Information processing apparatus

Abstract

The present invention relates to an information processing device for calculating a travel time, which is the time for a vehicle traveling on a road to arrive at a target location from a predetermined location, the information processing device comprising a control unit for executing: estimating a traffic flow rate, which is the number of vehicles passing through the target location within a predetermined unit time; a first traffic volume estimation unit that estimates a first traffic volume, which is the number of vehicles in a first section, on the basis of a vehicle density, which is the density of vehicles present in the first section, the first section being a section of a road from the prescribed point to the target point; the vehicle density is calculated on the basis of travel information acquired from vehicles traveling in the first section; and calculating a first travel time, which is a travel time in the first section, on the basis of the traffic flow rate and the first traffic volume.

Description

Technical Field

[0001] The present disclosure relates to calculation of travel time. Background Art

[0002] There are many known traffic condition prediction technologies. In this regard, for example, Japanese Patent Application Laid-Open No. 2021-71995 discloses a traffic condition prediction system that predicts the traffic condition of a road based on a reference cumulative traffic volume prediction value at a reference point at the most upstream of the prediction object range and a most downstream simulated cumulative traffic volume prediction value at a most downstream point of the prediction object range. Japanese Patent Application Laid-Open No. 2021-71995 discloses that the prediction object range is a range predicted based on the inference result of the traffic condition and the prediction result of the inflow of vehicles. Summary of the invention

[0003] An object of the present disclosure is to perform prediction with higher accuracy than before in calculating road travel time.

[0004] One aspect of an embodiment of the present disclosure is an information processing device that calculates a travel time from a predetermined point to a target point, wherein:

[0005] A control unit is provided, which performs the following processing:

[0006] Estimate the number of vehicles passing the target location within a specified unit time, i.e., the traffic flow rate;

[0007] estimating the number of vehicles in the first section, i.e., a first traffic volume, based on a vehicle density, i.e., a density of vehicles existing in a first section, the first section being a section of a road from the predetermined point to the target point, the vehicle density being calculated based on travel information obtained from vehicles traveling in the first section; and

[0008] The travel time in the first section, that is, the first travel time is calculated based on the traffic flow rate and the first traffic volume.

[0009] In addition, as another aspect, there may be a method executed by the above-mentioned apparatus, a program for causing a computer to execute the method, or a computer-readable storage medium storing the program in a non-transitory manner.

[0010] According to the present disclosure, it is possible to predict the traffic conditions of roads with higher accuracy than before. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Hereinafter, features, advantages, technical and industrial significance of exemplary embodiments of the present invention will be described with reference to the accompanying drawings, in which like reference numerals represent like components, wherein:

[0012] Figure 1 This is a diagram showing an overview of processing executed by a server device according to the embodiment.

[0013] Figure 2 This is a diagram for explaining components included in a server device according to an embodiment.

[0014] Figure 3 This is a flowchart of a process executed by a control unit of a server device according to an embodiment.

[0015] Figure 4 This is a flowchart of a process of calculating a travel time when a bottleneck point exists, which is executed by a control unit of a server device according to an embodiment.

[0016] Figure 5 It is a graph showing the relationship between vehicle speed, vehicle density, and traffic flow rate.

[0017] Figure 6 It is a diagram showing an example of setting a calculation interval. DETAILED DESCRIPTION

[0018] Systems for predicting traffic conditions are known.

[0019] For example, consider a system that predicts the time it takes for a vehicle to move from a predetermined point to a target point on a road, that is, the travel time. First, the system obtains data detected by roadside devices such as vehicle sensors installed on the road and detection data from multiple vehicles traveling on the road. Then, the system uses the data obtained from both parties to calculate the traffic flow rate (the amount of traffic passing per unit time) and the like, thereby predicting the travel time. At this time, the system mainly uses the data detected by the vehicle sensors and the like in the prediction of the traffic flow rate.

[0020] However, a vehicle sensor or the like is not necessarily installed at a location where an accident has occurred or a restricted location where a traffic condition different from the normal state has occurred. In addition, since vehicle sensors or the like cannot be installed as closely as at intervals of several kilometers from the perspective of installation cost, it is difficult for the system using data detected by vehicle sensors or the like to accurately detect congestion or traffic conditions different from the normal state.

