Road deterioration management system and road deterioration management method

Abstract

A road deterioration management system and a road deterioration management method capable of predicting deterioration of a road with high accuracy and appropriately managing the road. A road deterioration management system (10) is provided with a simulator (300) that simulates by a digital twin model simulating a management area (100), a plurality of IoT devices (130) that acquire data related to deterioration of a road, a data server (200) that collects and processes data acquired by the IoT devices, and a mobile body flow control system (110) that controls flow of mobile bodies in the management area. The simulator predicts a deterioration progress of the road based on data related to deterioration factors and deterioration conditions of the road acquired from the data server and using the digital twin model, adjusts the digital twin model based on a comparison result of observed data of the deterioration conditions acquired from the data server and the predicted deterioration progress, and adjusts parameters of the mobile body flow control system based on the predicted deterioration progress.

Description

Technical Field

[0001] This disclosure relates to systems and methods for managing the deterioration of roads laid in a managed area, and in particular to systems configured for management using IoT technology, and methods for management using IoT technology. Background Technology

[0002] Patent Document 1 discloses prior art related to the management of road deterioration. However, in addition to Patent Document 1, Patent Document 2 and Patent Document 3 can also be cited as examples of the level of technology in the field related to this disclosure.

[0003] According to the prior art disclosed in Patent Document 1, road lifespan is predicted, and vehicle traffic is controlled to extend road lifespan. Vehicle traffic control includes, for example, a total limit on the number of vehicles allowed to pass and a weight limit on vehicles exceeding a specified weight. In this prior art, to predict road lifespan, an axle load cell is installed on the road to measure the weight of passing vehicles, and road damage is calculated based on the weight using a predetermined formula.

[0004] However, vehicle weight is only one of the parameters affecting road deterioration, and the prediction results based on calculations are only a rough reference. Furthermore, in the aforementioned prior art, even if the deterioration of the entire road where the axle load meter is installed can be predicted, it is not possible to narrow down the prediction to a specific interval. In particular, on roads with frequent vehicle traffic, such as ordinary roads, it may only be possible to predict the deterioration near the location of the axle load meter.

[0005] Patent Document 1: Japanese Patent Application Publication No. 2020-086965

[0006] Patent Document 2: Japanese Patent Application Publication No. 2019-185443

[0007] Patent Document 3: International Publication No. 2023 / 042238 Summary of the Invention

[0008] This disclosure was made in view of the aforementioned issues. One object of this disclosure is to provide a road deterioration management system and a road deterioration management method capable of predicting road deterioration with high accuracy and managing it appropriately.

[0009] According to one embodiment of this disclosure, a road deterioration management system is a system for managing the deterioration of roads laid in a management area, comprising a simulator, multiple IoT devices, a data server, and a mobile body flow control system. The simulator is configured to simulate the deterioration of the management area using a digital twin model. The multiple IoT devices are configured to acquire data related to road deterioration from the management area. The data server is configured to collect and process the data acquired by the multiple IoT devices. Furthermore, the mobile body flow control system is configured to control the flow of mobile bodies within the management area.

[0010] The simulator is configured to perform at least the following three processes. The first process is the prediction of road deterioration progress using a digital twin model, based on data obtained from a data server related to the causes and conditions of road deterioration. The second process is the adjustment of the digital twin model based on a comparison between observed deterioration data obtained from the data server and the predicted deterioration progress. Furthermore, the third process is a parameter study based on the predicted deterioration progress, used to adjust the parameters of the moving body flow control system to suppress the development of road deterioration.

[0011] According to one embodiment of this disclosure, the road deterioration management method is a method for managing the deterioration of roads paved in a management area, comprising at least the following five steps: The first step is to acquire data related to road deterioration from the management area using multiple IoT devices. The second step is to collect and process the data acquired by the multiple IoT devices through a data server. The third step is to predict the deterioration progress of the road using a digital twin model simulating the management area, based on data related to the causes and conditions of road deterioration acquired from the data server. The fourth step is to adjust the digital twin model based on a comparison between observed data of the deterioration condition acquired from the data server and the predicted deterioration progress. Furthermore, the fifth step is to conduct parameter research based on the predicted deterioration progress, performing parameter adjustments to a mobile body flow control system that controls the flow of mobile bodies in the management area to suppress the development of road deterioration.

