Information processing device, information processing method, and program

Abstract

To estimate a traffic volume at an arbitrary point located in a free flow zone.SOLUTION: An information processing device comprises: an acquisition section for acquiring a time required for a vehicle, which is positioned at an observation point where a traffic volume is observed at a certain time to move between the observation point and an arbitrary point as a required time corresponding to the time; and an estimation section for estimating the traffic volume at the arbitrary point based on a first required time corresponding to a first time, a second required time corresponding to a second time, and the number of vehicles detected at the observation point from the first time to the second time.SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and a program.

Background Art

[0002] In recent years, technologies for determining the state of traffic flow based on data acquired by vehicle detectors have been developed. For example, technologies for estimating the traffic volume at a certain point or the number of vehicles within a certain section based on data acquired by vehicle detectors have been disclosed (see, for example, Patent Documents 1 to 3).

[0003] Also, a simulation technology has been disclosed in which the traffic volume observed at an observation point within a certain section is input to predict the traffic flow of a road including a traffic jam section (see, for example, Non-Patent Document 1).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Patent Document 3

Non-Patent Documents

[0005]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] In the above-mentioned Non-Patent Document 1, traffic flow is predicted using observed traffic volume measured by vehicle detectors installed on roads. However, if the observation point indicating the location where the vehicle detector is installed is located within a congested area, the observed traffic volume measured by the vehicle detector decreases, and there is a possibility that the reproduction of traffic flow, such as the peak time or length of congestion predicted by the simulation, may not be sufficiently obtained.

[0007] Therefore, the present invention has been made in view of the above problems, and the object of the present invention is to provide a novel and improved information processing device, information processing method, and program that can estimate the traffic volume at any point in a free watershed. [Means for solving the problem]

[0008] To solve the above problems, according to one aspect of the present invention, an information processing device is provided, comprising: an acquisition unit that acquires the time required for a vehicle located at an observation point for observing traffic volume at a certain time to travel between the observation point and an arbitrary point, as the required time corresponding to the said time; and an estimation unit that estimates the traffic volume at the arbitrary point based on a first required time corresponding to a first time, a second required time corresponding to a second time, and the number of vehicles detected at the observation point between the first time and the second time.

[0009] The observation point may be located downstream from the arbitrary point on the road along which the vehicle is traveling.

[0010] The estimation unit may estimate the traffic volume at the arbitrary location based on the difference between a third time obtained by subtracting the first required time from the first time, a fourth time obtained by subtracting the second required time from the second time, and the number of vehicles detected at the observation point between the first time and the second time.

[0011] The acquisition unit may acquire the required time based on probe data that associates the time, a kilometer post indicating the distance from a certain starting point on the road, and the speed of the vehicle at the point indicated by the kilometer post at the time.

[0012] The observation point, the arbitrary point, and the estimated start and end times for estimating traffic volume may be set in advance, and the acquisition unit may acquire the required time based on a plurality of probe data including the distance from the arbitrary point to the observation point and the distance from the start time to the end time.

[0013] The system further includes an interpolation unit that interpolates missing data at some time points in the probe data from the estimated start time to the estimated end time, based on probe data from other time points, and the acquisition unit may acquire the required time based on the interpolated data obtained by the interpolation unit or the probe data.

[0014] The interpolation unit may interpolate missing data from some kilometer posts in the probe data from the arbitrary point to the observation point based on probe data from other kilometer posts.

[0015] The interpolation unit may obtain the average value of the probe data before and after the missing data as the interpolated data.

[0016] The estimation unit may estimate the traffic volume based on a time list that includes a plurality of target times, each separated into a predetermined time interval between the estimated start time and the estimated end time.

[0017] The third time is the target time, and the fourth time may be the target time plus the time width.

[0018] The aforementioned time interval may be set to a value within a predetermined range.

[0019] The observation point may be located upstream of the arbitrary point on the road along which the vehicle travels.

[0020] The estimation unit may estimate the traffic volume at the arbitrary point based on the difference between a fifth time obtained by adding the first required time to the first time and a sixth time obtained by adding the second required time to the second time, and the number of vehicles detected at the observation point between the first time and the second time.

[0021] The number of vehicles detected at the observation point may be obtained by vehicle detectors arranged at at least one or more points on the road.

[0022] Further, in order to solve the above problems, according to another aspect of the present invention, there is provided an information processing method executed by a computer, including obtaining, as a required time corresponding to the time, the time required for a vehicle located at a time at an observation point for observing traffic volume to move between the observation point and an arbitrary point, and estimating the traffic volume at the arbitrary point based on a first required time corresponding to a first time, a second required time corresponding to a second time, and the number of vehicles detected at the observation point between the first time and the second time.

[0023] Further, in order to solve the above problems, according to another aspect of the present invention, there is provided a program that causes a computer to realize an acquisition function of obtaining, as a required time corresponding to the time, the time required for a vehicle located at a time at an observation point for observing traffic volume to move between the observation point and an arbitrary point, and an estimation function of estimating the traffic volume at the arbitrary point based on a first required time corresponding to a first time, a second required time corresponding to a second time, and the number of vehicles detected at the observation point between the first time and the second time.

Advantages of the Invention

[0024] As described above, according to the present invention, it is possible to estimate the traffic volume at an arbitrary point in a free flow region.

Brief Description of the Drawings

[0025] [Figure 1] This is an explanatory diagram illustrating an example of the overview of the information processing system according to this embodiment. [Figure 2] This is an explanatory diagram illustrating an example of the functional configuration of the data management device 10 according to this embodiment. [Figure 3] This is an explanatory diagram illustrating an example of probe data held by the probe data storage unit 131. [Figure 4] This is an explanatory diagram illustrating an example of track count data held by the track count data storage unit 133. [Figure 5] This is an explanatory diagram illustrating an example of the functional configuration of the traffic volume estimation device 20 according to this embodiment. [Figure 6] This is an explanatory diagram illustrating an example of the calculation process for the sum of tracking times according to this embodiment. [Figure 7] This is an explanatory diagram illustrating an example of the traffic volume estimation process according to this embodiment. [Figure 8] This is an explanatory diagram illustrating an example of the operation process of the tracking time sum calculation process according to this embodiment. [Figure 9] This is an explanatory diagram illustrating an example of the operation process of the traffic volume estimation process according to this embodiment. [Figure 10] This is an explanatory diagram to illustrate specific examples of observed and estimated traffic volume. [Figure 11] This is an example of data showing observed traffic flow during congestion. [Figure 12A] This is an example of data showing the results of a traffic flow simulation using observed traffic volume. [Figure 12B] This is an example of data showing the results of a traffic flow simulation using estimated traffic volume. [Figure 13] This is a block diagram showing the hardware configuration of the traffic volume estimation device 20 according to this embodiment. [Modes for carrying out the invention]

[0026] Preferred embodiments of the present invention will be described in detail below with reference to the attached drawings. In this specification and drawings, components having substantially the same functional configuration are denoted by the same reference numerals, and redundant descriptions will be omitted.

