Traffic management device, traffic management method and program

Abstract

A traffic management device (102) comprises an estimation unit (103) and a determination unit (104). The estimation unit (103) estimates the emission amounts of vehicles. The determination unit (104) uses said estimated emission amount to determine a toll for allowing passage of a subject vehicle through a prescribed road section.

Description

[Technical Field]

[0001] The present invention relates to a traffic management device. , intersection Management method and program Regarding. [Background technology]

[0002] Patent Document 1 describes a technique for calculating the amount of carbon dioxide emitted in excess due to traffic congestion (carbon dioxide emission amount) Z2.

[0003] Patent Document 2 states that the CO2 emission estimation model is constructed based on the following concept.

[0004] A. For the census section, traffic volume is estimated for all time periods of 365 days, for a total of 8,760 hours, and the QV formula is used to estimate vehicle-kilometers by travel speed for each time period. Note that the QV formula is set based on road traffic census data by road type, number of lanes, urban / non-urban area, signal density, and congested / non-congested area.

[0005] a. CO2 emissions are output via emission intensity by travel speed.

[0006] (c) For municipal roads, travel speeds are set at 18 km / h in urban areas and 28 km / h in non-urban areas (the average travel speed during congestion on general prefectural roads), regardless of the degree of congestion, and CO2 emissions are estimated.

[0007] Patent Document 3 describes a technology for estimating traffic volume.

[0008] Non-Patent Document 1 describes a technology for estimating carbon dioxide emissions. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] International Publication No. 2020 / 065972 [Patent Document 2] Japanese Patent Application Laid-Open No. 2007-328769 [Patent Document 3] Japanese Patent Application Publication No. 2018-055455 [Non-patent literature]

[0010] [Non-Patent Document 1] Tetsuhiro Ishizaka and three others, "Organization of Estimation Methods for CO2 Emissions from Automobile Traffic and Their Application," [online], [Retrieved December 20, 2021], Internet<URL:http: / / library.jsce.or.jp / jsce / open / 00039 / 200906_no39 / pdf / 35.pdf> Summary of the Invention [Problem to be solved by the invention]

[0011] However, even if it is possible to estimate carbon dioxide emissions using the techniques described in Patent Documents 1 and 2 and Non-Patent Document 1, it is difficult to reduce the amount of exhaust gas. Even if it is possible to estimate traffic volume using the technique described in Patent Document 3, it is difficult to reduce the amount of exhaust gas.

[0012] In view of the above-mentioned problems, an example of an object of the present invention is to provide a traffic management device, a traffic management system, a traffic management method, and a recording medium that solve the problem of reducing the amount of exhaust gas. [Means for solving the problem]

[0013] According to one aspect of the present invention, an estimation means for estimating an amount of exhaust gas from a vehicle; and a determination means for determining a toll for a target vehicle to travel a predetermined road section using the estimated exhaust gas amount. 、 The estimation means estimates the amount of exhaust gas in the road section from one or more vehicles that pass through the road section before the target vehicle. R A traffic management device is provided. According to one aspect of the present invention, an estimation means for estimating an amount of exhaust gas from a vehicle; a determination means for determining a toll for a target vehicle to travel through a predetermined road section using the estimated exhaust gas amount; The determination means determines the toll for the target vehicle by further using information about carbon dioxide absorbers in the vicinity of the road section. A traffic management device is provided. According to one aspect of the present invention, an estimation means for estimating an amount of exhaust gas from a vehicle; a determination means for determining a toll for a target vehicle to travel through a predetermined road section using the estimated exhaust gas amount; the estimation means estimates an amount of exhaust gas from the target vehicle in the road section; The determining means determines the toll for the target vehicle in another road section that the target vehicle will travel after traveling through the road section in which the amount of exhaust gas from the target vehicle has been estimated. A traffic management device is provided.

[0015] According to one aspect of the present invention, The computer Estimate the amount of vehicle exhaust gas, and determining a toll for a target vehicle to travel a predetermined road section using the estimated amount of exhaust gas. fruit, Estimating the amount of exhaust gas from the vehicle includes estimating the amount of exhaust gas from one or more vehicles traveling on the road section before the target vehicle. nothing A traffic management method is provided. According to one aspect of the present invention, The computer Estimate the amount of vehicle exhaust gas, determining a toll for a target vehicle to travel a predetermined road section using the estimated exhaust gas amount; Determining the toll includes determining the toll for the target vehicle further using information about carbon dioxide absorbers in the vicinity of the road section. A traffic management method is provided.

[0016] According to one aspect of the present invention, On the computer, Estimate the amount of vehicle exhaust gas, determining a toll for a target vehicle to travel a predetermined road section using the estimated exhaust gas amount; 、 Estimating the amount of exhaust gas from the vehicle includes estimating the amount of exhaust gas from one or more vehicles traveling on the road section before the target vehicle. Program Hmm. [Effects of the Invention]

[0017] According to the present invention, it is possible to reduce the amount of exhaust gas. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a diagram showing an overview of a traffic management system according to a first embodiment of the present invention. [Figure 2] FIG. 1 is a diagram illustrating an overview of a traffic management process according to a first embodiment of the present invention. [Figure 3] FIG. 1 is a view of road R as seen from above at time T1. [Figure 4] 1 is a diagram illustrating an example of a physical configuration of a generating device according to a first embodiment of the present invention. [Figure 5] 1 is a diagram illustrating an example of a physical configuration of a traffic management device according to a first embodiment of the present invention. [Figure 6] 5 is a flowchart showing an example of a generation process according to the first embodiment of the present invention. [Figure 7] 4 is a flowchart showing a detailed example of a traffic management process according to the first embodiment. [Figure 8] 4 is a flowchart showing a detailed example of an estimation process according to the first embodiment. [Figure 9] 10 is a flowchart showing a detailed example of an analysis process according to the first embodiment. [Figure 10] 10A and 10B are diagrams showing an example of images included in image information PI_i at times T1 and T2, and an example of the results of an analysis performed by the analysis model according to the first embodiment using the image information PI_i at times T1 and T2 as input data. [Figure 11] FIG. 2 is a diagram showing an example of an analysis result according to the first embodiment. [Figure 12] FIG. 4 is a diagram showing an example of an estimation result for each region according to the first embodiment. [Figure 13] FIG. 3 is a diagram showing an example of an estimation result of an entire road according to the first embodiment. [Figure 14] FIG. 10 is a diagram illustrating an example of the configuration of a traffic management system according to a second embodiment of the present invention. [Figure 15] 10 is a flowchart showing an example of a traffic management process according to a second embodiment of the present invention. [Figure 16] 10 is a flowchart showing a detailed example of an estimation process according to the second embodiment. [Figure 17] 10 is a flowchart showing a detailed example of an analysis process according to the second embodiment. [Figure 18] 10A and 10B are diagrams showing an example of images included in image information PI_i at times T1 and T2, and an example of the results of an analysis performed by an analysis model according to the second embodiment using the image information PI_i at times T1 and T2 as input data. [Figure 19] FIG. 10 is a diagram showing an example of vehicle model data according to the second embodiment. [Figure 20] FIG. 10 is a diagram showing an example of an analysis result according to the second embodiment. [Figure 21] FIG. 10 is a diagram illustrating an example of the configuration of a traffic management system according to a third embodiment of the present invention. [Figure 22] FIG. 10 is a diagram illustrating an example of a functional configuration of a traffic management device according to a third embodiment of the present invention. [Figure 23] 11 is a flowchart showing an example of a traffic management process according to a third embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0019] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In all the drawings, like components are designated by like reference numerals, and the description thereof will be omitted as appropriate.

[0020] <<Embodiment 1>> (Overview of the traffic management system 100) 1 is a diagram showing an overview of a traffic management system 100 according to a first embodiment of the present invention. The traffic management system 100 includes a generating device 101 and a traffic management device 102.

[0021] The generating device 101 generates information for estimating the amount of exhaust gas from a vehicle.

[0022] The traffic management device 102 includes an estimation unit 103 and a determination unit 104 .

[0023] The estimation unit 103 estimates the amount of exhaust gas emitted from the vehicle. The determination unit 104 uses the estimated amount of exhaust gas to determine the toll for the subject vehicle to travel through a predetermined road section.

[0024] (Overview of traffic management processing) FIG. 2 is a diagram showing an outline of the traffic management process according to this embodiment.

[0025] The estimation unit 103 estimates the amount of exhaust gas from the vehicle (step S101).

[0026] The determination unit 104 determines the toll for the subject vehicle to travel through a predetermined road section using the estimated exhaust gas amount (step S102).

[0027] According to this embodiment, it is possible to reduce the amount of exhaust gas.

[0028] A detailed example of the traffic management system 100 according to the first embodiment will be described below.

