Information processing device and information processing method

Abstract

To improve the prediction accuracy of a predictive movement time predicted for an object vehicle used for an on-demand type vehicle allocation service to require in order to travel a movement route.SOLUTION: According to the application, an information processing device includes an acquisition part for acquiring a predictive travel time calculated by a route retrieval service for calculating a predictive travel time predicted to require to travel a travel route of a vehicle from a departure place to a destination, a determination part for determining a correction coefficient to be used in calculating a predictive movement time predicted to require for an object vehicle to travel the movement route on the basis of an actual time required being a time required for the object vehicle used for the on-demand type vehicle allocation service to actually travel the movement route, and a calculation part for calculating the predictive movement time on the basis of the predictive travel time and the correction coefficient.SELECTED DRAWING: Figure 7

Description

Technical Field

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

Background Art

[0002] Conventionally, various techniques for predicting the travel time of a vehicle on a travel route from a departure point to a destination are known. For example, there is known a technique in which the required time on a route traveled in the past is recorded, and when traveling to the same destination on the same route again, the required time is predicted based on the data traveled in the past.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the above conventional technology, since the required time on a route traveled in the past is recorded, and when traveling to the same destination on the same route again, the required time is only predicted based on the data traveled in the past, it is not always possible to improve the prediction accuracy of the predicted travel time predicted to be required for a target vehicle used in an on-demand vehicle dispatch service to move along a travel route.

[0005] An object of the present application is to make it possible to improve the prediction accuracy of the predicted travel time predicted to be required for a target vehicle used in an on-demand vehicle dispatch service to move along a travel route.

Means for Solving the Problems

[0006] The information processing apparatus according to the embodiment includes an acquisition unit that acquires the predicted travel time calculated by a route search service that calculates a predicted travel time required to travel a travel route of a vehicle from a departure point to a destination, a determination unit that determines a correction coefficient used when calculating a predicted travel time predicted to be required for a target vehicle used in an on-demand vehicle dispatch service to actually move along the travel route based on the actual required time that is the time required for the target vehicle to actually move along the travel route corresponding to the travel route, and a calculation unit that calculates the predicted travel time based on the predicted travel time and the correction coefficient.

Effect of the Invention

[0007] According to one aspect of the embodiment, it is possible to improve the prediction accuracy of the predicted travel time predicted to be required for a target vehicle used in an on-demand vehicle dispatch service to move along a travel route.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Embodiments for Carrying Out the Invention

[0009] Hereinafter, embodiments for carrying out the information processing apparatus and the information processing method according to the present application (hereinafter referred to as "embodiments") will be described in detail with reference to the drawings. Note that the information processing apparatus and the information processing method according to the present application are not limited by this embodiment. Also, in the following embodiments, the same parts are denoted by the same reference numerals, and redundant descriptions are omitted.

[0010] (Embodiment) [1. Introduction] Conventionally, a route search service for calculating a predicted travel time predicted to be required for a vehicle to travel along a travel route from a departure point to a destination has been known. For example, the route search service records map information including the departure point and the destination. For example, the map information includes information on roads on which the vehicle can travel. For example, the route search service receives designations of a departure point and a destination from a user. Note that the route search service may acquire position information indicating the current location of the user from an in-vehicle device mounted on the user's vehicle or the user's terminal device instead of receiving a designation of the departure point from the user.

[0011] For example, when the route search service receives designations of a departure point and a destination from a user, it refers to the map information to search for a travel route of the vehicle from the departure point to the destination. Subsequently, the route search service calculates a predicted travel time predicted to be required for the vehicle to travel along the searched travel route. Also, the route search service provides the user with information regarding the travel route and the predicted travel time. Hereinafter, the travel route calculated by the route search service may be simply referred to as the "travel route". Also, the predicted travel time calculated by the route search service may be simply referred to as the "predicted travel time".

