Road disaster response support system and program, road management method
The integrated system leverages diverse data sources and machine-learning to rapidly assess and respond to road disasters, overcoming inefficiencies of visual inspections.
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- 葛西 章史
- Filing Date
- 2024-12-18
- Publication Date
- 2026-06-30
Smart Images
Figure 2026106696000001_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a road disaster response support system, a program, and a road management method for restoring and managing roads during disasters such as earthquakes.
Background Art
[0002] During large-scale disasters such as earthquakes, road administrators carry out emergency restoration (road opening) for the passage of emergency vehicles and Self-Defense Force vehicles engaged in disaster countermeasures, and then carry out road disaster responses such as emergency restoration and full restoration. An emergency vehicle is a vehicle defined by laws such as the Basic Law on Disaster Countermeasures and the Road Traffic Law. For example, it includes fire trucks, ambulances, disaster restoration work vehicles, doctor cars, road patrol cars, wreckers, vehicles of administrative agencies, vehicles of electric power companies, gas companies, and telephone companies, disaster countermeasure vehicles (such as drainage pump trucks, lighting trucks, disaster countermeasure headquarters vehicles, standby support vehicles, satellite communication vehicles, information collection vehicles, disaster countermeasure vehicles with drones, etc.), material transport vehicles, evacuation buses, etc.
Prior Art Documents
Patent Documents
[0003]
Patent Document 1
Summary of the Invention
Problems to be Solved by the Invention
[0004] Conventionally, the road has been visually inspected from patrols or helicopters, and based on the actual road conditions, it has been determined whether to carry out road disaster responses such as emergency restoration and emergency restoration during disasters such as earthquakes. However, there is a problem that it takes time and effort to judge only by visual patrol, and it is difficult to respond quickly. [[ID=,39]]Road disaster response mainly includes grasping the overall situation and issuing orders for the system, formulating basic policies for disaster response, giving command orders for restoration (emergency restoration, emergency restoration, full restoration), and public notification. Specifically, these include gathering information immediately after a disaster, securing communication and contact immediately after a disaster, establishing an operational system, responding to government headquarters and relevant ministries, conducting emergency inspections of roads and facilities immediately after a disaster, securing disaster relief equipment and materials, securing restoration equipment and materials, carrying out emergency restoration work, carrying out temporary restoration work, carrying out full-scale restoration work, restricting road traffic, ensuring road traffic, taking measures to prevent secondary disasters, emergency restoration of lifeline facilities, providing support to local governments, responding to disaster victims, and responding to voluntary support from volunteers, etc. [Means for solving the problem]
[0005] The above problems can be solved by the invention having the following configuration. [1] A road disaster response support system comprising: an information acquisition unit that acquires two or more pieces of information from among information on patrol status, information on fiber optic survey status, information on satellite survey status, information on weather conditions, information on vehicle driving status, and information on road service status; an analysis unit that analyzes the two or more pieces of information acquired by the information acquisition unit; and a decision unit that determines the need for road disaster response from part or all of the results of the analysis by the analysis unit, wherein the decision unit determines the need for road disaster response; and inputs the image data of a road taken as the subject of analysis from one or more pieces of information acquired by the information acquisition unit, along with the learning target image data of a road taken as the subject of learning, and the disaster status of the road into a learning model that has been machine-learned using the learning data, to output a road disaster status, and complements and corrects the output road disaster status based on the measurement data included in the one or more pieces of information. [2] A road disaster response support system comprising: an information acquisition unit that acquires two or more pieces of information from among information on patrol status, information on optical fiber survey status, information on satellite survey status, information on weather conditions, information on vehicle driving status, and information on road service status; an analysis unit that analyzes the two or more pieces of information acquired by the information acquisition unit; and a decision unit that determines the need for road disaster response from part or all of the results of the analysis by the analysis unit, wherein the decision unit determines the need for road disaster response; inputs the image data of a road photographed for analysis from one or more pieces of information acquired by the information acquisition unit to a learning model that has been machine-learned using learning image data of a road photographed and the disaster situation of the road, thereby outputting the road disaster situation; analyzing the severity of the road disaster situation based on the measurement data contained in the one or more pieces of information; and supplementing and correcting the information acquired by the information acquisition unit based on the results of the analysis. [3] A road disaster response support system comprising: an information acquisition unit that acquires two or more pieces of information from among information on patrol status, information on fiber optic survey status, information on satellite survey status, information on weather conditions, information on vehicle driving status, and information on road service status; an analysis unit that analyzes the two or more pieces of information acquired by the information acquisition unit; and a decision unit that determines the need for road disaster response from part or all of the results analyzed by the analysis unit, wherein the decision unit determines the need for road disaster response; an improvement unit that outputs road disaster status by inputting image data of a road taken from one or more pieces of information acquired by the