WO2024042691A1 - Dispositif d'estimation de trafic, procédé d'estimation de trafic et programme d'estimation de trafic - Google Patents

Dispositif d'estimation de trafic, procédé d'estimation de trafic et programme d'estimation de trafic Download PDF

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Publication number
WO2024042691A1
WO2024042691A1 PCT/JP2022/032129 JP2022032129W WO2024042691A1 WO 2024042691 A1 WO2024042691 A1 WO 2024042691A1 JP 2022032129 W JP2022032129 W JP 2022032129W WO 2024042691 A1 WO2024042691 A1 WO 2024042691A1
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Prior art keywords
traffic volume
traffic
road section
unit
travel time
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PCT/JP2022/032129
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English (en)
Japanese (ja)
Inventor
雅 高木
賢士 小宮
亮太 中田
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日本電信電話株式会社
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Priority to PCT/JP2022/032129 priority Critical patent/WO2024042691A1/fr
Publication of WO2024042691A1 publication Critical patent/WO2024042691A1/fr

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles

Definitions

  • the present invention relates to a traffic estimation device, a traffic estimation method, and a traffic estimation program.
  • map data indicating when to depart and where to travel
  • traffic demand data indicating when to depart and where to travel is obtained through questionnaire surveys such as person trip surveys or from vehicle probe data of connected cars.
  • the present invention has been made in view of the above, and an object of the present invention is to obtain traffic demand data that is easy to utilize.
  • the traffic estimation device includes a search unit that searches for a road section running parallel to a road section to be processed, among road sections where the traffic volume is known. and an estimating unit that estimates the travel time of the searched road section; A calculation unit that calculates the amount.
  • FIG. 1 is a diagram for explaining an overview of a traffic volume estimation device according to a first embodiment.
  • FIG. 2 is a schematic diagram illustrating a schematic configuration of a traffic volume estimation device according to the first embodiment.
  • FIG. 3 is a diagram for explaining the processing of the dividing section.
  • FIG. 4 is a diagram for explaining the processing of the distribution unit.
  • FIG. 5 is a flowchart showing a traffic volume estimation processing procedure according to the first embodiment.
  • FIG. 6 is a diagram for explaining an overview of a traffic volume estimation device according to the second embodiment.
  • FIG. 7 is a diagram for explaining an overview of a traffic volume estimation device according to the second embodiment.
  • FIG. 8 is a schematic diagram illustrating a schematic configuration of a traffic volume estimating device according to the second embodiment.
  • FIG. 9 is a flowchart showing a traffic volume estimation processing procedure according to the second embodiment.
  • FIG. 10 is a diagram illustrating an example of a computer that executes a traffic volume estimation program.
  • the traffic estimation device uses OD (Origin-Destination ) to estimate the data.
  • OD Oil-Destination
  • cross-sectional traffic data it is possible to obtain easily-utilized traffic demand data that has finer temporal and spatial granularity that can be used for traffic flow simulations than person trip surveys.
  • FIG. 1 is a diagram for explaining an overview of a traffic volume estimation device according to a first embodiment. Specifically, the traffic estimation device first extracts the arterial road network and cross-sectional traffic measurement points within the target area, as illustrated in FIG. 1(a). Next, the traffic estimation device defines an area with the measurement point as a boundary, as illustrated in FIG. 1(b). This is equivalent to converting cross-sectional traffic volume data of a main road into traffic demand data between adjacent areas.
  • the traffic estimation device connects the cross-sectional traffic volume between adjacent areas, that is, the traffic demand data, and derives the traffic volume on the main road within each area. This makes it possible to generate traffic demand data for traveling on arterial roads. Further, the traffic estimation device disperses the departure points and destinations at both ends of the traffic demand data within each area of the departure/arrival area, as illustrated in FIG. 1(d). That is, movement across areas is allowed only on main roads, and traffic demand data that always passes through the measurement point is generated. In this case, departure times are also dispersed within a predetermined time frame. This makes it possible to obtain traffic demand data with appropriate temporal and spatial granularity.
