Road disaster response support system, program, and road management method

Abstract

Conventionally, the road was visually inspected during patrols or the like to check the actual road conditions, and it was determined whether to respond to road disasters in the event of disasters or the like. However, there was a problem in that it took time and effort to make a judgment based only on visual patrols, making it difficult to respond quickly. 【Solution means】 An information acquisition unit that acquires two or more pieces of information among information on patrol status, information on optical fiber survey status, information on satellite survey status, information on weather status, information on vehicle driving status, and information on road service status; an analysis unit that analyzes two or more pieces of information acquired by the information acquisition unit; and a determination unit that determines the necessity of road disaster response from part or all of the results analyzed by the analysis unit. A road disaster response support system, program, and road management method characterized by comprising the above.

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 in the event of disasters such as earthquakes.

Background Art

[0002] During large-scale disasters such as earthquakes, road administrators perform emergency restoration (road opening) for the passage of emergency vehicles and vehicles of the Self-Defense Forces engaged in disaster countermeasures, and then carry out road disaster responses such as emergency restoration and full restoration. Emergency vehicles are vehicles defined by laws such as the Basic Law on Disaster Countermeasures and the Road Traffic Law. Examples include fire trucks, ambulances, disaster restoration work vehicles, doctor cars, road patrol cars, wrecker trucks, vehicles of administrative agencies, vehicles of power companies, gas companies, and telephone companies, disaster countermeasure vehicles (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 the actual road conditions have been observed to determine whether to carry out road disaster responses such as emergency restoration and emergency restoration during disasters such as earthquakes. However, there has been a problem that it takes time and effort to make a judgment based only on visual patrols, and it is difficult to respond quickly. Road disaster response mainly includes overall grasping, issuing of orders for the system, formulation of basic policies for disaster response, command and order for restoration (emergency restoration, emergency restoration, full restoration), dissemination of information, etc. Specifically, it includes information collection immediately after a disaster occurs, ensuring communication immediately after a disaster occurs, establishing an activity system, responding to the government headquarters and related ministries and agencies, conducting emergency inspections of roads and facilities immediately after a disaster occurs, securing disaster prevention equipment and materials, securing restoration equipment and materials, implementing emergency restoration work, implementing emergency recovery work, implementing full restoration work, restricting road access, ensuring road traffic, taking measures to prevent secondary disasters, implementing emergency restoration of lifeline facilities, providing support to local governments, responding to affected people, and responding to spontaneous support such as volunteers.

Means for Solving the Problems

[0005] The above problems can be solved by an invention having the following configuration. [1] An information acquisition unit that acquires information regarding meteorological conditions and information regarding patrol conditions, an analysis unit that analyzes the meteorological conditions and patrol conditions obtained from the information acquired by the information acquisition unit, and a determination unit that determines the necessity of road disaster response from the results analyzed by the analysis unit. The determination unit determines the necessity of road disaster response, the analysis unit judges the severity of the road disaster from the results analyzed, and when the road disaster is severe, one or more pieces of information among information regarding vehicle driving conditions, information regarding optical fiber survey conditions, information regarding satellite survey conditions, and information regarding road service conditions are acquired by the information acquisition unit, the acquired one or more pieces of information are analyzed by the analysis unit, and the determination unit determines the necessity of road disaster response from the analyzed results. A road disaster response support system characterized by this. [2] A program for causing a computer to function as the road disaster response support system according to claim 1. [3] 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 the patrol conditions obtained from the acquired information, determines the necessity for road disaster response from the analyzed results, judges the severity of the road disaster from the analyzed results, and when the road disaster is severe, acquires one or more pieces of information among information on vehicle driving conditions, information on optical fiber survey conditions, information on satellite survey conditions, and information on road service conditions, analyzes using the acquired one or more pieces of information, and determines the necessity for road disaster response from the analyzed results. A road management method characterized by this.

Effect of the Invention

[0006] The present invention can quickly respond to road disasters during disasters such as earthquakes.

Brief Explanation of the Drawings

[0007]

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Embodiments for Carrying Out the Invention

[0008] Hereinafter, with reference to the drawings, an embodiment of the road disaster response support system of the present invention will be described. Note that the embodiment described below does not unduly limit the content of the present disclosure described in the claims. Also, not all of the configurations described in this embodiment are essential constituent elements of the present disclosure. In addition, each individual configuration constituting the feature group can also be an invention.