[0029] <Details of Embodiment 1> Figure 3 is a diagram showing road R as viewed from above at time T1. Road R runs from entrance gate G1 to exit gate G2. This figure shows an example in which multiple vehicles C are traveling on the road. Note that there may be one or more vehicles C.

[0030] Road R corresponds to a road section of road R. Road R includes multiple target areas P_i that are set for each section along the traffic direction of road R. The target areas P_1 to P_M according to this embodiment are virtual areas obtained by dividing the entire road R from entrance gate G1 to exit gate G2 into M areas without any gaps.

[0031] Here, i is an integer between 1 and M, inclusive, and M is an integer greater than or equal to 2. The same applies hereinafter.

[0032] It should be noted that there may be only one target area P. One or more target areas P may be set in a part of the road R. The multiple target areas P may be set on the road R with intervals between them.

[0033] In detail, as shown in the figure, the traffic management system 100 includes a plurality of generation devices 101_1 to 101_M associated with target areas P_1 to P_M, respectively, a traffic management device 102, an ID acquisition device 105a, and a fare adjustment device 105b. The generation devices 101_1 to 101_M, the ID acquisition device 105a, and the fare adjustment device 105b are facilities attached to the road R.

[0034] The generation devices 101_1 to 101_M, the ID acquisition device 105a, and the settlement device 105b are each connected to the traffic management device 102 via a network N. The network N is a communication network constructed using wired or wireless means or a combination thereof.

[0035] Therefore, each of the generation devices 101_1 to 101_M and the traffic management device 102 can transmit and receive information to and from each other. The ID acquisition device 105a and the traffic management device 102 can transmit and receive information to and from each other. The settlement device 105b and the traffic management device 102 can transmit and receive information to and from each other.

[0036] The generation device 101_i photographs a target area P_i. In response to the photographing, the generation device 101_i generates image information PI_i including an image of the target area P_i. That is, the image information PI_i is information obtained in response to photographing a road R. When there are one or more vehicles C traveling on the road R, the image information PI_i includes one or more vehicles C.

[0037] The image information PI_i according to this embodiment further includes the shooting time.

[0038] The generating device 101_i continuously transmits the image information PI_i to the traffic management device 102 via the network N in real time.

[0039] The above-mentioned "generation device 101" (see FIG. 1) corresponds to any one of the generation devices 101_1 to 101_M (i.e., generation device 101_i). Note that the number of target regions P may be one as described above, and in this case, the number of generation devices 101 may also be one.

[0040] The ID acquisition device 105a is a device for detecting a vehicle C entering a road R. The ID acquisition device 105a is installed at the entrance gate G1.

[0041] The ID acquisition device 105a wirelessly communicates with an on-board device (not shown) mounted on the vehicle C. The on-board device is, for example, a device that configures an ETC (Electronic Toll Collection System) together with the ID acquisition device 105a, the settlement device 105b, and the like.

[0042] The ID acquisition device 105a detects a vehicle C that has entered road R by communicating with an on-board device, and acquires a vehicle ID (Identifier) ​​from the on-board device. The vehicle ID is information for identifying the vehicle C. The ID acquisition device 105a transmits the vehicle ID to the traffic management device 102 via the network N.

[0043] The settlement device 105b is a device for detecting a vehicle C exiting from the road R and settling the toll for passing through the road R for the detected vehicle C as the subject vehicle.

[0044] In this embodiment, the settlement device 105b is installed at the exit gate G2. When the vehicle C exits the road R, the settlement device 105b communicates with the vehicle C and the traffic management device 102. Then, the settlement device 105b settles the toll of the target vehicle in accordance with the toll determined by the traffic management device 102.

[0045] The traffic management device 102 determines the toll for the subject vehicle C passing through the exit gate G2 where the settlement device 105b is installed, to travel on the road R.

[0046] The estimation unit 103 acquires image information PI_i from each of the generation devices 101_i via the network N. The estimation unit 103 estimates the amount of exhaust gas from the vehicle C using the image information PI_i acquired from the generation device 101_i. That is, the image information PI_1 to PI_M according to this embodiment are an example of information for estimating the amount of exhaust gas from the vehicle C.

[0047] Note that the information for estimating the amount of exhaust gas from the vehicle C is not limited to image information, and may be, for example, sensor information including the concentration of exhaust gas (e.g., a gas of a specific component contained in exhaust gas, such as carbon dioxide) in the target area P. In this case, each of the generation devices 101_i may be equipped with a sensor that measures the concentration of exhaust gas in the target area and generate sensor information including the concentration of exhaust gas. The estimation unit 103 may acquire the sensor information from each of the generation devices 101_i via the network N and estimate the amount of exhaust gas from the vehicle C using the sensor information.

[0048] The amount of exhaust gas from vehicle C is, for example, the amount of carbon dioxide (CO2) emitted from vehicle C traveling on road R. Note that the amount of exhaust gas is not limited to the amount of carbon dioxide (CO2). The amount of exhaust gas may be, for example, the total amount of exhaust gas from vehicle C, or the amount of a specific component gas in the exhaust gas from vehicle C. A greenhouse gas is suitable as the specific component gas. CO2 is an example of a greenhouse gas.

[0049] The determination unit 104 determines the toll for the target vehicle to travel on the road R, using the exhaust gas amount estimated by the estimation unit 103. Fee The determination unit 104 transmits the toll of the target vehicle to the settlement device 105b via the network N.

[0050] In this embodiment, as described above, the settlement device 105b settles the toll when the vehicle C exits the road R. Therefore, the target vehicle is the vehicle C passing through the exit gate G2 where the settlement device 105b is installed. That is, in this embodiment, each vehicle C traveling on the road R becomes a target vehicle when it passes through the exit gate G2.

[0051] (Physical configuration of traffic management system 100) The traffic management system 100 is physically composed of a generation device 101_i and a traffic management device 102 connected via a network N. The generation device 101_i and the traffic management device 102 are each composed of a single physically different device.

[0052] The generating device 101_i and the traffic management device 102 may be physically configured as a single device, in which case the generating device 101_i and the traffic management device 102 are connected using an internal bus 1010 (described later) instead of the network N. Also, one or both of the generating device 101_i and the traffic management device 102 may be physically configured as multiple devices connected via an appropriate communication line such as the network N.

[0053] (Physical configuration of generation device 101_i) The generation device 101_i is physically an image capturing device such as a camera, etc. As shown in FIG. 4 , the generation device 101_i includes a bus 1010, a processor 1020, a memory 1030, a storage device 1040, a network interface 1050, a sensor 1060, and an optical system 1070.

[0054] The bus 1010 is a data transmission path for transmitting and receiving data among the processor 1020, memory 1030, storage device 1040, network interface 1050, and sensor 1060. However, the method of connecting the processor 1020 and the like to each other is not limited to bus connection.

[0055] The processor 1020 is implemented by a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or the like.

[0056] The memory 1030 is a main storage device realized by a RAM (Random Access Memory) or the like.

[0057] The storage device 1040 is an auxiliary storage device realized by a hard disk drive (HDD), a solid state drive (SSD), a memory card, a read only memory (ROM), or the like. The storage device 1040 stores program modules for realizing the functional units of the generating device 101. The processor 1020 reads each of these program modules into the memory 1030 and executes them, thereby realizing the function corresponding to the program module.

[0058] The network interface 1050 is an interface for connecting the generating device 101 to the network N.

[0059] The sensor 1060 is an image sensor that converts an image into an electrical signal. The optical system 1070 is a lens or the like that is used together with the sensor 1060.

[0060] (Physical configuration of traffic management device 102) The traffic management device 102 is physically, for example, a general-purpose computer. As shown in Fig. 5, the traffic management device 102 has a bus 1010, a processor 1020, a memory 1030, a storage device 1040, and a network interface 1050 similar to those of the generation device 101. The traffic management device 102 further has an input interface 1080 and an output interface 1090.

[0061] The input interface 1080 is an interface for the user to input information, and is composed of, for example, a touch panel, a keyboard, a mouse, etc. The output interface 1090 is an interface for presenting information to the user, and is composed of, for example, a liquid crystal panel, an organic EL (Electro-Luminescence) panel, etc.

[0062] Each of the ID acquisition device 105a and the settlement device 105b is provided with a communication interface for communicating with an in-vehicle device (not shown) in addition to the components that the traffic management device 102 physically has.

[0063] (Operation of the traffic management system 100) Now, the operation of the traffic management system 100 will be described with reference to the drawings.

[0064] (Generation process according to the first embodiment) 6 is a flowchart showing an example of the generation process according to this embodiment. The generation process is a process for generating information for estimating the amount of exhaust gas from a vehicle. Each of the generation devices 101_1 to 101_M repeatedly executes the generation process during operation.

[0065] When the generation device 101_i photographs the target area P_i, it generates image information PI_i including an image of the target area P_i in response to the photograph (step S201). The generation device 101_i transmits the image information PI_i generated in step S201 to the traffic management device 102 via the network N (step S202). The generation device 101_i executes step S201 again.