[0027] <<1. Overview of the Information Processing System>> One embodiment of the present invention relates to an information processing system that enables the estimation of traffic volume at any point in a free watershed.

[0028] In recent years, there has been a great deal of development in simulation techniques or technologies for generating predictive models to predict traffic flow. Such simulation techniques for predicting traffic flow may, for example, utilize observed traffic volume obtained from vehicle detection devices. In the following explanation, the simulation techniques for predicting traffic flow may be referred to as "traffic flow simulations."

[0029] However, the meaning of observed traffic volume measured using a vehicle detector can change depending on the traffic flow conditions at the observation point where the vehicle detector is installed. For example, traffic flow conditions include free flow, congested flow, and jammed flow. Specifically, traffic flow speeds of "55 km / h or more" are defined as "free flow," traffic flow speeds in the range of "40 km / h to 55 km / h" are defined as "congested flow," and traffic flow speeds of "40 km / h or less" are defined as "jammed flow." However, the definitions of free flow, congested flow, and jammed flow are not limited to the traffic flow speeds mentioned above.

[0030] For example, if an observation point where a vehicle detector is installed is located within a congested area, the vehicle speed will be lower. This may result in a decrease in the number of vehicles detected per unit of time, even if there are actually many vehicles around the vehicle detector.

[0031] Therefore, during times when an observation point equipped with a vehicle detector is located within a congested watershed, the observed traffic volume obtained by the vehicle detector may show a value equivalent to the observed traffic volume obtained by the vehicle detector when the observation point is located within a free watershed.

[0032] As a result, when using observed traffic volumes from observation points located within congested areas in traffic flow simulations, there was a risk that the reproducibility of congestion peak times or congestion lengths could not be sufficiently obtained. In other words, it is desirable to use traffic volumes that have been corrected for the reduction in traffic volume associated with congestion in traffic flow simulations.

[0033] Therefore, in one embodiment of the present invention, it becomes possible to estimate the traffic volume at any point (for example, a point in a free watershed) based on the number of vehicles detected at an observation point. The mechanism for estimating the traffic volume at an arbitrary point will be explained below. First, with reference to Figure 1, an overview of the information processing system according to one embodiment of the present invention will be described.

[0034] Figure 1 is an explanatory diagram illustrating an example of the overview of the information processing system according to this embodiment. As shown in Figure 1, the information processing system according to one embodiment of the present invention includes a plurality of driving history data collectors 1, a plurality of vehicle detectors 2, a data management device 10, and a traffic volume estimation device 20.

[0035] The driving history data collector 1 and the vehicle detector 2 are connected to the data management device 10 via network 5A. The data management device 10 is also connected to the traffic volume estimation device 20 via network 5B. Networks 5A and 5B may be the same or different. Hereafter, networks 5A and 5B may be collectively referred to as network 5.

[0036] For example, network 5 may include public networks such as telephone lines, the Internet, and satellite networks, as well as LANs (Local Area Networks) or WANs (Wide Area Networks). Network 5 may also include dedicated network lines such as IP-VPNs (Internet Protocol-Virtual Private Networks).

[0037] <Driving history data collector 1> Multiple driving history data collectors 1 according to this embodiment are installed on highways and receive vehicle probe driving history data (hereinafter simply referred to as driving history data) from on-board devices installed in each vehicle.

[0038] More specifically, the in-vehicle device sequentially accumulates driving history data, which includes the time the vehicle equipped with the device traveled, and location information (e.g., latitude and longitude) and speed information associated with that time. The driving history data collector 1 then receives the accumulated driving history data from the vehicle traveling on the highway.

[0039] Furthermore, the driving history data collector 1 transmits the driving history data acquired from each vehicle to the data management device 10. For example, the driving history data collector 1 may transmit the accumulated driving history data to the data management device 10 at regular intervals.

[0040] <Vehicle Detector 2> The vehicle detector 2 according to this embodiment is installed at predetermined intervals on the highway and acquires information about vehicles passing through the observation range of the vehicle detector 2 at predetermined time intervals (time widths) as traffic data.

[0041] For example, vehicle detector 2 acquires various information as traffic data, such as the date and time for each predetermined time interval, the average speed of vehicles that passed through the observation range associated with that date and time, and the number of vehicles. The traffic data may also include information assigned to each vehicle detector 2, such as the traffic ID (Identification), route code, and installation location.

[0042] Furthermore, the vehicle detector 2 transmits the acquired traffic data to the data management device 10. For example, the vehicle detector 2 may transmit the accumulated traffic data to the data management device 10 at regular intervals.

[0043] <Data management device 10> The data management device 10 according to this embodiment is a device for managing information on expressways. Here, with reference to Figure 2, an example of the functional configuration of the data management device 10 will be described.

[0044] Figure 2 is an explanatory diagram illustrating an example of the functional configuration of the data management device 10 according to this embodiment. As shown in Figure 2, the data management device 10 according to this embodiment comprises a communication unit 110, a control unit 120, and a storage unit 130.

[0045] (Communications Department 110) The communication unit 110 according to this embodiment has the function of sending and receiving data. For example, the communication unit 110 receives driving history data from the driving history data collector 1. The communication unit 110 also receives track count data from the vehicle detector 2.

[0046] Furthermore, the communication unit 110 transmits various data, such as probe data, track count data, or master data, stored in the storage unit 130 to the traffic volume estimation device 20, in accordance with the control unit 120.

