Information processor

Abstract

To provide more accurate forecasts of traffic conditions on roads than before.SOLUTION: An information processor for calculating a travel time taken by vehicles 10 traveling on a road from a predetermined point to a target point includes a control unit 110 for executing processes of: estimating a traffic flow rate indicating a number of the vehicles 10 passing through the target point within a predetermined unit of time; estimating a first traffic volume indicating a number of vehicles in a first section which is a section of the road from the predetermined point to the target point based on a vehicle density which is the density of the vehicles in the first section and is calculated from travel information obtained from the vehicles 10 traveling on the first section; and calculating a first travel time which is the travel time in the first section based on the traffic flow rate and the first traffic volume.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] FIELD This disclosure relates to travel time calculations. [Background technology]

[0002] Many traffic condition prediction techniques are known. For example, Patent Document 1 discloses a traffic condition prediction system that predicts the traffic conditions of a road based on a reference cumulative traffic volume prediction value at a reference point at the most upstream of a prediction target range predicted based on an estimated traffic condition and a predicted vehicle inflow volume, and a most downstream pseudo cumulative traffic volume prediction value at a most downstream point of the prediction target range. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-71995 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure aims to perform prediction with higher accuracy than conventional methods in calculating road travel times. [Means for solving the problem]

[0005] One aspect of the present disclosure is to An information processing device for calculating a travel time from a predetermined point to a target point, a control unit that executes the following operations: estimating a traffic flow rate, which is the number of vehicles passing through the target point within a predetermined unit time; estimating a first traffic volume, which is the number of vehicles in a first section, which is a section of a road from the predetermined point to the target point, based on a vehicle density, which is the density of vehicles present in the first section, calculated from travel information acquired from vehicles traveling in the first section; and calculating a first travel time, which is the travel time in the first section, based on the traffic flow rate and the first traffic volume. It is an information processing device.

[0006] Further, other aspects include a method executed by the above-mentioned device, a program for causing a computer to execute the method, or a computer-readable storage medium non-transitoryly storing the program. Effect of the Invention

[0007] According to the present disclosure, it is possible to predict road traffic conditions with higher accuracy than before. [Brief description of the drawings]

[0008] [Figure 1] FIG. 4 is a diagram showing an overview of a process executed by a server device according to the embodiment. [Diagram 2] FIG. 2 is a diagram for explaining components of a server device according to the embodiment. [Diagram 3] 5 is a flowchart of a process executed by a control unit of the server device according to the embodiment. [Figure 4] 11 is a flowchart of a process for calculating a travel time when a bottleneck point is present, which is executed by a control unit of the server device according to the embodiment. [Diagram 5] 1 is a diagram showing the relationship between vehicle speed, vehicle density, and traffic flow rate. [Figure 6] FIG. 13 is a diagram showing an example of setting a calculation interval. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Systems for predicting traffic conditions are known.

[0010] For example, consider a system that predicts travel time, which is the time it takes for a vehicle to move on a road from a specified point to a target point. First, the system acquires data detected by roadside devices such as vehicle detectors installed on the road, and probe data from multiple vehicles traveling on the road. The system then predicts travel time by calculating traffic flow rates (passing traffic volume per unit time) and the like using both of the acquired data. In this case, the system mainly uses data detected by vehicle detectors, etc., particularly for predicting traffic flow rates.

[0011] However, vehicle detectors are not necessarily installed at locations where traffic conditions different from the steady state occur, such as accident locations or regulated locations. In addition, vehicle detectors cannot be installed densely, such as at intervals of several kilometers, from the viewpoint of installation costs, so it is difficult for the system, which uses data detected by vehicle detectors, to accurately detect congestion or traffic conditions different from the steady state. To address these issues, it is preferable that the system does not use data detected by vehicle detectors, etc. to detect congestion or traffic conditions other than a steady state, and to predict traffic flow rates and therefore travel times, etc. The information processing device in this embodiment solves this problem.