[0012] Conventionally, technologies related to on-demand vehicle dispatching services are known. Here, an on-demand vehicle dispatching service is a service that dispatches vehicles that can accommodate multiple passengers in response to the requests of users who use the on-demand vehicle dispatching service (hereinafter sometimes referred to as "passengers"). In other words, an on-demand vehicle dispatching service is a mobility service provided by on-demand buses or shared taxis. For an on-demand bus, the driving operation may be performed by an autonomous driving system mounted on the vehicle instead of a driver performing the driving operation.

[0013] In addition, in order to provide an on-demand vehicle dispatching service, it is necessary to determine the operation schedule of the target vehicle (hereinafter sometimes simply referred to as "target vehicle") used for the on-demand vehicle dispatching service. Specifically, the operation schedule of the target vehicle is determined based on the predicted travel time (hereinafter sometimes simply referred to as "predicted travel time") that is predicted to be required for the target vehicle to move along the travel route corresponding to the driving route. Also, the predicted travel time may be calculated based on the predicted driving time.

[0014] However, there may be a difference between the actual required time (hereinafter sometimes abbreviated as "actual required time"), which is the time required for the target vehicle used for the on-demand vehicle dispatching service to actually move along the travel route corresponding to the driving route, and the predicted travel time. FIG. 1 is a scatter diagram showing the relationship between the predicted travel time and the actual required time according to the comparative technology. In FIG. 1, in a graph with the predicted travel time (unit: minutes) on the horizontal axis and the actual required time (unit: minutes) on the vertical axis, a point group corresponding to the data showing the relationship between the predicted travel time and the actual required time according to the comparative technology is plotted. In FIG. 1, when the predicted travel time and the actual required time match, the points corresponding to the data are distributed on the straight line L1. However, in reality, there are many points distributed at positions away from the straight line L1. That is, there are many cases where the difference between the predicted travel time and the actual required time is relatively large.

[0015] For example, the data corresponding to point P1 included in region R1 in the graph shown in FIG. 1 is distributed below straight line L1, so the actual required time is shorter than the predicted travel time. Specifically, for the data corresponding to point P1, the predicted travel time was 35 minutes, while the actual required time was 17 minutes. That is, for the data corresponding to point P1, the predicted travel time was 18 minutes longer than the actual required time. Thus, the comparative technique tended to calculate a longer predicted travel time compared to the actual required time (i.e., it had too much of a margin).

[0016] As described above, the operation schedule of the target vehicle is determined based on the predicted travel time. However, when the difference between the predicted travel time and the actual required time is large, it becomes difficult to establish an efficient operation schedule. Here, an efficient operation schedule refers to an operation schedule that transports more passengers in a shorter time. For example, in the case of the data corresponding to point P1 in FIG. 1, since 18 minutes of the difference between the predicted travel time and the actual required time is wasted, the operation schedule of the target vehicle determined based on the predicted travel time cannot be said to be efficient. Therefore, a technique for improving the prediction accuracy of the predicted travel time that is predicted to be required for the target vehicle used in an on-demand vehicle dispatch service to move along a travel route is desired.

[0017] [2. Configuration Example of Information Processing Apparatus] FIG. 2 is a diagram showing a configuration example of an information processing apparatus according to an embodiment. As shown in FIG. 2, the information processing apparatus according to the embodiment, information processing apparatus 100 has a communication unit 110, a storage unit 120, and a control unit 130. Note that information processing apparatus 100 may have an input unit (e.g., a keyboard, a mouse, etc.) that receives various operations from an administrator or the like of information processing apparatus 100, and a display unit (e.g., a liquid crystal display, etc.) for displaying various information.

[0018] (Communication Unit 110) The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. The communication unit 110 is connected to a network either wired or wirelessly, and transmits and receives information to and from, for example, an in-vehicle device mounted on a target vehicle used for an on-demand vehicle dispatch service. Here, the in-vehicle device is an information processing device used by the driver of the target vehicle. For example, the in-vehicle device may be a terminal device (such as a smartphone) held by the driver of the target vehicle.

[0019] (Memory unit 120) The memory unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. For example, the memory unit 120 stores passenger information, reservation information, driving information, and performance information generated by the acquisition unit 131.