[0021] In order to address such issues, it is preferred that the system does not use data detected by vehicle sensors or the like in detecting congestion or traffic conditions that are different from normal conditions, or in predicting traffic flow rates or travel times.

[0022] The information processing device of this embodiment solves the above-mentioned problems.

[0023] An information processing device involved in one form of the present disclosure is an information processing device for calculating the travel time from a specified location to an object location, wherein the device comprises a control unit that performs the following processing: inferring the number of vehicles passing through the above-mentioned object location within a specified unit time, namely, the traffic flow rate; inferring the number of vehicles in the above-mentioned first section, namely, the first traffic volume, based on the density of vehicles existing in the first section, namely, the vehicle density, wherein the above-mentioned first section is a section of the road from the above-mentioned specified location to the above-mentioned object location, and the above-mentioned vehicle density is calculated based on the driving information obtained from the vehicle traveling in the above-mentioned first section; and calculating the above-mentioned travel time in the above-mentioned first section, namely, the first travel time, based on the above-mentioned traffic flow rate and the above-mentioned first traffic volume.

[0024] The target point is the end of the section for calculating the first travel time. The target point may be typically the destination of the vehicle.

[0025] The traffic flow rate is a rate obtained by converting the traffic volume (the number of vehicles passing through) measured at a predetermined point into the traffic volume per unit time (typically, one hour).

[0026] For example, when there is a location where traffic is congested due to a bottleneck or the like, the traffic flow rate at that location may be significantly lower than the normal traffic flow rate. The traffic flow rate may change depending on the location of the bottleneck, the cause of the congestion, and the like.

[0027] The control unit may estimate the traffic flow rate of the target point based on the probe information transmitted from the vehicle, for example, or may store and utilize the actual value of the traffic flow rate of each target point.

[0028] The first section is a section from a predetermined point to a target point. The first section is a section to be calculated for the first travel time.

[0029] The first traffic volume is the number of vehicles existing in the first section.

[0030] The travel information is information indicating the speed, traveling direction, etc. of the vehicle acquired from the vehicle traveling on the road. The travel information may be probe information collected from the vehicle traveling in the first section.

[0031] The vehicle density indicates the degree of traffic congestion, and is expressed as the number of vehicles per unit distance (eg, 1 km).

[0032] The control unit estimates the first traffic volume (the traffic volume from the target point to the predetermined point) based on the vehicle density calculated from the driving information obtained from the vehicles traveling in the first section. Since there is a certain correlation between the speed of the vehicle and the vehicle density, when the driving information includes speed information, etc., the vehicle density of the vehicles traveling in the first section can be estimated by a known method. In addition, when the vehicle density is known, the first traffic volume can be estimated by multiplying it by the length of the first section.

[0033] The control unit 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 in the first section are known, the time required to travel through the first section can be calculated.

[0034] As a result, the information processing device according to the present disclosure can calculate the travel time with higher accuracy than before, even without using data acquired by vehicle sensors or the like.

[0035] In addition, when there is a bottleneck point that is a cause of congestion on the road, the control unit may divide the first section with the bottleneck point as a boundary to set a plurality of calculation sections. The control unit may estimate the number of vehicles existing in the calculation section, i.e., the second traffic volume, by multiplying the vehicle density calculated in each of the calculation sections by the section length of the calculation section for each of the plurality of calculation sections. The control unit may calculate the travel time from the front end to the end of each of the calculation sections, i.e., the second travel time, based on the second traffic volume and the traffic flow rate at the bottleneck point for each of the plurality of calculation sections. The control unit may calculate the first travel time by summing up the second travel times of all the calculation sections.

[0036] When there is a bottleneck on the vehicle route, traffic conditions may change with the bottleneck as a boundary. In view of this, in such a case, the first section may be divided with the bottleneck as a boundary, and travel time may be calculated for each of the divided sections and the travel time may be totaled.

[0037] The second traffic volume can be the number of vehicles existing in each calculation section.