  • FIG. 2 is a schematic diagram illustrating a schematic configuration of a traffic volume estimation device according to the first embodiment.
  • the traffic estimation device 10 of this embodiment is realized by a general-purpose computer such as a personal computer, and includes an input section 11, an output section 12, a communication control section 13, a storage section 14, and a control section 15. Be prepared.
  • the input unit 11 is realized using an input device such as a keyboard or a mouse, and inputs various instruction information such as starting a process to the control unit 15 in response to an input operation by an operator.
  • the output unit 12 is realized by a display device such as a liquid crystal display, a printer, or the like. For example, the output unit 12 displays the results of traffic volume estimation processing, which will be described later.
  • the communication control unit 13 is realized by a NIC (Network Interface Card) or the like, and controls communication between an external device and the control unit 15 via a telecommunication line such as a LAN (Local Area Network) or the Internet.
  • a NIC Network Interface Card
  • the communication control unit 13 controls communication between the control unit 15 and a management device that manages various information such as map data, PoI information, facility information, and population distribution in the area to be processed.
  • the storage unit 14 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk.
  • a processing program for operating the traffic volume estimating device 10 data used during execution of the processing program, and the like are stored in advance, or are temporarily stored each time processing is performed.
  • the storage unit 14 may be configured to communicate with the control unit 15 via the communication control unit 13.
  • the storage unit 14 may also acquire and store in advance various information necessary for the traffic volume estimation process described below, such as map data, PoI information of large facilities, etc. in the area to be processed, and population distribution. .
  • the control unit 15 is realized using a CPU (Central Processing Unit) or the like, and executes a processing program stored in a memory. Thereby, the control unit 15 functions as an acquisition unit 15a, a division unit 15b, a connection unit 15c, and a distribution unit 15d, as illustrated in FIG. Note that each or a part of these functional units may be implemented in different hardware. Further, the control unit 15 may include other functional units.
  • a CPU Central Processing Unit
  • the acquisition unit 15a acquires cross-sectional traffic volume indicating the traffic volume at a predetermined measurement point on the main road. For example, the acquisition unit 15a acquires a main road network in the area to be processed, a cross-sectional traffic volume measurement point, and a cross-sectional traffic volume at the measurement point.
  • the acquisition unit 15a acquires map data, PoI information of large facilities, etc. within the processing target area, population distribution, etc.
  • the acquisition unit 15a acquires this information via the input unit 11 or from a management device or the like via the communication control unit 13.
  • the acquisition unit 15a may cause the storage unit 14 to store the acquired information.
  • the dividing unit 15b divides an area including alleys around the main road on the map at traffic measurement points. That is, the dividing unit 15b divides the area to be processed using the measurement point on the main road as a boundary. Thereby, the cross-sectional traffic volume data of the main road is converted into traffic demand data between divided adjacent areas.
  • the dividing unit 15b determines alleys around the main road to be included in each divided area. For example, the dividing unit 15b determines the alleys included in the area based on the travel time through the alleys. That is, the dividing unit 15b determines the alley to be associated with each section between the measurement points on the main road, not based on the geometric distance, but based on the travel time in consideration of one-way streets, speed limits, and the like.
  • FIG. 3 is a diagram for explaining the processing of the dividing section.
  • the dividing unit 15b lists alleys associated with each section of the main road. At this time, if the traffic volume measurement points are different for the up line and the down line of the main road, the dividing unit 15b manages the areas divided by the up line and the down line.
  • section B#01 and section B#12+section B#13 of the main road are distinguished. Therefore, there is an alley associated with section B#01 of the main road indicated by a solid line in FIG. 3(a), an alley associated with section B#12 indicated by a dashed line in FIG. 3(b), and an alley indicated by a dotted line It is managed separately from the alley associated with B#13.
  • the dividing unit 15b excludes alleys that straddle the divided area from the alleys included in the area.