information acquisition unit to a learning model that has been machine-learned using learning image data of a road taken and the disaster status of the road, and that complements and corrects the output road disaster status based on measurement data included in the one or more pieces of information, and improves the results analyzed by the analysis unit or the judgment criteria of the decision unit or the results determined by the decision unit. [4] A program for causing a computer to function as the road disaster response support system described in claim 1, claim 2, or claim 3. [5] A road management method using a computer, wherein the computer acquires two or more pieces of information via a network from among information on patrol status, information on optical fiber survey status, information on satellite survey status, information on weather conditions, information on vehicle driving status, and information on road service status; analyzes the two or more acquired pieces of information, determines the need for road disaster response from part or all of the results of the analysis; inputs the image data of the road taken from one or more acquired pieces of information to be analyzed, along with the image data of the road taken to be learned and the disaster status of the road to a learning model that has been machine-learned using the learning data, to output the road disaster status; and complements and corrects the output road disaster status based on the measurement data included in the one or more pieces of information. [6] A road management method using a computer, wherein the computer acquires two or more pieces of information via a network from among information on patrol status, information on optical fiber survey status, information on satellite survey status, information on weather conditions, information on vehicle driving status, and information on road service status; analyzes the two or more acquired pieces of information, determines the need for road disaster response from part or all of the results of the analysis; outputs the road disaster status by inputting the image data of the road taken from one or more acquired pieces of information to a learning model that has been machine-learned using the learning image data of the road taken and the disaster status of the road; analyzes the severity of the road disaster status based on the measurement data included in the one or more pieces of information; and complements and corrects the acquired information based on the results of the analysis. [7] A road management method using a computer, wherein the computer acquires two or more pieces of information via a network from among information on patrol status, information on optical fiber survey status, information on satellite survey status, information on weather conditions, information on vehicle driving status, and information on road service status; analyzes the two or more acquired pieces of information and determines the need for road disaster response from part or all of the results of the analysis; outputs a road disaster situation by inputting the analysis target image data of a road photographed from one or more acquired pieces of information, the learning target image data of a road photographed, and the disaster situation of the road into a learning model that has been machine-learned using the learning data; and complements and corrects the output road disaster situation based on the measurement data included in the one or more pieces of information and improves the analyzed results or judgment criteria or determined results. [Effects of the Invention]
[0006] This invention enables rapid response to road disasters during earthquakes and other disasters. [Brief explanation of the drawing]
[0007] [Figure 1] This is a system configuration diagram relating to the road disaster response support system of the present invention. [Figure 2] This is a flowchart illustrating an example of a patrol procedure. [Figure 3] This is a flowchart illustrating an example of a fixed-point camera setup. [Figure 4] This figure shows an example of a patrol situation. [Figure 5] This figure shows an example of the status of fiber optic cable surveys. [Figure 6] This is a diagram illustrating an example of satellite survey status. [Figure 7] This figure shows an example of weather conditions. [Figure 8] This is a diagram illustrating an example of vehicle driving conditions. [Figure 9]This flowchart illustrates an example of the flow of the road disaster response support system of the present invention. [Figure 10] This figure shows an example of the criteria used in the decision-making unit of the present invention. [Figure 11] This figure shows an example of the hardware configuration for the road disaster response support system of the present invention. [Modes for carrying out the invention]
[0008] An embodiment of the road disaster response support system of the present invention will be described below with reference to the drawings. Note that this embodiment described below does not unduly limit the content of the claims of this disclosure. Furthermore, not all of the configurations described in this embodiment are essential components of this disclosure. In addition, individual configurations constituting the feature group may also constitute an invention.
[0009] Figure 1 is a system configuration diagram relating to the road disaster response support system 600 of the present invention. The road disaster response support system 600 includes an information acquisition unit 610 that acquires information on patrol status, optical fiber survey status, satellite survey status, weather conditions, and vehicle driving status; an analysis unit 620 that analyzes the patrol status, optical fiber survey status, satellite survey status, weather conditions, and vehicle driving status obtained from the information acquired by the information acquisition unit 610; and a decision unit 630 that comprehensively determines the necessity of road disaster response based on the results analyzed by the analysis unit 620. The road disaster response support system 600 of this embodiment is connected to a patrol status provision server 100, an optical fiber survey status provision server 200, a satellite survey status provision server 300, a weather status provision server 400, and a vehicle driving status provision server 500 via a network NW. To understand the patrol situation, Figure 1 shows only one vehicle Vh and terminal device TM, but multiple vehicles Vh and terminal devices TM may be connected to the network NW. In order to grasp the disaster situation and the like, only one fixed-point camera CAM is shown in FIG. 1, but a plurality of fixed-point cameras CAM may be connected to the network NW.