[0038] The second travel time may be the time taken for the vehicle to move from the front end to the rear end of each calculation section.

[0039] Thus, the information processing device according to the present disclosure can calculate the travel time based on the traffic flow rate of each section with the bottleneck point as the boundary, and calculate the travel time that is more in line with the actual situation by summing them up.

[0040] Furthermore, the control unit may determine the vehicle density based on a travel speed of a vehicle obtained from the vehicle traveling in the first section.

[0041] Since there is a certain correlation between the speed of a vehicle and the vehicle density, the vehicle density can be obtained by using the speed information included in the detection information obtained from the vehicle.

[0042] In addition, the control unit may acquire the speeds of the plurality of vehicles at the bottleneck point as the travel information. The control unit may estimate the traffic flow rate at the bottleneck point based on the vehicle density at the bottleneck point and the average speed of the plurality of vehicles at the bottleneck point.

[0043] The average speed of a plurality of vehicles can be obtained from the driving information obtained from the vehicles. In addition, as described above, the vehicle density can be estimated from the speed of the vehicles. Therefore, by using this information, the traffic flow rate at the bottleneck point can be estimated.

[0044] In addition, the control unit may obtain 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. The control unit may correct the first traffic volume based on the inflow traffic volume and the outflow traffic volume.

[0045] When there is a grade separation or an intersection in the first section, the first traffic volume in the first section may not be estimated correctly because of inflow and outflow at the intermediate point. In order to cope with this situation, the first traffic volume may be corrected based on the inflow and outflow traffic volumes. This can improve the calculation accuracy of the travel time.

[0046] Hereinafter, specific embodiments of the present disclosure will be described based on the drawings. Unless otherwise specified, the hardware configuration, module configuration, functional configuration, etc. described in each embodiment do not limit the technical scope of the disclosure to these.

[0048] Reference Figure 1 The following describes an overview of processing performed by the server device according to the embodiment. Figure 11 is a diagram showing an overview of processing performed by a server device 100 according to an 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 the traffic conditions of a road based on driving information obtained from a vehicle 10 and calculates the time taken by the vehicle 10 to travel from a predetermined location to a target location, that is, the travel time. The server device 100 is configured to be able to communicate with a plurality of vehicles 10.

[0049] First, the server device 100 acquires the travel information of the vehicle 10. The travel information of the vehicle 10 is detection information including the speed of the vehicle 10 and the like.

[0050] First, the case where there is no bottleneck point between the predetermined point and the target point will be described. Figure 1 As shown in (A), the server device 100 estimates the traffic flow rate ( Figure 1 Here, the traffic flow rate refers to the amount of traffic (the number of vehicles) passing through the target point per hour.

[0051] Next, the server device 100 estimates the vehicle density ( Figure 1 (2)). The vehicle density is a numerical value indicating the congestion condition of the road, and can be expressed by the number of vehicles 10 existing per 1 km. The server device 100 obtains the speed of the vehicle 10 from the vehicle 10 as the driving information, and estimates the vehicle density based on the corresponding speeds of the plurality of vehicles 10.

[0052] Since there is a certain correlation between the speed of a vehicle and the vehicle density, the vehicle density in the target section can be estimated by using the speed of the vehicle traveling in the target section.

[0053] The server device 100 estimates 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.

[0054] Next, the server device 100 calculates the time required to move from the predetermined location to the target location, that is, the first travel time ( Figure 1 (4)).

[0055] Next, the case where a bottleneck point is included between the predetermined point and the target point is described. Figure 1As shown in (B), the server device 100 determines a bottleneck point where traffic is congested between a predetermined point and a target point based on the speeds of the plurality of vehicles 10. The server device 100 divides the area between the predetermined point and the target point using the bottleneck point as a boundary, and uses each of the bottleneck points as a calculation interval.

[0056] Next, the server device 100 estimates the traffic flow rate at the bottleneck point which is the end of the calculation section.

[0057] Next, the server device 100 estimates the vehicle density in each calculation interval for each calculation interval.

[0058] The server device 100 estimates the traffic volume in each calculation section, that is, the section traffic volume, 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.

