Road surface damage prediction device, road surface repair support device, road surface damage prediction system, road surface repair support system, road surface damage prediction method, and road surface damage prediction program

Abstract

Based on the results of aggregating indicator values ​​representing road surface damage over a certain period of time, the timing of road surface damage is predicted. [Solution] The server 20 comprising the road surface damage prediction device includes a calculation unit 24 that calculates an index value representing road surface damage at a target location, linked to location information, at regular intervals; an aggregation unit 25 that aggregates the calculated values ​​at regular intervals in a time series; and a prediction unit 26 that predicts when the road surface will be damaged based on the aggregation results.

Description

Technical Field

[0001] The present invention relates to a road surface damage prediction device, a road surface repair support device, a road surface damage prediction system, a road surface repair support system, a road surface damage prediction method, and a road surface damage prediction program.

Background Art

[0002] There is known a system that determines whether a road surface during travel is a good road or a bad road and checks for road damage.

[0003] Patent Document 1 discloses a road surface condition determination system that can determine the condition of a road surface. An acceleration sensor built into an in-vehicle device is mounted on a vehicle and detects the acceleration in the vertical direction of the vehicle. The in-vehicle device transmits the acceleration to a server. The server obtains, for each vehicle, the variance value of the absolute values of the accelerations detected while traveling a predetermined distance within a predetermined speed range based on the received acceleration, and determines the road surface condition on which the vehicle has traveled based on the obtained variance value.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Conventional technologies such as Patent Document 1 are systems for determining the real-time road surface condition, not systems for predicting the future, and no plans have been made for road repairs in anticipation of future damage.

[0006] [[ID=四十二]] For example, road maintenance managers in local governments and other organizations formulate annual budgets for road repairs. However, these plans are based on empirical knowledge, such as predicting road deterioration based on past experience and securing a budget equivalent to past budgets. They are not utilizing data collected from road damage assessment systems to formulate more appropriate repair plans. If the timing of road surface damage could be predicted using this data, it would be possible to formulate more appropriate repair plans.

[0007] The present invention relates to a road surface damage prediction device, a road surface repair support device, a road surface damage prediction system, a road surface repair support system, a road surface damage prediction method, and a road surface damage prediction program that can appropriately predict when a road surface will be damaged. [Means for solving the problem]

[0008] To achieve the aforementioned objectives, the road surface damage prediction device according to the present invention has the following features. A calculation unit that calculates an index value representing road surface damage at a target location, linked to location information, at regular intervals, A summation unit that aggregates the calculated values ​​for each of the aforementioned fixed periods in a time series, The system includes a prediction unit that predicts the timing of damage to the road surface based on the aggregated results, Road surface damage prediction device.

[0009] To achieve the aforementioned objectives, the road surface repair support device according to the present invention has the following features. The road surface damage prediction device described above, The system includes a presentation unit that presents the time predicted by the prediction unit as the time when repairs to the road surface are required. Road surface repair support device.

[0010] To achieve the aforementioned objectives, the road surface damage prediction system according to the present invention has the following features. The road surface damage prediction device described above, An in-vehicle device that collects data used to calculate the aforementioned index value and transmits it to the road surface damage prediction device in association with the location information, A road surface damage prediction system equipped with the following features.

[0011] To achieve the aforementioned objectives, the road surface repair support system according to the present invention has the following features. The road surface repair support device described above, An in-vehicle device that collects data used to calculate the aforementioned index value and transmits it to the road surface repair support device in association with the location information, A road surface repair support system equipped with [features / equipment].

[0012] To achieve the aforementioned objectives, the road surface damage prediction method according to the present invention is characterized by the following: The steps include: calculating an index value representing road surface damage at a target location, linked to location information, at regular intervals; The steps include: aggregating the calculated values ​​for each period in a time series; The method includes the step of predicting the timing of damage to the road surface based on the aggregated results. Method for predicting road surface damage.

[0013] To achieve the aforementioned objectives, the road surface damage prediction program according to the present invention has the following features. The steps include: calculating an index value representing road surface damage at a target location, linked to location information, at regular intervals; The steps include: aggregating the calculated values ​​for each period in a time series; The steps include predicting the timing of damage to the road surface based on the aggregated results, A road surface damage prediction program that is executed by a computer. [Effects of the Invention]

[0014] According to the present invention, it is possible to accurately predict when the road surface will be damaged.