[0010] The terminal device TM, the fixed-point camera CAM, the patrol situation providing server 100, the optical fiber survey situation providing server 200, the satellite survey situation providing server 300, the weather situation providing server 400, the vehicle running situation providing server 500, and the road disaster response support system 600 communicate via the network NW. The network NW includes, for example, some or all of a WAN (Wide Area Network), a LAN (Local Area Network), the Internet, a provider device, a wireless base station, a dedicated line, a satellite line, and the like. Note that the communication method can be not only via the network NW but also data transfer via a memory card. Also, data can be downloaded / 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 or a tablet terminal. The terminal device TM may be a communication-type drive recorder or a stationary in-vehicle device mounted on the vehicle Vh, or may be equipped with an image analysis function by AI (Artificial Intelligence). The terminal device TM has a built-in road patrol application that cooperates with the patrol situation 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] Figure 2 is a flowchart showing an example of the flow of a patrol. The terminal device TM starts collecting position information, acceleration information, video, etc. (S2) by pressing the patrol start button of the road patrol app (S1). After the patrol ends, by pressing the patrol end button of the road patrol app (S3), the position information, acceleration information, video, etc. of the terminal device TM are transmitted to the patrol situation providing server 100 (S4). Based on the measurement information transmitted from the terminal device TM, the patrol situation providing server 100 determines the presence or absence of unevenness on the road surface and specifies the position of the road surface determined to be uneven. Also, based on the transmitted video / images, it determines the damage situation of the road surface and specifies the position of the road surface determined to be a risk location.
[0013] The fixed-point camera CAM is installed on buildings, roadside poles, poles, etc. around locations prone to waterlogging, such as roads (highways, main arterial roads, roads with heavy traffic, main bus routes, roads connecting to schools, public facilities, emergency hospitals, roads along mountains and mountainous areas, etc.), underpasses (under roads that are dug-down at intersections), roads along rivers, and roads along the sea. The fixed-point camera CAM is a communicable live camera, web camera, network camera, etc. The fixed-point camera CAM may also be a small unmanned aerial vehicle camera such as a communication-type drive recorder or drone, or may have an image analysis function by AI (artificial intelligence). The fixed-point camera CAM has a camera app built-in for communicating with the patrol situation providing server 100. The fixed-point camera CAM has a position measuring device such as a lens, image sensor, 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] Figure 3 is a flowchart showing an example of the workflow for a fixed-point camera CAM. The fixed-point camera CAM collects road conditions periodically or intermittently (S5), and periodically or intermittently transmits video, location information, date and time information, etc. to the patrol status provision server 100 (S6). The patrol status server 100 determines the extent of road surface damage, etc., based on video and images transmitted from the fixed-point camera CAM, and identifies the locations determined to be at risk.
[0015] The patrol status provision server 100 provides patrol status to the road disaster response support system 600 via the network NW. The provided patrol status is road-specific information and includes some or all of the following: road damage, road collapse, roadbed washout, roadway collapse, pavement damage, gutter damage, uneven road surface, liquefaction, snow cover / snow quality / freezing on the road surface, collapsed buildings, vehicle traffic history, power outages, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, volcanic activity damage, tornado damage, landslides, mudslides, mudslides, slope failures, tunnel collapses, shoulder collapses, fallen trees, rockfalls, road erosion, total bridge loss, river breaches, river flooding, inundation, dense fog, avalanches, blizzards, presence of accident vehicles / stuck vehicles, etc., and other disaster conditions or things that hinder vehicle traffic. Figure 4 shows an example of a patrol situation, where road damage location 110 is shown in black on the map.
[0016] The optical fiber survey status provision server 200 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., detects vibration patterns corresponding to the driving conditions of vehicles on the roads, etc., based on the backscattered light, and obtains information on the driving conditions of vehicles on the roads, etc., and the surrounding road conditions, etc., from the detected vibration patterns and a learning model. The fiber optic survey status provision server 200 provides fiber optic survey status to the road disaster response support system 600 via the network NW. The provided fiber optic survey status is road-specific information and includes some or all of the following: vehicle traffic history, traffic volume, traffic congestion, sudden vehicle stops, traffic accidents, snow accumulation on the road surface, tunnel collapses, accidents, power outages, water leaks / outages, earthquake prediction information, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, tornado damage, fiber optic cable breaks / disconnections, etc., which may hinder vehicle traffic. Figure 5 shows an example of the fiber optic survey situation, with the collapsed section 210 inside the tunnel indicated in black on the map.