[0020] (Control unit 130) The control unit 130 is a controller, and is realized, for example, by various programs (corresponding to an example of an information processing program) stored in a storage device inside the information processing device 100 being executed with the RAM as a working area by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. Also, the control unit 130 is a controller and is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0021] The control unit 130 has an acquisition unit 131, a determination unit 132, a calculation unit 133, and a provision unit 134 as functional units, and realizes or executes the operations of information processing described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in FIG. 2, and may be any other configuration as long as it can perform the information processing described later. Also, each functional unit represents the function of the control unit 130, and does not necessarily have to be physically distinct.

[0022] (Acquisition Unit 131) The acquisition unit 131 acquires attribute information regarding the attributes of users (hereinafter referred to as "passengers") who use an on-demand car-hailing service. For example, the acquisition unit 131 acquires the passenger's attribute information from the terminal device used by the passenger. For example, the acquisition unit 131 acquires information regarding the passenger's age, gender, residential area, etc. as the passenger's attribute information. When the acquisition unit 131 acquires the passenger's attribute information, it generates passenger information associating information that can identify the passenger (such as a passenger ID) with the passenger's attribute information, and stores the passenger information in the storage unit 120.

[0023] In addition, the acquisition unit 131 receives reservation content information regarding the content of the reservation for the target vehicle (hereinafter abbreviated as "target vehicle") used in the on-demand car-hailing service. For example, the acquisition unit 131 receives the reservation content information from the terminal device used by the passenger. For example, the acquisition unit 131 receives the reservation content information from each of a plurality of passengers. For example, the acquisition unit 131 receives, as the reservation content information, information that can identify the passenger (such as a passenger ID), as well as information on the departure location, destination, departure time, arrival time, and the availability of sharing rides. When the acquisition unit 131 receives the reservation content information, it generates reservation information associating information that can identify the reservation (such as a reservation ID) with the reservation content information, and stores the reservation information in the storage unit 120.

[0024] In addition, the acquisition unit 131 determines the departure location and the destination of the target vehicle based on the reservation information. Subsequently, when the acquisition unit 131 determines the departure location and the destination of the target vehicle, it calls a route search service via an API (Application Programming Interface) and calculates the driving route of the vehicle from the departure location to the destination using the route search service. Further, the acquisition unit 131 calculates the predicted driving time that is predicted to be required to drive the driving route of the vehicle from the departure location to the destination using the route search service. In this way, the acquisition unit 131 acquires the predicted driving time calculated by the route search service that calculates the predicted driving time required to drive the driving route of the vehicle from the departure location to the destination. When the acquisition unit 131 acquires the predicted driving time, it generates driving information in which information that can identify the driving route (such as a driving route ID), the driving route, and the predicted driving time are associated with each other, and stores the driving information in the storage unit 120.

[0025] In addition, the acquisition unit 131 acquires position history information regarding the position information history of the target vehicle from an in-vehicle device mounted on the target vehicle. Subsequently, the acquisition unit 131 estimates the actual departure location and the destination of the target vehicle by comparing the position history information of the target vehicle with the information indicating the departure location and the destination of the target vehicle included in the operation schedule of the target vehicle. Further, the acquisition unit 131 estimates the actual movement route of the target vehicle (hereinafter, may be described as the "actual movement route") based on the position history information of the target vehicle and the information indicating the actual departure location and the destination of the target vehicle. Here, the actual movement route corresponds to the movement route corresponding to the driving route calculated by the route search service.

[0026] In addition, the acquisition unit 131 estimates the departure time (hereinafter, may be referred to as the "actual departure time") when the target vehicle departs from the actual departure place based on the position history information of the target vehicle. Further, the acquisition unit 131 estimates the arrival time (hereinafter, may be referred to as the "actual arrival time") when the target vehicle arrives at the actual destination based on the position history information of the target vehicle. Subsequently, the acquisition unit 131 calculates the time from the actual departure time to the actual arrival time as the actual required time. In this way, the acquisition unit 131 acquires the actual required time, which is the time required for the target vehicle used in the on-demand vehicle dispatch service to actually move along the movement route corresponding to the driving route. When the acquisition unit 131 acquires the actual required time, it generates performance information associating information that can identify the actual movement route (such as a movement route ID), the position history information corresponding to the actual movement route, the actual required time, and information that can identify the driving route corresponding to the actual movement route (such as a driving route ID), and stores the performance information in the storage unit 120.

