Information processing apparatus

Abstract

To predict a traffic jam with higher accuracy than before.SOLUTION: The information processing device predicts traffic demands at a plurality of destinations, and predicts the number of vehicles traveling to each destination per day based on past route information. Then, the information processing apparatus determines a time at which congestion occurs in the first section based on the number of vehicles passing through the first section for each time zone calculated from the route information, and estimates a travel time in the target section.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to congestion prediction. [Background technology]

[0002] There is a technology for predicting road congestion. For example, Patent Document 1 discloses a traffic congestion prediction model creation device that selects population data of a group that has a relatively high contribution to traffic congestion data at a traffic congestion prediction location, and builds a traffic congestion prediction model by regression analysis based on the selected population data and the traffic congestion data at the traffic congestion prediction location. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-066930 Summary of the Invention [Problem to be solved by the invention]

[0004] With the development of machine learning, it is expected that models for predicting traffic congestion will be developed in the future. The present disclosure aims to predict traffic congestion with higher accuracy than conventional methods. [Means for solving the problem]

[0005] One aspect of the present disclosure is an information processing device having a control unit that executes the following: estimating a traffic flow rate, which is the number of multiple vehicles that pass a first point per unit time; estimating a traffic volume, which is the number of vehicles present in a first section at a predetermined time point, based on a vehicle density, which is the density of vehicles in a first section, calculated based on travel information, which is information regarding travel of multiple vehicles traveling in the first section from a predetermined point to the first point; and estimating a travel time for the first section based on the traffic flow rate and the traffic volume, wherein the control unit further estimates, for each of multiple destinations included in OD information, which is past route information of multiple vehicles, a first number, which is the predicted number of vehicles that will travel to the destination on a target day, based on the first information; calculating, for each of the multiple destinations, a first ratio, which is the ratio for each departure point to the first number of vehicles heading to each destination, based on the OD information; and multiplying the first number calculated for each destination by the first ratio calculated for each departure point. calculates a second number, which is the predicted number of vehicles traveling from the departure point to the destination in one day, for each OD pair that is a pair of an departure point and a destination, by multiplying the calculated number by the first ratio; calculates a third number, which is the predicted number of vehicles traveling through the first section, for each OD pair by multiplying each of the second numbers calculated for each OD pair by the ratio of vehicles traveling through the first section obtained from the OD information; calculates a fourth number, which is the predicted number of vehicles traveling through the first section in a specific time period, for each OD pair based on the third number calculated for each OD pair and information related to a time period related to a passage through the first section obtained from the OD information; determines a first time, which is the time at which the number of vehicles traveling through the first section exceeds a predetermined threshold, based on the fourth number calculated for each OD pair; and estimates the travel time for the first section by applying a traffic flow rate in a congestion flow to the first section, with a time period including the first time as a congestion occurrence time period.

[0006] Another aspect is a program for causing a computer to execute the information processing method executed by the information processing device, or a computer-readable storage medium non-temporarily storing the program. [Effects of the Invention]

[0007] According to the present disclosure, traffic congestion can be predicted with higher accuracy than conventional methods. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 2 is a diagram showing an outline of a process executed by a server device according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating components of a server device according to an embodiment. [Figure 3] 6 is a flowchart of a process executed by a control unit of a server device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] (overview) There is technology to predict road congestion.

[0010] For example, a conventional information processing device for predicting traffic congestion is known. The information processing device acquires population data around a departure point and selects population data that has a relatively high contribution to traffic congestion data at a traffic congestion prediction location. The information processing device then performs a regression analysis based on the selected population data and the traffic congestion data at the traffic congestion prediction location to construct a traffic congestion prediction model.

[0011] In this way, the above-mentioned conventional technology predicts congestion based on the demographic trends around the departure point by constructing a congestion prediction model based on population data around the departure point. However, this method cannot accurately predict possible congestion if there are no changes in the demographic trends around the departure point. Here, a case where there are no changes in the demographic trends around the departure point can be considered as a case where there is no prior population influx into the departure point, such as when a vehicle departs from the vehicle user's residence.

[0012] However, when a user travels to a destination, the outbound journey usually departs from the user's place of residence, and therefore accurate traffic congestion prediction is required even in such cases.

