Failure prediction device, toll collection system, failure prediction method, and program

Abstract

To provide a failure prediction device, a toll collection system, a failure prediction method, and a program capable of predicting a failure of a step board.SOLUTION: A failure prediction device includes: a first acquisition section for acquiring the number of axles of a vehicle traveling on a step board of a lane; a second acquisition section for acquiring vehicle information of the vehicle; a determination section for determining a weighting coefficient from the acquired vehicle information; an integration section for calculating a correction value obtained by multiplying the acquired number of the axles of the vehicle by the determined weighting coefficient, and then calculating an integration value by integration of the correction value; and a determination section for determining that the step board 6 is in a deteriorated state when the integration value is equal to or more than a predetermined determination value.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present disclosure relates to a failure prediction device, a toll collection system, a failure prediction method, and a program. [Background technology]

[0002] The Electronic Toll Collection System (ETC) used on toll roads can collect tolls from vehicles traveling in the toll gate lane by communicating wirelessly with the vehicles.

[0003] Some electronic toll collection systems use a vehicle type discrimination device installed on the lane of the toll booth to discriminate the vehicle type (classification of ordinary car, large vehicle, etc.) of a passing vehicle. For example, the vehicle type discrimination device measures the number of axles of the vehicle by counting the number of pressures by the tires through a footplate-type pressure detection sensor (hereinafter also referred to as "footplate" or "footplate unit") laid on the road surface of the lane, and discriminates the vehicle type based on the measurement result.

[0004] In electronic toll collection systems, the above-mentioned footplate is known to have a relatively high frequency of failure due to the nature of vehicles passing directly above it. In addition, when the footplate breaks down, the lane must be closed for repair, and the impact of repairs in the event of a breakdown is significant. For this reason, the footplate is considered to be a device that requires high predictive maintenance.

[0005] It is empirically known that the probability of footboard failure increases with the number of passing axles, and predictive maintenance using the number of passing axles as an indicator is conceivable. For example, Patent Document 1 discloses a method for detecting failures in footboards that have built-in axle load scales. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2020-042580 A Summary of the Invention [Problem to be solved by the invention]

[0007] Patent Document 1 discloses a failure prediction method that detects failure of a step having an integrated axle load scale by comparing multiple pieces of measurement information obtained for vehicles having the same vehicle characteristic information. However, the method disclosed in Patent Document 1 does not take into account the characteristics of the tread, which deteriorates due to the load of vehicles passing directly above it and the resulting vibrations, etc., and therefore there is room for improvement in terms of failure prediction accuracy.

[0008] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a failure prediction device, a toll collection system, a failure prediction method, and a program that can predict pedal failures. [Means for solving the problem]

[0009] The failure prediction device disclosed herein comprises a first acquisition unit that acquires the number of axles of a vehicle traveling on a tread of a lane, a second acquisition unit that acquires vehicle information of the vehicle, a determination unit that determines a weighting coefficient from the acquired vehicle information, an integration unit that calculates a correction value by multiplying the acquired number of axles of the vehicle by the determined weighting coefficient and then accumulates the correction value to calculate an accumulated value, and a determination unit that determines that the tread is in a deteriorated state if the accumulated value is equal to or greater than a predetermined determination value.

[0010] The failure prediction method disclosed herein includes the steps of acquiring the number of axles of a vehicle traveling on a step of a lane, acquiring vehicle information of the vehicle, determining a weighting coefficient from the acquired vehicle information, calculating a correction value by multiplying the acquired number of axles of the vehicle by the determined weighting coefficient, and then accumulating the correction value to calculate an integrated value, and determining that the step is in a deteriorated state if the integrated value is equal to or greater than a predetermined judgment value.

[0011] The program of the present disclosure executes the steps of acquiring the number of axles of a vehicle traveling on a tread of a lane, acquiring vehicle information of the vehicle, determining a weighting coefficient from the acquired vehicle information, calculating a correction value by multiplying the acquired number of axles of the vehicle by the determined weighting coefficient, and then accumulating the correction value to calculate an integrated value, and determining that the tread is in a deteriorated state if the integrated value is equal to or greater than a predetermined judgment value. Effect of the Invention

[0012] The failure prediction device, toll collection system, failure prediction method, and program disclosed herein make it possible to predict failure of a pedal. [Brief description of the drawings]

[0013] [Figure 1] 1 is a schematic perspective view of a fee collection system according to a first embodiment of the present disclosure. [Diagram 2] 1 is a block diagram of a failure prediction device according to a first embodiment of the present disclosure. [Diagram 3] 2 is a flowchart of a failure prediction method according to the first embodiment of the present disclosure. [Figure 4] 2 is an example of a database recorded by the failure prediction device according to the first embodiment of the present disclosure. [Diagram 5] 2 is an example of a database recorded by the failure prediction device according to the first embodiment of the present disclosure. [Figure 6] FIG. 4 is a block diagram of a failure prediction device according to a second embodiment of the present disclosure. [Figure 7] 13 is a schematic perspective view of a toll collection system according to a modified example of the present disclosure. FIG. [Figure 8] FIG. 2 is a block diagram of a failure prediction device according to a first modified example of the present disclosure. [Figure 9] 11 is a flowchart of a failure prediction method according to a first modified example of the present disclosure. [Figure 10] FIG. 11 is a block diagram of a failure prediction device according to a second modified example of the present disclosure. [Figure 11]11 is a flowchart of a failure prediction method according to a second modified example of the present disclosure. [Figure 12] 2 is an example of a hardware configuration of a computer equipped with a failure prediction device according to each embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, each embodiment of the present disclosure will be described with reference to the drawings. In all the drawings, the same or corresponding components are denoted by the same reference numerals, and common descriptions will be omitted.

[0015] First Embodiment A failure prediction device 7 according to a first embodiment will be described with reference to the drawings.

[0016] (Overall configuration of the toll collection system) The toll collection system 1 of this embodiment is a system that is installed, for example, at one of the toll gates TL (entrance toll gate or main line toll gate) of an expressway, which is a toll road, and collects from users of the expressway a toll amount related to the vehicle AA in which the user is riding.

[0017] As shown in Fig. 1, vehicle AA is traveling on lane LN leading from the general road to the expressway via toll booth TL, which is the entrance toll booth. Vehicle AA is a vehicle with multiple axles, such as a light vehicle, a regular vehicle, or a large vehicle.

