of the Snow Country GX platform described in any one of [1], [2], [4], [5], [7],

[11] ,

[13] ,

[15] , or

[19] ; ​​(B) at least one of the following functions (1) through (10): (1) The road snow removal unit and robotics unit described in [3], based on the evaluation information and timeline derived by the analysis unit, to perform snow removal on an autonomous snowplow, snow removal drone, or snow removal robot. (2)[6] A function to generate start, stop, or switch operation commands for snow removal work; a function to calculate the roof snow risk level for each house based on information about the house, information about snow accumulation around the house, and information about weather conditions, using the information acquisition unit and the analysis unit described in (2)[6], and to derive the evaluation information that reflects the roof snow risk level; a function to index at least one of the following in each region or living area based on the classification results and the evaluation results of the urban structure: priority for snow removal, snow removal efficiency, maintenance costs, or degree of community cooperation. A function to calculate the snow removal score and, based on the snow removal score and at least one of the following indicators: relocation of residents or businesses, reallocation of local resources, development of snow melting channels, or regional reconstruction; (4)[9], the Urban Development Department extracts candidate sites from the vacant lots or vacant houses that can be used as snow storage sites based on information on vacant lots or vacant houses, weather conditions, traffic conditions, and snow removal status, and the road structure, snow removal conditions, snow removal routes, and location conditions of the candidate sites, A function to generate a proposed snow storage site layout by evaluating at least one of the following: traffic safety and impact on the surrounding environment; (5)

[10] , a function by the Urban Development Department to evaluate the urgency and impact of collapse of vacant houses that are at risk of collapse based on information on vacant houses and the snow removal status, and to select vacant houses as candidates for snow storage sites for each construction section based on the evaluation results and the snow removal status; (6)

[12] , a function by the Analysis Department and Information Provision Department to provide basic information for constructing a MaaS platform, as well as snow removal status, road conditions, traffic conditions, and transportation supply status.Based on information on weather conditions, the system generates mobility suggestion information that dynamically optimizes at least one of the following: departure time, travel route, or mode of transport, and outputs such mobility suggestion information for areas where reduced safety due to poor road conditions or increased environmental load due to traffic congestion is predicted, (7)

[14] described by the Financial Settlement Management Department, which generates GX economic cycle information based on at least one of the following: environmental CO2 reduction amount based on the estimation results of the carbon cycle model, traffic CO2 reduction amount estimated based on the traffic congestion index or driving efficiency index, or snow removal CO2 reduction amount calculated based on the snow removal implementation plan and snow removal response status, and based on the GX economic cycle information, which performs at least one of the following: application for issuance of carbon credits, cooperation with registration bodies, calculation of estimated sales amount, or reallocation of revenue, (8)

[16] described by the Infrastructure Maintenance Department and the Road Clearing Department, which generates performance information of snow removal contractors and road maintenance contractors, and information on equipment and materials The Snow Country GX platform integrates and manages information on the availability of personnel and the types and scope of work that both contractors can handle in a common format, generates a road maintenance plan based on preventive maintenance indicators, supports the decision on work coordination and equipment allocation for implementing preventive maintenance for road maintenance based on a comprehensive management system, and further functions to dynamically optimize road clearing plans in the event of a disaster in conjunction with the preventive maintenance indicators and the comprehensive management system; (9)

[17] , the Snow Country GX platform integrates multiple types of road maintenance plans, snow removal plans, and road clearing plans to generate regional resilience information that integrates road maintenance, snow removal operations, and road clearing in the event of a disaster in a single, integrated manner; (10)

[18] , the Snow Country GX platform calculates at least one of the following for adaptation funds related to adaptation measures to prepare for disasters caused by climate change, based on the regional resilience information: the required amount, allocation policy, or procurement plan.

[22] A road management method based on KPIs and GX indicators using a computer, wherein the computer registers a pre-formulated snow removal plan (including at least the route or work section, the snow removal contractor in charge, and the snow removal implementation method) that includes at least one road that contributes to securing the passage of emergency vehicles, derives evaluation information for each road or management unit based on information acquired through information acquisition processing via a network, and the evaluation information includes an achievement index that represents the degree of achievement or deviation from the KPI or GX indicator with respect to at least one of the following operational requirements: (1) efficiency of snow removal work, (2) advancement of automation or autonomy of snow removal work, A road management method characterized by performing the process of formulating or updating a snow removal implementation plan based on the evaluation information, targeting at least one road that can be designated as a road clearing route or emergency transport route, or a road leading to such a road clearing route or emergency transport route, based on the evaluation information and the registered snow removal plan. (3) Maintaining sidewalks or living spaces through community-collaborative snow removal activities, (4) Optimizing sustainable urban structure, mobility, or urban development, (5) Promoting the utilization of snow as a resource through a carbon cycle model, (6) Establishing a GX economic cycle or generating financial resources, (7) Strengthening the region through the linkage of infrastructure maintenance and disaster resilience. [Effects of the Invention]

[0006] The Snow Country GX Platform is an operational platform that optimizes snow removal operations based on KPIs and GX indicators, and includes an information acquisition unit, an analysis unit, a road snow removal unit, etc. In the Snow Country GX Platform, the road snow removal unit registers pre-formulated snow removal plans (including at least the route or work section, the snow removal contractor in charge, and the snow removal implementation method) that include at least one road that contributes to ensuring the passage of emergency vehicles, and the analysis unit derives evaluation information for each road or management unit from the information acquired by the information acquisition unit. This evaluation information includes achievement indicators that represent the degree of achievement or deviation from the aforementioned KPI or GX indicator with respect to one or more of the following operational requirements. (1) Efficiency improvement of snow removal operations (2) Enhancement of automation or autonomy in snow removal operations (3) Maintenance of sidewalks or living spaces through regional collaborative snow removal activities (4) Optimization of sustainable urban structures, mobility, or urban development (5) Promotion of snow resource utilization based on the carbon cycle model (6) Establishment of the GX economic cycle or revenue generation (7) Strengthening of regions through the collaboration between infrastructure maintenance and disaster resilience Based on the evaluation information and the registered snow removal plan, the Snow Country GX Platform can formulate or update the snow removal implementation plan for at least one or more roads that can be designated as road opening routes or emergency transportation routes, or roads leading to such road opening routes or emergency transportation routes, by optimizing according to the evaluation information. This enables objective determination of necessity and priority based on multi-source data integration, dynamic implementation plans for each road or management unit, transparent performance management starting from KPI and GX indicators, and learning improvement for the next fiscal year, suppression of duplication and delays, improvement of cost-effectiveness, integration with public transportation, emergency transportation, and road opening, linkage with environmental evaluations such as CO2, strengthening of audits, disclosure, and settlement, automatic collection of performance, digital certification, and enhancement of contract matching. It also realizes an improvement in citizen satisfaction, an increase in the operational efficiency of road managers and contractors, and contributes to fair and explainable prioritization.

Brief Description of Drawings

[0007] [Figure 1] It is a system configuration diagram related to the snow removal judgment support system of the present invention. [Figure 2] It is a flowchart showing an example of the patrol process. [Figure 3] It is a flowchart showing an example of the process of a fixed-point camera. [Figure 4] It is a diagram showing an example of the road surface condition. [Figure 5] It is a cross-sectional view showing an example of the road surface condition (unevenness of the snow-covered road surface). [Figure 6] It is a cross-sectional view showing an example of the road surface condition (thickness of the compacted snow on the road surface). [Figure 7] It is a diagram showing an example of traffic conditions. [Figure 8] It is a diagram showing an example of road space conditions. [Figure 9] It is a diagram showing an example of weather conditions. [Figure 10] It is a diagram showing an example of vehicle driving conditions. [Figure 11] It is a flowchart diagram showing an example of the flow of an example of the snow removal and snow clearing judgment support system of the present invention. [Figure 12] It is a diagram showing an example of the judgment criteria in the decision-making part of the present invention. [Figure 13] It is a diagram showing an example of the hardware configuration related to the snow removal and snow clearing judgment support system of the present invention. [Figure 14]Figure 14 schematically shows the configuration of the Snow Country GX platform of the present invention, indicating that the Snow Country GX platform incorporates a snow removal decision support system and a snow removal operation platform. Figure 14 schematically shows the configuration of the Snow Country GX platform of the present invention, indicating that the Snow Country GX platform incorporates a conventional snow removal decision support system and a snow removal operation platform, and has a higher layer above it that integrates functions such as KPI / GX indicator management, financial / settlement / revenue distribution management, environmental value assessment / CO2 reduction amount calculation, carbon credit / GX economic cycle management, contract / incentive matching, audit / accountability management, and related organization cooperation / information disclosure. Figure 1 is a block diagram schematically showing the basic configuration of the snow removal decision support system that makes up the Snow Country GX platform shown in Figure 14. In this figure, the main functional modules such as the information acquisition unit, analysis unit, and decision unit are shown, and the process of acquiring and analyzing various information such as road surface conditions, traffic conditions, weather conditions, and vehicle driving conditions is shown. On the other hand, the present invention also envisions embodiments that include, in addition to these, components such as a road snow removal unit that manages resident notification information, snow removal information, and snow removal implementation plans, and an information provision unit that provides information to external parties. Figure 11 is a typical processing flow showing the flow of information acquisition, analysis, and decision processing, and is an auxiliary diagram for understanding embodiments of the present invention. Figure 12 is a table showing examples of decisions on whether or not snow removal is necessary when various types of information (road surface, traffic, road space, weather, and vehicle driving information) are combined in a complex manner, and should be understood as an example of the analysis processing and decision criteria in the present invention.Furthermore, the present invention may include additional configurations that perform, in addition to snow removal priority analysis and implementation plan formulation, adjustment processing of implementation timing based on predictive information, workload evaluation, snow removal vehicle operation prediction, optimization of snow removal routes to snow disposal sites, feasibility determination based on remaining budget, processing of requests for external support, weighting processing that takes into account the distribution of medical facilities, welfare facilities, educational facilities, and vulnerable road users, as well as advanced automation or autonomy of snow removal work, maintenance of sidewalks or living spaces through community-collaborative snow removal activities, optimization of sustainable urban structure, mobility or urban development, promotion of snow resource utilization through carbon cycle models, establishment of GX economic circulation or financial resource creation, and evaluation and policy planning related to regional resilience through the linkage of infrastructure maintenance and disaster resilience, in conjunction with evaluation using KPIs or GX indicators. These are one embodiment of the Snow Country GX Platform and, although not shown in Figures 1 to 12 and Figure 14, can be appropriately combined or expanded within the scope of the functional configuration claimed in this application. The hardware configuration shown in Figure 13 is a general-purpose configuration that can also be applied to embodiments of the Snow Country GX platform. [Modes for carrying out the invention]

[0008] The following describes one embodiment of the present invention. The Snow Country GX Platform integrates and analyzes diverse information to formulate and update snow removal and clearing plans for each road or management unit, and operates in an integrated manner from dispatch, allocation, and progress management to visualization and evaluation of results and improvement for the next period. The Snow Country GX Platform functions by incorporating conventional snow removal and clearing decision support systems (information acquisition unit, analysis unit, decision unit, road snow removal and clearing unit, etc.) and snow removal and clearing operation platforms, and above them is an operational platform equipped with a higher layer that integrates functions such as KPI and GX indicator management, financial, settlement and revenue distribution management, environmental value assessment and CO2 reduction amount calculation, carbon credit and GX economic cycle management, contract and incentive matching, audit and accountability management, and cooperation with related organizations and information disclosure. The Snow Country GX Platform, as its basic configuration, includes an information acquisition unit, an analysis unit, and a road snow removal unit. Depending on the implementation, it may further include at least one of the following: a robotics unit, an urban development unit, an environmental unit, a financial settlement management unit, an infrastructure maintenance unit, a road clearing unit, an information provision unit, or an AI management unit. Each of these units works in conjunction with data acquired by the information acquisition unit, evaluation information derived by the analysis unit, various planning information such as snow removal plans, road maintenance plans, and road clearing plans, as well as KPIs and GX indicators, and divides the work to perform tasks such as snow removal operations, optimization of urban structure and mobility, resource utilization of snow based on environmental assessment and carbon cycle models, construction of a GX economic cycle, infrastructure maintenance and road clearing during disasters, and provision of information to external parties and ensuring accountability. From a functional standpoint, the Yukiguni GX platform includes an information infrastructure responsible for acquiring, storing, and normalizing information; analysis for estimating and weighting necessity, urgency, and impact; execution for optimizing plans and managing command, allocation, and time zone switching; and improvement and governance responsible for recording, auditing, publishing, settling, and learning improvement logic from performance data. KPIs consist of indicators such as safety, mobility, impact on daily life, work quality, efficiency, and environment, and are used to optimize plans, adjust priorities, and conduct post-implementation evaluations based on the degree of achievement and deviation. The road traffic environment refers to the overall operational status, which consists of the interrelationships of road surface, traffic, public transport, weather, infrastructure health, snow disposal sites, emergency transport, road clearing, citizen notifications, implementation plans, equipment, personnel, budget, and environmental impact. This is modeled spatiotemporally and reflected in KPIs and GX indicators. A management unit refers to a unit subject to evaluation and aggregation, including routes, work sections, districts, road types, contractors, employees, etc. KPIs are calculated for each management unit, and planning, allocation, deployment, completion evaluation, settlement, and next-term allocation are managed consistently. CO2 is a notation for carbon dioxide. It should be noted that the road clearing implementation plan described herein is a plan for road clearing during natural disasters, and differs in purpose and evaluation indicators from the snow removal implementation plan for maintenance management under snowy conditions. However, in this embodiment, the two plans can cross-reference or exchange data via a common information infrastructure (for example, correcting the priority of snow removal in accordance with the priority securing of emergency transport routes, and updating the estimation of whether roads can be cleared and passed through based on feedback of snow removal results), but such exchange is optional and not an essential component of the present invention. The configurations and processes described below are examples and can be substituted, added, deleted, or combined without departing from the spirit of the invention as described in the claims. <Reference Guide> The configuration and functionality equivalent to the snow removal decision support system included in the Snow Country GX platform are described in paragraph 0009 and subsequent paragraphs. Embodiments of the configuration and functions equivalent to the snow removal and clearing operation platform included in the Snow Country GX platform are described in paragraph 0046 and subsequent paragraphs. The higher-level functions and integrated operation implementations of the Snow Country GX platform are described in paragraph 0093 and subsequent paragraphs. The hardware configuration of the Snow Country GX Platform, Snow Removal Operation Platform, and Snow Removal Decision Support System is described in paragraph 0137.

[0009] Figure 1 is a system configuration diagram relating to the snow removal decision support system 600 of the present invention. The snow removal decision support system 600 includes an information acquisition unit 610 that acquires information on road surface conditions, traffic conditions, road space conditions, weather conditions, and vehicle driving conditions; an analysis unit 620 that analyzes the road surface conditions, traffic conditions, road space conditions, weather conditions, and vehicle driving conditions obtained from the information acquired by the information acquisition unit 610; and a decision unit 630 that determines the necessity of snow removal based on the results analyzed by the analysis unit 620. In this embodiment, the snow removal decision support system 600 is connected to the road surface condition provision server 100, traffic condition provision server 200, road space condition provision server 300, weather condition provision server 400, and vehicle driving condition provision server 500 via a network NW. To understand the road surface conditions, Figure 1 shows only one vehicle Vh and terminal device TM, but multiple vehicles Vh and terminal devices TM may be connected to a network NW. Although Figure 1 shows only one fixed-point camera (CAM) to understand the road space conditions, multiple fixed-point cameras (CAM) may be connected to a network (NW). Furthermore, the present invention includes a road snow removal unit that formulates and manages the resident notification status and snow removal status, which are core information in the snow removal decision support system 600, as well as the snow removal implementation plan, which is a core component, and an information provision unit that provides information to external parties, etc. On the other hand, functions for acquiring road service status, fiber optic survey status, and satellite survey status, as well as components such as infrastructure maintenance, road clearing, prediction, and improvement units, may be provided as expandable configurations that can be added to the system as needed.

[0010] The terminal device TM, fixed-point camera CAM, road surface condition server 100, traffic condition server 200, road space condition server 300, weather condition server 400, vehicle driving condition server 500, and snow removal decision support system 600 communicate via a network NW. The network NW includes some or all of the following: a WAN (Wide Area Network), a LAN (Local Area Network), the Internet, provider equipment, wireless base stations, dedicated lines, etc. Furthermore, data can be exchanged not only via the network (NW) but also via a memory card. Downloading and uploading data via the network (NW) is also acceptable. Furthermore, in this invention, snow removal information and resident notification information are core information elements in the snow removal decision support system 600, and functions for acquiring and processing them are provided as an essential component of the system. This information is acquired via a network NW through a dedicated server, an in-vehicle terminal device, a fixed-point camera, a resident notification server, etc. On the other hand, road service information, fiber optic survey information, satellite survey information, etc., are positioned as optional extended information that can be acquired according to the operational purpose, and may be additionally provided to the snow removal decision support system 600 as needed.

[0011] Terminal devices TM are used by passengers riding in vehicles Vh. Terminal devices TM include mobile phones such as smartphones and tablet devices. Vehicles Vh are mainly road maintenance patrol vehicles / road patrol cars (patrol vehicles such as garbage trucks, compactor trucks, and trash collection trucks are also acceptable). The terminal device TM may be a communication-type drive recorder mounted on the vehicle Vh, a stationary in-vehicle device, or it may be equipped with AI (artificial intelligence) image recognition capabilities. The vehicle Vh may also be equipped with a subsurface cavity detection function (a technology that irradiates electromagnetic waves from the road surface downwards and estimates the location of cavities and buried pipes from the reflected waves), and the vehicle Vh may be a subsurface cavity detection vehicle. The terminal device TM has a built-in road patrol application that works in conjunction with the road surface condition provision server 100. The terminal device™ includes a positioning device such as a GPS (Global Positioning System) receiver, a communication device for connecting to a network NW, input / output devices such as a G-sensor (accelerometer), camera, and touch panel, and a processor such as a CPU (Central Processing Unit).

[0012] Figure 2 is a flowchart illustrating an example of a patrol. The terminal device TM starts collecting location information, acceleration information, video, images, etc. when the patrol start button of the road patrol app is pressed (S1) (S2). After the patrol is completed, pressing the "end patrol" button on the road patrol app (S3) transmits the terminal device TM's location information, acceleration information, video, images, etc. to the road surface condition provision server 100 (S4). The road surface condition server 100 determines whether there are any bumps or unevenness on the road surface based on the measurement information transmitted from the terminal device TM, and identifies the location of the road surface that is determined to be uneven. Furthermore, the location information, acceleration information, video, images, etc. transmitted to the road surface condition server 100 may be measurement data from private cars, taxis, trucks, etc.

[0013] Fixed-point cameras (CAMs) are installed on roads (major arterial roads, roads with heavy traffic, major bus routes, roads important for transporting snow to disposal sites, roads connecting to schools, public facilities, and emergency hospitals, etc.) and on buildings around intersections, roadside pillars, poles, etc. Fixed-point cameras (CAMs) include communication-enabled live cameras, web cameras, and network cameras. The fixed-point camera CAM may be a connected dashcam or a small unmanned aerial vehicle camera such as a drone, or it may be equipped with AI (artificial intelligence) image recognition capabilities. The fixed-point camera CAM has a built-in camera application that communicates with the road space condition provision server 300. A fixed-point camera (CAM) includes a lens, an image sensor, a positioning device such as a GPS (Global Positioning System) receiver, a communication device for connecting to a network (NW), and a processor such as a CPU (Central Processing Unit).

[0014] Figure 3 is a flowchart showing an example of the workflow for a fixed-point camera CAM. The fixed-point camera CAM collects road spatial conditions periodically or intermittently (S5), and periodically or intermittently automatically transmits video / images, location information, date and time information, etc. to the road spatial conditions provision server 300 (S6). The road space condition provision server 300 determines the height of snowdrifts (snow piles on the road shoulder, hereafter omitted) based on video and images transmitted from the fixed-point camera CAM, and identifies the location of road spaces that have been determined to be dangerous.

[0015] The road surface condition provision server 100 provides road surface conditions to the snow removal decision support system 600 via the network NW. The road surface conditions provided are road-specific information and include some or all of the following: unevenness of the snow-covered road surface, thickness of compacted snow on the road surface, quality of snow on the road surface, snow accumulation on the road surface, snowmelt on the snow-covered road surface, freezing of the road surface, bowl-shaped deformation of the snow-covered road surface, rutting of the snow-covered road surface, snow accumulation on the road surface, unevenness (flatness) of the road surface, cracks, rutting, cavities beneath the road surface, road damage, road collapse, flooding, and whether or not the road width has decreased due to snow accumulation. Figure 4 shows an example of road surface conditions, with areas 110, where the snow-covered road surface is severely uneven, indicated in black on the map. The provided road surface conditions may be quantified in the analysis unit 620 as traffic obstruction score, freezing risk, road surface damage risk, etc., and used as input information for priority determination processing. In addition, the infrastructure maintenance unit may evaluate the long-term deformation risk such as cracks and cavities, and perform processing to determine whether repairs are necessary and to reflect this in the road maintenance plan.

[0016] Figure 5 is a cross-sectional view showing an example of road surface conditions (unevenness of a snow-covered road surface), where unevenness due to snow accumulation is indicated by a dashed line.

[0017] On snow-covered roads, snow melts easily near manholes, creating a step between the road surface and the compacted snow surface. Figure 6 is a cross-sectional view showing an example of road surface conditions (compacted snow thickness on the road surface). By determining the height of the step near the manhole from acceleration information from the terminal device TM, the compacted snow thickness of 130 can be determined.

[0018] The traffic information server 200 provides traffic information to the snow removal decision support system 600 via the network NW. The traffic information provided is road-specific information and includes some or all of the following: traffic volume, passing speed, average speed, and whether or not there is congestion or traffic jams. Figure 7 shows an example of traffic conditions, with areas 210 experiencing severe congestion indicated in black on the map. The traffic conditions provided may be converted and quantified in the analysis unit 620 into traffic flow scores, congestion risk, traffic disruption trends, etc., and integrated with other information (weather information, vehicle driving information, road space conditions, etc.) for use in determining the priority of snow removal.

[0019] The road space condition provision server 300 provides road space conditions to the snow removal decision support system 600 via the network NW. The provided road space conditions are road-specific information and include some or all of the following: avalanches, snowdrifts, height of snow levees due to snow accumulation, presence or absence of poor visibility at intersections due to snow levees, presence or absence of road width reduction due to snow accumulation, presence or absence of stuck large vehicles, presence or absence of accident vehicles, etc. Figure 8 shows an example of road space conditions, with the dangerous locations 310, where snow dams are high, indicated in black on the map. The provided information may be quantified in the analysis unit 620 as a traffic obstruction score, visibility impairment score, etc., and used to evaluate whether snow removal work is necessary in the road snow removal unit.

[0020] The weather information server 400 provides weather information to the snow removal decision support system 600 via the network NW. The weather information provided is regional information and includes some or all of the following: time, weather (sunny, rainy, snowy, etc.), temperature, humidity, snowfall amount, snow depth, snow density / weight, snow quality, moisture content, snow load, wind speed, forecast (snowfall amount / snow depth, etc.), presence or absence of warnings (blizzard, heavy snow, etc.) and advisories (heavy snow, windstorm, avalanche, etc.), record-breaking short-term heavy rain information, landslide disaster warning information, earthquake information, etc. Figure 9 shows an example of weather conditions, where snowfall amount 410 and warning 420 are displayed as numbers and letters. Weather conditions are used in the short-term snow removal forecast processing by the determination unit 630, and are utilized in the forecasting unit for medium- to long-term snowfall risk forecasting. Furthermore, the analysis unit 620 may be configured to process wind speed, snowfall amount, temperature, etc., in a multifactorial manner to calculate a snow removal difficulty score for each road.

[0021] The vehicle driving status provision server 500 provides vehicle driving status to the snow removal decision support system 600 via the network NW. The provided vehicle driving status is road and route-specific information obtained from vehicles such as route buses and loop buses, and includes some or all of the following: bus delay information (bus delay status) relative to the winter timetable (timetable that takes into account traffic conditions during the winter), bus route delay information, bus driving position (which lane it is driving in out of three lanes on one side, etc.), number of vehicles in front of the bus and number of vehicles alongside the bus, whether or not the road width has been reduced due to snow accumulation, skidding locations, tire slip locations, tire lock locations, and locations where sudden braking occurred. If there is no winter timetable, the bus delay time relative to the normal timetable may be used. Vehicle driving status can also be provided as open data in the Dynamic Bus Information Format (GTFS Realtime), including the latest route information (vehicle ID, route name, delay, estimated departure and arrival times, passing times, etc.), vehicle location information (vehicle latitude and longitude, approach information, congestion level, etc.), and operational information (service suspension, detour, accident, stuck, road obstruction, images of the front and rear of the bus, etc.). Furthermore, ETC2.0 probe information (such as driving history, speed, travel time, congestion, locations with frequent sudden decelerations, and traffic records based on vehicle-to-infrastructure communication, etc.) may be used to supplement the vehicle driving conditions. Furthermore, vehicle driving conditions may include data obtained from connected cars (private cars, taxis, trucks, garbage trucks, delivery vehicles, vehicles covered by auto insurance with drive recorders provided by insurance companies, etc.) from vehicle sensors such as temperature, locations of sudden braking, locations of skidding, locations of tire slippage, locations of tire lock-up, locations of ABS activation, unevenness of snowy road surfaces, ruts in snowy road surfaces, pits in snowy road surfaces, thickness of compacted snow on the road surface, reduction in road width due to snow accumulation, unevenness (flatness) of the road surface, locations of cavities under the road surface, avalanches, road obstacles, cracks, rutting, flooding, road damage, road collapses, road surface freezing, road surface snow accumulation, road surface snow quality, identification of impassable areas, traffic history, traffic volume, traffic congestion, passing speed, average speed, acceleration from probe information, and presence or absence of snowfall from wiper operation status. Figure 10 shows an example of vehicle driving conditions, where the area 510 where the tires are spinning is shown in black on the map. Various driving obstruction conditions or abnormal vehicle behavior are automatically classified and weighted by an AI (artificial intelligence) model in the analysis unit 620, and the configuration may be such that tire slippage locations and sections with frequent skidding are strongly reflected in the snow removal priority. In addition, delay data may be used by the improvement unit to retrain the judgment model.

