Snow removal decision support system and program, road management method

Abstract

Visual patrols are conducted to carry out snow removal work on snow-covered roads, but there is inconsistency in the judgment of the timing of snow removal work depending on the experience of the patrol officers, etc. This causes delays in snow removal work and reduces road traffic functions during the winter. [Solution] A snow removal decision support system and program, and road management method, comprising an information acquisition unit that acquires two or more pieces of information from the following: information on road surface conditions, information on traffic conditions, information on road space conditions, information on weather conditions, information on vehicle driving conditions, information on road service conditions, and information on snow removal conditions; an analysis unit that analyzes the two or more pieces of information acquired by the information acquisition unit; a decision unit that determines the need for snow removal based on some or all of the results analyzed by the analysis unit; and a road snow removal unit that formulates an implementation plan for snow removal, in which the decision unit determines the need for snow removal.

Description

[Technical Field]

[0001] The present invention relates to optimizing snow removal decisions on snow-covered road surfaces. [Background technology]

[0002] In cold, snowy regions, in order to maintain urban functions and ensure smooth road traffic during the winter, it is necessary to remove snow from roads when it accumulates, and visual patrols are carried out to determine when snow removal work should be carried out. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Takikawa City Civil Engineering Division, "Snow Removal in Takikawa City," About Snow Removal, [online], February 18, 2025, Takikawa City, [Retrieved February 21, 2025], Internet<URL:https: / / www.city.takikawa.lg.jp / site / jyohaisetu / > [Non-patent document 2] Hiroo Town Construction and Waterworks Division, "Regarding Snow Removal on Town Roads," Snowplow Dispatch Standards, [online], Date of publication unknown, Hiroo Town, [Retrieved February 21, 2025], Internet<URL:https: / / www.town.hiroo.lg.jp / kurashi / seikatsu / josetsu / choudounojosetsu / > Summary of the Invention [Problem to be solved by the invention]

[0004] Visual patrols are used to determine when snow removal work should be carried out, but there is variation in the judgment of when snow removal work should be carried out depending on the experience of the patrol officers, etc. This causes delays in snow removal work and reduces road traffic functions during the winter. [Means for solving the problem]

[0005] The above problems can be solved by the invention having the following configuration. [1] A snow removal decision support system comprising: an information acquisition unit that acquires two or more pieces of information selected from the group consisting of information on road surface conditions, information on traffic conditions, information on road space conditions, information on weather conditions, information on vehicle driving conditions, information on road service conditions, and information on snow removal conditions; an analysis unit that analyzes the two or more pieces of information acquired by the information acquisition unit; a decision unit that determines the need for snow removal based on some or all of the results of the analysis by the analysis unit; and a road snow removal unit that formulates a snow removal implementation plan, wherein the decision unit determines the need for snow removal, and when snow removal is required, a snow removal plan including a road clearance route that has been formulated in advance is registered in the road snow removal unit, and the road snow removal implementation plan is formulated by the road snow removal unit based on the snow removal plan registered in the road snow removal unit and the two or more pieces of information acquired by the information acquisition unit or the results of the analysis by the analysis unit. [2] An information acquisition unit that acquires two or more pieces of information from among information on road surface conditions, information on traffic conditions, information on road space conditions, information on weather conditions, information on vehicle driving conditions, information on road service conditions, and information on snow removal conditions; an analysis unit that analyzes the two or more pieces of information acquired by the information acquisition unit; a decision unit that determines the need for snow removal based on part or all of the results of the analysis by the analysis unit; and a road snow removal unit that formulates an implementation plan for snow removal, wherein the decision unit determines the need for snow removal, and the image data of the road to be analyzed, which is taken from one or more pieces of information acquired by the information acquisition unit, is used to analyze the road. The road snow removal decision support system is characterized in that it outputs the road snow removal status by inputting the image data of the learning subject and the snow accumulation and snow removal status of the road into a learning model that has undergone machine learning using learning data, and complements and corrects the output road snow removal status based on the measurement data contained in the one or more pieces of information, and when snow removal is necessary, a snow removal plan including a pre-planned road clearing route is registered in the road snow removal unit, and the road snow removal implementation plan is made by the road snow removal unit based on the snow removal plan registered in the road snow removal unit and two or more pieces of information acquired by the information acquisition unit or the results of analysis by the analysis unit. [3] A snow removal decision support system, which is a system of [1] or [2], characterized in that it transmits to a mobile terminal device or a fixed terminal device one or more pieces of information acquired by the information acquisition unit, the results of analysis by the analysis unit, the need for snow removal determined by the decision unit, or the snow removal implementation plan formulated by the road snow removal unit. [4] A program for causing a computer to function as the snow removal decision support system described in any one of [1] to [3]. [5] A road management method using a computer, wherein the computer acquires two or more pieces of information from a network, including information on road surface conditions, information on traffic conditions, information on road space conditions, information on weather conditions, information on vehicle driving conditions, information on road service conditions, and information on snow removal conditions, analyzes the acquired two or more pieces of information, determines the need for snow removal based on some or all of the analysis results, and if snow removal is required, registers a snow removal plan including a road clearance route that has been formulated in advance, and makes a snow removal implementation plan based on the registered snow removal plan and the acquired two or more pieces of information or the analyzed results. [6] A road management method using a computer, wherein the computer acquires two or more pieces of information from a network among information on road surface conditions, information on traffic conditions, information on road space conditions, information on weather conditions, information on vehicle driving conditions, information on road service status, and information on snow removal status, analyzes the acquired two or more pieces of information, and determines the need for snow removal from some or all of the analysis results, inputs image data of a road to be analyzed from one or more pieces of information acquired, image data of a road to be learned, and the snow accumulation and snow removal status of the road into a learning model that has been machine-learned using learning data, thereby outputting the road snow removal status, supplements and corrects the output road snow removal status based on measurement data included in the one or more pieces of information, and when snow removal is required, registers a snow removal plan including a road clearance route that has been formulated in advance, and makes a snow removal implementation plan based on the registered snow removal plan, the acquired two or more pieces of information, the analyzed results, or the supplemented and corrected road snow removal status. [7] A road management method according to [5] or [6], characterized in that the acquired one or more pieces of information, the analyzed results, the determined need for snow removal, or the formulated snow removal implementation plan are transmitted to a mobile terminal device or a fixed terminal device. [Effects of the Invention]