[0015] The present invention has been briefly described above. Further details of the present invention will be clarified by referring to the accompanying drawings and reading through the embodiments for carrying out the invention described below. [Brief explanation of the drawing]

[0016] [Figure 1] Figure 1 is a system configuration diagram showing a configuration example of a road surface damage prediction system and a road surface repair support system according to an embodiment of the present invention. [Figure 2] Figure 2 is a block diagram showing a configuration example of an in-vehicle device. [Figure 3] Figure 3 is a block diagram showing a configuration example of a server. [Figure 4] Figure 4 is a schematic diagram showing an example of data of acceleration dispersion values and a map showing positions where these data are measured. [Figure 5] Figure 5 is a schematic diagram of a road surface for explaining an operation example of a prediction unit. [Figure 6] Figure 6 is a flowchart showing a schematic procedure of a road surface damage prediction method performed by a server according to an embodiment. [Figure 7] Figure 7 is a flowchart showing a schematic procedure of a road surface damage prediction method performed by the server again after performing the road surface damage prediction of FIG. 6. [Figure 8] Figure 8 is a screen displayed on the display screen of the administrator PC as a result of the first road surface damage prediction. [Figure 9] Figure 9 is a screen displayed on the administrator PC as a result of performing the road surface damage prediction again after the first road surface damage prediction of FIG. 8. [Figure 10] Figure 10 is a screen displayed on the administrator PC as a result of changing a coefficient in consideration of an increase or decrease in the number of vehicle passages and performing the road surface damage prediction after the first road surface damage prediction of FIG. 8.

Embodiments for Carrying Out the Invention

[0017] Specific embodiments of the present invention will be described below with reference to the respective figures.

[0018] As shown in Figure 1, the road surface damage prediction system 1 according to this embodiment is a system that predicts when the road surface will be damaged. The road surface damage prediction system 1 comprises an on-board unit 10 mounted on a vehicle V and a server 20 that can communicate with the on-board unit 10.

[0019] The on-board unit 10 is installed in a vehicle V, such as a truck, and collects operational data (so-called digital tachograph data) including the driving status of the vehicle V. The collected operational data is used, for example, to create a daily report based on a day's worth of operational information. Specifically, the on-board unit 10 records operational data such as the vehicle V's speed, driving time, driving distance, and the start and end times of the operational work. The on-board unit 10 can be wirelessly connected to a network N, such as the Internet, for example, via wireless communication.

[0020] Server 20 constitutes the road surface damage prediction device, which forms the core of the road surface damage prediction system 1. Server 20 is a computer device capable of communicating with other devices via the network N. Server 20 is operated by, for example, a business operator (e.g., a transportation company) that operates vehicle V.

[0021] Figure 1 also shows the administrator PC (personal computer) 30. The administrator PC 30 is a communication terminal used by the vehicle manager of the operator of the vehicle V, and can communicate with other devices via the network N. The communication terminal is not limited to a fixed-installation type like a business PC, but may also be a portable device such as a tablet or smartphone.

[0022] Next, the in-vehicle unit 10 will be described. The in-vehicle unit 10 comprises a control unit 11, a position information acquisition unit 12, an acceleration sensor 13, and a communication unit 14. The control unit 11 is the main processing unit (computer) responsible for controlling the in-vehicle unit 10. The control unit 11 reads various programs stored in memory (not shown) and causes each part of the in-vehicle unit 10 to execute predetermined processes.

[0023] The position information acquisition unit 12 acquires the position information of the vehicle V using, for example, GPS (Global Positioning System). The acceleration sensor 13 detects the acceleration accompanying the movement of the vehicle V, and in this embodiment in particular, it can detect the vertical acceleration of the vehicle V. The vehicle V moves vertically due to unevenness and damage to the road surface, and the acceleration sensor 13 can also detect the acceleration generated at that time.