[0059] Next, the server device 100 calculates the second travel time, which is the time required to move from end to end of each calculation section, based on the traffic flow rate at each bottleneck point and the section traffic volume in the calculation section corresponding to each bottleneck point.

[0060] Finally, the server device 100 calculates the first travel time ( ) which is the time required to move from the predetermined point to the target point by summing up the second travel time of each calculation section. Figure 1 (5)).

[0061] In this way, the server device 100 sets the calculation section, estimates the traffic flow rate at the end of the calculation section, estimates the traffic volume of the calculation section, and calculates the travel time corresponding to the calculation section based on the estimated traffic volume and the traffic flow rate at the end.

[0062] When there is a bottleneck point on the route, the server device 100 sets a plurality of calculation sections with the bottleneck as a boundary, calculates the travel time for each calculation section, and totals the calculated travel time.

[0063] Thus, the server device 100 can predict the traffic conditions based only on the travel information of the vehicle 10 without using data detected by roadside devices such as vehicle sensors.

[0064] According to the above configuration, the travel time can be appropriately calculated even without using a roadside device.

[0065] Next, each element constituting the server device 100 will be described in detail. Figure 2 This is a diagram for explaining the components of the server device 100 according to the embodiment.

[0066] The server device 100 according to the present embodiment includes a control unit 110 , a storage unit 120 , and a communication unit 130 .

[0067] The control unit 110 is implemented by a processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit) and a memory. The control unit 110 includes an acquisition unit 111, a setting unit 112, a calculation unit 113, and an estimation unit 114 as functional modules. These functional modules can be implemented by the control unit 110 executing a program.

[0068] The acquisition unit 111 acquires travel information and the like from the vehicle 10. The travel information is composed of detection information of the vehicle 10 including the speed and the traveling direction of the vehicle 10 and the like.

[0069] When there is a bottleneck point, which is a location where vehicles 10 are congested, on the road of the calculation target section of the first travel time, the setting unit 112 divides the first section with the bottleneck point as a boundary to set a plurality of calculation sections.

[0070] When there is no bottleneck point on the road in the calculation target section, the first section is not divided.

[0071] The calculation unit 113 calculates the travel time in the first section, that is, the first travel time, based on the traffic flow rate and the section traffic volume estimated by the estimation unit 114. Here, the first travel time refers to the time taken for the vehicle 10 to move from a predetermined location to a target location. In addition, the section traffic volume when there is no bottleneck in the calculation target section and the calculation target section is not divided is the first traffic volume. In addition, the section traffic volume when there is a bottleneck in the calculation target section and the calculation target section is divided is the second traffic volume.

[0072] In addition, when the first section is divided due to the presence of a bottleneck point, the calculation unit 113 calculates the second travel time for each of the plurality of calculation sections based on 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 front end to the end of the calculation section in each calculation section divided with the bottleneck as the boundary. Furthermore, the calculation unit 113 calculates the first travel time by summing up the second travel times of each of the calculation sections.

[0073] The inference unit 114 infers the traffic flow rate at the target location or the bottleneck location determined by the setting unit 112. Here, the traffic flow rate is the number of vehicles 10 passing through the target location or the bottleneck location within a specified unit time. For example, the traffic flow rate may be a specified value determined for each location, or may be obtained by calculation. Specifically, the inference unit 114 may infer the traffic flow rate at the target location or the bottleneck location based on the vehicle density and the average speed of a plurality of vehicles 10 at the location. The above-mentioned vehicle density may be the density at the target location or the bottleneck location inferred from the speed of the vehicle 10.

[0074] In addition, the estimation unit 114 estimates the traffic volume in the calculation section or the first section composed of a plurality of calculation sections (section traffic volume). The estimation unit 114 may calculate the number of vehicles 10 that have passed through the section of the road from the predetermined point to the target point, that is, the predetermined point in the first section, as the section traffic volume based on the vehicle density. Here, the vehicle density is the density of vehicles existing in the first section calculated based on the travel information obtained from the vehicle 10 traveling in the first section. The estimation unit 114 may determine the vehicle density based on the travel speed of the vehicle 10 obtained by the vehicle 10.