[0018] Hereinafter, the direction in which the lane LN extends (the ±X direction in FIG. 1) will be referred to as the "lane direction", and the expressway side of the lane LN in the lane direction (the +X direction in FIG. 1) will be referred to as the "downstream". Additionally, the general road side of the lane LN in the lane direction (the -X direction in FIG. 1) will be referred to as the "upstream". Furthermore, the width direction of the lane LN is referred to as the lane width direction (±Y direction in FIG. 1), and the height direction of the vehicle AA is referred to as the up-down direction (±Z direction in FIG. 1).

[0019] An island IS is laid on both sides of the lane LN, and at least some of the devices constituting the toll collection system 1 are installed thereon. For example, as shown in Fig. 1, a plurality of lanes LN are laid side by side at a toll booth TL. In this case, an island IS is laid between each of the adjacent lanes LN.

[0020] In this embodiment, the fee collection system 1 performs billing processing using a wireless communication system. The toll collection system 1 is a device that performs wireless communication processing (hereinafter simply referred to as "wireless communication") between vehicles AA attempting to pass through a toll gate TL from upstream to downstream of lane LN, and performs charging processing related to the type of vehicle AA, etc. For example, the toll collection system 1 may be part of a system that constitutes an electronic toll collection system (ETC: Electronic Toll Collection System (registered trademark), also called an "automated toll collection system").

[0021] As shown in FIG. 1, the toll collection system 1 includes a vehicle type discrimination device 2, a communication antenna 3, a lane server 4, and a failure prediction device 7.

[0022] As shown in FIG. 2, the vehicle type discrimination device 2, the communication antenna 3, the lane server 4, and the failure prediction device 7 are connected to each other via a dedicated line or a communication line. For example, the fee collection system 1 further includes a billing processing unit (not shown) that handles a series of billing processes, and outputs the acquired information, information on the determined billing amount, etc. to a higher-level device 9 installed in a remote location via a dedicated line or a communication line.

[0023] (Vehicle type identification device) The vehicle type discrimination device 2 is a device that discriminates the vehicle type of the vehicle AA traveling on the lane LN. The vehicle type discrimination device 2 is provided on the most upstream side (+X side) of the toll collection system 1 in the lane direction. In this embodiment, the vehicle type discrimination device 2 includes a first vehicle detector 10, a footboard 6, and a license plate reader 5. The vehicle type discrimination device 2 discriminates the vehicle type of the vehicle AA that has entered the lane LN based on various information such as the number of axles detected by the pedal 6 and the license plate information obtained by the license plate reader 5. The vehicle type discrimination device 2 further acquires other information, such as the vehicle length and height of the vehicle AA that has entered the lane LN, detected by various sensors of the toll collection system 1, to discriminate the vehicle type of the vehicle AA. For example, vehicle types are divided into five categories: "light cars / two-wheelers," "ordinary cars," "medium-sized cars," "large cars," and "extra-large cars." The vehicle type discrimination device 2 outputs the discriminated vehicle type to the failure prediction device 7 and the lane server 4 as vehicle information. For example, the toll collection system 1 charges the toll according to the type of vehicle AA identified by the vehicle type discrimination device 2.

[0024] (Configuration of the first vehicle detector) As shown in FIG. 1, the first vehicle detector 10 is configured to be able to detect a vehicle AA at an entry detection position XA in the lane direction. The first vehicle detector 10 is installed on an island IS facing the lane LN. When a plurality of lanes LN are arranged side by side, a first vehicle detector 10 is provided for each lane LN. In this case, the first vehicle detector 10 is installed on an island IS adjacent to each lane LN, facing each lane LN.

[0025] The first vehicle detector 10 includes a light emitter / receiver 21 . The light emitter / receiver 21 is disposed at an approach detection position XA. In this embodiment, the light emitter / receiver 21 constitutes a transmission type vehicle detector. The light emitter / receiver 21 emits and receives light over the entire vehicle height direction of the vehicle AA within a vertical plane parallel to the lane width direction at the entry detection position XA (within a plane perpendicular to the ±X directions). 1, the light projector / receiver 21 has a pair of a light projector 21A that projects light and a light receiver 21B that receives the light, with the lane LN in between. In this case, the light projector / receiver 21 receives the light projected by the light projector 21A when the vehicle AA is not present at the entry detection position XA, and does not receive the light projected by the light projector 21A when the vehicle AA is present at the entry detection position XA. This allows the first vehicle detector 10 to detect the vehicle AA entering the entry detection position XA. In other embodiments, the first vehicle detector 10 may be a reflective vehicle detector.

[0026] (Stepping board configuration) The tread 6 acquires a detection signal corresponding to the stepping on of the tires of the vehicle AA detected by the first vehicle detector 10. The tread 6 is configured to be capable of detecting the position and range where it is stepped on, and by combining this with the detection results of the first vehicle detector 10, the number of axles, tread width, and tire width of the vehicle AA can be identified. The step board 6 and the first vehicle detector 10 are located at the same position in the lane direction (±X direction). The pedal 6 outputs the number of axles of the vehicle AA to the failure prediction device 7.

[0027] (Configuration of license plate reader) The license plate reading device 5 acquires a photographed image including the license plate of the vehicle AA detected by the first vehicle detector 10. The license plate reading device 5 performs a predetermined image analysis process on the acquired captured image and obtains license plate information of the photographed license plate, i.e., the vehicle number and classification number engraved on the license plate, the size and color of the license plate, etc.

[0028] When multiple lanes LN are lined up, a license plate reading device 5 is provided for each lane LN. In this case, the license plate reading device 5 is installed on an island IS adjacent to each lane LN. In this case, each license plate reading device 5 outputs the acquired license plate information to a higher-level device 9. This allows the higher-level device 9 to accumulate license plate information acquired from a vehicle AA passing through a toll gate TL, regardless of the lane LN that the vehicle AA is traveling on and the time of day.

[0029] The license plate reading device 5 photographs the entry detection position XA in the lane direction. Furthermore, the license plate reading device 5 obtains a photographed image including the license plate of the vehicle AA at the timing when the first vehicle detector 10 detects the vehicle AA entering the entry detection position XA. By capturing an image in this manner, the license plate reading device 5 can obtain a captured image including the license plate of the vehicle AA while the passage of the vehicle AA is being detected by the first vehicle detector 10. This makes it easier for the license plate reading device 5 to distinguish between the vehicle AA preceding the vehicle AA and the vehicle AA following the vehicle AA, and obtain an image including the license plate of the vehicle AA.