[0006] By determining the optimal timing for snow removal work on snow-covered roads, the present invention can prevent delays in snow removal work and deterioration of road traffic functions, thereby providing road users with a safe and secure road traffic environment. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a system configuration diagram of a snow removal decision support system according to the present invention. [Figure 2] FIG. 10 is a flowchart illustrating an example of a patrol flow. [Figure 3] FIG. 10 is a flowchart illustrating an example of a flow of a fixed camera. [Figure 4] FIG. 2 is a diagram illustrating an example of a road surface condition. [Figure 5] FIG. 1 is a cross-sectional view showing an example of road surface conditions (unevenness of snow-covered road surface). [Figure 6] FIG. 2 is a cross-sectional view showing an example of road surface conditions (thickness of packed snow on the road surface). [Figure 7] FIG. 1 is a diagram illustrating an example of a traffic situation. [Figure 8] FIG. 1 is a diagram illustrating an example of a road space situation. [Figure 9] FIG. 10 is a diagram illustrating an example of weather conditions. [Figure 10] FIG. 2 is a diagram illustrating an example of a vehicle driving situation. [Figure 11] 1 is a flowchart illustrating an example of the flow of a snow removal decision support system according to the present invention. [Figure 12] FIG. 10 is a diagram showing an example of a determination criterion in a determination unit of the present invention. [Figure 13] 1 is a diagram illustrating an example of a hardware configuration of a snow removal decision support system according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, one embodiment of the snow removal decision support system of the present invention will be described with reference to the drawings. Note that the embodiment described below does not unduly limit the content of the present disclosure described in the claims. Furthermore, not all of the configurations described in this embodiment are necessarily essential components of the present disclosure. In addition, each individual configuration that constitutes a feature group can also be an invention.

[0009] 1 is a system configuration diagram of a snow removal decision support system 600 of the present invention. 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 need for snow removal based on the results of the analysis by analysis unit 620. The snow removal decision support system 600 of the embodiment is communicatively connected to a road surface condition providing server 100, a traffic condition providing server 200, a road space condition providing server 300, a weather condition providing server 400, and a vehicle driving condition providing server 500 via a network NW. Although FIG. 1 shows only one vehicle Vh and one terminal device TM for grasping road surface conditions, a plurality of vehicles Vh and terminal devices TM may be connected to the network NW. Although only one fixed camera CAM is shown in FIG. 1 to grasp the road space situation, multiple fixed cameras CAM may be connected to the network NW.

[0010] The terminal device TM, fixed camera CAM, road surface condition providing server 100, traffic condition providing server 200, road space condition providing server 300, weather condition providing server 400, vehicle traveling condition providing server 500, and snow removal decision support system 600 communicate via a network NW. The network NW includes, for example, some or all of a WAN (Wide Area Network), LAN (Local Area Network), the Internet, a provider device, a wireless base station, a dedicated line, etc. The communication method is not limited to the network NW, but data can also be sent and received via a memory card. Data can also be downloaded and uploaded via the network NW.

[0011] The terminal device TM is used by a user riding in the vehicle Vh. The terminal device TM is a mobile phone such as a smartphone, a tablet device, or the like. The vehicle Vh is mainly a road management patrol vehicle or road patrol car (it may also be a patrol vehicle such as a garbage truck, compactor truck, or refuse collection truck). The terminal device TM may be a communication-type drive recorder mounted on the vehicle Vh or a stationary in-vehicle device, and may be equipped with an image recognition function using AI (artificial intelligence). The terminal device TM has a road patrol application installed therein that cooperates with the road surface condition providing server 100 . The terminal device TM has a positioning device such as a GPS (Global Positioning System) receiver, a communication device for connecting to the network NW, an input / output device such as a G sensor (acceleration sensor), a camera, and a touch panel, and a processor such as a CPU (Central Processing Unit).