[0012] According to one embodiment of this disclosure, various data related to road deterioration can be collected from a relatively wide area of ​​the management zone using multiple IoT devices and stored in a data server. Furthermore, by retrieving data related to the causes and conditions of road deterioration from the data server and inputting it into a digital twin model, the rate of road deterioration can be predicted. Since the digital twin model is adjusted based on the comparison between observed data of the deterioration condition and the predicted rate of deterioration, the accuracy of the predicted rate of deterioration can be maintained at a high level. By conducting parameter studies based on this high-accuracy prediction of the rate of deterioration, the parameters of the mobile flow control system can be appropriately adjusted to suppress the development of road deterioration. Attached Figure Description

[0013] Figure 1 This is a diagram illustrating an outline of a road deterioration management system according to one embodiment of the present disclosure.

[0014] Figure 2 This is a diagram illustrating a specific example of the composition of a management area governed by a road deterioration management system.

[0015] Figure 3 This diagram illustrates a specific example of the data server configuration required for a road deterioration management system.

[0016] Figure 4 This is a diagram illustrating a specific example of the structure of a digital twin simulator within a road deterioration management system.

[0017] Figure 5 This is a flowchart representing the method for determining measures corresponding to the predicted rate of degradation.

[0018] Explanation of reference numerals in the attached figures

[0019] 10…Road deterioration management system, 100…Management area, 110…Mobile flow control system, 111…Entry control system, 112…Traffic control system, 120…Object of concern, 121…Vehicle, 122…Road, 123…Traffic control information display, 130…IoT device, 131…Surveillance camera, 132…Vehicle-mounted sensing device, 133…Environmental sensing device, 200…Data server, 210…Data collection unit, 220…Data storage unit, 230…Data conversion unit, 240…Data output unit, 300…Digital twin simulator, 310…Data processing unit, 320…Road deterioration model, 330…Benchmark comparison unit, 340…Solution generation unit, 350…Digital entry control system, 360…Digital traffic control system, 370…Result output unit. Detailed Implementation

[0020] 1. Overview of the Road Deterioration Management System

[0021] One embodiment of the road deterioration management system disclosed herein is a system that uses IoT technology to manage the deterioration of roads paved in a management area. Hereinafter, using… Figure 1 This section provides an overview of the road deterioration management system.

[0022] like Figure 1As shown, a management area 100 is defined in the road deterioration management system 10. The management area 100 is an area containing roads that are the management objects of the road deterioration management system 10. Typically, a management area 100 refers to a closed city, such as some smart cities or demonstration cities, isolated from its surroundings by walls and gates. However, administrative districts, which lack physical barriers to their surrounding areas, may also become management areas 100. Furthermore, a management area 100 may also include a road network connecting numerous roads. Additionally, areas such as shopping malls or theme parks, where only sidewalks are provided, may also become management areas 100.

[0023] The road deterioration management system 10 includes a mobile body flow control system 110. The mobile body flow control system 110 is a system installed in the management area 100 and controls the flow of mobile bodies that affect road deterioration. When the road deterioration management system 10 manages the carriageway, the mobile bodies controlled by the mobile body flow control system 110 refer to vehicles, namely cars, buses, trucks, motorcycles, scooters, and other two-wheeled vehicles. When the road deterioration management system 10 manages the sidewalk, the mobile bodies controlled by the mobile body flow control system 110 refer to people and small mobile vehicles permitted to travel on the sidewalk.

[0024] The road deterioration management system 10 includes numerous IoT devices 130. These IoT devices 130 are sensing devices installed in the management area 100 and connected to the Internet, used to acquire real-time data related to road deterioration. The IoT devices 130 can be either fixed-location or variable-location sensing devices. Furthermore, the IoT devices 130 can be dedicated sensing devices for road deterioration management or sensing devices that can be used for other purposes.

