Information processing device, method, program and storage medium

Abstract

Generates information suitable for calculating the required time. [Solution] The information processing device comprises an acquisition means, a calculation means, and a generation means. The acquisition means can acquire multiple pieces of positional information representing the positions of multiple moving objects. The calculation means calculates the estimated travel time required to traverse a set of links based on the positional information corresponding to adjacent sets of links. The generation means generates information regarding the travel time for each set of links.

Description

[Technical Field]

[0001] The present disclosure relates to an information processing apparatus, method, program, and storage medium. [Background Art]

[0002] A technology for calculating and displaying the required travel time of a route is known. For example, Patent Document 1 discloses a technology that displays the estimated arrival time of a route (candidate route) searched by route search together with the route. [Prior Art Documents] [Patent Documents]

[0003] [Patent Document 1] International Publication WO2011 / 141980 [Summary of the Invention] [Problem to be Solved by the Invention]

[0004] Generally, on a road before an intersection, the degree of congestion may differ depending on the traveling direction such as straight or right turn. If the required time of a route passing through such an intersection is determined based on the predicted travel time per link, there is a problem that the accuracy of the determined required time deteriorates.

[0005] One object of the present disclosure is, in view of the above problem, to provide an information processing apparatus, method, program, and storage medium capable of generating information suitable for calculating required travel time. [Means for Solving the Problem]

[0006] The invention recited in the claims is an acquisition means capable of acquiring a plurality of pieces of position information representing positions of a plurality of moving bodies from the plurality of moving bodies; a calculation means for calculating a travel time predicted to be required for passing through a pair of adjacent links, based on the position information corresponding to the pair of links; A generation means for generating information regarding the travel time for each set of links, It is an information processing device.

[0007] Furthermore, the invention described in the claims is, A method by which a computer performs an action. An acquisition process that allows for the acquisition of multiple positional information representing the position of multiple moving objects, A calculation step of calculating the estimated travel time required to traverse a set of links based on the position information corresponding to an adjacent set of links, The method comprises a generation step of generating information relating to the travel time for each set of links.

[0008] Furthermore, the invention described in the claims is, An acquisition means capable of acquiring multiple pieces of positional information representing the position of multiple moving objects from multiple moving objects, A calculation means for calculating the estimated travel time required to traverse a set of links based on the position information corresponding to an adjacent set of links, A generation means for generating information regarding the travel time for each set of links. It is a program that makes a computer function. [Brief explanation of the drawing]

[0009] [Figure 1] An example of a traffic information system configuration is shown. [Figure 2] An example of the general configuration of a vehicle terminal is shown. [Figure 3] An example of a general configuration of a server device is shown. [Figure 4] This represents a link network near a certain intersection. [Figure 5] This is an example of a data structure for an individual linked database. [Figure 6] This document outlines the method for calculating predicted travel times for Link ID "1100U" and Link ID "1101U". [Figure 7] This is an example of the first data structure for a combinational linked database. [Figure 8] This is an example of a second data structure of a combination link DB. [Figure 9] This is an example of a third data structure of a combination link DB. [Figure 10] This illustrates an overview of required time calculation according to a comparative example. [Figure 11] This illustrates an overview of required time calculation according to the present embodiment. [Figure 12] This is a display example of a map reflecting the calculated required time. [Figure 13] This is an example of a flowchart showing a processing procedure related to updating traffic information. [Figure 14] This is an example of a flowchart showing a processing procedure related to displaying an estimated arrival time. [Figure 15] This is an example of a flowchart showing a procedure related to route search processing. [Figure 16] This is a first display example related to congestion degree display. [Figure 17] This is a second display example related to congestion degree display. [Figure 18] This is a third display example related to congestion degree display. [Figure 19] This is a fourth display example related to congestion degree display. [Figure 20] This is a fifth display example related to congestion degree display. [Figure 21] This is an example of a flowchart showing a procedure of display processing related to congestion degree. DESCRIPTION OF EMBODIMENTS

[0010] In one preferred embodiment of the present invention, an information processing apparatus comprises: an acquisition unit capable of acquiring a plurality of pieces of position information representing positions of a plurality of mobile bodies from the plurality of mobile bodies; a calculation unit that calculates a travel time predicted to be required for passing through a pair of adjacent links based on the position information corresponding to the pair of links; and a generation unit that generates information related to the travel time for each of the pairs of links.

[0011] The above-described information processing device comprises an acquisition means, a calculation means, and a generation means. The acquisition means can acquire multiple pieces of positional information representing the positions of multiple moving objects. The calculation means calculates the estimated travel time required to traverse a set of links based on the positional information corresponding to adjacent sets of links. The generation means generates information regarding the travel time for each set of links. According to this embodiment, the information processing device can generate information regarding the estimated travel time for each set of links that a vehicle travels through in succession. Such information can be suitably used to calculate the required travel time for a route that accurately considers the combination of links that a vehicle travels through in succession.

[0012] In one embodiment of the above-described information processing device, a database is generated as information regarding the travel time, which shows, for each set of links, a correction time for calculating the travel time of the set of links from the travel time individually predicted for each of the links.

[0013] In another embodiment of the information processing device described above, the generation means generates a database showing the travel time for each set of links as information regarding the travel time.

[0014] In another embodiment of the above-described information processing device, the generation means generates a graph structure as information about the travel time, which includes nodes indicating individual travel times for each link and connected edges indicating correction times for the travel time for each set of links.

[0015] In another embodiment of the information processing device described above, the information processing device further includes output means for outputting information relating to the travel time corresponding to a specified set of links.

[0016] In another embodiment of the information processing device described above, the generating means generates at least information regarding the travel time of the set of links connected at the intersection.

[0017] In another embodiment of the information processing device described above, the generating means generates at least information regarding the travel time of a set of links, including a second link whose link length is less than a predetermined distance, or whose travel rate calculated based on the position information is less than a predetermined rate.

[0018] In another preferred embodiment of the present invention, a computer method comprising: an acquisition step of acquiring multiple positional information representing the positions of multiple moving bodies from a plurality of moving bodies; a calculation step of calculating the estimated travel time required to traverse a set of links based on the positional information corresponding to adjacent sets of links; and a generation step of generating information relating to the travel time for each set of links. By executing this method, the computer can generate information relating to the estimated travel time for each set of links through which a vehicle travels in succession.

[0019] In yet another embodiment of the present invention, the program causes a computer to function as an acquisition means capable of acquiring multiple positional information representing the positions of multiple moving bodies, a calculation means that calculates the estimated travel time required to traverse a set of links based on the positional information corresponding to adjacent sets of links, and a generation means that generates information relating to the travel time for each set of links. By executing this program, the computer can generate information relating to the estimated travel time for each set of links through which a vehicle travels in succession. Preferably, the program is stored in a storage medium. [Examples]

[0020] Preferred embodiments of the present invention will be described below with reference to the drawings.

[0021] <First Example> (1) System Configuration Figure 1 shows an example of the configuration of a traffic information system. The traffic information system estimates the degree of road congestion based on probe information collected from multiple vehicles via a network, and provides information based on the congestion estimation results to the vehicles. The traffic information system has a vehicle terminal 1 that moves with the vehicle and a server device 2. Although Figure 1 shows only one vehicle as an example, in reality there are multiple vehicles that supply probe information to the server device 2.

[0022] Vehicle terminal 1 moves with the vehicle in which the user of this system is riding and provides driving assistance to the user, who is the occupant of the vehicle. The aforementioned driving assistance may include displaying a map with traffic information, route searching from the current location to the destination, route guidance, vehicle control related to automated driving to the destination, and other optional driving assistance. The vehicle on which vehicle terminal 1 is installed is called the "target vehicle". Vehicle terminal 1 functions as a user interface for driving assistance, receiving input from the user and presenting information to the user. In this embodiment, vehicle terminal 1 performs driving assistance such as displaying a map, searching for a route from the target vehicle's current location to the destination, and route guidance based on information received from server device 2. In addition, vehicle terminal 1 generates probe information (i.e., floating car data), which is driving information that includes at least the location information and time information (timestamp) of the target vehicle, at predetermined intervals, and transmits the generated probe information to server device 2. For example, vehicle terminal 1 generates probe information at intervals of one second and transmits the generated probe information to server device 2. The probe information may include vehicle location information as well as arbitrary data related to the vehicle's state generated by sensors installed on the vehicle. The probe information includes a user ID and a link ID, which are identifiers representing the user of the transmitting vehicle. The link ID is information about the road link after map matching processing by vehicle terminal 1.

[0023] Vehicle terminal 1 may be a navigation device installed in the target vehicle that provides route guidance to a set destination, or it may be a user's mobile device such as a smartphone with an application installed that implements route guidance and other functions. Vehicle terminal 1 may also be integrated into the target vehicle.

[0024] Server device 2 generates information necessary for the driving assistance provided by vehicle terminal 1 and supplies the generated information to vehicle terminal 1, thereby causing vehicle terminal 1 to perform driving assistance to the user. For example, server device 2 generates traffic information based on probe information supplied from vehicle terminal 1 and causes vehicle terminal 1 to perform driving assistance based on the generated traffic information. Server device 2 may also be a system (cloud system) consisting of multiple devices or computers that collaborate using cloud computing technology, etc. Server device 2 is an example of an "information processing device".

[0025] (2) Device configuration Figure 2 shows an example of the schematic configuration of the vehicle terminal 1. The vehicle terminal 1 mainly consists of a communication unit 11, a storage unit 12, an input unit 13, a control unit 14, a sensor group 15, a display unit 16, and a sound output unit 17. Each element within the vehicle terminal 1 is interconnected via a bus line 10.