[0022] As a method for assessing road width reduction due to snow accumulation, in addition to using camera functions and image recognition functions of smartphones, connected dashcams, and fixed-point cameras installed in vehicles to determine the snow accumulation situation, it is also acceptable to use the same functions to determine road width reduction from the number of lanes and the number of vehicles side by side. Furthermore, it is also acceptable to determine the lane position (which lane it is traveling in out of three lanes on one side, or which lane it is traveling in out of two lanes on one side, etc.) from the latitude and longitude of a moving route bus, etc., and to determine road width reduction due to snow accumulation. The results of the width reduction determination may be integrated with traffic conditions, driving abnormalities, lane driving patterns, etc., and used for obstacle evaluation by the analysis unit 620 and weight adjustment processing by the improvement unit.

[0023] The road service status provision server provides road service status to the snow removal decision support system 600 via the network NW. The information regarding road service status is road-specific information and is mainly managed by road service providers (such as the Japan Automobile Federation), and includes some or all of the following: rescue requests (date, time, location, rescue details, etc.), rescue requests due to abnormal weather (date, time, location, rescue details, etc.), rescue requests due to natural disasters (date, time, location, rescue details, etc.), dead batteries, locked-out keys, running out of gas, flat tires, wheels coming off / falling off, flooding / submersion, recovery from snowy / mud roads, accidents, slips, disaster / damage situations, vehicle towing / transportation, removal / towing / transportation of abandoned vehicles, removal / towing / transportation of damaged vehicles, removal / towing / transportation of accident vehicles, location information of stuck vehicles, situation of stuck vehicles, road conditions, traffic conditions, EV charging availability, vehicle inspection results, etc. The frequency of vehicle jamming and abnormal rescue requests may be quantified by the analysis unit 620 as an index of difficulty of passage and used as a factor in determining the priority of decisions.

[0024] The optical fiber survey status provision server utilizes optical fiber sensing technology, which uses optical fibers as sensors. It receives backscattered light from communication optical fibers contained in cables laid on roads, etc., and detects vibration patterns corresponding to the vehicle driving conditions on the road, etc., based on the backscattered light. From the detected vibration patterns and the learned model, it is possible to understand road conditions such as the presence or absence of snow, changes in road surface conditions, the presence of compacted snow or bumps, frozen areas, vehicle congestion or lagging, underground cavities, and impassable areas. Furthermore, by analyzing the intensity, frequency changes, and continuous abnormal patterns of waveforms of minute vibrations propagating through the ground, it is possible to detect the risk of underground cavities and signs of ground deformation. If necessary, the sensing results can be corroborated and supplemented by images acquired from fixed-point cameras connected to optical fibers. The fiber optic survey status provision server provides fiber optic survey status to the snow removal decision support system 600 via the network NW. The information provided includes snow accumulation, compaction, and freezing conditions for each road, unevenness of the snow-covered road surface, vehicle traffic history, traffic volume, traffic congestion, sudden vehicle stops, accident trends, history of freezing and slipping, road surface unevenness, road damage, road collapses, and the possibility of voids under the road surface. This information is used to identify areas where passage is difficult due to snow accumulation and areas with high snow removal priority. This enables fiber optic sensing to continuously and widely monitor road conditions even during times when ground patrols cannot be conducted, contributing to more accurate and immediate decisions regarding snow removal and clearing. The obtained waveforms may be analyzed by AI (artificial intelligence), visualized and quantified as a spatiotemporal distribution of road surface abnormalities and freezing risk, and input to the analysis unit 620 and the determination unit 630.

[0025] The satellite survey status server uses satellite remote sensing technology with artificial satellites equipped with SAR (Synthetic Aperture Radar), optical sensors, microwave sensors, etc., to detect snow cover, compacted snow, snowdrifts, road surface freezing, unevenness of snow-covered road surfaces, areas where snow removal has been completed and not, impassable areas, cavities under the road surface, road damage, road collapses, and flooding. This uses satellite-acquired data such as optical images, SAR images, temperature data, scattering intensity values, polarization information, and phase information to detect time-series changes in snow cover conditions and abnormalities in road surface conditions with high accuracy. Furthermore, by applying interferometric analysis techniques such as InSAR (Interferometric SAR) and DInSAR (Differential Interferometric SAR), it is possible to comprehensively understand subtle height changes due to compacted snow and freezing, changes after snow removal, and areas where snow has accumulated. This allows for the detection of snow depth, remaining compacted snow, and areas where snow removal has been insufficient. The satellite survey status provision server transmits snow accumulation data, estimated snow removal requirements, snow removal completion evaluations, and traffic impact indicators to the snow removal decision support system 600 via the network (NW). This information is compiled and organized by route, work section, or local road, and used in conjunction with other information sources to make decisions regarding snow removal. The determination of whether snow removal has been completed or not, and the traffic impact index, are integrated and evaluated in the analysis unit 620, and may be used for priority decisions in the decision unit 630 and for disaster risk assessment in the prediction unit.

[0026] The resident reporting status server collects and manages information regarding complaints, requests, and reports from residents. It supports multiple communication methods, including AI (artificial intelligence) voice reporting, reporting via SNS (e.g., LINE), web form reporting, email reporting, smartphone app reporting, voice assistant reporting, and traditional telephone reporting with input of interview results by staff. The resident reporting status server provides information regarding resident reporting status to the snow removal decision support system 600 via the network. "AI-based voice notification reception" refers to a system that automatically analyzes voice notification content transmitted via telephone or other voice input means using AI (artificial intelligence) technologies such as speech recognition and natural language processing. This enables quantitative understanding of notification content and automatic storage and learning of notification information without the need for human intervention by operators. The AI-powered voice notification system analyzes the content of the notification (e.g., uncleared snow, snowdrifts, ice, road obstruction, etc.) obtained through dialogue with the caller, as well as the location of the notification (address, facility name, landmark, etc.), using speech recognition and natural language processing, and automatically registers it in the notification database. In addition to SNS notifications and web notifications, citizen-participation notification platforms (e.g., FixMyStreet Japan) also handle posted data, including notification content and location information, in the same way. This data includes text entered by the caller, photos, GPS coordinates, and multiple-choice items, and is handled together with notification information entered by local government officials. The collected reports are classified by location, content, and method of reporting, and organized into report density maps and complaint histories for each route or work section. These are used as basic information by the road snow removal department when assigning priorities for snow removal, and by linking them with the local population or number of households, it becomes possible to make decisions that balance the number of reports with the scope of impact. Resident notification information is automatically sorted by AI (artificial intelligence), and only notifications related to snow removal are extracted. After extraction, the urgency (road impassable, road clearing route, in front of hospital facilities, etc.), geographical characteristics of the target area (main roads / local roads, width, traffic volume, etc.), notification density, and population or household distribution are evaluated to derive priorities. This information is then subjected to integrated analysis by the analysis unit 620 and used as decision-making material by the decision unit 630. In particular, it functions as a source of information that supports highly accurate decisions that influence the necessity, timing, and method of snow removal (daytime / nighttime, etc.). Furthermore, the resident reporting status information may be configured to evaluate its reliability by cross-referencing it with external sensing information such as satellite survey data and fiber optic survey data, and then weighting and prioritizing it accordingly. This makes it possible to improve the reliability and reproducibility of decisions by mutually complementing multiple information sources, without relying on subjective judgment.

[0027] The snow removal status provision server provides snow removal status to the snow removal decision support system 600 via the network NW. The information regarding snow removal status is road-specific and is mainly managed by road administrators. It includes some or all of the following: information on snow removal routes (main roads, etc.) and work sections (local roads, etc.), road conditions, traffic conditions, information on snow removal operators, types and number of snow removal vehicles (shovels, graders, rotary snowplows, dozers, dump trucks, spreaders), prior snow removal plans and work guidelines, snow removal implementation standards, snow removal completion standards, snow removal work plans, dispatch orders to snow removal operators, snow removal implementation status, snow removal work status (including the current location of snow removal vehicles and video / images from snow removal vehicles), snow removal operation records and snow removal performance information (operating hours, location information, history, etc.), snow removal work daily reports, patrol results, budget management, settlement management, complaints and requests, and information on snow disposal sites and snow storage areas. Furthermore, road administrators formulate snow removal plans and guidelines in advance, and the following are planned: snow removal implementation system (organization, implementation system, patrols, snow-related consultation desk, snow removal contractors, snow removal vehicles, snow removal work evaluation, snow removal dispatch orders, public awareness activities, etc.), snow removal classification (main roads, auxiliary main roads, suburban main roads, fully contracted work areas, designated contracted work areas, local roads, road clearing routes, etc.), snow removal implementation methods (snow removal standards, snow removal dispatch standards, snow removal methods, snow removal duration, snow removal time, snow removal time, routes, snow disposal sites, anti-slip measures by applying de-icing agents, etc.), and types of snow removal work (normal snow removal work, fresh snow removal work, road surface leveling work, widening snow removal work, transport snow removal work, intersection snow removal work, bottleneck work, narrow road snow removal work, snow peeling work, de-icing agent application work, etc.). In snow removal operations, after patrols by road administrators and snow removal companies, snow removal implementation plans and work plans are formulated, snow removal work is carried out, and patrols are also conducted after the snow removal work is completed. This information may be used as data for prioritizing and deciding on the implementation of snow removal and clearing operations. Furthermore, information corresponding to the details of snow removal work and responses (work status, operational records, daily work reports, patrol results, etc.) may be stored within the system as snow removal information. This information regarding snow removal and clearing conditions may be used in the analysis unit 620 for correlation analysis between work performance and changes in road surface, evaluation of the relationship between working time and finish quality, and confirmation of spatiotemporal consistency with the location where complaints occurred. Furthermore, the improvement department may readjust and optimize its decision-making logic and allocation policies through actions such as detecting deviations from work guidelines and implementation standards, evaluating whether snow removal responses are sufficient or insufficient, and comparing the performance of each snow removal contractor. In addition, the road snow removal department may implement measures such as revising the snow removal implementation plan, redesigning the work sequence, dynamically adjusting resource allocation, and optimizing the use of snow disposal sites, based on these analysis results and performance evaluations. Furthermore, patrol results and finish evaluation information from in-vehicle video and images (conventional methods) can also be used as feedback learning data, and the configuration may contribute to improving the accuracy of judgment models and AI (artificial intelligence) processing. In addition, images, videos, and acceleration sensor data collected by patrol vehicles regarding the road surface conditions after snow removal may also be included as information used for performance evaluation and feedback learning. Furthermore, this information, along with information regarding the status of snow removal and clearing, reflects changes in road surface conditions and the finished state. In this specification, the snow removal and clearing information may be configured to partially include elements of road surface information. This information can be used by the road snow removal department to formulate snow removal plans, assign priorities, and decide whether to perform snow removal during the day or at night. It may also be structured to be integrated and analyzed with other information as needed. In this specification, the evaluation information derived by the snow removal decision support system 600, specifically the evaluation information regarding the necessity and urgency of snow removal, will be referred to as "evaluation information (conventional)" for convenience and will be described separately from the evaluation information in the snow removal operation platform and the Snow Country GX platform described later. In this specification, information regarding snow removal and clearing conditions is used as a broad concept that includes at least work plans, dispatch orders, implementation status, work status, operational records, daily reports, patrol results, etc., and depending on the context, may also include information regarding snow removal and clearing conditions from paragraph 0046 onwards.

[0028] The information acquisition unit 610, which operates in the snow removal decision support system 600, acquires road surface conditions from the road surface condition provision server 100, traffic conditions from the traffic condition provision server 200, road space conditions from the road space condition provision server 300, weather conditions from the weather condition provision server 400, vehicle driving conditions from the vehicle driving conditions provision server 500, road service conditions from the road service conditions provision server, fiber optic survey conditions from the fiber optic survey conditions provision server, satellite survey conditions from the satellite survey conditions provision server, resident notification conditions from the resident notification conditions provision server, and snow removal conditions from the snow removal conditions provision server via the network NW. Road surface conditions provided by the road surface condition provision server 100 are stored as road surface information 640. Traffic conditions provided by the traffic condition provision server 200 are stored as traffic information 650. Road space conditions provided by the road space condition provision server 300 are stored as road space information 660. Weather conditions provided by the weather condition provision server 400 are stored as weather information 670. Vehicle driving conditions provided by the vehicle driving conditions provision server 500 are stored as vehicle driving information 680. Road service conditions provided by the road service condition provision server are stored as road service information. Fiber optic survey conditions provided by the fiber optic survey condition provision server are stored as fiber optic survey information. Satellite survey conditions provided by the satellite survey condition provision server are stored as satellite survey information. Resident notification conditions provided by the resident notification condition provision server are stored as resident notification information. Snow removal conditions provided by the snow removal condition provision server are stored as snow removal information. The information acquired by the information acquisition unit 610 may include at least two of the following types of information, including at least information on resident reports: road surface conditions, traffic conditions, road space conditions, weather conditions, vehicle driving conditions, road service conditions, fiber optic survey conditions, satellite survey conditions, resident report conditions, and snow removal conditions. It is not necessary to acquire all of the information. Furthermore, this information is passed on to the analysis unit 620, decision unit 630, road snow removal unit, improvement unit, etc., and used for judgment, analysis, learning, etc. in each unit. The information acquisition unit 610 may also be configured to manage information by adding metadata such as the type of information, source, acquisition frequency, acquisition time, and related route information, enabling dynamic and real-time information utilization. Furthermore, the information acquisition unit 610 may use AI methods (machine learning models, rule-based inference, optimization or search algorithms, probabilistic models, or some or all of heuristic processing) to perform preprocessing such as data extraction, normalization, noise reduction, attribute estimation, spatiotemporal correction, and anonymization before storing the data. These models may be updated through online learning or additional learning depending on the operational status. Furthermore, the information acquisition unit 610 may be configured to acquire information from external organizations such as administrative agencies (police, fire department, Self-Defense Forces, etc.), infrastructure operators (telecommunications, electricity, gas, water, sewage, etc.), transportation operators (railways, buses, etc.), snow removal companies, construction and civil engineering companies, and tourist facility operators. This information may include requests regarding disasters, recovery, and snow removal, traffic disruptions, facility conditions, and evacuation support needs. Furthermore, the information acquisition unit 610 may be configured to dynamically select and limit the types of information to be acquired according to the system's purpose, status, load, etc. For example, if there is a concentration of resident reports, it may be configured to prioritize the acquisition of highly relevant information such as report information, traffic information, and population density.

[0029] The analysis unit 620, which operates within the snow removal decision support system 600, analyzes various types of information acquired by the information acquisition unit 610, including road surface information 640, traffic information 650, road space information 660, weather information 670, vehicle travel information 680, road service information, fiber optic survey information, satellite survey information, resident notification information, and snow removal information, for each type of information, and stores the analysis results 690. The analysis unit 620 is one of the core components of the snow removal decision support system 600 and performs individual and integrated analysis based on the acquired information. The analysis results 690 may be visualized in the form of a map display, tabular format, time-series display, graph, etc., and may be configured to contribute to subsequent decision-making processes and decisions. The analysis unit 620 may also have an integrated analysis function that allows for the cross-referencing and comparison of multiple pieces of information, in addition to individual analysis. For example, by mapping optical fiber vibration data, satellite image data, and vehicle driving anomaly data to the same area and comparing and aggregating the anomaly scores of each piece of information, it becomes possible to evaluate the overlap and accuracy of anomaly occurrences at that location and derive the necessity of snow removal with high accuracy. This complements the limitations of individual information and enables decision-making based on the consistency of multiple pieces of information.

[0030] Furthermore, the analysis unit 620 may incorporate an AI (artificial intelligence) model, for example, using a neural network or a decision tree-based machine learning model, to take multiple pieces of information as input and generate a snow removal necessity score (numerical or class classification) for each location as output. The output score is used as a decision criterion in the decision unit 630 and for prioritizing in the road snow removal unit. Furthermore, the analysis unit 620 may be configured to provide the analyzed information to the improvement unit as training data or update data, contributing to improving the accuracy of the decision model and retraining the model. Specifically, by using effect indicators obtained after snow removal (such as a decrease in resident reports, improvement in traffic speed, and changes in the number of complaints) as training signals and performing online learning to sequentially adjust the model weights, adaptive decisions can be made according to regional characteristics and seasonal trends. The training targets include date and time, location, content of reports, report density, complaint classification, congestion occurrence, traffic volume, snow accumulation conditions, snow removal history, etc., making it possible to construct statistical trends and reproducible decision logic for snow removal decisions. In addition, the analysis unit 620 may be configured to detect signs of disaster occurrence, and may be configured to perform a disaster screening analysis different from snow removal judgment using multiple types of abnormal information (such as a sudden increase in reports, traffic disruptions, weather warnings, abnormal vibrations of optical fibers, and surface anomalies in satellite images). Disaster analysis is performed in parallel with normal processing based on indicators and models specialized for disaster judgment. In addition, resident reports are subjected to AI (artificial intelligence)-based content sorting, urgency assessment, report density aggregation, geographical characteristic assessment of the target area, and integration with regional population or household numbers. The analysis unit 620 comprehensively evaluates these multi-stage evaluation results and has the function to assess the necessity and priority of snow removal in each region with high accuracy. Furthermore, the analysis unit 620 may be configured to focus on the time or period of information acquisition and perform weighting processing according to the reliability, immediacy, and impact on traffic of the information. Alternatively, the analysis unit 620 may be configured to weight the reliability or priority of various types of information (e.g., road surface information, traffic information, road space information, weather information, vehicle driving information, road service information, fiber optic survey information, satellite survey information, resident report information, snow removal information, etc.) obtained by the information acquisition unit 610 according to the time of acquisition, and perform analysis processing based on this weighting. Such weighting processing according to time of day may also be utilized in deciding whether to carry out snow removal during the day or at night. For example, if resident reports tend to be concentrated in the morning, a configuration that places more emphasis on the density of reports during that time period, or a configuration that distinguishes satellite data provided at night from daytime information and adjusts its weight accordingly, can enable highly reliable decisions based on the time axis. Furthermore, the analysis unit 620 may be configured to analyze the causal relationship between factors influencing changes in road conditions, trends in the occurrence of bad roads, and weather conditions or snow removal / clearing responses using statistical or machine learning methods. This makes it possible to accurately grasp the mechanisms of bad road occurrence and the effectiveness of snow removal / clearing responses, contributing to improved preventative dispatch decisions and priority evaluations. Furthermore, the snow removal necessity score is also used as one of the sub-scores that make up the snow removal evaluation score (overall score) as described in paragraph 0032.

[0031] These analysis results 690 are transmitted to the decision unit 630 and used as information to help determine the necessity of snow removal. They are also distributed to various components such as the road snow removal unit, road clearing unit, infrastructure maintenance unit, improvement unit, and prediction unit as needed, contributing to improved judgment and management accuracy across the entire system. Furthermore, the analysis results 690 may be visualized in conjunction with a GIS (Geographic Information System), and may be configured to contribute to feedback learning of the decision model through comparison with snow removal implementation history, comparative analysis with snow removal method selection history, and evaluation of implementation effectiveness. The system could also be structured to grasp trends in snow accumulation and road conditions by cross-referencing and analyzing past weather and traffic information. Furthermore, the system may be structured to analyze the causal relationship between factors influencing changes in road conditions, trends in the occurrence of bad roads, and weather conditions or snow removal measures, using statistical or machine learning methods. This would allow for a more accurate understanding of the mechanisms of bad road occurrence and the effectiveness of countermeasures, contributing to priority assessment and preventive decision-making. These analysis results can be used as information on changes in road conditions regarding the necessity and urgency of snow removal, and may be structured to contribute to priority assessment and work decisions. The outputted snow removal necessity score and snow removal evaluation score (paragraph 0032) can be displayed on a GIS (Geographic Information System) for each road section or area on the map, and may be color-coded (e.g., in a heatmap format) according to the score level. This allows operators and decision-makers to intuitively grasp high-priority areas and supports quick response decisions. Note that the information to be analyzed is not limited to all information; analysis may be performed using any combination of information depending on the target area, operational system, processing purpose, etc. Furthermore, by performing processes such as matching image data with non-image data (e.g., comparing image analysis results with optical fiber data), spatiotemporal interpolation processing for areas with frequent anomalies (for nighttime and severe weather conditions), and processing to improve the responsiveness of disaster response decisions (multifaceted risk assessment using AI and XAI), the system can be configured to provide advanced decision-making support that does not rely on a single sensor. Furthermore, the system may be configured to obtain distribution information regarding the population or number of households in a region from statistical information databases or administrative information, and to integrate this information with the density and urgency of resident reports, thereby enabling snow removal decisions that are appropriate to the population size and density of the region. In addition, the system may be configured to dynamically adjust the weighting coefficients of each analysis indicator based on the regional attributes. For example, in areas with a large elderly population, the urgency of reports may be given a higher weight, and in areas near main roads, the weighting of traffic impact may be increased, thereby realizing priority evaluations that are tailored to the specific circumstances of each region. Furthermore, the analysis unit 620 may be configured to perform priority weighting processing using distribution information on vulnerable road users (elderly people, people requiring care, schoolchildren, people with disabilities) in the target area, as well as location information of medical facilities, welfare facilities, and educational facilities, and to correct the priority of snow removal and clearing operations for each road from the perspective of prioritizing welfare and the protection of human lives. Traditionally, decision-making has been primarily based on empirical rules and rules, making it difficult to integrate and evaluate large amounts of unstructured information (resident reports, images, social media, vibration data, etc.). In contrast, this invention uses AI (artificial intelligence) to learn and estimate the nonlinear relationships and time-series trends between this information, enabling highly sophisticated and reproducible decision-making processes. Furthermore, the snow removal necessity score generation process performed by the analysis unit 620 is comprised of multivariate analysis or machine learning methods that take multiple pieces of information as input. For example, variables such as resident report density, traffic speed reduction rate, number of days without snow removal, snow depth, temperature, snowfall forecast, and past snow removal history are used as features, and these are normalized or categorized to construct the input vector. The learning model may be a decision tree-based classification / regression model such as Random Forest or XGBoost (eXtreme Gradient Boosting), or a numerical regression model using a three-layer deep neural network. The model's training data will be based on past snow removal results (whether or not a team was dispatched, priority, regional population composition, number of complaints, etc.), and weight optimization will be performed using supervised learning. The output is defined as a score from 0 to 100 for each target area, with a higher score indicating a greater urgency for snow removal. This score, stored as analysis result 690, is used by the decision unit 630 to determine whether or not action is necessary through threshold determination, etc. This configuration enables the analysis unit 620 to integrate and analyze both standardized information (such as sensor data) and non-standardized information (such as resident reports and video analysis results), achieving flexible decision-making support that goes beyond conventional rule-based processing.

[0032] Based on multiple pieces of information acquired from the information acquisition unit 610, the analysis unit 620 evaluates indicators such as the necessity, urgency, traffic impact, and difficulty of snow removal for each road or management unit, including the snow removal necessity score generated in paragraph 0030, and derives integrated evaluation information (conventional) that includes these indicators. The evaluation information (conventional) is expressed in numerical score or hierarchical rank format and is used in formulating snow removal implementation plans. Furthermore, the analysis unit 620 evaluates the workload for each work section based on predicted snow quality, moisture content, snow load, etc. The workload is converted into indicators such as required work time, heavy equipment load, and number of workers, and expressed as a workload score. If a high workload is predicted, adjustments are made by bringing forward the implementation date or reallocating resources. Furthermore, the analysis unit 620 acquires time-series data on future snowfall, temperature fluctuations, rainfall, and other forecast information (temperature, humidity, snow quality, moisture content, snow load) based on short-term weather forecasts. Based on this forecast information, it determines the start time for snow removal preparation work and identifies work sections that require prior attention. This improves the ability to respond to sudden snowfall and freezing, and increases the accuracy of planning. Furthermore, predictive information regarding snow load may be used to evaluate the risk that the load will pose to traffic or road structures. If the predicted snow load exceeds a threshold, the priority of work in the affected section will be increased, and a snow removal and clearing plan that takes this risk into consideration will be formulated. Regarding the evaluation of workload, the workload for each section is numerically scored by setting load conversion coefficients linked to the average working speed (km / h) for each heavy machine and fuel and labor costs, based on past snow removal daily reports and operational records (snow depth, operating hours, fuel consumption, road length, number of vehicles, etc.). Specifically, the required workload (t / km or cubic meters / km) is calculated according to the length, width, and snow depth of the target section, and the time or cost required to process that workload is estimated. This enables relative workload comparisons with other sections and the leveling of operational plans. Furthermore, regarding the utilization of short-term weather forecast information, time-series data such as the probability of snowfall, temperature, wind speed, humidity, and presence or absence of rainfall for each region will be obtained from a Web API or publicly available data from the Japan Meteorological Agency, and snow removal decisions will be made based on statistical weather pattern analysis. For example, if snow removal was frequently required in the past under the conditions of "temperature below -2°C" and "probability of snowfall of 70% or higher," the system will be configured to increase the likelihood of pre-dispatch decisions when these conditions are met. Regression analysis, Bayesian inference, random forests, or time-series forecasting models (e.g., LSTM) may be used for the model. The snow removal evaluation score is calculated as a weighted average of various factors such as the necessity, urgency, traffic impact, and difficulty of the work. If this score exceeds a predetermined threshold, the decision unit 630 is notified that the area is a candidate for priority response or pre-deployment. Furthermore, the score calculation logic and threshold may be configured to be editable externally as a parameter setting file according to regional characteristics and administrative policies. The analysis unit 620 may perform a process to evaluate the consistency with the budget using past performance information such as trends in changes in priority in the target area, operating hours, and snow removal unit costs. Alternatively, the system may be configured to calculate estimated implementation costs based on the required working hours, number of vehicles, and number of personnel for each potential snow removal solution, and then determine whether or not to implement the solution based on a comparison with the budget. Furthermore, the evaluation information (conventional) may be structured to derive an integrated score by applying predetermined weights to multiple indicators. For example, a weighted average score such as "traffic impact 70 points, workload 30 points" may be used for the overall evaluation. The system may also include a process of classifying the data into categories such as "priority response," "normal response," and "delayed response" based on the score. This clarifies the basis for decisions made when formulating snow removal plans in road snow removal departments, thereby increasing practicality. Note that the snow removal evaluation score in the snow removal decision support system and the contractor evaluation score in the snow removal operation platform are distinguished from each other.