[0025] There are multiple objects of interest 120 within the management area 100. Objects of interest 120 are either controlled objects of the mobile flow control system 110, or objects affected by the mobile flow control system 110's control over the flow of mobile bodies, and are observed by the IoT device 130. In other words, the control results of the mobile flow control system 110 are physically reflected in the objects of interest 120 and digitized through observation by the IoT device 130. However, the objects observed by the IoT device 130 also include objects other than those controlled by the mobile flow control system 110 and those affected by the control of the mobile body's flow.

[0026] The road deterioration management system 10 includes a data server 200. The form of the data server 200 is not limited; it can be a single server or a collection of multiple servers. IoT devices 130 connect to the data server 200 via the Internet. The data server 200 is configured to collect and store various real-time data acquired by the IoT devices 130.

[0027] The road deterioration management system 10 includes a digital twin simulator 300. The digital twin simulator 300 is a device that uses a digital twin model simulating the management area 100. A data server 200 is connected to the input of the digital twin simulator 300, and a mobile flow control system 110 is connected to its output. The digital twin simulator 300 and the data server 200 can be connected via a network, located on the same computer, or share hardware. The digital twin simulator 300 is configured to acquire simulation data from the data server 200 and provide the mobile flow control system 110 with control data generated based on the simulation.

[0028] 2. Specific examples of the composition of management areas

[0029] Next, use Figure 2 A specific example of the composition of management area 100 will be explained. In this specific example, management area 100 is a closed city isolated from the surrounding area by walls and gates.

[0030] The mobile flow control system 110 installed in the management area 100 of a closed urban area includes an entry control system 111. The entry control system 111 controls the entry of mobile vehicles into the management area 100, particularly vehicles 121 using the roads of the management area 100. The entry control system 111 is configured, for example, to switch between allowing and stopping vehicle entry into the management area 100 by controlling the entry gates. Furthermore, the entry control system 111 is configured to control the number of vehicles 121 entering per unit time and the number of vehicles 121 entering per time period by controlling the opening and closing of the entry gates. The parameters used to control the operation of the entry control system 111 are adjusted based on control data input from the digital twin simulator 300. However, in the case where the management area 100 is an open area like a typical urban area, such an entry control system cannot be installed.

[0031] The mobile flow control system 110 includes a traffic control system 112 that controls traffic within the management area 100. The traffic control system 112 is a system that can be installed in open areas such as typical urban areas. The specific objects controlled by the traffic control system 112 are traffic control information displays 123, such as traffic lights, road signs, and road markings. The traffic control system 112 is configured to change the flow of vehicles 121 by switching the display of the traffic control information displays 123. For example, to suppress vehicles from entering a deteriorating road 122, the display of the traffic control information displays 123 can be appropriately controlled, prohibiting entry to that road 122 based on time periods, or causing vehicles to detour around that road 122. The parameters used to control the actions of the traffic control system 112 are adjusted based on control data input from the digital twin simulator 300.

[0032] Vehicles 121 entering the management area 100 are subject to entry control by the entry control system 111. Roads 122 within the management area 100 are also subject to entry control by the entry control system 111 and the flow control of vehicles 121 by the traffic control system 112. Furthermore, the traffic control information display 123 is a controlled object of the traffic control system 112. In this specific example, any one of the vehicles 121, roads 122, and traffic control information display 123 is an object of interest 120 and an object of observation for the IoT device 130.

[0033] The IoT device 130 includes surveillance cameras 131 installed at various locations within the management area 100. The surveillance cameras 131 are configured to capture images of the road 122 for point-to-point observation of the road 122's condition and the types of vehicles 121 passing through it (trucks, dump trucks, buses, regular cars, etc.). By analyzing the images captured by the surveillance cameras 131, real-world data related to the deterioration of the road 122 can be obtained. This real-world data related to the deterioration of the road 122 includes, for example, data on road deterioration conditions such as cracks and subsidence caused by vehicles passing through the road, as well as traffic volume and vehicle types—data related to the factors contributing to road deterioration.