[0013] Therefore, in order to accurately predict congestion on the outbound journey as well, it is preferable that the information processing device predicts congestion based on, for example, traffic demand at the vehicle's destination, rather than the demographics of the departure point, which does not contribute to the prediction of congestion on the outbound journey.

[0014] An information processing device according to one aspect of the present disclosure includes: an information processing device having a control unit that executes the following: estimating a traffic flow rate, which is the number of multiple vehicles that pass a first point per unit time; estimating a traffic volume, which is the number of vehicles present in a first section at a predetermined time point, based on a vehicle density, which is the density of vehicles in a first section, calculated based on travel information, which is information regarding travel of multiple vehicles traveling in the first section, which is a section from a predetermined point to the first point; and estimating a travel time for the first section based on the traffic flow rate and the traffic volume, wherein the control unit further estimates, for each of multiple destinations included in OD information, which is past route information of multiple vehicles, a first number, which is the predicted number of vehicles that will travel to the destination on a target day, based on the first information; calculating a first ratio for each of the plurality of destinations, which is a ratio for each departure point to the first number of vehicles heading to each destination, by multiplying the first number of vehicles calculated for each destination by the first ratio calculated for each departure point to calculate a second number of vehicles, which is a predicted number of vehicles heading from the departure point to the destination in one day, for each OD pair that is a pair of departure point and destination; multiplying each of the second numbers of vehicles calculated for each OD pair by a ratio of vehicles passing through the first section obtained from the OD information to calculate a third number of vehicles, which is a predicted number of vehicles passing through the first section, for each OD pair; and calculating a third number of vehicles, which is a predicted number of vehicles passing through the first section in a specific time period, based on the third number of vehicles calculated for each OD pair and information on a time period during which vehicles pass through the first section obtained from the OD information. The method performs the following operations: calculating a fourth number of vehicles for each OD pair, which is the predicted number of vehicles that will pass between the first and second sections; determining a first time at which the number of vehicles passing through the first section will exceed a predetermined threshold based on the fourth number of vehicles calculated for each OD pair; and estimating the travel time in the first section by applying a traffic flow rate in a congestion flow to the first section, with a time period including the first time set as a congestion occurrence time period.

[0015] The control unit estimates the traffic volume, which is the number of vehicles present in the first section, based on the vehicle density in the first section, and estimates the travel time for the first section based on the traffic flow rate and the estimated traffic volume.

[0016] Traffic flow rate is the number of vehicles that can pass through a specific point per unit time. Traffic flow rate can differ between normal times and when there is congestion.

[0017] The first section is a section on a road from a predetermined point to a first point. Here, the first point refers to a point that may become a bottleneck for traffic congestion. In other words, the first section is a section where traffic congestion is likely to occur constantly due to traffic concentration. The information processing device according to the present disclosure estimates travel time for the first section.

[0018] Vehicle density is the number of vehicles per unit distance.

[0019] Traffic volume is the number of vehicles in the first section. Traffic volume is calculated by multiplying the length of the first section by the vehicle density.

[0020] Travel time is the time it takes to travel from one point to another.

[0021] If the length and vehicle density of the first section are known, the traffic volume, which is the number of vehicles present in the first section, can be estimated, and the travel time for the first section can be estimated based on the estimated traffic volume and traffic flow rate. Furthermore, if the future vehicle density in the first section is known, the future travel time can be estimated. For example, it is conceivable to estimate the number of vehicles that will pass through the first section in the future based on OD information, which is past route information of multiple vehicles, and thereby estimate the future travel time in the first section.

[0022] However, because OD information is generated based on data transmitted from probe cars, it is not possible to calculate the predicted number of all vehicles from OD information alone. Therefore, the control unit calculates the ratio of vehicles heading to multiple destinations included in the OD information that pass through the first section, and multiplies this by the predicted number of vehicles heading to each destination in one day to estimate the number of vehicles passing through the first section.

[0023] First, for each of the multiple destinations included in the OD information, the control unit calculates the predicted number of vehicles heading to the target destination on the target day (the total number of vehicles heading to the target destination from all departure points; hereinafter, referred to as the first number) based on the first information.