[0026] The communication unit 11 communicates data with the server device 2 based on the control of the control unit 14. For example, based on the control of the control unit 14, the communication unit 11 transmits probe information regarding the driving status of the target vehicle, which is identified based on the data output by the sensor group 15, to the server device 2. In another example, based on the control of the control unit 14, the communication unit 11 receives information from the server device 2 that is necessary for controlling the output of the display unit 16 and the sound output unit 17.

[0027] The storage unit 12 is composed of various types of memory, including RAM (Random Access Memory), ROM (Read Only Memory), and non-volatile memory (including hard disk drives, flash memory, etc.). The storage unit 12 stores programs and software (including applications installed on the vehicle terminal 1) that enable the vehicle terminal 1 to perform predetermined processes. The aforementioned applications may be any applications that provide content (including driving assistance) to the user on the vehicle terminal 1. The storage unit 12 is also used as working memory for the control unit 14. The programs executed by the vehicle terminal 1 and other information may be stored in external devices other than the storage unit 12 that communicate with the vehicle terminal 1 (including server devices), a storage medium that can be attached to or removed from the vehicle terminal 1, or any other storage medium.

[0028] The input unit 13 is a user interface that accepts user input, and examples of the input unit 13 include buttons, touch panels, remote controllers, and voice input devices. The display unit 16 displays information based on the control of the control unit 14. Examples of the display unit 16 include displays and projectors. The sound output unit 17 outputs sound based on the control of the control unit 14. Examples of the sound output unit 17 include speakers.

[0029] The sensor group 15 includes various sensors that perform sensing of the state of the target vehicle or the environment outside the vehicle. The sensor group 15 has an external sensor 18 and an internal sensor 19. The external sensor 18 is one or more sensors for recognizing the surrounding environment of the target vehicle, such as a camera, lidar, radar, ultrasonic sensor, infrared sensor, or sonar. The internal sensor 19 is a sensor for positioning the vehicle, such as a GNSS (Global Navigation Satellite System) receiver, gyro sensor, IMU (Inertial Measurement Unit), vehicle speed sensor, or a combination thereof. The sensor group 15 only needs to have sensors that output data from which the control unit 14 can directly or indirectly derive the position of the target vehicle (i.e., by performing arbitrary position estimation) from the output of the sensor group 15.

[0030] The control unit 14 includes processors such as a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit), and controls the entire vehicle terminal 1.

[0031] Furthermore, the processing performed by the control unit 14 is not limited to being implemented by software through a program, but may also be implemented by a combination of hardware, firmware, and software. Additionally, the processing performed by the control unit 14 may be implemented using a user-programmable integrated circuit, such as an FPGA (Field-Programmable Gate Array) or a microcontroller. In this case, the program executed by the control unit 14 in this embodiment may be implemented using this integrated circuit.

[0032] The configuration of the vehicle terminal 1 shown in Figure 2 is an example, and various modifications may be made to the configuration shown in Figure 2. For example, at least one of the input unit 13, the display unit 16, and the sound output unit 17 may be provided inside the target vehicle as an external device to the vehicle terminal 1, and the generated signals may be supplied to the vehicle terminal 1. Also, at least some of the sensors in the sensor group 15 may be sensors installed in the target vehicle. In this case, the vehicle terminal 1 may acquire information output by the sensors installed in the target vehicle from the target vehicle based on a communication protocol such as CAN (Controller Area Network).

[0033] Figure 3 shows an example of the schematic configuration of server device 2. Server device 2 mainly consists of a communication unit 21, a storage unit 22, and a control unit 24. Each element within server device 2 is interconnected via a bus line 20.

[0034] The communication unit 21 includes a communication antenna and a communication transceiver, and performs data communication with external devices such as the vehicle terminal 1 based on the control of the control unit 24.

[0035] The storage unit 22 is composed of various types of memory, such as RAM, ROM, and non-volatile memory. The storage unit 22 stores programs for the server device 2 to perform predetermined processes. The storage unit 22 is also used as working memory for the control unit 24. Note that the programs executed by the server device 2 may be stored in storage media other than the storage unit 22.

[0036] Furthermore, the memory unit 22 stores map data 5, a probe database (DB:DataBase) 6, and traffic information 7.

[0037] Map data 5 is registered data related to maps. Map data 5 includes, for example, a road database (DB) that represents the road network using combinations of nodes and links, and a spot database (DB) that is a database of spots. Spots registered in the spot database may be any facility, or any location that could potentially be set as a destination.

[0038] The road database registers link IDs, which are the identification information for links, and each link ID is associated with corresponding link attribute information. Examples of link attribute information include location information including the start and end points of the link, the number of lanes, the direction of travel for each lane (for example, the link IDs that can be traveled for each lane), the link length, and the identification information (node ​​ID) of the node to which the link connects. Similarly, the road database assigns node IDs, which are the identification information for nodes, and each node ID is associated with corresponding node attribute information. Examples of node attribute information include the node's location information and the link ID to which the node is connected. Hereafter, links are assumed to exist for each of the two directions on a road, excluding one-way streets, and the start and end points of a link are determined according to the direction of travel of vehicles traveling on that link.

[0039] The probe DB6 is a database that stores probe information received from each vehicle on the road. Traffic information 7 is information about the traffic conditions of links included in the road DB, and includes the individual link DB61 and the combined link DB62. The individual link DB61 is a database that associates information about the predicted time required for a vehicle to pass through individual links (also called "predicted travel time") with the corresponding link ID. The combined link DB62 is a database that associates information about the predicted travel time required for a vehicle to pass through multiple adjacent links in succession with the corresponding link IDs of multiple links. Traffic information 7, including the individual link DB61 and the combined link DB62, is generated by the control unit 24 based on the probe DB6. Details of the data structure and generation method of the individual link DB61 and the combined link DB62 will be described later. The individual link DB61 is an example of first information, and the combined link DB62 is an example of second information.

[0040] The control unit 24 includes processors such as a CPU and GPU, and controls the entire server device 2.

[0041] For example, the control unit 24 updates the probe DB6 based on probe information received by the communication unit 21. The control unit 24 also generates traffic information 7 based on the updated probe DB6. In this case, the control unit 24 may use probe information generated within a predetermined time from the current time to update the traffic information 7. For example, the control unit 24 may consider probe information from the last 30 minutes as the target of collection, and probe information that is no longer the target of collection may not be used in updating the traffic information 7, etc. However, when the control unit 24 analyzes congestion trends by time of day, day of the week, and / or by a specific event, it may also include older probe information that is no longer the target of collection in the data to be analyzed.

[0042] In another example, when the control unit 24 receives a route search request from the vehicle terminal 1 via the communication unit 21, specifying the destination, current location, and route search conditions, it performs a route search based on the current location and destination specified in the search request, referring to the road database of the map data 5. In this case, the control unit 24 may use any route search algorithm, such as Dijkstra's algorithm, to search for a recommended route. The control unit 24 then transmits the search result, which shows one or more searched routes, to the vehicle terminal 1. If information indicating a route specified by the user is supplied from the vehicle terminal 1, the control unit 24 sets the specified route as the recommended route (also called the "recommended route") for the target vehicle. Subsequently, the control unit 24 stores the route information indicating the set recommended route in the storage unit 22. The route information may also be information indicating the recommended route on a lane-by-lane basis. Therefore, in the case of a recommended route that passes through a link with multiple lanes, the route information may include information indicating which lane is recommended to pass through among the multiple lanes.

[0043] The control unit 24 functions as a computer or the like that executes a program. The processing performed by the control unit 24 is not limited to being implemented by software through a program; it may also be implemented by a combination of hardware, firmware, and software. The control unit 24 functions as an "acquisition means," "calculation means," "generation means," "search means," "setting means," "output means," "transmission means," "determination means," "display control means," and a computer or the like that that executes a program.

[0044] Note that the configuration of server device 2 shown in Figure 3 is just one example, and various modifications may be made to the configuration shown in Figure 3.

[0045] (3) Individual Link Database Next, we will explain specific examples of the data structure and generation method of the individual link DB61.

[0046] Figure 4 shows the link network near a certain intersection. In Figure 4, the corresponding link ID is clearly indicated for each section of road. Figure 5 is an example of the data structure of the individual link DB61. The link ID is, for example, a combination of a four-digit identification number that identifies the road section and the identification symbol "U" or "D" indicating the direction of travel (uphill or downhill).

[0047] Figure 4 clearly shows the link IDs for each road section assigned identification numbers from "1001" to "1005". Furthermore, the individual link DB61 shown in Figure 5 includes "Link ID" and "Predicted Travel Time" as columns.

[0048] The "Link ID" field shown in Figure 5 stores the link IDs included in the road data. As an example, Figure 5 clearly shows five records where the "Link ID" is "1001U" through "1005U".

[0049] The "Predicted Travel Time" field stores the predicted time (in seconds) it took the vehicle supplying the probe information to traverse the target link. This predicted value is calculated based on multiple probe data associated with the same vehicle, including position information on the target link, as described later. Note that the individual link DB61 may include columns other than "Link ID" and "Predicted Travel Time". For example, the columns of the individual link DB61 may further include "Link Length," which indicates the length of the target link, and "Number of Lanes," which indicates the number of lanes included in the target link. The predicted travel time is an example of the first travel time.

[0050] In this example, different link IDs are assigned to the same road section depending on the direction of travel. Alternatively, a unique link ID may be assigned to each road section. In this case, the individual link DB61 will be associated with a predicted travel time for each pair of link ID and identification information for the direction of travel (uphill or downhill).

