Snow removal decision support system and program, road management method

Abstract

The present invention aims to provide snow removal decision support technology that utilizes diverse information such as resident reports, snow removal status, road surface conditions, traffic conditions, weather information, and various sensor data to accurately determine the necessity of snow removal, prioritize snow removal, and support the dynamic formulation and updating of snow removal implementation plans. [Solution] The present invention is a computer-based snow removal decision support system / program or road management method. The computer acquires and analyzes multiple pieces of information, including information on resident reporting status, to determine the necessity of snow removal. If the necessity of snow removal is determined, it assigns priorities to each road and dynamically formulates and updates a snow removal implementation plan. Furthermore, it accumulates acquired information, analysis results, and decision results as learning data and continuously improves accuracy using AI. Moreover, it is configured to be applicable to disaster judgment and road infrastructure maintenance management.

Description

Technical Field

[0001] The present invention relates to a technology for assisting in the implementation judgment of snow removal work on snow-covered roads, and particularly to a snow removal judgment support system that dynamically judges and manages the necessity and priority of snow removal by using resident notification information and various external information.

Background Art

[0002] In snowy and cold regions, in order to maintain urban functions in winter and ensure smooth road traffic, it is necessary to appropriately carry out snow removal work on roads during snowfall. This can minimize traffic disruptions and impacts on daily life. Conventionally, when judging the necessity of snow removal, visual patrols by local government staff and notification information such as phone calls from residents have been the main means, and the uneven distribution of information and the subjectivity of judgment have been issues. In recent years, the utilization of AI (artificial intelligence) analysis and sensing technology has been progressing, but these mainly rely on some sensing means and devices, and the fact is that they have not yet achieved wide-area and planned operation across the entire local government.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, advanced technologies have been developed for snow removal and snow clearing decisions and work management, such as AI (artificial intelligence) technology that analyzes images acquired by cameras mounted on patrol vehicles to understand the conditions of snow accumulation and snow embankments (for example, Patent Document 1), and vibration sensing technology that estimates snow accumulation conditions based on optical fiber cables laid on roads (for example, Patent Document 2). Furthermore, interactive information provision systems (for example, Patent Document 3) have been proposed that use AI (artificial intelligence) to analyze the content of users' messages via SNS (Social Networking Service) or chat applications and present relevant information. However, while each of these technologies has its own merits, they are limited to specific information sources and individual functions, and do not comprehensively support the overall assessment and prioritization of snow removal and clearing, or the development of dynamic implementation plans. The present invention aims to provide a highly practical snow removal decision support system that uses information reported by residents as core information and integrates and analyzes diverse information such as patrol information, various sensor information, traffic conditions, and weather information to accurately and flexibly determine the necessity of snow removal and clearing, enabling prioritization and the formulation and updating of dynamic implementation plans. Furthermore, the present invention goes beyond the one-time task of snow removal and clearing, embodying a new infrastructure management concept of "self-improving snow removal decision support" that comprehensively collects, analyzes, and judges weather characteristics, traffic characteristics, and citizen needs for each region, and dynamically and continuously optimizes operational policies based on the results. [Means for solving the problem]