[0024] The first information is information used to determine the number of vehicles at a predetermined destination, such as information about events held at the predetermined destination, calendar information, etc.

[0025] Next, the control unit calculates a first ratio, which is a ratio for each departure point to the number of vehicles (first number) heading to each of the multiple destinations included in the OD information. The first ratio is calculated for each of the multiple destinations included in the OD information. The control unit multiplies the first number by the first ratio calculated for each departure point, and calculates the number of vehicles between the departure point and the destination. The second number of vehicles is calculated for each pair of an origin and a destination (hereinafter referred to as an OD pair).

[0026] In addition, the control unit calculates the predicted number of vehicles (third number) that will pass through the first section for each OD pair by multiplying each of the second numbers calculated for each OD pair by the ratio of vehicles passing through the first section obtained from the OD information.

[0027] Furthermore, the control unit calculates a fourth number, which is the predicted number of vehicles that will pass through the first section during a specific time period, for each OD pair based on the third number calculated for each OD pair and information regarding the time period during which vehicles will pass through the first section obtained from the OD information. The OD information includes information about the time period when vehicles passed through the first section. Using this information, it is possible to determine the predicted number of vehicles (fourth number) that will pass through the first section in a certain time period. By summing up the fourth numbers calculated for multiple OD pairs, the total number of passing vehicles in the first section can be estimated for each future time period.

[0028] The control unit also calculates the first time based on the fourth number calculated for each OD pair.

[0029] The first time is the time when the total of the fourth number of vehicles in the first section exceeds a threshold at which the flow of vehicles on the road transitions from free flow to congested flow. When the total of the fourth number of vehicles exceeds the threshold, it means that congestion will occur in the first section. In other words, the first time is the time when congestion will occur in the first section.

[0030] Since the first time is the time when congestion occurs, the travel time for the first section changes from that time onward. Therefore, the control unit calculates the travel time for the first section using the traffic flow rate in the congestion flow for the time period including the first time.

[0031] In this way, an information processing device according to one embodiment of the present disclosure predicts traffic demand at a destination and, based on the number of vehicles heading to the destination, estimates the number of vehicles heading to the destination from multiple departure points based on past route information.The information processing device then estimates the number of vehicles passing through a first section between the departure point and the destination from the estimated number of vehicles and determines the time when congestion will occur in the first section.The information processing device then estimates the travel time to the destination by applying the traffic flow rate in the congestion flow during the time period when congestion occurs.

[0032] With this configuration, the information processing device according to the present disclosure can predict traffic congestion with higher accuracy than conventional methods.

[0033] Specific embodiments of the present disclosure will be described below with reference to the accompanying drawings. Unless otherwise specified, the hardware configuration, module configuration, functional configuration, etc. described in each embodiment are not intended to limit the technical scope of the disclosure to those configurations.

[0034] (Embodiment) [Outline of processing performed by the server device] An overview of processing executed by a server device according to an embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram illustrating an overview of processing executed by a server device 100 according to an embodiment. An information processing device according to an aspect of the present disclosure is realized as the server device 100. In this embodiment, the server device 100 predicts traffic demand at a vehicle's destination, and determines the occurrence time of traffic congestion in a specific section based on the predicted traffic demand.

[0035] First, the server device 100 predicts the number of vehicles that will pass through a specific section (a "first section" in the present disclosure) in a specific time period based on the past driving history of a plurality of vehicles. First, the server device 100 predicts the number of vehicles heading to the destinations included in the OD information, based on the OD information that is information on the past routes of the vehicles.

[0036] At this time, the server device 100 predicts the traffic demand for each destination (that is, how many vehicles will head to the target destination in a day) based on event information and the like at multiple destinations included in the OD information.

[0037] Next, the server device 100 calculates the number of vehicles departing from a certain departure point and arriving at a certain destination for each pair of departure point and destination (OD pair) based on the OD information. Then, based on the route information included in the OD information, it predicts the number of vehicles passing through a target section during a specific time period. Here, the target section is a section of a predetermined length starting from a bottleneck point of congestion.