[0024] The communication unit 14 functions as a transmission unit that transmits vehicle V operation data, vehicle V location information acquired by the location information acquisition unit 12, vehicle V acceleration detected by the acceleration sensor 13, location information, and the time corresponding to the acceleration to the server 20, administrator PC 30, etc. The communication unit 14 transmits the operation data, etc., to the server 20, administrator PC 30, etc., via the network N, for example, at predetermined intervals. The communication unit 14 also functions as a reception unit that receives information from the server 20, administrator PC 30, etc.

[0025] Figure 3 is a block diagram showing an example configuration of a server 20 according to the embodiment. The server 20 comprises a control unit 21, a communication unit 22, a storage unit 23, a calculation unit 24, an aggregation unit 25, a prediction unit 26, and an acquisition unit 27.

[0026] The control unit 21 is the arithmetic processing unit (computer) that is primarily responsible for controlling the server 20. The control unit 21 reads programs stored in memory, storage unit 23, etc. (not shown) and causes each part of the server 20 to execute predetermined processes.

[0027] The communication unit 22 functions as a receiving unit that receives vehicle operation data, location information, acceleration, etc. from the server 20 via the network N, and also functions as a transmitting unit that sends information to the server 20, administrator PC 30, etc.

[0028] The memory unit 23 is a storage device that stores various data and programs. The memory unit 23 stores various data transmitted and received by the communication unit 22, calculation results by the calculation unit 24 (described later), and aggregation results by the aggregation unit 25. The memory unit 23 also stores operational data, location information, acceleration, etc., collected by multiple vehicles V at multiple locations, as well as calculation results and aggregation results. The memory unit 23 may store a road surface damage prediction program (described later) that performs road surface damage prediction, and the control unit 21 can read the road surface damage prediction program from the memory unit 23 and execute road surface damage prediction.

[0029] The calculation unit 24 calculates an index value representing road surface damage at target locations where the vehicle V travels, linking it to location information at regular intervals. The target location refers not only to a single point on the road surface but also to a section (target section) spanning two points on the road surface. The target location can be arbitrarily determined, for example, to a location with a high volume of traffic. Road surface damage refers to irregularities, scratches, peeling, etc., that occur on the road surface of the road traveled by the vehicle V, and which may hinder the vehicle's operation. The type of index value and acquisition method are not particularly limited. The index value may be, for example, the variance value of the vertical acceleration of the vehicle V detected by the acceleration sensor 13 of the onboard unit 10, as described later. Alternatively, the index value may be a value obtained by detecting road surface damage from image data captured by a camera installed on the vehicle V and quantifying the number or degree of damage.

[0030] Location information corresponding to road surface damage is obtained from the location information acquisition unit 12 of the in-vehicle unit 10. The period for calculating the index value is not particularly limited, but for example, if the calculation unit 24 calculates the index value every month, it becomes easier to predict the timing of road surface damage on a monthly basis. The calculation unit 24 calculates the index value for multiple target locations.

[0031] The aggregation unit 25 aggregates the calculated values ​​for each period calculated by the calculation unit 24 in a time series. The aggregation unit 25 can also aggregate the number of vehicles passing through each period based on the vehicle location information obtained from the location information acquisition unit 12. The aggregation unit 25 aggregates the calculated values ​​from the calculation unit 24 in a time series for multiple target locations.

[0032] The prediction unit 26 predicts the timing of road surface damage, that is, the timing when the degree of road surface deterioration will become such that it will impede vehicle traffic, based on the aggregation results of the aggregation unit 25. The method used by the prediction unit 26 to predict the timing of road surface damage is not particularly limited, but for example, the prediction unit 26 predicts the timing of road surface damage as the timing when the number of vehicles passing through the target point is expected to reach a predetermined number, based on the aggregation results of the aggregation unit 25. For example, if the index value is the variance value of acceleration as described above, the prediction unit 26 may predict the timing of road surface damage by estimating the period until the variance value exceeds a predetermined threshold. The prediction unit 26 may predict the timing of road surface damage based on the aggregation results at one designated target point, or it may predict the timing of road surface damage at one or more other target points by using information collected by the in-vehicle device 10.