[0012] An information processing device according to an embodiment of the present disclosure includes: An information processing device that calculates a travel time from a specified point to a target point includes a control unit that executes the following operations: estimating a traffic flow rate, which is the number of vehicles passing through the target point within a specified unit time; estimating a first traffic volume, which is the number of vehicles in a first section, which is a section of a road from the specified point to the target point, based on a vehicle density, which is the density of vehicles present in the first section, calculated from driving information acquired from vehicles traveling on the first section; and calculating a first travel time, which is the travel time in the first section, based on the traffic flow rate and the first traffic volume.

[0013] The target point is an end of the section for which the first travel time is calculated. The target point may typically be the destination of the vehicle.

[0014] The traffic flow rate is the traffic volume (the number of passing vehicles) measured at a specific point converted into the traffic volume per unit time (typically one hour). For example, when there is a location where congestion occurs due to a bottleneck or the like, the traffic flow rate at that location may be significantly lower than the traffic flow rate under normal circumstances. The traffic flow rate may vary depending on the location of the bottleneck, the cause of the congestion, etc. The control unit may, for example, estimate the traffic flow rate at a target point based on probe information transmitted from a vehicle, or may hold and use an actual value of the traffic flow rate for each target point.

[0015] The first section is a section from a predetermined point to a target point. The first section is a section for which the first travel time is to be calculated.

[0016] The first traffic volume is the number of vehicles existing in the first section.

[0017] The travel information is information obtained from a vehicle traveling on a road and indicates the speed, traveling direction, etc. of the vehicle. The travel information may be probe information collected from a vehicle traveling in the first section.

[0018] Vehicle density indicates the degree of traffic congestion, and is expressed as the number of vehicles per unit distance (for example, 1 km).

[0019] The control unit estimates a first traffic volume (traffic volume from a target point to a specified point) based on a vehicle density calculated from travel information acquired from vehicles traveling in the first section. Since there is a certain correlation between vehicle speed and vehicle density, if the travel information includes speed information, etc., the vehicle density of vehicles traveling in the first section can be estimated by a known method. In addition, if the vehicle density is known, the first traffic volume can be estimated by multiplying it by the length of the first section. The control unit then calculates the first travel time using both the traffic flow rate corresponding to the target point and the estimated first traffic volume. If the traffic flow rate at the beginning of the first section and the traffic volume within the first section are known, the time required to pass through the first section can be calculated.

[0020] As a result, the information processing device according to the present disclosure can calculate travel time with higher accuracy than before, without using data acquired by vehicle detectors or the like.

[0021] In addition, when a bottleneck point that is a cause of congestion is present on the road, the control unit may divide the first section at the bottleneck point to set a plurality of calculation sections, multiply the vehicle density calculated for each of the plurality of calculation sections by the section length of the calculation section to estimate a second traffic volume that is the number of vehicles present in the calculation section, calculate a second travel time, which is the travel time from the beginning to the end of the calculation section in each of the plurality of calculation sections, from the second traffic volume and the traffic flow rate at the bottleneck point, and calculate the first travel time by summing up the second travel times of all the calculation sections.

[0022] If there is a bottleneck on the vehicle route, the traffic conditions may change at the bottleneck. In such a case, the first section may be divided at the bottleneck, and the travel time may be calculated for each of the divided sections, and the calculated travel times may be added together.

[0023] The second traffic volume may be the number of vehicles existing in each calculation section.

[0024] The second travel time may be the time it takes for a vehicle to travel from the beginning to the end of each calculated section.

[0025] As a result, the information processing device according to the present disclosure can calculate travel times based on the traffic flow rates of each section with a bottleneck point as the boundary, and then add these up to calculate travel times that are more in line with reality.

[0026] The control unit may determine the vehicle density based on a travel speed of a vehicle acquired by the vehicle traveling in the first section.

[0027] Since there is a certain correlation between vehicle speed and vehicle density, the vehicle density can be obtained by using the speed information contained in the probe information acquired by the vehicle.

[0028] In addition, the control unit may acquire the speeds of multiple vehicles at the bottleneck point as the driving information, and estimate the traffic flow rate at the bottleneck point based on the vehicle density at the bottleneck point and the average speed of the multiple vehicles at the bottleneck point.

[0029] The average speed of multiple vehicles can be calculated from the driving information acquired from the vehicles. As mentioned above, vehicle density can be estimated from vehicle speed. Therefore, by using this information, the traffic flow rate at a bottleneck point can be estimated.