[0027] (Determination unit 132) Before explaining the process of the determination unit 132, the correction coefficients used when calculating the predicted movement time predicted to be required for the target vehicle to move along the movement route will be explained. FIG. 3 is a graph showing the relationship between the predicted driving time and the predicted movement time according to the embodiment. The calculation unit 133 calculates the predicted movement time predicted to be required for the target vehicle to move along the movement route corresponding to the driving route based on the predicted driving time acquired by the acquisition unit 131. In FIG. 3, when the calculation unit 133 sets the predicted driving time as x, the predicted movement time as y, and the two types of correction coefficients used when calculating the predicted movement time as a and b respectively, the calculation unit 133 calculates the predicted movement time using the linear function formula represented by "y = a * x + b". In FIG. 3, among the two types of correction coefficients, a is called the movement time coefficient. The movement time coefficient a corresponds to the slope of the linear function represented by "y = a * x + b". Also, among the two types of correction coefficients, b is called the boarding preparation coefficient. The boarding preparation coefficient b corresponds to the intercept of the linear function represented by "y = a * x + b".

[0028] FIG. 4 is a diagram for explaining a correction coefficient according to an embodiment. In FIG. 4, when the predicted travel time x is "10" (minutes), the travel time coefficient a corresponding to the slope is "1", and the boarding preparation coefficient b corresponding to the intercept is "0", the calculation unit 133 calculates the predicted travel time as "10" (minutes). Further, when the predicted travel time x is "10" (minutes), the travel time coefficient a corresponding to the slope is "0.8", and the boarding preparation coefficient b corresponding to the intercept is "0", the calculation unit 133 calculates the predicted travel time as "8" (minutes). Thus, when the travel time coefficient a corresponding to the slope is reduced from "1" to "0.8", the predicted travel time calculated by the calculation unit 133 is shortened from "10" (minutes) to "8" (minutes). That is, reducing the travel time coefficient a has the effect of shortening the predicted travel time calculated by the calculation unit 133 (compression effect when the predicted travel time is large). Further, when the predicted travel time x is "10" (minutes), the travel time coefficient a corresponding to the slope is "0.8", and the boarding preparation coefficient b corresponding to the intercept is "3", the calculation unit 133 calculates the predicted travel time as "11" (minutes). Thus, when the boarding preparation coefficient b corresponding to the intercept is increased from "0" to "3", the predicted travel time calculated by the calculation unit 133 is lengthened from "8" (minutes) to "11" (minutes). That is, increasing the boarding preparation coefficient b has the effect of lengthening the predicted travel time calculated by the calculation unit 133 (effect of providing a time margin overall).

[0029] The determination unit 132 determines a correction coefficient used when calculating the predicted travel time predicted to be required for the target vehicle to move along the travel route based on the actual required time, which is the time required for the target vehicle used for the on-demand car-sharing service to actually move along the travel route corresponding to the travel route. Specifically, the determination unit 132 determines the correction coefficient so as to reduce the magnitude of the difference between the predicted travel time and the actual required time. More specifically, the determination unit 132 determines the correction coefficient for each region based on the actual required time for each region related to the travel route.

[0030] FIG. 5 is a diagram for explaining a process of determining a correction coefficient for each region according to an embodiment. In FIG. 5, a map MP1 including a grid region obtained by dividing a region including areas A to F into a grid is shown. The areas A to F may be areas divided for each administrative region such as municipalities, or may be areas divided according to characteristics of the areas such as the amount of traffic or the amount of crowds. For example, the determination unit 132 determines a correction coefficient for each grid region based on the actual required time for each grid region obtained by dividing the region related to the travel route into a grid. For example, the determination unit 132 acquires information regarding the travel route of the vehicle acquired by the acquisition unit 131. Further, the determination unit 132 refers to the storage unit 120 and acquires map information of the region including the travel route of the vehicle acquired by the acquisition unit 131. For example, the determination unit 132 acquires map information obtained by dividing the region including the travel route into a grid. In FIG. 5, a state is shown in which the departure place calculated by the route search service is included in the grid region A2 and the destination calculated by the route search service is included in the grid region B3. Further, a state is shown in which the travel route RT1 of the vehicle from the departure place to the destination calculated by the route search service passes through the grid regions A2, B1, B2, and B3.