[0066] Each of the generating devices 101_1 to 101_M repeatedly executes such a generating process. As a result, the generating devices 101_1 to 101_M continuously transmit, in real time, to the traffic management device 102, image information PI_1 to PI_M including images of the target areas P_1 to P_M associated with each of them.

[0067] (Traffic management process according to the first embodiment) 7 is a flowchart showing a detailed example of the traffic management process according to this embodiment. The traffic management device 102 repeatedly executes the traffic management process during operation.

[0068] Road R is typically a toll road. However, road R may also be a road that is normally free to travel. In this case, the traffic management device 102 may start the traffic management process in response to a user instruction, for example, when the amount of exhaust gas on road R exceeds a threshold. Also, the traffic management device 102 may end the traffic management process in response to a user instruction.

[0069] The estimation unit 103 estimates the amount of exhaust gas from the vehicle using the image information PI_1 to PI_M (step S101).

[0070] FIG. 8 is a flowchart showing a detailed example of the estimation process (step S101).

[0071] The estimation unit 103 acquires the image information PI_1 to PI_M from the generation devices 101_1 to 101_M, respectively, via the network N (step S101a). The estimation unit 103 holds the acquired image information PI_1 to PI_M.

[0072] The estimation unit 103 analyzes the image information PI_1 to PI_M acquired in step S101 (step S101b).

[0073] FIG. 9 is a flowchart showing a detailed example of the analysis process (step S101b) according to this embodiment.

[0074] The estimation unit 103 repeats steps S101b_2 to S101b_4 for each of the image information PI_1 to PI_M acquired in step S101a (step S101b_1; loop A).

[0075] The estimation unit 103 analyzes the image information PI_i, and when the image information PI_i includes one or more vehicles C, assigns a vehicle ID to each of the one or more vehicles C according to the vehicle ID acquired from the ID acquisition device 105a (step S101b_2).

[0076] Details of step S101b_2 will be described taking as an example a case where a target region P_i at time T2 is analyzed using an analytical model trained by machine learning. The analytical model is a model for analyzing image information PI_i.

[0077] Time T2 is a time after time T1. Time T2 is, for example, the time when the generation device 101_i captured the most recent image. Time T1 is, for example, the time when the generation device 101_i captured the image immediately before time T2.

[0078] Note that time T1 is not limited to immediately before time T2, but may be any time before time T2 when the generation device 101_i captures an image.

[0079] The estimation unit 103 inputs the image information PI_i at time T2 and the image information PI_i at time T1 into the analysis model. The image information PI_i at time T2 is the image information PI_i including the shooting time T2. The image information PI_i at time T1 is the image information PI_i including the shooting time T1.

[0080] In response to these inputs, if one or more vehicles C are included in the image information PI_i at time T2, the analysis model outputs the vehicle ID of each of the vehicles C included in the image information PI_i.

[0081] The input data to the analytical model during learning is a plurality of pieces of image information obtained by photographing the road R. The plurality of pieces of image information is typically image information of consecutive frames in a moving image or of frames at predetermined time intervals. In machine learning, the analytical model performs supervised learning using training data. The training data includes, for example, information identifying each of the vehicles C included in the plurality of pieces of image information as a correct answer.

[0082] 10 is a diagram showing an example of images included in the image information PI_i at times T1 and T2, and the results of an analysis performed by an analysis model using the image information PI_i at times T1 and T2 as input data. In the figure, the vehicle C included in the image information PI_i at time T2 is shown by a solid line, and the vehicle C included in the image information PI_i at time T1 is shown by a dotted line.

[0083] In the example shown in the figure, image information PI_i at time T1 includes vehicles C1, C2, and a portion of vehicle C3. As a result of analyzing image information PI_i at time T1, the vehicle IDs of vehicles C1 to C3 are identified as "001," "002," and "003," respectively.

[0084] The analysis model uses, for example, the vehicle number included in the image to determine whether or not the vehicle C is common between the image information PI_i at time T1 and time T2 (commonality of the vehicle C).

[0085] As a result, the analytical model determines that the images of image information PI_i at time T1 and time T2 share a common vehicle C located at the bottom of the figure. The analytical model also determines that the images of vehicle C located at the top of the figure share a common vehicle. In other words, the analytical model determines that image information PI_i at time T2 includes vehicle C1 and parts of vehicles C2 and C4.

[0086] In such a case, as shown in the figure, the analysis model assigns the same vehicle IDs "001" and "002" to vehicles C1 and C2 included in image information PI_i at time T2 as vehicles C1 and C2 included in image information PI_i at time T1. The estimation unit 103 assigns to vehicle C4 included in image information PI_i at time T2, for example, vehicle ID "004" that was assigned to vehicle C4 in the analysis of other image information PI_i+1 at time T1.

[0087] In this way, the analytical model assigns the same vehicle ID to common vehicles C.

[0088] Here, the image information PI_M associated with the target area P_M including the gate G1 includes the vehicle C that has newly entered the road R from the gate G1. Therefore, the estimation unit 103 can use the image information PI_M to link the vehicle C that has newly entered the road R with the vehicle ID acquired from the ID acquisition device 105a.

[0089] For example, the estimation unit 103 associates the vehicle C that has newly entered the road R with the vehicle ID based on the photographing time included in the image information PI_M and the time when the ID acquisition device 105a acquired the vehicle ID.

[0090] As a result, the vehicle C that has entered the road R is identified while traveling on the road R using the vehicle ID acquired by the ID acquisition device 105a.

[0091] The estimation unit 103 may determine the commonality of the vehicle C by using various known image processing techniques such as using image feature amounts.

[0092] Referring again to FIG. The estimation unit 103 generates an analysis result 107 based on the result of the process at step S101b_2 (step S101b_3).

[0093] 11 is a diagram showing an example of the analysis result 107. The analysis result 107 is information that associates an area ID, a time, and a vehicle ID.

[0094] The area ID is information for identifying the target area P_i corresponding to the image information PI_i that is the target of analysis. The time is the shooting time included in the image information PI_i that is the target of analysis.

[0095] The vehicle ID is a vehicle ID assigned in step S101b_2 using the image information PI_i that is the target of analysis.

[0096] The analysis result 107 shown in the figure includes the results of analysis performed based on the image information PI_i at the above-mentioned times T1 and T2.

[0097] Referring again to FIG. The estimation unit 103 holds the analysis result 107 generated at step S101b_3 (step S101b_4). After executing steps S101b_2 to S101b_4 for each of the image information PI_1 to PI_M, the estimation unit 103 returns to the estimation process (step S101).

[0098] Referring again to FIG. The estimation unit 103 estimates the amount of exhaust gas from the vehicle C on the road R using the analysis result 107 (step S101c).

[0099] In detail, for example, the estimation unit 103 uses an estimation model to estimate the amount of exhaust gas from each vehicle C in each of the target areas P_1 to P_M and the amount of exhaust gas from each vehicle C on the entire road. The estimation model is a model for estimating the amount of exhaust gas. Information input to the estimation model is the analysis result 107.

[0100] (Example of estimation model 1) The estimation model according to this embodiment is a model that assumes that the amount of exhaust gas per unit time changes depending on the speed of the vehicle C.

[0101] The amount of exhaust gas per unit time according to speed (emission coefficient) may be determined as appropriate, for example, based on the average amount of exhaust gas at each speed of a vehicle group made up of various vehicles C. The vehicle group may be made up of multiple types of vehicles C (described in detail below). Furthermore, the composition ratio of each type of vehicle C making up the vehicle group may be the same as the composition ratio of each type of vehicle C making up the vehicle group traveling on road R.

[0102] The emission coefficient may be a value obtained experimentally based on a sensor (e.g., a flow sensor, a CO2 sensor) attached to vehicle C, or may be a value determined based on a value listed in the catalog of vehicle C, etc.

[0103] An example of such an estimation model is the model expressed by equations (1) to (3).

number

[0104]

number

[0105]

number

[0106] Here, the exhaust gas amount H is the exhaust gas amount for each vehicle on the entire road R.

[0107] The exhaust gas amount Gi is the amount of exhaust gas of the vehicle C in the target region P_i. In other words, the exhaust gas amount Gi is the amount of exhaust gas of the vehicle C for each region.

[0108] K(Vi) is the emission coefficient. As described above, the emission coefficient according to this embodiment is the amount of exhaust gas per unit time of the vehicle C according to the speed.

[0109] Vi is the vehicle speed.

[0110] TLi is the length of time that the vehicle C is present in the target area P_i. TLi can be calculated as the time difference between when the vehicle C enters the target area P_i and when the vehicle C leaves the target area P_i.

[0111] RLi is the length of the target region P_i along the traffic direction of the road R.