  • the alley shown surrounded by an ellipse in FIG. 3(b) is not associated with either section B#12 or section B#13 because both ends straddle the measurement point. This makes it possible to prevent the traffic volume of the main road from deviating from the cross-sectional traffic volume in the processing of the distribution unit 15d, which will be described later.
  • the connecting unit 15c connects the cross-sectional traffic volumes between the divided areas and derives the traffic volume of the main road in each area. For example, the connecting unit 15c generates a route along a specific highway and connects the cross-sectional traffic volumes of areas on the generated route, thereby creating a route on the highway as illustrated in FIG. 1(c). Derive the traffic volume of the route. In this way, the connecting unit 15c generates a route along the main road and generates traffic demand data for the main road.
  • the connecting unit 15c identifies the departure area or destination area of each traffic using at least one of the distribution data of the average trip length or the statistical data of the right-left-turn-straight ratio at a predetermined intersection. That is, the connecting unit 15c generates a route along the main road by referring to the average trip length, right/left turn/straight ratio, etc., and specifies the departure area or destination area of each traffic. Note that the connecting unit 15c may estimate the average trip length and the right/left turn/straight ratio from the vehicle probe data.
  • the dispersion unit 15d disperses the departure points or destinations in each of the specified departure areas or destination areas among the divided areas. That is, as illustrated in FIG. 1(d), the distribution unit 15d distributes the routes of the traffic demand data of the main road so that vehicles enter and exit the alley as well.
  • the dispersion unit 15d distributes departure points or destinations by randomly selecting alleys within the departure area or the destination area.
  • the dispersion unit 15d may disperse the departure points or destinations according to the daytime population distribution or nighttime population distribution within the departure area or destination area.
  • the dispersion unit 15d may disperse the departure points or destinations using PoI information such as the number of people that can be accommodated in a large commercial facility or the like in the departure area or the destination area, the number of cars that can be accommodated in a parking lot, etc. This makes it possible to generate traffic demand data with fine spatial granularity and ease of use.
  • FIG. 4 is a diagram for explaining the processing of the dispersion section.
  • the distribution unit 15d causes traffic passing through the area to pass on the main road of the area.
  • the dispersion unit 15d controls traffic passing through an area that flows in from outside the area and flows out of the area from the end point of the main road in the area. Be sure to pass through the measurement point and do not disperse the departure point or destination. This prevents the traffic volume on the main road from deviating from the cross-sectional traffic volume.
  • the distribution unit 15d may further distribute the departure times at the departure points within a predetermined time frame. This makes it possible to generate traffic demand data with finer time granularity and ease of use.
  • FIG. 5 is a flowchart showing a traffic volume estimation processing procedure according to the first embodiment.
  • the flowchart in FIG. 5 is started, for example, at the timing when the user performs an operation input instructing to start.
  • the acquisition unit 15a acquires cross-sectional traffic volume indicating the traffic volume at a predetermined measurement point on the main road (step S1). For example, the acquisition unit 15a acquires a main road network in the area to be processed, a cross-sectional traffic volume measurement point, and a cross-sectional traffic volume at the measurement point. The acquisition unit 15a also acquires map data, PoI information of large facilities, etc. within the processing target area, population distribution, and the like.
  • the dividing unit 15b divides an area including alleys around the main road on the map at the measurement point (step S2). That is, the dividing unit 15b divides the area to be processed using the measurement point on the highway as a boundary, and determines alleys around the highway to be included in each divided area.
  • the dividing unit 15b determines the alleys to be included in the area based on the travel time through the alleys. That is, the dividing unit 15b determines the alley to be associated with each section between the measurement points on the main road, not based on the geometric distance, but based on the travel time in consideration of one-way streets, speed limits, and the like. At this time, the dividing unit 15b excludes alleys that straddle the divided area from the alleys included in the area.
  • the connecting unit 15c connects the cross-sectional traffic volumes between the divided areas and derives the traffic volume of the main road in each area (step S3).