[0031] In FIG. 5, the determination unit 132 determines correction coefficients for each of the grid regions A2, B1, B2, and B3 based on the actual required time in each of the grid regions A2, B1, B2, and B3. Specifically, the determination unit 132 determines a travel time coefficient a1 and a boarding preparation coefficient b1 in the grid region A2 based on the actual required time in the grid region A2. For example, the determination unit 132 refers to the actual performance information in the storage unit 120 and extracts a plurality of actual performance information regarding the travel route passing through the grid region A2 from the actual performance information. For example, the determination unit 132 extracts a plurality of actual performance information regarding the travel route corresponding to the section of the grid region A2 in the travel route RT1 from the actual performance information. Subsequently, the determination unit 132 calculates the actual required time x1' required for the target vehicle to move within the grid region A2 based on each of the extracted plurality of actual performance information. For example, the determination unit 132 calculates the actual required time x1' (hereinafter, may be described as "the actual required time x1' in the grid region A2") required for the target vehicle to move within the grid region A2 based on the position history information corresponding to the travel route passing through the grid region A2 and the map information obtained by dividing the area including the travel route into a grid. For example, the determination unit 132 calculates the actual required time x1' in the grid region A2 based on the time when the target vehicle is located at the departure point of the grid region A2 and the time when the target vehicle is located at the intersection of the boundary between the grid region A2 and the grid region B1 and the travel route RT1. For example, the determination unit 132 calculates the average of the difference times between the time when the target vehicle is located at the departure point of the grid region A2 and the time when the target vehicle is located at the intersection of the boundary between the grid region A2 and the grid region B1 and the travel route RT1 as the actual required time x1' in the grid region A2.

[0032] Further, the determination unit 132 acquires information (such as a travel route ID) that can identify the travel route included in each of the plurality of performance information extracted, and extracts travel information regarding the travel route passing through the grid area A2 from the travel information in the storage unit 120. For example, the determination unit 132 extracts travel information regarding the travel route RT1 from the travel information. Subsequently, the determination unit 132 calculates, based on each of the plurality of pieces of extracted travel information, a predicted travel time x1 that is predicted to be required for the target vehicle to travel within the grid area A2. For example, the determination unit 132 calculates the predicted travel time required for the target vehicle to travel the travel route RT1, the total distance of the travel route RT1, and the distance (for example, the travel distance from the starting point of the grid area A2 to the intersection of the boundary between the grid area A2 and the grid area B1 and the travel route RT1) that the target vehicle travels within the grid area A2, and calculates a predicted travel time x1 (hereinafter, may be described as "predicted travel time x1 in the grid area A2") that is predicted to be required for the target vehicle to travel within the grid area A2.

[0033] Further, when the determination unit 132 calculates the predicted travel time x1 in the grid area A2, it calculates a predicted movement time y1 in the grid area A2 using the formula of a linear function represented by "y1 = a1 * x1 + b1". Specifically, the determination unit 132 substitutes the predicted travel time x1 into the formula of the linear function represented by "y1 = a1 * x1 + b1" to calculate the predicted movement time y1.

[0034] Further, when the determination unit 132 calculates the predicted travel time y1 in the grid area A2, it determines the travel time coefficient a1 and the boarding preparation coefficient b1 in the grid area A2 so as to reduce the magnitude of the difference between the predicted travel time y1 in the grid area A2 and the actual required time x1' in the grid area A2. For example, the determination unit 132 uses the least squares method to determine the travel time coefficient a1 and the boarding preparation coefficient b1 such that the sum of the squares of the differences between the predicted travel time y1 in the grid area A2 and the actual required time x1' in the grid area A2 is minimized. For example, the determination unit 132 avoids the situation where the predicted travel time y1 is shorter than the actual required time x1' by 5 minutes or more (a delay of 5 minutes or more), and under the second constraint condition that the boarding preparation coefficient b1 is a positive number, it solves the optimization problem of minimizing the sum of the squares of the differences between the predicted travel time y1 in the grid area A2 and the actual required time x1' in the grid area A2, thereby determining the travel time coefficient a1 and the boarding preparation coefficient b1 in the grid area A2.