[0034] The IoT device 130 includes an onboard sensing device 132 mounted on the vehicle 121. The onboard sensing device 132 includes an accelerometer, a GPS sensor, and a vehicle speed sensor. Based on the vertical impact of the vehicle 121 measured by the accelerometer, data related to the location of deterioration, such as the collapse site, and the deterioration status of the road 122 can be obtained. Data related to traffic congestion can be obtained from the GPS and vehicle speed sensors, serving as data related to the factors contributing to road deterioration. Traffic congestion easily exacerbates the deterioration of the road 122. The onboard sensing device 132 can acquire real-time data related to the deterioration of the road 122 from various locations along the road 122 where the vehicle 121 travels.

[0035] The IoT device 130 includes environmental sensing devices 133 installed at various locations within the management area 100. The environmental sensing devices 133 include sensors for sunlight, rainfall, temperature, and pH. Real-time data related to environmental factors and meteorological conditions affecting the deterioration of the road 122 can be acquired from the environmental sensing devices 133, serving as data related to the factors contributing to road deterioration.

[0036] In addition to the aforementioned sensing devices, the IoT device 130 also includes weight sensors embedded in the road 122 and transmitters for individual vehicle information (vehicle type information) of the vehicle 121. Furthermore, in cases where the road is a sidewalk, the IoT device 130 may also include smartphones or smartwatches. The GPS sensors built into these devices can obtain data related to the movement of people within the managed area 100, and based on posture changes detected by accelerometers and gyroscopes, locations where people are likely to trip can be determined.

[0037] 3. Specific examples of the composition of a data server

[0038] Next, use Figure 3 A specific example illustrating the configuration of data server 200 is provided. For example... Figure 3 As shown, the data server 200 includes a data collection unit 210, a data storage unit 220, a data conversion unit 230, and a data output unit 240. Each of these units is implemented through the cooperation of a processing circuit, which is the hardware of the data server 200, and a memory and a program stored in the memory and executed by the processing circuit.

[0039] The data collection unit 210 connects to the Internet and collects various real-world data acquired by the IoT device 130. The data storage unit 220 stores the real-world data collected by the data collection unit 210 in a memory according to the data type. The format of the real-world data stored in the memory varies depending on the type of IoT device 130 that acquired the data. The data conversion unit 230 converts the real-world data stored in the memory into a format that the digital twin simulator 300 can process. The data output unit 240 outputs the converted real-world data, processed by the data conversion unit 230, to the digital twin simulator 300 according to the requests from the digital twin simulator 300.

[0040] 4. Specific examples of the composition of a digital twin simulator

[0041] Next, use Figure 4 A specific example illustrating the structure of the digital twin simulator 300 is provided. Figure 4 As shown, the digital twin simulator 300 includes a data processing unit 310, a road deterioration model 320, a benchmark comparison unit 330, a scheme generation unit 340, a digital entry control system 350, a digital traffic control system 360, and a result output unit 370. Each of these units is implemented through the cooperation of a processing circuit, which is the hardware of the digital twin simulator 300, and a memory and a program stored in the memory and executed by the processing circuit.

[0042] The data processing unit 310 obtains the data required for simulation in the digital twin simulator 300 from the data server 200. The data processing unit 310 inputs the required data into the road deterioration model 320, the benchmark comparison unit 330, the digital access control system 350, and the digital traffic control system 360.

[0043] The road deterioration model 320 is a digital twin model simulating management area 100, and is a machine learning model constituting the prediction of the deterioration progress of road 122 within management area 100. The road deterioration model 320 has parameters causally related to the deterioration progress of road 122, such as physical characteristics, traffic volume, environmental factors, meteorological conditions, maintenance history, and current deterioration status. The road deterioration model 320 is input with real data corresponding to each parameter obtained from data server 200, i.e., real data related to the deterioration factors and deterioration status of road 122. However, the real data used in the road deterioration model 320 does not necessarily have to be real-time or near real-time data; historical data can also be used. When historical data is used, a weighting is applied based on the data's age. The road deterioration model 320 predicts the deterioration progress according to pre-defined intervals for road 122. The road deterioration model 320 can also predict the deterioration progress at specific locations where deterioration such as cracks and settlement is detected.