[0047] (Control unit 120) The control unit 120 in this embodiment controls the overall operation of the data management device 10. For example, the control unit 120 causes the various data held in the storage unit 130 to be transmitted to the communication unit 110.

[0048] Furthermore, the control unit 120 converts the driving history data received from the driving history data collector 1 into probe data.

[0049] For example, the control unit 120 generates probe data that includes the highway route code and kilometer posts indicating the distance from the starting point of the highway to a certain location, based on the location information included in the driving history data. The control unit 120 also generates probe data that includes the average speed over a preset time or section width, based on the time information, location information, and speed information included in the driving history data.

[0050] (Storage unit 130) The storage unit 130 according to this embodiment has the function of holding software and various data. As shown in Figure 2, the storage unit 130 according to this embodiment includes a probe data storage unit 131, a track count data storage unit 133, and a master data storage unit 135.

[0051] The probe data storage unit 131 according to this embodiment holds the probe data generated by the control unit 120. An example of probe data will be described below with reference to Figure 3.

[0052] Figure 3 is an explanatory diagram illustrating an example of probe data held by the probe data storage unit 131. The probe data storage unit 131 stores probe data that associates, for example, the target date and time PD1, the route code PD2, the direction PD3 indicating whether it is an uphill or downhill direction, the kilometer post PD4, and the average speed of traffic flow PD5 for each predetermined time width and section width, as shown in Figure 3.

[0053] For example, if the section width is set to 100m (0.1km) and the time width to 1 minute, the average speed PD5 may represent the average speed over 1 minute for a group of vehicles traveling within the 100m section.

[0054] The Torakan data storage unit 133 according to this embodiment stores Torakan data received from the vehicle detector 2. An example of Torakan data will be described below with reference to Figure 4.

[0055] Figure 4 is an explanatory diagram illustrating an example of the trakkan data held by the trakkan data storage unit 133. The trakkan data storage unit 133 holds trakkan data that associates, for example, the date and time TD1 for each time interval (for example, every minute), the route code TD2 on which the vehicle detector 2 is installed, the trakkan ID TD3 which is the identification information of the vehicle detector, the average speed TD4, and the number of vehicles TD5.

[0056] The master data storage unit 135 according to this embodiment stores master data of the vehicle detector 2, which associates the identification information of the vehicle detector 2, the Trakan ID, with the route code on which the vehicle detector 2 is installed and the installation location.

[0057] <Traffic volume estimation device 20> The traffic volume estimation device 20 according to this embodiment is an example of an information processing device for estimating traffic volume. The traffic volume estimation device 20 acquires the time required for a vehicle located at an observation point at a given time to travel between the observation point and an arbitrary point, as the required time corresponding to that time.

[0058] Furthermore, the traffic volume estimation device 20 estimates the traffic volume at an arbitrary point based on a first travel time corresponding to a first time, a second travel time corresponding to a second time, and the number of vehicles detected at observation points between the first and second time.

[0059] In this embodiment, particular ingenuity has been applied to the traffic volume estimation device 20 among the information processing systems described above. Specific examples of the configuration and operation of the traffic volume estimation device 20 according to this embodiment will be described in detail below.

[0060] <<2. Example of Functional Configuration of Traffic Volume Estimation Device 20>> Figure 5 is an explanatory diagram illustrating an example of the functional configuration of the traffic volume estimation device 20 according to this embodiment. As shown in Figure 5, the traffic volume estimation device 20 according to this embodiment comprises a communication unit 210, an operation display unit 220, and a control unit 230.

[0061] <Communications Department 210> The communication unit 210 according to this embodiment has the function of sending and receiving data. For example, the communication unit 210 receives probe data, track count data, and master data from the data management device 10.

[0062] Furthermore, the communication unit 210 may transmit the estimated traffic volume at any point estimated by the traffic volume estimation unit 235 to an external system (for example, a traffic flow simulator).

[0063] <Operation display section 220> The operation display unit 220 according to this embodiment includes the function of an input unit for inputting the following: an observation point KP (kilometer post) indicating the distance from the starting point of the highway to the observation point; an arbitrary point KP indicating the distance from the starting point of the highway to an arbitrary point; the start and end times of traffic volume estimation at the arbitrary point KP; and the aggregation time range for aggregating traffic volume. The operation display unit 220 also includes the function of a display unit for displaying the traffic volume at the arbitrary point estimated by the traffic volume estimation unit 235. Here, the observation point refers to the location where the vehicle detector 2 is installed.

[0064] The input function can be implemented, for example, by a touch panel. The display function can be implemented, for example, by a CRT (Cathode Ray Tube) display, a liquid crystal display (LCD) display, or an OLED (Organic Light Emitting Diode) display. Note that the input and operation functions may be configured separately.

[0065] <Control Unit 230> The control unit 230 according to this embodiment controls the overall operation of the traffic volume estimation device 20. As shown in Figure 5, the control unit 230 according to this embodiment includes an estimation condition receiving unit 231, a probe data interpolation unit 232, a loop processing determination unit 233, a tracking time sum calculation unit 234, and a traffic volume estimation unit 235.

[0066] (Estimated Condition Reception Unit 231) In this embodiment, the estimation condition receiving unit 231 receives the observation point KP, an arbitrary point KP, the start time and end time of traffic volume estimation at the arbitrary point KP, and the time range for aggregating traffic volume, which are input via the operation display unit 220.

[0067] Furthermore, the estimation condition receiving unit 231 outputs the observation point KP, the arbitrary point KP, and the start and end times of traffic volume estimation at the arbitrary point KP to the probe data interpolation unit 232. The estimation condition receiving unit 231 also outputs the observation point KP, the arbitrary point KP, the start and end times of traffic volume estimation at the arbitrary point KP, and the aggregation time range for aggregating traffic volume to the loop processing determination unit 233. In the following explanation, the start time of traffic volume estimation at the arbitrary point KP may be expressed as the estimation start time, and the end time of traffic volume estimation at the arbitrary point KP may be expressed as the estimation end time. Also, the time range for aggregating traffic volume may be expressed as the aggregation time range.