[0033] The acquisition unit 27 acquires data including vertical acceleration detected by the acceleration sensor 13 in the on-board unit 10 installed in the vehicle V that has passed the target point as described above. The on-board unit 10 links the data including acceleration with the corresponding location information, and the communication unit 14 transmits the data including acceleration and the location information to the server 20. The communication unit 22 of the server 20 receives the data including acceleration and the location information, and the acquisition unit 27 acquires the data including vertical acceleration. As described above, the calculation unit 24 calculates the variance value of the acceleration included in the data acquired by the acquisition unit 27 as an index value.

[0034] The calculation unit 24, aggregation unit 25, prediction unit 26, and acquisition unit 27 may be software function units realized by the control unit 21 reading the road surface damage prediction program from the storage unit 23.

[0035] The administrator PC 30, in combination with the server 20, provides a road surface repair support device that presents the timing (road surface repair cycle) when road surface repairs are necessary and assists in road surface repairs. The administrator PC 30 functions as a presentation unit, presenting the timing predicted by the prediction unit 26 as the timing when road surface repairs are necessary. The administrator PC 30 has a display device such as a display, which displays a map and presents the repair timing, for example, as described later.

[0036] Furthermore, the road surface repair support device and the in-vehicle unit 10 constitute a road surface repair support system 2 that indicates when road surface repairs are necessary and assists in repairing the road surface.

[0037] Figure 4 shows an example of a map that displays the acceleration variance data calculated by the calculation unit 24 and the locations (location information) where this data was measured. The three sets of variance data D11 to D13 shown in Figure 4 represent the distribution of vertical acceleration variance measured when a vehicle travels on road Rm1 on map M1 within the range from position coordinate P01 to position coordinate P02.

[0038] The three sets of variance data, D11 to D13, are variance values ​​obtained from acceleration measurements taken by vehicles traveling at different times (e.g., with a difference of several months). Furthermore, the average of multiple variance values ​​measured at the same location by multiple vehicles traveling at approximately the same speed was used. Variance data D13 and D12 indicate a state where the road surface is not deteriorated, while variance data D11 exceeds a threshold (e.g., 0.05), indicating a state where the road surface is deteriorated or damaged.

[0039] In the example shown in Figure 4, time-series changes in variance data D11 to D13 are observed for each location (latitude PL) on the road Rm1. Such time-series changes in variance are thought to reflect the effects of irregularities and steps, such as newly occurring damage to the road surface. Therefore, it is possible to predict to some extent the presence of road surface damage from the variance data D11 to D13 shown in Figure 4.

[0040] Figure 5 is a schematic diagram of the road surface to illustrate an example of the operation of the prediction unit 26. The prediction unit 26 can predict when the road surface will be damaged by using, for example, the variance data D11 to D13, which have been acquired in the past and are shown in Figure 4. As shown in the upper part of Figure 5, the prediction unit 26 can predict when the road surface will change from the state of a flat, undamaged reference road surface SR0 to the state of a damaged road surface SR1 (see the lower part of Figure 5), where a damaged area AD exists.

[0041] In other words, the prediction unit 26 can predict that areas with uneven or uneven surfaces, i.e., areas with damage, exist at locations where the variance of the vehicle's vertical acceleration is large. However, since the effects of damage in areas not actually passed by the vehicle's wheels are not reflected in the acceleration, it may not be able to predict damage actually present on the road surface.

[0042] Figure 6 is a flowchart illustrating the outline of the road surface damage prediction method implemented by the server 20 according to the embodiment. A road manager, such as a local government or other road maintenance manager, determines the target locations for predicting road surface damage and requests the operator of the server 20 to perform the prediction. The operator inputs the target locations and the analysis period to be analyzed into the server 20 using a predetermined interface (step S1). In other words, in step S1, the target locations to be analyzed are specified. The analysis period can be determined by considering the past repair history at the target locations, for example, the period from the most recent repair to the present can be specified as the analysis period.

[0043] The calculation unit 24 refers to the data including the vehicle's acceleration and location information received from the in-vehicle device 10 and calculates an index value representing road surface damage at the input target point at regular intervals (step S2). The index value here is the variance of acceleration as shown in Figure 4.

[0044] The aggregation unit 25 aggregates the calculated index values ​​for each period calculated by the calculation unit 24 in a time series (step S3). Furthermore, the aggregation unit 25 aggregates the number of vehicles that passed through the target point during a certain period (number of passing vehicles) based on the vehicle location information obtained from the on-board device 10 of each vehicle (step S4).