[0030] The control unit may also acquire inflow traffic volume from an interchange or junction on the road and outflow traffic volume from the interchange or junction on the road, and correct the first traffic volume based on the inflow traffic volume and the outflow traffic volume.

[0031] If there is an interchange or a junction in the first section, inflow and outflow of traffic occurs at the midpoint, so the first traffic volume in the first section may not be estimated correctly. To deal with this, the first traffic volume may be corrected based on the inflow and outflow traffic volumes. This can improve the accuracy of the travel time calculation.

[0032] Specific embodiments of the present disclosure will be described below with reference to the drawings. Unless otherwise specified, the hardware configuration, module configuration, functional configuration, and the like described in each embodiment are not intended to limit the technical scope of the disclosure to only those.

[0033] (Embodiment) An overview of the processing performed by the server device according to the embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an overview of the processing performed by the server device 100 according to the embodiment. Here, the server device 100 is an example of an information processing device according to the present disclosure. The server device 100 is a device that estimates road traffic conditions based on travel information acquired from the vehicle 10 and calculates a travel time that is the time it takes for the vehicle 10 to travel from a predetermined point to a target point. The server device 100 is configured to be able to communicate with a plurality of vehicles 10.

[0034] First, the server device 100 acquires driving information of the vehicle 10. The driving information of the vehicle 10 is probe information that includes the speed of the vehicle 10 and the like.

[0035] First, a case where there is no bottleneck point between a predetermined point and a target point will be described. As shown in (A) of Fig. 1, the server device 100 estimates the traffic flow rate of the target point ((1) of Fig. 1). Here, the traffic flow rate is the passing traffic volume (number of vehicles) per hour at the target point.

[0036] Next, the server device 100 estimates the vehicle density in the section from the predetermined point to the target point ((2) in FIG. 1). The vehicle density is a value indicating the degree of congestion on the road, and may be expressed as the number of vehicles 10 present per kilometer. The server device 100 acquires the speed of the vehicles 10 as driving information from the vehicles 10, and estimates the vehicle density based on the speeds of the multiple vehicles 10.

[0037] Since there is a certain correlation between vehicle speed and vehicle density, the vehicle density in a target section can be estimated by using the speed of the vehicles traveling in that section.

[0038] Then, the server device 100 estimates the section traffic volume, which is the traffic volume in the section from the predetermined point to the target point, based on the estimated vehicle density. The section traffic volume can be obtained by multiplying the estimated vehicle density by the length of the target section.

[0039] Next, the server device 100 calculates a first travel time, which is the time it takes to travel from the specified point to the target point, based on the traffic flow rate at the target point and the section traffic volume in the section from the specified point to the target point ((4) in Figure 1).

[0040] Next, a case where a bottleneck point is included between a predetermined point and a target point will be described. As shown in (B) of Fig. 1, the server device 100 identifies bottleneck points, which are points where traffic is stagnating between the predetermined point and the target point, from the speeds of multiple vehicles 10. Then, the server device 100 divides the area from the predetermined point to the target point at the bottleneck points, and sets each section as a calculation section.

[0041] Next, the server device 100 estimates the traffic flow rate at the bottleneck point which is the end point of the calculation section.

[0042] Next, the server device 100 estimates the vehicle density in each calculation section.

[0043] Then, the server device 100 estimates the section traffic volume, which is the traffic volume in each calculation section, for each calculation section based on the estimated vehicle density. The section traffic volume can be obtained by multiplying the estimated vehicle density by the length of the calculation section. Next, the server device 100 calculates a second travel time, which is the time it takes to travel from one end of each calculated section to the other end, based on the traffic flow rate at each bottleneck point and the section traffic volume in the calculated section corresponding to each bottleneck point.

[0044] Finally, the server device 100 calculates the first travel time, which is the time it takes to travel from the predetermined point to the target point, by adding up the second travel times for each calculation section ((5) in FIG. 1).