[0075] In addition, the estimation unit 114 may calculate the number of vehicles 10 included in the calculation section set by dividing the first section with the bottleneck point as the section traffic volume. Here, the section traffic volume is a specific example of the second traffic volume.

[0076] The storage unit 120 is a main storage device such as a RAM or ROM, an EPROM, a hard disk drive, and an auxiliary storage device such as a removable medium. The auxiliary storage device stores an operating system (OS), various programs, various tables, etc., and by executing the programs stored therein, various functions that meet the prescribed purposes of each part of the control unit 110 can be realized. However, some or all of the functions can also be realized by a hardware circuit such as an ASIC or FPGA.

[0077] The storage unit 120 stores data and the like used or generated in the processing performed by the control unit 110. In addition, the storage unit 120 may temporarily store vehicle information obtained from the vehicle 10, or inflow and outflow traffic from a grade separation or an intersection in a road including a section for calculating the first travel time obtained from an external server device.

[0078] The communication unit 130 is composed of 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). In addition, the communication unit 130 may be a communication circuit that performs wireless communication using LTE (Long Term Evolution), or a communication circuit that performs communication based on LPWA (Low Power Wide Area). In addition, the communication unit 130 may be a communication circuit that performs wireless communication using Wi-Fi (registered trademark).

[0079] Next, the specific content of the processing performed by the server device 100 will be described. Figure 3 This 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 to be calculated for the first travel time, the server device 100 executes the following process for the entire section to be calculated for the first travel time.

[0080] In addition, the server device 100 may start the operation when receiving a trigger such as a request for providing information related to the travel time from the user of the vehicle 10. Figure 3 Processing shown.

[0081] First, in S10, the estimation unit 114 estimates the traffic flow rate at the target location, which is the end of the interval for calculating the first travel time. The estimation unit 114 may estimate the traffic flow rate at the target location based on the vehicle density at the target location and the speeds of the plurality of vehicles 10 at the target location. The estimation unit 114 may obtain the vehicle density based on the average speed of the plurality of vehicles 10 passing through the target location, which is obtained by the obtaining unit 111.

[0082] Q=KV ···Formula (1)

[0083] Q: traffic flow rate (traffic volume), K: vehicle density, V: average speed

[0084] Here, the average speed can be expressed by the following formula (Underwood formula).

[0085] V=Vf·e －( K / Kc)···Formula (2)

[0086] V: average speed, Vf: maximum speed, K: vehicle density, Kc: critical density

[0087] According to formula (2), the value of vehicle density K can be calculated, and thus the traffic flow rate Q can be calculated according to formula (1).

[0088] The relationship between the average speed V, vehicle density K, and traffic flow rate (traffic volume) Q in formula (2) is as follows: Figure 5 shown. Figure 5 This is a diagram showing the relationship between the speed of the vehicle, the vehicle density, and the traffic flow rate. The traffic flow rate (traffic volume) Q increases with the increase of the average speed V, but decreases when it exceeds the speed at which the maximum value is reached. On the road, when the speed of the vehicle 10 is lower than the speed at which the traffic volume reaches the maximum value, it is a congested flow, and when the speed is higher than the speed at which the maximum value is reached, it is a free flow. As described above, when it is determined to be a congested flow based on the speed of the vehicle 10, the traffic volume determined by the constraints on the road is handled as the traffic flow rate.

[0089] 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.

[0090] In addition, the acquisition unit 111 may acquire information related to the blinker operation, information related to the steering amount, an image captured by a vehicle-mounted camera, etc. as vehicle information acquired from the vehicle 10. Furthermore, the estimation unit 114 may determine the driving position of the vehicle 10 based on the information related to the blinker operation, information related to the steering amount, an image captured by a vehicle-mounted camera, etc., and determine the driving lane in which the vehicle 10 is traveling. Furthermore, the estimation unit 114 may estimate the traffic flow rate of each lane and use a value obtained by summing up the traffic flow rates of each lane as the traffic flow rate at the target point.