[0038] Next, the server device 100 determines the time when a traffic jam will occur based on the number of vehicles passing through the target section during a specific time period. The server device 100 determines the time when the flow of vehicles in the target section will transition to a traffic jam flow.

[0039] Then, the server device 100 applies the traffic flow rate of the congestion flow to the target section during the time period including the time when congestion occurs in the target section, and estimates the travel time required to travel through the target section.

[0040] Traffic flow rate is the traffic volume (number of passing vehicles) measured at a specific point converted into traffic volume per unit time (typically one hour). For example, if there is a location where congestion is occurring due to a bottleneck, the traffic flow rate at that location may be significantly lower than the normal traffic flow rate. Traffic flow rate can vary depending on the location of the bottleneck, the cause of the congestion, etc.

[0041] By applying the traffic flow rate in the congestion flow to the congestion section, the travel time in the target section when congestion occurs can be calculated.

[0042] As described above, the server device 100 predicts the traffic demand for each of multiple destinations and predicts the number of vehicles heading to each destination for each departure point based on the OD information.The server device 100 then predicts the future number of vehicles passing through the target section based on the number of vehicles calculated for each OD pair, determines the time when congestion will occur in the target section, and estimates the travel time for the time period including that time.This allows the server device 100 to predict congestion with higher accuracy than before.

[0043] [Server device configuration] Next, a description will be given of the hardware and software configuration of the server device 100. Fig. 2 is a diagram illustrating the components of the server device 100 according to the embodiment.

[0044] The server device 100 can be configured as a computer having a processor (CPU, GPU, etc.), a main memory device (RAM, ROM, etc.), and an auxiliary memory device (EPROM, hard disk drive, removable media, etc.). The auxiliary memory device stores an operating system (OS), various programs, various tables, etc., and by executing the programs stored therein, various functions (software modules) that meet predetermined purposes, as described below, can be realized. However, some or all of the functions may be implemented as hardware modules using hardware circuits such as ASICs, FPGAs, etc. It may be expressed.

[0045] The server device 100 includes a control unit 110, a storage unit 120, and a communication unit .

[0046] The control unit 110 is a computing unit that executes predetermined programs to realize various functions of the server device 100. The control unit 110 can be realized by a hardware processor such as a CPU. The control unit 110 may also be configured to include RAM, ROM (Read Only Memory), cache memory, etc.

[0047] In this embodiment, the control unit 110 included in the server device 100 is configured to have six software modules: a first prediction unit 111, a second prediction unit 112, a third prediction unit 113, a fourth prediction unit 114, a time determination unit 115, and an estimation unit 116. Each software module may be realized by the control unit 110 (e.g., a CPU) executing a program stored in the storage unit 120. Note that the information processing executed by the software modules is synonymous with the information processing executed by the control unit 110 (e.g., a CPU).

[0048] The first prediction unit 111 estimates a first number, which is the number of vehicles heading to a specified destination in one day, based on first information, which is information for predicting traffic demand, which is the number of vehicles present at a specified destination on a target day. The first prediction unit 111 estimates a first number of vehicles for each of a plurality of destinations included in OD information, which is information on the past routes of a plurality of vehicles.

[0049] The second prediction unit 112 calculates a ratio (hereinafter referred to as a first ratio) for each departure point for each of the multiple destinations included in the OD information. Then, the second prediction unit 112 calculates a second number by multiplying the first number calculated for each destination by the first ratio. The second number is the predicted number of vehicles traveling in one day from a certain departure point to a target destination, and is calculated for each combination of departure point and destination (OD pair).

[0050] The third prediction unit 113 multiplies the second number calculated for each OD pair by the passing rate, which is the rate of vehicles passing through the target section obtained from the OD information, to calculate a third number, which is the number of vehicles passing through the target section in one day. The third number is calculated for each OD pair.

[0051] The fourth prediction unit 114 multiplies the third number calculated for each OD pair by the time period ratio, which is the ratio for each time period during which vehicles pass through the target section, obtained from the OD information, to calculate a fourth number, which is the number of vehicles passing through the target section in a specific time period. The fourth number is calculated for each OD pair and time period.