[0017] The satellite survey status provision server 300 utilizes satellite remote sensing technology, which involves observations by artificial satellites equipped with SAR (Synthetic Aperture Radar), optical sensors, microwave sensors, etc. It uses data (scattering intensity values, phase information, polarization information, etc.) and images (optical images, SAR images, etc.) observed by the artificial satellites to acquire road conditions, vehicle conditions, etc., from the difference between before and after a disaster. Furthermore, AI (artificial intelligence) analysis may be used to compare and detect road conditions and vehicle conditions before and after a disaster, or to detect road conditions and vehicle conditions during a disaster. The satellite survey status provision server 300 provides satellite survey status to the road disaster response support system 600 via the network NW. The provided satellite survey status is road-specific information and includes some or all of the following: collapsed buildings, landslides, mudslides, soil runoff, slope failures, tunnel collapses, road damage, roadway collapses, shoulder collapses, fallen trees, falling rocks, total bridge loss, river breaches, river flooding, inundation, avalanches, vehicle traffic records, ground subsidence, power outages, water leaks / outages, earthquake prediction information, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, tornado damage, forest damage, crop damage, presence of accident vehicles / stuck vehicles, etc., as well as any other information that may obstruct vehicle traffic or cause other disasters. Figure 6 shows an example of the satellite survey situation, where the landslide area 310 is shown in black on the map.
[0018] The weather information server 400 provides weather information to the road disaster response 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, rainfall, snowfall, snow depth, wind speed, special warnings (heavy rain, strong winds, storm surge, high waves, heavy snow, blizzard), warnings (heavy rain, strong winds, flood, heavy snow, blizzard, etc.), record-breaking short-term heavy rain information, landslide warning information, earthquake prediction information, earthquake information, tsunami information, eruption information, information on volcanic activity, typhoon information, tornado information, and disaster status. Figure 7 shows an example of weather conditions, where warning 410 and seismic intensity 420 are indicated by letters and numbers.
[0019] The vehicle driving status provision server 500 utilizes automotive sensing technology and acquires various data from vehicles such as connected cars, including vehicle driving status and surrounding road conditions. The vehicle driving status provision server 500 provides vehicle driving status to the road disaster response support system 600 via the network NW. The provided vehicle driving status is road-specific information obtained from vehicles (including electric vehicles) such as private cars, taxis, buses, and trucks, and includes some or all of the following: temperature, sudden braking locations, skidding locations, tire spin locations, tire lock locations, ABS activation locations, flooded locations from vehicle sensors, etc.; power outages, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, tornado damage, river flooding, liquefaction phenomena, road obstacles from camera images, etc.; collapsed buildings, landslides, slope failures, tunnel collapses, total bridge losses, road damage and impassable areas from 3D data, etc.; vehicle traffic history (including separate data for regular cars and large vehicles), traffic volume, traffic congestion, passing speed, average speed, acceleration from probe information (including ETC2.0), etc.; and presence or absence of rainfall or snowfall from wiper operation status, etc., including some or all of the disaster situation and things that hinder vehicle traffic. Furthermore, data may be acquired using autonomous driving technology (sensing technology for autonomous vehicles), or it may be detected and identified through AI (artificial intelligence) analysis, such as disaster conditions or vehicle obstruction information. Figure 8 shows an example of vehicle traffic conditions, and areas 510 where there is no record of vehicle traffic are shown in black on the map.
[0020] The information acquisition unit 610, which operates in the road disaster response support system 600, acquires patrol status from the patrol status provision server 100, fiber optic survey status from the fiber optic survey status provision server 200, satellite survey status from the satellite survey status provision server 300, weather conditions from the weather conditions provision server 400, and vehicle driving status from the vehicle driving status provision server 500 via the network NW. Patrol status provided by patrol status server 100 is stored as patrol information 640. Fiber optic survey status provided by fiber optic survey status server 200 is stored as fiber optic survey information 650. Satellite survey status provided by satellite survey status server 300 is stored as satellite survey information 660. Weather conditions provided by weather status server 400 are stored as weather information 670. Vehicle driving status provided by vehicle driving status server 500 is stored as vehicle driving information 680. Furthermore, the information acquisition unit 610 may also be equipped with information regarding road service status. This information is road-specific and is mainly managed by road service providers (such as the Japan Automobile Federation). It includes some or all of the following: rescue requests (date, time, location, rescue details, etc.), rescue requests due to extreme 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 snow / mud, accidents, falls, disaster / damage situations, vehicle towing / transportation, removal / towing / transportation of abandoned vehicles, removal / towing / transportation of damaged vehicles, removal / towing / transportation of accident vehicles, road conditions, traffic conditions, EV charging availability, vehicle inspection results, etc. The road service status is stored as road service information. Furthermore, the information acquired by the information acquisition unit 610 may be limited to only some of the following: patrol status, fiber optic survey status, satellite survey status, weather conditions, vehicle driving status, and road service status.