[0044] The benchmark comparison unit 330 obtains the predicted deterioration progress from the road deterioration model 320 and the observed deterioration data contained in the actual data from the data processing unit 310. The benchmark comparison unit 330 compares the predicted deterioration progress with the observed data and adjusts the road deterioration model 320, i.e., performs hyperparameter tuning, to reduce the deviation between the predicted deterioration progress and the observed data. The benchmark comparison unit 330 repeatedly performs the adjustment of the road deterioration model 320 at a constant period to maintain the road deterioration model 320 as the latest state reflecting the current deterioration state of the road 122.

[0045] In addition, the benchmark comparison unit 330 compares the predicted deterioration progress with the prescribed benchmark value and determines the measures corresponding to the predicted deterioration progress. Figure 5 This is a flowchart illustrating the method for determining measures corresponding to the predicted deterioration progress based on the baseline comparison unit 330. The baseline comparison unit 330 determines the measures corresponding to the predicted deterioration progress according to this flowchart.

[0046] First, in step S01, the reference value comparison unit 330 obtains the predicted deterioration progress from the road deterioration model 320. Next, in step S02, the reference value comparison unit 330 determines whether the predicted deterioration progress is above a first reference value. If the predicted deterioration progress is above the first reference value, it is determined that even adjusting the parameters of the moving body flow control system 110 is insufficient to suppress the deterioration of road 122, and the reference value comparison unit 330 proceeds to step S04. In step S04, the reference value comparison unit 330 recommends maintenance of road 122 to the manager who manages the management area 100.

[0047] If the predicted degradation progress is less than the first reference value, the reference value comparison unit 330 executes step S03. In step S03, the reference value comparison unit 330 determines whether the predicted degradation progress is above the second reference value. The second reference value is set to a value smaller than the first reference value. If the predicted degradation progress is above the second reference value, the reference value comparison unit 330 executes step S05. In step S05, the reference value comparison unit 330 decides to adjust the parameters of the moving body flow control system 110.

[0048] If the predicted deterioration progress is less than the second benchmark value, the benchmark comparison unit 330 executes step S06. In step S06, the benchmark comparison unit 330 determines that no countermeasures are needed for the deterioration of road 122 under the current conditions. In other words, the benchmark comparison unit 330 selects standby processing based on the prediction result of the road deterioration model 320.

[0049] Return to Figure 4The description of the configuration of the digital twin simulator 300 continues. In the reference value comparison unit 330, if it is determined that the parameters of the moving body flow control system 110 need to be adjusted, this decision is notified to the scheme generation unit 340. The scheme generation unit 340 is configured to conduct parameter research for adjusting the parameters of the moving body flow control system 110 to suppress the deterioration of the road 122.

[0050] The scheme generation unit 340 uses the digital access control system 350 and the digital traffic control system 360 to conduct parameter studies. These are digital simulations of the real-world access control system 111 and traffic control system 112, reflecting the latest state of the real world based on real-time data obtained from the data server 200. Parameters studied include, for example, the opening time and frequency of the entrance gates, the display of traffic lights (e.g., the switching between red and green lights), and the display content of road signs and markings (e.g., permitted traffic direction, speed limits, etc.). The scheme generation unit 340 calculates the optimal values ​​for each parameter of the access control system 111 and traffic control system 112 that are most effective in suppressing the deterioration of the road 122.

[0051] A specific example of a scheme generated by the scheme generation unit 340 will be explained. In the following specific example, it is assumed that the road 122 within the management area 100 contains multiple roads, such as road A and road B, and their deterioration status is observed together.

[0052] The first specific example is a scenario where both Road A and Road B are observed to be deteriorating at relatively high rates. In this scenario, the solution generation unit 340 generates, for example, a solution to reduce overall traffic volume. In this solution, for example, the optimal values ​​of each parameter are calculated to restrict entry into the management area 100 through the control of the entrance gate.

[0053] The second specific example is a scenario where the deterioration rate is faster on road A, while the deterioration rate is very slow on road B. In this scenario, the scheme generation unit 340 generates, for example, a scheme to reduce traffic volume on road A and increase traffic volume on road B, so as not to reduce the overall traffic volume, in other words, not to impair the convenience of the residents of the street. In this scheme, for example, the optimal values ​​of each parameter are calculated and the change of permitted traffic direction is controlled by the display of road signs.