[0033] In the snow removal decision support system 600, "evaluation information (conventional)" refers to analysis results derived for each road or management unit based on multiple pieces of information acquired by the information acquisition unit 610, and includes at least the priority of snow removal response. Specifically, it includes various indicators based on the necessity of snow removal, urgency, traffic impact, difficulty of work, amount of snow, risk of freezing, traffic history, density of resident reports, remaining budget, distribution of vulnerable road users (elderly, those requiring care, schoolchildren, people with disabilities), and the location of medical, welfare, and educational facilities. These indicators may be configured to dynamically adjust their weighting according to regional attributes and facility distribution. Furthermore, these evaluation indicators can be integrated with AI (artificial intelligence) weather and traffic forecasting models and urgency assessments of resident reports, and can be structured as evaluation information (conventional) that includes predictions of short-term response needs. In addition, evaluation information (conventional) can be expressed in numerical score or hierarchical rank format and used in planning processes by road snow removal departments. Moreover, evaluation information (conventional) of the finished product based on patrol results and in-vehicle images may also be incorporated as feedback learning data and used in constructing evaluation information (conventional).

[0034] In the snow removal decision support system 600, "decision information" refers to information related to decisions regarding the necessity, urgency, priority, timing of response, and feasibility of snow removal, derived by the analysis unit 620 or the improvement unit. The decision information is generated based on various information and evaluation information (conventional) obtained from the information acquisition unit 610 and may be configured to be used for formulating snow removal implementation plans, assisting local government decisions, determining the necessity of external support, and dynamic resource reallocation processing. The decision information is expressed in score format or judgment categories and is used as the basis for decision-making in subsequent processing.

[0035] The analysis unit 620 may perform analysis based on a specific definition of "bad road" in order to evaluate the necessity and priority of snow removal. Here, a bad road refers to a road condition in which vehicle travel becomes difficult due to unevenness of the snow-covered road surface, compacted snow thickness, snow quality, snow accumulation, freezing, snowmelt, bowl-shaped deformation, rutting, etc. The analysis unit 620 may be configured to perform a bad road evaluation for each target section using one or more of these indicators and to calculate a bad road score that will be useful in determining the priority of snow removal. For example, in evaluating unevenness, the amplitude and variability are calculated based on the vertical fluctuation values ​​obtained from the vehicle's acceleration sensor, and these are quantitatively expressed as an unevenness index. Furthermore, by setting thresholds and evaluating in stages (e.g., an amplitude of 5 mm or more is a cautionary level, and 15 mm or more is a level requiring snow removal), it contributes to the standardization of on-site judgment. Furthermore, compacted snow thickness can be evaluated using directly measured values ​​or estimated step amounts from acceleration, etc., as a guideline for thickness. In addition, regarding road surface conditions, image data obtained from fixed-point cameras, in-vehicle cameras, drones, smartphones, etc., may be used to perform image analysis such as object detection and image segmentation to recognize snow accumulation, snow piles, freezing, snowmelt, etc., and convert them into a road condition index. The analysis unit 620 may include a process that uses these road condition indicators to calculate the percentage of road conditions exceeding a predetermined standard for each management unit, which is a division of the road into route units or work sections. The system may then perform scoring based on a tiered evaluation according to the percentage (e.g., 0-20% = low, 21-50% = medium, over 51% = high) and reflect this in the priority order for snow removal. Furthermore, the analysis unit 620 may be configured to manage road information in units of branch numbers using a GIS (Geographic Information System) and to perform detailed evaluations of individual sections on main roads and local roads. This configuration makes it possible to identify localized hazardous points, such as between intersections and at points of gradient change, in addition to the entire route, contributing to the prioritization and efficiency of snow removal and clearing operations. Furthermore, the system may be configured to evaluate the reliability of information for the relevant section based on factors such as the mileage and frequency of patrol vehicles, and to perform correction and exclusion processing if the reliability falls below a predetermined threshold. In addition, the analysis unit 620 may use multiple pieces of information (such as road conditions, traffic, weather, vehicle movement, and road services) to correct or weight average the snow removal priority score using a pre-configured rule-based logic. This configuration enables consistent decision-making based on both rules and actual measurement data. Furthermore, the analysis unit 620 in this embodiment may be configured to comprehensively analyze distribution information of vulnerable road users (elderly people, people requiring care, schoolchildren, people with disabilities, etc.) in the target area, weather forecast values ​​related to snow load, and snow removal response history and regional characteristics for each management unit, and generate a unique risk score that numerically represents the urgency of snow removal response for each road or work section. This score is used for priority evaluation and correction processing of response order. Furthermore, when formulating snow removal plans for road snow removal departments, it is also possible to group target areas by priority based on evaluation information (conventional methods) and create schedules for each time slot and work method. For example, by prioritizing early morning work on routes and areas requiring emergency response, and utilizing times with low traffic volume, it is possible to create a system that balances efficiency and reliability by arranging work in a time-slot format.

[0036] The decision unit 630, which operates in the snow removal decision support system 600, makes an integrated and comprehensive decision on the necessity of snow removal based on some or all of the analysis results 690 of various information (road surface information 640, traffic information 650, road space information 660, weather information 670, vehicle travel information 680, road service information, fiber optic survey information, satellite survey information, resident notification information, and snow removal information) analyzed by the analysis unit 620, and stores the decision result 695. The judgment result 695 may include the priority of snow removal at each location, the snow removal necessity score, the recommended time of day for snow removal (daytime or nighttime), or whether disaster response is necessary. The judgment result 695 may be displayed in a way that visualizes it on a map, in a list, in a time-series display, or on a real-time monitoring screen such as a dashboard. Furthermore, the decision unit 630 may be configured to support or automate decision-making using machine learning or rule-based AI (artificial intelligence) models, learning statistical trends, thresholds, correlations, etc., of the analysis results 690 to improve and expedite snow removal decisions. Furthermore, the decision unit 630 may be configured to perform a process to determine whether disaster response is necessary (access to disaster prevention bases, road clearing, securing emergency transport routes, etc.) if a disaster occurs or its signs are detected, in addition to snow removal decision-making. In the event of a disaster, the system switches from the normal snow removal decision-making logic to disaster response decision criteria, and appropriate prioritization and response policies are formulated. Furthermore, the decision unit 630 may be configured to cooperate with the road snow removal unit and, based on the judgment result 695, determine whether snow removal is necessary for the target road, its priority, and the time of implementation (daytime / nighttime), and to support the process of formulating and updating a snow removal implementation plan based on this information. This ensures consistency between judgment and plan, and improves the efficiency of on-site response. Furthermore, the decision unit 630 may be configured to forecast the likelihood of needing snow removal in the near future (for example, a few hours to the next day) based on information such as weather forecasts, snow removal performance, and trends in resident reports. In addition to weather forecast information, various types of information obtained by the information acquisition unit 610 (e.g., road surface information, traffic information, road space information, vehicle driving information, road service information, fiber optic survey information, satellite survey information, etc.) may be used in an integrated manner in the future forecasting process of the decision unit 630. Functionally, this forecasting function differs from the prediction unit, which deals with medium- to long-term risks related to extreme snowfall and snow damage, and is intended for short-term operational decisions and notifications. The forecast results are handed over to the road snow removal unit and used for prior snow removal preparation, provisional setting of priorities, and consideration of response time slots. Furthermore, the judgment result 695 is shared with components such as the Road Clearing Department, Infrastructure Maintenance Department, and Improvement Department, and is used as input information for each department's processing and implementation plan formulation. Furthermore, the decision result 695 may be configured to be notified and provided to external parties (local government officials, snow removal businesses, residents, etc.) via the information provision unit, and the information provision unit may be configured to have the function of notifying in real time the decision result from the decision unit 630 (snow removal necessity, priority, forecast results, etc.) and the medium- to long-term risk forecast result from the forecast unit via a web dashboard, notification email, smartphone push notification, administrative system linkage API, etc. In addition, the judgment result 695 may be used to optimize and retrain the judgment logic in the improvement unit, thus supporting the system's self-improvement. This enables feedback learning with actual snow removal results and complaint information, contributing to improved accuracy in snow removal and clearing. Thus, the decision unit 630 integrates and evaluates the diverse information acquired and analyzed by the analysis unit 620, and functions as a component for making decisions on snow removal, disaster response, and short-term snow removal forecasts. This enables comprehensive and flexible decision-making support, not only for snow removal but also for infrastructure maintenance and disaster response. Furthermore, the snow removal decision support system 600 may be configured to include a function that dynamically re-evaluates and reconstructs analysis results and response plans when information regarding disasters, damage, and recovery changes. This enables flexible decision support that can respond immediately to sudden changes in on-site conditions.

[0037] Figure 11 is a flowchart showing an example of the flow of the snow removal decision support system 600. The information acquisition unit 610 periodically acquires information on road surface conditions from the road surface condition provision server 100 (for example, every few minutes) (S10). The information acquisition unit 610 periodically acquires information on traffic conditions from the traffic condition provision server 200 (for example, every few minutes) (S11). The information acquisition unit 610 periodically acquires information on road space conditions from the road space condition provision server 300 (for example, every few minutes) (S12). The information acquisition unit 610 periodically acquires information on weather conditions from the weather condition provision server 400 (for example, every few minutes) (S13). The information acquisition unit 610 periodically acquires information on vehicle driving conditions from the vehicle driving condition provision server 500 (for example, every few minutes) (S14). The analysis unit 620 then extracts areas where the road width has been significantly reduced due to snow accumulation from the road surface information 640 (S15). The analysis unit 620 extracts sections where traffic congestion is continuously occurring from the traffic information 650 (S16). The analysis unit 620 extracts dangerous areas where snowdrifts are high (for example, about 1m or more) and visibility is poor from the road space information 660 (S17). The analysis unit 620 extracts areas where the amount of snowfall since the start of the snowfall has reached 10cm or more from the weather information 670 (S18). The analysis unit 620 extracts sections where bus delays have continued for 30 minutes or more from the vehicle driving information 680 (S19). Next, based on the results of the above analysis, the decision unit 630 makes a comprehensive determination of the necessity of snow removal and, if necessary, makes a decision to execute the appropriate action (S20). Figure 11 shows an example of a typical processing flow and is not limited thereto. In this embodiment, various types of information not shown, such as snow removal information, resident notification information, fiber optic survey information, satellite survey information, and road service information, can also be acquired by the information acquisition unit 610 and may be subject to analysis processing by the analysis unit 620. These analysis results are used in the decision unit 630 for snow removal decisions and prioritization, and this information may also be used by the road snow removal unit to formulate snow removal implementation plans and by the improvement unit to optimize decision logic.

[0038] Figure 12 shows an example of the criteria used by the decision unit 630. The decision unit 630 can determine the necessity of snow removal measures based on triggers such as: heavy snowfall of 10 cm / h or more (1st element) 631; road width reduced by snow accumulation resulting in bus delays of 30 minutes or more (2nd element) 632; snow melting due to sunny weather, causing severe unevenness on snow-covered roads and worsening traffic congestion (3rd element) 633; ​​heavy snow warning issued, causing vehicles to slip and skid, as well as congestion due to vehicles getting stuck in the snow (4th element) 634; and frequent tire lock-ups on icy roads at -10°C, resulting in self-inflicted accidents and congestion (5th element) 635. Furthermore, some or all of the judgment results 695 determined by the decision unit 630, the analysis results 690 derived by the analysis unit 620, and the various information (road surface information, traffic information, road space information, weather information, vehicle driving information, road service information, fiber optic survey information, satellite survey information, resident notification information, snow removal information) acquired by the information acquisition unit 610 can be used to send email notifications to snow removal companies, provide data to road administrators' GIS (Geographic Information System), link to autonomous driving systems and MaaS (Mobility as a Service), provide information to government agencies (police, fire department, Self-Defense Forces, etc.) and the mass media, and make information publicly available to road users and residents (homepage, smartphone app, etc.) through the information provision unit, etc. It should be noted that these information provision processes do not necessarily need to go through the information provision unit, allowing for structural flexibility. As an example of the function of disclosing information to road users and residents, the following information may be provided: unevenness of the snow-covered road surface, thickness of compacted snow on the road surface, traffic congestion status, camera images, height of snow levees around routes and intersections, future snowfall forecasts and snow depth, bus route delay information, locations where vehicles are stuck, snow removal orders and implementation status, snow removal orders and implementation status, patrol results, etc. Furthermore, this snow removal decision support system is characterized by being configured to transmit this information or the analyzed results, judgment results, snow removal and snow removal implementation plans, etc., to mobile terminal devices (mobile phones, smartphones, tablet devices, laptops, game consoles, etc.) or fixed terminal devices (desktop computers, smart TVs, set-top boxes, digital signage, kiosk terminals, car navigation systems, car display audio systems, etc.). Figure 12 shows an example of typical decision criteria and is not limited to this. In this embodiment, various types of information not shown, such as snow removal information, resident notification information, fiber optic survey information, satellite survey information, and road service information, are also used as decision-making factors by the decision unit 630. Furthermore, these decision results may be used for formulating snow removal implementation plans by the road snow removal unit, optimizing the decision logic by the improvement unit, and processing notifications by the information provision unit.

[0039] The snow removal decision support system 600 is configured to include an improvement unit that continuously improves some or all of the analysis results 690 (including the analysis method) obtained by the analysis unit 620, the decision results 695 output by the decision unit 630, and some or all of the decision criteria. The improvement unit is a component that realizes "continuous improvement" in the snow removal decision support system 600, and aims to improve decision accuracy, execution appropriateness, resident satisfaction, etc., by forming a learning, verification, and reconstruction loop within a series of processes such as acquisition, analysis, judgment, execution, and evaluation of various information. The Improvement Department includes re-evaluation and retraining functions, including those for AI (artificial intelligence) analysis methods themselves. It uses technologies such as statistical processing, machine learning, and deep learning to improve information acquisition methods, analysis logic, judgment criteria, and prioritization algorithms. For example, this includes optimizing threshold settings, redefining evaluation items, and revising input information selection criteria. Furthermore, the improvement unit may include a function to analyze the causal relationship between factors influencing changes in road conditions, trends in road deterioration, and weather conditions or snow removal measures. This allows for understanding the effectiveness of countermeasures and the mechanisms of occurrence, contributing to improved accuracy in decision-making logic and prioritization. Furthermore, the Improvement Unit uses road surface information, traffic information, road space information, weather information, vehicle driving information, road service information, fiber optic survey information, satellite survey information, resident report information, and snow removal information acquired by the Information Acquisition Unit 610 as input data, and performs learning processing and model improvement based on the accumulated history of analysis results 690 and judgment results 695 obtained from this data. In addition, the Improvement Unit may be configured to use various information obtained by the Information Acquisition Unit 610 (e.g., road surface information, traffic information, road space information, weather information, vehicle driving information, road service information, fiber optic survey information, satellite survey information, resident report information, snow removal information, etc.) as learning targets, in addition to the analysis results from the Analysis Unit 620, the judgment results from the Decision Unit 630, and the implementation status of the Road Snow Removal Unit. In particular, by coordinating with the resident report status provision server, the system may be configured to dynamically reconfigure the priority criteria and timing of snow removal based on triggers such as the recurrence of reports or an increase in complaints after snow removal. Furthermore, the improvement unit may be configured to collect and analyze traffic congestion information, recurrence of reports and complaints, and road conditions (remaining snow, ruts, uneven surfaces, reduced width, etc.) obtained by patrol vehicles (including not only road administrators but also snow removal companies), and feed this back into future decisions and implementation plans, thereby enabling highly accurate evaluation and improvement of the consistency between actual results and decisions. The Improvement Department is responsible for optimizing the formulation of future snow removal plans by analyzing the correlation between the snow removal implementation plan and priority assignment results formulated by the Road Snow Removal Department, the implementation history after snow removal, snow removal quality, snow removal completion time, remaining snow conditions, and feedback from residents (complaints and further reports). Furthermore, the improvement unit may be configured to continuously improve prediction models related to road damage and subsurface cavities in cooperation with the infrastructure maintenance unit, and to optimize the algorithms for determining road clearing routes and priority locations in cooperation with the road clearing unit. In addition, it may be configured to improve the prediction accuracy of the models to be improved in accordance with the results of future snowfall, traffic, and disaster predictions in cooperation with the prediction unit. The learning timing in the improvement unit is selected as appropriate according to the operational situation, such as nighttime batch processing, periodic learning at regular intervals, or trigger processing when an exceptional surge in complaints or abnormal weather is detected. Furthermore, the learning model can be configured to apply multiple methods, such as deep learning, decision trees, random forests, and reinforcement learning, depending on the application. Furthermore, the improvement unit may be configured to evaluate the relationship between the time-of-day weighting results in the analysis unit 620 and the actual snow removal results, resident satisfaction, and re-reporting status, and to optimize the weighting parameters for subsequent attempts. This makes it possible to optimize the decision-making system by taking into account reporting trends, differences in traffic volume, and the difficulty of snow removal according to the time of day. In this invention, the relationship between changes in road conditions (e.g., occurrence and resolution of bad roads), implementation details, and weather conditions is analyzed as a causal model using regression analysis, correlation analysis, etc., and the effectiveness of snow removal is quantitatively evaluated. The improvement department accumulates past snow removal results, weather conditions, road conditions, and trends in resident reports as learning data, and continuously improves the evaluation results and judgment model output through supervised learning. In particular, error feedback through consistency analysis is emphasized. Furthermore, the improvement unit may also be equipped with a function to statistically analyze the errors and discrepancies between the snow removal necessity score, workload score, predicted snowfall amount, and priority correction information based on population density output by the analysis unit 620 and the actual snow removal implementation results. This allows for the detection of excesses or deficiencies (overestimation or underestimation) in the score design logic, and enables the refinement of the entire analysis logic, such as resetting thresholds or reviewing weighting parameters. Furthermore, a mechanism may be provided to dynamically adjust the confidence coefficient for each information source by evaluating the correlation between the integrated analysis results in the analysis unit 620 (e.g., aggregated values ​​of anomaly scores from multiple sources) and the actual traffic disruptions, number of complaints, sections where snow removal was not completed, etc. Furthermore, in generating scores using AI (artificial intelligence) models, it is also possible to use the analysis results of XAI (explainable AI) in conjunction with the AI ​​model to evaluate the validity and transparency of the reasoning behind the decisions, and to reinforce the training data or revise the feature selection for models that lack explainability. Furthermore, the improvement unit may be configured to analyze the consistency between information on changes in road conditions after snow removal, traffic impact information, and weather information, and the snow removal response details automatically recorded by the system. This allows for a quantitative understanding of the relationship between actual road changes and response results, and enables continuous feedback processing to refine and optimize the snow removal implementation plan or response logic. These processes may be performed by either the analysis unit 620 or the improvement unit.

[0040] In this specification, "management information" refers to attribute information associated with road sections in the target area, and includes information such as the type of management unit (route unit, work section unit, etc.), the managing entity (municipalities, prefectures, road administrators, etc.), past snow removal and clearing performance, road width, route type (main roads, local roads, etc.), budget allocation, location of available construction companies, and the number of operating machines. This information is used by the Analysis Department 620 or the Improvement Department to determine whether snow removal and clearing is feasible, to adjust response policies, or to correct priorities.

[0041] The snow removal decision support system 600 may be configured to include a prediction unit that predicts future road conditions and disaster risk in the medium to long term in preparation for special events such as extreme snowfall and snow damage. The prediction unit estimates future large-scale snowfall, traffic paralysis, and disaster risk based on the analysis results 690 accumulated by the analysis unit 620, the history of decision results 695 output by the decision unit 630, past snow removal implementation history, complaint trends, weather change trends, topography, road structure, etc. The prediction unit may be configured to quantitatively or probabilistically predict, for example, the possibility of traffic disruptions, simultaneous vehicle jams at multiple locations, concentrated reporting areas, or the likelihood of snow damage reaching disaster levels, when snowfall of 100 cm or more is expected within 24 hours. This will enable the formulation of wide-area countermeasures, emergency resource deployment, and road clearing preparations in advance, separate from normal snow removal decisions. Furthermore, the prediction unit can learn time-series fluctuations, geographical distribution, similarities with past disaster patterns, etc., using an AI (artificial intelligence) model, and output high-resolution future predictions. Input factors may include past snowfall history, temperature trends, wind direction and speed, snow density, snow removal delays, resident report volume and complaint density, traffic flow attenuation trends, stuck vehicle occurrence history, road surface images, etc. Furthermore, the prediction unit may be configured to work in conjunction with the analysis unit 620 and the improvement unit to improve the reliability of the prediction results and retrain the model, and it may also be equipped with functions to periodically verify the error of the prediction and the deviation from reality. This makes it possible to continuously improve the prediction accuracy. The prediction results are provided to the Infrastructure Maintenance Department or the Road Clearing Department and are used to identify areas where snow removal is difficult and to plan road clearing routes. Furthermore, information based on the prediction results is integrated and evaluated as a risk score through the Analysis Department 620, and may be used by the Improvement Department to tune the prediction model and decision logic. In this embodiment, the prediction unit is functionally distinct from the short-term snow removal "forecast" based on weather forecasts performed by the decision unit 630, and is configured to prepare for medium- to long-term risks such as large-scale snow damage and complex disasters.

[0042] The snow removal decision support system 600 is activated when the decision unit 630 determines the need for snow removal, and includes a road snow removal unit that formulates and manages a snow removal implementation plan for the target road. The road snow removal unit is configured to respond flexibly not only to routine snow removal during normal times, but also to large-scale responses caused by heavy snowfall of disaster magnitude. Local governments (road administrators) formulate snow removal plans and snow removal work guidelines in advance, which include the following: snow removal implementation system (organization, implementation system, patrols, snow-related consultation desk, snow removal contractors, snow removal vehicles, snow removal work evaluation, snow removal dispatch orders, public awareness activities, etc.), snow removal classification (main roads, auxiliary main roads, suburban main roads, fully contracted work areas, designated contracted work areas, local roads, road clearing routes, etc.), snow removal implementation methods (snow removal standards, snow removal dispatch standards, snow removal methods, snow removal length, snow removal time, snow removal time, routes, snow disposal sites, slip prevention measures such as de-icing agent application), and types of snow removal work (normal snow removal work, fresh snow removal work, road surface leveling work, widening snow removal work, transport snow removal work, intersection snow removal work, bottleneck work, narrow road snow removal work, snow peeling work, de-icing agent application work, etc.). This information is registered in advance with the road snow removal department and used as reference information when the system formulates implementation plans. The Road Snow Removal Unit has the primary function of formulating and updating dynamic snow removal implementation plans. Based on various information acquired from the Information Acquisition Unit 610, the analysis results 690 from the Analysis Unit 620, and the judgment results 695 from the Decision Unit 630, it determines the priority order for each target area and route, the working hours (day and night), and the work categories. The Road Snow Removal Unit may also be configured to determine the appropriate working hours for snow removal based on the analysis results of the acquired information, and to formulate and update the snow removal implementation plan based on that determination. In determining the working hours, for example, while nighttime is often suitable for snow removal under normal circumstances due to low traffic volume, in the event of heavy snowfall of disaster magnitude causing traffic disruptions, the system may be configured to prioritize life-saving efforts and carry out snow removal during the daytime. This makes it possible to improve the safety, efficiency, and resident satisfaction of the implemented work. Furthermore, priority adjustments and re-evaluations are carried out by integrating and processing multiple pieces of information, including resident reporting status information. In this specification, the snow removal implementation plan is distinct from the annual snow removal plan predetermined by the local government. Instead, it refers to a dynamic implementation plan that is formulated and updated daily or in real time based on the latest information obtained by the snow removal decision support system 600. Similarly, the assignment of priorities is also structured to be constantly updated and re-evaluated. The areas targeted for snow removal are linked to predefined "route" or "work section" units, and the road snow removal department sets and updates priorities, work types, and implementation timings for each of these management units. By linking route codes or work section IDs with various information (report density, snow removal history, complaint history, etc.), it becomes possible to formulate highly accurate work plans. Furthermore, the road snow removal unit may incorporate an AI (artificial intelligence) analysis and decision-making model. This model can use various sensor information, camera images, and past snow removal records as training data to construct a model that estimates snow accumulation trends and the risk of traffic disruptions. For example, the snow conditions obtained through image recognition may be input into the training model, the degree of snow removal needed to be corrected may be output, and the implementation plan may be adjusted based on this. Furthermore, conventional integrated evaluation processing that is not based on AI (artificial intelligence) can also be carried out in parallel. This includes configurations that integrate resident reporting density, reporting methods (SNS, telephone, etc.), traffic delay information, snow depth, presence or absence of visibility obstruction, and snowplow location information, and then perform prioritization using non-AI weighting processing. Furthermore, the analysis unit 620 or the decision unit 630 may compare the implementation cost for each candidate response with the current remaining budget, automatically adjust the priority of the responses if the budget is exceeded, and register the response as a candidate for requesting external support (wide-area cooperation). This makes it possible to implement a flexible and sustainable snow removal plan even under financial constraints. The road snow removal and clearing unit may work in cooperation with the improvement unit to continuously evaluate and improve the appropriateness and effectiveness of the snow removal and clearing implementation plan based on images and acceleration data from patrol vehicles obtained after snow removal, as well as the occurrence of complaints and repeat reports. Alternatively, the system may be configured to dynamically adjust the work plan according to the time of day with the most reports, the time of day with the most traffic, etc., by utilizing the results of applying time-based weighting analyzed by the analysis unit 620. Furthermore, by coordinating with the infrastructure maintenance department and the road clearing department, it may be possible to share information about road damage and areas difficult to clear that are discovered during snow removal operations, and to coordinate repair and clearing work. In addition, by coordinating with the forecasting department, it may be possible to identify locations where snow removal is expected to be difficult in the future and areas where snow damage is anticipated, and incorporate preventative measures. In a resource-sharing network formed through collaboration among multiple municipalities or snow removal companies, clustering processing is introduced to optimize the matching of resource demand and supply, using resources such as work vehicles, workers, and snow disposal sites as nodes. The Road Snow Removal Department also has the function of managing and recording the status of snow removal (work time, work duration, vehicle history, etc.) and the road surface condition after snow removal (remaining snow, ruts, unevenness, etc.). This information is used for explaining to residents, evaluating work, and settling accounts with snow removal contractors. In addition, the department can disclose and notify residents, businesses, and related organizations of the plan and implementation status through the Information Provision Department. Thus, the Road Snow Removal Department is a core component that activates based on decisions made by the Decision-Making Department 630 and other departments, dynamically and flexibly formulates and updates prioritized snow removal implementation plans, and achieves highly efficient snow removal operations that satisfy citizens through cooperation with other departments. Furthermore, the road snow removal department may also record information regarding the snow removal contractor's performance (date and time of implementation, amount of snow removed, distance covered, time taken, etc.) and use this information for settlement processing based on contracts and for visualizing work performance. Furthermore, the road snow removal unit may be configured to dynamically formulate and update a snow removal schedule using the most efficient combination, by predicting the efficiency of each work section, taking into account the number of operational snow removal vehicles and workers, travel time, standby status, etc. Furthermore, in snow removal, the system may be configured to execute a route selection algorithm that minimizes the cost function, taking into account the remaining capacity and real-time congestion status of each snow disposal site, access distance, and road (access route) congestion status. The cost function is configured to include parameters such as time, fuel consumption, congestion rate, and response time. Furthermore, the road snow removal unit may be configured to dynamically adjust the work order, resource allocation, and response time for each management unit by comprehensively referring to information such as the snow removal necessity score, workload score, and predicted snowfall amount output by the analysis unit 620. For example, it may be configured to compare scores in multiple management units and immediately raise the priority of sections with scores above a threshold, or to automatically generate a preventative deployment plan when disaster-level snowfall is predicted. In addition, it is possible to define priority areas as zones based on the spatial distribution and temporal changes of scores and perform scheduling processing to carry out concentrated snow removal in those zones. This makes it possible to reflect the sophisticated judgment results of the analysis unit 620 in the snow removal plan in a responsive and flexible manner. Furthermore, snow removal information, information regarding the status of snow removal, and information regarding the implementation status of snow removal are used as substantially synonymous in the context, in accordance with the definition in paragraph 0027, and include not only the acquired primary data but also secondary data generated, aggregated, and processed by each department. This decision may be made by weighting and integrating evaluation results such as the snow removal necessity score (sub-score) in paragraph 0030, the snow removal evaluation score (overall score) in paragraph 0032, and the workload score. Furthermore, these acquisition and generation categories are not mutually exclusive and are cross-referenced through the evaluation information repository (referring to evaluation information for the snow removal operation platform and the Snow Country GX platform), and are cyclically reflected in the implementation plan and operational decisions.