[0009] FIG. 1 is a system configuration diagram related 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 regarding the patrol situation, information regarding the optical fiber survey situation, information regarding the satellite survey situation, information regarding the weather situation, and information regarding the vehicle running situation, and a patrol situation, an optical fiber survey situation, a satellite survey situation, a weather situation, and a vehicle running situation obtained from the information acquired by the information acquisition unit 610. An analysis unit 620 that analyzes the vehicle running situation, and a determination unit 630 that comprehensively determines the necessity of road disaster response from the results analyzed by the analysis unit 620. The road disaster response support system 600 of the embodiment is communicably connected to a patrol situation providing server 100, an optical fiber survey situation providing server 200, a satellite survey situation providing server 300, a weather situation providing server 400, and a vehicle running situation providing server 500 through a network NW. In order to grasp the patrol situation, only one vehicle Vh and terminal device TM are shown in FIG. 1, but a plurality of 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 inspection situation providing server 200, the satellite inspection 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, etc. Note that the communication method can be not only via the network NW but also by exchanging data via a memory card. Also, it is acceptable to download / upload data 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 app 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 is completed, 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 columns, poles, etc. around locations prone to waterlogging, such as roads (expressways, major arterial roads, roads with heavy traffic, major bus routes, roads connecting schools, public facilities, emergency hospitals, roads along mountains and in mountainous areas), underpasses (the lower 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 be a small unmanned aerial vehicle camera such as a communication type drive recorder or drone, or may be equipped with an image analysis function by AI (artificial intelligence). The fixed-point camera CAM has a built-in camera app that communicates with the patrol situation providing server 100. The fixed-point camera CAM has 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] Figure 3 is a flowchart showing an example of the flow of the fixed-point camera CAM. The fixed-point camera CAM periodically or intermittently collects road conditions (S5) and periodically or intermittently automatically transmits video, position information, date and time information, etc. to the patrol situation providing server 100 (S6). Based on the video and images transmitted by the fixed-point camera CAM, the patrol situation providing server 100 determines the damage situation of the road surface, etc., and identifies the risk locations and the positions determined as such.

[0015] The patrol situation providing server 100 provides the patrol situation to the road disaster response support system 600 via the network NW. The provided patrol situation is information for each road, including some or all of the disaster situations and obstacles to vehicle passage, such as road damage, road subsidence, roadbed outflow, roadway collapse, pavement damage, side ditch damage, unevenness of the road surface, liquefaction phenomenon, snow accumulation, snow quality, freezing on the road surface, collapsed buildings, vehicle passing records, power outages, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, volcanic activity damage, tornado damage, landslide, earthslide, earthflow, slope collapse, tunnel collapse, road shoulder collapse, fallen trees, rockfalls, road scouring, bridge total loss, river breach, river flooding, waterlogging, thick fog, avalanche, ground blizzard, presence or absence of accident vehicles and stranded vehicles, etc. FIG. 4 is a diagram showing an example of the patrol situation, and the road damage location 110 is displayed in black on the map.

[0016] The optical fiber inspection situation providing server 200 utilizes the optical fiber sensing technology that uses the optical fiber as a sensor, receives the backscattered light from the communication optical fiber included in the cable laid on the road or the like, detects the vibration pattern corresponding to the driving situation of the vehicle on the road or the like based on the backscattered light, and obtains the driving situation of the vehicle on the road or the like and the surrounding road conditions, etc. from the detected vibration pattern and the learning model. The optical fiber inspection situation providing server 200 provides the optical fiber inspection situation to the road disaster response support system 600 via the network NW. The provided optical fiber inspection situation is information for each road, including some or all of the disaster situations and obstacles to vehicle passage, such as vehicle passing records, traffic volume, traffic congestion, sudden stops of vehicles, traffic accidents, snow accumulation on the road surface, collapses in tunnels, accidents, power outages, water leakage and water cut-off, earthquake prediction information, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, tornado damage, optical fiber interruption and disconnection locations, etc. FIG. 5 is a diagram showing an example of the optical fiber survey situation, and the collapse location 210 in the tunnel is shown in black on the map.