[0052] Based on the fourth number calculated for each OD pair and time period, the time determination unit 115 determines the first time at which the number of vehicles passing through the target section exceeds the threshold at which the flow of vehicles on the road in the target section transitions from free flow to congested flow.

[0053] The estimation unit 116 estimates the travel time of a route from a specified point to a specified destination via the first point by determining a time period including the first time as a congestion occurrence time period and applying a traffic flow rate in the congestion flow to the target section.

[0054] The storage unit 120 is a means for storing information, and is configured with storage media such as RAM, a magnetic disk, a flash memory, etc. The storage unit 120 stores programs executed by the control unit 110, data used by the programs, etc. In this embodiment, the storage unit 120 stores the above-mentioned OD information.

[0055] The communication unit 130 is a communication interface for connecting the server device 100 to an external network. The communication unit 130 is configured to be able to communicate with an external device via, for example, a wireless LAN or a cellular communication network such as 3G, 4G, or 5G.

[0056] 2 is an example, and all or part of the illustrated functions may be performed using a dedicated circuit. Also, programs may be stored or executed using a combination of a main memory and an auxiliary memory other than those illustrated.

[0057] [Server device processing] Next, specific details of the processing performed by the server device 100 according to an embodiment of the present disclosure will be described. Fig. 3 is a flowchart of the processing performed by the control unit 110 of the server device 100 according to the embodiment. Fig. 3 describes the processing performed by the server device 100 to determine the time of congestion occurrence based on the traffic demand at the destination and estimate the travel time for the target section.

[0058] The server device 100 starts step S10 when it receives a request from a vehicle under its control. Alternatively, the server device 100 may start step S10 when it receives an input from a vehicle user via an in-vehicle terminal or the like. Alternatively, the server device 100 may periodically start the illustrated process.

[0059] The processing of steps S10 to S12 is executed for each of the multiple destinations included in the OD information. First, in step S10, the first prediction unit 111 estimates a first number, which is the number of vehicles heading to the destination to be processed in one day, based on information for predicting traffic demand (first information). For example, the first prediction unit 111 may predict the number of vehicles heading to the destination to be processed based on information about scheduled events to be held at the destination to be processed, calendar information, past performance values, etc.

[0060] In step S11, the second prediction unit 112 tallyes trips heading to the target destination from the OD information by departure point, and calculates the ratio of each departure point to the total number of vehicles heading to the target destination. That is, the second prediction unit 112 calculates a first ratio for each departure point, which is the ratio of vehicles heading to the target destination from a certain departure point to vehicles heading to the target destination from all departure points.

[0061] In step S12, the second prediction unit 112 predicts the number of vehicles heading to the target destination in one day for each departure point based on the first ratio for each departure point calculated in step S11. Specifically, the second prediction unit 112 may multiply the first number by the first ratio. Then, the first prediction unit 111 and the second prediction unit 112 repeat steps S10 to S12 for all destinations.

[0062] Next, in step S13, the third prediction unit 113 predicts a third number, which is the number of vehicles that will pass through the target section in one day. Specifically, the third prediction unit 113 multiplies each of the second numbers calculated for each OD pair by the rate (passing rate) of vehicles passing through the target section obtained from the OD information, to calculate the third number, which is the predicted number of vehicles that will pass through the target section, for each OD pair.

[0063] Next, in step S14, the fourth prediction unit 114 multiplies the third number of vehicles calculated for each OD pair by a time period ratio, which is the ratio of vehicles passing through the target section in each of the multiple time periods, to all vehicles passing through the target section, to calculate a fourth number of vehicles, which is the number of vehicles passing through the target section for each time period.The fourth prediction unit 114 calculates the fourth number of vehicles, which is the number of vehicles passing through the target section for each time period, by summing the fourth numbers calculated for each OD pair by time period. can calculate the number of vehicles passing through the target section during a specific time period.

[0064] Next, in step S15, the time determination unit 115 determines the time at which the flow of vehicles in the target section transitions from free flow to congested flow. Specifically, based on the fourth number of vehicles calculated for each OD pair and time period, the time determination unit 115 determines a first time at which the fourth number of vehicles exceeds a threshold at which the flow of vehicles on the road in the target section transitions from free flow to congested flow.