[0112] When the vehicle C leaves the target region P_i, the estimation unit 103 acquires the exhaust gas amount Gi of each of the vehicle C in each of the target regions P_1 to P_M using equations (2) and (3). The estimation unit 103 generates an estimation result 108 for each region including the exhaust gas amount Gi.

[0113] 12 is a diagram showing an example of the region-specific estimation result 108. The estimation result 108 is information that associates the region ID, time, vehicle ID, and exhaust gas amount by region.

[0114] The area ID is information for identifying the target area P_i from which the vehicle C, identified using the associated vehicle ID, left. The time is the time when the vehicle C left the target area P_i. The exhaust gas amount by area is the exhaust gas amount Gi of the vehicle C in the target area P_i.

[0115] In the example of Fig. 10, vehicle C3 is not present in target region P_i at time T2, and therefore exits target region P_i at time T1. Fig. 12 includes an example of region-specific estimation results 108 generated when vehicle C3 exits target region P_i.

[0116] Furthermore, the estimation unit 103 acquires the exhaust gas amount H of the vehicle C using equation (1), for example, when the vehicle C leaves the road R. When the vehicle C leaves the road R, for example, the vehicle C passes through the exit gate G2, when it leaves the target area M, when it leaves the target area M, etc.

[0117] The estimation unit 103 outputs the estimation result 10 for the entire road including the exhaust gas amount H of the vehicle C (target vehicle) leaving the road R. 9 Generate.

[0118] 13 is a diagram showing an example of the estimation result 109 for the entire road. The estimation result 109 is information that associates the time, the vehicle ID, and the vehicle-specific exhaust gas amount H for the entire road R.

[0119] The time is the time when vehicle C, identified using the associated vehicle ID, leaves road R. The vehicle-specific exhaust gas amount on the entire road R is the exhaust gas amount H on the entire road R of vehicle C, identified using the associated vehicle ID.

[0120] In the figure, vehicle C3 with vehicle ID "003" is 1 This includes 109 examples of estimation results for the entire road when leaving road R at

[0121] Referring again to FIG. The estimation unit 103 holds the estimation results 108 and 109 generated in step S101c (step S101d), and returns to the traffic management process.

[0122] Referring again to FIG. The determination unit 104 determines whether or not there is a target vehicle (S103). The target vehicle is vehicle C passing through the exit gate G2.

[0123] In detail, for example, when vehicle C passes through exit gate G2, the settlement device 105b wirelessly communicates with an on-board device (not shown) of vehicle C. Using this communication, the settlement device 105b detects the target vehicle and obtains the vehicle ID of the target vehicle from the on-board device.

[0124] When the settlement device 105b detects the target vehicle, it transmits a determination instruction to determine the toll to the traffic management device 102 via the network N. The determination instruction includes the vehicle ID of the target vehicle that the settlement device 105b has acquired from the in-vehicle device.

[0125] When the determination instruction is not acquired from the settlement device 105b, the determination unit 104 determines that there is no target vehicle (step S103; No). The estimation unit 103 executes step S101 again.

[0126] When a determination instruction is received from the settlement device 105b, the determination unit 104 determines that there is a target vehicle (step S103; Yes). In this case, the determination unit 104 obtains the estimation result 109 of the entire road related to the vehicle ID from the estimation unit 103 in accordance with the vehicle ID included in the determination instruction.

[0127] The determination unit 104 determines the toll for the target vehicle to travel on the road R based on the estimation result 109 for the entire road (step S102).

[0128] In detail, for example, the determination unit 104 acquires the amount of exhaust gas from the target vehicle on the entire road R based on the estimation result 109 for the entire road and the vehicle ID included in the determination instruction. The determination unit 104 determines the toll for the target vehicle according to a predetermined first determination rule. The determination unit 104 generates toll information including the determined toll. The toll information may further include the vehicle ID of the target vehicle.

[0129] The first decision rule is a rule for determining the toll. The first decision rule determines the relationship between the amount of exhaust gas H of the target vehicle on the entire road R and the toll using a formula, a table, etc.

[0130] The first decision rule, for example, defines a relationship in which the greater the amount of exhaust gas H of the target vehicle on the entire road R, the higher the toll fee. In accordance with such a first decision rule, the decision unit 104 decides the toll fee for the target vehicle so that the greater the amount of exhaust gas on road R, the higher the amount.

[0131] In detail, for example, the first decision rule includes a certain reference exhaust gas amount. When the exhaust gas amount Gi of the target vehicle is greater than the reference exhaust gas amount, the decision unit 104 sets a high toll for the target vehicle. Furthermore, when the exhaust gas amount Gi of the target vehicle is less than the reference exhaust gas amount, the decision unit 104 sets a low toll for the target vehicle. The reference exhaust gas amount may be a fixed value or a variable value. The reference exhaust gas amount may be, for example, a standard value for a certain area, a set target value, or the like.

[0132] The determination unit 104 transmits the fee information to the settlement device 105b via the network N (step S104). The estimation unit 103 executes step S101 again.

[0133] The settlement device 105b acquires the toll information transmitted in step S104 from the determination unit 104 via the network N. The settlement device 105b wirelessly communicates with an on-board device (not shown) of the target vehicle, and performs a toll settlement process for the target vehicle in accordance with the toll information.

[0134] So far, the first embodiment of the present invention has been described.

[0135] According to this embodiment, the traffic management device 102 includes an estimation unit 103 and a determination unit 104. The estimation unit 103 estimates the amount of exhaust gas from a vehicle. The determination unit 104 determines the toll for the target vehicle to travel on road R using the estimated amount of exhaust gas.

[0136] This can motivate the user of the vehicle to reduce the amount of exhaust gas, thereby making it possible to reduce the amount of exhaust gas.

[0137] The estimation unit 103 estimates the amount of exhaust gas from the target vehicle on the road R.

[0138] This allows the amount of exhaust gas from the target vehicle to be used to determine the toll for the target vehicle to pass through a predetermined target area P. For example, by setting the toll for a target vehicle with a low amount of exhaust gas lower than the toll for a target vehicle with a high amount of exhaust gas, it is possible to motivate users of the target vehicle to reduce their exhaust gas emissions. Therefore, it becomes possible to reduce the amount of exhaust gas.

[0139] The determination unit 104 determines the toll for the target vehicle on the road R for which the amount of exhaust gas from the target vehicle has been estimated.

[0140] This can motivate the user of the vehicle to reduce the amount of exhaust gas, thereby making it possible to reduce the amount of exhaust gas.

[0141] The determination unit 104 determines the toll for the target vehicle so that the larger the estimated amount of exhaust gas, the higher the amount.

[0142] This can motivate the user of the vehicle to reduce the amount of exhaust gas, thereby making it possible to reduce the amount of exhaust gas.

[0143] (Variation 1) In the first embodiment, an example has been described in which the generation devices 101_1 to 101_M are imaging devices such as cameras. In the first modification, an example will be described in which the generation devices 101_1 to 101_M are on-board devices mounted on a vehicle C. Note that, in this modification as well, the number of generation devices 101 may be one.

[0144] The generating devices 101_1 to 101_M according to this modification generate vehicle information CI_1 to CI_M of the vehicle C on which each of the generating devices 101_1 to 101_M is mounted, instead of the image information PI_1 to PI_M according to the first embodiment. Each of the generating devices 101_1 to 101_M transmits the generated vehicle information to the traffic management device 102 via a network N. The network N may include road-to-vehicle communication, vehicle-to-vehicle communication, etc. using wireless communication.

[0145] The vehicle information includes, for example, at least one of a vehicle number and a vehicle ID. The vehicle information may further include one or more of a vehicle model, a type of combustion, an amount of exhaust gas, a speed of the vehicle C, an accelerator opening (acceleration), and the like.

[0146] The generating device 101_i may be physically configured in the same manner as the traffic management device 102.

[0147] In this modification, the estimation unit 103 executes the same analysis process (step S101_b) as in the first embodiment, using vehicle information CI_1 to CI_M instead of the image information PI_1 to PI_M according to the first embodiment. That is, the estimation unit 103 according to this modification estimates the amount of exhaust gas on the road R, using vehicle information related to each of the one or more vehicles C generated by an on-board device mounted on each of the one or more vehicles C.

[0148] However, in cases where the vehicle information includes a vehicle ID, or where the vehicle information includes a vehicle number that is used as the vehicle ID, step S101B_2 does not need to be executed.

[0149] Then, the estimation unit 103 executes an analysis process (step S101_b) using the vehicle information CI_1 to CI_M, thereby generating an analysis result 107 similar to that of the first embodiment. Therefore, the estimation unit 103 can use the analysis result 107 to generate estimation results 108 and 109 similar to those of the first embodiment. Therefore, the toll can be determined similarly to the first embodiment.