[0030] (Communication antenna configuration) The communication antenna 3 is mounted above the lane LN by a gantry GN. The communication antenna 3 performs wireless communication processing with the vehicle-mounted device α of the vehicle AA. Specifically, the communication antenna 3 is configured to be able to transmit and receive electromagnetic waves of a predetermined frequency (for example, about 5.8 GHz), and performs wireless communication with the vehicle-mounted device α mounted on the arriving vehicle AA via the electromagnetic waves. When a plurality of lanes LN are arranged side by side, a communication antenna 3 is provided for each lane LN.

[0031] The communication antenna 3 performs wireless communication with the vehicle-mounted device α mounted on the vehicle AA at the timing when the first vehicle detector 10 detects the vehicle AA entering the entry detection position XA. By performing wireless communication in this manner, the communication antenna 3 can perform wireless communication with the in-vehicle device α mounted on the vehicle AA while the passage of the vehicle AA is being detected by the first vehicle detector 10. Therefore, the communication antenna 3 can easily distinguish between the vehicle AA preceding the vehicle AA and the vehicle AA following the vehicle AA, and perform wireless communication with the in-vehicle device α mounted on the vehicle AA.

[0032] (Lane Server Configuration) The lane server 4 is installed on the island IS. The lane server 4 acquires the identification information of the on-board device α mounted on the vehicle AA entering the toll gate TL. At that time, the lane server 4 acquires the identification information based on information acquired by wireless communication with the on-board device α via the communication antenna 3.

[0033] When a plurality of lanes LN are arranged, a lane server 4 may be provided for each lane LN. In this case, the lane server 4 is installed on an island IS adjacent to each lane LN, facing each lane LN. Each lane server 4 outputs the acquired identification information and the vehicle information from the vehicle type discrimination device 2 to the higher-level device 9 .

[0034] (Configuration of failure prediction device 7) As shown in FIG. 2, the failure prediction device 7 functionally comprises a first acquisition unit 71, a second acquisition unit 72, a decision unit 76, an integrating unit 77, and a judgment unit 78. The failure prediction device 7 further includes a record processing unit 75 and a notification unit 79.

[0035] The first acquisition unit 71 acquires, from the tread 6, the number of axles of the vehicle AA traveling on the lane LN. The second acquisition unit 72 acquires, from the vehicle type identification device 2, the “vehicle information” of the vehicle AA traveling on the lane LN. In the first embodiment, the vehicle information includes the "model" of the vehicle AA. In the first embodiment, the second acquisition unit 72 acquires, as the vehicle information, the vehicle type of the vehicle AA from the vehicle type discrimination device 2. Note that the vehicle information may include license plate information of the vehicle AA, or may include identification information of the vehicle AA.

[0036] The record processing unit 75 records the vehicle information of the vehicle AA traveling on the lane LN in association with the number of axles of the vehicle AA. The record processing unit 75 records the vehicle information and the number of axles acquired for the vehicle AA in the same lane LN and at the same timing in association with each other. The record processing unit 75 records the acquired license plate information, identification information, vehicle type, and total vehicle weight each time the vehicle AA arrives at the toll gate TL in association with each other.

[0037] The determination unit 76 determines the weighting coefficients from the acquired vehicle information. In the first embodiment, the determination unit 76 determines the weighting coefficient according to the vehicle model. For example, the determination unit 76 determines the weighting coefficient as follows: 0.8 if the vehicle type is a “light car / two-wheeler”, 1.0 if the vehicle type is a “standard vehicle”, 1.1 if the vehicle type is a “medium-sized vehicle”, 1.3 if the vehicle type is a “large vehicle”, and 1.5 if the vehicle type is an “extra-large vehicle”. The determination unit 76 may determine a predetermined value according to the total vehicle weight of each vehicle type as the weighting coefficient. For example, when the vehicle types are "light car / two-wheeler" and "ordinary car", the determination unit 76 may determine the weighting coefficient based on the median, average, mode, etc. of the total vehicle weight. Also, for example, when the vehicle types are "medium-sized car", "large car", and "extra-large car", the determination unit 76 may determine the weighting coefficient based on the value obtained by multiplying the maximum value of the total vehicle weight by 0.8.

[0038] The accumulating unit 77 calculates a correction value by multiplying the acquired number of axles of the vehicle by the weighting coefficient determined by the determining unit 76, and then accumulates the correction values ​​to calculate an accumulated value.

[0039] When the integrated value is equal to or greater than a predetermined judgment value, the judgment unit 78 judges that the tread 6 is in a deteriorated state. The deteriorated state indicates the degree to which the tread 6 has deteriorated from the start of use of the tread 6 until the judgment by the judgment unit, and although the deteriorated state has not yet reached a breakdown state in which the equipment cannot be used, it can be regarded as a state in which it is not desirable to continue using it as it is.

[0040] The notification unit 79 issues a warning when the determination unit 78 determines that the tread 6 is in a deteriorated state. The notification unit 79 also notifies the lane number on which the tread 6 determined to be in a deteriorated state is installed. 1, the failure prediction device 7 is connected to a monitoring device MN in an office R that manages a toll booth TL, and a notification unit 79 notifies the monitoring device MN that the step 6 is in a deteriorated state. At that time, when a notification that the step 6 is in a deteriorated state is confirmed by a display or the like on the monitoring device MN, an operator may replace the step 6.

[0041] (operation) The operation of the failure prediction device 7 of the first embodiment will be described with reference to FIG. The operation of the failure prediction device 7 corresponds to the failure prediction method of this embodiment.