[0043] In this embodiment, the information acquisition unit 610 can acquire information regarding the operational status of work vehicles and workers, as well as their availability, from information sources shared via systems or networks individually owned by multiple local governments, multiple road administrators (e.g., the Ministry of Land, Infrastructure, Transport and Tourism, prefectures, municipalities, etc.) or snow removal companies. In this context, "availability information" refers to information including not only currently operational resources but also their potential availability and free schedules for a certain period in the future. Furthermore, deployment location and mobility (response and coverage area), operating hours and shift status (available time slots), reservation status and assignment schedule (avoiding overlap with other tasks), and communication availability (prerequisites for remote collaboration) may also be included as availability information. Furthermore, the information acquisition unit 610 also acquires information regarding the utilization status of the snow disposal site, such as the current congestion status of incoming snow, the remaining acceptance capacity, and the incoming snow schedule, as needed. This information is used to optimize resource reallocation and wide-area coordination in the road snow removal and clearing unit. Based on the information acquired above, the Road Snow Removal Department dynamically reallocates snow removal resources across multiple municipalities and road administrators (wide-area cooperation). This process optimizes the process, including budget adjustments and consistency of implementation plans, minimizing delays and regional disparities in snow removal.

[0044] The snow removal and clearing decision support system 600 may also be configured to include an infrastructure maintenance unit responsible for evaluating the soundness of road infrastructure and making maintenance decisions. The infrastructure maintenance unit is configured to detect and manage the impact on the road surface due to snow removal and clearing, as well as road damage that is likely to worsen due to snow accumulation, freezing, and snow removal work, and to rationally determine the priority of repairs. Furthermore, in the event of a disaster, the damage assessment results for damaged roads can be linked with the road clearing unit and decision unit 630 to enable integrated operation with disaster response processing. The Infrastructure Maintenance Department may use at least two types of information acquired by the Information Acquisition Unit 610, such as satellite survey information, fiber optic survey information, vehicle driving information, road space information, and resident report information, to perform correlation analysis on road surface abnormalities (vibration intensity, changes in vehicle behavior, abnormalities in video, report content, etc.) and calculate a risk score for subsurface cavities, structural damage, etc. An AI (artificial intelligence) model such as a neural network may be used to calculate the risk score, and accumulated traffic history and weather data may also be used as training data. In this detection process, various types of information obtained by the Information Acquisition Unit 610 (e.g., road surface information, traffic information, road space information, vehicle driving information, fiber optic survey information, satellite survey information, resident report information, etc.) may be used in any combination. Furthermore, for areas deemed high-risk, the presence, depth, and shape of cavities can be obtained through on-site ground surveys (e.g., ground-penetrating radar surveys, camera image acquisition, vibration measurements, etc.), and this data can be re-input as training data for the AI ​​(artificial intelligence) model to continuously improve prediction accuracy. In addition, the system can be configured to visualize the basis for score calculation using XAI (explainable AI) technology, efficiently learn from limited data using active learning, or improve model performance through data augmentation processing using GAN (Generative Adversarial Network), etc. The infrastructure maintenance department's scope of road damage includes uneven road surfaces, cracks, rutting, voids beneath roads, road subsidence, road collapse, structural tilting and deformation, and damage to gutters and shoulders. It has the function of making repair decisions or proposing appropriate responses to these issues. Furthermore, it is responsible for all aspects of maintenance support, including recording, classifying, and prioritizing damaged areas, proposing inspections, and supporting repair contractors. Furthermore, the Infrastructure Maintenance Department may collaborate with the Improvement Department to enhance the accuracy of assessments regarding the necessity of road surface repairs, and may also contribute to real-time detection of damage trends by utilizing road space information and changes in vehicle traffic patterns provided by the Analysis Department 620. If necessary, it can also collaborate with the Prediction Department to make preventative repair proposals based on predictions of future road damage risks. Thus, the Infrastructure Maintenance Department is equipped with functions that contribute to both support for repairs during normal times and rapid decision-making during disasters, and through organic cooperation with other components, it realizes increased efficiency and reduced risk in infrastructure maintenance and management. In this specification, "infrastructure maintenance response" includes recording, classifying, and managing damaged areas, developing inspection plans, assigning priorities, proposing repairs, assisting with contractor arrangements, or similar processes.

[0045] The snow removal decision support system 600 may be configured to include a road clearing unit to support road clearing operations during disasters. The road clearing unit is configured to dynamically formulate and update a road clearing implementation plan based on various information acquired when a disaster occurs and the decision and analysis results provided by each component. Road clearing involves quickly carrying out minimal measures such as debris removal and road surface repair to secure passable routes (road clearing routes) for the purpose of enabling the passage of emergency vehicles, etc., for the purpose of life-saving and rescue operations, emergency supply transport, and recovery support. In abnormal situations such as large-scale disasters or torrential snowfalls, it is an initial response carried out prior to emergency restoration. Local governments (road administrators) formulate road clearing plans in advance, which include road clearing bases (disaster prevention bases, support troop bases, supply depots, etc.), road clearing routes connecting them (wide-area movement routes, access routes, routes within disaster-stricken areas, etc.), and action plans (work timelines) to be taken when a disaster occurs. This information is registered in advance with the road clearing department. After a disaster occurs, the Road Clearing Unit comprehensively evaluates areas that are difficult to pass, locations where vehicles are likely to get stuck, building collapse risks, etc., based on disaster, traffic, and reporting-related information collected by the Information Acquisition Unit 610, the integrated analysis results of the Analysis Unit 620, and the disaster judgment results of the Decision Unit 630, and identifies feasible road clearing routes. Furthermore, the road clearing unit may be configured to use an AI (artificial intelligence) model to analyze disaster images and traffic flow scores, and select the optimal road clearing route from multiple candidate routes. Optical fiber sensing, satellite images, traffic delay data, patrol cameras, and SNS reports are used as input information, and processing to correct abnormal values ​​and false detections is realized in cooperation with the improvement unit. After determining the road clearing route, the Road Clearing Department automatically generates an implementation plan (Road Clearing Implementation Plan) including the order of road clearing (work timeline), and presents and notifies the road administrator. The work timeline indicates the order and chronological action plan of the road clearing work to be carried out. Alternatively, or in conjunction with it, the Road Clearing Implementation Plan may be formulated based on the priority of each road. The Road Clearing Implementation Plan, as referred to here, includes a work timeline as one of its components, which organizes the start time, processing order, and required time of road clearing work in chronological order based on information on road clearing bases and road clearing routes that have been registered in advance. This implementation plan may also include a process to optimize priorities and implementation order in order to ensure the speed of initial response in the event of a disaster. If necessary, this implementation plan will also be distributed to fire departments, police, medical institutions, and related businesses through the Information Provision Department and used for initial response. In this specification, the road clearing implementation plan differs from the static road clearing plans established by local governments during normal times. Instead, it refers to a dynamic implementation plan that is formulated and updated daily or in real time after a disaster occurs, based on the latest information obtained by the snow removal decision support system 600. Furthermore, the Road Clearing Department, in cooperation with the Improvement Department, will analyze the results of patrols after road clearing (traffic volume, road surface conditions, complaint occurrences, etc.) to evaluate the effectiveness of the road clearing route and reflect the findings in future improvements. The system may also be structured to collaborate with the Prediction Department to identify risk areas that may become difficult to clear in the future and incorporate countermeasures into the plan. In addition, collaboration with the Infrastructure Maintenance Department will enable route adjustments and repair coordination that take into account the risk of road damage along the road clearing route. In this way, the Road Clearing Unit, using registered road clearing bases, road clearing routes, and work timelines as basic information, works in cooperation with the Decision Unit 630, Analysis Unit 620, Prediction Unit, Improvement Unit, Infrastructure Maintenance Unit, etc., to accurately and quickly formulate and update road clearing implementation plans. As a result, the Snow Removal Decision Support System 600 is configured to flexibly respond not only to normal snow removal decisions but also to road clearing decisions and their implementation during disasters. The Road Clearing Department may be configured to support rapid disaster response through cooperation with other departments (Infrastructure Maintenance Department, Road Snow Removal Department, Improvement Department, etc.), by formulating and managing a road clearing implementation plan that includes road clearing bases (disaster prevention warehouses, support unit bases, etc.), road clearing routes (wide-area connecting roads, emergency access roads, etc.), and timelines regarding the order of work, based on the decisions of the Decision Department 630. Furthermore, these processes and data may be operated in conjunction with higher-level functions of the snow removal operation platform and the Snow Country GX platform (management of KPIs and GX indicators, financial, settlement and revenue distribution management, environmental value assessment and CO2 reduction calculation, carbon credit and GX economic cycle management, contract and incentive matching, audit and accountability management, cooperation with related organizations and information disclosure, etc.), and road clearing implementation plans may be cross-referenced with snow removal implementation plans to adjust priorities and equipment allocation.

[0046] In the following paragraphs, we will describe in detail the snow removal and clearing operation platform in this embodiment (the subsystem responsible for the snow removal and clearing domain of the Snow Country GX platform), including the framework for designing and optimizing KPIs and achievement indicators, the operation of timelines and milestones, collaboration with related organizations and authority decisions, allocation of materials and equipment and selection of bases, switching to disaster mode, dynamic redeployment and automatic progress determination, environmental assessment (CO2 calculation, public transport correction, baseline normalization, explicit determination of calculation boundaries and assignment of uncertainty), reflection of eco priorities, electronic document and contract matching, quality-linked settlement, audit, disclosure and reporting, contractor evaluation and employee compensation, incentive linkage, allocation for the next fiscal year and visualization feedback, budget upper limit management and phased control, monetization of environmental value, regional fund, user burden option, usage tracking disclosure, and robotics execution (these are examples, and all or part of them may be implemented by configurations with equivalent functions). In this embodiment, the following departments are newly described and will be concisely defined and described below: the Environment Department (CO2 calculation, public transport adjustment, baseline normalization, specification of calculation boundaries and assignment of uncertainty), the Financial Settlement Management Department (electronic documents, contract matching, budget limit management, regional fund, monetization of environmental value, user burden), the Contractor Support Department (contractor evaluation score, employee return, incentives, allocation for the next fiscal year), and the Robotics Department (commands to robotics equipment, safe-side transition). The Information Provision Department is not a newly established component but an existing one, and its roles as an audit, administrative, and resident dashboard and usage tracking will be clarified in this specification. The snow removal operation platform circulates information acquisition, analysis, planning, command, and monitoring through an evaluation information repository. The information acquisition unit 610 collects road surface, traffic, weather, resident reports, work vehicle location, operation, and on-site images in chronological order, and the analysis unit 620 formats this information into achievement indicators for each management unit. The road snow removal unit compares evaluation information with pre-registered plans (routes, work sections, assigned snow removal contractors, snow removal implementation methods, etc.) and formulates or updates snow removal implementation plans based on KPIs, prioritizing the securing of emergency transport routes and road clearing routes, minimizing wasted movement and waiting time, and reducing required time and fuel consumption. The environment unit adds CO2 emission and reduction amounts to the evaluation information based on work performance and traffic changes, and the financial settlement management unit performs settlements by matching electronic documents with contract terms, returning control parameters from a budgetary perspective to operations as needed. The contractor support unit develops contractor evaluation scores based on quality, efficiency, safety, etc., and reflects them in allocation and incentives. The Information Provision Department generates output for audits, administration, and residents, after applying the necessary anonymization. The Robotics Department commands work parameters based on the plan and road traffic environment, and collects the execution results. Furthermore, the information regarding snow removal status in this specification is not limited to acquired primary data, but may also include secondary data (evaluation information, achievement indicators, electronic documents, notification / approval history, etc.) generated, aggregated, and processed in each department. These acquired and generated categories are not mutually exclusive and can be cross-referenced via the evaluation information repository. In addition, in this specification, snow removal information, information regarding snow removal status, and information regarding the implementation status of snow removal are used substantially synonymously depending on the context. Note that the contractor evaluation score in the snow removal operation platform and the snow removal evaluation score in the snow removal decision support system are distinct.

[0047] The following definitions are used in relation to the snow removal and clearing operation platform according to this embodiment, and unless otherwise specified, they shall have the following meanings (these are illustrative and not limiting to this application). (1) Work vehicles: This refers to vehicles used for snow removal and clearing, such as snowplows, snow dump trucks, and snow spreading vehicles (including these). (2) Vehicle driving status: This refers to traffic conditions based on driving probes related to general traffic vehicles (bus operation information, ETC2.0, connected cars, etc.), and is distinct from operational information of work vehicles. (3) Timeline (including milestones): Timeline: A progress plan for each road / management unit (e.g., route / work section), which arranges when, who, what, and how in chronological order. It may also include operational attributes such as priority, snow removal standards, and completion targets. Milestone: A time reference used in a timeline, including start time, end time, time zone, phase boundary, etc. Operational attributes of the timeline may be associated with and maintained for these milestones as needed. (4) Evaluation information: This refers to a collection of operational records, including achievement indicators, auxiliary indicators, uncertainties, etc., that are stored in association with management units and time. Furthermore, evaluation information managed by the conventional snow removal decision support system is referred to as "evaluation information (conventional)" and is distinguished from evaluation information in the snow removal operation platform. (5) Achievement indicators (corresponding to KPIs): These are indicators of the degree of achievement or deviation from KPIs, including at least some of the following: secured road width, intersection opening ratio, required time (including completion time), overlapping driving rate, refreezing risk, fuel-derived emissions, etc. (6) Road surface condition sensors: These include various sensors (contact / non-contact IR, laser / radar / ultrasonic, optical camera / AI analysis, etc.) that are mounted on work vehicles or robotic equipment (including snow removal robots) or installed on the road surface or roadside and are capable of acquiring at least some of the road surface temperature, friction coefficient, snow depth, compacted snow hardness, and freezing / slush determination. (7) Snowfall moisture content: This refers to the percentage of water contained in snow. (8) Snow density: This is an index that indicates the degree of snow compaction and firmness, and may be estimated by on-site image analysis, road surface condition sensors, vehicle probes, etc. (9) Road surface water film thickness: Refers to the thickness of the water film or slush layer on the road surface. (10) Freeze-thaw cycle: Refers to the repeated cycle of thawing and refreezing. (11) Operating status of the blade snow chute: This refers to the operating status such as up / down, angle, and opening degree based on signals such as hydraulic stroke or encoder, and may be acquired online via CAN or IoT gateway. (12) AI methods: including machine learning models (supervised, unsupervised, self-supervised, and reinforcement learning), rule-based inference, optimization or search algorithms, probabilistic models, and some or all heuristics. (13) Example data and preprocessing: This may include in-vehicle ECUs (electronic control units), sensors (cameras, LiDAR, IMU, road surface temperature, fuel flow rate, etc.), work logs, contract tables, expense ledgers, past performance, manual input, externally distributed files, etc., and may be normalized, imputed, coordinate transformed, time-series windowed, etc. Furthermore, the evaluation information and achievement indicators defined by these terms may also be used as a common data model for managing KPIs and GX indicators, conducting environmental value assessments, and managing carbon credits and GX economic cycles within the Snow Country GX Platform.

[0048] The analysis unit 620 organizes the collected data into a unified format and maps it to the perspectives of secured width, intersection opening ratio, required time, overlapping traverse rate, signs of refreezing, and fuel-derived emissions to derive achievement indicators. The achievement indicators may be corrected for differences in season and time of day, and may also retain features that contribute to understanding trends. The road snow removal unit links these indicators to setting objectives and adjusts plans through differential updates, etc., while prioritizing the maintenance of passability on emergency transport routes and road clearing routes. Furthermore, the road snow removal department aims to simultaneously reduce travel and waiting times, shorten working hours, control fuel consumption, and narrow KPI deviations, based on registered routes / sections, assigned contractors, time restrictions, and safety conditions. Updates will prioritize changes with a small impact (such as rearranging work sequences or reassigning assigned sections) and will be applied in a way that does not disrupt site continuity. These changes will be applied in stages while monitoring changes in evaluation information and achievement indicators. By minimizing the amount of changes based on estimated arrival times and vehicle directions, both recovery speed and work quality will be ensured. Furthermore, the road snow removal department will treat the operation of emergency vehicles, the operation of public transportation, and instructions from road administrators as prerequisites for its timeline. In sections where notifications or instructions have been received, priorities and work standards will be immediately reviewed. For updates requiring authorization (such as changes to work hours or calls for support from a wider area), an approval flow will be automatically assigned, and the approval history will be linked to evaluation information and maintained. This will enable the rapid reconcentration of resources on emergency transport routes and road clearing routes. In addition, the road snow removal department will review its equipment allocation and base locations in accordance with snowfall and traffic distribution. Fueling, inspection, and standby bases will be re-selected in accordance with demand distribution, and entry and exit routes will be reviewed in accordance with changes in road opening status and congestion. The addition of temporary bases and adjustment of supply vehicle routes may be done by using different time slots so as not to interfere with the routes of work vehicles.

[0049] The snow removal operation platform generates a timeline for each road / management unit that corresponds to the road traffic environment, based on at least one of the following: road surface conditions, traffic conditions, road space conditions, weather conditions, vehicle driving conditions, resident reports, and snow removal status, acquired by the information acquisition unit 610. The road snow removal unit sets and outputs milestones (including at least one start time and end time) for each route / work section, and also sets and outputs priority or snow removal criteria in the timeline, and maintains the timeline and milestones in association with evaluation information, KPIs, and achievement indicators.

[0050] The road snow removal unit determines whether it is necessary to switch to disaster mode based on evaluation information and at least one piece of information acquired by the information acquisition unit 610, and identifies emergency transport routes and road clearing routes as priority sections. The switching result is updated and reflected in the timeline (progress plan including milestones) corresponding to the road traffic environment, and resource allocation (dispatch order, assigned sections, reassignment of support) is written back to the timeline (including allocation to personnel, contractors, and bases). Each change in switching determination, priority section, and allocation is version-controlled along with time information. This process is saved in association with evaluation information, KPIs, and achievement indicators.

[0051] The road snow removal unit maintains priorities or snow removal criteria in the timeline (progress plan including milestones), and associates them with milestones (including at least one start time and end time). It monitors at least one of the following against the milestones: the location of work vehicles, work status, on-site images (or videos), traffic indicators, and snow removal status. It maintains delay criteria (delay threshold, allowable deviation, SLA target value) and estimates delay factors based on the monitoring results. In the event of a delay, it performs replanning (including recalculation of deployment order, assigned sections, and support reassignment) that includes at least one of the following: rearranging the work order, adopting alternative routes, or redeploying support from other work sections. It updates and outputs the timeline corresponding to the road traffic environment. The update results are associated with evaluation information, KPIs, and achievement indicators. The updated timeline is notified to relevant parties, and the notification history is associated with the evaluation information.

[0052] The road snow removal department will maintain KPIs (Key Performance Indicators) for each route and section as service levels, and will gradually increase the priority of sections showing signs of falling below the standards. If non-achievement or delays become apparent, they will implement measures such as calling for reinforcements, notifying relevant parties, and providing information to residents in stages. Standards and thresholds may be set in light of past performance quotients or management standards.

[0053] The Environmental Department estimates the reduction in emissions and baseline ratios for each section, taking into account the fuel consumption, distance traveled, operating hours, and idling time of work vehicles, as well as traffic congestion, average travel speed, number of stops, and dwell time, and stores this information in the evaluation database. The calculation method may be adjusted using combinations of speed, stops, and dwell time.

[0054] The Environmental Department will calculate indicators of the impact on public transport from public transport operation information, delay data, connected car and ETC2.0 probe data, etc., and apply them as correction coefficients to the estimated CO2 reduction results. The correction may be designed to reflect contributions to the restoration of operations and the mobility benefits for residents, and the key points will be presented concisely when made public.

[0055] The Environmental Department normalizes the baseline based on temperature, snowfall amount, snowfall intensity, road surface temperature, time of day, snow moisture content, snow density, whether it is wet or dry snow, actual snow depth, road surface water thickness, and freeze-thaw cycle. The calculation boundary for CO2 emissions or CO2 reductions is clearly defined by selecting either tank-to-wheel or well-to-wheel, and variability in assumptions is assigned and stored as uncertainty intervals.

[0056] The Environmental Department will determine the reduction effect per unit of resource input and provide this to the Road Removal and Snow Clearing Department as a priority adjustment. This will prioritize sections where greater environmental benefits can be expected with the same amount of work. The Road Removal and Snow Clearing Department will adjust the adjustment gradually, ensuring that the intensity of the adjustment is not excessive, while also ensuring that emergency transport routes and road clearing routes are maintained.

[0057] The road snow removal unit integrates information such as the location of work vehicles, progress by section, fuel levels, operating status of work equipment, and crew shift information to reallocate vehicles with the aim of reducing travel distance, time required, and fuel consumption. If uneven progress or fuel shortages are detected in a section, alternative vehicles will be reallocated, and this will be immediately reflected in the timeline and milestones. Changes will be applied to the extent that they do not disrupt the current route as much as possible.

[0058] The Road Snow Removal Department acquires and updates constraints such as whether the same contractor or work unit is handling multiple routes / sections, as well as the contractor's available working hours, vehicle availability, and travel restrictions related to private snow removal operations. Based on these constraints, it dynamically adjusts at least one of the following: work sequence, start time, end time, assigned section, or connection timing (merging / separating), and updates and outputs a timeline (including milestones) that corresponds to the road traffic environment. This ensures the continuity and reach of operations while suppressing the overload of personnel and equipment.

[0059] The road snow removal unit calculates an efficiency score from travel distance, travel time, waiting time, working time, fuel consumption, and overlap rate, and integrates this with priority information included in the evaluation information to create a combined score (an allocation index created by combining priority information and efficiency score using normalization or a weighted average, etc.). Based on the overall score, the allocation may be reviewed, the updated results may be output, and the direction of improvement may be fed back to the field.

[0060] The road snow removal unit automatically determines whether work is complete for each section and identifies unworked areas (for example, unprocessed sections extracted from on-site images or driving trajectories) based on the position of the work vehicle, its trajectory, the operating status of the blade (snow-pushing plate) or snow chute (snow-throwing nozzle), on-site images (or videos), and the output of road surface condition sensors. If the unworked area exceeds a predetermined threshold, it generates and outputs a trigger for recalculating the allocation, returning to the dynamic redistribution flow.

[0061] The Financial Settlement Management Department adds timestamps and electronic signatures to the location, start and end times, operating hours, fuel consumption, operation logs of work equipment, and on-site images (or videos) of work vehicles, and stores them as electronic documents. Tamper detection may be implemented by maintaining a chain of change history. These electronic documents can be centrally managed and cross-referenced at one or more levels: per worker, per contractor, per route, or per work section.

[0062] The Financial Settlement Management Department matches the actual values ​​on the supporting documents with the unit price system for distance, time, number of trips, and output registered in the contract, and automatically calculates the settlement amount according to contractual regulations such as rounding and surcharges. If there are any discrepancies, it will be returned along with the reasoning.