[0091] Next, in S11, the estimation unit 114 estimates the 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 the first traffic volume.

[0092] For example, the traffic volume in the first section may be a value obtained by multiplying the vehicle density obtained from the average speed of the plurality of vehicles 10 in the first section by the section length of the first section. Alternatively, the vehicle density may be determined based on the distance to the preceding vehicle or the following vehicle detected by the vehicle 10.

[0093] In addition, the estimation unit 114 may derive a sampling ratio for traffic volume according to the day of the week or time period at a predetermined location in the first section based on statistical information on the number of vehicles 10. The estimation unit 114 may estimate the traffic volume in the first section based on the current number of vehicles 10 at a predetermined location in the first section.

[0094] Next, in 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 of the first section. The travel time in the first section is the time taken for the vehicle 10 to move from the front end to the end of the first section. The calculation unit 113 can divide the traffic volume in the first section by the traffic flow rate at the target point as the travel time in the first section. The travel time in the first section is a specific example of the first travel time.

[0095] In addition, after S12, the estimation unit 114 may compare the travel time in the first section calculated in S12 with the actual travel time in the first section of the vehicle 10. Furthermore, the estimation unit 114 may determine parameters such as the maximum speed Vf and the critical density Kc of the formula (2) so that the error between the two is minimized. The determined parameters may be used in the next and subsequent Figure 3 Used in the processing shown.

[0096] Thus, the server device 100 can calculate the travel time of the vehicle 10 in the first section based on the driving information of the vehicle 10 without using the data detected by the roadside device such as the vehicle sensor. Thus, the server device 100 can calculate the travel time of the vehicle 10 in the first section by a method with higher accuracy than before using the driving information of the vehicle 10 that can be obtained reliably without using the data detected by the roadside device such as the vehicle sensor. Therefore, the server device 100 can perform a prediction with higher accuracy than before in predicting the travel time.

[0097] Next, the processing performed by the server device 100 when the bottleneck point is included in the first section will be described. Figure 4 FIG. 1 is a flowchart of a process of calculating travel time when a bottleneck point exists, which is executed by the control unit 110 of the server device 100 according to the embodiment. Figure 3 The processing described in Figure 4 For the processing described in Figure 4 Description of the process and Figure 3 The same contents as those described in the above are omitted.

[0098] The server device 100 may start the process when receiving a trigger such as a request for providing information related to the travel time from the user of the vehicle 10. Figure 4 Processing shown.

[0099] First, in S20, the setting unit 112 divides the first section with the bottleneck point as a boundary to set a plurality of calculation sections. Here, the bottleneck point refers to the beginning point of the section where traffic congestion occurs. The bottleneck point may also be the beginning part of the section where the average speed of the passing vehicles 10 is less than a predetermined value. The setting unit 112 may determine the bottleneck point where congestion occurs based on the driving information obtained from the vehicle 10. Here, the driving information obtained from the vehicle 10 may be the speed of the vehicle 10 or the brake operation frequency of the vehicle 10.

[0100] For example, the setting unit 112 analyzes the transition relationship between the brake operation frequency and the speed of the plurality of vehicles 10 at the predetermined location in the first section within the predetermined period. The setting unit 112 obtains the probability distribution of the transition of the speed of the future vehicle 10 relative to the brake operation frequency at a certain time at the predetermined location. Furthermore, the setting unit 112 predicts the time when the probability of the traffic flow rate decreasing at the predetermined location is high based on the current brake operation frequency at the predetermined location, so that it can be determined when congestion occurs at the predetermined location and the bottleneck location can be determined.

[0101] Figure 6 : is a diagram showing an example of setting the calculation interval. At time t1 when only a free flow with a traffic flow speed of a certain level or higher is generated, the first interval is not divided, and the interval to be calculated is only interval 210. In contrast, at time t2 when a congested flow with a traffic flow speed of a certain level or lower is generated, the first interval is divided into two with the location of the congested flow (bottleneck location) as the boundary, and intervals 220 and 230 are set as calculation intervals. Similarly, at time tn when two congested flows are generated, the first interval is divided into three with the location of the congested flow (bottleneck location) as the boundary, and intervals 240, 250, and 260 are set as calculation intervals.