[0012] 2 is a flowchart showing an example of the flow of patrol. When the terminal device TM presses a patrol start button on the road patrol app (S1), it starts collecting location information, acceleration information, video, images, etc. (S2). After the patrol is completed, the user presses the patrol end button in the road patrol application (S3), and the terminal device TM transmits the location information, acceleration information, video, images, etc. to the road surface condition providing server 100 (S4). The road surface condition providing server 100 determines whether or not there are irregularities 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. The position information, acceleration information, video, images, etc. transmitted to the road surface condition providing server 100 may be measurement information from a private car, taxi, truck, etc.

[0013] Fixed camera CAMs are installed on roads (major arterial roads, roads with heavy traffic, major bus routes, roads important for transporting and clearing snow to snow dumps, roads connecting to schools, public facilities, and emergency hospitals), buildings around intersections, and roadside posts and poles. Fixed camera CAMs include live cameras, web cameras, and network cameras that can communicate. The fixed camera CAM may be a communication-type drive recorder or a small unmanned aerial vehicle such as a drone, or it may be equipped with image recognition capabilities using AI (artificial intelligence). The fixed camera CAM has a built-in camera application that communicates with the road space situation providing server 300. The fixed 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 the network NW, and a processor such as a CPU (Central Processing Unit).

[0014] 3 is a flowchart showing an example of the flow of the fixed camera CAM. The fixed camera CAM periodically or intermittently collects road space conditions (S5), and periodically or intermittently automatically transmits video / images, location information, date / time information, etc. to the road space condition providing server 300 (S6). The road space situation providing server 300 determines the height of snow banks (mountains of snow on the roadside, omitted below) based on the video and images transmitted from the fixed camera CAM, and identifies the location of road spaces determined to be dangerous areas.

[0015] The road surface condition providing server 100 provides road surface conditions via the network NW to the snow removal decision support system 600. The road surface conditions provided are information for each road, and include some or all of the following: road surface freezing, snow accumulation and snow quality, unevenness of the snow-covered road surface, ruts and mortars on the snow-covered road surface, thickness of packed snow on the road surface, and whether or not the road width has narrowed due to snow accumulation. FIG. 4 is a diagram showing an example of road surface conditions, where a portion 110 where the snow-covered road surface is extremely uneven is displayed in black on the map.

[0016] FIG. 5 is a cross-sectional view showing an example of road surface conditions (unevenness of a snow-covered road surface), and unevenness 120 of the road surface caused by snow is shown by a wavy line.

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

[0018] The traffic condition providing server 200 provides traffic conditions via the network NW to the snow removal decision support system 600. The traffic conditions provided are information for each road, and include some or all of the following: traffic volume, passing speed, average speed, and whether or not there is congestion or congestion. FIG. 7 is a diagram showing an example of traffic conditions, with locations 210 with heavy traffic congestion displayed in black on the map.

[0019] The road space condition providing server 300 provides the road space condition via the network NW to the snow removal decision support system 600. The road space condition provided is information for each road, and includes some or all of the following: avalanches, snowdrifts, height of snow banks caused by accumulated snow, poor visibility at intersections caused by snow banks, narrowing of road width caused by accumulated snow, whether large vehicles are stuck, whether there are any accident vehicles, etc. FIG. 8 is a diagram showing an example of road space conditions, where dangerous areas 310 with high snow banks are displayed in black on the map.

[0020] The weather condition providing server 400 provides weather conditions via the network NW to the snow removal decision support system 600. The weather conditions provided are information for each region, and include some or all of the following: time, weather (sunny, rainy, snowy, etc.), temperature, amount of snowfall, snow depth, wind speed, forecast (amount of snowfall, snow depth, etc.), and whether or not there are warnings (blizzards, heavy snow, etc.) and advisories (heavy snow, wind and snow, avalanches, etc.). FIG. 9 shows an example of weather conditions, where snowfall amount 410 and warning 420 are displayed in numbers and letters.

[0021] The vehicle driving status providing server 500 provides vehicle driving status information to the snow removal decision support system 600 via the network NW. The provided vehicle driving status information is information for each road and route obtained from vehicles such as route buses and circular buses, and includes some or all of the following: bus delay times and bus route delay information relative to the winter schedule (a timetable that takes winter traffic conditions into account), the bus's driving position (which lane out of three lanes in each direction the bus is driving in, etc.), the number of vehicles ahead of the bus, the number of vehicles lined up next to the bus, whether the road width has narrowed due to snow accumulation, areas of skidding, areas of tire spinning, areas of tire locking, areas of sudden braking, etc. If there is no winter schedule, bus delay times relative to the regular schedule may also be used. Vehicle driving conditions can be the latest route information (vehicle ID, route name, delays, predicted departure and arrival times, passing, etc.) in the open data dynamic bus information format (GTFS real-time), vehicle location information (vehicle latitude and longitude, approach information, congestion level, etc.), and operation information (service suspensions, detours, accidents, stuck buses, traffic obstructions, images before and after the bus, etc.). Furthermore, vehicle driving conditions may be obtained from connected cars (private cars, taxis, trucks, garbage trucks, delivery vehicles, cars covered by automobile insurance with dashcams provided by insurance companies, etc.) and may include information such as temperature, locations of sudden braking, skidding, tire spin, locked tires, and ABS activation from vehicle sensors, identification of avalanches, obstacles on the road, and impassable areas from camera footage and images, traffic history, traffic volume, traffic congestion, passing speed, average speed, and acceleration from probe information, and whether or not snow is falling from the wiper operation status. FIG. 10 is a diagram showing an example of a vehicle driving situation, where a location 510 where the tires are spinning is displayed in black on the map.