[0017] The satellite survey situation providing server 300 utilizes satellite remote sensing technology observed by artificial satellites equipped with SAR (Synthetic Aperture Radar), optical sensors, microwave sensors, etc. It acquires road conditions, vehicle conditions, etc. from differences before and after disasters using data (scattering intensity values, phase information, polarization information, etc.) and images (optical images, SAR images, etc.) observed by artificial satellites. Also, it may be one that compares and detects road conditions, vehicle conditions, etc. before and after disasters through AI (artificial intelligence) analysis, or one that detects and detects road conditions, vehicle conditions, etc. during disasters through AI (artificial intelligence) analysis. The satellite survey situation providing server 300 provides the satellite survey situation to the road disaster response support system 600 via the network NW. The provided satellite survey situation is information for each road and includes some or all of those that become disaster situations, obstacles to vehicle passage, etc., such as collapsed buildings, landslides, earth and sand collapses, earth and sand outflows, slope collapses, tunnel collapses, road damages, lane collapses, shoulder collapses, fallen trees, rockfalls, total bridge losses, river breaches, river floods, inundation, avalanches, vehicle passing records, ground subsidence, power outages, water leakage / water cut-off, earthquake prediction information, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, tornado damage, forest damage, crop damage, presence or absence of accident vehicles / stuck vehicles, etc. FIG. 6 is a diagram showing an example of the satellite survey situation, and the location 310 where a landslide has occurred is shown in black on the map.

[0018] The weather situation providing server 400 provides the weather situation to the road disaster response support system 600 via the network NW. The provided weather situation is information for each region 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, storm, high tide, wave, heavy snow, blizzard)·warnings (heavy rain, storm, flood, heavy snow, blizzard, etc.), record short-term heavy rain information, earth and sand disaster warning information, earthquake prediction information, earthquake information, tsunami information, volcanic eruption information, information on volcanic activities, typhoon information, tornado information, disaster situation, etc. FIG. 7 is a diagram showing an example of weather conditions, and the warning 410 and seismic intensity 420 are displayed in characters and numbers.

[0019] The vehicle driving condition providing server 500 utilizes the sensing technology of automobiles and acquires various data such as vehicle driving conditions and surrounding road conditions from vehicles such as connected cars. The vehicle driving condition providing server 500 provides the vehicle driving condition to the road disaster response support system 600 via the network NW. The provided vehicle driving condition is information for each road 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 points, skidding points, tire spin points, tire lock points, ABS activation points, waterlogging points from vehicle sensors, power outages, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, tornado damage, river flooding, liquefaction phenomena, obstacles on the road, collapsed buildings, landslides, slope collapses, tunnel collapses, total bridge losses, road damage, identification of impassable areas, vehicle passing records (including by private cars and large vehicles), traffic volume, traffic congestion, passing speed, average speed, acceleration, presence or absence of rainfall and snowfall from wiper operation status, etc., which may affect disaster situations and vehicle passage. Note that data may also be acquired by autonomous driving technology (sensing technology of autonomous vehicles), or disaster situations and vehicle obstruction information may be detected and detected by AI (artificial intelligence) analysis. FIG. 8 is a diagram showing an example of vehicle driving conditions, and the locations 510 where there is no vehicle passing record are displayed in black on the map.

[0020] The information acquisition unit 610 operating in the road disaster response support system 600 acquires the patrol condition from the patrol condition providing server 100, the optical fiber inspection condition from the optical fiber inspection condition providing server 200, the satellite inspection condition from the satellite inspection condition providing server 300, the weather condition from the weather condition providing server 400, and the vehicle driving condition from the vehicle driving condition providing server 500 via the network NW. The patrol status provided by the patrol status providing server 100 is stored as patrol information 640. The optical fiber inspection status provided by the optical fiber inspection status providing server 200 is stored as optical fiber inspection information 650. The satellite survey status provided by the satellite survey status providing server 300 is stored as satellite survey information 660. The weather status provided by the weather status providing server 400 is stored as weather information 670. The vehicle driving status provided by the vehicle driving status providing server 500 is stored as vehicle driving information 680. Furthermore, the information acquisition unit 610 may be provided with information regarding the load service status. The information regarding the load service status is information for each road, mainly information managed by load service operators (such as the Japan Automobile Federation), and includes rescue requests (date and time, location, rescue content, etc.), rescue requests due to abnormal weather (date and time, location, rescue content, etc.), rescue requests due to disasters (date and time, location, rescue content, etc.), battery run-up, key confinement, gas shortage, flat tire, wheel off / wheel drop, flooding / submersion, pulling out from snowy roads / muddy roads, accidents, slips, disaster / damage status, towing / transporting of vehicles, removal / towing / transporting of abandoned vehicles, removal / towing / transporting of damaged vehicles, removal / towing / transporting of accident vehicles, road conditions, traffic conditions, EV charging support, vehicle inspection results, etc., including some or all of them. The load service status is stored as load service information. Note that the information acquired by the information acquisition unit 610 may be only a part of the information on patrol status, optical fiber inspection status, satellite survey status, weather status, vehicle driving status, and load service status.