[0005] The above problems can be solved by the invention having the following configuration. The snow removal decision support system of the present invention has the function of acquiring and analyzing multiple pieces of information, including information on resident reporting status and information on snow removal status, to determine the necessity of snow removal and formulate a snow removal implementation plan. Furthermore, the present invention includes a road snow removal unit that manages and controls actual snow removal work based on the decision results, and an improvement unit that continuously improves the decision model, and these are configured as core components. These components enable flexible prioritization according to region and time of day, reflection of resident needs, and learning improvement by AI (artificial intelligence), realizing efficient and highly accurate snow removal support. An example of the configuration of the present invention is shown below in accordance with the claims. [1] A snow removal decision support system comprising: an information acquisition unit that acquires two or more pieces of information, including at least information on resident reporting status, from among information on road surface conditions, traffic conditions, road space conditions, weather conditions, vehicle driving conditions, road service status, fiber optic survey status, satellite survey status, resident reporting status, and snow removal status; an analysis unit that analyzes the two or more pieces of information acquired by the information acquisition unit; a decision unit that determines the necessity of snow removal measures based on the results of the analysis by the analysis unit; and a road snow removal unit that, when the necessity of snow removal measures is determined, assigns a priority to each road and executes the process of formulating and updating a snow removal implementation plan based on the priority, wherein the snow removal implementation plan is formulated and updated dynamically. A snow removal decision support system as described in [2][1], characterized in that the information regarding the status of resident reports is obtained by at least one of the following means: AI-based voice reporting, reporting via SNS, reporting via web form, or telephone interviews by staff and subsequent database registration. A snow removal decision support system according to [3] [1] or [2], wherein the information regarding the status of resident reports is used to extract reports related to snow removal based on the content of the reports, to evaluate the urgency from the extracted reports, to evaluate the necessity of snow removal considering the geographical characteristics of the target area identified by the content of the reports, and further to assign a priority to each road considering the density of reports and distribution information regarding the population or number of households in the target area. A snow removal decision support system according to any one of paragraphs [4], [1], or [3], wherein the road snow removal unit has a function to record the status of snow removal and to perform a process to support settlement of business results with snow removal contractors based on the record. A snow removal decision support system according to any one of paragraphs [5], [1] to [4], wherein the snow removal decision support system includes an AI-based improvement unit that continuously improves at least one of the following as learning data: the analysis results by the analysis unit, the decision results by the decision unit, the status of snow removal by the road snow removal unit, or various information obtained by the information acquisition unit, based on the learning data: the process of selecting information to be acquired by the information acquisition unit, the process of improving the analysis accuracy by the analysis unit, the process of optimizing the decision criteria by the decision unit, or the process of improving the accuracy of priority assignment by the road snow removal unit. A snow removal decision support system according to any one of paragraphs [6], [1] to [5], wherein the decision unit forecasts the possibility of snow removal becoming necessary in the near future based on various information obtained by the information acquisition unit, and has a notification function to provide relevant parties with information regarding the preparation or implementation of snow removal based on the forecast result. A snow removal decision support system according to any one of paragraphs [7][1] to [6], wherein the analysis unit weights the reliability or priority of various information obtained by the information acquisition unit according to the time period in which the information was acquired, and evaluates the necessity of snow removal based on the weighting. A snow removal decision support system according to any one of paragraphs [8], [1] to [7], wherein the road snow removal unit has a function to determine a suitable working time for snow removal based on various information obtained by the information acquisition unit, and to formulate a snow removal implementation plan based on said determination. A snow removal decision support system according to any one of paragraphs [9][1] to [8], wherein the snow removal decision support system detects unevenness, cracks, rutting, voids under the road surface, road damage, or road collapse on winter days or non-winter days without snowfall, based on various information obtained by the information acquisition unit, and utilizes the detection results for corresponding processing related to infrastructure maintenance. A snow removal decision support system according to any one of paragraphs

[10] , [1] to [9], wherein the analysis unit detects the occurrence of a disaster based on various information obtained by the information acquisition unit, the decision unit determines the necessity of snow removal due to the disaster based on the disaster detection result by the analysis unit, and the road snow removal unit formulates a road clearing implementation plan based on a road clearing route, road clearing bases, and work timeline or priority order related to road clearing, via the road clearing unit, based on the decision result by the decision unit.

[11] A program for causing a computer to function as a snow removal decision support system as described in any one of paragraphs [1] through

[10] .