[0022] As a method for understanding the narrowing of road width due to snow accumulation, it is acceptable to use camera and image recognition functions of smartphones, communication-type drive recorders, fixed-point cameras, etc. installed in vehicles to determine the snow accumulation situation, or to use the same functions to determine the narrowing of road width from the number of lanes and the number of vehicles side by side.It is also acceptable to determine the lane driving position of a route bus or other vehicle from the latitude and longitude (which lane out of three lanes on each side the bus is driving in, or which lane out of two lanes on each side the bus is driving in, etc.) and determine the narrowing of road width due to snow accumulation.

[0023] The information acquisition unit 610 operating in the snow removal decision support system 600 acquires road surface conditions from the road surface condition providing server 100, traffic conditions from the traffic condition providing server 200, road space conditions from the road space condition providing server 300, weather conditions from the weather condition providing server 400, and vehicle driving conditions from the vehicle driving condition providing server 500 via the network NW. The road surface conditions provided by the road surface condition providing server 100 are stored as road surface information 640. The traffic conditions provided by the traffic condition providing server 200 are stored as traffic information 650. The road space conditions provided by the road space condition providing server 300 are stored as road space information 660. The weather conditions provided by the weather condition providing server 400 are stored as weather information 670. The vehicle driving conditions provided by the vehicle driving condition providing server 500 are stored as vehicle driving information 680. Furthermore, the information acquisition unit 610 may store information related to road service status. The information related to road service status is information for each road 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 battery, locked keys in the car, running out of gas, flat tires, wheels coming off or falling off, flooding or submersion, recovery from snowy or muddy roads, accidents, slips and falls, disaster or damage status, vehicle towing or transportation, removal, towing or transportation of abandoned vehicles, removal, towing or transportation of damaged vehicles, removal, towing or transportation of accident vehicles, stuck location, road conditions, traffic conditions, EV charging capabilities, vehicle inspection results, etc. The road service status is stored as road service information. Additionally, the information acquisition unit 610 may store information regarding snow removal status. Information regarding snow removal status is road-specific and is primarily managed by road administrators. It may include some or all of the following: information regarding snow removal routes (main roads) and work areas (community roads); road conditions; traffic conditions; information regarding snow removal companies; types and numbers of snow removal vehicles (shovels, graders, rotary scissor trucks, dozers, dump trucks, spreaders); advance snow removal plans and work guidelines; snow removal implementation standards; snow removal completion standards; snow removal implementation plans; snow removal work plans; dispatch orders to snow removal companies; snow removal implementation status; snow removal work status (including the current locations of snow removal vehicles and video and images from snow removal vehicles); snow removal operation results (operating hours, location information, history, etc.); daily snow removal work reports; patrol results; budget management; settlement management; complaints and requests; and information regarding snow dumping sites and snow storage areas. The snow removal status is stored as snow removal information. The information acquired by the information acquisition unit 610 may be only a part of the road surface conditions, traffic conditions, road space conditions, weather conditions, vehicle driving conditions, road service conditions, and snow removal conditions.

[0024] The analysis unit 620 operating in the snow removal decision support system 600 analyzes each piece of information obtained from the information acquired by the information acquisition unit 610, including road surface information 640, traffic information 650, road space information 660, weather information 670, and vehicle driving information 680, and stores the analysis results as 690. The stored analysis results 690 can be viewed in map view or list view. The analysis unit 620 may also analyze road service information and snow removal information. Furthermore, the analysis unit 620 may have an analysis function using AI (artificial intelligence). An example of AI (artificial intelligence) is as follows. A snow removal decision support system characterized by inputting image data of the road to be analyzed, which is photographed from each piece of information acquired by the information acquisition unit (road surface information, traffic information, road space information, weather information, vehicle driving information, road service information, snow removal information), into a learning model that has undergone machine learning using learning data along with image data of the road to be learned and the snow accumulation and snow removal status of the road, to output the road snow removal status, analyze the severity of the road snow removal status based on the measurement data contained in each piece of information (date and time, address, latitude and longitude, weather data, each sensing data, passing speed, average speed, delay time, acceleration, vibration, vehicle data, probe data, etc.), and complement and correct the information acquired by the information acquisition unit based on the results of the analysis. A road management method characterized by inputting image data of the road to be analyzed, which is photographed from each piece of acquired information (road surface information, traffic information, road space information, weather information, vehicle driving information, road service information, and snow removal information), along with image data of the road to be learned and the snow accumulation and snow removal status of the road, into a learning model that has undergone machine learning using learning data, thereby outputting the road snow removal status, analyzing the severity of the road snow removal status based on the measurement data contained in each piece of information (date and time, address, latitude and longitude, weather data, each sensing data, passing speed, average speed, delay time, acceleration, vibration, vehicle data, probe data, etc.), and complementing and correcting the acquired information based on the results of the analysis. The information analyzed by the analysis unit 620 may be only a part of the road surface information 640, traffic information 650, road space information 660, weather information 670, vehicle driving information 680, road service information, and snow removal information.