[0045] In this way, the server device 100 sets the calculation section and estimates the traffic flow rate at the end of the calculation section. Then, the server device 100 estimates the traffic volume of the calculation section and calculates the travel time corresponding to the calculation section from the estimated traffic volume and the traffic flow rate at the end. When a bottleneck point is present on the route, the server device 100 sets a plurality of calculation sections around the bottleneck, calculates the travel time for each calculation section, and adds them up. This allows the server device 100 to predict traffic conditions based only on the travel information of the vehicle 10, without using data detected by roadside devices such as vehicle detectors.

[0046] According to this configuration, it becomes possible to properly calculate the travel time without using a roadside device.

[0047] Next, a detailed description will be given of each element constituting the server device 100. Fig. 2 is a diagram for explaining the components of the server device 100 according to the embodiment.

[0048] The server device 100 according to this embodiment includes a control unit 110, a storage unit 120, and a communication unit .

[0049] The control unit 110 is realized by a processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit) and a memory. The control unit 110 includes, as functional modules, an acquisition unit 111, a setting unit 112, a calculation unit 113, and an estimation unit 114. These functional modules may be realized by the control unit 110 executing a program.

[0050] The acquisition unit 111 acquires driving information and the like from the vehicle 10. The driving information is made up of probe information of the vehicle 10 including the speed, traveling direction, and the like of the vehicle 10.

[0051] The setting unit 112 determines whether the vehicle 10 is in a traffic jam on the road in the section for which the first travel time is to be calculated. When a bottleneck point, which is a point where the load is increased, is present, the first section is divided at the bottleneck point to set a plurality of calculation sections. If there is no bottleneck point on the road in the section to be calculated, the first section is not divided.

[0052] The calculation unit 113 calculates a first travel time, which is a travel time in the first section, based on the traffic flow rate and the section traffic volume estimated by the estimation unit 114. Here, the first travel time is the time it takes for the vehicle 10 to move from a predetermined point to a target point. Note that the first traffic volume is the section traffic volume when there is no bottleneck in the section to be calculated and the section to be calculated is not divided. Also, the second traffic volume is the section traffic volume when there is a bottleneck in the section to be calculated and the section to be calculated is divided.

[0053] Furthermore, when a bottleneck point exists and the first section is divided, the calculation unit 113 calculates the second travel time for each of the calculation sections from the section traffic volume estimated by the estimation unit 114 and the traffic flow rate at the bottleneck point. Here, the second travel time is the travel time from the beginning to the end of each calculation section divided by the bottleneck. Then, the calculation unit 113 calculates the first travel time by summing up the second travel times for all the calculation sections.

[0054] The estimation unit 114 estimates a traffic flow rate at the target point or the bottleneck point determined by the setting unit 112. Here, the traffic flow rate is the number of vehicles 10 passing through the target point or the bottleneck point within a predetermined unit time. For example, the traffic flow rate may be a predetermined value set for each point, or may be obtained by calculation. Specifically, the estimation unit 114 may estimate the traffic flow rate at the target point or the bottleneck point based on the vehicle density at the target point or the bottleneck point estimated from the speed of the vehicles 10 and the average speed of the multiple vehicles 10 at the point.

[0055] Furthermore, the estimation unit 114 estimates the traffic volume (section traffic volume) in the calculation section or in a first section consisting of a plurality of calculation sections. The estimation unit 114 may calculate, based on the vehicle density, the number of vehicles 10 that have passed a predetermined point in the first section, which is a section of a road from a predetermined point to a target point, as the section traffic volume. Here, the vehicle density is the density of vehicles present in the first section, calculated from travel information acquired from the vehicles 10 traveling in the first section. The estimation unit 114 may determine the vehicle density based on the travel speed of the vehicles 10 acquired by the vehicles 10.

[0056] Furthermore, the estimation unit 114 may calculate the number of vehicles 10 included in a calculation section set by dividing the first section at the bottleneck point as the section traffic volume. Here, the section traffic volume is a specific example of the second traffic volume.

[0057] The storage unit 120 is a main storage device such as a RAM or a ROM, an EPROM, a hard disk drive, and an auxiliary storage device such as a removable medium. An operating system (OS), various programs, various tables, and the like are stored in the auxiliary storage device, and by executing the programs stored therein, each function that matches the predetermined purpose of each part of the control unit 110 can be realized. However, some or all of the functions may be realized by a hardware circuit such as an ASIC or an FPGA.