[0042] The operation of the failure prediction device 7 is initiated, for example, when the first vehicle detector 10 detects a vehicle AA traveling on the lane LN and the vehicle type discrimination device 2 accordingly discriminates the vehicle type of the vehicle AA. First, as shown in FIG. 3, the first acquisition unit 71 acquires the number of axles of the vehicle AA traveling on the lane LN from the vehicle type identification device 2 (ST01: step of acquiring the number of axles). Following execution of ST01, the second acquisition unit 72 acquires the vehicle type of the vehicle AA from the vehicle type discrimination device 2 as vehicle information of the vehicle AA (ST02: step of acquiring vehicle information). Following execution of ST02, the determination unit 76 determines the weighting coefficient from the acquired vehicle information (ST03: step of determining weighting coefficient). In the first embodiment, the weighting coefficient is determined based on the vehicle type. Specifically, for example, the determination unit determines the weighting coefficient as follows: 0.8 if the vehicle type is a “light car / two-wheeler”, 1.0 if the vehicle type is a “standard vehicle”, 1.1 if the vehicle type is a “medium-sized vehicle”, 1.3 if the vehicle type is a “large vehicle”, and 1.5 if the vehicle type is an “extra-large vehicle”.

[0043] Following execution of ST03, the accumulation unit 77 calculates a correction value by multiplying the number of axles of the acquired vehicle AA by the determined weighting coefficient, and then accumulates the correction value to calculate an accumulated value (ST04: step of calculating an accumulated value). 4, the accumulator 77 calculates a correction value of 1.6 by multiplying the number of axles, 2, of vehicle AA, No. 1, by the weighting coefficient of 0.8 determined by the determiner 76 in accordance with "light vehicle / two-wheeler."The accumulator 77 calculates a correction value of 2.2 for vehicle No. 3, which traveled in the same lane number 1 as vehicle No. 1, and then calculates an accumulated value of 1.6 + 2.2 = 3.8. Each piece of information is recorded by being accumulated in the database DB in the order of arrival of each vehicle AA at the toll gate TL, that is, No. 1, No. 2, No. 3, . . . . Each lane number included in the database DB indicates the number of the lane LN in which the vehicle AA was traveling when each piece of information was acquired, among the multiple lanes LN of the toll gate TL.

[0044] Following execution of ST04, the determination unit 78 determines that the tread 6 is in a deteriorated state if the integrated value is equal to or greater than a predetermined determination value (ST05: step of determining the deteriorated state). Specifically, for example, the judgment value is 20,000 in the database DB shown in Fig. 5. Note that the judgment value may be a value other than 20,000. If the integrated value is not equal to or greater than the predetermined judgment value (ST05: No), the process returns to ST01 to obtain a new number of axes. On the other hand, if the integrated value is equal to or greater than the predetermined judgment value (ST05: Yes), the notification unit 79 issues a warning to the effect that the tread 6 is in a deteriorated state.

[0045] The operation of the failure prediction device 7 according to the first embodiment described above (FIG. 5) is an example, and the order of the processing steps and the judgment criteria can be changed within a range in which the same purpose can be achieved. For example, the steps related to the first acquisition unit and the second acquisition unit (step ST01 and step ST02, etc.) can be interchanged with each other.

[0046] (Action and Effects) According to one example of this embodiment, the failure prediction device 7 accumulates a correction value obtained by multiplying the number of axles of the vehicle AA by a weighting coefficient corresponding to the vehicle information, and can determine whether the tread 6 is in a deteriorated state based on this accumulated value. As a result, the failure prediction device 7 of this embodiment can include the number of passing axles and vehicle weight as indicators of the deterioration state, thereby improving the accuracy of failure prediction compared to devices that use only the number of passing axles as an indicator, and making it possible to more accurately predict when the tread 6 will fail.

[0047] Second Embodiment A failure prediction device 7 according to a second embodiment will be described with reference to the drawings. The configuration of the failure prediction device 7 of this embodiment is similar to that of the first embodiment, except for the points described below.

[0048] As shown in FIG. 6, the step 6 provided in the vehicle type discrimination device 2 further includes an axle load meter 11. The axle load meter 11 measures the axle load of the vehicle AA detected by the first vehicle detector 10, and acquires axle load information. The axle load meter 11 acquires axle load information for each axle of a vehicle AA having multiple axles.

[0049] In the second embodiment, the "vehicle information" of the vehicle AA acquired by the second acquisition unit includes "axle load information" of the vehicle AA. In the second embodiment, the second acquisition unit 72 acquires axle weight information of the vehicle AA from the vehicle type discrimination device 2 as the vehicle information. In the second embodiment, the determination unit 76 determines the weighting coefficient based on the axle weight, which is the axle weight information.

[0050] The lane server 4 acquires axle weight information of the vehicle AA that has entered the lane LN, which is detected by the axle weight meter 11 on the step 6 provided in the vehicle type identification device 2. Each lane server 4 outputs the axle weight information acquired from the vehicle type identification device 2 to the higher-level device 9.

[0051] The record processing unit 75 records the acquired license plate information, identification information, vehicle type, total vehicle weight, and axle weight information in association with each other each time a vehicle AA arrives at a toll gate TL.

[0052] The determination unit 76 determines the weighting coefficient based on the axle weight, which is the axle weight information. For example, the determination unit 76 determines the weighting coefficient as follows: 0.8 when the axle load is "less than 1000 kg", 1.0 when the axle load is "1000 kg or more and less than 2000 kg", 1.1 when the axle load is "2000 kg or more and less than 3000 kg", 1.3 when the axle load is "3000 kg or more and less than 4000 kg", and 1.5 when the axle load is "4000 kg or more". For example, the determination unit 76 may obtain the total vehicle weight by calculating the sum of the axle weights of the vehicles AA based on the axle weight information of the vehicles AA, and determine a predetermined value according to the total vehicle weight as the weighting coefficient.

[0053] (operation) The operation of the failure prediction device 7 of the second embodiment will be described. The operation of the second embodiment is similar to that of the first embodiment shown in FIG. 3 as follows.

[0054] The operation of the failure prediction device 7 is initiated, for example, when the first vehicle detector 10 detects a vehicle AA traveling on the lane LN and the vehicle type discrimination device 2 accordingly discriminates the vehicle type of the vehicle AA. First, as shown in FIG. 3, the first acquisition unit 71 acquires the number of axles of the vehicle AA traveling on the lane LN from the vehicle type identification device 2 (ST01: step of acquiring the number of axles). Following execution of ST01, the second acquisition unit 72 acquires vehicle information of the vehicle AA from the vehicle type discrimination device 2 (ST02: step of acquiring vehicle information). In the second embodiment, the vehicle information of the vehicle AA is the "axle weight" of the vehicle AA. Following execution of ST02, the determination unit 76 determines a weighting coefficient from the acquired vehicle information (ST03: step of determining weighting coefficient). In the second embodiment, the weighting coefficient is determined based on the axle weight. Specifically, for example, the determination unit 76 determines the weighting coefficient as follows: 0.8 when the axle weight is "less than 1000 kg", 1.0 when the axle weight is "1000 kg or more and less than 2000 kg", 1.1 when the axle weight is "2000 kg or more and less than 3000 kg", 1.3 when the axle weight is "3000 kg or more and less than 4000 kg", and 1.5 when the axle weight is "4000 kg or more".