[0025] The decision unit 630 operating in the snow removal decision support system 600 comprehensively determines the need for snow removal from part or all of the analysis results 690 for each piece of information analyzed by the analysis unit 620 (which may include road service information and snow removal information), and stores the result as a decision result 695. The stored judgment results 695 can be checked on a map display or a list display. Furthermore, the decision unit 630 may be equipped with an analysis function, a judgment function, and a decision function using AI (artificial intelligence). Furthermore, snow removal decision support system 600 may include an information providing unit that provides to the outside some or all of the analysis results 690, the judgment results 695, and the information acquired by the information acquisition unit (road surface information, traffic information, road space information, weather information, vehicle driving information, road service information, snow removal information), etc. By including an information providing unit, it is possible to obtain the effect of widely disseminating information related to snow removal, etc.

[0026] 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 (e.g., every few minutes) from the road surface condition providing server 100 (S10). The information acquisition unit 610 periodically acquires information on traffic conditions (e.g., every few minutes) from the traffic condition providing server 200 (S11). The information acquisition unit 610 periodically acquires information on road space conditions (e.g., every few minutes) from the road space condition providing server 300 (S12). The information acquisition unit 610 periodically acquires information on weather conditions (e.g., every few minutes) from the weather condition providing server 400 (S13). The information acquisition unit 610 periodically acquires information on vehicle driving conditions (e.g., every few minutes) from the vehicle driving condition providing server 500 (S14). The analysis unit 620 then extracts locations where road width is severely reduced from the road surface information 640 (S15). The analysis unit 620 extracts locations where traffic congestion continues to be severe from the traffic information 650 (S16). The analysis unit 620 extracts dangerous locations where snow banks are high (snow banks of approximately 1 m or more) from the road space information 660 (S17). The analysis unit 620 extracts locations where the amount of snowfall from the start of snowfall is 10 cm or more from the weather information 670 (S18). The analysis unit 620 extracts locations where buses are delayed by 30 minutes or more from the vehicle travel information 680 (S19). Next, based on the results of the above analysis, the decision unit 630 comprehensively decides whether snow removal is necessary (S20). Furthermore, it may also be equipped with information from road users and residents (disaster information, damage information, relief information, restoration information, requests, inquiries, complaints regarding snow removal, etc.) using an app-based consultation reception system or SNS (Social Networking Service), information from government (police, fire department, Self-Defense Forces, etc.), infrastructure operators (telecommunications, electricity, gas, water, sewerage, etc.), transportation operators (railways, buses, etc.), snow removal and removal companies, construction and civil engineering companies (including construction industry associations), garbage collection companies, delivery companies, tourism operators (inns, hotels, tourist facilities, roadside stations, etc.), designated public institutions (Disaster Countermeasures Basic Act) (disaster information, damage information, relief information, restoration information, requests, inquiries, complaints regarding snow removal, etc.), and information from the government's Emergency Disaster Response Headquarters and Emergency Disaster Response Headquarters.

[0027] 12 is a diagram showing an example of the judgment criteria in the decision unit 630. In the case of heavy snowfall of 10 cm / h or more (element 1) 631, when road width is reduced due to snow accumulation and bus delays are 30 minutes or more (element 2) 632, when snow melts on clear skies and the snow-covered road surface becomes more uneven, causing heavy traffic congestion (element 3) 633, when a heavy snow warning is issued and cars slip and skid, causing cars to get stuck in the snow and causing congestion (element 4) 634, when brakes do not work on frozen roads at -10°C (frequent tire locks) and there is congestion due to accidents resulting in self-inflicted damage (element 5). At 635, the decision unit 630 decides whether snow removal is necessary. Furthermore, some or all of the judgment results 695 determined by the decision unit 630, the analysis results 690, and the information acquired by the information acquisition unit (road surface information, traffic information, road space information, weather information, vehicle driving information, road service information, snow removal information) may be sent via an information provision unit (described in paragraph 0025) to snow removal companies via email, to provide data to a GIS (Geographic Information System) for road managers, to provide data to automated driving systems, to MaaS (Mobility as a Service), to government agencies (police, fire departments, Self-Defense Forces, etc.) and the media, and to provide information to road users and residents (on websites, smartphone apps, etc.) (although this does not necessarily have to be via the information provision unit). Examples of the information disclosure function to road users and residents are as follows: A snow removal decision support system characterized by transmitting one or more pieces of information acquired by an information acquisition unit (unevenness of snow-covered road surfaces, thickness of packed snow on road surfaces, traffic congestion, camera images, height of snow banks around routes and intersections, future snowfall forecasts and snow depths, bus route delay information, stuck vehicle locations, snow removal orders and status, snow removal orders and status, patrol results, etc.) or the results of analysis by an analysis unit, or the need for snow removal determined by a decision unit, or an implementation plan for snow removal formulated by a road snow removal and removal unit, to a mobile terminal device (mobile phone, smartphone, tablet terminal, laptop computer, game console, etc.) or a fixed terminal device (desktop computer, smart TV, set-top box, digital signage, kiosk terminal, car navigation, car display audio, etc.).