[0102] Next, in S21, the inference unit 114 infers the traffic flow rate at each bottleneck point. For example, the acquisition unit 111 acquires the speeds of the plurality of vehicles 10 passing through each bottleneck point. Furthermore, the inference unit 114 calculates the average speed based on the speeds of the plurality of vehicles 10 acquired by the acquisition unit 111. Furthermore, the inference unit 114 infers the traffic flow rate at each bottleneck point based on the vehicle density at the bottleneck point and the average speed of the plurality of vehicles 10 at the bottleneck point. Specifically, the inference unit 114 can calculate the traffic flow rate at each bottleneck point according to equation (1) and equation (2).

[0103] The estimation unit 114 may calculate 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 plurality of vehicles 10 at each bottleneck point. At the bottleneck point, the traffic volume expressed by equation (1) is treated as the traffic flow rate.

[0104] The traffic flow rate calculated in S21 may be a value obtained by multiplying the traffic flow rate Q calculated according to equations (1) (2) by the number of lanes of the road. In addition, at a bottleneck location where congestion occurs stably, the inference unit 114 may use the value obtained by averaging past traffic flow rate data as the traffic flow rate. In addition, at a bottleneck location caused by restrictions on the road due to construction or accidents, the inference unit 114 may use the value obtained by subtracting the number of restricted lanes from the total number of lanes and dividing it by the total number of lanes and multiplying it by the traffic flow rate Q calculated according to equations (1) (2) as the traffic flow rate.

[0105] Next, in S22, the estimation unit 114 estimates the traffic volume existing in each section, that is, the section traffic volume, for each of the calculation sections. 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. In addition, for example, the estimation unit 114 may distinguish the congested flow section and the free flow section based on information related to the end of the congestion determined by the setting unit 112 based on the average speed of the plurality of vehicles 10, and use the number of waiting queues 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.

[0106] When the acquisition unit 111 can obtain the congestion length based on traffic information from VICS (Vehicle Information and Communication System) (registered trademark), etc., the estimation unit 114 can calculate the number of vehicles in the waiting queue of the congested flow section based on the congestion length. The estimation unit 114 can use the number of vehicles in the waiting queue as the section traffic volume in each calculation section.

[0107] Next, in S23, the calculation unit 113 calculates the travel time in each calculation interval, that is, the second travel time, based on the interval traffic volume and the traffic flow rate at each bottleneck point. The second travel time is the time taken for the vehicle 10 to move from the end to the end of each calculation interval. The calculation unit 113 obtains the interval traffic volume estimated by the estimation unit 114 and the traffic flow rate at each bottleneck point to calculate the second travel time.

[0108] Next, in S24, the calculation unit 113 calculates the first travel time by summing up the second travel times of all the calculated 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 the end of the first section to the opposite end.

[0109] Second embodiment

[0110] In the first embodiment, the traffic volume in the calculation target section is estimated based on the speed data transmitted from the vehicle. However, when there is a grade separation or an intersection in the middle of the section, the traffic volume may not be accurately calculated due to the inflow and outflow of vehicles from the branch road in the middle. Therefore, in order to more accurately grasp the traffic volume, in the second embodiment, the estimation unit 114 uses the inflow or outflow of traffic from the grade separation or the intersection to correct the traffic flow rate in the first section or each calculation section.

[0111] The acquisition unit 111 acquires the inflow traffic volume from the grade separation or the intersection and the outflow traffic volume from the grade separation or the intersection in the road including the section for calculating the first travel time. The acquisition unit 111 may query an external server device to acquire the inflow traffic volume from the grade separation or the intersection and the outflow traffic volume from the grade separation or the intersection.

[0112] Furthermore, the calculation unit 113 calculates the first travel time or the second travel time of the vehicle 10 using the traffic volume included in the first section or each calculation section, or the traffic volume in the section near the grade separation or the intersection that exists near the first section (hereinafter referred to as the traffic volume near the branch section). At this time, the inference unit 114 corrects the change in the traffic volume at the specified location at the time when the vehicle 10 is considered to pass through the first section. Among them, the inference unit 114 can calculate the traffic volume near the branch section based on the branch rate. The branch rate refers to the ratio of the number of vehicles 10 that have passed the specified grade separation or intersection to the number of vehicles 10 that merge into the first section or branch from the first section.