[0021] The analysis unit 620 operating in the road disaster response support system 600 analyzes the patrol information 640, optical fiber inspection information 650, satellite survey information 660, weather information 670, and vehicle driving information 680 obtained from the information acquired by the information acquisition unit 610 for each piece of information, and stores the result as analysis result 690. The stored analysis result 690 can be confirmed by map display, list display, etc. Further, the analysis unit 620 may be provided with an analysis function and an analysis function by AI (Artificial Intelligence). An example of AI (Artificial Intelligence) is as follows. The road disaster response support system is characterized in that image data of an analysis target of a road captured from each piece of information (patrol information, optical fiber survey information, satellite survey information, weather information, vehicle running information, road service information) acquired by an information acquisition unit is input into learning target image data of the road captured and a learning model in which machine learning is performed on the disaster situation of the road using learning data, so as to output the road disaster situation, and based on measurement data (date and time, address, latitude and longitude, seismic intensity, weather data, landslide disaster data, each sensing data, acceleration, vibration, vehicle data, probe data, three-dimensional data, etc.) included in each piece of information, the severity of the road disaster situation (earthquakes with a seismic intensity of 6-weak or higher, wide-area disasters such as floods, snow disasters, volcanic activities, landslides, etc.) is analyzed, and based on the severity of the road disaster situation, the information acquired by the information acquisition unit is complemented and corrected. The road management method is characterized in that image data of an analysis target of a road captured from each piece of information (patrol information, optical fiber survey information, satellite survey information, weather information, vehicle running information, road service information) acquired is input into learning target image data of the road captured and a learning model in which machine learning is performed on the disaster situation of the road using learning data, so as to output the road disaster situation, and based on measurement data (date and time, address, latitude and longitude, seismic intensity, weather data, landslide disaster data, each sensing data, acceleration, vibration, vehicle data, probe data, three-dimensional data, etc.) included in each piece of information, the severity of the road disaster situation (earthquakes with a seismic intensity of 6-weak or higher, wide-area disasters such as floods, snow disasters, volcanic activities, landslides, etc.) is analyzed, and based on the severity of the road disaster situation, the acquired information is complemented and corrected. Furthermore, the analysis unit 620 may also analyze the road service information. Note that the information analyzed by the analysis unit 620 may also be only a part of the patrol information 640, optical fiber survey information 650, satellite survey information 660, weather information 670, vehicle running information 680, and road service information.

[0022] The determination unit 630 operating in the road disaster response support system 600 comprehensively determines the need for road disaster response 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 stores it as the determination result 695. The stored determination result 695 can be confirmed by map display, list display, etc. Also, the determination unit 630 may be equipped with analysis, judgment, and decision-making functions using AI (artificial intelligence). Furthermore, the road disaster response support system 600 may be equipped with an information providing unit that provides part or all of the analysis results 690, determination results 695, etc. to the outside. By providing the information providing unit, the effect of widely informing the public about public relations, etc. in road disaster response can be obtained.