[0051] Here, the method for generating each record in the individual link DB61 will be explained with reference to Figure 6. Figure 6 is a diagram showing an overview of the method for calculating the predicted travel time for link ID "1100U" and link ID "1101U". In Figure 6, data points P1 to P14, which are identified every second by probe information, are clearly shown on the road. Data points P1 to P14 represent the trajectory of a vehicle's past position. As a preprocessing step, the vehicle terminal 1 performs a process (so-called map matching) to correct the data points so that each data point is on a link, based on matching the position information of each link included in the road DB of the map data 5 with the data points, and identifies the link ID corresponding to each data point after map matching. Then, the vehicle terminal 1 transmits probe information including the identified link ID and the position information of the data point to the server device 2. Alternatively, instead of identifying the link ID of each data point based on the probe information, the server device 2 may perform map matching based on the position information of the data point and the position information of each link to identify the link ID for each data point.

[0052] When Server Device 2 calculates the predicted travel time for Link ID "1100U", it predicts the travel time of the vehicle that provided probe information on Link ID "1100U" based on the nine time-series data points P2 to P10 present on Link ID "1100U". In this case, Server Device 2 predicts the vehicle's link travel time based on the distance between data points P2 and P10 at both ends of the link and the time difference between the timestamps corresponding to data points P2 and P10 at both ends. Note that probe information is not available between the first data point P2 on Link ID "1100U" and the immediately preceding data point P1 on another link, and between the last data point P10 on Link ID "1100U" and the immediately following data point P11 on another link.

[0053] In this case, first, server device 2 calculates the traversal rate for link ID "1100U" by determining the ratio of the distance between data points P2 and P10 at both ends to the link length. Here, the link length is "90m" and the distance between data points P2 and P10 is "72m", so server device 2 determines that the traversal rate for link ID "1100U" is "80%". Next, server device 2 assumes that the calculated traversal rate and the time difference between timestamps are proportional, and calculates the time difference between timestamps when the traversal rate is 100% as the link travel time. In this case, the time difference between timestamps between data points P2 and P10 corresponding to an 80% traversal rate (i.e., the travel time at an 80% traversal rate) is "8 seconds", so server device 2 calculates the predicted link time as "10 seconds". Server device 2 calculates a predicted travel time for link ID "1100U" for each vehicle that has provided time-series probe information showing the travel history for link ID "1100U" using the process described above. Server device 2 then determines a representative value of the calculated predicted value (which may be the mean, median, or other statistical representative value) as the predicted travel time to be associated with link ID "1100U" in the individual link DB 61.

[0054] Similarly, when Server Device 2 calculates the predicted travel time for link ID "1101U", it predicts the travel time for the vehicle that provided the probe information on link ID "1101U" based on the three time-series data points P11 to P13 present on link ID "1101U". Here, the link length is "25m" and the distance between the data points P11 and P13 at both ends is "15m", so Server Device 2 identifies that the completion rate of link ID "1100U" is "60%". The difference in timestamps between data points P11 and P13 corresponding to a 60% completion rate (i.e., the time taken to reach a 60% completion rate) is "2 seconds", so Server Device 2 calculates the predicted value for the link as "3.3 seconds". Server Device 2 calculates the predicted travel time for link ID "1101U" for each vehicle that provided time-series probe information showing the driving history on link ID "1101U". The server device 2 then sets a representative value of the predicted travel time calculated for link ID "1101U" as the predicted travel time associated with link ID "1101U" in the individual link DB 61.

[0055] As described above, the server device 2 calculates the predicted travel time for each link based on the probe information used to update the individual link DB61, and updates the predicted travel time associated with the link ID.

[0056] (4) Combined Link Database Next, we will describe specific examples of the data structure and generation method of the combined link DB62. In the following description, the combined link DB62 will be described as information that associates predicted travel time or correction time with each pair of adjacent links. However, the combined link DB62 may also be information that associates predicted travel time or correction time with three or more predetermined numbers of adjacent links.

[0057] Figure 7 shows an example of the first data structure of the combination link DB62. The combination link DB62 in this first data structure example is a database that stores the predicted travel time for sets of links that vehicles may pass through consecutively. The combination link DB62 includes "combination link ID" and "combination travel time" as columns. In Figure 7, records for some of the link sets that make up the road network shown in Figure 4 are explicitly shown.

[0058] The "Combination Link ID" field stores the combination link ID, which is the identification information for the combination link. A combination link is a group of links that a vehicle may pass through consecutively, and in this case, it represents a pair of adjacent links. The combination link ID is a string formed by connecting the link IDs of the two links that make up the combination link with an arrow "→" in the order that the vehicle passes through them.

[0059] The "Combined Travel Time" field stores the combined travel time, which is an estimated value of the travel time required to traverse the corresponding combined links in sequence. The combined travel time is calculated based on time-series probe information, including location information on the target combined links. The combined travel time is an example of the second travel time.

[0060] Here, the combined travel time for the combined link ID "1002U→1004U" is 120 seconds, and the sum of the predicted travel times for links ID "1002U" and "1004U" based on the individual link DB61 is 100 seconds (= 60 seconds + 40 seconds). In this way, depending on the combination of links, there may be a discrepancy between the combined travel time of a combined link and the sum of the predicted travel times of the links that make up that combined link. For example, in a link with multiple lanes for each direction of vehicle movement, the time required to pass through may differ depending on the lane the vehicle is using. Therefore, the combined link DB62 stores combined travel times that take into account such differences in congestion for each direction of movement and show the appropriate travel time for each combined link.

[0061] Here, we will provide a supplementary explanation regarding the problems with calculating the travel time to a destination using only the individual link DB61. Short links such as the link ID "1101U" mentioned above tend to have a low completion rate. The shorter the completion rate, the less the travel time through the link connections is taken into account in the calculation, and the first problem is that information about unused sections is not reflected in the predicted value. Consequently, the accuracy of the predicted travel time for links with a low completion rate will be poor. In particular, there tends to be a large number of short links in urban areas, resulting in the generation of individual link DB61 with low accuracy in predicted travel time. And when the estimated arrival time to the destination is calculated using such an individual link DB61, the accuracy of the calculated estimated arrival time will be poor.

[0062] Furthermore, the travel time for a link before an intersection can differ significantly depending on whether the route goes straight or turns right. Therefore, a second problem arises: if the estimated travel time to a destination is calculated based solely on a database that shows predicted travel times on a link-by-link basis, such as the individual link DB61, the accuracy of the calculated travel time will be poor, especially for routes that pass through roads where only the right-turn lane is congested.

[0063] Taking the above into consideration, the server device 2 generates a combination link DB 62 containing the predicted travel time for each pair of adjacent links, and further uses the combination link DB 62 to calculate the travel time for the route to the destination. This allows the server device 2 to effectively suppress the deterioration of travel time accuracy caused by the first and second problems. Specifically, even if there are links with short link lengths, the combination travel time of combination links including those links can be extracted from the link DB 62 and used to calculate the travel time for the route. Thus, the deterioration of travel time accuracy caused by the first problem is suppressed. Furthermore, by referring to the link DB 62 and using the combination travel time of combination links including links before and after passing through an intersection, the travel time for the route passing through the intersection can also be calculated with high accuracy. Thus, the deterioration of travel time accuracy caused by the second problem is suppressed.

[0064] Next, we will provide further explanation on how to generate records in the combined link DB62, again referring to Figure 6.

[0065] For example, consider a combined link consisting of two links, link ID "1100U" and link ID "1101U". In this case, server device 2 calculates the combined travel time using data points P2 to P13 corresponding to link IDs "1100U" and "1101U". In this case, the travel rate is higher than that of the individual links because the combined link has a length of 115m (=90m + 25m), allowing server device 2 to calculate a more accurate predicted value for the travel time of the combined link.

[0066] Furthermore, even when a pair of links that change direction and connect at an intersection node are designated as a combined link, the server device 2 calculates a predicted travel time for the combined link based on data points derived from time-series probe information of vehicles that have passed through the combined link. This allows the server device 2 to accurately predict the travel time for the combined link, even in sections where right turns are congested, such as the pair of link IDs "1002U" and "1004U" in Figure 4.

[0067] In this manner, the server device 2 calculates a predicted travel time for the combination links that each vehicle has traversed, based on the time-series probe information obtained for each vehicle. The server device 2 then aggregates the predicted travel times of the vehicles that have traversed each combination link and calculates a representative value (which may be the mean, median, or other statistical representative value) of the calculated predicted values. The server device 2 then considers the calculated representative value as the combination travel time and records it in the combination link DB 62, linked to the corresponding combination link ID.

[0068] Furthermore, it is not necessary for the combined travel times of all combination links that may be passed by vehicles consecutively to be stored in the combination link DB62; the combined travel times of only some of the combination links that may be passed by vehicles consecutively may be stored in the combination link DB62.

[0069] In the first example, server device 2 selects a combination of links to connect at an intersection as a combination link. In other words, server device 2 selects a link that connects to multiple links at an intersection and a combination link that includes those multiple links. Server device 2 then calculates the combined travel time of the selected combination link and stores the calculated combined travel time in the combination link DB62, associating it with the corresponding combination link ID. Even in this case, server device 2 can identify the travel times of adjacent links at an intersection by referring to the combination link DB62, and can calculate the accurate route time that takes into account the differences in congestion for each direction of travel.

[0070] In the second example, the server device 2 selects a combination of links that includes a link with a link length less than a predetermined distance or a completion rate less than a predetermined percentage. The predetermined distance and predetermined percentage are, for example, default values ​​determined in advance, taking into account link length or completion rate that may cause a decrease in the accuracy of travel time. The server device 2 then calculates the combined travel time of the selected combination of links and stores the calculated combined travel time in the combination link DB 62, associating it with the corresponding combination link ID. In this case as well, the server device 2 can identify the travel time of a link with a link length less than a predetermined distance or a completion rate less than a predetermined percentage by referring to the combination link DB 62 as a combined travel time that is the sum of the travel times of the links adjacent to that link. Therefore, the server device 2 can calculate the accurate required time of the route without causing a decrease in accuracy due to the short link length or low completion rate. The first and second examples described above also apply when the combination link DB 62 has the second or third data structure example described below.