[0055] Following execution of ST03, the accumulation unit 77 calculates a correction value by multiplying the number of axles of the acquired vehicle AA by the determined weighting coefficient, and then accumulates the correction value to calculate an accumulated value (ST04: step of calculating an accumulated value). Specifically, for example, if vehicle No. 1 AA has two axles and the axle weights are 2100 kg and 1900 kg, the calculation unit 77 calculates a correction value 1.1 by multiplying the one axle number of 1 by the weighting coefficient 1.1 determined by the determination unit 76 in accordance with "2000 kg or more and less than 3000 kg". Furthermore, the calculation unit 77 calculates a correction value 1.0 by multiplying the other axle number of 1 by the weighting coefficient 1.0 determined by the determination unit 76 in accordance with "1000 kg or more and less than 2000 kg", and then calculates an accumulated value of 1.1 + 1.0 = 2.1.

[0056] Following execution of ST04, if the integrated value is equal to or greater than a predetermined judgment value, the judgment unit 78 judges whether or not the tread 6 is in a deteriorated state (ST05: step of judging a deteriorated state). If the integrated value is not equal to or greater than the predetermined judgment value ("No" in ST05), the process returns to execution of ST01 and a new number of axes is obtained. On the other hand, if the integrated value is equal to or greater than the predetermined judgment value ("Yes" in ST05), the notification unit 79 issues a warning to the effect that the tread 6 is in a deteriorated state.

[0057] (Action and Effects) According to this embodiment, the failure prediction device 7 has the same functions and effects as those of the first embodiment.

[0058] (Modification) For example, the following modified examples are possible.

[0059] <Variation 1> In the above-described exemplary embodiments, weighting is performed based on the vehicle information of the vehicle. However, weighting may also be performed based on the outside air temperature information of the pedal 6.

[0060] A failure prediction device 7 according to the first modification will be described with reference to the drawings (FIGS. 7 and 8). The configuration of the failure prediction device 7 of the first modification is similar to that of the first or second embodiment, except for the points described below.

[0061] 7, the first vehicle detector 10 includes a temperature sensor 12 that measures the outside air temperature in the vicinity of the tread 6. Note that the temperature sensor 12 may be another temperature sensor that measures the outside air temperature in the vicinity of the tread 6 (for example, a temperature sensor installed at the toll gate TL).

[0062] The vehicle type discrimination device 2 acquires outside air temperature information measured by the temperature sensor 12 . The vehicle type discrimination device 2 outputs the acquired outside air temperature information to the lane server 4.

[0063] As shown in FIG. 8, the failure prediction device 7 includes a third acquisition unit 73. The third acquisition unit 73 acquires outside air temperature information relating to the outside air temperature in the vicinity of the tread 6 from the lane server 4.

[0064] Each time each vehicle AA arrives at the toll gate TL, the record processing unit 75 records the acquired license plate information, identification information, vehicle type, total vehicle weight, axle load information, and outside air temperature information in association with each other.

[0065] The determination unit 76 determines a weighting coefficient based on the vehicle information, and also determines a coefficient based on the acquired outside air temperature information. For example, the determination unit 76 may determine the coefficient based on the outside air temperature information as 1.0 when the outside air temperature is "less than 20° C." and 1.05 when the outside air temperature is "20° C. or higher." For example, the determination unit 76 may determine the weighting coefficient by multiplying the weighting coefficient based on the vehicle information by a coefficient based on the outside air temperature information.

[0066] For example, the integrating unit 77 may multiply the correction value calculated based on the weighting coefficient determined by the determining unit 76 by a coefficient based on the outside air temperature information determined by the determining unit 76 to further weight the correction value, and then integrate the result.

[0067] (operation) As shown in FIG. 9, the operation of the failure prediction device 7 of the first modification is similar to that of the first or second embodiment, except for the points described below.

[0068] The operation of the failure prediction device 7 is initiated, for example, when the first vehicle detector 10 detects a vehicle AA traveling on the lane LN and the vehicle type discrimination device 2 accordingly discriminates the vehicle type of the vehicle AA. Following execution of ST02, the third acquisition unit 73 acquires outside temperature information on the outside temperature of the step 6 from the lane server 4 (ST02A: step of acquiring outside temperature information). Following execution of ST02A, the determination unit 76 determines a weighting coefficient from the acquired vehicle information, and also determines a coefficient based on the acquired outside air temperature information (ST03: step of determining a weighting coefficient). Specifically, for example, the determination unit determines the coefficient based on the outside air temperature information to be 1.0 if the outside air temperature is "less than 20° C." and 1.05 if the outside air temperature is "20° C. or higher." Following execution of ST03, the accumulation unit 77 calculates a correction value by multiplying the number of axles of the acquired vehicle AA by the determined weighting coefficient, and then multiplies the calculated correction value by a coefficient based on the outside air temperature information to further weight it before accumulating it.

[0069] The operation of the failure prediction device 7 according to the above-mentioned modified example 1 (FIG. 9) is an example, and the order of the processing steps and the judgment criteria can be changed within a range in which the same purpose can be achieved. For example, the steps related to the first acquisition unit, the second acquisition unit, and the third acquisition unit (step ST01, step ST02, step ST02A, etc.) can be interchanged with each other.

[0070] (Action and Effects) According to one example of this modified example, the failure prediction device 7 calculates a correction value by multiplying the number of axles of vehicle AA by a weighting coefficient corresponding to the vehicle information, and then multiplies the calculated correction value by a coefficient based on the outside air temperature information to further weight it, and then accumulates the result, and it is possible to determine whether or not the tread 6 is in a deteriorated state based on this accumulated value. As a result, the failure prediction device 7 of this modified example includes the number of passing axles, vehicle weight, and outside air temperature as indicators of the deterioration state, and has higher failure prediction accuracy than a device that uses only the number of passing axles as an indicator, and can more accurately predict when the tread 6 will fail.