[0021] The analysis unit 620, which operates within the road disaster response support system 600, analyzes the patrol information 640, optical fiber survey information 650, satellite survey information 660, weather information 670, and vehicle travel information 680 obtained from the information acquired by the information acquisition unit 610, for each piece of information, and stores the analysis results 690. The stored analysis results (690) can be viewed using map displays, list displays, etc. Furthermore, the analysis unit 620 may also analyze road service information. Furthermore, the analysis unit 620 may be equipped with AI (artificial intelligence) analysis and processing functions. An example of AI is as follows: This road disaster response support system is characterized by the following: it inputs image data of roads taken from various types of information (patrol information, fiber optic survey information, satellite survey information, weather information, vehicle driving information, road service information) acquired by the information acquisition unit into a learning model that has been machine-learned using training image data of roads and the disaster situation of the road in question, thereby outputting the road disaster situation; it analyzes the severity of the road disaster situation (earthquakes of magnitude 6 or higher, wind and flood damage, snow damage, volcanic activity, landslides, etc.) based on the measurement data contained in each type of information (date and time, address, latitude and longitude, seismic intensity, weather data, sediment disaster data, various sensing data, acceleration, vibration, vehicle data, probe data, 3D data, etc.) and complements and corrects the information acquired by the information acquisition unit based on the severity of the road disaster situation. A road management method characterized by inputting image data of roads taken from each acquired piece of information (patrol information, fiber optic survey information, satellite survey information, weather information, vehicle driving information, road service information) into a machine learning model that has been trained using training image data of roads and the disaster situation of the road in question, thereby outputting the road disaster situation, analyzing the severity of the road disaster situation (earthquakes of magnitude 6 or higher, wind and flood damage, snow damage, volcanic activity, landslides, etc.) based on the measurement data contained in each piece of information, and supplementing and correcting the acquired information based on the severity of the road disaster situation. Furthermore, the information analyzed by the analysis unit 620 may consist of only a portion of the following: patrol information 640, fiber optic survey information 650, satellite survey information 660, weather information 670, vehicle movement information 680, and road service information.
[0022] The decision unit 630, which operates in the road disaster response support system 600, comprehensively determines the necessity of road disaster response from some or all of the analysis results 690 for each piece of information (which may include road service information) analyzed by the analysis unit 620, and stores the result as a judgment result 695. The stored judgment results 695 can be viewed in map displays, list displays, etc. Furthermore, the decision unit 630 may be equipped with AI (artificial intelligence) analysis, judgment, and decision functions. Furthermore, the road disaster response support system 600 may include an information provision unit that provides some or all of the analysis results 690 and judgment results 695 to external parties. By including an information provision unit, it is possible to widely publicize public awareness and other aspects of road disaster response.
[0023] Figure 9 is a flowchart showing an example of the flow of the road disaster response support system 600. The information acquisition unit 610 periodically acquires information on patrol status from the patrol status provision server 100 (for example, every few minutes) (S10). The information acquisition unit 610 periodically acquires information on optical fiber survey status from the optical fiber survey status provision server 200 (for example, every few minutes) (S11). The information acquisition unit 610 periodically acquires information on satellite survey status from the satellite survey status provision server 300 (for example, every few minutes) (S12). The information acquisition unit 610 periodically acquires information on weather conditions from the weather conditions 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 status provision server 500 (for example, every few minutes) (S14). Then, the analysis unit 620 extracts road damage locations, etc. from patrol information 640 (S15). The analysis unit 620 extracts traffic records, etc. from optical fiber survey information 650 (S16). The analysis unit 620 extracts landslide locations, etc. from satellite survey information 660 (S17). The analysis unit 620 extracts earthquake and tsunami information, etc. from weather information 670 (S18). The analysis unit 620 extracts areas where travel is impossible, etc. from vehicle travel information 680 (S19). Next, based on the results of the above analysis, the decision unit 630 makes a comprehensive decision (S20) on whether road disaster response is necessary. Furthermore, the Road Disaster Response Support System 600 may also be equipped with information from road users via SNS (Social Networking Service) (disaster information, damage information, rescue information, etc.), information from government agencies (police, fire department, Self-Defense Forces, etc.), infrastructure operators (telecommunications, electricity, gas, water, sewage, etc.), transportation operators (railways, buses, ferries, airplanes, etc.), construction and civil engineering companies (including construction industry associations), tourism businesses (inns, hotels, tourist facilities, roadside stations, etc.), and designated public institutions (Disaster Countermeasures Basic Act) (disaster information, damage information, rescue information, recovery information, etc.), as well as information from the government's Emergency Disaster Countermeasures Headquarters and Emergency Disaster Countermeasures Headquarters.