[0045] The prediction unit 26 predicts when the road surface will be damaged based on the aggregation results from the aggregation unit 25. In making the prediction, the prediction unit 26 determines whether or not repair information regarding past repairs at the target location has been entered into the server 20 (step S5). If repair information has been entered (Yes in step S5), the process proceeds to step S6. If repair information has not been entered (No in step S5), the prediction unit 26 predicts when the road surface will be damaged based on the aggregation results from the aggregation unit 25, without taking the repair information into account (step S7). For example, the prediction unit 26 predicts the time when the number of vehicles passing through the target location is expected to reach a predetermined number, based on the aggregation results from the aggregation unit 25, as the time when the road surface will be damaged.

[0046] In step S6, the prediction unit 26 predicts when the road surface will be damaged, taking into account the repair information in addition to the aggregated results from the aggregation unit 25. The repair information includes at least one of the repair history and the repair method. If repair information is available, the prediction unit 26 predicts when the road surface will be damaged, taking into account the interval between repairs and whether the repairs were carried out using a specific method. For example, the prediction unit 26 performs a simulation based on the aggregated results from the aggregation unit 25, comparing them to the past, as follows: The prediction unit 26 checks how much the number of vehicles passing through the target location X has changed in the most recent period since repairs were carried out using method A seven years ago, for example, what percentage increase or decrease there has been per year since seven years ago. The prediction unit 26 can predict how many years later the road surface will be damaged by changing a coefficient according to this rate of change in the number of vehicles passing through and performing a simulation. Furthermore, the prediction unit 26 can take into account information such as the time elapsed between repairs using method B at point Y and the next repair, to predict how many years later the road surface will be damaged again if repairs using method B are carried out at target point X. In this way, the prediction unit 26 can perform simulations of repairs using different repair methods, regardless of the location.

[0047] Figure 7 is a flowchart outlining the procedure for predicting road surface damage, which is performed again by Server 20 after the road surface damage prediction shown in Figure 6. Even after performing road surface damage prediction as shown in Figure 6, the timing of road surface damage may change due to factors such as subsequent changes in road conditions. Therefore, the administrator may request the operator of Server 20 to perform the prediction again. The prediction may also be performed periodically.

[0048] After receiving a request from the administrator, the calculation unit 24 refers to the data including the vehicle's acceleration and location information received from the in-vehicle device 10 and calculates an index value representing road surface damage at the input target location at regular intervals (step S11). The index value here is the variance of acceleration as shown in Figure 4.

[0049] The aggregation unit 25 aggregates the calculated index values ​​for each fixed period calculated by the calculation unit 24 in a time series (step S12). Furthermore, the aggregation unit 25 aggregates the number of vehicles that passed through the target point during the fixed period based on the vehicle location information obtained from the on-board device 10 of each vehicle (step S13). The prediction unit 26 predicts again when the road surface will be damaged based on the aggregation results from the aggregation unit 25 (step S14).

[0050] The control unit 21 determines whether the newly predicted timing of road surface damage differs from the previous prediction (prediction in Figure 6) (step S15). If there is no difference (Yes in step S15), it reports the result to that effect (step S16). If there is a difference (No in step S15), it reports the result of the new road surface damage timing prediction (step S17).

[0051] According to this embodiment, the calculation unit 24 calculates an index value representing road surface damage, and the aggregation unit 25 aggregates this index value over time at regular intervals. Based on these results, the prediction unit 26 can predict when the road surface will be damaged. This allows road maintenance managers, such as local governments, to formulate highly accurate road surface repair plans. Consequently, it can contribute to securing an appropriate budget based on the predicted timing of road surface damage.

[0052] In particular, the calculation unit 24 calculates the variance value of acceleration as an index value, and the prediction unit 26 estimates the period until the variance value exceeds a predetermined threshold, thereby predicting when the road surface will be damaged. Since the timing of road surface damage can be predicted using the vertical acceleration obtained from the in-vehicle device, highly accurate timing predictions can be expected.