[0068] The various information received by the estimation condition reception unit 231 may be entered by the user via the operation display unit 220, or it may be automatically set by the system. In addition, any value may be set for the aggregation time range, but it is desirable to set a value within a predetermined range of, for example, 5 to 10 minutes, taking into account the balance between processing accuracy and speed.

[0069] (Probe data interpolation unit 232) The probe data interpolation unit 232 in this embodiment is an example of an interpolation unit, and when the missing probe data indicating missing probe data is only at some time points among the probe data from the estimated start time to the estimated end time, it interpolates the missing data based on probe data from other time points.

[0070] First, the probe data interpolation unit 232 receives the observation point KP, arbitrary location KP, estimated start time, and estimated end time from the estimation condition reception unit 231.

[0071] The probe data interpolation unit 232 then acquires probe data from the data management device 10, including the estimated start time and estimated end time at observation point KP and arbitrary point KP. For example, the probe data interpolation unit 232 may acquire probe data from the data management device 10 from probe data taken a predetermined time before the estimated start time to probe data taken a predetermined time after the estimated end time. The predetermined time here is a margin that can be used when interpolating missing data.

[0072] The probe data interpolation unit 232 then interpolates the missing probe data (i.e., the missing data) when there is a shortage of probe data for some of the time points included in the estimated start time to estimated end time.

[0073] Specifically, let's assume the estimated start time is "2020 / 4 / 29 15:29" and the estimated end time is "2020 / 4 / 29 15:39". Also, let's assume the probe data storage unit 131 manages probe data with a section width of 100m (0.1km) and a time width of 1 minute. In this case, the probe data interpolation unit 232 acquires probe data from the data management device 10 for each kilometer post (every 100m) from observation point KP to arbitrary point KP, for each time point (every minute) from "2020 / 4 / 29 15:29" to "2020 / 4 / 29 15:39".

[0074] Furthermore, if the predetermined time mentioned above is "5 minutes", the probe data interpolation unit 232 may acquire probe data from the data management device 10 for each time point from "2020 / 4 / 29 15:24" to "2020 / 4 / 29 15:44" at each kilometer post from observation point KP to arbitrary point KP.

[0075] Here, if the probe data for "2020 / 4 / 29 15:35" at any point KP is missing for reasons such as being lost, the probe data interpolation unit 232 may interpolate the missing probe data for "2020 / 4 / 29 15:35" based on probe data from other times.

[0076] For example, the probe data interpolation unit 232 may calculate probe data by interpolating missing values ​​based on the probe data for "2020 / 4 / 29 15:34" at an arbitrary location KP and the probe data for "2020 / 4 / 29 15:36" at an arbitrary location KP. More specifically, the probe data interpolation unit 232 may interpolate the average of the average speed at "2020 / 4 / 29 15:34" at an arbitrary location KP and the average of the average speed at "2020 / 4 / 29 15:36" at an arbitrary location KP as the average speed at "2020 / 4 / 29 15:35" at an arbitrary location KP.

[0077] Furthermore, the probe data interpolation unit 232 may interpolate missing data for some kilometer posts in the probe data from an arbitrary point KP to an observation point KP, based on probe data from other kilometer posts.

[0078] For example, suppose an arbitrary point KP is upstream and an observation point KP is downstream. Also, suppose that probe data is missing at a point 100m downstream from arbitrary point KP. In this case, the probe data interpolation unit 232 may interpolate the missing data for the point 100m downstream from arbitrary point KP based on the probe data at arbitrary point KP and the probe data at a point 200m downstream from arbitrary point KP. In the following explanation, the probe data interpolated by the probe data interpolation unit 232 may be referred to as interpolated data.

[0079] (Loop processing determination unit 233) In this embodiment, the loop processing determination unit 233 generates a list of times for executing the process of estimating traffic volume. First, the loop processing determination unit 233 obtains the observation point KP, an arbitrary point KP, the estimated start time, the estimated end time, and the aggregation time width. Then, the loop processing determination unit 233 generates a time list divided into aggregation time widths from the estimated start time to the estimated end time. Finally, the loop processing determination unit 233 outputs each time included in the generated time list to the traffic volume estimation unit 235.

[0080] (Tracking time sum calculation unit 234) The tracking time sum calculation unit 234 according to this embodiment is an example of an acquisition unit, and calculates the time required for a vehicle located at an observation point KP at a given time to travel between the observation point KP and an arbitrary point KP, as the required time corresponding to that time. In the following description, an example in which the arbitrary point KP is upstream and the observation point KP is downstream will be mainly described.

[0081] For example, the tracking time sum calculation unit 234 may calculate the time required for a vehicle that arrived at the observation point KP at a given time to travel from an arbitrary point KP to the observation point KP.

[0082] First, the tracking time sum calculation unit 234 obtains the observation point KP, arbitrary point KP, and target time from the traffic volume estimation unit 235. The tracking time sum calculation unit 234 also obtains interpolated probe data (interpolated data) from the probe data interpolation unit 232 and calculates the tracking time sum as the time required from the arbitrary point KP to the observation point KP at the target time. An example of the tracking time sum calculation process will be explained below with reference to Figure 6.

[0083] Figure 6 is an explanatory diagram illustrating an example of the calculation process for the sum of tracking times according to this embodiment. In the graph shown in Figure 6, the vertical axis represents time and the horizontal axis represents kilometer posts (KP). For example, if the interval width is set to 100m (0.1km) and the time interval to 1 minute, moving one square to the right on the horizontal axis corresponds to moving 100m downstream, and moving one square down on the vertical axis corresponds to moving 1 minute later.

[0084] Furthermore, in Figure 6, the state of traffic flow is represented by the intensity of the dots. For example, in the grid shown in Figure 6, the traffic flow state corresponding to the darker dots is congested flow, the traffic flow state corresponding to the lighter dots is crowded flow, and the traffic flow state corresponding to the grid with no dots is free flow.

[0085] First, the tracking time sum calculation unit 234 obtains the observation point KP, arbitrary point KP, and target time from the traffic volume estimation unit 235. Next, the tracking time sum calculation unit 234 sets the arbitrary point KP as the source X1, which is the starting point for calculating the tracking time sum, sets the observation point KP as the destination X2, which is the ending point for calculating the tracking time sum, and sets the target time as the tracking start time for the source KP.