[0063] The Financial Settlement Management Department uses the quality indicators for finished work (secured width, compacted snow thickness, degree of unevenness, snow dam height, intersection opening ratio, pedestrian crossing or bus stop opening ratio, refreezing suppression, etc.) provided by the Road Snow Removal Department as adjustment factors for settlement amounts, and links the basis to images, road surface condition sensors, and third-party verification to eliminate arbitrariness.

[0064] The Information Provision Department outputs electronic documents, settlement amounts, and adjusted settlement amounts as audit databases, administrative forms, and public dashboards for residents. When making the information public, personal and sensitive information is excluded or anonymized.

[0065] The contractor support department will create evaluation scores for each contractor based on finished quality indicators, completion time, response time, overlapping mileage rate, safety-related events, and the degree of resolution of resident complaints. These scores will be stored and output as contractor evaluation scores, including the evaluation score and the basis for calculation. Contractor evaluation information will function as weights for settlement and allocation decisions, and regional and seasonal differences may be normalized before comparison.

[0066] The contractor support department compiles employee-specific data such as crew identification, boarding / alighting times, mileage, working hours / waiting times, and workload indicators to create detailed operational performance statements. It presents recommended compensation amounts and allocation indicators, and issues warnings if the difference between these and actual payments exceeds a predetermined threshold.

[0067] The vendor support department applies an incentive coefficient based on the vendor evaluation score to the settlement and determines the adjusted settlement amount. The design policy and change history of the coefficient are recorded and used for season-by-season reviews.

[0068] The Contractor Support Department will determine the allocation ranking for the following year, the contract limit, and the evaluation adjustment coefficient for bidding, based on the contractor evaluation score history, the status of employee benefit programs, and the trends in safety-related events. Companies that consistently demonstrate high performance will receive appropriate preferential treatment.

[0069] The information provision department generates feedback reports for vendors (breakdown of vendor evaluation scores, employee work performance details, and incentive application results), and publishes summaries to residents with personal information removed or anonymized, to share the overall progress and areas for improvement in the region.

[0070] The Financial Settlement Management Department maintains the annual budget limit and the allocation limit for each item, and updates the remaining budget balance based on the accumulation of projected payments. If there is any indication that the remaining balance will fall below a predetermined threshold, it generates control parameters to restrict the frequency, time period, number of vehicles, and target work areas of the emergency response, and returns them to the operation department. Ensuring minimum passability of emergency transport routes and road clearing routes may be excluded from the restrictions.

[0071] The Financial Settlement Management Department simulates the end-of-period achievement rate based on weekly, monthly, and quarterly budget utilization rates, work order trends, snowfall forecasts, and past performance. If the risk of exceeding the budget is high, it will promptly suggest potential cost-cutting measures. The results are visualized to aid in decision-making.

[0072] The Financial Settlement Management Department combines priority information included in the evaluation information with the remaining budget to determine the implementation level in stages, such as reducing the target route, adjusting the work width, extending the re-implementation interval, and limiting the implementation to specific time periods. The Road Snow Removal Department then reflects these control parameters in the timeline (including milestones) and work orders.

[0073] The Information Provision Department will present the budget limit, cumulative expenditure, remaining budget, budget utilization rate, and end-of-period achievement rate simulation results as a public dashboard. The display granularity may be adjusted according to viewing permissions.

[0074] The Financial Settlement Management Department registers environmental value data (such as CO2 reductions), applies for carbon credit issuance, coordinates with registration organizations, calculates estimated sales amounts, allocates revenue by account, and links the results with evaluation information.

[0075] The Financial Settlement Management Department maintains the contribution amounts, allocation rules, activation conditions, and settlement methods for the regional fund, and calculates the grant amounts based on snowfall intensity, damage level, work demand, and remaining budget. The allocation is reflected in the operational plan, and contributions and grants are reconciled and settled at the end of the period.

[0076] The Financial Settlement Management Department manages application information, fee tables, service conditions, and fairness constraints for priority snow removal options on roads leading to tourist areas or farmland, roads surrounding public facilities (including medical facilities, welfare facilities, and educational facilities) (within a specified distance), or routes requested by residents. It adjusts the implementation level and priority based on applications. It generates invoice data and adjusts it to prevent bias.

[0077] The Information Provision Department tracks expenditures using source tags (CO2 credits, regional funds, user fees) and usage tags (work costs, personnel incentives, equipment upgrades, contingency funds) for new revenue sources, and publishes anonymized aggregated data.

[0078] Based on images, point clouds, and on-board ECU signals (control signals originating from the vehicle's electronic control unit), achievement indicators can be estimated using semantic partitioning or multimodal models that estimate road surface conditions (snow, ice, dry), snow dam areas, intersection opening ratios, and required width. Thresholds and weights may be updated as needed through online learning or transfer learning.

[0079] Based on SLA / KPI thresholds and management criteria, if-then rules or fuzzy inference determine when to switch to disaster mode, request assistance, or issue phased control orders, and reflect these decisions in the timeline (including milestones) and notification system.

[0080] Assignment, route, time window, and formation are used as decision variables, and at least one of the following methods may be used to minimize travel distance, time required, and fuel consumption, and to maximize KPI achievement: mixed integer programming, constraint satisfaction, local search, metaheuristics, and graph search (such as A* (A-star) and D* (D-star)).

[0081] Using Bayesian estimation, Kalman particle filters, and hidden Markov models, the distributions of friction coefficient, snow depth, refreezing probability, and completion time are sequentially estimated, and the results are stored as evaluation information with uncertainty intervals.

[0082] Under on-site constraints such as multiple assignments, shifts, and fuel levels, simple reordering and reassignment of support staff can be performed immediately using greedy methods or scoring, and this can be applied to replanning at the vehicle edge.

[0083] The robotics department, based on information about the road traffic environment, issues commands to the snow removal robots regarding routes and work parameters. In sections with unstable communication, the robots will execute autonomously according to a pre-distributed plan, and in the event of an abnormality, they may take a safer course of action such as retreating, remotely stopping, or handing over to human operators. The execution results are saved in the evaluation information.

[0084] It is embodied as a program that performs at least a portion of information acquisition, analysis, planning, financial settlement, environmental assessment, contractor support, information provision, and robotics control. This program may run on computers constituting the snow removal operation platform, or it may be executed in a distributed manner on a group of computers constituting the higher-level functions of the Snow Country GX platform.

[0085] The AI ​​method takes data and preprocessing results from the information acquisition unit 610 as input and performs inference and learning by combining machine learning, rule-based methods, optimization / search, probabilistic models, and heuristics to generate outputs such as plans, commands, coefficients, and scores. Learning may be updated through online learning or additional learning.

[0086] The computer registers pre-planned snow removal and clearing, including emergency transport routes and road clearing routes, via the network. It derives evaluation information based on acquired data and formulates or updates implementation plans through optimization based on achievement indicators. The updates are reflected in the timeline (including milestones), and the monitoring results are fed back into the evaluation information, which is then cyclically reflected in subsequent operations.

[0087] The analysis unit 620 progressively adjusts the objectives based on delay trends, uneven distribution of overlapping traffic, signs of refreezing, decreased intersection opening ratios, and at least some of the fuel-derived emissions. The results of the adjustments are reflected in the timeline (including milestones), and the progress and quality updates obtained from monitoring are fed back into the evaluation information. This cycle prioritizes maintaining the passability of emergency transport routes and road clearing routes while simultaneously reducing travel and waiting times, shortening travel time, and controlling fuel consumption.

[0088] The road snow removal unit may implement disaster mode switching based on a composite judgment derived from a combination of changes such as a sudden increase in snowfall intensity, deterioration of visibility, an increase in the density of resident reports, and a decrease in average speed by section. After switching, emergency transport routes and road clearing routes are fixed as priority sets, and the frequency and width of work on non-priority sets are temporarily reduced. Restoration will be carried out in stages according to improvements in recovery indicators such as improved visibility and congestion, reduction of unprocessed sections, and resumption of public transport operations, and the decisions will be saved as evaluation information in the history along with the approval flow. Furthermore, the combined judgment method may be used in conjunction with a threshold method for a single indicator, and the criteria for judgment and recovery may be changed in stages according to the operational policy.

[0089] The Environmental Department will calculate public transport impact indicators based on public transport operation information, delay data, connected car and ETC2.0 probe data, etc., and apply them as correction factors to estimate CO2 reduction amounts. The correction will reflect the status of bus stop openings and their contribution to the restoration of route operations, thereby improving the convenience of residents' mobility with the same input resources and suppressing the recurrence of congestion. The correction coefficient can be a monotonic function, a step function, or a piecewise linear function (based on its contribution to restoring service).

[0090] The Financial Settlement Management Department monitors the impact of the outputted control parameters as KPI deviations and changes in achievement indicators after the application of the timeline (including milestones). If the deviation widens, the controls are partially relaxed or released again, and then readjusted in stages based on future expenditure forecasts and the trend of the remaining balance. This establishes a bidirectional closed loop in which budget compliance and the maintenance of operational quality are mutually monitored. Ensuring minimum passability of emergency transport routes and road clearing routes may be excluded from the restrictions. Furthermore, if the KPI deviation widens after the suppression is applied, the control may be partially or completely re-released and readjusted in stages according to the trend of the remaining amount.

[0091] The Financial Settlement Management Department may add time information and the signature of the sender to location / trajectory, start / end times, operating hours, fuel consumption, equipment operation, and on-site images, and maintain a chain of change history. When matching with contract information, in addition to the unit price system for distance, time, number of times, and work completed, contractual regulations such as time-of-day surcharges, holiday handling, and rounding rules are mechanically applied. If a discrepancy is detected, it is returned with supporting documentation, and the recalculation history after correction is saved with the same key as the documentation. In addition to timestamps and digital signatures, chain records of change history (e.g., hash chains) may also be used to ensure consistency.

[0092] The contractor support department defines incentive coefficients based on at least some of the following: finished quality indicators, completion time, response time, overlap rate, safety-related events, and the convergence rate of resident reports. The coefficients are designed to add points for quality and safety improvements and deduct points for significant deviations, and can be cross-referenced with images, sensor results, and third-party verification as justification. Coefficient updates are reviewed in stages according to seasonal reviews, and the change history is stored for auditing purposes. Furthermore, the calculation of the coefficient may be changed in stages based on some or all of the following criteria: quality, safety, overlap rate, response time, and resolution of resident complaints.

[0093] In the following paragraphs, a configuration example corresponding to the claims of the present invention will be described as an embodiment of the Snow Country GX Platform in this embodiment. In this embodiment, the Snow Country GX Platform is configured to include at least an information acquisition unit 610, an analysis unit 620, and a road snow removal unit, and may further include a robotics unit, an urban development unit, an environmental unit, a financial settlement management unit, an infrastructure maintenance unit, a road clearing unit, an information provision unit, and an AI management unit that oversees these AIs, as needed. The Snow Country GX Platform integrates and realizes various functions related to snow removal operations, urban development and mobility, environmental value, infrastructure maintenance, and regional resilience based on various KPIs (Key Performance Indicators; hereinafter referred to as KPIs) and GX indicators. In this specification, "Snow Country GX Platform" refers to a regional management platform that includes functions for decision-making support and operational support related to snow removal, as well as for integrating and optimizing KPIs and GX indicators, including social indicators such as urban structure, energy circulation, financial resource creation, and regional resilience.

[0094] The following definitions are used in relation to the Snow Country GX platform according to this embodiment, and unless otherwise specified, they shall have the following meanings (these are illustrative and not limiting to this application). (1) KPI: "KPI" stands for Key Performance Indicator, and it is a general term for indicators used to quantitatively grasp the achievement status of operational targets related to road maintenance, snow removal operations, urban development, infrastructure maintenance, environmental value creation, and regional resilience in snowy regions. KPIs may also include operational level indicators such as efficiency, work quality, safety, cost, environmental impact, and resident satisfaction of snow removal operations. (2) GX indicators: "GX indicators" refer to indicators related to green transformation (GX), and include evaluation indicators for decarbonization, energy transition, resource recycling, and sustainable transformation of economic or social structures. In addition to decarbonization indicators such as CO2 emission reductions, energy efficiency, and carbon credit creation, GX indicators may also include investment amounts or financing scales related to climate change adaptation (for example, indicators showing the mobilization status of adaptation funds secured to reduce disaster risks due to climate change). GX indicators are calculated in combination with KPIs and represent the degree of achievement or deviation from sustainability in the three aspects of environment, society, and economy. (3) Timeline (including milestones): A "timeline" is a progress plan for each road or management unit (e.g., a route or work section), which arranges in chronological order who does what and when. Operational attributes such as priority, snow removal standards, snow removal methods, and completion targets may be associated with the timeline. A "milestone" is a time reference used in a timeline, such as a start time, end time, time zone, or phase boundary. The operational attributes of the timeline may be associated with these milestones and maintained as needed. (4) Evaluation information: "Evaluation information" refers to a collection of operational records, including achievement indicators, auxiliary indicators, uncertainties, risks, etc., that are stored in association with roads or management units and time. Evaluation information includes at least achievement indicators that represent the degree of achievement or deviation from KPIs and GX indicators, and may also include evaluation results related to the efficiency of snow removal and clearing operations, automation and autonomy, regional collaboration, urban structure, carbon cycle, GX economic cycle, and regional resilience. Evaluation information managed in the conventional snow removal and clearing decision support system is referred to as "Evaluation Information (Conventional)" and is distinguished from evaluation information in the Snow Country GX Platform. (5) Achievement indicators: An "achievement indicator" refers to an indicator that shows the degree of achievement or deviation from a KPI or GX indicator. Achievement indicators may include at least some of the following: secured road width, intersection opening ratio, required time (including completion time), overlapping mileage rate, refreezing risk, fuel-derived emissions, CO2 reduction amount, degree of community collaboration, and infrastructure health. (6) Snow removal activities through community collaboration, and the degree of community collaboration: "Community-based snow removal activities" refer to snow removal activities in which residents, PTAs, neighborhood associations, volunteer organizations, businesses, etc., participate collaboratively in maintaining sidewalks or living spaces, in addition to snow removal by the government. "Degree of community collaboration" refers to an indicator that comprehensively shows the availability of human resources, equipment, funds, or collaborative relationships in a community. The degree of community collaboration may be quantified by taking into account the number of people who can participate in snow removal activities, the number of small snow removal machines or robotics equipment available, the amount of fuel or funds that can be provided, and past community activity results, based on information on the provision system and willingness to collaborate of human and material resources held by the government, schools, boards of education, PTAs, neighborhood associations, or volunteer organizations, etc. (7) Urban structure: "Urban structure" refers to the spatial structure of a city, consisting of road networks, public transport hubs, infrastructure layout, and location patterns of residential, commercial, business, and public facilities. Urban structure is subject to evaluation and optimization of urban planning, including reduction or reorganization of road management length, based on factors such as roads subject to snow removal, traffic volume, maintenance costs, population distribution, and access to essential services. (8) Lifestyle-related indicators: "Lifestyle-related indicators" refer to indicators used to evaluate the quality of living infrastructure in a given area or living area, in addition to transportation convenience. Lifestyle-related indicators may include information on the distribution, proximity, and accessibility of medical facilities, educational facilities, commercial facilities, public transportation, government offices, welfare facilities, or community centers. (9) Snow removal score: The "snow removal score" is a score calculated by the Urban Development Department based on at least one indicator, such as the priority of snow removal in each region or living area, snow removal efficiency, maintenance costs, and degree of community cooperation. The snow removal score is used to support relocation of residents or businesses, reallocation of local resources, development plans for snow melting channels, and regional redevelopment, based on classification results, evaluation results of urban structure, and transportation convenience or lifestyle-related indicators. It should be noted that this score is distinct from the snow removal evaluation score in the snow removal decision support system. (10) Snow depth score: The "snow depth score" is an index that scores the snow depth situation for each region or area, taking into account time-series changes in the area around a house, based on information about snow depth around the house provided by residents. The snow depth score is generated by the analysis unit 620 and used as part of the evaluation information. (11) Roof snow risk level: The "roof snow risk level" is an index that indicates the degree of risk of roof snow for each house, calculated based on information about the house (including at least the shape and structure of the roof), information about snow accumulation around the house, or a snow accumulation score, as well as information about weather conditions (snowfall amount, temperature, solar radiation, etc.). (12) Impact of collapse: "Collapse impact" is an indicator that shows the magnitude of the impact on the surrounding area if a vacant house at risk of collapse were to collapse. It is evaluated based on factors such as traffic volume, proximity to school routes or evacuation routes, density of surrounding buildings, and proximity to public facilities or medical / welfare facilities. (13) Snow removal and clearing status: "Snow removal response status" refers to a broad definition of snow removal status, including not only the progress of snow removal work, but also the snow removal implementation plan, priorities, support system, dispatch history, and implementation results. Snow removal response status may be used to calculate the amount of CO2 reduction derived from snow removal or to dynamically optimize road clearing plans. (14) Basic Information: "Basic information" refers to the set of information used to build the MaaS platform, and may include at least one of the following: a mobility demand model, a road drivability model, a mode of transport selection model, a snow removal optimization model, and an environmental load model, as well as a priority matrix or urban structure scenario created by integrating the results of these analyses. (15) Model: A "model" refers to structured information used to analyze spatial and temporal relationships based on observational data, statistical data, or forecast data, in order to estimate future demand, supply, efficiency, or environmental impacts. Examples of models include mobility demand models, road drivability models, transportation method selection models, snow removal optimization models, environmental load models, forest absorption models, snow resource energy models, and carbon cycle models. (16) Snow removal optimization model: A "snow removal optimization model" refers to structured information used to estimate the efficiency or effectiveness of snow removal operations by representing the allocation of work for snowplows and snow removal vehicles on a road network, the priority of work areas, the reallocation of resources, and response times using mathematical or AI models, while considering traffic flow and weather conditions. (17) Forest absorption model and snow resource energy model: A "forest carbon sequestration model" is a model that estimates the amount of CO2 absorbed by forests in a region based on information about forest conditions (which may include forest distribution, growth data by tree species, carbon sequestration coefficients, soil carbon sequestration information, etc.). A "snow resource energy model" is a model that estimates the amount of energy consumption replaced by snow carbon sequestration or snow-derived energy utilization and the amount of CO2 emission reduction associated with such replacement, based on information about snow thermal energy utilization or snow-derived energy utilization, and information about electricity consumption or heat demand in the region. (18) Carbon cycle model: A "carbon cycle model" refers to a model that integrates the estimation results of a forest absorption model and a snow resource energy model to represent the regional-scale carbon cycle (flow of CO2 absorption and emission reduction) based on the use of forest resources and snow resource energy. The carbon cycle model is used to estimate regional carbon balances, residual emissions, potential CO2 reductions, etc., and its estimation results may be used to calculate GX indicators and GX economic cycle information. (19) Traffic congestion indicators and driving efficiency indicators: A "traffic congestion index" is an index that represents the degree of congestion for each road section, calculated based on bus operation information, connected car probe information, ETC2.0 probe information, etc. For example, it may be defined based on average travel time, delay time, travel time reliability, vehicle density, etc. A "driving efficiency index" is an index that represents driving efficiency, calculated based on similar vehicle driving conditions, taking into account the time required per unit distance, average speed, number of stops, fuel consumption, etc. (20) GX Economic Circulation Information: "GX Economic Circulation Information" refers to information obtained by normalizing at least one of the following into a common unit: environmental CO2 reductions based on the estimation results of a carbon cycle model, traffic-related CO2 reductions estimated based on traffic congestion indicators or driving efficiency indicators, or snow removal-related CO2 reductions calculated based on snow removal implementation plans and snow removal response status. The normalized result is then converted into a monetary value as environmental value data. GX Economic Circulation Information is used for decisions regarding carbon credit issuance applications, cooperation with registration organizations, calculation of estimated sales amounts, revenue redistribution, and fundraising through community-contributing funding methods. (21) Preventive maintenance indicators: A "preventive maintenance index" refers to an index that quantifies at least one of the following: the precursor to road damage, the risk of road damage progression, or the probability of serious road damage occurring. This index is based on a contribution rate model of road damage factors estimated from information on road damage in snowy regions, snow removal status, weather conditions, heavy vehicle traffic conditions, etc. (22) Regional resilience information: "Regional resilience information" refers to information that integrates multiple types of data, such as road maintenance plans, snow removal plans, and road clearing plans, to manage road maintenance, snow removal operations, and road clearing during disasters in snowy regions as a whole. Regional resilience information may be managed in conjunction with KPIs and GX indicators used to evaluate the degree of resilience achieved at the regional level. (23) Applicable funds: "Adaptation funds" refer to funds allocated to adaptation measures to prepare for disasters caused by climate change (such as strengthening road infrastructure, developing alternative routes, and introducing snow damage prevention equipment). Adaptation funds may include funds procured from local government budgets, national subsidies, carbon credit revenues, private investments, and community-based funding methods (such as hometown tax donations and crowdfunding). (24) Snow removal drones: A "snow removal drone" is a general term for a small, unmanned mobile vehicle (a flying or mobile vehicle equipped with a snow removal mechanism) that travels or flies autonomously or remotely in an outdoor environment to remove snow or ice. Unlike typical surveillance and photography drones, it may be configured to include a snow removal mechanism that exerts a physical effect on snow and ice, and a structure designed for operation in low-temperature and snowy environments. Snow removal drones may include at least those that fly in the air and those that travel on the ground. Any snow removal drone may be configured to travel or fly while acquiring information on snow depth, road surface conditions, presence of obstacles, etc., by being equipped with sensors such as cameras, distance sensors, GNSS receivers, and inertial measurement units (IMUs). The acquired images and sensor information may be used as input information for snow removal decision support and resource allocation optimization as one form of "mobile sensor" and "snow removal machine" in this Snow Country GX platform. (25) Snow storage area: A "snow storage area" refers to a space designated for temporarily accumulating snow removed from roads until it can be transported to a designated snow disposal site through snow removal operations. This concept includes at least a portion of road shoulders, passing areas near intersections, vacant lots, and parking lots, and its location, capacity, and usage are managed as temporary snow storage locations in the snow removal implementation plan. (26) Roads that may be designated as road clearing routes or emergency transport routes, and roads through which they pass: A "road clearing route" refers to a road route designated in a pre-formulated road clearing plan as a section that should be given priority for securing passage during a disaster for life-saving and rescue operations or the transport of emergency supplies. An "emergency transport route" refers to a road designated in a disaster prevention plan or other public plan of the national or local government as a route to be used for the emergency transport of personnel or supplies in the event of a disaster. "Roads that may be designated as road clearing routes or emergency transport routes" refers to roads that may fall under these categories of road clearing routes or emergency transport routes and that, based on past disaster response history, transportation network structure, access routes to medical and administrative centers, or AI analysis, are likely to be prioritized for clearing during disasters. Furthermore, "accessible roads" include roads that connect to the aforementioned routes and contribute to the efficiency of said road clearing or snow removal. (27) Roads that facilitate the passage of emergency vehicles: "Roads that contribute to ensuring the passage of emergency vehicles" refers to roads that play an important role in ensuring the safety and smooth passage of fire engines, ambulances, police vehicles, roadside assistance vehicles, and other emergency vehicles during disasters or in the event of accidents and medical emergencies during normal times. Such roads may include roads that can be designated as road clearing routes or emergency transport routes, and roads leading to them. Examples of "roads leading to these" include roads connecting to road clearing routes or emergency transport routes, roads connecting emergency transport routes to medical facilities or fire stations, and connecting roads that function as entrances and exits to disaster prevention bases or evacuation shelters.

[0095] The Snow Country GX Platform according to this embodiment is a Snow Country GX Platform based on KPIs and GX indicators, and comprises an information acquisition unit 610, an analysis unit, and a road snow removal unit. The road snow removal unit registers a pre-formulated snow removal plan (including at least the route or work section, the snow removal contractor in charge, and the snow removal implementation method) that includes at least one road that contributes to ensuring the passage of emergency vehicles. The analysis unit 620 derives evaluation information for each road or management unit from the information acquired by the information acquisition unit 610, and this evaluation information includes achievement indicators that represent the degree of achievement or deviation from KPIs or GX indicators related to at least one of the following operational requirements: (1) efficiency of snow removal work, (2) advanced automation or autonomy of snow removal work, (3) maintenance of sidewalks or living spaces through community-collaborative snow removal activities, (4) optimization of sustainable urban structure, mobility or urban development, (5) promotion of snow resource utilization through carbon cycle models, (6) establishment of GX economic cycle or financial resource creation, and (7) regional resilience through coordination of infrastructure maintenance and disaster resilience. The road snow removal unit is configured to formulate or update snow removal implementation plans based on the evaluation information and registered snow removal plans, targeting at least one road that can be designated as a road clearing route or emergency transport route, or roads leading to such a road clearing route or emergency transport route, through optimization based on the evaluation information.