[0150] The generation devices 101_1 to 101_M may include the same imaging device as in the first embodiment and the above-described in-vehicle device. Furthermore, as described above, they may also include a sensor. That is, the estimation unit 103 may estimate the exhaust gas amount using at least one of the carbon dioxide concentration on the road R, an image, and vehicle information acquired from the vehicle. The carbon dioxide concentration on the road R is, for example, the carbon dioxide concentration in each of the target areas P.

[0151] According to this modification, the same effects as those of the first embodiment are achieved.

[0152] (Variation 2) In the first embodiment, an example has been described in which the determination unit 104 determines the toll for the target vehicle on the road R on which the amount of exhaust gas from the target vehicle has been estimated. The determination unit 104 may determine the toll for the target vehicle on another road (not shown) on which the target vehicle will travel after traveling on the road R on which the amount of exhaust gas from the target vehicle has been estimated. The road on which the target vehicle will travel after traveling on road R is, for example, a road connecting to road R.

[0153] This also motivates the user of the vehicle to reduce the amount of exhaust gas, thereby making it possible to reduce the amount of exhaust gas.

[0154] <<Embodiment 2>> In the second embodiment, an example will be described in which the exhaust gas amount of a vehicle C is estimated by further using the type of the vehicle.

[0155] In this embodiment, the types of vehicles are categorized according to the configuration of the driving energy used by the vehicle C. In this case, the types of vehicles are, for example, electric vehicles, fuel cell vehicles (also called hydrogen vehicles), hybrid cars, and engine (internal combustion engine) vehicles.

[0156] An electric vehicle is a vehicle that uses an onboard storage battery that is charged externally and uses the power from that storage battery as its driving energy, while a fuel cell vehicle is a vehicle that uses electricity generated using externally supplied hydrogen as its driving energy.

[0157] A hybrid car is a car that uses both fuel and electricity as its driving energy, while an internal combustion engine car is a car that uses only fuel such as gasoline or diesel as its driving energy.

[0158] In this embodiment, for the sake of simplicity, the description that overlaps with that of the first embodiment will be omitted as appropriate.

[0159] 14 is a diagram showing the configuration of a traffic management system 200 according to the second embodiment of the present invention. The traffic management system 200 includes a traffic management device 202 instead of the traffic management device 102 according to the first embodiment. The traffic management device 202 includes an estimation unit 203 instead of the estimation unit 103 according to the first embodiment. Except for these, the traffic management system 200 may be configured similarly to the traffic management system 100 according to the first embodiment.

[0160] As in the first embodiment, the estimation unit 203 acquires image information PI_i from each of the generation devices 101_i via the network N. The estimation unit 203 estimates the amount of exhaust gas on the road R by using the image information PI_i acquired from the generation device 101_i.

[0161] The traffic management system 200 may be physically configured in the same manner as the traffic management system 100 according to the first embodiment.

[0162] (Operation of the traffic management system 200) The operation of the traffic management system 200 will now be described with reference to the drawings.

[0163] The generation process according to this embodiment may be the same as that of the first embodiment.

[0164] 15 is a flowchart showing an example of traffic management processing according to the second embodiment. The traffic management device 202 repeatedly executes traffic management processing during operation. The traffic management processing according to this embodiment includes an estimation processing (step S201) instead of the estimation processing (step S101) according to the first embodiment. Except for this point, the traffic management processing according to this embodiment may be the same as that of the first embodiment.

[0165] 16 is a flowchart showing a detailed example of the estimation process (step S201). The estimation process (step S201) includes steps S201b to S201c instead of steps S101b to S101c according to embodiment 1. Except for this point, the estimation process (step S201) according to this embodiment may be similar to that of embodiment 1.

[0166] FIG. 17 is a flowchart showing a detailed example of the analysis process (step S201b).

[0167] The estimation unit 103 repeats steps S201b_2 to S201b_5 for each of the image information PI_1 to PI_M acquired in step S101a (step S201b_1; loop B).

[0168] The estimation unit 203 analyzes the image information PI_i, and when the image information PI_i includes one or more vehicles C, similarly to the first embodiment, assigns a vehicle ID to each of the one or more vehicles C according to the vehicle ID acquired from the ID acquisition device 105a. The estimation unit 203 further identifies the vehicle type (step S201b_2). That is, the estimation unit 203 estimates the type of vehicle based on the image information PI_i.

[0169] The estimation unit 203 may acquire vehicle information as described in the first modification, and in this case, may estimate the type of vehicle using at least one of the vehicle model, the type of combustion, and the amount of exhaust gas.

[0170] The analysis model according to this embodiment differs from the analysis model according to the first embodiment in that it outputs the vehicle model in addition to the vehicle ID.

[0171] That is, when image information PI_i at time T2 and time T1 is input, the analysis model according to this embodiment outputs information associating the vehicle ID and vehicle model of each vehicle C included in the image information PI_i.

[0172] The input data during learning of the analytical model according to this embodiment may be the same as in embodiment 1. In the analytical model according to this embodiment, the training data during learning includes, for example, information for identifying each of the vehicles C included in the multiple pieces of image information, as well as the vehicle type of each of the vehicles C as the correct answer.

[0173] Fig. 18 is a diagram corresponding to Fig. 10. That is, Fig. 18 is a diagram showing an example of the results of an analysis performed by the analysis model according to this embodiment using images included in the image information PI_i at times T1 and T2 and the image information PI_i at times T1 and T2 as input data.

[0174] In the example shown in the figure, image information PI_i at time T1 includes vehicles C1 and C2 and a portion of vehicle C3. As a result of analyzing image information PI_i at time T1, the vehicle IDs and vehicle models of vehicles C1 to C3 are identified as "001 and vehicle model A," "002 and vehicle model B," and "003 and vehicle model C," respectively.

[0175] The analytical model determines the commonality of vehicle C, similarly to embodiment 1. As a result, similarly to embodiment 1, the analytical model determines that the image information PI_i at time T2 includes vehicle C1 and parts of vehicles C2 and C4.

[0176] In such a case, as shown in the figure, the analysis model assigns the same vehicle IDs to the vehicles C1 and C2 included in the image information PI_i at time T2 as those to the vehicles C1 and C2 included in the image information PI_i at time T1. 2 For the vehicles C1 and C2 included in the image information PI_i at time T1, the vehicle types "Vehicle Type A" and "Vehicle Type B" obtained based on the image information PI_i at time T1 are specified, respectively.

[0177] The estimation unit 203 assigns to the vehicle C4 included in the image information PI_i at time T2, for example, the vehicle ID "004" that was assigned to the vehicle C4 in the analysis of other image information PI_i+1 at time T1. In the example shown in the figure, the estimation unit 203 identifies the vehicle model of the vehicle C with the vehicle ID "004" as "vehicle model D" in accordance with the output of the analysis model.

[0178] The estimation unit 203 may identify the vehicle type using various known image processing techniques such as pattern matching.

[0179] Referring again to FIG. The estimation unit 203 identifies the type of vehicle for each of the vehicles C based on the vehicle type identified at step S201b_2 (step S201b_3).

[0180] Specifically, the estimation unit 203 identifies the type of vehicle using pre-stored vehicle model data 210. The vehicle model data 210 is data that associates the vehicle model with the vehicle type. Fig. 19 is a diagram showing an example of the vehicle model data 210.

[0181] The method for identifying the vehicle type is not limited to this. The above-described analysis model may output the vehicle type instead of the vehicle model.

[0182] In this case, the analytical model takes image information PI_i at time T1 and time T2 as input, and outputs information associating the vehicle ID of each vehicle C included in the image information PI_i with the vehicle type. The input data to the analytical model during training may be the same as the input data during training of the analytical model. The training data may include the vehicle type instead of the vehicle model.

[0183] Referring again to FIG. The estimation unit 203 generates an analysis result 207 based on the results of the processes in steps S201b_2 and S201b_3 (step S201b_4).

[0184] 20 is a diagram showing an example of the analysis result 207. The analysis result 207 is information that associates an area ID, a time, a vehicle ID, and a vehicle type.

[0185] The type of vehicle is the type of vehicle identified in step S101b_3 for vehicle C identified using the vehicle ID associated therewith.

[0186] The analysis result 207 shown in the figure includes the result of analysis performed based on the image information PI_i at the above-mentioned times T1 and T2.

[0187] Referring again to FIG. The estimation unit 203 holds the analysis result 207 generated at step S201b_4 (step S201b_5). After executing steps S201b_2 to S201b_5 for each of the image information PI_1 to PI_M, the estimation unit 203 returns to the estimation process (step S201).

[0188] The estimation unit 103 may identify the vehicle type using various known image processing techniques such as pattern matching.

[0189] Referring again to FIG. The estimation unit 203 estimates the amount of exhaust gas from the vehicle C on the road R using the analysis result 207 (step S201c).