[0071] <Variation 2> In the above-described exemplary embodiments, weighting is performed based on the vehicle information of the vehicle. However, weighting may be further performed based on speed-related information related to the speed of the vehicle.

[0072] The failure prediction device 7 according to the second modification will be described with reference to the drawings. The configuration of the failure prediction device 7 of the second modification is similar to that of the first or second embodiment, except for the points described below.

[0073] As shown in FIG. 7, the vehicle type discrimination device 2 further includes a second vehicle detector 13.

[0074] (Configuration of the second vehicle detector) The second vehicle detector is installed at a passage detection position XB downstream from the entry detection position XA of the lane LN by a predetermined distance ΔDs in the lane direction (±X direction). The second vehicle detector 13 according to the second modification has the same device configuration as the first vehicle detector 10. The second vehicle detector 13 detects the speed (passing speed) of the vehicle AA within a predetermined distance ΔDs by combining with the vehicle detection signal of the first vehicle detector 10. The lane server 4 calculates the speed of the vehicle AA using the time difference between the vehicle detection signal from the second vehicle detector and the vehicle detection signal from the first vehicle detector 10, and the known distance ΔDs. For example, if ΔDs is 10 m, the time of the vehicle detection signal from the first vehicle detector is 14:30:00, and the time of the vehicle detection signal from the second vehicle detector is 14:30:02, the lane saver 4 calculates 10 m / 2 s = 5 m / s = 18 km / h. Each lane server 4 outputs speed-related information related to the speed of each vehicle AA to the higher-level device 9.

[0075] As shown in FIG. 10, the failure prediction device 7 includes a fourth acquisition unit 74. The fourth acquisition unit 74 acquires speed-related information related to speed from the lane server 4.

[0076] Each time each vehicle AA arrives at the toll gate TL, the record processing unit 75 records the acquired license plate information, identification information, vehicle type, total vehicle weight, axle load information, and speed-related information in association with each other.

[0077] The determination unit 76 determines a weighting coefficient based on the vehicle information, and also determines a coefficient based on the acquired speed-related information. For example, the determination unit determines a coefficient based on the speed-related information as 1.0 when the speed is "less than 20 km / h", 1.05 when the speed is "20 km / h or more and less than 60 km / h", and 1.1 when the speed is "60 km / h or more".

[0078] For example, the accumulating unit 77 may multiply the correction value calculated based on the weighting coefficient determined by the determining unit 76 by a coefficient based on the speed-related information determined by the determining unit 76 to further weight the correction value, and then accumulate the result.

[0079] (operation) As shown in FIG. 11, the operation of the failure prediction device 7 of the second modification is similar to that of the first or second embodiment, except for the points described below.

[0080] The operation of the failure prediction device 7 is initiated, for example, when the first vehicle detector 10 detects a vehicle AA traveling on the lane LN and the vehicle type discrimination device 2 accordingly discriminates the vehicle type of the vehicle AA. Following execution of ST02, the fourth acquisition unit 74 acquires speed-related information from the lane server 4 (ST02B: a step of acquiring speed-related information). The lane server 4 calculates the speed of the vehicle AA using the time difference between the vehicle detection signal from the second vehicle detector and the vehicle detection signal from the first vehicle detector 10, and the known distance ΔDs. Specifically, for example, if ΔDs is 10 m, the time of the vehicle detection signal from the first vehicle detector is 14:30:00, and the time of the vehicle detection signal from the second vehicle detector is 14:30:02, the lane saver 4 calculates 10 m / 2 s = 5 m / s = 18 km / h.

[0081] Following execution of ST02B, the determination unit 76 determines a weighting coefficient from the acquired vehicle information, and also determines a coefficient based on the acquired speed-related information (ST03: step of determining a weighting coefficient). Specifically, for example, the determination unit 76 determines the coefficient based on the speed-related information to be 1.0 when the speed is “less than 20 km / h”, 1.05 when the speed is “20 km / h or more and less than 60 km / h”, and 1.1 when the speed is “60 km / h or more”. When the above-mentioned speed is 18 km / h, the determination unit 76 determines the coefficient based on the speed-related information to be 1.0, assuming that the speed is “less than 20 km / h”. Following execution of ST03, the accumulation unit 77 calculates a correction value by multiplying the number of axles of the acquired vehicle AA by the determined weighting coefficient, and then multiplies the calculated correction value by a coefficient based on the speed-related information to further weight it before accumulating it.

[0082] The operation of the failure prediction device 7 according to the above-mentioned modified example 2 (FIG. 11) is an example, and the order of the processing steps and the judgment criteria can be changed within a range in which the same purpose can be achieved. For example, the steps related to the first acquisition unit, the second acquisition unit, and the fourth acquisition unit (step ST01, step ST02, step ST02B, etc.) can be interchanged with each other.

[0083] (Action and Effects) According to one example of this modified example, the failure prediction device 7 calculates a correction value by multiplying the number of axles of vehicle AA by a weighting coefficient corresponding to the vehicle weight, and then multiplies the calculated correction value by a coefficient based on the speed-related information to further weight it, and then accumulates the result, and it is possible to determine whether or not the tread 6 is in a deteriorated state based on this accumulated value. As a result, the failure prediction device 7 of this modified example includes the number of passing axles, vehicle weight, and speed as indicators of the deterioration state, and has higher failure prediction accuracy than devices that use only the number of passing axles as an indicator, and can more accurately predict when the tread 6 will fail.

[0084] <Other Modifications> In the above-described embodiments, the vehicle type is identified by the vehicle type discriminator 2, but the vehicle type may be identified in any manner as long as the vehicle type can be identified. For example, the lane server 4 may identify the vehicle type.

[0085] In the above-described embodiments, the failure prediction device 7 is provided in an electronic toll collection system, but it may be provided in any system. As a modified example, the failure prediction device 7 may be provided in a system equipped with an automatic toll collection machine. As another modified example, the failure prediction device 7 may be provided at a manned toll booth where a toll collector collects tolls.