[0096] In this embodiment, the analysis unit 620 can set at least one of several types of KPIs and GX indicators as needed, corresponding to each of the operational requirements (1) to (7), including indicators calculated by the Road Snow Removal Department, Urban Development Department, Environmental Department, Financial Settlement Management Department, Infrastructure Maintenance Department, etc. For example, with respect to operational requirement (1) "Improved efficiency of snow removal work," KPIs may include the time required to complete snow removal for each route or work section, the overlapping travel rate, the processing time per operation, the operating rate of work vehicles, fuel consumption, etc., and GX indicators corresponding to these may include the reduction rate of CO2 emissions per unit length or per unit time, the reduction rate of fuel-derived emissions, etc. With respect to operational requirement (2) "Advanced automation or autonomy of snow removal work," KPIs may include the percentage of time used in autonomous driving mode, the processing time by autonomous driving, the unmanned operating time under remote monitoring, etc., and indicators related to the reduction of driver workload or improvement of safety achieved by these advancements may be adopted as GX indicators. Regarding operational requirement (3) "Maintaining sidewalks or living spaces through community-based snow removal activities," KPIs may be used such as the degree of community collaboration, the length of sidewalks maintained through community collaboration, and the width secured around living-related facilities, and social impact indicators that evaluate the maintenance and revitalization of local communities and the securing of opportunities for people with mobility difficulties to go out may be set as GX indicators. Regarding operational requirement (4) "Optimization of sustainable urban structure, mobility, or urban development," KPIs may be used such as the evaluation results of urban structure, transportation convenience score, living-related indicators, and public transport utilization rates, and indicators related to long-term population retention and mobility equity may be used as GX indicators. Regarding operational requirement (5) "Promoting the resource utilization of snow through carbon cycle models," KPIs may be used such as the amount of snow cooling energy used, the amount of snow-derived energy used, and the amount of electricity or fossil fuel-derived energy replaced by these, and the amount of CO2 emission reduction or carbon credit creation estimated by the carbon cycle model may be used as GX indicators. Regarding operational requirement (6) "Establishment of GX economic circulation or revenue generation," indicators showing the degree of self-sufficiency and expansion of the GX economic circulation in the region may be set as GX indicators, using KPIs such as the estimated amount of carbon credit sales, the monetary value of environmental value, the amount of environmental revenue returned to the region, and the percentage of snow removal costs or infrastructure maintenance costs covered by said revenue.Regarding operational requirement (7) "Regional resilience through coordination of infrastructure maintenance and disaster resilience," the following may be used as KPIs: the effect of reducing road damage risk based on preventive maintenance indicators, the degree of coordination between road clearing plans and road maintenance plans during normal times, the time required for road clearing during disasters, and the rate of securing rescue routes. The level of regional resilience evaluated by these may be used as a GX indicator. The analysis unit 620 calculates the degree of achievement or deviation for at least one of these KPIs and GX indicators and retains it as evaluation information. The road snow removal unit may, if necessary, perform multi-purpose optimization based on this evaluation information, combining prioritization of snow removal implementation plans, resource allocation, resetting of timelines and milestones, etc., for roads that can be designated as road clearing routes or emergency transport routes that contribute to securing the passage of emergency vehicles, as well as roads leading to them.

[0097] In one embodiment of the Snow Country GX platform according to this embodiment, the road snow removal unit generates a timeline (progress plan including milestones) corresponding to the road traffic environment based on at least one of the following acquired by the information acquisition unit 610: road surface conditions, traffic conditions, road space conditions, weather conditions, vehicle driving conditions, resident notification conditions, or snow removal conditions. It then sets and outputs at least one of the snow removal criteria, priority, or snow removal implementation method for the timeline, and sets and outputs milestones (including at least one start time and end time) for each route or work section, and the output is configured to be associated with evaluation information, KPIs, and achievement indicators.

[0098] In this embodiment, the timelines generated by the road snow removal unit may be managed as multiple types of timelines that reflect different priorities and snow removal standards for each route or work section. For example, the timelines may be stratified into main road timelines whose primary purpose is to ensure the passage of emergency vehicles and local road timelines whose primary purpose is to ensure the safety of school routes and maintain pedestrian spaces, and each timeline may be assigned a target completion time for each milestone, a maximum allowable delay time, and the amount of resources that need to be deployed (including at least one of the following: number of personnel, number of vehicles, and amount of fuel). Based on the evaluation information and the achievement status of the timelines, the road snow removal unit may perform a process to automatically replan part or all of the timeline and snow removal implementation plan if the deviation from the KPI or GX indicator exceeds a predetermined threshold. Furthermore, the analysis unit 620 may generate a draft standard timeline for the next season and beyond in advance based on the timeline operation history of past seasons, actual snow removal costs, traffic impact information, and statistical information on weather conditions. The road snow removal unit may then adjust the timeline parameters based on this draft standard timeline to align with the snow removal plan for the current year and register it. This configuration allows the Snow Country GX platform according to this embodiment to continuously improve the PDCA cycle of snow removal operations based on the timeline, achieving both increased efficiency in snow removal work, clarification of priorities, and improvement of GX indicators for the entire region.

[0099] In one embodiment of the Snow Country GX platform according to this embodiment, the Snow Country GX platform includes a robotics unit. The road snow removal unit performs processing to generate commands, monitor progress, or replan snow removal operations based on evaluation information derived by the analysis unit 620. The robotics unit performs processing to generate start commands, stop commands, or operation switching commands for snow removal operations corresponding to a timeline (progress plan including milestones), with at least one of the following as control targets: autonomous snow removal vehicles, snow removal drones, or robotics equipment (including at least a snow removal robot). The Snow Country GX platform is configured to associate this processing with evaluation information, KPIs, and achievement indicators.

[0100] In this embodiment, the robotics unit may internally store work section information or work task information associated with a timeline and milestones, and determine control parameters such as the start position, end position, travel route, work speed, blade opening, and snow removal direction for an autonomous snowplow, snow removal drone, or robotics equipment according to the time or phase on the timeline. As a result, in response to changes in traffic conditions, weather conditions, or evaluation information, the robotics unit can dynamically perform actions such as rearranging the work order, subdividing or merging work sections, and switching work modes (e.g., full lane snow removal mode, alternating one-way traffic mode, sidewalk priority mode, etc.) in cooperation with replanning by the road snow removal unit. From a safety perspective, the robotics unit may maintain geofencing and access restriction conditions based on map information for priority areas such as sidewalks, intersections, pedestrian crossings, bus stops, areas around schools, medical facilities, or welfare facilities. The robotics unit may also be configured to automatically control the movement of work vehicles, decelerate, stop, or request confirmation from remote operators in conjunction with obstacle detection information or surrounding traffic information acquired by the information acquisition unit 610. Furthermore, the robotics unit may be configured to feed back work performance information, fuel consumption information, work time information, and achievement indicators corresponding to KPIs to the analysis unit 620, and this feedback may be used to improve the timeline setting and resource allocation logic for future road snow removal operations. This makes it possible to continuously optimize work efficiency, reduction of fuel-derived CO2 emissions, worker safety, and consistency with regional collaboration for snow removal operations using robotics, based on KPIs and GX indicators.

[0101] In one embodiment of the Snow Country GX platform according to this embodiment, the information acquisition unit 610 acquires at least one piece of information on school routes (including at least school route information, pedestrian traffic information, student attributes, vehicle traffic information, or snow-related hazardous areas) from administrative bodies, schools, boards of education, PTAs, or local organizations (including at least neighborhood associations), specifically information on school routes that include snow-related hazardous areas. The analysis unit 620 scores the degree of danger on the school route based on the information on the school route acquired by the information acquisition unit 610. The road snow removal unit processes the results of the danger score and the degree of community cooperation to determine the priority of community-based snow removal activities, and is configured to associate this process with KPIs and achievement indicators.

[0102] The process of scoring the degree of danger on school routes may not only determine the presence or absence of dangerous areas due to snow, but may also be configured to comprehensively evaluate both static road structures and dynamic risks that fluctuate according to weather and traffic conditions. For example, the school route information may include road structure elements such as the presence and width of sidewalks, the distance from the roadway, the presence or absence of protective facilities such as guardrails, gradients, visibility at curves and intersections, the location of crosswalks and school zones, and bus stops and pick-up / drop-off points for vehicles. In addition to this information, the system may be configured to calculate a degree of danger score by time of day and weather conditions by combining it with past reports of falls and near misses, history of freezing and snowdrifts, traffic volume during rush hour, pedestrian traffic volume, and snow removal work performance information. The risk score may be calculated by breaking it down into multiple evaluation axes, such as "slipperiness due to snow," "poor visibility," "frequency of approaching vehicles," and "degree of separation between sidewalks and roadways." The overall risk level of school routes may be derived by weighting and aggregating the partial scores for each evaluation axis. The calculated risk score can be visualized as a school route map with each road / section color-coded, and may be used by the government, schools, boards of education, PTAs, or local organizations to share information about dangerous areas and consider improvement policies. The degree of community collaboration may be calculated for each school route based on factors such as the number of PTA and community volunteers who can participate, the availability of small snowplows and robotics equipment, the available time slots for activities, and past joint snow removal activity results. By combining the risk score and the degree of community collaboration, sections with both high risk and high levels of community collaboration can be prioritized for community-based snow removal activities, while sections with high risk but low levels of community collaboration can be reflected in the snow removal implementation plan as priority areas for the deployment of administrative work vehicles. This allows for the management of safety of sidewalks and living spaces during school hours as a KPI, even with limited resources, while clarifying the division of roles between community collaboration and targeted intervention by the administration. Furthermore, the risk score and the degree of community collaboration may be used in conjunction with the timeline. For example, corresponding to milestones during school commutes, priority sections on school routes can be marked as "priority snow removal sections" on the timeline, and operational policies such as bringing forward the start time of work, increasing the frequency of work, or securing temporary alternating one-way traffic can be automatically set. These operational results can be accumulated as achievement indicators in the form of "achievement rate of road surface conditions during school hours," "elimination rate of dangerous areas on school routes," and "trends in the number of complaints or accidents / near misses related to school routes," and may be configured to be used as basic data for feedback learning in the analysis unit 620 and for reviewing school routes and reorganizing routes in subsequent seasons.

[0103] In one embodiment of the Snow Country GX platform according to this embodiment, the information acquisition unit 610 acquires information about snow accumulation around houses (including at least snow depth) provided by residents. The analysis unit 620 is configured to generate a snow accumulation score that scores the snow accumulation situation for each region or area, taking into account the time-series changes in the area around the house, based on the information about snow accumulation around the house acquired by the information acquisition unit 610, and to derive evaluation information that reflects the snow accumulation score.

[0104] The snow depth score may be calculated not only as an evaluation of the snow depth at a given point in time, but also as an index that captures the increase or decrease in snow depth over time, as well as spatiotemporal biases that take into account the location conditions of the house. For example, information on snow depth around houses provided by residents may be acquired via a smartphone application, web form, call center input, or IoT sensors, and may include location information and acquisition time, as well as snow depth, overhang of snow cornices, presence or absence of snowdrifts, and the status of access to living areas. The acquired data may be mapped to a geographic mesh or management units such as roads and blocks, preprocessed with outlier removal and smoothing, and then aggregated as a snow depth score that takes time-series changes into account. The snow depth score may be calculated by decomposing the snow depth into multiple evaluation axes, such as the maximum snow depth within a certain period, the rate of increase, the degree of disruption to daily life routes, the history of snow removal, and the correlation with road surface conditions and weather conditions, and then weighting and combining the partial scores of each axis. This allows for assigning higher snow depth scores to areas with a large elderly population, areas with narrow sidewalks, and areas directly connected to public transportation access, even with the same snow depth, and can be used as an indicator to prioritize the allocation of limited snow removal resources. The generated snow depth scores can be accumulated for each road or area as part of the evaluation information and may be reflected in the formulation and updating of snow removal plans by road snow removal departments. For example, areas where the snow depth score exceeds a predetermined threshold may be automatically extracted as "priority areas" and their priority on the timeline may be increased. Furthermore, the distribution and trends of snow depth scores can be used as achievement indicators to evaluate the uneven distribution and improvement effects of snow removal services throughout the season, contributing to the verification and review of KPIs and GX indicators related to operational requirements such as the efficiency of snow removal work, the maintenance of sidewalks and living spaces through community collaboration, and the optimization of sustainable urban structures and mobility. This will enable detailed snow removal operations that are in line with local perceptions, based on resident-participatory data collection, and in the future, it will be possible to configure separate service modules or functions that are subject to separate patent applications, with snow depth scores as the main focus.

[0105] In one embodiment of the Snow Country GX platform according to this embodiment, the information acquisition unit 610 acquires information about houses provided by residents (including at least the shape and structure of the roof) and information about weather conditions (including at least the amount of snowfall, temperature, or solar radiation). The analysis unit 620 is configured to calculate a roof snow risk level indicating the degree of risk of roof snow for each house based on the information about the house, information about snow accumulation around the house or snow accumulation score, and information about weather conditions, and to derive evaluation information that reflects the roof snow risk level.

[0106] The information about a house used to calculate the roof snow risk level may include not only the shape and structure of the roof, but also the age of the building, renovation history, design conditions related to snow resistance, and past damage and repair history. This information may be obtained from self-reported information from residents, as well as from fixed asset registers, building permit registers, residential map data, damage records held by insurance companies, or analysis results of drone images and satellite images. For example, wooden houses that are older than a certain number of years, houses with single-slope or flat roofs that have poor snow removal capabilities, and houses with a history of snow damage repairs may be weighted to give a higher roof snow risk level even under the same snowfall conditions. The roof snow risk level may be calculated by combining a static vulnerability index based on the roof shape, structure, and age of each house, with the estimated results of dynamic load and snowmelt processes based on snowfall amount, temperature, solar radiation, wind direction and speed, etc. For example, weather conditions that make it easy for snow load to accumulate on the roof, such as continuous heavy snowfall in a short period, repeated melting and refreezing, and low temperatures that hinder snowmelt, may be detected, and an estimated roof snow load value for each house may be derived by integrating these over time. Alternatively, the magnitude of the impact of sliding or falling roof snow on sidewalks, entrances, roadways, etc., may be evaluated based on factors such as roof slope, presence or absence of snow guards, direction of snow fall, and overhang of eaves, and this may be reflected in the roof snow risk level as a separate element from the risk of structural collapse such as collapse. The roof snow risk level may be defined as a category value divided into multiple levels (e.g., low, medium, high, dangerous) or as a continuous value between 0 and 1, and may be stored as part of the evaluation information on a house or block basis. For groups of houses where the roof snow risk level exceeds a predetermined threshold, additional response candidates can be extracted by combining this with information such as school routes, evacuation routes, and the distribution of elderly households, such as assistance with roof snow removal, entry restrictions, changes to traffic routes, or revisions to priority snow removal plans. By systematically managing the roof snow risk level as evaluation information in this way, it becomes possible to improve the efficiency of snow removal work and ensure the safety of sidewalks and living spaces, as well as to position it as an independent functional block that could be offered as a standalone roof snow risk evaluation module or subject to divisional application in the future.

[0107] In one embodiment of the Snow Country GX platform according to this embodiment, the Snow Country GX platform is capable of registering or referencing at least one piece of urban planning information and further comprises a town development section. The town development section evaluates the maintenance level or priority of road management length in each region based on population dynamics information and snow removal cost information acquired or referenced by the information acquisition section 610, and based on the evaluation results, it performs processing to classify areas into one of the following: areas requiring intensive maintenance of snow removal, areas that can be maintained through regional cooperation, or areas that will be maintained in stages. Based on the classification results and urban planning information, the Snow Country GX platform performs processing to optimize road management length and evaluate urban structure in relation to the current plan, and is configured to associate this processing with KPIs, achievement indicators, and GX indicators.

[0108] The demographic information referenced by the Urban Development Department may include at least population density, aging rate, population by age group, household composition, future population projections, and residential distribution along school routes and commuter routes. This will allow for the identification of areas with a high concentration of children, students, the elderly, and vulnerable individuals, as well as areas where population decline is expected in the future, enabling the maintenance level of road management length to be evaluated according to regional characteristics rather than a uniform standard for each road type. Snow removal cost information may also include snow removal unit costs per length for each route or section, actual costs by year, contract unit prices, fuel and labor cost breakdowns, outsourcing costs, and impact on repair costs. By combining this with demographic information, indicators such as "maintenance cost per person," "maintenance cost per household," and "maintenance cost per elderly person" can be derived, allowing for quantitative comparison and evaluation of the maintenance level of road management length. In evaluating the maintenance level or priority of road management length, for example, trunk roads essential for accessing hospitals, schools, welfare facilities, commercial areas, and public transport hubs, or sections important as evacuation and relief routes during disasters, may be extracted as "areas requiring priority maintenance of snow removal," and these areas may be treated as areas where it is difficult to lower the maintenance level even under budget constraints. On the other hand, dead-end roads with a declining population and few residential units, or roads where alternative routes can be easily secured, may be classified as "areas where maintenance is possible through community cooperation" or "areas where maintenance will be carried out in stages." Instead of reducing the frequency of snow removal by the government, a division of roles and support schemes with community activities may be designed according to the degree of community cooperation and snow removal score. In this classification process, evaluation information based on KPIs and GX indicators can be referenced, and not only cost reduction can be considered, but also convenience of life, safety, environmental impact, and future urban structure scenarios can be comprehensively considered. In optimizing the length of road management and evaluating the urban structure in relation to the current plan, the system overlays land use plans, zoning regulations, height restrictions, urban planning road networks, public transportation networks, etc., included in urban planning information with classification results and snow removal cost information. This allows, for example, the visualization of excessive road management lengths in areas where consolidation should be pursued in the future, and the extraction of candidates such as "roads that should be maintained," "roads that should be considered for functional conversion in the future," and "roads that should be prioritized for reorganization as pedestrian spaces" based on lifestyle-related indicators and snow removal scores. Furthermore, these analysis functions and optimization proposal generation functions can be configured as an urban structure evaluation module independent of the snow removal decision support function, and can be positioned as a medium- to long-term urban development support function of the Snow Country GX platform in future separate applications and collaboration with other systems.

[0109] In one embodiment of the Snow Country GX Platform according to this embodiment, the Urban Development Department calculates a snow removal score based on the classification results and the evaluation results of the urban structure, which is an index of at least one of the following in each region or living area: priority for snow removal, snow removal efficiency, maintenance costs, or degree of regional cooperation. Based on the snow removal score and at least one of the transportation convenience or lifestyle-related indicators calculated by the Urban Development Department, the platform performs processing to generate information that supports at least one of the following: relocation of residents or businesses, reallocation of regional resources, development of snow melting channels, or regional restructuring. The Snow Country GX Platform is configured to associate this processing with KPIs, achievement indicators, and GX indicators.

[0110] The snow removal score calculated by the Urban Development Department may be structured as an index that normalizes the priority of snow removal, snow removal efficiency, maintenance costs, and the degree of community cooperation for each area onto a common scale, and then weights and integrates them as needed. For example, areas where main roads and bus routes are concentrated and essential for commuting to work and school may be weighted more highly based on transportation convenience and lifestyle-related indicators, while areas with alternative routes and declining populations may be scored based on evaluation axes centered on maintenance costs and the degree of community cooperation. Furthermore, when calculating the score, auxiliary indicators such as the amount of snowfall in winter, road surface conditions, number of days delayed in snow removal, number of complaints in previous years, and number of accidents and falls may be taken into consideration, and the weighting may be dynamically updated according to future climate change and population scenarios. Information to support relocation may be structured to visualize areas with low snow removal burden and high convenience of living, areas that are expected to be prioritized for maintenance by the government in the future, and areas where maintenance systems through community cooperation are relatively well-established, by combining snow removal scores, lifestyle-related indicators, and GX indicators. This information can then be presented to residents and businesses as a basis for making decisions when choosing a place to live or a location according to their own attributes and preferences. Information to support the relocation of regional resources may be structured to generate relocation scenarios based on snow removal scores and GX indicators for public facilities, commercial facilities, logistics hubs, or community hubs, supporting the transition to a compact urban structure suitable for snowy regions while reducing long-term maintenance costs and environmental burden. The support information for snowmelt ditch development plans may be structured to extract sections where snowmelt ditches should be prioritized for development, as well as candidate sections where existing irrigation canals and side ditches can be improved and utilized for both snowmelt and drainage, by referring to factors such as road longitudinal gradient, existing drainage network, past flooding history, and the possibility of utilizing snow as a cooling agent, in addition to snow removal scores. The support information for regional restructuring may be structured to generate multiple scenarios for district-specific restructuring policies (such as residential development zones, industrial cluster zones, and zones that prioritize snow resource utilization) by integrating snow removal scores, lifestyle-related indicators, traffic convenience evaluations based on a MaaS platform, and GX indicators based on a carbon cycle model. This support information may be provided as an output that can be used in the administrative planning process and dialogues with residents, and may be implemented as an independent module that can be linked with other information systems as needed.

[0111] In one embodiment of the Snow Country GX Platform according to this embodiment, the information acquisition unit 610 acquires or references fixed asset information or usage status information, including location information (including latitude and longitude), as information about vacant lots or vacant houses. Based on the information about vacant lots or vacant houses acquired or referenced by the information acquisition unit 610, weather conditions, traffic conditions, and snow removal status, the urban development unit extracts land from the vacant lots or vacant houses that can be used as snow storage sites, evaluates at least one of the following for the candidate sites: road structure, snow removal conditions, snow removal routes, location conditions, traffic safety, and impact on the surrounding environment, and performs processing to generate a snow storage site layout plan that contributes to improving snow removal efficiency or reducing the burden on the local environment. The Snow Country GX Platform is configured to associate this processing with KPIs, achievement indicators, and GX indicators.

[0112] When identifying land suitable for use as a snow storage area, evaluation may involve combining location information of vacant lots or empty houses with the type of roads the land connects to (main roads, secondary main roads, local roads, etc.), road width, presence or absence of intersections, as well as winter traffic volume and pedestrian traffic patterns. For example, land that is too close to school routes or routes used by the elderly may receive a reduced score from the perspective of traffic safety and visibility, while land located at a reasonable distance from main roads and with good access from snow removal routes may be given higher priority as a potential snow storage area. In evaluating snow removal conditions and snow disposal routes, indicators related to workability may be used, such as the slope of the site entrance and exit, site shape (whether it is a flag-shaped lot or not, frontage width, etc.), possibility of connection to existing side ditches, snow melting ditches, and drainage channels, and whether work machinery such as backhoes and dump trucks can turn around. This makes it possible to select candidate sites that comprehensively consider the number of vehicles to be used, working time, and fuel consumption in actual snow removal and disposal operations, rather than simply designating an empty plot of land as a snow storage area. Furthermore, for each candidate site, it may be possible to estimate the expected snow capacity that can be accepted and the risk of accumulation during continuous snowfall, and to simulate how well the snow disposal demand for the entire area can be covered by combining multiple candidate sites. In evaluating the site conditions and impact on the surrounding environment, indicators such as distance from surrounding residential areas, tolerance to noise, vibration, and dust (e.g., industrial, commercial, or residential areas), impact on views and scenery, sunlight and wind direction, and proximity to rivers, schools, and medical facilities may be used. For example, in areas where logistics hubs or factories are already located and large vehicles come and go on a daily basis, the impact on the living environment from additional dump truck traffic is relatively small, so the suitability as a snow disposal site may be highly rated. On the other hand, for sites adjacent to schools and childcare facilities, operational conditions may be designed to include restrictions on the hours of nighttime snow removal, taking into consideration safety and noise. Furthermore, when generating snow storage area layout proposals, evaluation indicators may include not only the snow removal efficiency for a single year, but also consistency with medium- to long-term land use and urban structure reorganization policies. For example, for vacant lots where the relocation of public facilities or conversion to residential areas is being considered in the future, a scenario may be presented in which they are used as temporary snow storage areas for a limited period, and a layout proposal may be generated that can be smoothly connected to the land use plan after the period ends. In addition, the amount of CO2 reduction resulting from the reduction in snow removal distance and the number of dump truck trips due to the snow storage area layout may be calculated as a GX indicator, and by correlating this with KPIs and achievement indicators, it may be possible to compare and examine multiple alternative layout proposals that balance snow removal efficiency and reduction of environmental impact. In this way, the snow storage area layout proposals can be used not only for administrative snow removal plans, but also as planning information that can be explained from the perspective of the regional environment and the GX economic cycle.

[0113] In one embodiment of the Snow Country GX Platform according to this embodiment, the urban development unit acquires, references, or inputs information on vacant houses that are at risk of collapse. Based on the snow removal score or the evaluation results of the urban structure, it evaluates the urgency and impact of the collapse of the vacant house, and based on the evaluation results and the snow removal status (including at least the snow removal status of surrounding roads) obtained by the information acquisition unit 610, it performs a process to select the site of the vacant house or the site after demolition as a candidate for a snow storage area for each construction section. The Snow Country GX Platform is configured to associate this process with KPIs, achievement indicators, and GX indicators.

[0114] Information regarding vacant houses at risk of collapse may include at least the building's structural type (wooden, steel frame, reinforced concrete, etc.), number of floors, age of the building, degree of deterioration (deterioration diagnosis results, cracks, tilting, roof damage, etc.), past disaster history (history of damage from earthquakes, heavy snow, floods), surrounding ground conditions, and information about the owner or whether or not there are plans for demolition. This information may be obtained or estimated from fixed asset registers, building registers, survey results of dangerous vacant houses, patrol records by local governments, resident reports, or on-site image analysis based on drone and vehicle traffic conditions. In assessing the urgency of collapse, the probability of collapse or partial collapse during this winter may be scored using explanatory variables such as roof snow risk level, assumed snow load, building's remaining structural strength (estimated value based on structural type, age, and degree of deterioration), unstable site conditions such as on slopes or retaining walls, and recent weather forecasts (snowfall amount, temperature trends, wind direction and speed, etc.). In assessing the impact of collapse, the type of road that could be blocked if the vacant house collapses (main road, secondary road, local road), proximity to school routes, evacuation routes, and emergency transport routes, surrounding building density, and distance to public facilities, medical facilities, and welfare facilities may be used to index the magnitude of the impact on transportation functions and infrastructure. This makes it possible to conduct a comprehensive risk assessment that considers both the urgency and impact of collapse. In linking with snow removal status, vacant houses with a high risk of collapse and concentrated snow removal loads on surrounding roads may be identified by referring to the snow removal score, snow removal implementation plan, and actual snow removal response status for each work section. In this case, multiple operational patterns may be evaluated as forms of selecting vacant houses as potential snow storage sites, such as a scenario in which the site after the demolition of an aging vacant house is used as a medium- to long-term snow storage site, and a scenario in which a part of the site is used as a small snow storage space during the temporary period until demolition. For each scenario, in addition to contributions to KPIs such as shortening snow removal distance, reducing the number of dump truck trips, and suppressing snow removal costs, contributions to GX indicators such as reducing the cost of acquiring new land for snow storage site development, accelerating measures against aging vacant houses, and improving the surrounding environment and landscape may be calculated, and the structure may be configured to optimize the reduction of collapse risk and the securing of snow storage sites as an integrated whole. This will enable the integrated handling of measures against dangerous vacant houses and snow removal operations, and the formulation of plans that contribute to regional resilience and urban reconstruction.