[0053] Furthermore, the aggregation unit 25 aggregates the number of vehicles that passed through the target location in time series, in addition to the calculated values ​​for each period (steps S4, S13), and the prediction unit 26 can predict when the road surface will be damaged, taking the number of vehicles into account. Since it is possible to predict when the road surface will be damaged based on the time-series aggregation of the number of vehicles that passed through the target location within a certain period and the index value during that period, a highly accurate timing prediction can be expected.

[0054] Furthermore, the prediction unit 26 may predict the timing of road surface damage according to the rate of change in the number of vehicles passing through. For example, the higher the rate of change, the earlier the timing may be predicted. Since the timing of road surface damage can be predicted by also taking into account the rate of change in the number of vehicles passing through the target point, a highly accurate timing prediction can be expected.

[0055] Furthermore, the prediction unit may also take into account repair information, including at least one of the past road surface repair history and repair methods, to predict when the road surface will be damaged. At least one of the repair history and repair process may be entered into the server in advance, and in the example in Figure 6, step S5 determines whether or not the repair process has been entered. By taking into account road surface repair information as well, a highly accurate timing prediction can be expected.

[0056] When the server 20, acting as a road surface damage prediction device, predicts when the road surface will be damaged as described above, the communication unit 22 of the server 20 transmits the prediction result to the administrator PC 30. The administrator PC 30 functions as a display unit that presents the time predicted by the prediction unit 26 of the server 20 as the time when road surface repairs will be necessary. The administrator PC 30 has a display device such as a display, which displays a map and presents the repair time, for example, as described later.

[0057] This system uses index values ​​representing road surface damage, aggregated over time, to indicate when road repairs are needed. This allows road maintenance managers, such as local governments, to develop highly accurate road repair plans, thereby contributing to securing appropriate budgets for road repairs.

[0058] The administrator PC30 may also present the road surface repair plan for each target section, categorized by the time remaining until repair is due. This allows for the development of a road surface repair plan tailored to the level of each target section.

[0059] Additionally, the administrator PC30 may display the repair schedule on the map. This allows for a clear and easy-to-understand display of the repair schedule for each target location on the map.

[0060] Figures 8 to 10 are schematic diagrams of the map displayed on the administrator PC30's display device and the screen examples showing the level and repair time corresponding to the time until repair is needed. Level 1 road surfaces have a low degree of damage and require repair after 3 years, meaning the time until repair is long. Level 2 road surfaces have a moderate degree of damage and require repair within 1 to 3 years, meaning the time until repair is moderate. Level 3 road surfaces have a high degree of damage and require repair within 1 year, meaning the time until repair is short.

[0061] Figure 8 shows the screen displayed on the administrator PC30 after the first road surface damage prediction. Figures 9 and 10 show the screen displayed on the administrator PC30 after performing a second road surface damage prediction after the first prediction in Figure 8, taking into account the increase or decrease in the number of passing vehicles and changing the coefficients. In the example in Figure 9, although there are sections where the number of passing vehicles has increased compared to the first road surface damage prediction, the simulation results (level for each section) are the same as in Figure 8, which shows the results of the first prediction. In the example in Figure 10, after the first road surface damage prediction in Figure 8, there are sections where the level has risen by one level compared to the first road surface damage prediction due to the increase in the number of passing vehicles. The administrator can easily grasp the level corresponding to the time until repair from the display screen for each target section and formulate a road surface repair plan.

[0062] The administrator PC30 may also directly display the repair date for each target section in the format of "The expected repair date is in [Year] [Month]" or "The expected repair date is in [Number] months."

[0063] In this embodiment, the administrator PC 30 functions as a display unit that indicates the timing of repairs, but the function of the display unit may also be possessed by the server 20, in which case the server 20 constitutes the road surface repair support device.

[0064] It should be noted that the present invention is not limited to the embodiments described above, and various modifications can be adopted within the scope of the present invention. For example, the present invention is not limited to the embodiments described above, and can be modified, improved, etc. as appropriate. Furthermore, the material, shape, dimensions, number, placement, etc. of each component in the embodiments described above are arbitrary and not limited as long as they can achieve the present invention.