[0086] Furthermore, the tracking time sum calculation unit 234 obtains interpolated probe data from the probe data interpolation unit 232. Subsequently, the tracking time sum calculation unit 234 repeatedly performs the tracking time sum calculation process from the tracking source X1 to the tracking destination X2 at the tracking start time T1.

[0087] [Calculation process for sum of tracking times] First, the tracking time sum calculation unit 234 obtains the current time (initially the tracking start time T1) and the average speed of the vehicle at the current kilometer post (initially the tracking source X1) from the probe data. Then, it obtains information about the average speed from the probe data at the current time (initially the tracking start time T1) and the current kilometer post (initially the tracking source X1).

[0088] The tracking time sum calculation unit 234 then multiplies the time from the current time to the next time by the average speed to calculate the distance traveled to the next time. For example, initially, the current time is the tracking start time T1, and the next time is time T11. In this case, the tracking time sum calculation unit 234 multiplies the time from the tracking start time T1 to time T11 (the time width of the probe data, for example, 1 minute) by the average speed to calculate the distance traveled from the tracking start time T1 to time T11.

[0089] Next, the tracking time sum calculation unit 234 determines whether the sum of the tracking source X1 and the total distance traveled has reached the next kilometer post. Here, the next kilometer post is kilometer post X11 for tracking source X1 and kilometer post X12 for kilometer post X11 in Figure 6.

[0090] For example, the tracking time sum calculation unit 234 determines whether the sum of the tracking source X1 and the total distance traveled has reached the distance to kilometer post X11 (which is the interval width of the probe data, for example, 100m).

[0091] If the sum of the tracking source X1 and the total distance traveled has not yet reached the next kilometer post, the tracking time sum calculation unit 234 stores the time from the current time to the next time as the time required to move from the tracking source X1 to the tracking destination X2.

[0092] On the other hand, if the sum of the tracking source X1 and the total distance traveled reaches the next kilometer post, the tracking time sum calculation unit 234 calculates and stores the time required for that distance by dividing the distance from the current kilometer post to the next kilometer post by the average speed. Furthermore, the tracking time sum calculation unit 234 stores the distance from the current kilometer post to the next kilometer post as the distance traveled.

[0093] The tracking time sum calculation unit 234 then repeatedly performs the above process until the sum of the tracking source X1 and the total distance traveled reaches the tracking destination X2, and by adding up the accumulated time, calculates the time when the vehicle arrives at the tracking destination X2 as the tracking end time T2. The tracking time sum calculation unit 234 then calculates the time required to travel from the tracking source X1 to the tracking destination X2 as the tracking time sum DT (i.e., tracking end time T2 - tracking start time T1).

[0094] (Traffic volume estimation section 235) The traffic volume estimation unit 235 according to this embodiment is an example of an estimation unit, and estimates the traffic volume at an arbitrary point KP. The traffic volume estimation unit 235 estimates the traffic volume at the arbitrary point, for example, based on the first required time corresponding to the first time, the second required time corresponding to the second time, and the number of vehicles detected at the observation point between the first time and the second time.

[0095] [Traffic volume estimation process] First, the traffic volume estimation unit 235 receives the observation point KP, the arbitrary point KP, the aggregation time width, and one target time included in the time list from the loop processing determination unit 233. Subsequently, the traffic volume estimation unit 235 acquires information indicating the track ID of the vehicle detector 2 located at the observation point KP input from the loop processing determination unit 233 (hereinafter, may be expressed as ID information) from the data management device 10.

[0096] Furthermore, the traffic volume estimation unit 235 acquires the number of vehicles (or observed traffic volume) detected by the vehicle detector 2 corresponding to the track ID based on the track ID included in the ID information acquired from the data management device 10.

[0097] Then, the traffic volume estimation unit 235 acquires the sum of tracking times calculated by the tracking time sum calculation unit 234 as the required time for the vehicle to move between the arbitrary point and the observation point, and calculates the traffic volume at the arbitrary point KP from the target time to the target time + aggregation time width. Hereinafter, referring to FIG. 7, an example of the traffic volume estimation process according to this embodiment will be described in more detail.

[0098] FIG. 7 is an explanatory diagram for explaining an example of the traffic volume estimation process according to this embodiment. First, the traffic volume estimation unit 235 acquires one target time from the arbitrary point X1 (i.e., the tracking source X1), the observation point X2 (i.e., the tracking destination X2), the aggregation time width (time T5 - time T3), and the time list from the loop processing determination unit 233. In FIG. 7, an example where the target time is time T3 is shown.

[0099] Next, the traffic volume estimation unit 235 obtains the sum of tracking times from the tracking time sum calculation unit 234 to the observation point X2 at time T3. Then, the traffic volume estimation unit 235 calculates time T4 by adding the sum of tracking times to time T3, which is the time at observation point X2 corresponding to the arbitrary point X1 at time T3. Here, time T4 represents the time when a vehicle that departed from arbitrary point X1 at time T3 arrives at observation point X2.

[0100] Specifically, if time T3 is "15:00" and the sum of tracking times from an arbitrary upstream point X1 to a downstream observation point X2 at "15:00" is "3 minutes", then the time at observation point X2 corresponding to the time "15:00" at arbitrary point X1 is "15:03".

[0101] Next, the traffic volume estimation unit 235 obtains the sum of tracking times from the tracking time sum calculation unit 234 for the target time + aggregation time range (hereinafter referred to as time T5) from an arbitrary point X1 to an observation point X2. Then, the traffic volume estimation unit 235 calculates time T6 by adding the sum of tracking times to time T5, which is the time at observation point X2 corresponding to arbitrary point X1 at time T5. Here, time T6 indicates the time when a vehicle that departed from arbitrary point X1 at time T5 arrives at observation point X2.

[0102] Specifically, if time T3 is "15:00" and the aggregation time interval is "5 minutes", then time T5 will be "15:05". Also, if the sum of tracking times from arbitrary point X1 to observation point X2 at time T5 is "4 minutes", then the time at observation point X2 corresponding to the time "15:05" at arbitrary point X1 will be "15:09".