[0058] The storage unit 120 stores data and the like used or generated in the processing performed by the control unit 110. The storage unit 120 may also temporarily store vehicle information acquired from the vehicle 10, or inflow and outflow traffic volumes from an interchange or a junction on a road including a section for which the first travel time is to be calculated, acquired from an external server device.

[0059] The communication unit 130 is configured with a communication circuit that performs wireless communication. The communication unit 130 may be, for example, a communication circuit that performs wireless communication using 4G (4th Generation) or a communication circuit that performs wireless communication using 5G (5th Generation). The communication unit 130 may also be a communication circuit that performs wireless communication using LTE (Long Term Evolution) or a communication circuit that performs wireless communication using LPWA (Low Power Wide Area). The communication unit 130 may be a communication circuit that performs communication using a standard wireless communication area (Wi-Fi).

[0060] Next, a specific content of the process performed by the server device 100 will be described. Fig. 3 is a flowchart of the process executed by the control unit 110 of the server device 100 according to the embodiment. When there is no bottleneck point on the road including the section for which the first travel time is to be calculated, the server device 100 executes the following process for the entire section for which the first travel time is to be calculated.

[0061] The server device 100 may start the process shown in FIG. 3 when receiving a trigger such as a request for information on travel time from the user of the vehicle 10.

[0062] First, in step S10, the estimation unit 114 estimates a traffic flow rate at a target point that is an end point of a section for which the first travel time is to be calculated. The estimation unit 114 may estimate the traffic flow rate at the target point based on the vehicle density at the target point and the speed of the multiple vehicles 10 at the target point. The estimation unit 114 may obtain the vehicle density from the average speed of the multiple vehicles 10 that have passed through the target point, which is acquired by the acquisition unit 111.

[0063] Q=KV...Equation (1) Q: traffic flow rate (traffic volume), K: vehicle density, V: average speed Here, the average velocity may be expressed by the following formula (Underwood formula). V = Vf e -(K / Kc) ...Equation (2) V: average speed, Vf: maximum speed, K: vehicle density, Kc: critical density The vehicle density K can be calculated from equation (2), and the traffic flow rate Q can be calculated from equation (1).

[0064] The relationship between the average speed V, vehicle density K, and traffic flow rate (traffic volume) Q in equation (2) is shown in FIG. 5. FIG. 5 is a diagram showing the relationship between vehicle speed, vehicle density, and traffic flow rate. The traffic flow rate (traffic volume) Q increases as the average speed V increases, but decreases when the speed exceeds a maximum value. On a road, if the speed of the vehicle 10 is lower than the speed at which the traffic volume is maximum, the road is a congested flow, and if the speed is higher, the road is a free flow. As described above, when the speed of the vehicle 10 is judged to be a congested flow, the traffic volume, which is determined from the constraints on the road, is treated as the traffic flow rate.

[0065] The estimation unit 114 may reflect past statistical information of the travel information acquired from the vehicle 10 in the estimation of the traffic flow rate Q.

[0066] Furthermore, the acquisition unit 111 may acquire information related to turn signal operation, information related to steering amount, images captured by an in-vehicle camera, and the like, as vehicle information acquired from the vehicle 10. The estimation unit 114 may then identify the traveling position of the vehicle 10 and determine the traveling lane in which the vehicle 10 is traveling, based on the information related to turn signal operation, information related to steering amount, images captured by an in-vehicle camera, and the like. The estimation unit 114 may then estimate the traffic flow rate for each lane, and determine the total value of the traffic flow rates for each lane as the traffic flow rate at the target point.

[0067] Next, in step S11, the estimation unit 114 estimates a traffic volume in the first section based on the vehicle density in the first section. The traffic volume in the first section is a specific example of a first traffic volume.

[0068] For example, the traffic volume in the first section may be a value obtained by multiplying the vehicle density calculated from the average speed of the multiple vehicles 10 in the first section by the section length of the first section. The vehicle density may also be determined based on the distance between the vehicle 10 and a preceding vehicle or a following vehicle sensed by the vehicle 10.