  • the connecting unit 15c generates a route along a specific highway and connects the cross-sectional traffic volumes of areas on the generated route, thereby deriving the traffic volume of the route on the highway.
  • the connecting unit 15c uses at least one of the distribution data of the average trip length or the statistical data of the right/left turn/go straight ratio at a predetermined intersection to generate a route along the main road and determine the origin of each traffic. Identify the area or destination area and generate traffic demand data.
  • the dispersion unit 15d disperses the departure points or destinations in each of the specified departure areas or destination areas among the divided areas (step S4).
  • the distribution unit 15d distributes the routes of traffic demand data for main roads so that vehicles enter and exit alleys as well.
  • the dispersion unit 15d causes traffic passing through the area to pass on the main road of the area so that it always passes through the measurement point.
  • the distribution unit 15d may distribute the departure times at the departure points within a predetermined time frame.
  • the distribution unit 15d outputs the generated traffic demand data (step S5). This completes a series of traffic volume estimation processes.
  • the traffic estimation device of the second embodiment applies a balanced distribution method to cross-sectional traffic data for sections where no traffic counters (traffic counters) are installed to measure cross-sectional traffic, and the traffic volume is known. Estimating the traffic volume on routes without trackers installed from the traffic volume on the route. In this way, by using cross-sectional traffic data, it is possible to obtain traffic data with fine granularity both temporally and spatially.
  • FIGS. 6 and 7 are diagrams for explaining an overview of a traffic volume estimation device according to the second embodiment. Specifically, as shown in FIG. 6(a), if there are multiple routes connecting the departure point and destination, the driver attempts to select the route with the minimum travel time.
  • the travel time depends on the traffic volume and changes depending on the congestion/congestion situation of the selected route.
  • This relationship between traffic volume and travel time is formulated, and as illustrated in FIG. 6(c), there is a point at which the balance of demand and supply of traffic (traffic volume distribution) on a route becomes balanced.
  • the well-known first principle of Wardrop will converge to the state that ⁇ the travel times of all routes used are equal and are shorter than, or at best equal to, the travel times of routes that are not used.'' To establish.
  • the traffic estimation device selects a road section where the traffic volume is known and runs parallel to this road section. Explore. Then, as shown in Figure 7(b), the balanced allocation method is applied to the road section where the tracker is installed and the traffic volume is known (route 1) and the road section for which the traffic volume is to be estimated (route 2). By applying this, the traffic volume of route 2 is estimated.
  • the travel time in the parallel section where the traffic volume is known (route 1) and the travel time in the section where the traffic volume is estimated (route 2) are approximately the same.
  • a simulation is performed to estimate the traffic volume in the estimation target section.
  • FIG. 8 is a schematic diagram illustrating a schematic configuration of a traffic volume estimating device according to the second embodiment.
  • the traffic volume estimating device 10a shown in FIG. 8 is different from the first one shown in FIG. This is different from the traffic estimation device 10 of the embodiment. Descriptions of other functional units similar to those of the traffic volume estimating device 10 shown in FIG. 2 will be omitted.
  • the acquisition unit 15a acquires cross-sectional traffic volume indicating the traffic volume at a predetermined measurement point on the main road.
  • the search unit 15e searches for a road section running parallel to the road section to be processed, among road sections with known traffic volume. For example, the search unit 15e searches for a road section of an arterial road whose both ends are the same as the road section whose traffic volume is unknown. Then, the search unit 15e obtains the traffic volume of the searched road section using the cross-sectional traffic volume at each measurement point on the main road.
  • the estimation unit 15f estimates the travel time of the searched road section (parallel section). For example, the estimation unit 15f performs a known traffic flow simulation to estimate the travel time at each time in the parallel section.
  • the estimation unit 15f may estimate the travel time using a predetermined relationship between the traffic volume and travel time in the searched road section. For example, the estimating unit 15f may express the relationship between the traffic volume and travel time in the road section illustrated in FIG. 6(b) using a formula, and estimate the travel time using the formula.