[0103] Thus, if the sum of tracking times from an arbitrary point X1 to observation point X2 at time T3 is different from the sum of tracking times from an arbitrary point X1 to observation point X2 at time T5, the aggregated time width (time T5 - time T3) and the tracking time width (time T6 - time T4) will show different values. Such differences in the sum of tracking times can occur when the observation point is located within a congested watershed.

[0104] The traffic volume estimation unit 235 then acquires the ID information of the vehicle detector 2 located at observation point X2, and based on the traffic count ID included in the ID information, it acquires the number of vehicles detected by the vehicle detector 2 corresponding to the traffic count ID during the tracking time period (time T6-time T4).

[0105] The traffic volume estimation unit 235 then assigns the number of observed vehicles detected at observation point X2 during the tracking time period (time T6-time T4) as the estimated number of vehicles that passed through an arbitrary point X1 during the aggregation time period (time T5-time T3).

[0106] The traffic volume estimation unit 235 estimates the traffic volume at an arbitrary point X1 by dividing the estimated number of vehicles by the difference between time T3 and time T5 (i.e., the aggregation time range). Specifically, the traffic volume estimation unit 235 estimates the traffic volume at an arbitrary point based on the difference between the third time (time T3), which is obtained by subtracting the first required time (sum of tracking times corresponding to time T4) from the first time (time T4), and the fourth time (time T5), which is obtained by subtracting the second required time (sum of tracking times corresponding to time T6) from the second time (time T6), and the number of vehicles detected at the observation point between the first time (time T4) and the second time (time T6). In the following explanation, the traffic volume at an arbitrary point X1 estimated by the traffic volume estimation unit 235 may be referred to as the estimated traffic volume.

[0107] For example, if the number of vehicles observed at observation point X2 between time T4 and time T6 is 300, then the observed traffic volume at observation point X2 is 3000 vehicles / hour (= 300 vehicles ÷ 6 minutes × 60 minutes / hour), while the estimated traffic volume at arbitrary point X1 is 3600 vehicles / hour (= 300 vehicles ÷ 5 minutes × 60 minutes / hour).

[0108] According to the traffic volume estimation process described above, the traffic volume estimation unit 235 can obtain an estimated traffic volume for an arbitrary point X1 in a free watershed different from the observation point X2, and it may be possible to obtain a traffic volume that suppresses the effect of a decrease in observed traffic volume that may occur when the observation point X2 is in a congested watershed.

[0109] The above describes an example of the functional configuration of the traffic volume estimation device 20 according to this embodiment. Next, an example of the operation processing of the traffic volume estimation device 20 according to this embodiment will be described with reference to Figures 8 and 9.

[0110] <<3. An example of the operation flow of the traffic volume estimation device 20 according to this embodiment>> <Calculation process for summing tracking times> Figure 8 is an explanatory diagram illustrating an example of the operation process of the tracking time sum calculation process according to this embodiment. First, the tracking time sum calculation unit 234 sets the source KP, the destination KP, and the tracking start time (S101).

[0111] Next, the tracking time sum calculation unit 234 acquires the probe data interpolated by the probe data interpolation unit 232 (i.e., interpolated data) (S103).

[0112] Next, the tracking time sum calculation unit 234 obtains the speed from the probe data by referring to the current time and the current kilometer post, and calculates the distance traveled to the next time by multiplying the time from the current time to the next time (the time width of the probe data, for example, 1 minute) by the speed (S105).

[0113] The tracking time sum calculation unit 234 then determines whether the total distance obtained by adding the total distance traveled to the tracking source kilometer post is less than the next kilometer post (S107). If the total distance is less than the next kilometer post (S107: Yes), the process proceeds to S109; if the total distance is equal to or greater than the next kilometer post (S107: No), the process proceeds to S111.

[0114] If the total distance obtained by adding the total distance traveled to the starting kilometer post is less than the next kilometer post (S107: Yes), the tracking time sum calculation unit 234 uses the time from the current time to the next time as the required time (S109).

[0115] If the total distance obtained by adding the total distance traveled to the starting kilometer post is equal to or greater than the next kilometer post (S107: No), the tracking time sum calculation unit 234 calculates the time required for that distance by dividing the distance to the next kilometer post from the total distance by the average speed (S111), and sets the distance to the next kilometer post as the travel distance (S113).

[0116] The tracking time sum calculation unit 234 then determines whether the total distance obtained by adding the total distance traveled to the tracking source kilometer post is less than the tracking destination kilometer post (S115). The processes S105 to S115 are repeated until the total distance reaches the tracking destination kilometer post. If the total distance reaches the tracking destination kilometer post (S115 / Yes), the tracking time sum calculation unit 234 according to this embodiment terminates its processing.

[0117] <Traffic volume estimation process> Figure 9 is an explanatory diagram illustrating an example of the operation process of the traffic volume estimation process according to this embodiment. First, the traffic volume estimation unit 235 obtains the observation point KP, the arbitrary point KP, the target time, and the aggregation time range from the estimation condition reception unit 231 (S201).

[0118] Next, the traffic volume estimation unit 235 obtains the sum of tracking times for time a (target time) and calculates time A corresponding to time a (S203).

[0119] Next, the traffic volume estimation unit 235 obtains the sum of tracking times for time b (target time + aggregation time range) and calculates time B corresponding to time b (S205).

[0120] Next, the traffic volume estimation unit 235 acquires the number of vehicles detected at observation point KP between time points A and B, and assigns this number of vehicles as the estimated number of vehicles at arbitrary point KP between time points A and B (S207).

[0121] Next, the traffic volume estimation unit 235 estimates the traffic volume at arbitrary point KP by dividing the estimated number of vehicles at arbitrary point KP by the time difference between time a and b (S209), and the traffic volume estimation unit 235 according to this embodiment terminates its processing.

[0122] According to the embodiment described above, a variety of effects can be obtained. For example, if the observation point is in a congested watershed and the observed traffic volume has decreased due to the slowing down of vehicles, the traffic volume estimation unit 235 can obtain the estimated traffic volume of any point KP in a free watershed where the traffic volume has not decreased.