[0028] The snow removal decision-making support system 600 may include an improvement unit that improves some or all of the analysis results 690, the judgment results 695, and the decision criteria used by the decision unit 630 (an example of the judgment criteria is shown in FIG. 12). Improvements can be made by inputting various data (data, images) related to snow removal into the snow removal decision-making support system 600, and the inclusion of an improvement unit can improve the accuracy of snow removal responses. An example flow is as follows: Various data (data, images) related to snow removal are input to the improvement unit → history of the analysis results 690 and judgment results 695 is referenced → analysis by the improvement unit → improvement results by the improvement unit → feedback (review of accuracy improvements, etc.). Examples of various data include hypothesis data, verification data, sample data, training data, past data, current data, and future data (future data on abnormal weather that may or may not occur once every few decades, data on abnormal weather that may occur in the future, data on abnormal weather that humanity has never experienced before, etc.). The improvement unit may also be equipped with analytical functions and improvement functions using AI (artificial intelligence). Examples of AI (artificial intelligence) are as follows: A snow removal decision support system characterized by having an improvement unit that inputs image data of a road to be analyzed, which is photographed from each piece of information acquired by an information acquisition unit (road surface information, traffic information, road space information, weather information, vehicle driving information, road service information, and snow removal information), into a learning model that has undergone machine learning using learning data along with image data of a road to be learned and the snow accumulation and snow removal status of the road, to output the road snow removal status, and complements and corrects the output road snow removal status based on the measurement data contained in each piece of information (date and time, address, latitude and longitude, weather data, each sensing data, passing speed, average speed, delay time, acceleration, vibration, vehicle data, probe data, etc.), and improves the results analyzed by the analysis unit, the judgment criteria in the decision unit, or the results decided by the decision unit. A road management method characterized by inputting image data of a road to be analyzed, which is photographed from each piece of acquired information (road surface information, traffic information, road space information, weather information, vehicle driving information, road service information, and snow removal information), into a learning model that has undergone machine learning using learning data along with image data of a road to be learned and the snow accumulation and snow removal status of that road, thereby outputting the road snow removal status, and complementing and correcting the output road snow removal status based on the measurement data contained in each piece of information (date and time, address, latitude and longitude, weather data, each sensing data, passing speed, average speed, delay time, acceleration, vibration, vehicle data, probe data, etc.), thereby improving the analyzed results, judgment criteria, or determined results.

[0029] The snow removal decision support system 600 may include a prediction unit that predicts the need for snow removal response by, for example, referring to the history of the analysis results 690 and the judgment results 695. Providing a prediction unit in the snow removal decision support system 600 enables advance preparation, planning, and public relations for snow removal response (snow removal forecast, snow removal forecast, etc.). An example flow is as follows: Various data (data, images, etc.) related to snow removal are input to the prediction unit → the history of the analysis results 690 and the judgment results 695 is referred to → analysis, etc. by the prediction unit → prediction results by the prediction unit → action (planning, public relations, etc.). Examples of various data include hypothesis data, verification data, sample data, training data, past data, current data, and future data (future data on abnormal weather that may or may not occur once every few decades, data on abnormal weather that may occur in the future, and data on abnormal weather that humanity has never experienced before). The prediction unit may also be equipped with analytical and prediction functions using AI (artificial intelligence).

[0030] The snow removal decision support system 600 can be effective in one or more of the following conditions: road surface condition, traffic condition, road space condition, weather condition, vehicle driving condition, road service condition, and snow removal condition. Furthermore, by using two or more elements, a synergistic effect can be obtained.