[0086] In one example of each of the above-described embodiments, the failure prediction device 7 is provided separately from the footboard 6 and the lane server 4, but the failure prediction device 7 may be provided in any manner. As a modified example, the failure prediction device 7 may be provided in the footboard 6, and as yet another modified example, the failure prediction device 7 may be provided in the lane server 4.

[0087] In one example of each of the above-described embodiments, the failure prediction device 7 is applied to a separate lane type toll collection system 1 installed on an island IS, but as a variant, the failure prediction device 7 may be applied to a free-flow type toll collection system.

[0088] <Computer hardware configuration> In each of the above-mentioned embodiments, a program for realizing various functions of the failure prediction device 7 is recorded in a computer-readable recording medium, and the program recorded in the recording medium is read into a computer system such as a microcomputer and executed to perform various processes. Here, the various processes of the CPU of the computer system are stored in the form of a program in a computer-readable recording medium, and the computer reads and executes the program to perform the various processes. In addition, the computer-readable recording medium refers to a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, a semiconductor memory, etc. In addition, the computer program may be distributed to a computer via a communication line, and the computer that receives the program may execute the program.

[0089] In each of the above-described embodiments, an example of the hardware configuration of a computer that executes a program for implementing various functions of the failure prediction device 7 will be described.

[0090] As shown in FIG. 12, the computer 80 provided in the failure prediction device 7 includes a CPU 801, a memory 802, a storage / playback device 803, an input output interface (hereinafter referred to as "IO I / F") 804, and a communication interface (hereinafter referred to as "communication I / F") 805.

[0091] The memory 802 is a medium such as a Random Access Memory (hereinafter referred to as “RAM”) that temporarily stores data used by the programs executed by the failure prediction device 7. The storage / playback device 803 is a device for storing data, etc. in external media such as CD-ROM, DVD, and flash memory, and for playing back data, etc. from external media. The IO I / F 804 is an interface for inputting and outputting information between the failure prediction device 7 and other devices. The communication I / F 805 is an interface for communicating with other devices via a communication line such as the Internet or a dedicated communication line.

[0092] <Other embodiments> Although several embodiments of the present disclosure have been described above, these embodiments are presented as examples and are not intended to limit the scope of the present disclosure. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the gist of the present disclosure. These embodiments and their modifications are included in the scope and gist of the disclosure.

[0093] <Additional Notes> The failure prediction device 7, the toll collection system, the failure prediction method, and the program described in each embodiment can be understood, for example, as follows.

[0094] (1) The failure prediction device 7 of the first embodiment includes a first acquisition unit 71 that acquires the number of axles of vehicle AA traveling on a tread 6 of a lane, a second acquisition unit 72 that acquires vehicle information of vehicle AA, a determination unit 76 that determines a weighting coefficient from the acquired vehicle information, an accumulation unit 77 that calculates a correction value by multiplying the acquired number of axles of vehicle AA by the determined weighting coefficient and then accumulates the correction value to calculate an accumulated value, and a determination unit 78 that determines that the tread 6 is in a deteriorated state if the accumulated value is equal to or greater than a predetermined determination value.

[0095] According to this embodiment, the failure prediction device 7 accumulates a correction value obtained by multiplying the number of axles of the vehicle AA by a weighting coefficient according to the vehicle weight, and can determine whether the step 6 is in a deteriorated state based on this accumulated value. For this reason, the failure prediction device 7 includes the number of passing axles and vehicle weight as indicators of the deterioration state, and has higher failure prediction accuracy than devices that use only the number of passing axles as an indicator, and can more accurately predict when the tread 6 will fail. Therefore, the failure prediction device 7 can predict failure of the step 6.

[0096] (2) The failure prediction device 7 according to the second aspect is the failure prediction device 7 of (1) further including a notification unit 79 that issues a warning when the step 6 is determined to be in a deteriorated state.

[0097] According to this embodiment, the failure prediction device 7 can issue a warning when it is determined that the step 6 is in a deteriorated state.

[0098] (3) A failure prediction device 7 according to a third aspect is the failure prediction device 7 of (1) or (2), in which the vehicle information is the vehicle model.

[0099] According to the present embodiment, the failure prediction device 7 includes the number of passing axles and the vehicle weight according to the vehicle type as indicators of the deterioration state, and has higher failure prediction accuracy than a device that uses only the number of passing axles as an indicator, and can more accurately predict when the tread 6 will fail.

[0100] (4) The failure prediction device 7 according to a fourth aspect is the failure prediction device 7 according to (3), in which the weighting coefficient is a predetermined value according to the total vehicle weight of each vehicle type.

[0101] According to the present embodiment, the failure prediction device 7 includes the number of passing axles and the total vehicle weight as indicators of the deterioration state, and has higher failure prediction accuracy than a device that uses only the number of passing axles as an indicator, and can more accurately predict when the tread 6 will fail.

[0102] (5) A failure prediction device 7 according to a fifth aspect is the failure prediction device 7 described in (1) or (2), in which the vehicle information is an axle load measured by an axle load meter 11 provided on the tread 6.

[0103] According to the present embodiment, the failure prediction device 7 includes the number of passing axles and axle load as indicators of the deterioration state, and has higher failure prediction accuracy than a device that uses only the number of passing axles as an indicator, and can more accurately predict when the tread 6 will fail.

[0104] (6) A sixth aspect of the failure prediction device 7 is a failure prediction device 7 of any of (1) to (5), further including a third acquisition unit that acquires outside air temperature information of the step 6, and the integration unit 77 multiplies the calculated correction value by a coefficient based on the outside air temperature information, further weights the value, and then integrates the value.

[0105] According to the present embodiment, the failure prediction device 7 includes the number of passing axles, vehicle weight, and outside air temperature information as indicators of the deterioration state, and has higher failure prediction accuracy than a device that uses only the number of passing axles as an indicator, and can more accurately predict when the tread 6 will fail.

[0106] (7) The seventh aspect of the failure prediction device 7 is a failure prediction device 7 described in any of (1) to (6), further including a fourth acquisition unit 74 that acquires speed-related information related to the speed of the vehicle AA, and the integration unit 77 multiplies the calculated correction value by a coefficient based on the speed-related information to further weight it and then integrates it.

[0107] According to the present embodiment, the failure prediction device 7 includes the number of passing axles, vehicle weight, and speed as indicators of the deterioration state, and has higher failure prediction accuracy than a device that uses only the number of passing axles as an indicator, and can more accurately predict when the tread 6 will fail.