[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 (for example, every few minutes) acquires the patrol status from the patrol status providing server 100 (S10). The information acquisition unit 610 periodically (for example, every few minutes) acquires the optical fiber survey status from the optical fiber survey status providing server 200 (S11). The information acquisition unit 610 periodically (for example, every few minutes) acquires the satellite survey status from the satellite survey status providing server 300 (S12). The information acquisition unit 610 periodically (for example, every few minutes) acquires the weather status from the weather status providing server 400 (S13). The information acquisition unit 610 periodically (for example, every few minutes) acquires the vehicle driving status from the vehicle driving status providing server 500 (S14). Then, the analysis unit 620 extracts road damage locations, etc. from the patrol information 640 (S15). The analysis unit 620 extracts traffic performance, etc. from the optical fiber survey information 650 (S16). The analysis unit 620 extracts locations where landslides have occurred, etc. from the satellite survey information 660 (S17). The analysis unit 620 extracts earthquake and tsunami information, etc. from the weather information 670 (S18). The analysis unit 620 extracts areas where driving is impossible, etc. from the vehicle driving information 680 (S19). Next, based on the results of the above analysis, the determination unit 630 comprehensively determines the necessity for road disaster response (S20). Furthermore, information from road users utilizing SNS (Social Networking Service) (disaster information, damage information, rescue information, etc.), information from the government (police, fire department, Self-Defense Forces, etc.), infrastructure providers (communications, electricity, gas, water supply, sewerage, etc.), transportation providers (railways, buses, ferries, airplanes, etc.), construction and civil engineering providers (including construction industry associations), tourism providers (hotels, inns, tourist facilities, roadside stations, etc.), designated public agencies (Basic Law on Disaster Countermeasures) (disaster information, damage information, rescue information, restoration information, etc.), and information from the government's Emergency Disaster Countermeasures Headquarters and Emergency Disaster Response Headquarters may be provided to the road disaster response support system 600.

[0024] FIG. 10 is a diagram showing an example of the judgment criteria in the determination unit 630. When a tsunami occurs (1 element) 631, when a slope collapse occurs and the optical fiber is disconnected (2 elements) 632, when there is road damage and collapsed buildings and no vehicle traffic record (3 elements) 633, when a heavy rain warning is issued and the tires spin due to landslide and the vehicle cannot move forward and the traffic congestion is severe (4 elements) 634, when a heavy snow warning is issued and there is snow accumulation on the road surface and the ABS is activated and the traffic congestion is severe and there are a large number of stranded vehicles (5 elements) 635, etc., the determination unit 630 makes a determination on road disaster response. Furthermore, the judgment result 695 determined by the determination unit 630 and the like can be used through the information providing unit (described in paragraph 0022) and the like, to provide functions such as an e-mail notification function to the road administrator, a data providing function to the road information board system, a data providing function to the road restoration visible map (a map that unifies and displays the road restoration status, main disaster-affected locations and disaster situations, road traffic restrictions, intercity travel times, vehicle speed data, vehicle passing records, population mesh data, etc. on a web map), a data providing function to the car navigation system, a data providing function to the automatic driving system, a data providing function to MaaS (Mobility as a Service), an information providing function to the administration (police, fire department, Self-Defense Forces, etc.) and the mass media, and an information disclosure function to road users (homepage, smartphone application, etc.). In addition, it may be used for detour guidance, evacuation route guidance, instructions for ensuring the passing routes of emergency vehicles and Self-Defense Forces vehicles engaged in disaster countermeasures, road opening operations, etc., emergency material transportation support, medical activity support, support for disaster victims, acceptance of volunteers, and road opening plans and road restoration plans.

[0025] The road disaster response support system 600 may include an improvement unit that improves some or all of the analysis result 690, the judgment result 695, the judgment criteria in the determination unit 630 (an example of the judgment criteria is described in FIG. 10), etc. It can be improved by importing various data (data, images, etc.) related to disasters into the road disaster response support system 600, and by providing an improvement unit, the effect of improving the accuracy of road disaster response can be obtained. An example of the process is as follows. Input various data (data, images, etc.) related to disasters into the improvement unit → Refer to the history of the analysis result 690, the judgment result 695, etc. → Analysis and analysis by the improvement unit → Improvement result by the improvement unit → Feedback (review of accuracy improvement, etc.). The various data include, for example, hypothesis data, verification data, sample data, teacher data, past data, current data, future data (data related to large-scale disasters that occur once in several decades or not at all, data related to large-scale disasters that may occur in the future, data related to large-scale disasters that humanity has never experienced, etc.). Further, the improvement unit may be equipped with analysis, analysis, and improvement functions by AI (artificial intelligence). An example of AI (artificial intelligence) is as follows. The image data of the analysis target of the road taken from each piece of information (patrol information, optical fiber survey information, satellite survey information, weather information, vehicle running information, road service information) acquired by the information acquisition unit is input into the learning target image data of the road taken and the learning model in which machine learning is performed on the disaster situation of the road using the learning data, so as to output the road disaster situation, and based on the measurement data (date and time, address, latitude and longitude, seismic intensity, weather data, landslide disaster data, each sensing data, acceleration, vibration, vehicle data, probe data, three-dimensional data, etc.) included in each piece of information, the output road disaster situation is complemented and corrected, and the result analyzed by the analysis unit or the judgment criterion in the decision unit or the result determined by the decision unit is improved. The road disaster response support system is characterized by comprising an improvement unit. The image data of the analysis target of the road taken from each piece of information (patrol information, optical fiber survey information, satellite survey information, weather information, vehicle running information, road service information) acquired is input into the learning target image data of the road taken and the learning model in which machine learning is performed on the disaster situation of the road using the learning data, so as to output the road disaster situation, and based on the measurement data (date and time, address, latitude and longitude, seismic intensity, weather data, landslide disaster data, each sensing data, acceleration, vibration, vehicle data, probe data, three-dimensional data, etc.) included in each piece of information, the output road disaster situation is complemented and corrected, and the analyzed result or judgment criterion or determined result is improved. The road management method is characterized by this.