[0031] The snow removal decision support system 600 may be equipped with a road snow removal unit that formulates a snow removal implementation plan. By equipping the snow removal decision support system 600 with a road snow removal unit, it is possible to achieve the effect of quickly and systematically removing snow in the event of a disaster or disaster-level heavy snowfall. Road administrators formulate snow removal plans and guidelines in advance, which include the snow removal implementation structure (organization, implementation structure, patrols, snow consultation desk, snow removal contractors, snow removal vehicles, snow removal work evaluation, snow removal dispatch orders, public relations and awareness activities, etc.), snow removal classification (main roads, secondary main roads, suburban main roads, fully outsourced work sections, designated outsourced work sections, residential roads, road clearance 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 dumping areas, anti-freeze spraying to prevent slipping, etc.), and types of snow removal work (regular snow removal, new snow removal, road surface leveling, widening snow removal, snow transportation and removal, intersection snow removal, bottleneck area response, alley snow removal, snow removal, anti-freeze spraying, etc.). In snow removal work, after patrols by road managers and snow removal companies, implementation plans and work plans for snow removal are drawn up, and snow removal work is carried out, with patrols continuing even after the snow removal work has been completed. An example of an embodiment of the road snow removal unit in the snow removal decision support system 600 is as follows. A snow removal decision support system (including a road management method) characterized in that a snow removal plan (which may include road clearance routes) formulated in advance is registered in the road snow removal unit (this may be registered before snowfall or accumulation or after snowfall or accumulation), and the road snow removal unit makes a snow removal implementation plan based on the snow removal plan registered in the road snow removal unit and two or more pieces of information acquired by the information acquisition unit or the results of analysis by the analysis unit. In addition, the road snow removal unit may be equipped with analytical, planning, and analysis functions using AI (artificial intelligence). Examples of AI (artificial intelligence) are as follows: A snow removal decision support system characterized by the fact that the road snow removal situation is output by inputting image data of the road to be analyzed, which is photographed from each piece of information acquired by the information acquisition unit (road surface information, traffic information, road space information, weather information, vehicle driving information, road service information, and snow removal information), into a learning model that has undergone machine learning using learning data along with image data of the road to be learned and the snow accumulation and snow removal situation of the road, and the output road snow removal situation is supplemented and corrected based on the measurement data contained in each piece of information (date and time, address, latitude and longitude, weather data, each sensing data, passing speed, average speed, delay time, acceleration, vibration, vehicle data, probe data, etc.), and when snow removal is required, a pre-formulated snow removal plan (which may include road clearance routes) is registered in the road snow removal unit, and the road snow removal implementation plan is made by the road snow removal unit based on the snow removal plan registered in the road snow removal unit and two or more pieces of information acquired by the information acquisition unit, or the results of analysis by the analysis unit, or the supplemented and corrected road snow removal situation. A road management method characterized by inputting image data of a road to be analyzed, which is photographed from each piece of acquired information (road surface information, traffic information, road space information, weather information, vehicle driving information, road service information, and snow removal information), into a learning model that has undergone machine learning using learning data along with image data of a road to be learned and the snow accumulation and snow removal status of the road, thereby outputting the road snow removal status, and supplementing and correcting the output road snow removal status based on the measurement data contained in each piece of information (date and time, address, latitude and longitude, weather data, each sensing data, passing speed, average speed, delay time, acceleration, vibration, vehicle data, probe data, etc.), and when snow removal is required, registering a snow removal plan (which may include road clearance routes) that has been formulated in advance, and making a snow removal implementation plan based on the registered snow removal plan and two or more pieces of information acquired by the information acquisition unit, or the analyzed results, or the supplemented and corrected road snow removal status.

[0032] In typical disasters, the process is emergency restoration followed by full restoration. However, in large-scale disasters, emergency restoration (road clearance) must precede emergency restoration. Road clearance involves quickly clearing minimal debris and repairing simple uneven sections to open rescue routes for emergency vehicles conducting rescue and relief activities, providing emergency supplies, and restoring roads. Road administrators develop road clearance plans in advance, including road clearance bases (bases for support units, disaster prevention centers such as storage sites for supplies and equipment), road clearance routes (wide-area travel routes, access routes, and routes within the affected area), and timelines (specific action plans). Road clearance involves assessing the damage situation, implementing emergency measures (traffic restrictions, etc.) immediately after the disaster, formulating a road clearance implementation plan, and carrying out road clearance operations. Rapid implementation of emergency traffic restrictions allows for concentrated snow removal and the rapid rescue of stranded vehicles, minimizing snow damage and potentially expediting the restoration of open roads on expressways and trunk roads. It can also be expected to be effective in areas with little snowfall when heavy snowfall occurs locally over a short period of time.

[0033] <Hardware configuration> FIG. 13 illustrates an example of the hardware configuration of a terminal device TM, a fixed camera CAM, a road surface condition providing server 100, a traffic condition providing server 200, a road space condition providing server 300, a weather condition providing server 400, a vehicle driving condition providing server 500, and a snow removal decision support system 600. This diagram illustrates an example in which the terminal device TM is a mobile phone such as a smartphone. The terminal device TM includes, for example, a CPU 701, a RAM 702, a ROM 703, a secondary storage device 704 such as a flash memory, a touch panel 705, and a wireless communication module 706, all interconnected via an internal bus or a dedicated communication line. Application programs such as a road patrol app are downloaded via a network NW and stored in the secondary storage device 704. The fixed camera CAM includes, for example, a CPU 901, a RAM 902, a ROM 903, a secondary storage device 904 such as a flash memory, a lens / image sensor 905, and a communication device 906, all interconnected via an internal bus or a dedicated communication line. Application programs such as a camera application are downloaded via the network NW and stored in the secondary storage device 904 . Each server includes, for example, a NIC 801, a CPU 802, a RAM 803, a ROM 804, a secondary storage device 805 such as a flash memory or a HDD, and a drive device 806, all interconnected via an internal bus or a dedicated communication line. A portable storage medium such as an optical disk is attached to the drive device 806. A program stored in the secondary storage device 805 or the portable storage medium attached to the drive device 806 is loaded into the RAM 803 by a DMA controller (not shown) or the like, and executed by the CPU 802, thereby realizing the functional units 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 be implemented using cloud computing.