[0115] In one embodiment of the Snow Country GX platform according to this embodiment, the Snow Country GX platform comprises a town planning section and an environment section. The information acquisition section 610 acquires or references information on mobility demand and mobility supply status. The environment section calculates at least one traffic congestion index or driving efficiency index based on the vehicle driving status (including at least one of bus operation information, connected car probe information, or ETC2.0 probe information) acquired by the information acquisition section 610. The town planning section comprehensively evaluates the traffic flow, mobility demand, and snow removal response status in the region based on at least two different pieces of information from among information on mobility demand, mobility supply status, traffic congestion index, driving efficiency index, urban structure evaluation results, snow removal score, snow removal implementation plan, or urban plan, and derives basic information for constructing a MaaS platform that covers all means of transportation in the region, including private cars. The Snow Country GX platform is configured to associate this processing with KPIs, achievement indicators, and GX indicators.

[0116] Information regarding travel demand may include, at a minimum, location information such as place of residence, place of work, place of school, shopping locations, and medical / welfare locations; purpose of travel by time of day (commuting, going to school, going to the hospital, shopping, sightseeing, leisure, etc.); usage history by mode of transport; departure and arrival times; and information regarding route preferences. This information may be obtained from public transport operator boarding and alighting data, commuter pass / IC card boarding data, taxi / on-demand transport dispatch history, visitor statistics for roadside facilities, smartphone-derived location data, or from surveys / resident reporting apps. Information regarding the status of transportation supply may include at least one of the following: routes and operating sections, schedules and operating frequency, vehicle capacity, operating costs, and information on operational constraints during the winter season (sections where service is suspended, delays, reduced service information, etc.) for buses, trains, on-demand transport, taxis, car sharing, and bicycle sharing. In addition, infrastructure information constituting transportation supply may be maintained, such as the number of lanes in the road network, speed limits, intersection structure, location and capacity of parking lots, location of park-and-ride hubs, and the status of pedestrian and bicycle network development. By combining these, it is possible to quantify the supply potential, which represents how much of each mode of transportation is provided by time of day and by region. The Environmental Department may analyze average travel speed, travel time per link, congestion length, number of stops, and acceleration / deceleration patterns obtained from vehicle driving conditions to calculate traffic congestion and driving efficiency indicators for the entire network or specific routes. For example, the system may be configured to score congestion and efficiency using the ratio to free-flow travel time, driving time or stopping time per unit distance, or fuel consumption or CO2 emissions per unit of travel demand. Furthermore, by combining these indicators with snow removal and weather conditions, the impact of snowfall on travel efficiency and the extent to which snow removal measures have improved it can be evaluated as part of the GX indicator and environmental load model. The Urban Development Department may integrate information on transportation demand and transportation supply to construct zone- and time-of-day-based transportation demand models and transportation mode selection models. By combining this with traffic congestion indicators, driving efficiency indicators, snow removal scores, snow removal implementation plans, urban structure evaluation results, and urban planning, multiple MaaS scenarios can be compared and evaluated. Examples of scenarios include modifying snow removal implementation plans to prioritize trunk bus routes, school bus routes, and medical access routes; introducing or increasing the frequency of on-demand transportation and shared taxis during the winter; establishing or consolidating park-and-ride hubs; and combining the reorganization of local roads and pedestrian networks with community-based snow removal. For each scenario, average travel time, delay risk, accessibility to medical, educational, and commercial facilities, user transportation cost burden, and transportation-related CO2 emissions and energy consumption may be evaluated as KPIs and GX indicators and output as basic information for constructing a MaaS platform. This basic information can include, at a minimum, recommended operating frequencies by mode of transport and time of day, road sections and bus routes that should be prioritized for maintenance, recommended routes based on road conditions, proposed fare and incentive designs to encourage modal shift from private cars to public transport and shared mobility, and recommended travel patterns for local residents, businesses, and tourists. The basic information is maintained in a format that can be shared by road administrators, public transport operators, MaaS operators, and municipal urban planning departments, and may be distributed in cooperation with the information provision department described later and external MaaS applications. By configuring it in this way, it becomes possible to optimize snow removal operations and mobility service planning and operation in an integrated manner, achieving both ensuring winter mobility and promoting long-term GX (Ground Transportation) including during normal times.

[0117] In one embodiment of the Snow Country GX platform according to this embodiment, the Snow Country GX platform includes an information provision unit. The analysis unit 620 comprehensively analyzes information (including at least weather forecasts) regarding snow removal status, road conditions, traffic conditions, transportation supply status, and weather conditions based on basic information for constructing a MaaS platform (e.g., a mobility demand model, a road drivability model, a mode of transport selection model, a snow removal optimization model, and an environmental load model). From the analysis results, it generates mobility suggestion information that dynamically optimizes at least one of the following: departure time, travel route, or travel schedule for the mode of transport. The information provision unit processes and outputs mobility suggestion information for areas where a decrease in safety due to poor road conditions or an increase in environmental load due to traffic congestion is predicted, and the Snow Country GX platform is configured to associate this processing with KPIs, achievement indicators, and GX indicators.

[0118] Mobility suggestion information may include, at a minimum, candidate departure times adjusted according to each user's attributes and purpose, multiple candidate routes and their respective travel times and delay risks, and a selection of modes of transportation, including combinations of public transport, on-demand transport, taxis, private cars, walking, etc. For example, for elderly people and children commuting to school or medical appointments, the system may be configured to suggest routes that avoid steep slopes with a high risk of falls and routes with a high risk of freezing, and prioritize routes where snow removal on sidewalks is carried out as a priority, as well as routes that prioritize highly barrier-free transfer points. On the other hand, for logistics vehicles and commercial vehicles, the system can be linked with snow removal implementation plans and road drivability models to suggest optimal delivery sequences and waiting locations that take into account snow accumulation, congestion, and traffic restrictions. The information provision department may output mobility suggestion information as push notifications to individual terminals, as a dashboard-style visualization screen, or as an API that can be linked to external route search services and MaaS applications. This will allow residents, tourists, businesses, public transport operators, road administrators, etc., to refer to recommended schedules and route information based on the same analysis results from their respective perspectives, enabling them to adjust winter travel behavior based on unified guidelines. In particular, the system may be configured to prioritize outputting suggestions encouraging changes in departure times or switching to public transport during times when reduced safety due to poor road conditions is expected, and suggestions encouraging peak shifting or modal shifting in areas where increased environmental burden due to traffic congestion is expected. The analysis unit 620 continuously incorporates real-time updates on snow removal status, traffic conditions, and weather conditions, re-evaluates the validity of already outputted mobility suggestion information, and may dynamically update recommended departure times, routes, and modes of transport as needed. This ensures that even in the event of sudden heavy snowfall, unexpected accidents causing traffic congestion, or delays in snow removal operations, users can continue to be presented with travel schedules that reflect the latest conditions. The updated suggestions are recorded along with their correspondence to safety indicators (number of accidents, degree of fall risk reduction, etc.), convenience indicators (average travel time, delay time, etc.), and environmental impact indicators (traffic-related CO2 emissions, energy consumption, etc.) set as KPIs, and may be used for future verification and improvement, as well as for enhancing the overall operation of the Snow Country GX platform.

[0119] In one embodiment of the Snow Country GX platform according to this embodiment, the Snow Country GX platform includes an environmental unit. The information acquisition unit 610 acquires or references information on forest conditions, information on snow cooling utilization or snow-derived energy utilization, and information on electricity consumption or heat demand in the region. Based on the information on forest conditions, the environmental unit constructs a forest absorption model to estimate the amount of CO2 absorbed by forest absorption in the region, and based on the information on snow cooling utilization or snow-derived energy utilization, and information on electricity consumption or heat demand in the region, constructs a snow resource energy model to estimate the amount of energy consumption replaced by snow cooling utilization or snow-derived energy utilization and the amount of CO2 emission reduction associated with such replacement. Furthermore, the environmental unit integrates the results of the forest absorption model and the snow resource energy model to construct a carbon cycle model that represents the carbon cycle at a regional scale based on forest resources and snow resource energy utilization, outputs the estimated carbon cycle results based on the carbon cycle model, and the Snow Country GX platform is configured to associate the estimated results with KPIs, achievement indicators, and GX indicators.

[0120] Information regarding forest conditions may include, at a minimum, information on forest distribution, tree species composition, age class composition, number of standing trees, timber volume, logging history, conservation classification, and ownership type. The forest carbon sequestration model may be configured to estimate annual and regional CO2 absorption by combining this information with information on meteorological conditions such as temperature and precipitation, and soil characteristics, and by applying carbon sequestration coefficients or growth curves for each forest type. Furthermore, the spatial resolution and estimation accuracy of the model may be improved by incorporating forest cover rate and tree height distribution obtained through satellite remote sensing or aerial laser measurements. Information regarding the use of snow cooling or snow-derived energy may include the scale of snow cooling facilities, the status of snow cooling in refrigerated and frozen warehouses, the status of snow-derived energy utilization in snow melting pipes, snow melting tanks, snow melting ditches, etc., and the status of collaboration with heat pumps and district heating systems. The snow resource energy model may be configured to estimate the amount of electricity and fuel consumed that can be replaced by snow resource energy utilization, and the resulting reduction in CO2 emissions, by area and application, using this information along with electricity rates, emission intensity for each fuel type, and the efficiency of existing equipment. This makes it possible to quantitatively grasp the environmental benefits of effectively utilizing snow collected through snow removal as an energy source, on an urban or regional scale. The carbon cycle model may be configured to calculate net emissions over a certain period, the gap to achieving net-zero emissions, or additional reduction potential by combining the amount of CO2 absorbed by forests, the amount of CO2 emission reductions estimated by the snow resource energy model, and baseline emission scenarios for the transportation, industrial, and residential sectors in the region. The estimation results of the carbon cycle model can be used to compare and evaluate multiple scenarios for forest management plans and snow resource energy utilization plans. For example, the differences in the degree of achievement against KPIs and GX indicators may be visualized for each combination of measures such as strengthening forest thinning and afforestation, increasing the number of snow cooling facilities, and improving the efficiency of snow melting infrastructure. With such a configuration, the Snow Country GX Platform can support the planning and verification of carbon cycle plans that consider snow removal, energy, and forest management as an integrated whole.

[0121] In one embodiment of the Snow Country GX platform according to this embodiment, the Snow Country GX platform includes a financial settlement management unit. The financial settlement management unit normalizes the amount of CO2 reduction from each source to a common unit based on at least one of the following: the amount of CO2 reduction from the environment based on the estimation results of a carbon cycle model, the amount of CO2 reduction from traffic estimated based on a traffic congestion index or a driving efficiency index, or the amount of CO2 reduction from snow removal calculated based on a snow removal implementation plan and snow removal response status. It then converts the normalized result into a monetary value as environmental value data and generates GX economic circulation information at the municipal or regional level corresponding to the scope of CO2 reduction calculation. Furthermore, based on the GX economic circulation information, the financial settlement management unit performs at least one of the following processes: application for issuance of carbon credits, cooperation with registration organizations, calculation of estimated sales amount, redistribution of revenue, or fundraising through community contribution type funding means (e.g., hometown tax donation, crowdfunding). The Snow Country GX platform is configured to associate these processes with KPIs, achievement indicators, and GX indicators.

[0122] The CO2 reductions from environmental sources, traffic sources, and snow removal, handled by the Financial Settlement Management Department, each have different calculation basis. Therefore, when normalizing them to a common unit, the emission intensity for each emission source category, fuel type, power source composition, standard emission coefficients by time of day and season, and comparison results with a standard scenario (assumed emissions when reduction measures are not implemented) may be used. For example, the electricity replacement from the use of snow resources may be aggregated by time of day and region as CO2 equivalent amounts based on the emission intensity of the power grid, the reductions from alleviating traffic congestion and improving driving efficiency may be aggregated by vehicle type and road type, and the reductions from optimizing snow removal implementation plans may be aggregated by time of day and region based on the fuel consumption and fuel type of the work vehicles. These values ​​may be managed as environmental value data with metadata such as calculation period, target area, calculation method, and confidence interval, and the structure may be such that it can keep up with future changes in carbon credit schemes and voluntary market requirements. In converting environmental value data into monetary value, at least one of the following may be used: domestic and international carbon credit market prices, government reference prices, internal carbon prices, or valuation unit prices independently set by local governments. The potential sale amount or internal benefit amount may be calculated for each CO2 reduction. The GX economic circulation information may also include an allocation table based on the contribution of each sector (snow removal, road maintenance, mobility, energy, etc.) and entity (local government, road administrator, business operators, community organizations, etc.). Based on this allocation table, for example, a scenario may be compared and evaluated in which a portion of the revenue corresponding to the CO2 reduction from snow removal is used to subsidize the introduction of high-efficiency snowplows, hydrogen snowplows, and robotics equipment, to improve the safety of school routes, to address snow on roofs, to develop snow storage facilities, to upgrade and improve the efficiency of snow melting ditches, or to cover the costs of developing regional MaaS. Furthermore, the Financial Settlement Management Department may generate a dashboard that visualizes the correspondence between GX economic cycle information and KPIs and GX indicators, enabling comparison of investment amounts, operating costs, environmental benefits, and social benefits for each scenario. For example, under the same budget constraints, it may be configured to evaluate multiple combinations of measures such as improving snow removal efficiency, alleviating traffic congestion, strengthening forest management, and expanding the use of snow resource energy, and to present a funding allocation plan that maximizes the achievement of KPIs and GX indicators. With such a configuration, the Snow Country GX Platform can not only calculate and settle environmental value, but also play a role in supporting the planning and review of long-term GX economic cycles and regional resilience strategies.

[0123] In one embodiment of the Snow Country GX platform according to this embodiment, the Snow Country GX platform includes an infrastructure maintenance unit. The information acquisition unit 610 acquires multiple types of information, including information on road damage in snowy regions (including damage caused by freeze-thaw cycles or snow removal operations that occur at least during the winter), information on snow removal conditions, information on weather conditions, and information on the traffic conditions of large vehicles (including at least ETC information, traffic volume survey data, camera-derived data, or weight estimation sensor-derived data). Based on the multiple types of information, the infrastructure maintenance unit estimates a contribution rate model of road damage factors, including factors specific to snowy regions, and is configured to perform processing to calculate a preventive maintenance index that quantifies at least one of the following: signs of road damage, risk of road damage progression, or probability of serious road damage occurring.

[0124] The contribution rate model for road damage factors used by the Infrastructure Maintenance Department may be configured to estimate the relationship between multiple explanatory variables, including time-series data (number of freeze-thaw cycles, minimum and maximum temperatures, road surface temperature, frequency and type of snow removal work, volume and proportion of heavy vehicle traffic, amount of de-icing agent applied, pavement structure, year of commencement of service or repair history, etc.) and damage indicators such as road surface crack rate, rutting amount, step amount, and number of pothole occurrences, using a statistical model or machine learning model. This contribution rate model may be constructed individually for each road attribute, such as route type (main roads, local roads, etc.), alignment conditions, gradient, and drainage conditions. Even on the same route, separate models may be set up for sections with different lane directions or sections with different structural conditions, such as bridges and tunnels. Preventive maintenance indicators may be calculated by combining the remaining soundness estimated from the contribution rate model, the rate of damage progression, and the probability of falling below the target service level within a predetermined planning period, and then scoring or ranking each section. For example, sections exceeding a certain threshold may be classified as "recommended for early repair," sections near the threshold as "priority monitoring," and sections well below the threshold as "normal management." These classifications can then be used to select repair menus (replacement, overlay, partial repair, etc.) and to consider the timing, method, and resources to be used for repairs. This configuration allows for the visualization of the impact of road damage factors, including those specific to snowy regions, while suppressing life cycle costs within a limited maintenance budget and formulating a rational preventive maintenance strategy from the perspective of KPIs and GX indicators.

[0125] In one embodiment of the Snow Country GX platform according to this embodiment, the Snow Country GX platform further comprises a road clearing unit. The Snow Country GX platform integrates and manages performance information of snow removal contractors and road maintenance contractors, availability information of equipment and personnel owned by both contractors, and information on the types of contractors and the scope of work they can handle in a common format. Based on this integrated management, it performs processing to propose and configure a year-round comprehensive management system based on the management system for snow removal. The infrastructure maintenance unit generates a road maintenance plan based on preventive maintenance indicators and performs processing to support the coordination of operations and the determination of equipment allocation for implementing preventive maintenance for road maintenance based on the comprehensive management system. The road clearing unit is configured to dynamically optimize the road clearing plan in the event of a disaster in cooperation with the preventive maintenance indicators and the comprehensive management system.

[0126] The comprehensive management system may be structured to integrate and manage performance information for each snow removal and road maintenance company on a route or section basis (types of work performed, working hours, workload, quality evaluation, complaint handling history, etc.), availability information of owned vehicles, heavy machinery, equipment, and personnel, and information on contract types and the scope of work that can be handled, all linked together using a common code system and georeference information. This makes it possible to centrally coordinate dispatch orders and vehicle scheduling during the winter snow removal season with year-round road maintenance operations such as pavement repair, ditch cleaning, and slope repair on a single platform. Based on this comprehensive management system, the Infrastructure Maintenance Department and the Road Clearing Department may simulate which contractors, which equipment and personnel, when and in what order should be deployed to sections with high preventive maintenance indicators, and use this to optimize road maintenance plans during normal times and to dynamically revise road clearing plans during disasters. For example, if the risk of road damage is high in a particular work section, availability information for both the snow removal contractor and the road maintenance contractor responsible for that section can be referenced. In winter, a plan could be proposed to combine snow removal and minor repairs on the same route. In the event of a disaster, preventive maintenance indicators and road clearing priorities could be combined to prioritize the allocation of support units to segments that are at high risk of damage but are also important for road clearing. This configuration integrates snow removal operations, road maintenance, and road clearing in a mutually complementary manner, allowing for the efficient use of limited human and material resources from the perspective of KPIs and GX indicators.

[0127] In one embodiment of the Snow Country GX Platform according to this embodiment, the Snow Country GX Platform integrates multiple types of road maintenance plans, snow removal plans, and road clearing plans, and processes to generate regional resilience information that comprehensively manages road maintenance, snow removal operations, and road clearing during disasters in snow country regions, and is configured to associate this regional resilience information with KPIs, achievement indicators, and GX indicators.

[0128] Regional resilience information may not simply be a list of road maintenance plans, snow removal plans, and road clearing plans, but may also be structured as multi-layered information that comprehensively aggregates the functions, vulnerabilities, and operational policies of each segment constituting the road network in snowy regions. For example, for each segment, the importance of road maintenance during normal times, the priority and burden of snow removal during winter, the priority of road clearing during disasters and the availability of alternative routes, as well as corresponding timelines and milestones, may be linked and maintained together with spatial information. Furthermore, regional resilience information may also include simulation results and evaluation indicators such as which routes should be secured and on what time axis, and in which sections preventive maintenance should be brought forward, in response to anticipated snowfall / heavy snow, road disasters, and climate change scenarios. With such a structure, it becomes possible to evaluate road maintenance, snow removal operations, and road clearing during disasters not only in terms of optimizing individual tasks, but also integrally from the perspective of maintaining transportation, logistics, and living functions throughout the region, and to visualize them in conjunction with KPIs and GX indicators. Furthermore, regional resilience information can be used in conjunction with local government-formulated regional disaster prevention plans, climate change adaptation plans, and urban planning master plans, and can also be utilized as basic data for considering future road network reorganization and investment allocation policies.

[0129] In one embodiment of the Snow Country GX platform according to this embodiment, the Snow Country GX platform performs a process to calculate at least one of the following for adaptation funds related to adaptation measures to prepare for disasters caused by climate change, based on regional resilience information, and is configured to associate this process with KPIs, achievement indicators, and GX indicators.

[0130] In this embodiment, "adaptation funds" refers to the total investment capital necessary to implement adaptation measures to strengthen road maintenance, snow removal operations, and road clearing against climate change impacts, including heavy snowfall and road disasters. The required amount of adaptation funds may be calculated by combining the vulnerability, risk, expected damage scale, and service level targets (such as emergency transport time, trunk network security rate, and evacuation route security status) for each road segment or area included in regional resilience information with unit price information or cost functions for each adaptation measure, such as the development and renewal of snow melting ditches, securing snow storage areas, raising roads and reinforcing slopes, repairing bridges and tunnels, and developing information infrastructure, and calculating the total amount for each scenario. The allocation policy may be structured to derive priority allocation ratios and investment sequences at the municipal level, living area level, or road type level based on weighting of factors such as vulnerability, demographics, distribution of facilities for the elderly and vulnerable, importance of access to logistics and medical bases, and CO2 reduction potential related to GX indicators. The procurement plan may be structured to design cash flows by combining local government budgets, national and prefectural subsidies, bond issuance, private funds, revenues utilizing environmental values ​​such as carbon credits, or funding inflows from community-contributing funding sources, on an annual and phase-by-year basis. This makes it possible to visualize the correspondence between the required amount, allocation policy, and procurement plan for adaptive funds based on regional resilience information and performance indicators represented by KPIs and GX indicators, and to present in an explainable way how much should be invested in which adaptive measures under limited financial resources.

[0131] In one embodiment of the Snow Country GX platform according to this embodiment, the Snow Country GX platform comprises a town planning department, an environmental department, an infrastructure maintenance department, and a road clearing department, and further comprises an AI management department that oversees the AI ​​of each of these departments. The AI ​​Management Unit is configured to take at least one of the following as inputs: evaluation information output by at least one of the Analysis Unit 620, Road Snow Removal Unit, Urban Development Unit, Environmental Unit, Infrastructure Maintenance Unit, and Road Clearing Unit; various plan information (including at least one of the snow removal plan, road maintenance plan, and road clearing plan); or various indicator information (including at least one of the GX indicator, achievement indicator, traffic congestion indicator, driving efficiency indicator, lifestyle-related indicator, preventive maintenance indicator, and estimated results of the carbon cycle model). It then performs inference or learning (including online learning or additional learning) using an AI method (including at least one of the following: machine learning model, rule-based inference, optimization or search algorithm, probabilistic model, and heuristic processing), and outputs at least one parameter update information or control policy information to improve the achievement of KPIs, achievement indicators, and GX indicators to at least one of the Analysis Unit 620, Road Snow Removal Unit, Urban Development Unit, Environmental Unit, Infrastructure Maintenance Unit, or Road Clearing Unit.

[0132] In this embodiment, the AI ​​Management Unit is responsible for centrally managing the setting, evaluation, and updating of AI methods used in a distributed manner across the Urban Planning Unit, Environment Unit, Infrastructure Maintenance Unit, Road Clearing Unit, Analysis Unit 620, and Road Snow Removal Unit. For example, the AI ​​Management Unit may manage training datasets, feature definitions, evaluation indicators, search spaces, constraints, hyperparameters, etc., within a common framework for demand forecasting models and evaluation information generation models in the Analysis Unit 620, snow removal optimization models in the Road Snow Removal Unit, urban structure scenario generation models in the Urban Planning Unit, carbon cycle models in the Environment Unit, preventive maintenance models in the Infrastructure Maintenance Unit, and road clearing plan optimization models in the Road Clearing Unit. The AI ​​Management Unit may also aggregate evaluation information and various indicator information across the board and perform multi-objective optimization and weighting adjustments to derive control policy information that balances multiple KPIs and GX indicators, rather than maximizing only a single KPI. Furthermore, the AI ​​Management Department may monitor changes in data distribution and operational policies over time, and automatically issue a trigger for retraining or present retraining candidates or parameter update candidates to operators if model performance falls below a predetermined threshold or if the achievement rate against KPIs deteriorates. In addition, the AI ​​Management Department may have a function to generate contribution information and scenario comparison information that explains which input factors influenced KPIs or GX indicators to what extent, based on the output control policy information and parameter update information, and visualize this as a dashboard or report. With such a configuration, the AI ​​Management Department can centrally and continuously manage the design, evaluation, and updating of AI methods in each department, streamlining the learning cycle of the entire Yukiguni GX platform and enabling stable improvement in the achievement rate against KPIs and GX indicators in long-term operation.

[0133] In the present invention, the analysis unit 620, the road snow removal unit, the robotics unit, the urban development unit, the environmental unit, the financial settlement management unit, the infrastructure maintenance unit, the road clearing unit, and the information provision unit may each generate various evaluation information, planning information, or indicator information using AI methods (including at least one of machine learning models, rule-based inference, optimization or search algorithms, probabilistic models, and heuristic processing) for inference or learning processing. These AI methods may be configured to take at least a portion of the data acquired by the information acquisition unit 610 or the pre-processing results of such data as input to generate at least a portion of the information output by each of the above units, such as demand forecasts, snow removal implementation plans, road clearing plans, urban structure scenarios, carbon cycle model estimation results, indicators related to preventive maintenance, indicators related to finance or the GX economic cycle, and visualization information. Furthermore, the AI ​​methods, learning models, inference logic, and other AI configurations used in relation to the snow removal operation platform and snow removal decision support system described herein are positioned as specific examples of AI methods in the snow removal operation platform and snow removal decision support system embedded in the Snow Country GX platform, and may be used as is, or extended or combined and applied in the information acquisition unit, analysis unit, road snow removal unit, robotics unit, urban development unit, environmental unit, financial settlement management unit, infrastructure maintenance unit, road clearing unit, or information provision unit of the Snow Country GX platform.