[0024] Figure 10 shows an example of the criteria used by the decision unit 630. The decision unit 630 makes decisions regarding road disaster response in the following cases: when a tsunami occurs (1 element) 631; when a slope collapses and optical fibers are severed (2 elements) 632; when there is road damage and collapsed buildings and no history of vehicle traffic (3 elements) 633; when a heavy rain warning is issued and mudslides cause tires to spin, preventing vehicles from moving forward and resulting in severe traffic congestion (4 elements) 634; when a heavy snow warning is issued, there is snow on the road surface, ABS is activated, and there is severe traffic congestion with many stranded vehicles (5 elements) 635, etc. Furthermore, the decision result 695 determined by the decision unit 630 may be provided through the information provision unit (described in paragraph 0022) to include functions such as email notification to road administrators, data provision to road information board systems, data provision to road restoration visualization maps (which unify and display road restoration status, major damaged locations and damage status, road traffic restrictions, intercity travel time, vehicle speed data, vehicle traffic history, population mesh data, etc., on a web map), data provision to car navigation systems, data provision to autonomous driving systems, data provision to MaaS (Mobility as a Service), information provision to government agencies (police, fire departments, Self-Defense Forces, etc.) and the mass media, and information disclosure to road users (homepage, smartphone app, etc.). It may also be used for detour guidance, evacuation route guidance, securing routes for emergency vehicles and Self-Defense Forces vehicles engaged in disaster response, directing road clearing operations, supporting the transport of emergency supplies, supporting medical activities, supporting disaster victims, accepting volunteers, and road clearing and road restoration plans.
[0025] The Road Disaster Response Support System 600 may be equipped with an improvement unit that improves some or all of the analysis results 690, judgment results 695, and judgment criteria in the decision unit 630 (an example of judgment criteria is shown in Figure 10). The Road Disaster Response Support System 600 can be improved by incorporating various disaster-related data (data, images, etc.), and by equipping it with an improvement unit, the accuracy of road disaster response can be improved. An example of the flow is as follows: Input various disaster-related data (data, images, etc.) into the improvement unit → Refer to the history of analysis results 690, judgment results 695, etc. → Analysis by the improvement unit → Improvement results by the improvement unit → Feedback (review of accuracy improvement, etc.). Various data include, for example, hypothesis data, verification data, sample data, training data, past data, present data, future data (future data on large-scale disasters that occur once every few decades, data on large-scale disasters that may occur in the future, data on large-scale disasters that humanity has never experienced before, etc.). Furthermore, the improvement unit may be equipped with AI (artificial intelligence) analysis, processing, and improvement functions. An example of AI is as follows: A road disaster response support system characterized by having an improvement unit that improves the results analyzed by the analysis unit or the judgment criteria of the decision unit or the results determined by the decision unit, by inputting the image data of roads to be analyzed, obtained from each piece of information (patrol information, fiber optic survey information, satellite survey information, weather information, vehicle driving information, road service information), and the learning model which has been machine-learned using the learning data and the disaster situation of the road, to output the road disaster situation, and by supplementing and correcting the output road disaster situation based on the measurement data contained in each piece of information (date and time, address, latitude and longitude, seismic intensity, weather data, sediment disaster data, various sensing data, acceleration, vibration, vehicle data, probe data, 3D data, etc.) and improving the results analyzed by the analysis unit or the judgment criteria of the decision unit or the results determined by the decision unit. A road management method characterized by inputting image data of roads taken from each acquired piece of information (patrol information, fiber optic survey information, satellite survey information, weather information, vehicle driving information, road service information) into a learning model that has been machine-learned using training image data of roads and the disaster situation of the road, thereby outputting the road disaster situation, and supplementing and correcting the outputted road disaster situation based on measurement data included in each piece of information (date and time, address, latitude and longitude, seismic intensity, weather data, sediment disaster data, various sensing data, acceleration, vibration, vehicle data, probe data, 3D data, etc.) to improve the analyzed results, judgment criteria, or determined results.