[0108] (8) The toll collection system 1 according to the eighth aspect includes a failure prediction device 7 according to any one of (1) to (7) and a footboard 6.

[0109] According to this embodiment, the toll collection system 1 accumulates a correction value obtained by multiplying the number of axles of the vehicle AA by a weighting coefficient according to the vehicle weight, and can determine whether the tread 6 is in a deteriorated state based on this accumulated value. For this reason, the toll collection system 1 includes the number of passing axles and vehicle weight as indicators of the deterioration state, and has a higher accuracy in predicting failures than devices that use only the number of passing axles as an indicator, and can more accurately predict when the tread 6 will fail. Therefore, the toll collection system 1 can predict failure of the pedal 6.

[0110] (9) A failure prediction method according to a ninth aspect includes the steps of acquiring the number of axles of vehicle AA traveling on a tread 6 of a lane, acquiring vehicle information about vehicle AA, determining a weighting coefficient from the acquired vehicle information, calculating a correction value by multiplying the acquired number of axles of the vehicle by the determined weighting coefficient, and then accumulating the correction value to calculate an accumulated value, and determining that the tread 6 is in a deteriorated state if the accumulated value is equal to or greater than a predetermined judgment value.

[0111] According to this embodiment, the failure prediction method accumulates a correction value obtained by multiplying the number of axles of the vehicle AA by a weighting coefficient according to the vehicle weight, and can determine whether the step 6 is in a deteriorated state based on this accumulated value. Therefore, the failure prediction method includes the number of passing axles and vehicle weight as indicators of the deterioration state, and has higher failure prediction accuracy than a device that uses only the number of passing axles as an indicator, and can more accurately predict when the tread 6 will fail. Therefore, the failure prediction method can predict failure of the step 6.

[0112] (10) The program relating to the tenth aspect executes the steps of acquiring the number of axles of vehicle AA traveling on the tread 6 of the lane, acquiring vehicle information for vehicle AA, determining a weighting coefficient from the acquired vehicle information, calculating a correction value by multiplying the acquired number of axles of the vehicle by the determined weighting coefficient, and then accumulating the correction value to calculate an accumulated value, and determining that the tread 6 is in a deteriorated state if the accumulated value is equal to or greater than a predetermined judgment value.

[0113] According to this embodiment, the failure prediction device 7 accumulates a correction value obtained by multiplying the number of axles of the vehicle AA by a weighting coefficient according to the vehicle weight, and can determine whether the step 6 is in a deteriorated state based on this accumulated value. For this reason, the failure prediction device 7 includes the number of passing axles and vehicle weight as indicators of the deterioration state, and has higher failure prediction accuracy than devices that use only the number of passing axles as an indicator, and can more accurately predict when the tread 6 will fail. Therefore, the failure prediction device 7 can predict failure of the step 6. [Explanation of symbols]

[0114] 1 Toll collection system 2 Vehicle type identification device 3. Communication Antenna 4 Lane Server 5. License plate reader 6 Treadboard 7. Failure prediction device 9. Upper device 10 First vehicle detector 11 Axle load meter 12 Temperature Sensor 13 Second vehicle detector 21 Emitter / receiver 21A Floodlight 21B Receiver 71 First Acquisition Department 72 Second Acquisition Department 73 Third Acquisition Department 74 Fourth Acquisition Department 75 Recording Processing Unit 76 Decision Section 77 Accumulator 78 Judgment section 79 Notification Department 80 Computer 801 CPU 792 Memory 793 Storage / playback device 794 IO interface 795 Communication I / F AA Vehicles DB Database GN Gantry IS Island LN Lane MN monitoring device R office TL Tollgate XA Entry Detection Position XB Passage detection position α Onboard equipment

Claims

1. A first acquisition unit that acquires the number of axles of a vehicle traveling on a lane tread; A second acquisition unit that acquires vehicle information of the vehicle; A determination unit that determines a weighting coefficient from the acquired vehicle information; an integrating unit that calculates a correction value by multiplying the acquired number of axles of the vehicle by the determined weighting coefficient, and then integrates the correction value to calculate an integrated value; A determination unit that determines that the tread is in a deteriorated state when the integrated value is equal to or greater than a predetermined determination value; A failure prediction device comprising:

2. The failure prediction device according to claim 1 , further comprising a notification unit that issues a warning when the footboard is determined to be in a deteriorated state.

3. The failure prediction device according to claim 1 , wherein the vehicle information is a vehicle model of the vehicle.

4. 4. The failure prediction device according to claim 3, wherein the weighting coefficient is a predetermined value according to the total vehicle weight of each vehicle type.

5. The failure prediction device according to claim 1 , wherein the vehicle information is an axle load measured by an axle load meter provided on the footboard.

6. Further, a third acquisition unit is provided for acquiring outside air temperature information of the step board, The failure prediction device according to claim 1 , wherein the integrating unit multiplies the calculated correction value by a coefficient based on the outside air temperature information to further weight the correction value and then integrates the weighted correction value.

7. A fourth acquisition unit that acquires speed-related information related to a speed of the vehicle, The failure prediction device according to claim 1 , wherein the integrating unit multiplies the calculated correction value by a coefficient based on the speed-related information to further weight the correction value and then integrates the weighted correction value.

8. The failure prediction device according to any one of claims 1 to 7, The footboard; A toll collection system comprising:

9. Obtaining the number of axles of a vehicle traveling on a lane tread; obtaining vehicle information of the vehicle; determining weighting coefficients from the acquired vehicle information; calculating a correction value by multiplying the acquired number of axles of the vehicle by the determined weighting coefficient, and then accumulating the correction value to calculate an integrated value; determining that the step is in a deteriorated state when the integrated value is equal to or greater than a predetermined determination value; A failure prediction method comprising:

10. The computer of the failure prediction device Obtaining the number of axles of a vehicle traveling on a lane tread; obtaining vehicle information of the vehicle; determining weighting coefficients from the acquired vehicle information; calculating a correction value by multiplying the acquired number of axles of the vehicle by the determined weighting coefficient, and then accumulating the correction value to calculate an integrated value; determining that the step is in a deteriorated state when the integrated value is equal to or greater than a predetermined determination value; A program that executes the following.

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