[0069] In addition, the estimation unit 114 may derive a sampling ratio for traffic volume by day of the week or time of day at a specified point in the first section based on statistical information on the number of vehicles 10, and estimate the traffic volume in the first section from the current number of vehicles 10 at the specified point in the first section.

[0070] Next, in step S12, the calculation unit 113 calculates the travel time in the first section based on the traffic flow rate at the target point and the traffic volume in the first section. The travel time in the first section is the time it takes for the vehicle 10 to move from the beginning to the end of the first section. The calculation unit 113 may obtain the travel time in the first section by dividing the traffic volume in the first section by the traffic flow rate at the target point. The travel time in the first section is a specific example of the first travel time.

[0071] After step S12, the estimation unit 114 may compare the travel time in the first section calculated in step S12 with the actual travel time of the vehicle 10 in the first section. Then, the estimation unit 114 may determine parameters such as the maximum speed Vf and the critical density Kc in the formula (2) that minimize the error between the two. The determined parameters may be used in the process shown in FIG. 3 from the next time onward.

[0072] This allows the server device 100 to calculate the travel time of the vehicle 10 in the first section based on the travel information of the vehicle 10, without using data detected by roadside devices such as vehicle detectors. This allows the server device 100 to calculate the travel time of the vehicle 10 in the first section by a more accurate method than before, using the travel information of the vehicle 10 that can be reliably acquired, without using data detected by roadside devices such as vehicle detectors. Therefore, the server device 100 can predict the travel time with higher accuracy than before.

[0073] Next, a process performed by the server device 100 when a bottleneck point is included in the first section will be described. Fig. 4 is a flowchart of a process for calculating travel time when a bottleneck point is included, which is executed by the control unit 110 of the server device 100 according to the embodiment. The process described in Fig. 4 is executed instead of the process described in Fig. 3. Of the processes described in Fig. 4, a description of the same contents as those described in Fig. 3 will be omitted.

[0074] The server device 100 may start the process shown in FIG. 4 when receiving a trigger such as a request for information on travel time from the user of the vehicle 10.

[0075] First, in step S20, the setting unit 112 divides the first section at the bottleneck point to set a plurality of calculation sections. Here, the bottleneck point refers to the head point of a section where traffic congestion occurs. The bottleneck point may also refer to the head part of a section where the average speed of the passing vehicle 10 is equal to or less than a predetermined value. The setting unit 112 may determine the bottleneck point where congestion occurs based on travel information acquired from the vehicle 10. Here, the travel information acquired from the vehicle 10 may be the speed of the vehicle 10 or the frequency of brake operation of the vehicle 10.

[0076] For example, the setting unit 112 may analyze the transition relationship between the braking operation frequency and the speed of a plurality of vehicles 10 at a predetermined point in the first section over a predetermined period of time, and obtain a probability distribution of future transitions in the speed of the vehicle 10 relative to the braking operation frequency at a certain time at the predetermined point. The setting unit 112 may then determine when congestion will occur at the predetermined point and identify a bottleneck point by predicting a time when the traffic flow rate at the predetermined point is highly likely to decrease, based on the current braking operation frequency at the predetermined point.

[0077] 6 is a diagram showing an example of setting the calculation section. At time t1 when only free flow with a traffic flow speed equal to or higher than a certain level occurs, the first section is not divided, and the section to be calculated is only section 210. In contrast, at time t2 when one congestion flow with a traffic flow speed equal to or lower than a certain level occurs, the first section is divided into two sections with the congestion flow point (bottleneck point) as the boundary, and sections 220 and 230 are set as the calculation sections. Similarly, at time tn when two congestion flows occur, the first section is divided into three sections with the congestion flow point (bottleneck point) as the boundary, and sections 240, 250, and 260 are set as the calculation sections.