[0071] Figure 8 shows an example of the second data structure of the combined link DB62. The combined link DB62 in this second data structure example is a database that stores correction values ​​to be applied when calculating the predicted travel time of a combined link from the predicted travel time for each link (i.e., the predicted travel time stored in the individual link DB61). The combined link DB62 includes "combined link ID" and "correction time" as columns. In Figure 8, records for some of the link combinations that make up the road network shown in Figure 4 are clearly shown.

[0072] The "Combination Link ID" field stores the combination link ID, which is the identification information for the combination link.

[0073] The "Correction Time" field stores the correction time that should be applied when calculating the travel time required to pass through the corresponding combination links consecutively, based on the individual link DB61. For example, the correction time for combination link ID "1001U→1002U" is 0 seconds. Therefore, server device 2 recognizes that the time required to pass through the link with link ID "1001U" and the link with link ID "1002U" consecutively is 90 seconds (=30 seconds + 60 seconds), referring to the individual link DB61 in Figure 4. On the other hand, the correction time for combination link ID "1002U→1003U" is "-20 seconds". Therefore, server device 2 recognizes that the time required to pass through the link with link ID "1002U" and the link with link ID "1003U" consecutively is 90 seconds (=60 seconds + 50 seconds - 20 seconds). Furthermore, the correction time for the combined link ID "1002U→1004U" is "+20 seconds". Therefore, server device 2 recognizes that the time required to traverse the link with link ID "1002U" and the link with link ID "1004U" consecutively is 120 seconds (=60 seconds + 40 seconds + 20 seconds).

[0074] An example of how to generate the combination link DB62 related to the second data structure example is provided. Based on the time-series probe information obtained for each vehicle, the server device 2 calculates the combined travel time for each combination link, similar to the method for generating the combination link DB62 related to the first data structure example. Next, for each combination link, the server device 2 calculates the difference between the calculated combined travel time and the sum of the predicted travel times of the individual links recorded in the individual link DB61 as a correction time. Then, the server device 2 stores the calculated correction value in the combination link DB62, linked to the corresponding combination link ID.

[0075] Figure 9 shows an example of the third data structure of the combination link DB62. The combination link DB62 in this third data structure example has a graph structure that includes nodes showing individual predicted travel times for each link, and connecting edges showing correction times for the predicted travel times for each combination link. In Figure 9, as an example, the server device 2 considers a set of link IDs that differ only in direction, identified by "U" or "D", as the same link (indicated as "road link" in Figure 9), and represents information for each link, such as predicted travel time, as a node. The server device 2 also represents the correction time for each combination of continuously passable links as connecting edges that connect the nodes representing the links. Each node also includes information indicating the four-digit identification number of the corresponding road section, its length (link length), and the travel time per link for each direction. The travel time included in the node is consistent with the predicted travel time per link stored in the individual link DB61.

[0076] Server device 2 can identify the combined travel time of any combined link by referring to the combined link DB 62 related to the third data structure example. For example, to identify the combined travel time of the combined link of link ID "1002U" and link ID "1003U", server device 2 first refers to the nodes with IDs "1002" and "1003" corresponding to link IDs "1002U" and "1003U". Then, server device 2 obtains the travel times ("60 seconds" and "50 seconds") of these nodes corresponding to direction "U". Server device 2 also obtains the correction time ("-20 seconds") of the connecting edge between the two nodes. Then, server device 2 calculates the combined travel time of the combined link of link ID "1002U" and link ID "1003U" (90 seconds = 60 seconds + 50 seconds - 20 seconds) based on the obtained travel time and correction time.

[0077] Furthermore, the combination link DB62 related to the third data structure example includes information equivalent to the individual link DB61 as a node. Therefore, if the combination link DB62 related to the third data structure example is stored in the storage unit 22, the individual link DB61 does not need to be stored in the storage unit 22.

[0078] (5) Specific examples of calculating required time Figure 10 shows an overview of the calculation of travel time in a comparative example where travel time is calculated by referring only to the individual link DB61. Here, a comparative example is shown in which the travel time for a route where the endpoint of link ID "1001U" is the starting point "Start" and the endpoint of link ID "1005U" is the goal point "Goal" is calculated using the individual link DB61. This route consists of road sections indicated by link IDs "1001U", "1002U", "1004U", and "1005U".

[0079] In this case, server device 2 refers to the records in the individual link DB61 corresponding to the link IDs "1001U", "1002U", "1004U", and "1005U" that make up the route, and calculates the route duration of "150 seconds" as follows. 30 seconds + 60 seconds + 40 seconds + 20 seconds = 150 seconds

[0080] On the other hand, this estimated travel time is likely inaccurate because it does not take into account the difference in travel time between vehicles going straight and vehicles turning right on link ID "1002U".

[0081] Figure 11 shows an overview of the time calculation method in this embodiment, which calculates the required time by referring to both the individual link DB 61 and the combined link DB 62. Here, an example is shown in which the required time for the route in the comparative example shown in Figure 10 is calculated using the individual link DB 61 shown in Figure 5 and the combined link DB 62 of the first data structure example shown in Figure 7.

[0082] In this case, first, server device 2 extracts records from the combined link DB62 corresponding to all combined links composed of combinations of link IDs "1001U", "1002U", "1004U", and "1005U" that constitute the route. Specifically, server device 2 extracts the combined travel times of "90 seconds", "120 seconds", and "60 seconds" from the records corresponding to the combined link IDs "1001U→1002U", "1002U→1004U", and "1004U→1005U", respectively. Next, it identifies the overlapping link IDs "1002U" and "1004U" among the records extracted from the combined link DB62, and extracts the predicted travel times of "60 seconds" and "40 seconds" corresponding to the identified link IDs from the individual link DB61. Then, Server Device 2 subtracts the predicted travel times of the overlapping link IDs, "60 seconds" and "40 seconds," from the sum of the combined travel times of "90 seconds," "120 seconds," and "60 seconds" extracted from the combined link DB62. This gives Server Device 2 a total route time of "170 seconds." Specifically, Server Device 2 performs the following calculation: 90 seconds + 120 seconds + 60 seconds - 60 seconds - 40 seconds = 170 seconds

[0083] As described above, the estimated travel time of "150 seconds" calculated based solely on the individual link DB61 differs from the estimated travel time of "170 seconds" calculated based on both the individual link DB61 and the combined link DB62. Furthermore, the estimated travel time of "170 seconds" calculated based on both the individual link DB61 and the combined link DB62 more accurately reflects the degree of congestion when making a right turn at link ID "1002U," and is therefore more precise.

[0084] Furthermore, even when the server device 2 refers to the combination link DB62 related to the second data structure example shown in Figure 8 and the third data structure example shown in Figure 9, it can calculate the same required time as in the example shown in Figure 11.

[0085] For example, when referring to the combination link DB62 related to the second data structure example shown in Figure 8, the server device 2 first calculates the total predicted travel time for each link, "150 seconds," similar to the comparative example. Next, the server device 2 identifies that the total correction time for the records corresponding to the combination link IDs "1001U→1002U," "1002U→1004U," and "1004U→1005U" is "+20 seconds" (=0 seconds + 20 seconds + 0 seconds). Then, the server device 2 calculates the required time for the route as "170 seconds," obtained by adding the total correction time of "+20 seconds" to the total predicted travel time for each link, "150 seconds."

[0086] Furthermore, when referring to the combination link DB62 related to the third data structure example shown in Figure 9, the server device 2 refers to the individual link DB61 or node and calculates the total predicted travel time for each individual link, which is "150 seconds". The server device 2 then identifies the connecting edges of road links present on the route: the connecting edge between "1001" and "1002", the connecting edge between "1002" and "1004", and the connecting edge between "1004" and "1005". The server device 2 then calculates the required time for the route as "170 seconds", obtained by adding the total correction time indicated by the identified connecting edges, which is "+20 seconds", to the total predicted travel time for each link, which is "150 seconds".

[0087] Figure 12 shows an example of a map display reflecting the calculated travel time. Based on the display information received from the server device 2, the vehicle terminal 1 displays the screen shown in Figure 12 on the display unit 16. Here, a recommended route to the destination spot "Facility A" is presented. Note that the screen shown in Figure 12 may be a screen that presents the searched recommended route, or it may be a screen that is displayed when the vehicle is moving along the recommended route.

[0088] Server device 2 displays a current location marker 60 indicating the target vehicle's current location and a route line 61 representing the recommended route on a map, based on probe information received from vehicle terminal 1 and route information related to the set recommended route. Server device 2 also refers to the combination link DB 62 and identifies the predicted travel time corresponding to each link included in the recommended route and the combined travel time or correction time corresponding to the combination links included in the recommended route, and calculates the required time for the recommended route. Server device 2 then provides an estimated arrival time field 62 on the screen, which shows the estimated arrival time (14:11 in this case) based on the calculated required time and the current time.

[0089] As described above, the server device 2 can present recommended routes and also provide users with highly accurate estimated arrival times that reflect actual traffic conditions. Even when multiple candidate routes are presented on the map before route selection, the server device 2 may calculate the travel time corresponding to each candidate route by referring to the individual link DB61 and the combination link DB62, and display the estimated arrival time based on the calculated travel time on the vehicle terminal 1, associated with each candidate route.