[0034] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0035] 100: Road surface condition server 200: Traffic information server 300: Road space situation providing 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 driving information 690:Analysis results 695: Judgment result

Claims

1. an information acquisition unit that acquires two or more pieces of information among information on road surface conditions, information on traffic conditions, information on road space conditions, information on weather conditions, information on vehicle driving conditions, and information on snow removal conditions; an analysis unit that analyzes the two or more pieces of information acquired by the information acquisition unit; a decision unit that evaluates the analysis results (e.g., snow-covered road conditions, traffic congestion due to snow, snow accumulation conditions on roads, snowfall forecasts, bus route delays, and snow removal work conditions) obtained by the analysis unit based on predetermined criteria and determines the need for snow removal; Register a pre-established snow removal plan, including the route or construction section (including cases where at least a part of the route or construction section is set as a road clearance route), the snow removal company in charge, and the snow removal dispatch criteria; and a road snow removal and disposal unit that formulates a dispatch command to the snow removal business operator as part of a snow removal and disposal implementation plan according to the analysis results, When the necessity of the snow removal is determined, Based on the snow removal plan registered in the road snow removal unit and the analysis results analyzed by the analysis unit, A snow removal decision support system characterized by formulating the dispatch command to the snow removal business operator for each of the routes or each of the work sections.

2. The snow removal decision support system according to claim 1, the information acquisition unit acquires one or more image data relating to the road surface conditions, the road space conditions, the vehicle driving conditions, or the snow removal conditions, and one or more measurement data relating to the road surface conditions, the traffic conditions, the weather conditions, the vehicle driving conditions, or the snow removal conditions; A snow removal decision support system characterized in that at least one of the improvement unit, which performs improvement processing to improve the accuracy of snow removal responses, or the analysis unit, is equipped with an artificial intelligence analysis function that inputs the image data and measurement data acquired by the information acquisition unit into a trained model and outputs road snow removal conditions (e.g., the snow-covered road surface conditions, traffic congestion caused by the snow, snow accumulation conditions on the road, snowfall amount or presence or absence of snowfall, and the snow removal work conditions).

3. The snow removal decision support system according to claim 1, One or more pieces of information acquired by the information acquisition unit, the analysis results analyzed by the analysis unit, the necessity of snow removal determined by the decision unit, or one or more of the dispatch commands to the snow removal business operators formulated by the road snow removal and disposal unit, A snow removal decision support system characterized in that it transmits information to at least one of a mobile terminal device and a fixed terminal device.

4. A program for causing a computer to function as the snow removal decision support system according to any one of claims 1 to 3.

5. A road management method using a computer, comprising: The computer communicates with the network via acquiring two or more pieces of information from among information on road surface conditions, information on traffic conditions, information on road space conditions, information on weather conditions, information on vehicle driving conditions, and information on snow removal conditions; Analyzing the two or more pieces of information obtained; Evaluating the analysis results (e.g., snow-covered road conditions, traffic congestion due to snow, snow accumulation conditions on roads, snowfall forecasts, bus route delays, and snow removal work conditions) based on predetermined criteria to determine the need for snow removal; Register a pre-established snow removal plan, including the route or construction section (including cases where at least a part of the route or construction section is set as a road clearance route), the snow removal company in charge, and the snow removal dispatch criteria; When the necessity of the snow removal is determined, Based on the registered snow removal plan and the analyzed analysis results, A road management method characterized by formulating a dispatch command to the snow removal business operator as part of a snow removal implementation plan for each of the routes or construction sections.

6. 6. The road management method according to claim 5, The computer communicates with the network via acquiring one or more image data relating to the road surface conditions, the road space conditions, the vehicle driving conditions, or the snow removal conditions, and one or more measurement data relating to the road surface conditions, the traffic conditions, the weather conditions, the vehicle driving conditions, or the snow removal conditions; A road management method characterized by inputting the acquired image data and measurement data into a trained model and performing an analysis using artificial intelligence to output road snow removal conditions (e.g., the snow-covered road surface conditions, traffic congestion caused by the snow, snow accumulation conditions on the road, snowfall amount or presence or absence of snowfall, and the status of the snow removal work).

7. 6. The road management method according to claim 5, The computer communicates with the network via One or more of the acquired information, the analyzed analysis results, the determined necessity of snow removal, or the formulated dispatch command to the snow removal business operator, A road management method characterized by transmitting the information to at least one of a mobile terminal device and a fixed terminal device.

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