[0190] As in the first embodiment, the estimation unit 203 uses an estimation model for estimating the amount of exhaust gas to estimate the amount of exhaust gas from each vehicle C in each of the target regions P_1 to P_M and the amount of exhaust gas H from each vehicle C on the entire road R. Information input to the estimation model is the analysis result 207.

[0191] In this embodiment, the estimation model used by the estimation unit 203 is different from that in embodiment 1. The estimation model according to this embodiment will be described.

[0192] (Example of estimation model 2) The estimation model according to this embodiment includes a model for each vehicle type. The estimation model is a model that assumes that the amount of exhaust gas per unit time of each vehicle C is constant for each vehicle type. In other words, the model for each vehicle type according to this embodiment represents the amount of exhaust gas per unit time using a constant that is determined for each vehicle type.

[0193] The amount of exhaust gas per unit time (emission coefficient) for each type of vehicle may be determined as appropriate, for example, based on the amount of exhaust gas when each type of vehicle C travels at a predetermined speed. The predetermined speed may be determined as appropriate, for example, the average travel speed on road R. The emission coefficient may be a value obtained experimentally based on a sensor (e.g., a flow sensor, a CO2 sensor) attached to vehicle C, or may be a value determined based on a value listed in a catalog for vehicle C, etc.

[0194] Examples of such estimation models include the model expressed by the above-mentioned formula (1) and the following formula (4).

number

[0195] K(Mi) is an emission coefficient. The emission coefficient according to this embodiment is the amount of exhaust gas per unit time for each type of vehicle.

[0196] Mi is the type of vehicle.

[0197] When the vehicle C leaves the target region P_i, the estimation unit 203 acquires the exhaust gas amount Gi of each of the vehicle C in each of the target regions P_1 to P_M using equation (4). The estimation unit 203 generates estimation results 108 for each region (see FIG. 12) similar to those in the first embodiment.

[0198] Furthermore, similar to the first embodiment, when the vehicle C leaves the target region M, that is, when the vehicle C leaves the road R, the estimation unit 203 uses the formula (1) to acquire the amount of exhaust gas H of the vehicle C. The estimation unit 203 generates an estimation result 109 for the entire road similar to the first embodiment.

[0199] The processing from step S101d onwards is the same as in the first embodiment, and therefore the description will be omitted.

[0200] So far, the second embodiment of the present invention has been described.

[0201] According to this embodiment, the estimation unit 203 estimates the amount of exhaust gas on the road R using the type of vehicle C traveling on the road R.

[0202] This allows for a more accurate estimation of the amount of exhaust gas on road R. This provides a stronger incentive for users of the target vehicles to reduce the amount of exhaust gas. This makes it possible to further reduce the amount of exhaust gas.

[0203] (Variation 3) In the second embodiment, the types of vehicles are described as electric vehicles, fuel cell vehicles (also called hydrogen vehicles), hybrid cars, and engine (internal combustion engine) vehicles, but the types of vehicles are not limited to these.

[0204] Vehicle types classified by the configuration of driving energy may be further subdivided, or the multiple vehicle types exemplified here may be combined into one. For example, engine vehicles may be further subdivided into gasoline cars, diesel cars, etc. Also, for example, electric vehicles and fuel cell vehicles may be combined into one classification as vehicles whose driving energy is solely electric. Furthermore, the criteria for classifying vehicle types are not limited to the configuration of driving energy. Vehicle types may also be, for example, vehicle models.

[0205] This also provides the same effect as in the second embodiment.

[0206] (Variation 4: Estimation Model Example 3) The estimation model according to this modification is similar to the estimation model according to embodiment 2 in that it includes multiple models for each vehicle type. The estimation model according to this modification expresses the amount of exhaust gas (emission coefficient) per unit time for each vehicle type as a function with the running state as a variable.

[0207] The function that defines the emission coefficient is, for example, a function that represents the average CO2 emissions of vehicle C by vehicle type, and may be determined experimentally based on a sensor (e.g., a flow sensor, a CO2 sensor) attached to vehicle C, or may be determined based on values ​​listed in the catalog of vehicle C.

[0208] Examples of such estimation models include the model expressed by the above-mentioned formula (1) and the following formula (5).

number

[0209] K(X, Y) is the emission coefficient. As described above, the emission coefficient according to this modification is the amount of exhaust gas per unit time for each vehicle type.

[0210] K(X, Y) includes vehicle type X and driving state Y as variables. Therefore, this emission coefficient is the amount of exhaust gas per unit time of vehicle C when the vehicle type is X and the driving state is Y. Variations As described above, K(X, Y) is a function determined for each vehicle type X, and has variables of the driving state Y including the driving speed, acceleration, idling stop state, and load amount.

[0211] RSi represents the driving state, and is a vector quantity whose components include one or more values ​​such as driving speed, acceleration, idling stop status, and load weight.

[0212] The idling stop status indicates whether or not the stopped vehicle C is idling stopped. For example, the value of the idling stop status may be set to a predetermined value corresponding to whether or not the vehicle is idling. Specifically, for example, idling may be set to "1" and not idling may be set to "0."

[0213] The traveling state may be acquired by the estimation unit 203, for example, in the analysis process (step S201b). For example, in step S201b_2, the estimation unit 203 acquires some or all of the traveling speed, acceleration, idling stop state, and load weight based on the analysis of the image information PI_i.

[0214] For example, the estimation unit 203 acquires the traveling speed and acceleration based on the change in position and time difference of the vehicle C included in a plurality of pieces of image information PI_i having different image capture times.

[0215] For example, the estimation unit 203 acquires the idling stop state based on the result of comparing the vibration of the vehicle C with a predetermined threshold. For example, if the vibration of the vehicle C is equal to or greater than the threshold, the estimation unit 203 determines that the vehicle is not idling stopped. If the vibration of the vehicle C is less than the threshold, the estimation unit 203 determines that the vehicle is idling stopped.

[0216] For example, the estimation unit 203 obtains the load amount based on the amount of sinking of the vehicle C. The estimation unit 203 may obtain the amount of sinking of the vehicle C by comparing the vehicle height of the vehicle C obtained by analyzing the image information PI_i that is the target of analysis with the standard vehicle height of a vehicle of the same model as the vehicle C.

[0217] Furthermore, for example, the vehicle information may include one or more of a driving speed, an acceleration, an idling stop status, and a load weight. If the vehicle C is equipped with a weight sensor, the in-vehicle device can generate vehicle information including the load weight obtained using the weight sensor.

[0218] In such a case, in step S201b_2, the estimation unit 203 may acquire the traveling speed, the acceleration, the idling stop state, and the load amount based on the analysis of the vehicle information.

[0219] Book Variations According to this, the estimation unit 203 estimates the amount of exhaust gas on the road R using the traveling state of the vehicle C traveling on the road R.

[0220] This allows for a more accurate estimation of the amount of exhaust gas on road R. This provides a stronger incentive for users of the target vehicles to reduce the amount of exhaust gas. This makes it possible to further reduce the amount of exhaust gas.

[0221] <<Embodiment 3>> In the first and second embodiments, the toll is paid when passing through the exit gate G2, that is, after passing through the road R. However, the toll may be paid in advance. 1 An example of settling the fare when passing through will be explained.

[0222] 21 is a diagram showing the configuration of a traffic management system 300 according to the third embodiment of the present invention. The traffic management system 300 includes a plurality of generating devices 101_1 to 101_M similar to those of the first embodiment. The traffic management system 300 also includes a traffic management device 302 that replaces the traffic management device 102 according to the first embodiment. Furthermore, the traffic management system 300 includes a fare adjustment device 305a and another fare adjustment device 305b.

[0223] The fare adjustment device 305a and the fare adjustment device 305b are facilities attached to the road R. The fare adjustment device 305a is installed at the entrance gate G1. The fare adjustment device 305b is installed at the exit gate G2.

[0224] The settlement device 305a and the settlement device 305b are each connected to the traffic management device 302 via the network N. Therefore, the settlement device 305a and the traffic management device 302 can transmit and receive information to and from each other. The settlement device 305b and the traffic management device 302 can transmit and receive information to and from each other.

[0225] The toll settlement device 305a is a device that detects a vehicle C entering a road R and settles the toll for passing through the road R for the detected vehicle C as a target vehicle.

[0226] The settlement device 305a wirelessly communicates with an on-board device (not shown) mounted on the vehicle C. The on-board device is a device that constitutes ETC together with the settlement devices 305a, 305b, for example.

[0227] The settlement device 305a detects vehicle C that has entered road R by communicating with the on-board device and acquires a vehicle ID from the on-board device. The settlement device 305a transmits a first determination instruction including the vehicle ID of the target vehicle acquired from the on-board device to the traffic management device 102 via the network N. In response to the first determination instruction, the settlement device 305a acquires the toll for the target vehicle from the traffic management device 302. The settlement device 305a settles the toll for the target vehicle in accordance with the toll determined by the traffic management device 302.