[0134] Furthermore, in this embodiment, the Snow Country GX platform may include a data quality evaluation model that determines whether the data acquired by the information acquisition unit 610 contains missing values, abnormal values, sensor failures, communication interruptions, or outliers. The data quality evaluation model may be configured to calculate a confidence score for each data based on at least one of time-series patterns, spatial correlations, past operational performance, and consistency with other types of sensor data, and to perform processing to exclude, supplement, or weight-reduce data whose confidence score falls below a predetermined threshold. Furthermore, the Snow Country GX platform may be configured to use a distributed learning model or federated learning model that works in conjunction with server devices distributed among multiple municipalities, road administrators, or snow removal businesses, allowing each server device to train or update a partial model using local datasets, and then aggregating at least one of the model parameters or gradient information calculated as a result. Furthermore, the Snow Country GX Platform may be configured to use a digital twin model that simulates multiple candidate snow removal and road clearing operational policies over time, taking at least one of the following as inputs: virtual snow conditions, traffic demand, population distribution, financial constraints, and GX policy parameters. It may also be configured to estimate and integrate at least one of the following: snow removal costs, delay time, accident risk, infrastructure degradation, carbon dioxide emissions, and carbon credit creation. Furthermore, the Snow Country GX platform may be configured to calculate at least one of the following for the control policy information or parameter update information: contribution information for each input factor, sensitivity analysis results, or comparison results with alternative scenarios, visualize these results on a dashboard screen, and provide an interface for operators to review, approve, correct, or revert the results. With such a configuration, the Snow Country GX platform can automatically monitor the quality of input data, improve model performance through distributed learning that reflects the knowledge of multiple stakeholders, pre-evaluate policy effects based on virtual scenarios, and ensure explainability and governance based on human review, thereby improving the achievement level of KPIs and GX indicators in a long-term and stable manner. Furthermore, these processes of data quality evaluation, distributed learning, digital twin simulation, and explainability enhancement may be performed by at least one of the AI ​​management unit, the analysis unit 620, or other processing units.

[0135] The present invention can also be realized as a program that enables a computer to implement each of the functions of the Snow Country GX platform described above. Such a program includes a sequence of instructions that causes the computer to function as at least part of the information acquisition unit 610, the analysis unit 620, the road snow removal unit, the robotics unit, the urban development unit, the environmental unit, the financial settlement management unit, the infrastructure maintenance unit, the road clearing unit, and the information provision unit. By executing these sequence of instructions, the processor can cause each of the aforementioned units to perform the processing described herein.

[0136] Furthermore, the present invention can also be embodied in a form in which the functions of the Snow Country GX Platform are implemented as a road management method. This road management method includes a series of processes consisting of registering snow removal plans, deriving evaluation information based on acquired information, and formulating or updating snow removal implementation plans based on the evaluation information, and these processes may be configured to be performed automatically or semi-automatically by a computer.

[0137] <Hardware Configuration> Figure 13 shows an example of the hardware configuration of a terminal device TM, a fixed-point camera CAM, a road surface condition provision server 100, a traffic condition provision server 200, a road space condition provision server 300, a weather condition provision server 400, a vehicle driving condition provision server 500, and a snow removal decision support system 600. This figure shows an example where the terminal device TM is a mobile phone such as a smartphone. The terminal device TM has a configuration in which, for example, a CPU 701, RAM 702, ROM 703, a secondary storage device 704 such as flash memory, a touch panel 705, and a wireless communication module 706 are interconnected by an internal bus or a dedicated communication line. Application programs such as a road patrol app are downloaded via the network NW and stored in the secondary storage device 704. The fixed-point camera CAM has a configuration in which, for example, a CPU 901, RAM 902, ROM 903, a secondary storage device 904 such as flash memory, a lens / image sensor 905, and a communication device 906 are interconnected by an internal bus or a dedicated communication line. Application programs, such as camera apps, are downloaded via the network (NW) and stored in the secondary storage device 904. These devices are connected to each other via the network (NW) to enable communication. Each server has a configuration in which components such as a NIC 801, CPU 802, RAM 803, ROM 804, secondary storage devices 805 such as flash memory or HDDs, and a drive device 806 are interconnected by an internal bus or dedicated communication line. A portable storage medium such as an optical disc is mounted on the drive device 806. Programs stored in the secondary storage device 805 or the portable storage medium mounted on the drive device 806 are loaded into the RAM 803 by a DMA controller (not shown) and executed by the CPU 802, thereby realizing the functional parts of each server. Road surface information 640, traffic information 650, road space information 660, weather information 670, vehicle driving information 680, analysis results 690, and judgment results 695 are stored in the secondary storage device 805. Each server may also be in a cloud computing environment. These computing resources are also commonly used in embodiments of the Snow Country GX platform. Furthermore, the snow removal decision support system 600 may be configured to communicate with various servers that handle the acquisition and processing of snow removal information, resident notification information, road service information, fiber optic survey information, satellite survey information, etc. Furthermore, the overall configuration of the snow removal and clearing decision support system 600 consists of a computing environment (cloud or on-premise) equipped with the memory, processor, and storage area necessary for processing each component, in order to implement each component, such as the information acquisition unit 610, analysis unit 620, decision unit 630, improvement unit, prediction unit, road snow removal and clearing unit, road clearing unit, infrastructure maintenance unit, and information provision unit. The higher-level functions of the Snow Country GX Platform (KPI / GX indicator management, financial / settlement / revenue distribution management, environmental value assessment / CO2 reduction calculation, carbon credit / GX economic cycle management, contract / incentive matching, audit / accountability management, related organization collaboration / information disclosure, etc.) can also be implemented on the same computing environment and may be in a single-instance or distributed configuration. Furthermore, the configuration may include computing resources such as GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), or AI accelerators for executing the AI ​​(artificial intelligence) models used in each component. Note that this configuration is just one example of the hardware configuration shown in Figure 13, and other configurations (edge ​​device configuration, IoT node configuration, etc.) may be used depending on the embodiment.

[0138] Although embodiments of the present invention have been described above with reference to the drawings, the present invention is not limited to these embodiments or illustrated configurations. For example, embodiments including configurations not shown but described herein, such as information on snow removal status, information on resident reporting status, road snow removal unit, improvement unit, prediction unit, road clearing unit, infrastructure maintenance unit, and information provision unit, are also included in the technical scope of the present invention. Furthermore, the Snow Country GX Platform of the present invention may be configured to include a snow removal decision support system and a snow removal operation platform, and can integrate higher-level functions such as KPI / GX indicator management, financial / settlement / revenue distribution management, environmental value assessment / CO2 reduction amount calculation, carbon credit / GX economic cycle management, contract / incentive matching, audit / accountability management, and related organization cooperation / information disclosure, and all or part of these functions may be selectively implemented. Moreover, the functions of the Snow Country GX Platform can be deployed in any computing environment such as cloud, on-premise, or edge, and may be configured as a single device or distributed across server groups and terminal groups.Therefore, the present invention can be modified, altered, or substituted in various ways without departing from its gist. [Explanation of Symbols]

[0139] 100: Road surface condition provision server 200: Traffic information server 300: Road space information server 400: Weather information server 500: Vehicle driving status server 600: Snow Removal Decision Support System 610: Information acquisition department 620: Analysis Department 630: Decision Section 640: Road surface information 650: Traffic information 660: Road Spatial Information 670: Weather information 680: Vehicle operation information 690:Analysis results 695: Judgment result

Claims

1. A snow country GX platform based on KPIs and GX indicators, It is equipped with an information acquisition unit, an analysis unit, and a road snow removal unit. The aforementioned road snow removal department shall register a pre-formulated snow removal plan that includes at least one road that contributes to ensuring the passage of emergency vehicles, including at least the route or work section, the snow removal contractor in charge, and the method of snow removal implementation. The analysis unit derives evaluation information for each road or management unit from the information acquired by the information acquisition unit, and the evaluation information includes an achievement index that represents the degree of achievement or deviation from the KPI or GX index for at least one of the following operational requirements. (1) To improve the efficiency of snow removal work, (2) Advanced automation or autonomy of snow removal operations, (3) Maintenance of sidewalks or living spaces through community-based snow removal activities, (4) Optimization of sustainable urban structure, mobility, or urban development, (5) Promoting the utilization of snow as a resource using a carbon cycle model, (6) Establishment of a GX economic cycle or creation of financial resources, (7) Strengthening regional resilience through the coordination of infrastructure maintenance and disaster resilience. Based on the evaluation information and the registered snow removal plan, the aforementioned road snow removal unit shall Snow Country GX Platform, characterized by formulating or updating snow removal and clearing plans for at least one road that may be designated as a road clearing route or an emergency transport route, or at least one road leading to such a road clearing route or emergency transport route, through optimization based on the aforementioned evaluation information.

2. The Snow Country GX platform according to claim 1, The aforementioned road snow removal unit generates a timeline corresponding to the road traffic environment based on at least one of the following: road surface conditions, traffic conditions, road space conditions, weather conditions, vehicle driving conditions, resident reports, or snow removal conditions, acquired by the aforementioned information acquisition unit. The system outputs the snow removal criteria, priority, or snow removal method for the relevant timeline, and For each of the aforementioned routes or work sections, set and output milestones that include at least one start time or end time. The Snow Country GX platform is characterized in that it can associate the output with the evaluation information, the KPI, and the achievement indicator.

3. The Snow Country GX platform according to claim 1, The aforementioned Snow Country GX platform is equipped with a robotics unit, Based on the evaluation information derived by the analysis unit, the road snow removal unit shall The system performs at least one of the following processes related to snow removal operations: generating instructions, monitoring progress, or replanning. The robotics unit, based on at least one of the following: the command provided by the road snow removal unit, the evaluation information provided by the analysis unit, or the information acquisition unit acquired regarding the road traffic environment, With at least one of the following as the target of control: an autonomous snowplow, a snow removal drone, or robotic equipment, The process generates start, stop, or switch command for snow removal operations corresponding to the timeline. The Snow Country GX platform is characterized in that it can associate the processing with the evaluation information, the KPI, and the achievement indicator.

4. The Snow Country GX platform according to claim 1, The aforementioned information acquisition unit receives information from government agencies, schools, boards of education, PTAs, or local organizations. Obtain at least one piece of information regarding school routes that includes dangerous areas, The analysis unit scores the degree of danger on the school route based on the information on the school route obtained by the information acquisition unit. The aforementioned road snow removal unit processes the results of the risk assessment and the degree of community cooperation to determine the priority of community-based snow removal activities. The Snow Country GX platform is characterized by being able to associate the said process with the KPI and the achievement indicator.

5. The Snow Country GX platform according to claim 1, The aforementioned information acquisition unit acquires information about snow accumulation around houses provided by residents, including at least the snow depth. The analysis unit generates a snow depth score that scores the snow depth situation for each region or area, taking into account the time-series changes in the snow depth around the house, based on the information on snow depth around the house obtained by the information acquisition unit. A snow country GX platform characterized by performing a process to derive the evaluation information that reflects the snow depth score.

6. The Snow Country GX platform according to claim 5, The information acquisition unit acquires information about the house provided by the residents, including at least the shape and structure of the roof, and information about weather conditions. Based on the information regarding the house, the information regarding snow accumulation around the house or the snow accumulation score, and the information regarding the weather conditions, the analysis unit shall We calculate the roof snow risk level, which indicates the degree of risk from snow on the roof of each house, A snow country GX platform characterized by performing a process to derive the evaluation information that reflects the roof snow risk level.

7. The Snow Country GX platform according to claim 1, The aforementioned Snow Country GX platform is capable of registering or referencing at least one piece of urban planning information, and further includes an urban development department. The aforementioned Urban Development Department, based on the demographic information and snow removal cost information acquired or referenced by the aforementioned Information Acquisition Department, evaluates the maintenance level or priority of road management length in each region. Based on the evaluation results, the areas are classified into one of the following categories: areas requiring priority maintenance of snow removal, areas where maintenance is possible through community cooperation, or areas where phased maintenance is required. Based on the classification results and the urban planning information, the Snow Country GX Platform performs processing to optimize the length of road management in relation to the current plan and to evaluate the urban structure. A snow country GX platform characterized in that the process can be associated with the KPI, the achievement indicator, and the GX indicator.

8. The Snow Country GX platform according to claim 7, Based on the classification results and the evaluation results of the urban structure, the aforementioned urban development department A snow removal score is calculated that uses at least one of the following indicators for each region or living area: priority of snow removal, snow removal efficiency, maintenance costs, or degree of community cooperation. Based on the snow removal score and at least one of the transportation convenience or lifestyle-related indicators calculated by the Urban Development Department, the system generates information to support at least one of the following: relocation of residents or businesses, reallocation of local resources, development of snow melting channels, or regional redevelopment. The Snow Country GX Platform is characterized in that it can associate the processing with the KPI, the achievement indicator, and the GX indicator.

9. The Snow Country GX platform according to claim 7, The aforementioned information acquisition unit acquires or references fixed asset information or usage status information, including location information, as information relating to vacant land or vacant houses. Based on the information regarding vacant lots or empty houses, weather conditions, traffic conditions, and snow removal status obtained or referenced by the Information Acquisition Unit, the Urban Development Department shall From the vacant lots or vacant houses in question, we will select the plots of land that can be used as snow storage areas. The road structure, snow removal conditions, snow removal routes, location conditions, traffic safety, and impact on the surrounding environment of the selected sites are evaluated, This process generates a proposed layout for snow storage areas that contributes to improving snow removal efficiency or reducing the burden on the local environment. The Snow Country GX Platform is characterized in that it can associate the processing with the KPI, the achievement indicator, and the GX indicator.

10. The Snow Country GX platform according to claim 7, The aforementioned urban development department acquires, references, or inputs information regarding vacant houses that are at risk of collapse. Based on the snow removal score or the evaluation results of the urban structure, We will assess the urgency and impact of the collapse of the vacant house in question. Based on the evaluation results and the snow removal status obtained by the information acquisition unit, a process is performed to select the site of the vacant house or the site after demolition as a candidate for a snow storage area for each work section. The Snow Country GX Platform is characterized in that it can associate the processing with the KPI, the achievement indicator, and the GX indicator.

11. The Snow Country GX platform according to claim 1, The aforementioned Snow Country GX platform includes an urban development department and an environmental department, The information acquisition unit acquires or refers to information regarding transportation demand and transportation supply status. The aforementioned environmental unit performs a process to calculate at least one of the traffic congestion index or driving efficiency index based on the vehicle driving conditions acquired by the information acquisition unit. The Urban Development Department, based on at least two different pieces of information from among the information on transportation demand, the information on transportation supply status, the traffic congestion index, the driving efficiency index, the urban structure evaluation results, the snow removal score, the snow removal implementation plan, or the urban plan, We comprehensively evaluate the traffic flow, mobility demand, and snow removal / clearing situation in the region. This process derives basic information for building a MaaS platform that covers all modes of transportation in the region, including private cars. The Snow Country GX Platform is characterized in that it can associate the processing with the KPI, the achievement indicator, and the GX indicator.

12. The Snow Country GX platform according to claim 11, The aforementioned Snow Country GX platform is equipped with an information provision unit. Based on the basic information for constructing the MaaS platform, the analysis unit performs the following: We will comprehensively analyze information regarding snow removal and clearing conditions, road conditions, traffic conditions, the aforementioned transportation supply conditions, and weather conditions. Based on the results of the analysis described above, mobility suggestion information is generated that dynamically optimizes at least one of the following: departure time, travel route, or travel schedule for the mode of transport. The information provision unit performs the process of outputting the mobility suggestion information regarding areas where a decrease in safety due to poor road conditions or an increase in environmental burden due to traffic congestion is predicted. The Snow Country GX Platform is characterized in that it can associate the processing with the KPI, the achievement indicator, and the GX indicator.

13. The Snow Country GX platform according to claim 1, The aforementioned Snow Country GX platform is equipped with an environmental section. The aforementioned information acquisition unit acquires or references information regarding forest conditions, information regarding the use of snow cooling or snow-derived energy, and information regarding electricity consumption or heat demand in the region. The aforementioned Environmental Department constructs a forest absorption model to estimate the amount of CO2 absorbed by forests in the region, based on the information regarding the forest conditions. Based on the information regarding the use of snow-based cooling or snow-derived energy, and the information regarding electricity consumption or heat demand in the region, a snow resource energy model is constructed to estimate the amount of energy consumption that can be replaced by the use of snow-based cooling or snow-derived energy, and the amount of CO2 emission reduction associated with such replacement. Furthermore, by integrating the results of the forest absorption model and the snow resource energy model, a carbon cycle model representing the regional-scale carbon cycle based on the use of forest resources and snow resource energy is constructed, and the estimated results of the carbon cycle based on the carbon cycle model are output. The Snow Country GX platform is characterized in that it can associate the estimation results with the KPI, the achievement index, and the GX index.

14. The Snow Country GX platform according to Claim 13, The aforementioned Snow Country GX platform includes a financial settlement management department, The aforementioned financial settlement management department shall, based on at least one of the following: the amount of CO2 reduction from the environment based on the estimation results of the carbon cycle model, the amount of CO2 reduction from traffic estimated based on the traffic congestion index or driving efficiency index, or the amount of CO2 reduction from snow removal calculated based on the snow removal implementation plan and snow removal response status, Further processing is performed to normalize the CO2 reduction amounts from each source to a common unit, convert the normalized result into monetary value as environmental value data, and generate GX economic circulation information at the municipal or regional level corresponding to the scope of the CO2 reduction calculation. Based on the aforementioned GX economic circulation information, at least one of the following processes is carried out: application for carbon credit issuance, cooperation with registration institutions, calculation of estimated sales amount, redistribution of profits, or fundraising through community-contributing financing means. The Snow Country GX Platform is characterized in that it can associate the processing with the KPI, the achievement indicator, and the GX indicator.

15. The Snow Country GX platform according to claim 1, The aforementioned Snow Country GX platform includes an infrastructure maintenance section. The information acquisition unit acquires multiple types of information, including information on road damage in snowy regions, including damage caused by freeze-thaw cycles or snow removal operations that occur at least during the winter, information on snow removal conditions, information on weather conditions, and information on the traffic conditions of large vehicles. The aforementioned infrastructure maintenance department estimates a contribution rate model for road damage factors, including factors specific to snowy regions, based on the aforementioned multiple types of information. The Snow Country GX platform is characterized by performing a process to calculate preventive maintenance indicators that quantify at least one of the following based on a contribution rate model of the road damage factors: the signs of road damage, the risk of road damage progression, or the probability of serious road damage occurring.

16. The Snow Country GX platform according to claim 15, The aforementioned Snow Country GX platform is further equipped with a road clearing section. The aforementioned Snow Country GX platform integrates and manages performance information of snow removal contractors and road maintenance contractors, availability information of equipment and personnel owned by both contractors, and information regarding the types of contractors and the scope of work they can handle in a common format. Based on this integrated management, it performs processing to propose and configure a year-round comprehensive management system based on the management system for snow removal. The Infrastructure Maintenance Department generates a road maintenance plan based on the preventive maintenance indicators and performs processing to support the coordination of operations and the allocation of materials and equipment for implementing preventive maintenance for road maintenance based on the comprehensive management system. The Snow Country GX platform is characterized in that the road clearing unit performs processing to dynamically optimize the road clearing plan in the event of a disaster, in conjunction with the preventive maintenance indicators and the comprehensive management system.

17. The Snow Country GX platform according to claim 16, The aforementioned Snow Country GX platform integrates multiple types of road maintenance plans, snow removal plans, and road clearing plans. This process generates regional resilience information that integrates road maintenance, snow removal operations, and road clearing during disasters in snowy regions. A snow country GX platform characterized by being able to associate the regional resilience information with the KPI, the achievement indicator, and the GX indicator.

18. The Snow Country GX platform according to claim 17, Based on the aforementioned regional resilience information, the Snow Country GX Platform performs a process to calculate at least one of the following for adaptation funds related to adaptation measures to prepare for disasters caused by climate change: the required amount, the allocation policy, or the procurement plan. A snow country GX platform characterized in that the process can be associated with the KPI, the achievement indicator, and the GX indicator.

19. The Snow Country GX platform according to claim 1, The aforementioned Snow Country GX platform comprises a town planning department, an environmental department, an infrastructure maintenance department, and a road clearing department, and further comprises an AI management department that oversees the AI ​​of each of these departments. The AI ​​Management Unit receives at least one of the evaluation information, planning information, or indicator information output by at least one of the following: the Analysis Unit, the Road Snow Removal Unit, the Urban Planning Unit, the Environment Unit, the Infrastructure Maintenance Unit, and the Road Clearing Unit. Perform inference or learning using AI methods, A snow country GX platform characterized by performing a process to output at least one of the following to at least one of the analysis unit, the road snow removal unit, the urban development unit, the environment unit, the infrastructure maintenance unit, or the road clearing unit: parameter update information or control policy information for improving the degree of achievement of the KPI, the achievement indicator, and the GX indicator.

20. A program for causing a computer to function as the Snow Country GX platform described in claim 1, The aforementioned computer is configured to function as at least one of the following departments: information acquisition department, analysis department, road snow removal department, robotics department, urban development department, environmental department, financial settlement management department, infrastructure maintenance department, road clearing department, or information provision department. Perform inference or learning using AI methods, The information acquisition unit takes at least a portion of the data acquired by the aforementioned information acquisition unit or the pre-processing results of said data as input. A program characterized by generating at least one piece of information output by at least one of the following units: the information acquisition unit, the analysis unit, the road snow removal unit, the robotics unit, the urban development unit, the environment unit, the financial settlement management unit, the infrastructure maintenance unit, the road clearing unit, or the information provision unit.

21. A program that includes a sequence of instructions to cause a computer to perform at least one of the following (A) or (B): (A) The functions of the Snow Country GX platform according to any one of claims 1, 2, 4, 5, 7, 11, 13, 15, and 19. (B) At least one of the following functions (1) through (10): (1) The road snow removal unit and robotics unit according to claim 3 have a function to generate a start command, stop command, or work switching command for an autonomous snow removal vehicle, snow removal drone, or robotics equipment based on evaluation information and a timeline derived by the analysis unit. (2) The function described in claim 6, which uses the information acquisition unit and the analysis unit to calculate the roof snow risk level of each house based on information about the house, information about snow accumulation around the house, and information about weather conditions, and to derive the evaluation information that reflects the roof snow risk level, (3) A function described in claim 8, which calculates a snow removal score based on the classification results and the evaluation results of the urban structure, which is an index of at least one of the following in each region or living area: priority of snow removal, snow removal efficiency, maintenance cost, or degree of community cooperation, and which generates information to support at least one of the following in each region or living area: relocation of residents or businesses, reallocation of local resources, development plan for snow melting ditches, or regional redevelopment, based on the snow removal score and at least one of the transportation convenience or lifestyle-related indexes. (4) The function described in claim 9, wherein the urban development department extracts candidate plots of land from the vacant lots or vacant houses that can be used as snow storage sites based on information regarding vacant lots or vacant houses, weather conditions, traffic conditions, and snow removal conditions, and evaluates at least one of the following factors for the extracted plots of land: road structure, snow removal conditions, snow removal routes, location conditions, traffic safety, and impact on the surrounding environment, in order to generate a snow storage site layout plan. (5) The function described in claim 10, in which the urban development department evaluates the urgency and impact of the collapse of a vacant house based on information regarding vacant houses at risk of collapse and the status of snow removal, and selects the vacant house as a candidate snow storage site for each construction area based on the evaluation results and the status of snow removal, (6) A function according to claim 12, which generates mobility proposal information that dynamically optimizes departure time, travel route, or at least one of the means of transport based on basic information for constructing a MaaS platform, as well as information on snow removal status, road conditions, traffic conditions, transport supply status, and weather conditions, and outputs such mobility proposal information for areas where a decrease in safety due to poor road conditions or an increase in environmental load due to traffic congestion is predicted. (7) The financial settlement management unit according to claim 14 generates GX economic cycle information based on at least one of the following: the amount of CO2 reduction from the environment based on the estimation results of the carbon cycle model, the amount of CO2 reduction from traffic estimated based on the traffic congestion index or the driving efficiency index, or the amount of CO2 reduction from snow removal calculated based on the snow removal implementation plan and the snow removal response status, and performs at least one of the following based on the GX economic cycle information: applying for the issuance of carbon credits, coordinating with registration bodies, calculating the estimated sale amount, or redistributing revenues. (8) The infrastructure maintenance unit and the road clearing unit described in claim 16 integrate and manage information on the performance record of snow removal contractors and road maintenance contractors, information on the availability of equipment and personnel, and information on the types of contractors and the scope of work they can handle in a common format, generate a road maintenance plan based on preventive maintenance indicators, support the coordination of operations and the allocation of equipment and materials for implementing preventive maintenance for road maintenance based on a comprehensive management system, and further function to dynamically optimize the road clearing plan in the event of a disaster in conjunction with the preventive maintenance indicators and the comprehensive management system. (9) The Snow Country GX Platform according to claim 17 has a function to generate regional resilience information that integrates multiple types of road maintenance plans, snow removal plans, and road clearing plans, and manages road maintenance, snow removal operations, and road clearing in snow country regions in an integrated manner. (10) The function of the Snow Country GX platform described in claim 18, which calculates at least one of the following for adaptive funds related to adaptation measures to prepare for disasters caused by climate change, based on the regional resilience information: the required amount, the allocation policy, or the procurement plan. A program characterized by causing a computer to execute something.

22. A road management method based on KPIs and GX indicators using a computer, The aforementioned computer, Register a pre-formulated snow removal plan that includes at least one road that contributes to ensuring the passage of emergency vehicles, including at least the route or work section, the snow removal contractor in charge, and the method of snow removal implementation. Based on information acquired through information acquisition processing via the network, evaluation information is derived for each road or management unit, and the evaluation information includes an achievement index that represents the degree of achievement or deviation from the KPI or GX index for at least one of the following operational requirements. (1) To improve the efficiency of snow removal work, (2) Advanced automation or autonomy of snow removal operations, (3) Maintenance of sidewalks or living spaces through community-based snow removal activities, (4) Optimization of sustainable urban structure, mobility, or urban development, (5) Promoting the utilization of snow as a resource using a carbon cycle model, (6) Establishment of a GX economic cycle or creation of financial resources, (7) Strengthening regional resilience through the coordination of infrastructure maintenance and disaster resilience. Based on the aforementioned evaluation information and the registered snow removal plan, A road management method characterized by performing a process to formulate or update a snow removal and clearing plan for at least one road that may be designated as a road clearing route or an emergency transport route, or a road leading to such a road clearing route or emergency transport route, by optimization based on the evaluation information.

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