[0113] In addition, for example, the inference unit 114 compares the current outbound traffic volume with the past outbound traffic volume corresponding to the information related to the similar destination or via point based on the information related to the destination or via point of the vehicle 10 on the outbound trip (such as the departure point, the arrival point, the entered cycloidal intersection, or the exited cycloidal intersection). Moreover, the inference unit 114 can correct the branching rate at the cycloidal intersection or intersection of the return trip by assuming that the return traffic volume will increase or decrease the increase or decrease of the outbound traffic volume relative to the past traffic volume. Here, for example, the outbound trip may be an up lane in a specified time period, and the return trip may be a down lane in a time period later than the specified time period.

[0114] Modifications

[0115] The above-described embodiment is merely an example, and the present disclosure can be implemented with appropriate modifications within the scope of the present disclosure.

[0116] For example, the processes and mechanisms described in the present disclosure can be implemented in combination as long as no technical contradiction occurs.

[0117] In addition, a process described as being performed by one device may be shared and executed by a plurality of devices. Alternatively, a process described as being performed by different devices may be executed by one device. In a computer system, it is possible to flexibly change which hardware configuration (server configuration) is used to implement each function.

[0118] The present disclosure can also be implemented by supplying a computer program installed with the functions described in the above-mentioned embodiments to a computer, and having one or more processors of the computer read out and execute the program. Such a computer program can be provided to the computer via a non-temporary computer-readable storage medium that can be connected to the system bus of the computer, or can be provided to the computer via a network. Non-temporary computer-readable storage media include, for example, any type of disks such as magnetic disks (floppy (registered trademark) disks, hard disk drives (HDDs), etc.), optical disks (CD-ROMs, DVD disks, Blu-ray disks, etc.), read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic cards, flash memories, optical cards, and any type of medium suitable for storing electronic commands.

Claims

1. An information processing device for calculating a travel time from a predetermined point to a target point, wherein: A control unit is provided, which performs the following processing: Inferring the number of vehicles passing the target location within a specified unit time, i.e., the traffic flow rate; estimating the number of vehicles in the first section, i.e., a first traffic volume, based on a vehicle density, i.e., a density of vehicles existing in a first section, the first section being a section of a road from the predetermined point to the target point, the vehicle density being calculated based on travel information obtained from vehicles traveling in the first section; and The travel time in the first section, ie, a first travel time, is calculated based on the traffic flow rate and the first traffic volume.

2. The information processing device according to claim 1, wherein: When there is a bottleneck point that is a cause of congestion on the road, the control unit divides the first section with the bottleneck point as a boundary to set a plurality of calculation sections, The control unit estimates the number of vehicles existing in the calculation interval, that is, the second traffic volume, for each of the plurality of calculation intervals, and estimates the second traffic volume by multiplying the vehicle density calculated for each of the calculation intervals by the interval length of the calculation interval. The control unit calculates the travel time from the front end to the end of each calculation interval, that is, the second travel time, for each of the plurality of calculation intervals, wherein the second travel time is calculated based on the second traffic volume and the traffic flow rate at the bottleneck point. The control unit calculates the first travel time by totaling the second travel times of all the calculation sections.

3. The information processing device according to claim 1 or 2, wherein: The control unit determines the vehicle density based on a travel speed of a vehicle obtained from the vehicle traveling in the first section.

4. The information processing device according to claim 2, wherein: The control unit obtains the speeds of a plurality of vehicles at the bottleneck point as the driving information, The control portion infers 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.

5. The information processing device according to claim 1 or 2, wherein: The control unit obtains the amount of traffic flowing into the road from the grade separation or the intersection and the amount of traffic flowing out of the road from the grade separation or the intersection, The control unit corrects the first traffic volume based on the incoming traffic volume and the outgoing traffic volume.

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