[0228] The settlement device 305b is a device for detecting a vehicle C exiting road R and for discounting the toll for the detected vehicle C. In this embodiment, an example will be described in which the toll is discounted by refunding at least a portion of the toll paid in advance. The method of discounting the toll is not limited to this, and the toll may be discounted by issuing a coupon or a discount ticket that can be used the next time the vehicle travels on road R.

[0229] The settlement device 305b communicates with an on-board device (not shown) mounted on the vehicle C wirelessly, similar to the settlement device 305a.

[0230] The settlement device 305b communicates with the vehicle-mounted device to came out of Vehicle C is detected and the vehicle ID is acquired from the in-vehicle device. The settlement device 305b transmits a second determination instruction including the vehicle ID of the target vehicle acquired from the in-vehicle device to the traffic management device 102 via the network N. In response to the second determination instruction, the settlement device 305b acquires the toll for the target vehicle from the traffic management device 302. The settlement device 305b settles the toll for the target vehicle in accordance with the toll determined by the traffic management device 302.

[0231] The traffic management device 302 determines the toll for a subject vehicle C passing through entrance gate G1 where settlement device 305a is installed, to travel along road R. The traffic management device 302 also determines the toll for a subject vehicle C passing through exit gate G2 where settlement device 305b is installed, to travel along road R.

[0232] 22 is a diagram showing the functional configuration of a traffic management device 302 according to this embodiment. The traffic management device 302 includes an estimation unit 103 similar to that of the first embodiment, and a determination unit 304 that replaces the determination unit 104 according to the first embodiment.

[0233] As in the first embodiment, the estimation unit 103 estimates the amount of exhaust gas from a target vehicle on a road R. The estimation unit 103 also estimates the amount of exhaust gas in a target region P from one or more vehicles C traveling on the road R ahead of the target vehicle.

[0234] The determination unit 304 determines the toll for the target vehicle to travel on the road R using the amount of exhaust gas estimated by the estimation unit 103.

[0235] In this embodiment, as described above, the settlement device 305a settles the toll when vehicle C enters road R. Therefore, the target vehicle is vehicle C passing through entrance gate G1 where the settlement device 305a is installed. In other words, in this embodiment, the vehicle entering road R is the target vehicle.

[0236] As described above, the settlement device 305b settles the toll refund when the vehicle C exits the road R. Therefore, the target vehicle is also the vehicle C passing through the exit gate G2 where the settlement device 305b is installed. That is, in this embodiment, each vehicle C traveling on the road R is also a target vehicle when it passes through the exit gate G2.

[0237] (Physical configuration of traffic management system 300) The traffic management device 302 may be physically configured similarly to the traffic management device 102 according to embodiment 1. Each of the settlement devices 305a and 305b may be physically configured similarly to the settlement device 105b according to embodiment 1.

[0238] (Operation of the traffic management system 300) The operation of the traffic management system 300 will now be described with reference to the drawings.

[0239] The generation process according to this embodiment may be the same as that of the first embodiment.

[0240] 23 is a flowchart showing an example of traffic management processing according to this embodiment. The traffic management device 302 repeatedly executes traffic management processing during operation.

[0241] The estimation unit 103 executes step S101 similar to that in the first embodiment.

[0242] The determination unit 304 determines whether or not there is a target vehicle (S303). As described above, the target vehicle is the vehicle passing through the entrance gate G1 and the vehicle C passing through the exit gate G2.

[0243] If neither the first determination instruction nor the second determination instruction is acquired, the determination unit 304 determines that there is no target vehicle (step S303; No). In this case, the estimation unit 103 executes step S101 again.

[0244] If either the first determination instruction or the second determination instruction is acquired, the determination unit 304 determines that a target vehicle is present (step S303; Yes). The determination unit 304 determines whether the acquired determination instruction is the first determination instruction (step S305).

[0245] (First decision Decision-making process when instructions are received) If it is determined that the instruction is the first determination instruction (step S305; Yes), the determination unit 304 determines the toll for the target vehicle to travel on the road R based on the estimation results 108 for each area (step S302a).

[0246] In detail, for example, the determination unit 304 obtains the exhaust gas amount of the vehicle C for the entire current road R based on the estimation results 108 for each area. The exhaust gas amount of the vehicle C for the entire current road R is the total amount of exhaust gas of the vehicle C traveling on the road R. For example, the determination unit 304 obtains the exhaust gas amount of the vehicle C for the entire current road by adding up the exhaust gas amounts for each area included in the estimation results 108 for each area that includes the current time (or a time within a predetermined range from the current time).

[0247] The determination unit 304 determines the toll for the target vehicle according to a predetermined second determination rule. The determination unit 304 generates toll information including the determined toll. The toll information may further include the vehicle ID of the target vehicle.

[0248] The second decision rule is a rule for determining the toll. The second decision rule determines the relationship between the amount of exhaust gas and the toll for the entire current road R using a formula, a table, etc.

[0249] The second decision rule, for example, defines a relationship in which the greater the amount of exhaust gas from vehicle C on the entire current road R, the higher the toll fee. In accordance with such a second decision rule, the decision unit 104 decides the toll fee for the target vehicle so that the greater the amount of exhaust gas on road R, the higher the amount.

[0250] The target vehicle is vehicle C that is about to enter road R. The determination unit 304 determines the toll for this target vehicle based on the amount of exhaust gas from vehicle C that is already traveling on road R. In other words, the determination unit 304 determines the toll for the target vehicle using the amount of exhaust gas on road R from one or more vehicles that travel on road R before the target vehicle.

[0251] The determination unit 304 transmits the fee information to each of the settlement devices 305a and 305b via the network N (step S304a). The estimation unit 103 executes step S101 again.

[0252] The settlement device 305a acquires the toll information from the determination unit 304 via the network N. The settlement device 305a wirelessly communicates with an on-board device (not shown) of the target vehicle, and performs a toll settlement process for the target vehicle in accordance with the toll information.

[0253] (Second decision Decision-making process when instructions are received) If it is determined that the instruction is not the first determination instruction (step S305; No), the determination unit 304 refers to the estimation result 109 for the entire road, and acquires the exhaust gas amount H of the vehicle ID included in the second determination instruction, i.e., the vehicle ID of the target vehicle, for the entire road R. The determination unit 304 determines the toll for the target vehicle according to the first determination rule similar to that of the first embodiment (step S302b).

[0254] In step S302b, the determination unit 304 generates toll information including the determined toll. The toll information may further include the vehicle ID of the target vehicle.

[0255] The determination unit 304 transmits the fee information to the settlement device 305b via the network N (step S304b). The estimation unit 103 executes step S101 again.

[0256] The settlement device 305b acquires the toll information transmitted in step S304a and step S304b. For the same target vehicle (i.e., target vehicles with the same vehicle ID), the toll included in the toll information transmitted in step S304a is designated as "toll M1." The toll included in the toll information transmitted in step S304b is designated as "toll M2."

[0257] If the toll M1 is greater than the toll M2, the settlement device 305b determines the difference as the refund amount. The settlement device 305b settles the refund of the toll for the target vehicle in accordance with the determined refund amount.

[0258] That is, the determination unit 304 according to this embodiment determines the toll for the target vehicle by further using the amount of exhaust gas from the target vehicle on the road R.

[0259] As described above, the toll M1 is a toll determined based on the amount of exhaust gas on road R of vehicle C that travels on road R before the target vehicle. The toll M2 is a toll determined based on the result of the target vehicle actually traveling on road R. By providing a refund, it is possible to settle the refund to the target vehicle that is estimated to have traveled on road R with a small amount of exhaust gas. This can motivate the user of the target vehicle to reduce the amount of exhaust gas.

[0260] So far, the third embodiment of the present invention has been described.

[0261] According to this embodiment, the estimation unit 103 estimates the amount of exhaust gas on the road R from one or more vehicles C traveling on the road R ahead of the target vehicle.

[0262] This makes it possible to determine the toll for the target vehicle to travel on road R using the amount of exhaust gas from vehicles traveling on road R before the target vehicle. This makes it possible to motivate users of the target vehicle to travel on road R, which has lower amounts of exhaust gas. At measurement points with low amounts of exhaust gas, vehicle C generally travels smoothly, and the amount of exhaust gas from vehicle C is often kept low. This makes it possible to reduce the amount of exhaust gas.

[0263] According to this embodiment, the estimation unit 103 further estimates the amount of exhaust gas from the target vehicle on the road section. The determination unit 304 determines the toll for the target vehicle using both the amount of exhaust gas on road R from one or more vehicles C traveling on road R before the target vehicle and the amount of exhaust gas from the target vehicle on road R.