[0090] (6) Processing flow Figure 13 is an example of a flowchart showing the processing procedure for updating traffic information 7.

[0091] First, the server device 2 receives probe information from multiple vehicles and stores the received probe information in the probe DB 6 (step S11). Next, the server device 2 determines whether or not it is time to update the traffic information 7 (step S12). The server device 2 determines that it is time to update the traffic information 7 if predetermined conditions are met. For example, the server device 2 determines that it is time to update the traffic information 7 if a predetermined amount of probe information has been newly accumulated since the last update of the traffic information 7. In another example, the server device 2 may determine that it is time to update the traffic information 7 if a predetermined amount or more of probe information has been newly accumulated since the last update of the traffic information 7.

[0092] Then, if the server device 2 determines that it is time to update the traffic information 7 (step S12; Yes), it calculates the predicted travel time for each link based on the probe information and updates the individual link DB 61 based on the calculation result (step S13). The server device 2 also calculates the combined travel time for each combined link based on the probe information and updates the combined link DB 62 based on the calculation result (step S14). In steps S13 and S14, the server device 2 may update the individual link DB 61 and combined link DB 62 using only probe information acquired up to a predetermined time before the present. The server device 2 may also delete records in the individual link DB 61 and combined link DB 62 based on probe information generated more than a predetermined time before the present.

[0093] The server device 2 then determines whether or not to terminate the traffic information update process 7 (step S15). For example, if a predetermined condition is met, the server device 2 determines that the traffic information update process 7 should be terminated. If the server device 2 determines that the traffic information update process 7 should be terminated (step S15; Yes), it terminates the process in the flowchart. On the other hand, if it determines that the traffic information update process 7 should not be terminated (step S15; No), the server device 2 returns to step S11.

[0094] Figure 14 is an example of a flowchart showing the processing procedure for displaying the estimated time of arrival.

[0095] First, the server device 2 determines whether or not there is a request to display the estimated arrival time of a route (step S21). Examples of the above-mentioned display requests include a request for the vehicle terminal 1 to continuously display the estimated arrival time on the set recommended route, and a request for the vehicle terminal 1 to display the estimated arrival times of one or more route candidates that have been searched or re-searched. If the server device 2 determines that there is no request to display the estimated arrival time of a route (step S21: No), it then proceeds to determine in step S21 whether or not there is a need for a request to display the estimated arrival time.

[0096] Meanwhile, if the server device 2 determines that there is a request to display the estimated arrival time of the route (step S21; Yes), it refers to the individual link DB 61 and the combined link DB 62 and calculates the travel time of the route (step S22). Then, the server device 2 displays the estimated arrival time based on the travel time calculated in step S22 on the vehicle terminal 1 (step S23).

[0097] (7) Other application examples The use of the individual link DB61 and the combined link DB62 is not limited to displaying estimated arrival times. For example, the server device 2 may use the individual link DB61 and the combined link DB62 when searching for a route that minimizes the total cost (sum of costs per link) calculated from one or more indicators. The one or more indicators mentioned above include travel time as at least one indicator, and other indicators of travel time may include tolls, number of traffic lights, road width, etc. Furthermore, the route search described above also includes searching for alternative routes to the route that is already being guided. In searching for alternative routes, the server device 2 searches for routes that have a lower total cost than the route that is currently being guided, due to changes in road congestion, etc., as alternative routes.

[0098] Server device 2 calculates the required time by referring to the individual link DB61 and the combined link DB62, in the same manner as in the embodiment described above, and calculates the cost corresponding to the calculated required time. In this case, server device 2 may determine the route that minimizes the total cost using any pathfinding method. The above-mentioned pathfinding methods include Dijkstra's algorithm and the A* algorithm.

[0099] Figure 15 is an example of a flowchart illustrating the steps involved in the pathfinding process.

[0100] First, the server device 2 determines whether there is a request to search for a recommended route or an alternative route (step S31). If there is neither a request to search for a recommended route nor an alternative route (step S31; No), the server device 2 continues to determine in step S31 whether there is a request to search for a route or an alternative route.

[0101] On the other hand, if there is a request to search for a recommended route or an alternative route (step S31; Yes), the server device 2 performs a route search using the cost based on the required time calculated by referring to the individual link DB 61 and the combined link DB 62 (step S32). As a result, the server device 2 determines the recommended route or alternative route that minimizes the total cost and outputs information about the determined route to the vehicle terminal 1.

[0102] (8) Variation The processing using individual link DB61 and combined link DB62 may be performed by the vehicle terminal 1 instead of the server device 2.

[0103] In this case, the vehicle terminal 1 acquires map data 5 and traffic information 7, etc. from the server device 2, or the map data 5 and traffic information 7, etc. are pre-stored in the storage unit 12 of the vehicle terminal 1. The vehicle terminal 1 then refers to the map data 5 and traffic information 7 and performs processing using the individual link DB 61 and the combined link DB 62 (including calculation of route time and route search using cost based on time) on behalf of the server device 2. In this case, the vehicle terminal 1 functions as an information processing device. The control unit 14 functions as an "acquisition means," "calculation means," "generation means," "search means," "setting means," "output means," "transmission means," "determination means," "display control means," and a computer that executes programs.

[0104] When vehicle terminal 1 performs processes such as calculating the required time for a route and searching for a route using the cost based on the required time, vehicle terminal 1 may request the server device 2 to provide the records of the individual link DB 61 and the combined link DB 62 necessary for vehicle terminal 1 to perform these processes. In this case, the server device 2 will send the records of the individual link DB 61 and the combined link DB 62 to vehicle terminal 1 in response to the request.

[0105] <Second Example> In the second embodiment, the server device 2 displays congestion images representing the degree of road congestion based on the individual link DB61 and combined link DB62 generated in the first embodiment on the vehicle terminal 1. In this case, if the map includes links that change direction and connect at an intersection, the server device 2 displays the congestion image for the link before the intersection and the congestion image for the link after the intersection together on the map. This allows the server device 2 to accurately grasp the congestion situation for each direction of travel before the intersection. Note that displaying two congestion images together means that there is a connection or continuity between the two congestion images, and it is sufficient if there is a gap between the two congestion images, at least in a manner that is recognizable to the user.

[0106] The configuration of the traffic information system, vehicle terminal 1, and server device 2 according to the second embodiment is the same as the configuration of the first embodiment described above, as shown in Figures 1 to 3. Furthermore, it is assumed that the traffic information 7, including the individual link DB 61 and the combined link DB 62, is generated according to the first embodiment. Also, as an example, the congestion level is assumed to have two stages: "high," which is considered congestion, and "low," which is otherwise. Note that the congestion level may have three or more indicator values. The server device 2 is an example of an information processing device that outputs a congestion level image representing the congestion level. Hereafter, the explanation will assume that the server device 2 performs display control of the display unit 16 of the vehicle terminal 1.

[0107] First, when the server device 2 controls the map display on the vehicle terminal 1, it refers to map data 5, etc., and identifies links that exist before an intersection (also called "intersection links"). Intersection links are links whose endpoints connect to nodes that represent intersections. In the example in Figure 4, these correspond to links with link IDs "1002U", "1003D", and "1004D".

[0108] Next, Server Device 2 identifies the combined travel time of the combined links, including the link before the intersection, by referring to the combined link DB62 (or the individual link DB61 and combined link DB62 if the combined link DB62 is the second data structure example). Then, Server Device 2 estimates the congestion level of the link before the intersection based on the identified combined travel time. In this case, Server Device 2 extracts the individual predicted travel times of links other than the link before the intersection from the individual link DB61, and considers the travel time obtained by subtracting the individual predicted travel times from the combined travel time of the combined link as the predicted travel time of the link before the intersection. Then, Server Device 2 calculates the speed based on the predicted travel time of the link before the intersection and the link length of the link before the intersection, and sets the congestion level according to the calculated speed. Furthermore, Server Device 2 sets the congestion level of links other than the link before the intersection according to the speed calculated from the individual predicted travel times based on the individual link DB61 and the link length.

[0109] Here, the pre-intersection link is connected to multiple links (also called "destination links") that are separated at the intersection according to the direction of vehicle movement, and the combined travel time with each destination link is stored in the combination link DB62. Therefore, the server device 2 calculates a congestion level for the pre-intersection link equal to the number of combination links (i.e., the number of destination links) that include the pre-intersection link recorded in the combination link DB62. Accordingly, the server device 2 displays a congestion level image for the pre-intersection link for each destination link.

[0110] Furthermore, Server Device 2 displays the congestion image of the link before the intersection, concatenated with the congestion image of the destination link. This allows Server Device 2 to accurately convey the traffic congestion situation for each direction of travel on the link before the intersection to the user. Other display variations are explained by referring to the display examples. The link before the intersection is an example of the first link, and the destination link is an example of the second link. Also, the congestion image of the link before the intersection is an example of the first congestion image, and the congestion image of the destination link is an example of the second congestion image.

[0111] Figure 16 shows a first example of displaying congestion levels. The first example displays a map of the area near the intersection shown in Figure 4, along with arrow lines (also called "congestion lines") indicating congestion levels. The congestion lines are an example of a congestion level image. The server device 2 generates display information based on map data 5 and traffic information 7, etc., and supplies the generated display information to the vehicle terminal 1. Based on the display information received from the server device 2, the vehicle terminal 1 displays the display screen shown in Figure 16 on the display unit 16. The server device 2 may also identify the current location of the target vehicle based on probe information etc. received from the vehicle terminal 1 and generate display information representing a map of the area around the identified current location, or it may generate display information representing a map of the area around a location specified by the input information supplied from the vehicle terminal 1.