[12] A road management method using a computer, wherein the computer acquires, via a network, at least two or more pieces of information from among information on road surface conditions, traffic conditions, road space conditions, weather conditions, vehicle driving conditions, road service conditions, optical fiber survey conditions, satellite survey conditions, resident notification conditions, and snow removal conditions, including at least information on resident notification conditions; analyzes the two or more pieces of information acquired; determines the necessity of snow removal measures based on the analysis results; assigns a priority to each road if the necessity of snow removal measures is determined; executes the process of formulating and updating a snow removal implementation plan based on the priority; and the snow removal implementation plan is formulated and updated dynamically. A road management method as described in

[13]

[12] , characterized in that the information relating to the status of resident reports is obtained by at least one of the following means: AI-based voice reporting, reporting via social media, reporting via web form, or telephone interviews by staff and subsequent database registration. A road management method according to

[14]

[12] or

[13] , wherein the information regarding the status of resident reports is used to extract reports concerning snow removal based on the content of the reports, to evaluate the urgency from the extracted reports, to evaluate the necessity of snow removal considering the geographical characteristics of the target area identified by the content of the reports, and further to assign a priority to each road considering the density of reports and distribution information regarding the population or number of households in the target area. A road management method according to any one of paragraphs

[15] ,

[12] , or

[14] , wherein the computer records the status of snow removal and clearing operations and, based on the record, performs processing to support settlement of business results with snow removal and clearing contractors. A road management method according to any one of paragraphs

[16] ,

[12] , to

[15] , characterized in that the computer stores at least one of the following as learning data: the analysis results, the judgment results, the status of snow removal and clearing, or various acquired information; and based on the learning data, it continuously performs a process using AI to perform at least one of the following: a process for selecting information to be acquired, a process for improving the accuracy of the analysis, a process for optimizing the judgment criteria, or a process for improving the accuracy of assigning priorities. A road management method according to any one of paragraphs

[17] ,

[12] , or

[16] , characterized in that the computer predicts the possibility of snow removal becoming necessary in the near future based on various information acquired, and performs a process of notifying relevant parties of information regarding the preparation or implementation of snow removal based on the prediction results. A road management method according to any one of paragraphs

[18] ,

[12] , or

[17] , characterized in that the computer weights the various types of information acquired according to the time period in which they were acquired, in terms of their reliability or priority, and performs a process to evaluate the necessity of snow removal based on the weighting. A road management method according to any one of paragraphs

[19] ,

[12] , or

[18] , characterized in that the computer determines a suitable time period for snow removal based on various acquired information, and performs a process of formulating the snow removal implementation plan based on said determination. A road management method according to any one of paragraphs

[20] ,

[12] , or

[19] , characterized in that the computer detects risks related to road infrastructure maintenance based on various information acquired on winter days or non-winter days when there is no snowfall, and utilizes information regarding said risks in response processing related to infrastructure maintenance. A road management method according to any one of paragraphs

[21] ,

[12] , or

[20] , characterized in that the computer detects the occurrence of a disaster based on various information acquired, determines the necessity of snow removal due to the disaster based on the results of the disaster detection, and performs the process of formulating a road clearing implementation plan based on a road clearing route, road clearing bases, and a work timeline or priority order for road clearing based on the determination. In this specification, "winter days" refers to days during the winter period when snowfall is expected, and "non-winter days" refers to days that belong to periods other than the snowy season, i.e., days that belong to spring, summer, and autumn. Furthermore, "risks related to road infrastructure maintenance" includes phenomena that require road maintenance and management measures, such as uneven road surfaces, road damage, road collapse, cracks, rutting, road subsidence, pavement deterioration, and voids beneath the road surface. [Effects of the Invention]

[0006] According to the present invention, by using the information received from residents as core information and integrating it with traffic conditions, weather information, sensing information, etc., it is possible to determine and prioritize the optimal need for snow removal on snow-covered road surfaces. This will prevent delays and duplication of snow removal work, enabling planned and rapid snow removal on necessary roads. Furthermore, it will allow for the development and notification of flexible snow removal plans, including nighttime and disaster response, providing a safe and secure road traffic environment for road users, while also contributing to increased efficiency for local governments (road administrators) and snow removal companies. Furthermore, this invention significantly improves the accuracy, flexibility, and citizen satisfaction of snow removal decisions by comprehensively integrating information types (road surface, weather, driving conditions, reports, etc.) that were limited in conventional technology, and by using AI (artificial intelligence) to make decisions that take into account regional characteristics and temporal variations. In addition, by reconstructing and optimizing the decision model based on feedback such as complaints, repeat reports, and road damage, self-improving social infrastructure management becomes possible. [Brief explanation of the drawing]