[0078] Next, in step S21, the estimation unit 114 estimates the traffic flow rate at each bottleneck point. For example, the acquisition unit 111 acquires the speeds of the multiple vehicles 10 passing through each bottleneck point. Then, the estimation unit 114 calculates an average speed from the speeds of the multiple vehicles 10 acquired by the acquisition unit 111. Then, the estimation unit 114 estimates the traffic flow rate at each bottleneck point based on the vehicle density at the bottleneck point and the average speed of the multiple vehicles 10 at the bottleneck point. Specifically, the estimation unit 114 may obtain the traffic flow rate at each bottleneck point by formula (1) and formula (2).

[0079] The estimation unit 114 may determine the traffic flow rate at each bottleneck point as a value obtained by multiplying the vehicle density at each bottleneck point by the average speed of the multiple vehicles 10 at each bottleneck point. At the bottleneck point, the traffic volume expressed by formula (1) is treated as the traffic flow rate.

[0080] The traffic flow rate calculated in step S21 may be a value obtained by multiplying the traffic flow rate Q calculated from formulas (1) and (2) by the number of lanes on the road. In addition, at a bottleneck point where congestion occurs regularly, the estimation unit 114 may determine the traffic flow rate as an average value of past traffic flow rate data. In addition, at a bottleneck point where congestion occurs due to road restrictions due to construction, accidents, or the like, the estimation unit 114 may determine the traffic flow rate by multiplying the traffic flow rate Q calculated from formulas (1) and (2) by a value obtained by subtracting the number of regulated lanes from the total number of lanes and dividing the result by the total number of lanes.

[0081] Next, in step S22, the estimation unit 114 estimates the section traffic volume, which is the traffic volume present in each calculation section, for each calculation section. The section traffic volume may be a value obtained by multiplying the vehicle density in each calculation section by the section length of each calculation section. Also, for example, the estimation unit 114 may distinguish between a congested flow section and a free flow section from information on the tail of the congestion determined by the setting unit 112 based on the average speed of the multiple vehicles 10, and may set the number of queuing vehicles in the congested flow section as the section traffic volume in each calculation section. The section traffic volume is a specific example of the second traffic volume.

[0082] In addition, when the acquisition unit 111 can acquire the congestion length from traffic information from VICS (Vehicle Information and Communication System) (registered trademark) or the like, the estimation unit 114 may calculate the number of queuing vehicles in the congestion flow section from the congestion length, and use the calculated number of queuing vehicles as the section traffic volume in each calculated section.

[0083] Next, in step S23, the calculation unit 113 calculates a second travel time, which is a travel time in each calculation section, from the section traffic volume and the traffic flow rate at each bottleneck point. The time is the time it takes for the vehicle 10 to move from one end of each calculation section to the other end. The calculation unit 113 acquires the section traffic volume estimated by the estimation unit 114 and the traffic flow rate at each bottleneck point to calculate the second travel time.

[0084] Next, in step S24, the calculation unit 113 calculates the first travel time by summing up the second travel times of all the calculation sections. The first travel time is the travel time in the entire first section, and is the time it takes for the vehicle 10 to move from one end of the first section to the opposite end.

[0085] Second Embodiment In the first embodiment, the traffic volume in the calculation target section is estimated based on data such as speed transmitted from the vehicle. However, if there is an interchange or junction in the middle of the section, the traffic volume may not be calculated accurately due to the inflow and outflow of vehicles from the branch roads in the middle. Therefore, in the second embodiment, in order to grasp the traffic volume more accurately, the estimation unit 114 corrects the traffic flow rate in the first section or each calculation section using the inflow or outflow traffic volume from the interchange or junction.

[0086] The acquisition unit 111 acquires the inflow traffic volume from an interchange or a junction and the outflow traffic volume from the interchange or the junction on the road including the section for which the first travel time is to be calculated. The acquisition unit 111 may acquire the inflow traffic volume from the interchange or the junction and the outflow traffic volume from the interchange or the junction by inquiring of an external server device.

[0087] Then, the calculation unit 113 calculates the first travel time or the second travel time of the vehicle 10 using the traffic volume (hereinafter, referred to as traffic volume near the branching section) that exists in the first section or each calculation section, or in a section near an interchange or junction in the vicinity of the first section. At that time, the estimation unit 114 corrects the change in the traffic volume at a predetermined point included in the first section at the time when the vehicle 10 is assumed to pass the point. The estimation unit 114 may calculate the traffic volume near the branching section based on a branching rate. The branching rate is the ratio of the number of vehicles 10 that merge into the first section or branch off from the first section to the number of vehicles 10 that have passed through a predetermined interchange or junction.