[0112] Server device 2 displays congestion lines 71U, 71D, 72Ua, 72Ub, 72D, 73U, 73D, 74U, 74Da, and 74Db on the map, indicating the degree of congestion for each link. Here, congestion lines 71U and 71D correspond to link IDs "1001U" and "1001D" in Figure 4, congestion lines 72Ua and 72Ub correspond to link ID "1002U", and congestion line 72D corresponds to link ID "1002D". Furthermore, congestion lines 73U and 73D correspond to link IDs "1003U" and "1003D", congestion line 74U corresponds to link ID "1004U", and congestion lines 74Da and 74Db correspond to link ID "1004D". Here, as an example, dashed arrows represent a high degree of congestion, and dotted arrows represent a low degree of congestion. For the sake of explanation, the line type of the congested lines is defined here according to the degree of congestion, but instead, the color of the congested lines may be defined according to the degree of congestion. In this way, server device 2 displays congested lines that have a form (color or line type) according to the degree of congestion, but have a common shape regardless of the degree of congestion.

[0113] As shown in Figure 16, on the road of link ID "1002U" corresponding to the link before the intersection, congestion lines 72Ua and 72Ub are provided, corresponding to the destination links of link ID "1002U". Congestion line 72Ua represents the degree of congestion when a vehicle travels straight from link ID "1002U" to link ID "1003U", and congestion line 72Ub represents the degree of congestion when a vehicle turns right from link ID "1002U" to link ID "1004U".

[0114] Server device 2 identifies the congestion level of links other than the link before the intersection based on the predicted travel time associated with the link IDs "1002D", "1003U", and "1004U" of the links other than the link before the intersection. Server device 2 then displays the congestion lines 72D, 73U, and 74U, which correspond to the identified congestion levels, on the roads with the corresponding link IDs "1002D", "1003U", and "1004U", respectively.

[0115] Furthermore, Server Device 2 displays congestion lines 72Ua and 72Ub, which represent the congestion level of the intersection-pre-link "1002U," by connecting them to the congestion lines of their respective destination links. Specifically, Server Device 2 aligns the endpoint of congestion line 72Ua (i.e., the tip of the arrow) with the starting point of congestion line 73U of the corresponding destination link, link ID "1003U." Additionally, Server Device 2 bends the tip of congestion line 72Ua toward the starting point of congestion line 74U of the corresponding destination link, link ID "1004U," so that the endpoint of congestion line 72Ua aligns with the starting point of congestion line 74U. In this case, congestion line 72Ub, which represents the congestion level for right turns, is displayed further to the right on the road of link ID "1002U" than congestion line 72Ua, which represents the congestion level for going straight. This allows the server device 2 to clearly indicate to the user which direction the congestion line displayed on the intersection link represents. In the example in Figure 16, the user can easily understand that the congestion is high in the right-turn direction of intersection link "1002U" and low in the straight-ahead direction of intersection link "1002U".

[0116] Similarly, Server Device 2 displays the endpoint (the tip of the arrow) of congestion line 71U for link ID "1001U," which vehicles pass through before the intersection link "1002U," by connecting it to the common starting point of congestion lines 72Ua and 72Ub, which represent the degree of congestion on the intersection link "1002U." In this way, Server Device 2 represents the connection relationships between links by the connections of congestion lines, even outside of intersections.

[0117] Furthermore, Server Device 2 displays congestion lines 74Da and 74Db, which represent the congestion level of the intersection-front link "1004D," by connecting them to the congestion lines of their respective destination links. Specifically, Server Device 2 bends the tip of congestion line 74Da toward the starting point of congestion line 72D, so that the endpoint of congestion line 74Da coincides with the starting point of congestion line 72D of the corresponding destination link, link ID "1002D." Similarly, Server Device 2 bends the tip of congestion line 74Db toward the starting point of congestion line 73U, so that the endpoint of congestion line 74Db coincides with the starting point of congestion line 73U, the corresponding destination link, link ID "1003U." In this case, congestion line 74Db, which represents the congestion level for right turns, is displayed further to the right on the road than congestion line 74Da, which represents the congestion level for left turns.

[0118] On the other hand, for the intersection link "1003D," there are two connected links, "1002D" and "1004U," but since the congestion level is the same for each connected link, the server device 2 displays only one congestion line 73D on the road. In this way, when the congestion level is the same for each connected link, it is possible to display only the congestion line representing the congestion level corresponding to at least one connected link, and omit the display of congestion lines representing the congestion levels of the other connected links. This reduces the number of congestion lines displayed and reduces the complexity of the display.

[0119] Furthermore, if the server device 2 can identify the number of lanes in the link before the intersection and the destination links that can be accessed from each lane (for example, the link ID of the destination link) based on the map data 5, it may display congestion lines representing the degree of congestion in the link before the intersection for each lane. In this case, the map data 5 includes information indicating the number of lanes in each link and the links that can be accessed from each of those lanes. The server device 2 then connects the end point of the congestion line for each lane to the start point of the congestion line of the destination link that can be accessed from each lane.

[0120] For example, consider a case where the link before the intersection indicated by link ID "1002U" has three lanes, with the left lane being for going straight, the center lane being for both going straight and turning right, and the right lane being for turning right. In this case, server device 2 determines the line type of the congestion line corresponding to the left lane based on the degree of congestion corresponding to the direction of travel to the corresponding destination link, link ID "1003U," and connects the end point of the congestion line to the starting point of congestion line 73U of link ID "1003U." Server device 2 also determines the line type of the congestion line corresponding to the right lane based on the degree of congestion corresponding to the direction of travel to the corresponding destination link, link ID "1004U," and connects the end point of the congestion line to the starting point of congestion line 74U of link ID "1004U." Furthermore, Server Device 2 determines the line type of congestion line corresponding to the center lane based on the congestion level (e.g., average congestion level) corresponding to both directions of travel to the corresponding destination links, Link IDs "1003U" and "1004U". Then, Server Device 2 branches the endpoint of the congestion line corresponding to the center lane so that it has two arrows, and connects them to the starting points of congestion lines 73U and 74U of Link IDs "1003U" and "1004U", respectively. In this way, Server Device 2 displays a total of three congestion lines for three lanes on the road of Link ID "1002U".

[0121] Furthermore, server device 2 may not display congestion information for links with a "low" congestion level, but may display a congestion line associated with the link for links with a "high" congestion level. The same applies to subsequent display examples.

[0122] Figure 17 shows a second example of displaying congestion levels. This second example shows a map of the area near a road junction along with congestion lines. The server device 2 generates display information based on map data 5 and traffic information 7, and supplies the generated display information to the vehicle terminal 1. Based on the display information received from the server device 2, the vehicle terminal 1 displays the display screen shown in Figure 17 on the display unit 16.

[0123] Server device 2 displays congestion lines 75Ua, 75Ub, 76U, 77U, and 78U on the map. Here, congestion lines 75Ua, 75Ub, 76U, and 77U represent the degree of congestion on the links connecting the main road 64a, where the target vehicle's current location is, to the branching point 63 where the side road 64b branches off. The branching point is a type of intersection.

[0124] First, server device 2 identifies the congestion level of links other than the intersection link based on the predicted travel time associated with the link ID of the links other than the intersection link. Then, server device 2 displays congestion lines 76U, 77U, and 78U, which correspond to the identified congestion levels, on the corresponding roads.

[0125] Furthermore, Server Device 2 displays congestion lines 75Ua and 75Ub, which represent the degree of congestion of the pre-intersection links located before the branching point 63. Congestion line 75Ua represents the degree of congestion in the direction of vehicles moving toward the side road 64b, and congestion line 75Ub represents the degree of congestion in the direction of vehicles traveling straight toward the main road 64a. Here, the congestion is high in the direction of vehicles moving toward the side road 64b due to congestion on the side road 64b. Server Device 2 identifies the degree of congestion of the pre-intersection links heading toward the side road 64b based on the combined travel time of the combined links of the pre-intersection links at branching point 63 and the connecting links corresponding to the side road 64b, and displays congestion line 75Ua, which represents the identified degree of congestion. Similarly, Server Device 2 identifies the degree of congestion of the pre-intersection links in the straight-ahead direction based on the combined travel time of the combined links of the pre-intersection links at branching point 63 and the connecting links in the straight-ahead direction, and displays congestion line 75Ub, which represents the identified degree of congestion.

[0126] Furthermore, Server Device 2 displays congested lines 75Ua and 75Ub connected to the corresponding congested lines of the destination links. Specifically, Server Device 2 bends the tip of congested line 75Ua along the side road 64b so that the endpoint of congested line 75Ua coincides with the starting point of congested line 77U. Also, Server Device 2 displays the endpoint of congested line 75Ub overlapping with the starting point of congested line 76U.

[0127] In this way, even when the degree of congestion differs for each direction near a branching point, the server device 2 can display these congestion levels in a way that is easily understood by the user.

[0128] Server device 2 may also display the congestion levels of both combination links registered in combination link DB62 together using a single congestion line.

[0129] Figure 18 shows a third display example regarding the display of congestion levels. In this third display example, the server device 2 displays a map of the same display area as in the second display example shown in Figure 17, and displays the congestion levels of both combination links registered in the combination link DB 62 together using a single congestion line.

[0130] Server device 2 displays congestion lines 75UA and 75UB for two combined links, including the pre-intersection link at branching point 63. In this case, server device 2 identifies the degree of congestion of the combined link between the pre-intersection link located before branching point 63 and the destination link corresponding to the main road 64a after branching point 63, based on the combined travel time of the combined link and the total link length of the combined link. Server device 2 then displays the congestion line 75UB, which represents the identified degree of congestion, in association with the combined link. Server device 2 also identifies the degree of congestion of the combined link between the pre-intersection link located before branching point 63 and the destination link corresponding to the side road 64b, based on the combined travel time of the combined link and the total link length of the combined link. Server device 2 then displays the congestion line 75UA, which represents the identified degree of congestion, along the combined link.