[0054] The result output unit 370 outputs the optimal values ​​of each parameter calculated by the scheme generation unit 340 as control data to the access control system 111 and the traffic control system 112. Based on the control data input from the result output unit 370, the optimal values ​​of each parameter of the access control system 111 and the traffic control system 112 are set.

[0055] The entry control system 111 controls the number of vehicles 121 entering the management area 100 based on input control data. The traffic control system 112 controls the traffic in the management area 100 based on input control data. Thus, the flow of vehicles 121 within the management area 100 is controlled to prevent deterioration of the road 122.

[0056] 5. Effects

[0057] According to the road deterioration management system 10 of this embodiment, various real-world data related to the deterioration of road 122 can be collected through numerous IoT devices 130 present in the management area 100. The collected real-world data is collected and stored by a data server 200, and then converted into a form that can be processed in a digital twin simulator 300. Furthermore, by retrieving real-world data related to the causes and conditions of road 122's deterioration from the data server 200 and inputting it into the digital twin simulator 300, the deterioration progress of road 122 can be predicted. The road deterioration model 320 of the digital twin simulator 300 is adjusted based on a comparison between observed data of the deterioration condition and the predicted deterioration progress, thus maintaining a consistently high level of accuracy in predicting the deterioration progress. By studying the parameters of the mobile flow control system 110 based on this high-accuracy prediction of the deterioration progress, the parameters of the mobile flow control system 110 can be adjusted to optimal values ​​to suppress the development of road 122's deterioration.

Claims

1. A road deterioration management system, characterized in that, have: The simulator simulates the aforementioned management area using a digital twin model. Multiple IoT devices acquire data related to the deterioration of the aforementioned roads from the aforementioned management area; The data server collects and processes data acquired by the aforementioned multiple IoT devices; and The mobile body flow control system controls the flow of mobile bodies within the aforementioned management area. The above simulator is configured to perform the following processing: Based on the data obtained from the aforementioned data server related to the causes and conditions of the aforementioned road deterioration, the aforementioned digital twin model is used to predict the deterioration progress of the aforementioned road. Based on the comparison between the observed degradation data obtained from the aforementioned data server and the predicted degradation progress, the aforementioned digital twin model is adjusted; and Based on the above-predicted deterioration progress, a parameter study is conducted to adjust the parameters of the aforementioned mobile flow control system in order to suppress the deterioration of the aforementioned road.

2. The road deterioration management system according to claim 1, characterized in that, The simulator is configured to perform the parameter study when the predicted deterioration rate is less than the first benchmark value, and to recommend maintenance for the road when the predicted deterioration rate is greater than the first benchmark value.

3. The road deterioration management system according to claim 2, characterized in that, The simulator is configured to perform the parameter study when the predicted degradation progress is greater than or equal to a second benchmark value that is smaller than the first benchmark value, and to perform standby processing when the predicted degradation progress is less than the second benchmark value.

4. The road deterioration management system according to any one of claims 1 to 3, characterized in that, The aforementioned IoT devices include at least one of a surveillance camera that captures images of the aforementioned road and a sensor possessed by the aforementioned mobile body.

5. The road deterioration management system according to any one of claims 1 to 3, characterized in that, The aforementioned mobile vehicle flow control system includes at least one of a traffic control system for controlling traffic within the aforementioned management area and an entry control system for controlling the entry of the aforementioned mobile vehicles into the aforementioned management area.

6. A method for managing road deterioration, characterized in that, It includes the following steps: Data related to the deterioration of the aforementioned roads was obtained from the aforementioned management area using multiple IoT devices; Data is collected and processed from the aforementioned multiple IoT devices via a data server; Based on data obtained from the aforementioned data server related to the causes and conditions of road deterioration, a digital twin model simulating the aforementioned management area was used to predict the deterioration progress of the aforementioned roads. Based on the comparison between the observed degradation data obtained from the aforementioned data server and the predicted degradation progress, the aforementioned digital twin model is adjusted; and Based on the aforementioned predicted deterioration progress, a parameter study was conducted to suppress the development of road deterioration by adjusting the parameters of a mobile body flow control system that controls the flow of mobile bodies in the aforementioned management area.

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