[0088] Also, for example, the estimation unit 114 compares the current traffic volume on the outbound route with past traffic volume on the outbound route linked to information on a similar destination or route based on information on the destination or route of the vehicle 10 on the outbound route (for example, information on the departure point, arrival point, entered interchange, or exited interchange, etc.). The estimation unit 114 may then correct the branching rate at an interchange or junction on the outbound route, assuming that the traffic volume on the return route increases or decreases by the amount of increase or decrease in the traffic volume on the outbound route from the past traffic volume. Here, for example, the outbound route may be an upbound lane in a specified time period, and the return route may be an outbound lane in a time period after the specified time period.

[0089] (Modification) The above-described embodiment is merely an example, and the present disclosure can be modified and implemented as appropriate without departing from the spirit and scope of the present disclosure.

[0090] For example, the processes and means described in this disclosure can be freely combined and implemented as long as no technical contradiction occurs.

[0091] Furthermore, the processes described as being performed by one device may be shared and executed by multiple devices. Alternatively, the processes described as being performed by different devices may be executed by one device. The hardware configuration (server configuration) used to achieve this can be flexibly changed.

[0092] The present disclosure can also be realized by supplying a computer program implementing the functions described in the above embodiments to a computer, and having one or more processors of the computer read and execute the program. Such a computer program may be provided to the computer by a non-transitory computer-readable storage medium connectable to the system bus of the computer, or may be provided to the computer via a network. Non-transitory computer-readable storage media include, for example, any type of disk, such as a magnetic disk (floppy disk, hard disk drive (HDD), etc.), an optical disk (CD-ROM, DVD disk, Blu-ray disk, etc.), a read-only memory (ROM), a random access memory (RAM), an EPROM, an EEPROM, a magnetic card, a flash memory, an optical card, and any type of medium suitable for storing electronic instructions. [Explanation of symbols]

[0093] 10. Vehicle 100 Server device 110... Control unit 111...Acquisition part 112 Setting section 113... Calculation section 114... Estimation section 120...Storage section 130 Communications Department 210, 220, 230, 240, 250, 260... Section

Claims

1. An information processing device for calculating a travel time from a predetermined point to a target point, Estimating a traffic flow rate, which is the number of vehicles passing through the target point within a predetermined unit time; estimating a first traffic volume, which is the number of vehicles in a first section, which is a section of a road from the predetermined point to the target point, based on a vehicle density, which is the density of vehicles present in the first section, calculated from travel information acquired from vehicles traveling in the first section; calculating a first travel time, which is the travel time in the first section, based on the traffic flow rate and the first traffic volume; A control unit for executing Information processing device.

2. The control unit is When a bottleneck point that is a cause of congestion is present on the road, the first section is divided at the bottleneck point to set a plurality of calculation sections; multiplying the vehicle density calculated for each of the calculation sections by a section length of the calculation section to estimate a second traffic volume, which is the number of vehicles present in the calculation section; calculating, for each of the plurality of calculation sections, a second travel time, which is the travel time in each of the calculation sections from a start point to a finish point of the calculation section, from the second traffic volume and the traffic flow rate at the bottleneck point; The first travel time is calculated by adding up the second travel times of all the calculation sections. The information processing device according to claim 1 .

3. The control unit is determining the vehicle density based on a travel speed of a vehicle acquired by the vehicle traveling in the first section; 3. The information processing device according to claim 1 or 2.

4. The control unit is As the travel information, speeds of a plurality of vehicles at the bottleneck point are acquired; estimating the traffic flow rate at the bottleneck point based on the vehicle density at the bottleneck point and an average speed of a plurality of vehicles at the bottleneck point; The information processing device according to claim 2 .

5. The control unit is Acquiring an inflow traffic volume from an interchange or a junction on the road and an outflow traffic volume from the interchange or the junction on the road; correcting the first traffic volume based on the inflow traffic volume and the outflow traffic volume; 3. The information processing device according to claim 1 or 2.

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