[0007] [Figure 1] It is a system configuration diagram related to the snow removal and snow clearing judgment support system of the present invention. [Figure 2] It is a flowchart showing an example of the flow of patrol. [Figure 3] It is a flowchart showing an example of the flow of a fixed-point camera. [Figure 4] It is a diagram showing an example of road surface conditions. [Figure 5] It is a cross-sectional view showing an example of road surface conditions (undulations of snow-covered road surface). [Figure 6] It is a cross-sectional view showing an example of road surface conditions (snow compacting thickness 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 running conditions. [Figure 11] It is a flowchart showing an example of the flow 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]This figure shows an example of the hardware configuration related to the snow removal decision support system of the present invention. Figure 1 is a schematic block diagram showing the basic configuration of the snow removal decision support system of the present invention. In this figure, the configuration mainly shows the main functional modules such as the information acquisition unit, analysis unit, and decision unit, but in the present invention, in addition to these, the configuration may also include a function that acquires and processes information on resident reporting status, information on snow removal status, etc. as core information, a road snow removal unit that specifically formulates and manages snow removal implementation plans, an improvement unit that improves the decision model using AI (artificial intelligence), etc. These components are not shown in Figure 1, but they function as important components in the embodiments of the present invention. Figure 11 shows an overview of a typical processing flow from information acquisition to analysis and decision in the snow removal decision support system, and is a supplementary diagram for understanding embodiments of the present invention. In the present invention, information on resident reporting status and information on snow removal status, etc. are also considered to be extremely important decision materials in determining the necessity of snow removal and prioritizing snow removal, but since this information is not explicitly shown in Figure 12, Figure 12 should be understood as merely an example. [Modes for carrying out the invention]

[0008] Hereinafter, with reference to the drawings, one embodiment of the snow removal decision support system according to the present invention will be described in detail. The embodiments described below are merely examples to facilitate understanding of the present invention and do not unduly limit the technical scope of the invention. Each component and functional configuration described herein may be modified, substituted, deleted, or added as necessary, based on the gist of the invention as described in the claims, and are included within the technical scope insofar as they produce similar effects. Furthermore, the various components and means described herein can be considered as independent inventions, or they can be combined to form new inventive forms. In this specification, "core components" refers to the following parts that are particularly central to processing and coordinating functions in the overall configuration of the snow removal decision support system: the analysis unit, the decision unit, the improvement unit, and the road snow removal unit. Furthermore, "core information" refers to information that is primarily subject to analysis, judgment, and control based on these core components, and includes information on resident reporting status, snow removal status, and road surface conditions.

[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, in this invention, the snow removal decision support system is configured to include, as core components, a road snow removal unit that handles information on resident reporting status, information on snow removal status, formulation and implementation management of snow removal plans, and an improvement unit that continuously improves the decision model. On the other hand, information regarding road service status, fiber optic survey status, satellite survey status, as well as infrastructure maintenance, road clearing, forecasting, and information provision departments, are components that may 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, the function of providing information on resident reporting status and snow removal status is provided in the snow removal decision support system 600 and functions as a core component. This information is acquired via a network NW through a dedicated server or terminal device. On the other hand, information regarding road service status, fiber optic survey status, and satellite survey status may be acquired as needed, and these can be added as optional extended information.

[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, roadside pillars, poles, etc., around intersections. 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 provided road surface conditions are road-specific information and include some or all of the following: road surface freezing, road surface snow accumulation and snow quality, unevenness of the snow-covered road surface, ruts and pits on the snow-covered road surface, road surface compaction thickness, road surface unevenness (flatness), 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.

[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.

[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.