[0035] As described above, the determination unit 132 determines the travel time coefficient a1 and the boarding preparation coefficient b1 in the grid area A2 based on the actual required time in the grid area A2. Similarly, the determination unit 132 determines the travel time coefficient a2 and the boarding preparation coefficient b2 in the grid area B1 based on the actual required time in the grid area B1. Further, the determination unit 132 determines the travel time coefficient a3 and the boarding preparation coefficient b3 in the grid area B2 based on the actual required time in the grid area B2. Further, the determination unit 132 determines the travel time coefficient a4 and the boarding preparation coefficient b4 in the grid area B3 based on the actual required time in the grid area B3.

[0036] In the above-described example, the determination unit 132 determines the correction coefficient for each grid area obtained by dividing the area related to the movement route into a grid for each region as the correction coefficient for each region. However, the correction coefficient for each region is not limited to this. For example, assume that the areas A to F shown in FIG. 5 are areas divided for each administrative region. At this time, the determination unit 132 determines the correction coefficient for each of the areas A to F divided for each administrative region based on the actual required time in each of the areas A to F divided for each administrative region as the correction coefficient for each region. Further, assume that the areas A to F shown in FIG. 5 are areas divided according to the characteristics of the areas such as the amount of traffic or the amount of crowding. At this time, the determination unit 132 determines the correction coefficient for each of the areas A to F divided according to the characteristics of the area based on the actual required time in each of the areas A to F divided according to the characteristics of the area.

[0037] In the above-described FIG. 3, the calculation unit 133 calculates the predicted travel time using the formula of the linear function represented by "y = a * x + b", and the determination unit 132 determines the travel time coefficient a and the boarding preparation coefficient b based on the actual required time. However, the calculation unit 133 may calculate the predicted travel time using a formula other than the formula of the linear function represented by "y = a * x + b", and the determination unit 132 may determine the correction coefficient included in a formula other than the formula of the linear function represented by "y = a * x + b" based on the actual required time. For example, the calculation unit 133 may calculate the predicted travel time y using a multi-dimensional function such as a quadratic function or a cubic function related to the predicted travel time x including the correction coefficient. Further, the determination unit 132 may determine the correction coefficient included in a multi-dimensional function such as a quadratic function or a cubic function based on the actual required time.

[0038] (Calculation unit 133) The calculation unit 133 calculates the predicted travel time based on the predicted travel time and the correction coefficient. Specifically, the calculation unit 133 calculates the predicted travel time based on the correction coefficient for each region. For example, the calculation unit 133 calculates the predicted travel time based on the correction coefficient for each grid region. For example, the calculation unit 133 calculates the predicted travel time for each grid region based on the correction coefficient for each grid region. Subsequently, the calculation unit 133 calculates the overall predicted travel time by adding up the predicted travel times for each grid region.

[0039] In FIG. 5, the calculation unit 133 calculates the predicted travel time y1'' in the grid region A2 based on the travel time coefficient a1 and the boarding preparation coefficient b1 in the grid region A2 determined by the determination unit 132 and the predicted travel time x1'' in the grid region A2. For example, the determination unit 132 substitutes the predicted travel time x1'' in the grid region A2 into the formula of the linear function represented by "y1 = a1 * x1 + b1" to calculate the predicted travel time y1'' in the grid region A2. Similarly, the calculation unit 133 calculates the predicted travel time y2'' in the grid region B1 based on the travel time coefficient a2 and the boarding preparation coefficient b2 in the grid region B1 determined by the determination unit 132 and the predicted travel time x2'' in the grid region B1. Further, the calculation unit 133 calculates the predicted travel time y3'' in the grid region B2 based on the travel time coefficient a3 and the boarding preparation coefficient b3 in the grid region B2 determined by the determination unit 132 and the predicted travel time x3'' in the grid region B2. Also, the calculation unit 133 calculates the predicted travel time y4'' in the grid region B3 based on the travel time coefficient a4 and the boarding preparation coefficient b4 in the grid region B3 determined by the determination unit 132 and the predicted travel time x4'' in the grid region B3.