[0026] The Road Disaster Response Support System 600 may be equipped with a prediction unit that predicts the need for road disaster response by referring to the history of analysis results 690 and judgment results 695. By equipping the Road Disaster Response Support System 600 with a prediction unit, it is possible to prepare, plan, and train for road disaster response in advance. An example of the flow is as follows: Input various disaster-related data (data, images, etc.) into the prediction unit → Refer to the history of analysis results 690 and judgment results 695, etc. → Analysis by the prediction unit → Prediction results by the prediction unit → Action (planning, training, etc.). Various types of data include, for example, hypothesis data, verification data, sample data, training data, past data, present data, future data (future data on large-scale disasters that occur once every few decades, data on large-scale disasters that may occur in the future, and data on large-scale disasters that humanity has never experienced before). Furthermore, the prediction unit may be equipped with AI (artificial intelligence) analysis, analytical, and prediction functions.
[0027] The Road Disaster Response Support System 600 can be effective based on one or more of the following factors: patrol status, fiber optic survey status, satellite survey status, weather conditions, vehicle traffic conditions, and road service status. Furthermore, synergistic effects can be achieved by combining two or more elements. One specific example is that by acquiring wide-area information (changes in wide-area infrastructure facilities and transportation networks) through satellite surveys (satellite remote sensing technology) and local information (local changes in road facilities and roads, etc.) through fiber optic surveys (fiber optic sensing technology), it becomes possible to monitor both wide-area and local areas. Next, by adding vehicle driving conditions (automobile sensing technology), it becomes possible to grasp diverse and advanced road and surrounding conditions, maximizing the advantages of each and achieving synergistic effects. Furthermore, by enabling rapid response to road disasters (including grasping the overall situation, issuing orders, directing and commanding restoration efforts, and publicizing information), it becomes possible to minimize damage, implement emergency, temporary, and full-scale restorations according to the disaster situation, and expect early recovery of the affected areas.
[0028] The Road Disaster Response Support System 600 may be implemented in the following manner. By having the analysis unit determine the severity of the road disaster (earthquakes of magnitude 6 or higher, widespread disasters such as wind and flood damage, snow damage, volcanic activity, and landslides), it is possible to reduce the cost of the system while enabling a rapid response according to the road disaster situation. A road disaster response support system comprising: an information acquisition unit that acquires information on weather conditions and information on patrol conditions; an analysis unit that analyzes the weather conditions and patrol conditions obtained from the information acquired by the information acquisition unit; and a decision unit that determines the necessity of road disaster response based on the results of the analysis by the analysis unit, wherein the decision unit determines the necessity of road disaster response, the analysis unit determines the severity of the road disaster based on the results of the analysis, and if the road disaster is severe, the information acquisition unit acquires one or more pieces of information from among information on vehicle driving conditions, information on fiber optic survey conditions, information on satellite survey conditions, and information on road service conditions, the analysis unit analyzes the acquired one or more pieces of information, and the decision unit determines the necessity of road disaster response based on the results of the analysis. A road management method using a computer, wherein the computer acquires information on weather conditions and information on patrol conditions via a network, analyzes the weather conditions and patrol conditions obtained from the acquired information, determines the need for road disaster response from the analysis results, judges the severity of the road disaster from the analysis results, and if the road disaster is severe, acquires one or more pieces of information from among information on vehicle driving conditions, information on fiber optic survey conditions, information on satellite survey conditions, and information on road service conditions, analyzes the acquired one or more pieces of information, and determines the need for road disaster response from the analysis results.
[0029] <Hardware Configuration> Figure 11 shows an example of the hardware configuration of a terminal device TM, a fixed-point camera CAM, a patrol status provision server 100, an optical fiber survey status provision server 200, a satellite survey status provision server 300, a weather status provision server 400, a vehicle driving status provision server 500, and a road disaster response 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 road patrol apps 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. Patrol information 640, fiber optic survey information 650, satellite survey 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.