[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, snowfall amount, snow depth, snow density / weight, 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.

[0021] The vehicle driving status 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 times and bus route delay information relative to the winter timetable (a timetable that takes into account traffic conditions during the winter), the bus's current position (which lane it is traveling in out of three lanes on one side, etc.), the number of vehicles in front of the bus and the number of vehicles alongside it, 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, bus delay times 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, 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.

[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.

[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 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 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, stuck locations, road conditions, traffic conditions, EV charging availability, vehicle inspection results, etc.

[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.

[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.

[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 (described in paragraph 0035) when assigning priorities for snow removal, and by considering them in conjunction 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 report information is automatically sorted by AI (artificial intelligence), and only reports 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.), report density, and population or household distribution are evaluated to derive priorities. This information is then subjected to integrated analysis by the analysis department and used as a basis for decision-making by the decision-making department. In particular, it functions as a source of information that supports highly accurate decisions that influence the necessity, timing, and method (daytime / nighttime, etc.) of snow removal. 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) and work sections (local roads), road conditions, traffic conditions, information on snow removal companies, 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 companies, 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 operational results (operating hours, location information, history, etc.), snow removal work reports, patrol results, budget management, settlement management, complaints and requests, and information on snow disposal sites and snow storage areas. Furthermore, images, videos, and acceleration sensor data collected by patrol vehicles regarding road surface conditions after snow removal may also be included as information used for evaluating the quality of snow removal and for 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 is used by the Road Snow Removal Department (described in paragraph 0035) to formulate snow removal plans, assign priorities, and decide whether to perform snow removal during the day or at night, and is analyzed in conjunction with other information as needed.

[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, the decision unit 630, the road snow removal unit (described in paragraph 0035), the improvement unit (described in paragraph 0033), 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 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 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. 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 (described in paragraph 0035). Furthermore, the analysis unit 620 may be configured to provide the analyzed information to the improvement unit (described in paragraph 0033) as training data or update data, contributing to improving the accuracy of the decision model and retraining the model. The training data may include date and time, location, report content, report density, complaint classification, congestion occurrence, traffic volume, snow accumulation status, 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. These analysis results 690 are transmitted to the decision unit 630 and used as information to help determine the necessity of snow removal. Furthermore, they are distributed as needed to various components such as the road snow removal unit (described in paragraph 0035), road clearing unit (described in paragraph 0037), infrastructure maintenance unit (described in paragraph 0036), improvement unit (described in paragraph 0033), and prediction unit (described in paragraph 0034), contributing to improving the overall system's judgment and management accuracy. In addition, 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 matching with snow removal implementation history, comparative analysis with snow removal method selection history, and evaluation of implementation effectiveness. Note that the analysis target information is not limited to all information; analysis may be performed using any combination 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 enable snow removal decisions that are tailored to the size of the population and the degree of population concentration in a region, by obtaining distribution information regarding the population or number of households in a region from a statistical information database or administrative information, and integrating this information with the density and urgency of resident reports. 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.

[0030] 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 work 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, 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 (described in paragraph 0035) 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. This forecasting function is functionally different from the prediction unit (described in paragraph 0034) 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 (described in paragraph 0035) and used for prior snow removal preparation, provisional setting of priorities, and consideration of response time periods. Furthermore, the judgment result 695 is shared with components such as the Road Clearing Department (described in paragraph 0037), the Infrastructure Maintenance Department (described in paragraph 0036), and the Improvement Department (described in paragraph 0033), and is used as input information for processing and formulating implementation plans for each department. 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 (described in paragraph 0033), 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, functioning as a core 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.

[0031] 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 resident notification status information, snow removal status information, fiber optic survey status information, satellite survey status information, and road service status 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 furthermore, this information may be used by the road snow removal unit (described in paragraph 0035) for formulating snow removal implementation plans, and by the improvement unit (described in paragraph 0033) for optimizing decision logic, etc.