  • the calculation unit 15g calculates the traffic volume of the road section to be processed so that the estimated travel time and the travel time of the road section to be processed are the same. For example, various traffic volumes are applied to the road section to be processed, and a traffic volume that is the same as the travel time estimated for the searched road section is calculated.
  • the calculation unit 15g may calculate the traffic volume using a predetermined relationship between the traffic volume and travel time in the road section to be processed.
  • the traffic volume of the road section to be processed may be calculated using an equation representing the relationship between the traffic volume and travel time in the road section.
  • the traffic volume estimating device 10a estimates the traffic volume of the road section to be processed whose traffic volume is unknown. This makes it possible to generate traffic demand data for the road section to be processed.
  • FIG. 9 is a flowchart showing a traffic volume estimation processing procedure according to the second embodiment.
  • the flowchart in FIG. 9 is started, for example, at the timing when the user performs an operation input instructing to start.
  • the acquisition unit 15a acquires cross-sectional traffic volume indicating the traffic volume at a predetermined measurement point on the main road, similarly to the first embodiment described above. Furthermore, the search unit 15e searches for a road section running parallel to the road section to be processed, among the road sections for which the traffic volume is known (step S1). For example, the search unit 15e searches for a road section of an arterial road whose both ends are the same as the road section whose traffic volume is unknown. Then, the search unit 15e obtains the traffic volume of the searched road section using the cross-sectional traffic volume at each measurement point on the main road.
  • the estimation unit 15f estimates the travel time of the searched road section (step S2). For example, the estimation unit 15f estimates the travel time using a predetermined relationship between the traffic volume and the travel time in the searched road section.
  • the calculation unit 15g calculates the traffic volume of the road section to be processed so that the estimated travel time and the travel time of the road section to be processed are the same (step S3). For example, the calculation unit 15g calculates the traffic volume using a predetermined relationship between the traffic volume and travel time in the road section to be processed. This generates traffic demand data for the road section to be processed.
  • calculation unit 15g outputs the generated traffic demand data (step S4). This completes the series of traffic volume estimation processes.
  • the traffic estimation device 10 of the first embodiment and the traffic estimation device 10a of the second embodiment described above may be devices that cooperate. For example, using the route of the traffic demand data generated by the traffic volume estimating device 10 of the first embodiment, the traffic volume estimating device 10a of the second embodiment determines the route for which the traffic volume running parallel to the route is unknown. estimates the traffic volume. In that case, the traffic estimation device 10 of the first embodiment and the traffic estimation device 10a of the second embodiment may be implemented in the same hardware.
  • the search unit 15e searches for a road section running parallel to the road section to be processed, among road sections where the traffic volume is known.
  • the estimation unit 15f estimates the travel time of the searched road section.
  • the calculation unit 15g calculates the traffic volume of the road section to be processed so that the estimated travel time and the travel time of the road section to be processed are the same.
  • the search unit 15e obtains the known traffic volume of the road section using the cross-sectional traffic volume that indicates the traffic volume at a predetermined measurement point on the main road.
  • the traffic volume estimating device 10a applies an equal distribution method to the traffic volume of a road section where the traffic volume is unknown, such as where a truck is not installed, to the known traffic volume such as cross-sectional traffic volume data. Estimated by. Therefore, by using the cross-sectional traffic data, the traffic volume estimation device 10a can obtain traffic data with fine granularity both temporally and spatially.
  • the estimation unit 15f estimates the travel time using a predetermined relationship between the traffic volume and travel time in the searched road section, and the calculation unit 15g estimates the travel time and traffic volume in the road section to be processed.
  • the traffic volume is calculated using a predetermined relationship. This makes it possible to estimate the traffic volume of a road section where the traffic volume is unknown with high accuracy.
  • the traffic estimation devices 10 and 10a can be implemented by installing a traffic estimation program that executes the above-mentioned traffic estimation processing into a desired computer as package software or online software.