[0264] In this way, by determining the toll using the amount of exhaust gas from vehicles traveling on road R before the target vehicle, it is possible to motivate users of the target vehicle to travel on road R with lower exhaust gas emissions. Also, even if the target vehicle travels on road R with a relatively high amount of exhaust gas, by determining the toll using the amount of exhaust gas from the target vehicle, it is possible to motivate users of the target vehicle to reduce the amount of exhaust gas emitted by the target vehicle. Therefore, it is possible to reduce the amount of exhaust gas.

[0265] According to this embodiment, processing is performed to discount at least a portion of the determined toll based on the amount of exhaust gas from the target vehicle on the road R.

[0266] As a result, as described above, it is possible to motivate the user of the target vehicle to reduce the amount of exhaust gas, and therefore it is possible to reduce the amount of exhaust gas.

[0267] (Variation 5) In the third embodiment, an example has been described in which the determination unit 304 performs processing to discount the determined toll based on the amount of exhaust gas from the target vehicle on road R. The determination unit 304 may perform processing to discount the determined toll based on at least one of the amount of exhaust gas from the target vehicle on road R and driving information indicating the driving state of the target vehicle on road R.

[0268] As described above, the driving state includes the driving speed, acceleration, idling stop status, load weight, etc. The driving information may include some or all of the driving speed, acceleration, idling stop status, load weight, etc.

[0269] As explained in the fourth modification, the traveling state can be acquired based on one or both of the image information PI_i and the vehicle information.

[0270] This modification also provides the same effects as the third embodiment.

[0271] (Variation 6) The determination unit 104, 304 may further use absorber information about carbon dioxide absorbers (CO2 absorbers) in the vicinity of the road section to determine the toll for the target vehicle. CO2 absorbers are plants such as trees, paints and materials with the function of absorbing CO2, and such paints and materials are installed on the walls of buildings, for example. The information about the CO2 absorbers includes at least one of the following: the location where the CO2 absorbers are installed, the number and area of ​​the CO2 absorbers installed, and a value indicating the CO2 absorption capacity of the installed CO2 absorbers. The determination unit 104, 304 may, for example, obtain the absorber information from an external device via the network N.

[0272] The determination unit 104, 304 may further use the absorber information to adjust the toll determined using the amount of exhaust gas on the road. Examples of adjustment methods include reducing the toll for road R where the amount of CO2 absorbers is greater than a reference value, and increasing the toll for road sections where the amount of CO2 absorbers is less than a reference value.

[0273] According to this modification, it is possible to motivate users of target vehicles to travel on roads R where the CO2 absorbers absorb a large amount of CO2, thereby making it possible to reduce the amount of exhaust gas.

[0274] Although the embodiments and modifications of the present invention have been described above with reference to the drawings, these are merely examples of the present invention, and various other configurations can also be adopted.

[0275] In addition, although the flowcharts used in the above description show multiple steps (processes) in a sequential order, the order of steps performed in each embodiment is not limited to the order shown. In each embodiment, the order of the steps shown in the drawings can be changed as long as it does not cause any problems in terms of content. Furthermore, the above-described embodiments and variations can be combined as long as the content is not contradictory.

[0276] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes.

[0277] 1. an estimation means for estimating an amount of exhaust gas from a vehicle; and a determination means for determining a toll for a target vehicle to travel a predetermined road section using the estimated exhaust gas amount. Traffic management device. 2. The estimation means estimates the amount of exhaust gas in the road section from one or more vehicles that pass through the road section before the target vehicle. 1. A traffic management device as described in Appendix 1. 3. The estimation means further estimates an amount of exhaust gas from the target vehicle in the road section, The determination means determines the toll for the target vehicle using both the amount of exhaust gas in the road section of one or more vehicles that pass through the road section before the target vehicle and the amount of exhaust gas of the target vehicle in the road section. 1. A traffic management device as described in Appendix 2. 4. The determination means determines the toll for the target vehicle by further using information about carbon dioxide absorbers in the vicinity of the road section. A traffic management device as described in Appendix 3. 5. The estimation means estimates the amount of exhaust gas from the target vehicle on the road section. 1. A traffic management device as described in Appendix 1. 6. The determining means determines the toll for the target vehicle on the road section for which the amount of exhaust gas from the target vehicle has been estimated. A traffic management device as described in Appendix 5. 7. The determining means determines the toll for the target vehicle in another road section that the target vehicle will travel after traveling through the road section in which the amount of exhaust gas from the target vehicle has been estimated. A traffic management device as described in Appendix 5. 8. The estimation means estimates the exhaust gas amount using at least one of a carbon dioxide concentration in the road section, an image, and vehicle information acquired from a vehicle. 10. A traffic management device according to any one of claims 1 to 4. 9. The determining means further performs processing to discount at least a portion of the determined toll based on at least one of the amount of exhaust gas from the target vehicle on the road section and driving information indicating the driving state of the target vehicle on the road section. 10. A traffic management device according to any one of claims 1 to 8. 10. The determining means determines the toll for the target vehicle so that the larger the estimated amount of exhaust gas, the higher the amount. 10. A traffic management device according to any one of clauses 1 to 9. 11. A traffic management device according to any one of appendices 1 to 10; a generating device that generates information for estimating the amount of exhaust gas from the vehicle; Traffic management system. 12. The computer Estimate the amount of vehicle exhaust gas, and determining a toll for a target vehicle to travel a predetermined road section using the estimated exhaust gas amount. Traffic management methods. 13. On the computer, Estimate the amount of vehicle exhaust gas, A recording medium having a program recorded thereon for determining a toll for a target vehicle to travel a specified road section using the estimated exhaust gas amount. 11. On the computer, Estimate the amount of vehicle exhaust gas, A program for executing the step of determining a toll for a target vehicle to travel a predetermined road section using the estimated exhaust gas amount. [Explanation of symbols]

[0278] 100,200,300 Traffic Management System 101 Generator 102,202,302 Traffic management equipment 103,203 Estimation part 104,304 Decision Section 105a ID acquisition device 105b,305a,305b Settlement device 107,207 Analysis results 108,109,208 Estimation results 210 Vehicle Data G1 Entrance Gate G2 Exit Gate

Claims

1. an estimation means for estimating an amount of exhaust gas from a vehicle; a determination means for determining a toll for a target vehicle to travel through a predetermined road section using the estimated exhaust gas amount; The estimation means estimates the amount of exhaust gas in the road section from one or more vehicles that pass through the road section before the target vehicle. Traffic management device.

2. The estimation means further estimates an amount of exhaust gas from the target vehicle in the road section, The determination means determines the toll for the target vehicle using both the amount of exhaust gas in the road section of one or more vehicles that pass through the road section before the target vehicle and the amount of exhaust gas of the target vehicle in the road section. The traffic management device of claim 1 .

3. an estimation means for estimating an amount of exhaust gas from a vehicle; a determination means for determining a toll for a target vehicle to travel through a predetermined road section using the estimated exhaust gas amount; The determination means determines the toll for the target vehicle by further using information about carbon dioxide absorbers in the vicinity of the road section. Traffic management device.

4. The estimation means estimates the amount of exhaust gas from the target vehicle on the road section. A traffic management device according to claim 1 or 3.

5. The determining means determines the toll for the target vehicle on the road section for which the amount of exhaust gas from the target vehicle has been estimated.

5. The traffic management device of claim 4.

6. an estimation means for estimating an amount of exhaust gas from a vehicle; a determination means for determining a toll for a target vehicle to travel through a predetermined road section using the estimated exhaust gas amount; the estimation means estimates an amount of exhaust gas from the target vehicle in the road section; The determining means determines the toll for the target vehicle in another road section that the target vehicle will travel after traveling through the road section in which the amount of exhaust gas from the target vehicle has been estimated. Traffic management device.

7. The estimation means estimates the exhaust gas amount using at least one of a carbon dioxide concentration in the road section, an image, and vehicle information acquired from a vehicle. A traffic management device according to any one of claims 1 to 3 and 6.

8. The computer Estimate the amount of vehicle exhaust gas, determining a toll for a target vehicle to travel a predetermined road section using the estimated exhaust gas amount; Estimating the amount of exhaust gas from the vehicle includes estimating the amount of exhaust gas from one or more vehicles traveling on the road section before the target vehicle. Traffic management methods.

9. The computer Estimate the amount of vehicle exhaust gas, determining a toll for a target vehicle to travel a predetermined road section using the estimated exhaust gas amount; Determining the toll includes determining the toll for the subject vehicle further using information about carbon dioxide absorbers in the vicinity of the road section. Traffic management methods.

10. On the computer, Estimate the amount of vehicle exhaust gas, determining a toll for a target vehicle to travel a predetermined road section using the estimated exhaust gas amount; The program in which estimating the amount of exhaust gas from the vehicle includes estimating the amount of exhaust gas in the road section from one or more vehicles that travel on the road section before the target vehicle.

Images