[0032] 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 judgment criteria and is not limited to this. In this embodiment, various types of information not shown, such as resident notification status information, snow removal status information, fiber optic survey status information, satellite survey status information, and road service status information, are also used as judgment criteria by the decision unit 630. Furthermore, these judgment results may be used for formulating snow removal implementation plans by the road snow removal unit (described in paragraph 0035), for optimizing the judgment logic by the improvement unit (described in paragraph 0033), and for notification processing by the information provision unit.

[0033] 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 the decision criteria. The improvement unit is a core component that realizes "continuous improvement" in the snow removal decision support system 600, and aims to improve decision accuracy, execution validity, and resident satisfaction 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 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 (described in paragraph 0035), 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 (described in paragraph 0036), and to optimize the algorithm for determining road clearing routes and priority locations in cooperation with the road clearing unit (described in paragraph 0037). In addition, it may be configured to improve the prediction accuracy of the model to be improved in accordance with the results of future snowfall, traffic, and disaster predictions in cooperation with the prediction unit (described in paragraph 0034). 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.

[0034] 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 (described in paragraph 0036) or the Road Clearing Department (described in paragraph 0037) and are reflected in the prior identification of areas where snow removal is difficult and in the planning of road clearing routes. In addition, the 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, and is configured to prepare for medium- to long-term risks such as large-scale snow damage and complex disasters.

[0035] 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. 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 (described in paragraph 0036) and the Road Clearing Department (described in paragraph 0037), the system may be configured to share information on road damage and areas where road clearing is difficult discovered during snow removal operations, and to coordinate with repair and clearing operations. In addition, by coordinating with the Prediction Department, it is possible to identify in advance locations where snow removal is expected to be difficult or areas where snow damage is anticipated, and to incorporate preventative measures. 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 the decisions made by the Decision Department 630, 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.

[0036] 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.

[0037] 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 analyze disaster images and traffic disruption scores using an AI (artificial intelligence) model and select the optimal road clearing route from multiple candidate routes. Input information such as fiber optic sensing, satellite images, traffic delay data, patrol cameras, and SNS reports are used, 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 structured to support rapid disaster response through cooperation with other departments (Infrastructure Maintenance Department, Road Snow Removal Department, Improvement Department, etc.), by formulating and managing road clearing implementation plans, including 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-Making Department.

[0038] <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 and stored in the secondary storage device 904. 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. Note that each server may also be cloud computing. Furthermore, the snow removal decision support system 600 may be configured to communicate with each corresponding server in order to acquire and process resident notification information, snow removal information, road service information, fiber optic survey information, satellite survey information, etc. Furthermore, the snow removal decision support system 600 is configured with a computing environment (cloud or on-premise) equipped with the memory, processor, and storage area necessary for processing each component, such as the information acquisition unit 610, analysis unit 620, decision unit 630, improvement unit, prediction unit, road snow removal unit, road clearing unit, infrastructure maintenance unit, and information provision unit. In addition, the system may be configured to include computing resources such as a GPU (Graphics Processing Unit), TPU (Tensor Processing Unit), or AI accelerator for executing the AI ​​(artificial intelligence) models used in each component. Furthermore, 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.

[0039] 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 resident reporting status, information on snow removal status, road snow removal unit, improvement unit, prediction unit, road clearing unit, infrastructure maintenance unit, and information provision unit, are also included within the technical scope of the present invention. Therefore, the present invention can be modified, altered, or substituted in various ways without departing from its essence. [Explanation of symbols]