[0030] Although embodiments for carrying out the present invention have been described above using examples, the present invention is not limited in any way to these embodiments, and various modifications and substitutions can be made without departing from the spirit of the present invention. [Explanation of symbols]
[0031] 100: Patrol status server 200: Fiber Optic Survey Status Provision Server 300: Satellite Survey Status Server 400: Weather information server 500: Vehicle driving status server 600: Road disaster response support system 610: Information acquisition department 620: Analysis Department 630: Decision Section 640: Patrol Information 650: Fiber Optic Survey Information 660: Satellite Survey 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 patrol status information, fiber optic survey status information, satellite survey status information, weather conditions information, vehicle driving status information, and road service status information. An analysis unit that analyzes two or more pieces of information acquired by the information acquisition unit, A decision unit that determines the necessity of road disaster response based on some or all of the results analyzed by the aforementioned analysis unit, It is equipped with the above-mentioned determination unit which determines the necessity of responding to road disasters, A road disaster response support system characterized by inputting image data of roads to be analyzed, obtained from one or more pieces of information by the information acquisition unit, into a learning model that has been machine-learned using image data of roads and the disaster situation of the roads, thereby outputting a road disaster situation, and supplementing and correcting the outputted road disaster situation based on measurement data included in the one or more pieces of information.
2. An information acquisition unit that acquires two or more pieces of information from among patrol status information, fiber optic survey status information, satellite survey status information, weather conditions information, vehicle driving status information, and road service status information. An analysis unit that analyzes two or more pieces of information acquired by the information acquisition unit, A decision unit that determines the necessity of road disaster response based on some or all of the results analyzed by the aforementioned analysis unit, It is equipped with the above-mentioned determination unit which determines the necessity of responding to road disasters, A road disaster response support system characterized by inputting image data of roads taken from one or more pieces of information acquired by the information acquisition unit into a learning model that has been machine-learned using image data of roads taken as training data and the disaster situation of the roads, thereby outputting the road disaster situation, analyzing the severity of the road disaster situation based on the measurement data contained in the one or more pieces of information, and supplementing and correcting the information acquired by the information acquisition unit based on the results of the analysis.
3. An information acquisition unit that acquires two or more pieces of information from among patrol status information, fiber optic survey status information, satellite survey status information, weather conditions information, vehicle driving status information, and road service status information. An analysis unit that analyzes two or more pieces of information acquired by the information acquisition unit, A decision unit that determines the necessity of road disaster response based on some or all of the results analyzed by the aforementioned analysis unit, It is equipped with the above-mentioned determination unit which determines the necessity of responding to road disasters, A road disaster response support system characterized by comprising: an improvement unit that outputs a road disaster situation by inputting image data of a road to be analyzed from one or more pieces of information acquired by the information acquisition unit, along with image data of a road to be learned and the disaster situation of the road to a learning model that has been machine-learned using the learning data; an improvement unit that complements and corrects the output road disaster situation based on measurement data contained in the one or more pieces of information, and improves the results analyzed by the analysis unit or the judgment criteria of the decision unit or the results determined by the decision unit.
4. A program for causing a computer to function as a road disaster response support system according to claim 1, claim 2, or claim 3.
5. A road management method using computers, The computer acquires two or more pieces of information via the network from among information on patrol status, information on fiber optic survey status, information on satellite survey status, information on weather conditions, information on vehicle driving status, and information on road service status. By analyzing the two or more pieces of information obtained, Based on some or all of the analyzed results, the need for road disaster response will be determined. A road management method characterized by inputting image data of roads taken from one or more acquired pieces of information into a learning model that has been machine-learned using training image data of roads taken and the disaster status of the roads, thereby outputting a road disaster status, and supplementing and correcting the outputted road disaster status based on measurement data included in the one or more pieces of information.
6. A road management method using computers, The computer acquires two or more pieces of information via the network from among information on patrol status, information on fiber optic survey status, information on satellite survey status, information on weather conditions, information on vehicle driving status, and information on road service status. By analyzing the two or more pieces of information obtained, Based on some or all of the analyzed results, the need for road disaster response will be determined. A road management method characterized by inputting image data of roads taken from one or more acquired pieces of information into a learning model that has been machine-learned using training image data of roads and the disaster situation of the roads, thereby outputting the road disaster situation, analyzing the severity of the road disaster situation based on the measurement data contained in the one or more pieces of information, and supplementing and correcting the acquired information based on the results of the analysis.
7. A road management method using computers, The computer acquires two or more pieces of information via the network from among information on patrol status, information on fiber optic survey status, information on satellite survey status, information on weather conditions, information on vehicle driving status, and information on road service status. By analyzing the two or more pieces of information obtained, Based on some or all of the analyzed results, the need for road disaster response will be determined. A road management method characterized by inputting image data of a road taken from one or more acquired pieces of information into a learning model that has been machine-learned using image data of a road taken as training data and the disaster status of the road, thereby outputting a road disaster status, and improving the output road disaster status based on measurement data contained in the one or more pieces of information, thereby improving the analyzed result, judgment criteria, or determined result.