[0040] Further, when the calculation unit 133 calculates the predicted travel time y1´´ in the grid area A2, the predicted travel time y2´´ in the grid area B1, the predicted travel time y3´´ in the grid area B2, and the predicted travel time y4´´ in the grid area B3, the calculation unit 133 adds the predicted travel time y1´´ in the grid area A2, the predicted travel time y2´´ in the grid area B1, the predicted travel time y3´´ in the grid area B2, and the predicted travel time y4´´ in the grid area B3 to calculate the predicted travel time that is predicted to be required for the target vehicle to move along the travel route corresponding to the travel route RT1.

[0041] In the above-described example, the case where the calculation unit 133 calculates the predicted travel time for each grid area based on the correction coefficient for each grid area and calculates the overall predicted travel time by adding the predicted travel times for each grid area has been described. However, the process in which the calculation unit 133 calculates the predicted travel time based on the correction coefficient for each grid area is not limited to this. For example, the calculation unit 133 calculates the average of the correction coefficients in a plurality of grid areas including the travel route based on the correction coefficient for each grid area. Subsequently, the calculation unit 133 calculates the predicted travel time that is predicted to be required for the target vehicle to move along the travel route corresponding to the travel route based on the average of the correction coefficients in the plurality of grid areas including the travel route.

[0042] Further, when the calculation unit 133 calculates the predicted travel time, the calculation unit 133 determines the operation schedule of the target vehicle based on the predicted travel time. For example, the acquisition unit 131 uses an existing technique for determining the operation schedule of the target vehicle based on the predicted travel time to determine the operation schedule of the target vehicle that transports more passengers in a shorter time.

[0043] (Provision unit 134) When the operation schedule of the target vehicle is determined by the calculation unit 133, the provision unit 134 transmits information regarding the operation schedule determined by the calculation unit 133 to the in-vehicle device mounted on the target vehicle. The in-vehicle device receives the information regarding the operation schedule from the information processing device 100. The target vehicle operates according to the operation schedule received by the in-vehicle device.

[0044] [3. Information Processing Procedure] FIG. 6 is a flowchart showing a processing procedure by the information processing apparatus according to the embodiment. In FIG. 6, the acquisition unit 131 of the information processing apparatus 100 acquires the predicted travel time calculated by a route search service that calculates the predicted travel time required to travel the travel route of the vehicle from the departure point to the destination (step S101). Further, the determination unit 132 of the information processing apparatus 100 determines a correction coefficient used when calculating the predicted travel time predicted to be required for the target vehicle to move along the movement route based on the actual required time, which is the time actually required for the target vehicle used in the on-demand vehicle dispatch service to move along the movement route corresponding to the travel route (step S102). Further, the calculation unit 133 of the information processing apparatus 100 calculates the predicted travel time based on the predicted travel time and the correction coefficient (step S103).

[0045] [4. Modification Example] The processing according to the above-described embodiment may be implemented in various different forms other than the above embodiment.

[0046] FIG. 7 is a diagram for explaining the process of determining the correction coefficient for each travel distance according to the modification example. In FIG. 7, the difference from the above-described embodiment is that the determination unit 132 determines the correction coefficient for each travel distance based on the actual required time for each travel distance of the target vehicle. Specifically, the determination unit 132 determines the correction coefficient for each travel section based on the actual required time for each travel section obtained by dividing the travel distance of the target vehicle into travel sections at predetermined distances. In FIG. 7, the travel distance of the target vehicle from the departure point to the destination is 15 km, and the correction coefficient for each travel section is determined based on the actual required time for each travel section obtained by dividing the travel distance of the target vehicle into travel sections every 5 km. Hereinafter, the travel section with a distance from the departure point of 0 to 5 km may be described as the first travel section, the travel section with a distance from the departure point of 5 to 10 km may be described as the second travel section, and the travel section with a distance from the departure point of 10 to 15 km may be described as the third travel section.