[0040] 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. An information acquisition unit that acquires two or more pieces of information from among information on road surface conditions, traffic conditions, road space conditions, weather conditions, vehicle driving conditions, road service conditions, fiber optic survey conditions, satellite survey conditions, resident notification conditions, and snow removal conditions, including at least the information on resident notification conditions. An analysis unit that analyzes the two or more pieces of information acquired by the information acquisition unit, A decision unit determines the necessity of snow removal measures based on the analysis results obtained by the aforementioned analysis unit, Register a pre-formulated snow removal plan, including the route or section (including cases where at least a portion of the route or section is designated as an emergency transport route or road clearing route), the snow removal company responsible, and the criteria for dispatching snow removal services. The Road Snow Removal Department, in accordance with the analysis results obtained by the Analysis Department, formulates dispatch orders to snow removal businesses as part of a snow removal implementation plan (including, for example, the formulation of dispatch orders, work types, work time periods, and priorities), If the need for snow removal measures is determined, Based on the snow removal plan registered with the Road Snow Removal Department and the analysis results analyzed by the Analysis Department, A snow removal decision support system characterized by formulating the dispatch order to the snow removal business operator for each of the aforementioned routes or work sections.

2. A snow removal and clearing decision support system according to claim 1, The snow removal decision support system is characterized in that the information regarding the status of resident reports is obtained by at least one of the following means: AI-based voice notification reception, notification reception via SNS, notification reception via web form, or telephone interviews by staff and subsequent database registration.

3. A snow removal and clearing decision support system according to claim 1, The snow removal decision support system is characterized in that the information regarding the status of resident reports is used to extract reports related to snow removal based on the content of the reports, to evaluate the urgency from the extracted reports, to evaluate the necessity of snow removal using the geographical characteristics of the target area identified by the report content, and further to assign priorities to each route or work section by reflecting the report density and distribution information regarding the population or number of households in the target area.

4. A snow removal and clearing decision support system according to claim 1, The aforementioned road snow removal unit is equipped with a function to record the status of snow removal and to perform processing that supports settlement of business results with snow removal contractors based on the said record, thereby providing a snow removal decision support system.

5. A snow removal and clearing decision support system according to claim 1, The snow removal decision support system is characterized by comprising an AI-based improvement unit that continuously improves at least one of the following based on the learning data: the analysis results from the analysis unit, the decision results from the decision unit, the status of snow removal by the road snow removal unit, or various information acquired by the information acquisition unit; and the improvement unit that continuously improves at least one of the following based on the learning data: the information acquisition unit's selection process for information to be acquired; the analysis unit's improvement process for improving the analysis accuracy; the decision unit's optimization process for determining the decision criteria; or the road snow removal unit's accuracy improvement process for assigning priorities.

6. A snow removal and clearing decision support system according to claim 1, The snow removal decision support system is characterized in that the decision unit forecasts the possibility of the need for snow removal in the near future based on various information acquired by the information acquisition unit, and has a notification function to provide relevant parties with information regarding the preparation or implementation of snow removal based on the forecast result.

7. A snow removal and clearing decision support system according to claim 1, Based on the analysis results obtained by the analysis unit, the road snow removal unit shall Determine the type of snow removal work to be carried out. The aforementioned snow removal and clearing work type is selected from among normal snow removal and clearing, fresh snow removal, widening snow removal, road surface leveling, snow stripping (compacted snow treatment), bottleneck snow removal (spot snow removal), intersection snow removal, and transport snow removal, and is a snow removal and clearing decision support system.

8. A snow removal and clearing decision support system according to claim 1, The aforementioned road snow removal unit is equipped with a function to determine a suitable time of day for snow removal based on various information acquired by the aforementioned information acquisition unit. Furthermore, the snow removal decision support system is characterized in that the analysis unit weights the reliability or priority of the various types of information acquired by the information acquisition unit according to the time period during which the information was acquired, and uses the weighting results to evaluate the necessity of snow removal measures.

9. A snow removal and clearing decision support system according to claim 1, The aforementioned snow removal decision support system operates on winter days or non-winter days when there is no snowfall. A snow removal decision support system characterized by detecting at least one of the following based on various information acquired by the aforementioned information acquisition unit: unevenness of the road surface, cracks, rutting, voids beneath the road surface, road damage, or road collapse, and utilizing the detection results for corresponding processing related to infrastructure maintenance.