  • the information processing device can be made to function as the traffic volume estimation device 10, 10a.
  • the information processing device referred to here includes a desktop or notebook personal computer.
  • information processing devices include mobile communication terminals such as smartphones, mobile phones, and PHSs (Personal Handyphone Systems), as well as slate terminals such as PDAs (Personal Digital Assistants).
  • the functions of the traffic estimation devices 10 and 10a may be implemented in a cloud server.
  • FIG. 10 is a diagram showing an example of a computer that executes a traffic volume estimation program.
  • Computer 1000 includes, for example, memory 1010, CPU 1020, hard disk drive interface 1030, disk drive interface 1040, serial port interface 1050, video adapter 1060, and network interface 1070. These parts are connected by a bus 1080.
  • the memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM 1012.
  • the ROM 1011 stores, for example, a boot program such as BIOS (Basic Input Output System).
  • Hard disk drive interface 1030 is connected to hard disk drive 1031.
  • Disk drive interface 1040 is connected to disk drive 1041.
  • a removable storage medium such as a magnetic disk or an optical disk is inserted into the disk drive 1041, for example.
  • a mouse 1051 and a keyboard 1052 are connected to the serial port interface 1050.
  • a display 1061 is connected to the video adapter 1060.
  • the hard disk drive 1031 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. Each piece of information described in the above embodiments is stored in, for example, the hard disk drive 1031 or the memory 1010.
  • the traffic estimation program is stored in the hard disk drive 1031, for example, as a program module 1093 in which commands to be executed by the computer 1000 are written. Specifically, a program module 1093 in which each process executed by the traffic estimation device 10 described in the above embodiment is described is stored in the hard disk drive 1031.
  • data used for information processing by the traffic estimation program is stored as program data 1094 in, for example, the hard disk drive 1031.
  • the CPU 1020 reads out the program module 1093 and program data 1094 stored in the hard disk drive 1031 to the RAM 1012 as necessary, and executes each of the above-described procedures.
  • program module 1093 and program data 1094 related to the traffic volume estimation program are not limited to being stored in the hard disk drive 1031; for example, they may be stored in a removable storage medium and read by the CPU 1020 via the disk drive 1041 or the like. May be read.
  • the program module 1093 and program data 1094 related to the traffic estimation program are stored in another computer connected via a network such as a LAN or WAN (Wide Area Network), and read by the CPU 1020 via the network interface 1070. May be served.

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Abstract

Selon la présente invention, une unité de récupération (15e) récupère, parmi des sections de route pour lesquelles le trafic est connu, une section de route qui s'étend côte à côte avec une section de route soumise à un traitement. Une unité d'estimation (15f) estime un temps de déplacement de la section de route récupérée. Une unité de calcul (15g) calcule le trafic dans la section de route soumise à un traitement, de telle sorte que le temps de déplacement qui a été estimé et le temps de déplacement de la section de route soumis au traitement sont identiques.
PCT/JP2022/032129 2022-08-25 2022-08-25 Dispositif d'estimation de trafic, procédé d'estimation de trafic et programme d'estimation de trafic WO2024042691A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002063680A (ja) * 2000-08-22 2002-02-28 Toshiba Corp 交通管制システム
JP2004294371A (ja) * 2003-03-28 2004-10-21 Equos Research Co Ltd 所要時間データベースの作成方法および経路探索方法
WO2014024264A1 (fr) * 2012-08-08 2014-02-13 株式会社 日立製作所 Dispositif et procédé de prédiction de volume de trafic

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002063680A (ja) * 2000-08-22 2002-02-28 Toshiba Corp 交通管制システム
JP2004294371A (ja) * 2003-03-28 2004-10-21 Equos Research Co Ltd 所要時間データベースの作成方法および経路探索方法
WO2014024264A1 (fr) * 2012-08-08 2014-02-13 株式会社 日立製作所 Dispositif et procédé de prédiction de volume de trafic

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