[0131] Figure 19 shows a fourth example of a display related to congestion levels. The fourth example shows a map of the area around the five-way intersection 65 along with congestion lines. The server device 2 generates display information based on map data 5 and traffic information 7, etc., and supplies the generated display information to the vehicle terminal 1. Based on the display information received from the server device 2, the vehicle terminal 1 displays the display screen shown in Figure 19 on the display unit 16. At the five-way intersection 65, the pre-branching road 66 branches into four branching roads 67a to 67d. The pre-branching road 66 corresponds to the pre-intersection link, and the branching roads 67a to 67d correspond to the destination links. The five-way intersection 65 is an example of an intersection.

[0132] First, server device 2 identifies the congestion level of branch roads 67a to 67d based on the predicted travel time associated with the link IDs of branch roads 67a to 67d other than the link before the intersection. Then, server device 2 displays congestion lines 80U to 83U, which are line types corresponding to the identified congestion level, on the corresponding branch roads 67a to 67d.

[0133] Furthermore, Server Device 2 displays congestion lines 79Ua to 79Ud, which represent the degree of congestion corresponding to the four destination links (corresponding to branch roads 67a to 67d) on the branch road 66 corresponding to the intersection link at the five-way intersection point 65. Here, the degree of congestion in the direction toward branch roads 67a and 67d is "high," while the degree of congestion in the direction toward the other branch roads 67b and 67c is "low." Based on the combined travel time of the combined links corresponding to each of the branch roads 67a to 67d, Server Device 2 identifies the degree of congestion on the branch road 66 toward each of the branch roads 67a to 67d. Then, Server Device 2 displays congestion lines 79Ua to 79Ud, which represent the identified degree of congestion.

[0134] Furthermore, Server Device 2 displays congestion lines 79Ua to 79Ud by connecting them to congestion lines 80U to 83U on branch roads 67a to 67d, respectively. For example, Server Device 2 bends the tip of congestion line 79Ua along branch road 67a so that the end point of congestion line 79Ua coincides with the start point of congestion line 80U. Similarly, Server Device 2 bends the tips of congestion lines 79Ub to 79Ud along the corresponding branch roads so that the end points of congestion lines 79Ub to 79Ud coincide with the start points of congestion lines 81U to 83U.

[0135] Furthermore, if the map data 5 or the like includes information indicating the number of lanes in the link before the intersection and the destination links that can be accessed for each lane (for example, the link ID of the destination link), the server device 2 may display congestion lines representing the degree of congestion in the link before the intersection for each lane. For example, if the road before the junction 66 includes a total of 5 lanes, from the 1st lane to the 5th lane, the server device 2 will display a total of 5 congestion lines for each lane on the road before the junction 66. For example, the 1st lane is the lane that goes to the junction 67a, the 2nd lane is the lane that goes to the junction 67a and 67b, the 3rd lane is the lane that goes to the junction 67c, the 4th lane is the lane that goes to the junction 67c and 67d, and the 5th lane is the lane that goes to the junction 67d. In this case, the server device 2 identifies the combined travel time between the road before the junction 66 and the destination junction based on the combined link DB 62 for each lane, and determines the degree of congestion for each lane based on the identified combined travel time. The server device 2 then displays a congestion line for each lane, representing the determined level of congestion, connecting it to the congestion line of the branching road ahead.

[0136] Figure 20 shows a fifth display example regarding the display of congestion levels. In the fifth display example, as in the fourth display example, when the server device 2 displays a map of the area around the five-way intersection 65 along with congestion lines, it displays multiple congestion lines of the same line type on the same link as a single congestion line.

[0137] Server device 2 displays congestion lines 79Ua, 79Ubc, and 79Ud, which represent the degree of congestion corresponding to the four destination links (corresponding to branch roads 67a to 67d), on the branch road 66 corresponding to the link before the intersection at the five-way intersection point 65. Here, server device 2 recognizes that the degree of congestion on the branch road 66 heading towards branch road 67b and the degree of congestion on the branch road 66 heading towards branch road 67c are at the same level, and displays congestion line 79Ubc, which commonly represents these congestion lines, on the branch road 66. In this case, since congestion line 79Ubc represents the degree of congestion in both directions of movement towards branch roads 67b and 67c, server device 2 makes the tip of congestion line 79Ubc a bifurcated arrow and connects each arrow to the starting point of congestion lines 81U and 82U of branch roads 67b and 67c, respectively.

[0138] According to the fifth display example, the server device 2 can simplify the display regarding congestion by reducing the number of congestion lines displayed, thereby effectively suppressing the complexity of the display.

[0139] Figure 21 is an example flowchart showing the procedure for displaying congestion levels. Server device 2 repeatedly executes the process shown in Figure 21 when it wants vehicle terminal 1 to display congestion levels.

[0140] First, the server device 2 refers to the individual link DB 61 and the combined link DB 62 to obtain the predicted travel time and combined travel time for the links to be displayed (step S41). Then, based on the predicted travel time and combined travel time obtained in step S41, the server device 2 identifies the congestion level of the links to be displayed (step S42). In this case, for links before intersections, the server device 2 identifies the congestion level for each connected link based on the combined travel time.

[0141] The server device 2 causes the vehicle terminal 1 to display information regarding the identified congestion level (step S43). In this case, as shown in the first to fifth display examples above, if the congestion level differs depending on the connected link in the link before the intersection, the server device 2 displays information representing the respective congestion levels on the map, associating them with the link before the intersection.

[0142] Alternatively, the vehicle terminal 1 may perform the congestion level display processing using the individual link DB 61 and the combined link DB 62 instead of the server device 2. In this case, the vehicle terminal 1 may obtain the map data 5 and traffic information 7, etc. necessary for the display processing from the server device 2, or it may store the map data 5 and traffic information 7, etc. in the storage unit 12 in advance. Then, the vehicle terminal 1 refers to the map data 5 and traffic information 7 and performs the processing shown in Figure 21 on behalf of the server device 2. In addition, the server device 2 generates congestion level information, and the vehicle terminal 1 performs display control to display a congestion level image on the display unit 16 based on the congestion level information generated by the server device 2. That is, the vehicle terminal 1 may perform the processing in step S43 of Figure 21.

[0143] <Means of Disclosure> This specification discloses inventions relating to the following means 1, means 2, and means 3.

[0144] (Measure 1) Generally, the degree of congestion on roads leading to intersections can vary depending on the direction of travel, such as going straight or turning right. When the travel time for routes passing through such intersections is determined based on predicted travel times per link, the accuracy of the determined travel time suffers.

[0145] One of the objectives of Means 1 is to provide an information processing device, method, program, and storage medium capable of generating information suitable for calculating the required time, in view of the above-mentioned problems.

[0146] Means 1 are identified, for example, by the apparatus, method, and storage medium described in the following appendix. [Note 1] An acquisition means capable of acquiring multiple pieces of positional information representing the position of multiple moving objects from multiple moving objects, A calculation means for calculating the estimated travel time required to traverse a set of links based on the position information corresponding to an adjacent set of links, A generation means for generating information regarding the travel time for each set of links, An information processing device having [Note 2] The information processing device according to Appendix 1, wherein the generation means generates a database as information regarding the travel time, which shows, for each set of links, a correction time for calculating the travel time of the set of links from the travel time individually predicted for each of the links. [Note 3] The information processing device described in Appendix 1 generates a database showing the travel time for each set of links as information relating to the travel time. [Note 4] The information processing device according to Appendix 1, wherein the generation means generates a graph structure as information about the travel time, the graph structure including nodes representing individual travel times for each link and connected edges representing correction times for the travel time for each set of links. [Note 5] The information processing apparatus according to any one of the appendices 1 to 4, further comprising output means for outputting information relating to the travel time corresponding to the specified set of links. [Note 6] The information processing device according to any one of the appendices 1 to 5, wherein the generating means generates at least information regarding the travel time of the set of links connected at an intersection. [Note 7] The information processing device according to any one of the appendices 1 to 6, wherein the generating means generates at least information regarding the travel time of a set of links including a second link whose link length is less than a predetermined distance, or whose completion rate calculated based on the position information is less than a predetermined rate. [Note 8] A method by which a computer performs an action. An acquisition process that allows for the acquisition of multiple positional information representing the position of multiple moving objects, A calculation step of calculating the estimated travel time required to traverse a set of links based on the position information corresponding to an adjacent set of links, A method comprising a generation step of generating information relating to the travel time for each set of links. [Note 9] An acquisition means capable of acquiring multiple pieces of positional information representing the position of multiple moving objects from multiple moving objects, A calculation means for calculating the estimated travel time required to traverse a set of links based on the position information corresponding to an adjacent set of links, A generation means for generating information regarding the travel time for each set of links. A program that makes a computer function. [Note 10] A storage medium characterized by storing the program described in Appendix 9.

[0147] (Measure 2) Generally, the degree of congestion on roads leading to intersections can vary depending on the direction of travel, such as going straight or turning right. When the travel time for routes passing through such intersections is determined based on predicted travel times per link, the accuracy of the determined travel time suffers.

[0148] One of the objectives of Means 2 is to provide an information processing device, method, program, and storage medium that can accurately calculate the required time, in light of the above-mentioned problems.