[0123] Furthermore, the communication unit 210 transmits the estimated traffic volume at an arbitrary point KP located in the free flow zone where traffic volume has not decreased to an external system such as a traffic flow simulator. This allows the traffic flow simulator to obtain simulation results with improved reproducibility of congestion peak times and congestion lengths. The difference between the traffic flow simulation results using observed traffic volume and the traffic flow simulation results using estimated traffic volume will be explained with reference to Figures 10 to 12B.

[0124] <<4. Traffic Flow Simulation>> Figure 10 is an explanatory diagram illustrating specific examples of observed and estimated traffic volume. In the graph shown in Figure 10, the dashed line represents the observed traffic volume TV1 at the observation point, and the solid line represents the estimated traffic volume TV2 at an arbitrary point. Furthermore, in the example shown in Figure 10, the observation point KP is in a congested area during time period E, from around 60 minutes to 80 minutes into the simulation. Therefore, even though there are actually many vehicles at the observation point during time period E, the number of vehicles detected by the vehicle detector 2 decreases due to the slowing down of vehicles caused by congestion, and the observed traffic volume TV1 also decreases.

[0125] On the other hand, the estimated traffic volume TV2 obtained by the traffic volume estimation unit 235 according to this embodiment is larger than the observed traffic volume TV1 during time period E, suggesting that the effects of vehicle deceleration can be suppressed. The estimated traffic volume obtained here may be smoothed by applying an appropriate moving average, or corrected to represent the traffic volume for any given time interval.

[0126] Figure 11 shows an example of data illustrating observed traffic flow during congestion. In the graphs of Figures 11-12B, the horizontal axis represents kilometer posts and the vertical axis represents time. In the graphs of Figures 11-12B, the speed of traffic flow is represented by the intensity of the dots. For example, in the data shown in Figures 11-12B, the traffic flow speed is slower at times and kilometer posts with darker dots compared to times and kilometer posts with lighter dots. Conversely, the traffic flow speed is faster at times and kilometer posts without dots compared to times and kilometer posts with dots.

[0127] In the observed values ​​shown in Figure 11, time T peak This represents the state where the traffic congestion length is at its longest. The time T here is peak This falls within time zone E shown in Figure 10.

[0128] Figure 12A shows an example of data illustrating the results of a traffic flow simulation using observed traffic volume. For example, when observed traffic volume is used in a traffic flow simulation, the number of vehicles detected per hour at the observation point is small, which can lead to congested traffic being treated as equivalent to free flow.

[0129] As a result, time T peak As a result, simulation results can be output that show a shorter traffic congestion length than the actual traffic congestion length, as shown in Figure 12A.

[0130] Figure 12B shows an example of data illustrating the results of a traffic flow simulation using estimated traffic volume. As shown in Figure 12B, when the estimated traffic volume at an arbitrary point in the free watershed is used in the traffic flow simulation, the time T of the observed values ​​shown in Figure 11 peak At the same time T peak This results in the longest possible traffic jam length.

[0131] In other words, by using the estimated traffic volume at any point included in the free watershed, estimated by the traffic volume estimation unit 235 according to this embodiment, in the traffic flow simulation, it is possible to output results with improved reproducibility of congestion peak times and congestion lengths.

[0132] <<5. Hardware configuration of the traffic volume estimation device 20 according to this embodiment>> The information processing described above is realized through the collaboration of software and the hardware of the traffic volume estimation device 20 described below. The hardware configuration described below is also applicable to the data management device 10.

[0133] Figure 13 is a block diagram showing the hardware configuration of the traffic volume estimation device 20 according to this embodiment. The traffic volume estimation device 20 may include a CPU (Central Processing Unit) 202, a ROM (Read Only Memory) 204, a RAM (Randome Access Memory) 206, an internal bus 208, an input / output interface 211, a display unit 212, an input unit 213, an audio output unit 214, a storage unit 215, a drive 216, a network interface 217, and an external interface 218.

[0134] The CPU 202 functions as both an arithmetic processing unit and a control unit, controlling the overall operation of the traffic volume estimation device 20 according to various programs. By working in cooperation with the ROM 204, RAM 206, and software described later, the CPU 202 can realize functions such as the loop processing determination unit 233, the tracking time sum calculation unit 234, and the traffic volume estimation unit 235.

[0135] ROM204 stores the programs and calculation parameters used by CPU202. RAM206 temporarily stores the programs used in the execution of CPU202, as well as parameters that change as needed during its execution.

[0136] The CPU 202, ROM 204, and RAM 206 are interconnected by an internal bus 208, and are further connected to the display unit 212, input unit 213, audio output unit 214, storage unit 215, drive 216, network interface 217, and external interface 218, which will be described later, via an input / output interface 211.

[0137] The display unit 212 is a display device such as a CRT (Cathode Ray Tube) display device, a liquid crystal display (LCD), or an OLED (Organic Light Emitting Diode) device, and converts video data into video and outputs it. The input unit 213 may consist of input means for members to input information, such as a mouse, keyboard, touch panel, buttons, microphone, sensor, switch, and lever, and an input control circuit that generates an input signal based on the input from the member and outputs it to the CPU 202. The audio output unit 214 is an audio output device such as a speaker or headphones, and converts audio data into audio and outputs it.

[0138] The storage unit 215 is a device for storing data. The storage unit 215 may include a storage medium, a recording device for recording data on the storage medium, a reading device for reading data from the storage medium, and a deletion device for deleting data recorded on the storage medium. The storage unit 215 is composed of, for example, an HDD (Hard Disk Drive), an SSD (Solid Storage Drive), or a memory with equivalent functionality. This storage unit 215 drives the storage and stores programs executed by the CPU 202 and various data.

[0139] Drive 216 is a reader / writer for storage media and is either built into or external to the traffic volume estimation device 20. Drive 216 reads information stored on removable storage media such as magnetic disks, optical disks, magneto-optical disks, or semiconductor memory and outputs it to RAM 206. Drive 216 can also write information to removable storage media.

[0140] The network interface 217 is a communication interface composed of devices such as those used to connect to a communication network, such as the Internet. The network interface 217 may also be a wired LAN (Local Area Network) or wireless LAN compatible communication device, or a wired communication device that performs wired communication.

[0141] The external interface 218 is a connection interface consisting of connection ports for connecting external devices, such as a USB (Universal Serial Bus) port, an IEEE 1394 port, a SCSI (Small Computer System Interface) port, an RS-232C port, or an optical audio terminal.