[0065] Herein, the features of the embodiments of the road surface damage prediction device, road surface repair support device, road surface damage prediction system, road surface repair support system, road surface damage prediction method, and road surface damage prediction program according to the present invention are briefly summarized and listed below in [1] to

[12] .

[0066] [1] A calculation unit (24) that calculates an index value representing road surface damage at a target location at regular intervals, linked to location information, The aggregation unit (25) aggregates the calculated values ​​for each period in a time series, The system includes a prediction unit (26) that predicts the timing of damage to the road surface based on the aggregated results, Road surface damage prediction device (server 20).

[0067] According to the configuration described in [1] above, the timing of road surface damage is predicted based on the results of aggregating index values ​​representing road surface damage over a certain period of time. This allows road maintenance managers, such as local governments, to formulate highly accurate road surface repair plans. Therefore, it can contribute to securing appropriate budgets based on the predicted timing of road surface damage.

[0068] [2] The vehicle is equipped with an acquisition unit (27) that acquires data including vertical acceleration detected by an on-board device (10) installed in the vehicle that has passed the target point, The calculation unit calculates the variance of acceleration included in the data as the index value, The prediction unit estimates the period until the variance value exceeds a predetermined threshold and predicts the timing. The road surface damage prediction device described in [1] above.

[0069] According to the configuration described in [2] above, the timing of road surface damage can be predicted using the vertical acceleration obtained from the in-vehicle device.

[0070] [3] The aggregation unit aggregates the calculated values ​​for each period and the number of vehicles that passed through the target location in a time series, The prediction unit takes the number of units into consideration and predicts the timing. The road surface damage prediction device described in [2] above.

[0071] According to the configuration described in [3] above, it is possible to predict when road surface damage will occur based on the results of a time-series aggregation of the number of vehicles that passed through the target location within a certain period and the index value during that period.

[0072] [4] The prediction unit predicts the timing according to the rate of change of the number of units. The road surface damage prediction device described in [3] above.

[0073] According to the configuration described in [4] above, the timing of road surface damage can be predicted by also taking into account the rate of change in the number of vehicles passing through the target location.

[0074] [5] The prediction unit predicts the timing by taking into account repair information including at least one of the road surface repair history and repair method performed in the past. The road surface damage prediction device described in [4] above.

[0075] According to the configuration described in [5] above, the timing of road surface damage can be predicted by also taking into account road surface repair information.

[0076] [6] A road surface damage prediction device described in any one of [1] to [5] above, The system includes a presentation unit (administrator PC30) that presents the time predicted by the prediction unit as the time when repairs to the road surface will be necessary. Road surface repair support device.

[0077] According to the configuration described in [6] above, the timing for road surface repairs is indicated based on the results of aggregating indicator values ​​representing road surface damage over a certain period of time. This allows road maintenance managers, such as local governments, to formulate highly accurate road surface repair plans. Therefore, it can contribute to securing an appropriate budget for road surface repairs.

[0078] [7] The display unit displays the repair timing for each target section, categorized into levels according to the period until the repair timing. A road surface repair support device as described in [6].

[0079] According to the configuration described in [7] above, it becomes possible to formulate a road surface repair plan according to the level of each section in question.

[0080] [8] The display unit displays the repair period on the map. A road surface repair support device as described in [6].

[0081] According to the configuration described in [8] above, the repair schedule for each target location can be clearly displayed on the map.

[0082] [9] A road surface damage prediction device described in any one of [1] to [5] above, An in-vehicle device that collects data used to calculate the aforementioned index value and transmits it to the road surface damage prediction device in association with the location information, A road surface damage prediction system equipped with the following features.

[0083] According to the configuration described in [9] above, the road surface damage prediction device can predict when the road surface will be damaged using data collected by an on-board device installed in a vehicle that has passed through the target location.

[0084]

[10] The road surface repair support device described in [6] above, An in-vehicle device that collects data used to calculate the aforementioned index value and transmits it to the road surface repair support device in association with the location information, A road surface repair support system equipped with [features / equipment].

[0085] According to the configuration described in

[10] above, the road surface repair support device can use data acquired from an on-board device installed in a vehicle that has passed through the target location to indicate when road surface repairs should be performed.