[0149] Means 2 include, for example, the apparatus described in the appendix below. It is determined by the method and the storage medium. [Note 1] A first acquisition means for obtaining the first travel time of links included in the route for calculating the required time from first information indicating the first travel time which is the predicted travel time for each link, A second acquisition means for obtaining information regarding the second travel time of a set of links included in the route from second information regarding the second travel time, which is the travel time for each set of adjacent links, A calculation means for calculating the required time based on the first travel time acquired by the first acquisition means and the information regarding the second travel time acquired by the second acquisition means, An information processing device having [Note 2] A search means for searching for a recommended route from the aforementioned routes based on the time required, An output means for outputting information regarding the recommended route, The information processing device described in Appendix 1, further comprising the above. [Note 3] The output means is an information processing device as described in Appendix 2, which displays the recommended route on a map and displays the estimated time of arrival based on the estimated time required for the recommended route. [Note 4] The aforementioned recommended route is an alternative route to the currently guided route, if one already exists. The search means is an information processing device according to Appendix 2 or 3, which searches for the alternative route based on the required time. [Note 5] The second information indicates, for each set of links, a correction time for calculating the second travel time from the first travel time. The information processing device according to any one of the appendices 1 to 4, wherein the second acquisition means acquires the correction time linked in the second information to the set of links included in the route as information relating to the second travel time. [Note 6] The second information indicates the second travel time for each set of links, The information processing device according to any one of the appendices 1 to 4, wherein the second acquisition means acquires the second travel time, which is linked in the second information to the set of links included in the route, as information relating to the second travel time. [Note 7] The first acquisition means receives the first travel time from a server device that stores the first information and the second information, The second acquisition means is an information processing device according to any one of the appendices 1 to 6, which acquires information relating to the second travel time from the server device. [Note 8] The information processing device is a server device that controls the output of a vehicle terminal that provides guidance regarding the route, The information processing apparatus according to any one of the appendices 1 to 6, further comprising a transmission means for transmitting information relating to the time required to the vehicle terminal. [Note 9] A method by which a computer performs an action. A first acquisition step involves obtaining the first travel time for links included in the route for which the required time is calculated, from first information indicating the first travel time which is the predicted travel time for each link, A second acquisition step of obtaining information regarding the second travel time of the set of links included in the route from second information regarding the second travel time, which is the travel time for each set of adjacent links, A calculation means for calculating the required time based on the first travel time obtained in the first acquisition step and the information regarding the second travel time obtained in the second acquisition step, A method of having. [Note 10] A first acquisition means for obtaining the first travel time of links included in the route for calculating the required time from first information indicating the first travel time which is the predicted travel time for each link, A second acquisition means for obtaining information regarding the second travel time of a set of links included in the route from second information regarding the second travel time, which is the travel time for each set of adjacent links, A calculation means that calculates the required time based on the first travel time acquired by the first acquisition means and the information regarding the second travel time acquired by the second acquisition means. A program that makes a computer function. [Note 11] A storage medium characterized by storing the program described in Appendix 10.

[0150] (Measure 3) Approaching an intersection, the degree of congestion can vary depending on the direction of travel at the intersection. In such situations, there was a problem in accurately conveying the degree of congestion near the intersection to the user.

[0151] One of the objectives of means 3 is to provide an information processing device, method, program, and storage medium that can suitably present the degree of congestion near intersections, in view of the above-mentioned problems.

[0152] Means 3 are identified, for example, by the apparatus, method, and storage medium described in the following appendix. [Note 1] A means for obtaining the congestion level of links on the map to be displayed, The system includes a display control means that displays a congestion level image representing the congestion level in association with the link on the display unit, When the map includes a first link and a second link that change direction at an intersection, the display control means displays a first congestion image, which is a congestion image representing the congestion level of the first link, and a second congestion image, which is a congestion image representing the congestion level of the second link, together on the display unit. Information processing device. [Note 2] A third link is connected to the end of the first link, which is connected to the second link. The information processing apparatus according to Appendix 1, wherein the display control means displays a first congestion level image representing the congestion level of the first link in the direction toward the second link and a first congestion level image representing the congestion level of the first link in the direction toward the third link side by side on the display unit. [Note 3] The information processing apparatus according to Appendix 2, wherein the display control means displays a plurality of first congestion images side by side such that one end of each of the plurality of first congestion images is connected to one another, and the other end of each of the plurality of first congestion images is branched. [Note 4] The information processing apparatus according to Appendix 3, wherein the display control means displays the first congestion images side by side such that when a plurality of the first congestion images represent the same level of congestion, one end of each of the first congestion images is connected to the other, and the other end of each of the first congestion images is branched. [Note 5] The information processing apparatus according to any one of the appendices 1 to 4, wherein the display control means displays on the display unit an image that has a shape that is common regardless of the degree of congestion, and which corresponds to the degree of congestion represented by the congestion image. [Note 6] The information processing apparatus according to any one of the appendices 1 to 4, wherein the display control means displays, as the congestion level image, a line having a color or line type corresponding to the congestion level represented by the congestion level image on the display unit. [Note 7] An information processing device according to any one of the appendices 1 to 6, further comprising determination means for determining the congestion level of the first link and the congestion level of the second link based on first information representing the predicted travel time for each link and second information representing the predicted travel time for each pair of adjacent links. [Note 8] The information processing apparatus according to Appendix 7, wherein the display control means displays the congestion images representing the congestion levels of both the first and second links as the first congestion image and the second congestion image on the display unit when the travel time for the pair of the first and second links is included in the second information. [Note 9] The information processing device according to Appendix 7, wherein the display control means displays the same number of first congestion level images on the display unit as the number of link sets including the first link, based on the travel time associated with the set of links including the first link in the second information. [Note 10] The information processing device according to Appendix 7, wherein the display control means displays a number of first congestion images equal to the number of lanes on the display unit, based on map data indicating the number of lanes in the first link and the links that can be moved from each of the lanes, and the travel time associated in the second information with the set of links including the first link. [Note 11] A method by which a computer performs an action. The acquisition process involves obtaining the congestion level of links on the map to be displayed, The system includes a display control step of displaying a congestion level image representing the congestion level in association with the link on the display unit, In the display control step, if the map includes a first link and a second link that change direction and connect at an intersection, the first congestion image, which is a congestion image representing the congestion level of the first link, and the second congestion image, which is a congestion image representing the congestion level of the second link, are connected and displayed on the display unit. [Note 12] A means for obtaining the congestion level of links on the map to be displayed, Display control means for displaying the congestion level image representing the congestion level in association with the link on the display unit. To make the computer function as, The display control means is a program that, when the map includes a first link and a second link that change direction and connect at an intersection, displays a first congestion image, which is a congestion image representing the congestion level of the first link, and a second congestion image, which is a congestion image representing the congestion level of the second link, together on the display unit. [Note 13] A storage medium characterized by storing the program described in Appendix 12.

[0153] In each of the embodiments described above, the program can be stored using various types of non-transitory computer-readable medium and supplied to a control unit, which is a computer. Non-transitory computer-readable medium includes various types of tangible storage medium. Examples of non-transitory computer-readable medium include magnetic storage medium (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical storage medium (e.g., magneto-optical disks), CD-ROM (Read Only Memory), CD-R, CD-R / W, and semiconductor memory (e.g., mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (Random Access Memory)).

[0154] Although the present invention has been described above with reference to embodiments, the present invention is not limited to the above embodiments. Various modifications to the structure and details of the present invention can be made that are understandable to those skilled in the art within the scope of the present invention. That is, the present invention naturally includes the full disclosure, including the claims, and various modifications and alterations that those skilled in the art could make in accordance with the technical idea. Furthermore, each disclosure of the above-mentioned patent documents and other references is incorporated herein by reference. [Explanation of Symbols]

[0155] 1. Vehicle terminal 2 Server devices 5. Map data 6. Probe DB 7 Traffic information 11, 21 Communications Department 12, 22 Storage section 13, 23 Input section 14, 24 Control Unit 15 Sensor Groups 16 Display 17. Sound output section

Claims

1. An acquisition means capable of acquiring multiple pieces of positional information representing the position of multiple moving objects from multiple moving objects, A calculation means for calculating the estimated travel time required to traverse a set of links based on the position information corresponding to an adjacent set of links, A generation means for generating information regarding the travel time for each set of links, An information processing device having

2. The information processing apparatus according to claim 1, wherein the generation means generates a database as information relating to the travel time, which shows a correction time for each set of links to calculate the travel time of the set of links from the travel time predicted individually for each of the links.

3. The information processing apparatus according to claim 1, wherein the generation means generates a database showing the travel time for each set of links as information relating to the travel time.

4. The information processing apparatus according to claim 1, wherein the generation means generates a graph structure as information relating to the travel time, the graph structure including nodes representing individual travel times for each link and connected edges representing correction times for the travel time for each set of links.

5. The information processing apparatus according to claim 1, further comprising output means for outputting information relating to the travel time corresponding to a specified set of links.

6. The information processing apparatus according to claim 1, wherein the generation means generates at least information regarding the travel time of the set of links connected at an intersection.

7. The information processing apparatus according to claim 1, wherein the generating means generates at least information regarding the travel time of a set of links including a second link whose link length is less than a predetermined distance, or whose travel rate calculated based on the position information is less than a predetermined rate.

8. A method by which a computer performs an action. An acquisition process that allows for the acquisition of multiple positional information representing the position of multiple moving objects, A calculation step of calculating the estimated travel time required to traverse a set of links based on the position information corresponding to an adjacent set of links, A method comprising a generation step of generating information relating to the travel time for each set of links.

9. An acquisition means capable of acquiring multiple pieces of positional information representing the position of multiple moving objects from multiple moving objects, A calculation means for calculating the estimated travel time required to traverse a set of links based on the position information corresponding to an adjacent set of links, A generation means for generating information regarding the travel time for each set of links. A program that makes a computer function.

10. A storage medium characterized by storing the program described in claim 9.

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