[0142] <<6. Supplement>> Although preferred embodiments of the present invention have been described in detail above with reference to the attached drawings, the present invention is not limited to these examples. It is clear to any person with ordinary skill in the art to which the present invention belongs that various modifications or alterations can be conceived within the scope of the technical idea described in the claims, and these are also understood to fall within the technical scope of the present invention.

[0143] For example, although this specification mainly describes an example where an arbitrary point is upstream and the observation point is downstream, the observation point may be upstream and the arbitrary point may be downstream. In this case, the traffic volume estimation unit 235 may estimate the traffic volume at the arbitrary point based on the difference between a fifth time obtained by adding the sum of tracking times (first required time) corresponding to the first time from a first time and a sixth time obtained by adding the sum of tracking times (second required time) corresponding to the second time from a second time, and the number of vehicles detected at the observation point between the first time and the second time.

[0144] Furthermore, the steps in the operation of the traffic volume estimation device 20 according to this embodiment do not necessarily have to be processed chronologically in the order shown in the explanatory diagram. For example, each step in the operation of the traffic volume estimation device 20 may be processed in an order different from the order shown in the explanatory diagram, or may be processed in parallel.

[0145] Furthermore, it is possible to create computer programs that enable the hardware, such as the CPU, ROM, and RAM, built into the traffic volume estimation device 20 and the data management device 10 to perform functions equivalent to those of the respective configurations of the traffic volume estimation device 20 and the data management device 10 described above. [Explanation of symbols]

[0146] 1. Driving history data collector 2. Vehicle detector 10. Data Management Device 110 Communications Department 120 Control Unit 130 Storage section 131 Probe data storage unit 133. Track data storage unit 135 Master Data Storage Unit 20 Traffic volume estimation device 210 Communications Department 220 Operation display section 230 Control Unit 231 Estimated Condition Reception Department 232 Probe data interpolation unit 233 Loop Processing Determination Unit 234 Tracking Time Sum Calculation Unit 235 Traffic volume estimation section

Claims

1. An acquisition unit that acquires the time required for a vehicle located at a traffic volume observation point at a given time to travel between the observation point and an arbitrary point, as the required time corresponding to that time, An estimation unit that estimates the traffic volume at the arbitrary point based on a first required time corresponding to a first time, a second required time corresponding to a second time, and the number of vehicles detected at the observation point between the first time and the second time; Equipped with, The observation point is located downstream from the arbitrary point on the road along which the vehicle travels. The estimation unit, The traffic volume at the arbitrary point is estimated by dividing the number of vehicles detected at the observation point between the first time and the second time by the difference between the third time obtained by subtracting the first required time from the first time and the fourth time obtained by subtracting the second required time from the second time, and the number of vehicles detected at the observation point between the first time and the second time. Information processing device.

2. The acquisition unit is, The required time is obtained based on probe data that associates the time, a kilometer post indicating the distance from a certain starting point on the road, and the speed of the vehicle at the point indicated by the kilometer post at the aforementioned time. The information processing apparatus according to claim 1.

3. The aforementioned observation point, the aforementioned arbitrary point, and the estimated start time and estimated end time for the traffic volume estimation are set in advance. The acquisition unit is, The required time is obtained based on a plurality of probe data, including the distance from the arbitrary point to the observation point and the period from the estimated start time to the estimated end time. The information processing apparatus according to claim 2.

4. An interpolation unit, when there is missing data for some time points among the probe data from the estimated start time to the estimated end time, interpolates the missing data based on probe data from other time points. Furthermore, The acquisition unit is, The required time is obtained based on the interpolated data obtained by the interpolation unit or the probe data. The information processing apparatus according to claim 3.

5. The interpolation unit is, When there is a shortage of probe data for some kilometer posts from the aforementioned arbitrary point to the observation point, the missing data is interpolated based on the probe data for other kilometer posts. The information processing apparatus according to claim 4.

6. The interpolation unit is, The average value of the probe data before and after the missing data is obtained as interpolated data. The information processing apparatus according to claim 5.

7. The estimation unit, The traffic volume is estimated based on a time list that includes multiple target times, each separated into a predetermined time interval between the estimated start time and the estimated end time. The information processing apparatus according to claim 6.

8. The third time is the target time, and the fourth time is the target time plus the time width. The information processing apparatus according to claim 7.

9. The aforementioned time range is set to a value within a predetermined range. The information processing apparatus according to claim 7.

10. The number of vehicles detected at the aforementioned observation point is obtained by vehicle detectors placed at at least one location on the road. An information processing apparatus according to any one of claims 2 to 9.

11. The time taken for a vehicle located at a traffic volume observation point at a given time to travel between the observation point and an arbitrary point is obtained as the time required corresponding to that time. Based on the first required time corresponding to the first time, the second required time corresponding to the second time, and the number of vehicles detected at the observation point between the first and second time, the traffic volume at the arbitrary point is estimated. Includes, The observation point is located downstream from the arbitrary point on the road along which the vehicle travels. Estimating the traffic volume at the aforementioned arbitrary point is This includes estimating the traffic volume at the arbitrary point by dividing the number of vehicles by the difference between the difference between the first time and the second time, based on the difference between the third time obtained by subtracting the first required time from the first time and the fourth time obtained by subtracting the second required time from the second time and the number of vehicles detected at the observation point between the first time and the second time, A method of information processing performed by a computer.

12. On the computer, A function to acquire the time taken for a vehicle located at a traffic volume observation point at a given time to travel between the observation point and an arbitrary point, as the time required corresponding to that time, An estimation function that estimates the traffic volume at an arbitrary point based on a first required time corresponding to a first time, a second required time corresponding to a second time, and the number of vehicles detected at the observation point between the first time and the second time. To make it happen, The observation point is located downstream from the arbitrary point on the road along which the vehicle travels. The aforementioned estimation function, The system includes a function to estimate the traffic volume at an arbitrary point by dividing the number of vehicles by the difference between the difference between the first time and the second time, based on the difference between the third time obtained by subtracting the first required time from the first time and the fourth time obtained by subtracting the second required time from the second time and the number of vehicles detected at the observation point between the first time and the second time, program.

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