[0026] The road disaster response support system 600 may include a prediction unit that predicts the necessity of road disaster response by referring to histories such as the analysis result 690 and the judgment result 695. By providing the road disaster response support system 600 with a prediction unit, an effect can be obtained in which preparations, plans, and training for road disaster response can be carried out in advance. An example of the flow is as follows. Input various data (data, images, etc.) related to disasters into the prediction unit → Refer to histories such as the analysis result 690 and the judgment result 695 → Analysis, analysis, etc. by the prediction unit → Prediction result by the prediction unit → Action (planning, training, etc.). The various data include, for example, hypothesis data, verification data, sample data, teacher data, past data, current data, future data (data related to large-scale disasters that occur once in several decades or not at all, data related to large-scale disasters that may occur in the future, data related to large-scale disasters that humanity has never experienced), etc. Further, the prediction unit may be provided with an analysis function, an analysis function, and a prediction function by AI (artificial intelligence).

[0027] The road disaster response support system 600 can exert an effect with one or more elements among the patrol situation, the optical fiber survey situation, the satellite survey situation, the weather situation, the vehicle driving situation, and the road service situation. Furthermore, by using two or more elements, a synergistic effect can be obtained. As one specific example, a wide range of information (changes in wide-area infrastructure and transportation networks) is obtained by the satellite survey situation (satellite remote sensing technology), and local information (local changes in road facilities and roads, etc.) is obtained by the optical fiber survey situation (optical fiber sensing technology), enabling both wide-area and local monitoring. Next, by adding the vehicle driving situation (automobile sensing technology), diverse and advanced road and surrounding situations can be grasped, maximizing the advantages of each other and obtaining a synergistic effect. And, by being able to quickly carry out road disaster response (grasping the whole and issuing orders for the system, command orders for restoration, public notification, etc.), the disaster can be minimized, and emergency restoration, emergency restoration, full restoration, etc. according to the disaster situation become possible, and early recovery of the disaster-stricken area can be expected.

[0028] The road disaster response support system 600 may be the embodiment described below. By having an analysis unit determine the severity of road disasters (wide-area disasters such as earthquakes of intensity 5- on the Japanese seismic scale, floods, typhoons, snow disasters, volcanic activities, and landslides), it is possible to obtain the effect of enabling a prompt response according to the road disaster situation while suppressing the system cost. An information acquisition unit that acquires information regarding the meteorological situation and information regarding the patrol situation, an analysis unit that analyzes the meteorological situation and the patrol situation obtained from the information acquired by the information acquisition unit, and a determination unit that determines the necessity for road disaster response from the results analyzed by the analysis unit. The determination unit determines the necessity for road disaster response from the results analyzed by the analysis unit. The analysis unit determines the severity of the road disaster from the results analyzed. When the road disaster is severe, the information acquisition unit acquires one or more pieces of information among information regarding the vehicle driving situation, information regarding the optical fiber survey situation, information regarding the satellite survey situation, and information regarding the road service situation, and the analysis unit analyzes the one or more pieces of information acquired, and the determination unit determines the necessity for road disaster response from the results of the analysis. A road disaster response support system characterized by the above. A road management method using a computer, wherein the computer acquires information regarding the meteorological situation and information regarding the patrol situation via a network, analyzes the meteorological situation and the patrol situation obtained from the acquired information, determines the necessity for road disaster response from the results of the analysis, determines the severity of the road disaster from the results of the analysis, and when the road disaster is severe, acquires one or more pieces of information among information regarding the vehicle driving situation, information regarding the optical fiber survey situation, information regarding the satellite survey situation, and information regarding the road service situation, analyzes using the one or more pieces of information acquired, and determines the necessity for road disaster response from the results of the analysis. A road management method characterized by the above.