[0086]

[11] A step of calculating an index value representing road surface damage at the target location, linked to location information, at regular intervals, The steps include: aggregating the calculated values ​​for each period in a time series; The method includes the step of predicting the timing of damage to the road surface based on the aggregated results. Method for predicting road surface damage.

[0087] According to the configuration described in

[11] above, the timing of road surface damage is predicted based on the results of aggregating index values ​​representing road surface damage over time at regular intervals. This allows road maintenance managers, such as local governments, to formulate highly accurate road surface repair plans. Therefore, it can contribute to securing appropriate budgets based on the predicted timing of road surface damage.

[0088]

[12] A step of calculating an index value representing road surface damage at the target location, linked to location information, at regular intervals, The steps include: aggregating the calculated values ​​for each period in a time series; The steps include predicting the timing of damage to the road surface based on the aggregated results, A road surface damage prediction program that is executed by a computer.

[0089] According to the configuration described in

[12] above, the timing of road surface damage is predicted based on the results of aggregating index values ​​representing road surface damage over a certain period of time. This allows road maintenance managers, such as local governments, to formulate highly accurate road surface repair plans. Therefore, it can contribute to securing appropriate budgets based on the predicted timing of road surface damage. [Explanation of symbols]

[0090] 1. Road surface damage prediction system 2. Road surface repair support system 10 Onboard equipment 11 Control Unit 12 Location information acquisition section 13. Accelerometer 14 Communications Department 20 Servers (Road surface damage prediction devices) 21 Control Unit 22 Communications Department 23 Memory section 24 Calculation Section 25. Aggregation Department 26 Prediction Section 27 Acquisition Department 30 Administrator PC30 (presentation part)

Claims

1. A calculation unit that calculates an index value representing road surface damage at a target location, linked to location information, at regular intervals, A summation unit that aggregates the calculated values ​​for each of the aforementioned fixed periods in a time series, The system includes a prediction unit that predicts the timing of damage to the road surface based on the aggregated results, Road surface damage prediction device.

2. The system includes an acquisition unit that acquires data including vertical acceleration detected by an on-board device installed in a vehicle that has passed the aforementioned target point. The calculation unit calculates the variance of acceleration included in the data as the index value, The prediction unit estimates the period until the variance value exceeds a predetermined threshold and predicts the timing. The road surface damage prediction device according to claim 1.

3. The aggregation unit aggregates the calculated values ​​for each set period and the number of vehicles that passed through the target location in a time series. The prediction unit takes the number of units into consideration and predicts the timing. The road surface damage prediction device according to claim 2.

4. The prediction unit predicts the timing according to the rate of change of the number of units. The road surface damage prediction device according to claim 3.

5. The prediction unit predicts the timing by taking into account repair information, which includes at least one of the past repair history of the road surface and the repair method. The road surface damage prediction device according to claim 1.

6. A road surface damage prediction device according to any one of claims 1 to 5, The system includes a presentation unit that presents the time predicted by the prediction unit as the time when repairs to the road surface are required. Road surface repair support device.

7. The display unit presents the repair timing for each target section, categorized into levels according to the time remaining until the repair timing. The road surface repair support device according to claim 6.

8. The aforementioned display unit displays the repair period on the map. The road surface repair support device according to claim 6.

9. A road surface damage prediction device according to any one of claims 1 to 5, An in-vehicle device that collects data used to calculate the aforementioned index value and transmits it to the road surface damage prediction device in association with the location information, A road surface damage prediction system equipped with the following features.

10. A road surface repair support device according to claim 6, An in-vehicle device that collects data used to calculate the aforementioned index value and transmits it to the road surface damage prediction device in association with the location information, A road surface repair support system equipped with [features / equipment].

11. The steps include: calculating an index value representing road surface damage at a target location, linked to location information, at regular intervals; The steps include: aggregating the calculated values ​​for each period in a time series; The method includes the step of predicting the timing of damage to the road surface based on the aggregated results. Method for predicting road surface damage.

12. The steps include: calculating an index value representing road surface damage at a target location, linked to location information, at regular intervals; The steps include: aggregating the calculated values ​​for each period in a time series; The steps include predicting the timing of damage to the road surface based on the aggregated results, A road surface damage prediction program that is executed by a computer.

Images