[0065] Next, in step S16, the estimation unit 116 estimates the travel time for the target section by applying the traffic flow rate in the congestion flow for the congestion occurrence time period including the time determined in step S15. The travel time for the target section is the time it takes for a vehicle to travel from the beginning to the end of the target section. Specifically, the estimation unit 116 may determine the travel time for the target section by dividing the fourth number of vehicles, which is the traffic volume for each time period in the target section, by the traffic flow rate for the target section (traffic flow rate in the congestion flow).

[0066] The estimation unit 116 may estimate the traffic flow rate in the target section during congestion based on the vehicle density in the target section during congestion and the speeds of multiple vehicles in the target section during congestion. The estimation unit 116 may also calculate the vehicle density from the average speed of multiple vehicles that pass through the target section.

[0067] 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 value of vehicle density K can be calculated from equation (2), and the traffic flow rate Q can be calculated from equation (1).

[0068] As described above, the server device 100 in this embodiment predicts the number of vehicles heading to a destination in a day based on the traffic demand for the destination, and predicts the number of vehicles passing through a target section during a specific time period based on OD information.The server device 100 then determines the time when congestion will occur in the target section, and estimates the travel time for the target section by applying the traffic flow rate during congestion during the congestion time period.This allows the server device 100 to predict the time required to pass through congestion with higher accuracy than conventional methods.

[0069] In this embodiment, the target section is fixed, but if there are multiple sections where congestion due to traffic concentration is likely to occur constantly, the above-mentioned processing may be performed on multiple sections.

[0070] (Other variations) The above-described embodiment is merely an example, and the present disclosure may be modified as appropriate within the scope of the present disclosure. For example, the processes and means described in the present disclosure may be freely combined as long as no technical contradiction occurs.

[0071] The present disclosure can also be realized by supplying a computer program that implements 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, magnetic disks (floppy disks, hard disk drives (HDDs), etc.), optical disks (CD-ROMs, This includes any type of disk (such as a DVD disk, Blu-ray disk, etc.), read-only memory (ROM), random-access memory (RAM), EPROM, EEPROM, magnetic card, flash memory, optical card, and any type of medium suitable for storing electronic instructions. [Explanation of symbols]

[0072] 100 Server device 110 Control unit 111···First Prediction Section 112···Second Prediction Section 113···Third Prediction Section 114···Fourth Prediction Section 115...Time determination section 116... Estimation section 120...Storage section 130 Communications Department

Claims

[Claim 1] Estimating a traffic flow rate, which is the number of vehicles passing through the first point per unit time; Estimating traffic volume, which is the number of vehicles present in a first section at a predetermined time, based on vehicle density, which is the density of vehicles in a first section calculated based on travel information, which is information regarding travel of a plurality of vehicles traveling in the first section, which is a section from a predetermined point to the first point; estimating a travel time for the first section based on the traffic flow rate and the traffic volume; An information processing device having a control unit that executes The control unit further For each of a plurality of destinations included in the OD information, which is past route information of a plurality of vehicles, estimating a first number, which is a predicted number of vehicles heading to the destination on a target day, based on the first information; calculating, for each of the plurality of destinations, a first ratio that is a ratio for each departure point to the first number of vehicles heading to each destination based on the OD information; multiplying the first number calculated for each destination by the first ratio calculated for each departure point to calculate a second number, which is a predicted number of vehicles traveling from the departure point to the destination in one day, for each OD pair, which is a pair of departure point and destination; calculating a third number, which is a predicted number of vehicles that will pass through the first section, for each of the OD pairs by multiplying each of the second numbers calculated for each of the OD pairs by a rate of vehicles that will pass through the first section obtained from the OD information; and calculating, for each of the OD pairs, a fourth number of vehicles that is a predicted number of vehicles that will pass through the first section in a specific time period, based on the third number of vehicles calculated for each of the OD pairs and information related to a time period of passage through the first section obtained from the OD information; determining a first time at which the number of vehicles passing through the first section exceeds a predetermined threshold, based on the fourth number calculated for each of the OD pairs; determining a time period including the first time as a congestion occurrence time period, and applying a traffic flow rate in a congestion flow to the first section to estimate a travel time in the first section; To execute Information processing device.

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