[0029] <Hardware Configuration> FIG. 11 is a diagram showing an example of the hardware configuration of the terminal device TM, the fixed-point camera CAM, and 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. This figure shows an example in which the terminal device TM is a mobile phone such as a smartphone. The terminal device TM has, for example, a configuration in which 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 are interconnected by an internal bus or a dedicated communication line. Application programs such as a road patrol application are downloaded via the network NW and stored in the secondary storage device 704. The fixed-point camera CAM has, for example, a configuration in which 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 are interconnected by 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 has, for example, a configuration in which a NIC 801, a CPU 802, a RAM 803, a ROM 804, a secondary storage device 805 such as a flash memory or an HDD, and a drive device 806 are interconnected by an internal bus or a dedicated communication line. A portable storage medium such as an optical disk 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 expanded into the RAM 803 by a DMA controller (not shown) or the like and executed by the CPU 802, whereby the functional units of each server are realized. The patrol information 640, the optical fiber survey information 650, the satellite survey information 660, the weather information 670, the vehicle running information 680, the analysis result 690, and the judgment result 695 are stored in the secondary storage device 805. Note that each server may be a cloud computing.

[0030] As described above, the embodiments for carrying out the present invention have been described using the embodiments. However, the present invention is not limited to such embodiments, and various modifications and substitutions can be made without departing from the gist of the present invention.

Explanation of Signs

[0031] 100: Patrol Situation Provision Server 200: Optical Fiber Survey Situation Provision Server 300: Satellite Survey Situation Provision Server 400: Weather Situation Provision Server 500: Vehicle Travel Situation Provision Server 600: Road Disaster Response Support System 610: Information Acquisition Unit 620: Analysis Unit 630: Decision Unit 640: Patrol Information 650: Optical Fiber Survey Information 660: Satellite Survey Information 670: Weather Information 680: Vehicle Travel Information 690: Analysis Result 695: Judgment Result

Claims

1. An information acquisition unit that acquires information regarding weather conditions and information regarding patrol conditions obtained by a fixed-point camera, a smartphone, a tablet terminal, a drive recorder, or a small unmanned aerial vehicle camera; An analysis unit that analyzes the weather conditions and patrol conditions obtained from the information acquired by the information acquisition unit; A determination unit that determines the necessity for road disaster response from the results analyzed by the analysis unit; The road disaster response support system is provided with the determination unit for determining the necessity for road disaster response, The analysis unit determines the severity of the road disaster from the results analyzed. When the road disaster is severe due to an earthquake of intensity 6-weak or higher or a wide-area disaster, among the information regarding the vehicle driving conditions obtained by automotive sensing technology, the information regarding the optical fiber inspection conditions obtained by the optical fiber laid on the road, the information regarding the satellite inspection conditions obtained from the data observed by artificial satellites, and the information regarding the road service conditions managed by road service providers, one or more pieces of information are acquired by the information acquisition unit, the acquired one or more pieces of information are analyzed by the analysis unit, and the determination unit determines the necessity for road disaster response from the analyzed results.

2. A program for causing a computer to function as the road disaster response support system according to Claim 1.

3. A road management method using a computer, wherein the computer acquires, via a network, information regarding weather conditions and information regarding patrol conditions obtained by a fixed-point camera, a smartphone, a tablet terminal, a drive recorder, or a small unmanned aerial vehicle camera, analyzes the weather conditions and patrol conditions obtained from the acquired information, and determines the necessity for road disaster response from the analyzed results. Based on the analyzed results, judge the severity of road disasters. When road disasters are severe due to an earthquake of intensity 5 weak or higher or a wide-area disaster, among the information on vehicle driving conditions obtained by automotive sensing technology, the information on optical fiber inspection conditions obtained by optical fibers laid on the road, the information on satellite inspection conditions obtained from data observed by artificial satellites, and the information on road service conditions managed by road service operators, acquire one or more pieces of information via the network, analyze using the acquired one or more pieces of information, and determine the necessity for road disaster response based on the analyzed results. This is a road management method characterized by the above.

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