10. A snow removal and clearing decision support system according to claim 1, The analysis unit analyzes at least one of the following based on the various information acquired by the information acquisition unit: weather conditions, traffic conditions, or snow damage conditions. The possibility that the said route or section related to an emergency transport route or road clearing route is blocked, or that there is a traffic obstruction in the said route or section, is assessed. The snow removal decision support system is characterized in that the decision unit determines the necessity of the urgent snow removal response based on the evaluation results from the analysis unit.

11. A program for causing a computer to function as a snow removal decision support system according to any one of claims 1 to 10.

12. A road management method using computers, The computer acquires, via the network, two or more pieces of information, including at least the information regarding resident reports, from among information regarding 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. The two or more pieces of information obtained are analyzed, Based on the analysis results described above, the necessity of snow removal measures will be determined. Register a pre-formulated snow removal plan, including the route or section (including cases where at least a portion of the route or section is designated as an emergency transport route or road clearing route), the snow removal company responsible, and the criteria for dispatching snow removal services. If the need for snow removal measures is determined, Based on the registered snow removal plan and the analyzed results, A road management method characterized by performing a process to formulate dispatch orders to snow removal businesses as part of a snow removal implementation plan (for example, including the formulation of dispatch orders, work types, work time periods, and priorities) for each of the aforementioned routes or work sections.

13. A road management method according to claim 12, A road management method characterized by performing a process to acquire information regarding the status of resident reports by at least one of the following means: AI-based voice reporting, reporting via social media, reporting via web form, or telephone interviews by staff and subsequent database registration.

14. A road management method according to claim 12, The road management method is characterized in that the information regarding the status of resident reports is used to extract reports related to snow removal based on the content of the reports, to evaluate the urgency from the extracted reports, to evaluate the necessity of snow removal measures using the geographical characteristics of the target area identified by the content of the reports, and to assign priorities to each route or work section by reflecting the report density and distribution information regarding the population or number of households in the target area.

15. A road management method according to claim 12, The road management method is characterized in that the computer records the status of snow removal and clearing operations and, based on said records, performs processing to support settlement of business results with snow removal and clearing contractors.

16. A road management method according to claim 12, The road management method is characterized in that the computer stores at least one of the following as learning data: the analysis results, the judgment results, the status of snow removal and clearing, or the various types of information acquired; and based on the learning data, it continuously performs at least one of the following processes using AI: a process for selecting information to be acquired, a process for improving the accuracy of the analysis, a process for optimizing the judgment criteria, or a process for improving the accuracy of prioritization.

17. A road management method according to claim 12, A road management method characterized in that the computer predicts the likelihood of the need for snow removal in the near future based on the various information acquired, and performs a process of notifying relevant parties of information regarding the preparation or implementation of snow removal based on the prediction results.

18. A road management method according to claim 12, Based on the analysis results that have been analyzed, the computer Determine the type of snow removal work to be carried out. The aforementioned road management method is characterized by performing a process selected from among the following types of work: normal snow removal, fresh snow removal, widening snow removal, road surface leveling, snow stripping (compacted snow treatment), bottleneck snow removal (spot snow removal), intersection snow removal, and transport snow removal.

19. A road management method according to claim 12, The computer determines the appropriate time period for snow removal based on the various information acquired, Furthermore, the road management method is characterized by performing a process to evaluate the necessity of snow removal and clearing measures using the weighting results, which involves assigning weights to the reliability or priority of the various types of information acquired according to the time period in which the information was acquired.

20. A road management method according to claim 12, The road management method is characterized in that the computer detects risks related to road infrastructure maintenance based on the various information acquired, on winter days or non-winter days when there is no snowfall, and utilizes information regarding said risks in response processing related to infrastructure maintenance.

21. A road management method according to claim 12, The computer analyzes at least one of the following based on the acquired information: weather conditions, traffic conditions, or snow damage conditions. The possibility that the said route or section related to an emergency transport route or road clearing route is blocked, or that there is a traffic obstruction in the said route or section, is assessed. A road management method characterized by performing a process to determine the necessity of the aforementioned snow removal and clearing measures that require urgency, based on the evaluation results.

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