[0047] Specifically, the determination unit 132 determines a travel time coefficient a5 and a boarding preparation coefficient b5 in the first movement section based on the actual required time in the first movement section. For example, the determination unit 132 refers to the actual performance information in the storage unit 120 and extracts a plurality of pieces of actual performance information related to the travel route including the first movement section from the actual performance information. Subsequently, the determination unit 132 calculates an actual required time x5' required for the target vehicle to move in the first movement section based on each of the extracted plurality of pieces of actual performance information. For example, the determination unit 132 calculates an actual required time x5' required for the target vehicle to move in the first movement section (hereinafter, may be referred to as "the actual required time x5' in the first movement section") based on the position history information corresponding to the travel route including the first movement section and the distance of the first movement section. For example, the determination unit 132 calculates the actual required time x5' in the first movement section based on the departure time of the target vehicle and the time when the target vehicle is located at a point 5 km away from the departure place.

[0048] In addition, the determination unit 132 acquires information (such as a travel route ID) that can identify the travel routes included in each of the extracted plurality of pieces of actual performance information, and extracts travel information related to the travel route including the first movement section from the travel information in the storage unit 120. Subsequently, the determination unit 132 calculates a predicted travel time x5 predicted to be required for the target vehicle to travel in the first movement section based on each of the extracted plurality of pieces of travel information. For example, the determination unit 132 calculates a predicted travel time x5 predicted to be required for the target vehicle to travel in the first movement section (hereinafter, may be referred to as "the predicted travel time x5 in the first movement section") based on the predicted travel time required for the target vehicle to travel the travel route including the first movement section, the total distance of the travel route including the first movement section, and the distance (5 km) that the target vehicle travels in the first movement section among the travel route including the first movement section.

[0049] Further, when the determination unit 132 calculates the predicted travel time x5 in the first movement section, it calculates the predicted travel time y5 in the first movement section using the linear function formula represented by "y5 = a5 * x5 + b5". Specifically, the determination unit 132 substitutes the predicted travel time x5 into the linear function formula represented by "y5 = a5 * x5 + b5" to calculate the predicted travel time y5.

[0050] Also, when the determination unit 132 calculates the predicted travel time y5 in the first movement section, it determines the travel time coefficient a5 and the boarding preparation coefficient b5 in the first movement section so as to reduce the magnitude of the difference between the predicted travel time y5 in the first movement section and the actual required time x5' in the first movement section. For example, the determination unit 132 uses the least squares method to determine the travel time coefficient a5 and the boarding preparation coefficient b5 such that the sum of the squares of the differences between the predicted travel time y5 in the first movement section and the actual required time x5' in the first movement section is minimized. For example, the determination unit 132 avoids the situation where the predicted travel time y5 is shorter than the actual required time x5' by 5 minutes or more (a delay of 5 minutes or more) (the first constraint condition), and under the second constraint condition that the boarding preparation coefficient b5 is a positive number, solves the optimization problem of minimizing the sum of the squares of the differences between the predicted travel time y5 in the first movement section and the actual required time x5' in the first movement section, thereby determining the travel time coefficient a5 and the boarding preparation coefficient b5 in the first movement section.

[0051] As described above, the determination unit 132 determines the travel time coefficient a5 and the boarding preparation coefficient b5 in the first movement section based on the actual required time in the first movement section. Similarly, the determination unit 132 determines the travel time coefficient a6 and the boarding preparation coefficient b6 in the second movement section based on the actual required time in the second movement section. Further, the determination unit 132 determines the travel time coefficient a7 and the boarding preparation coefficient b7 in the third movement section based on the actual required time in the third movement section.

[0052] Further, the calculation unit 133 calculates the predicted travel time based on the correction coefficient for each travel distance. For example, the calculation unit 133 calculates the predicted travel time based on the correction coefficient for each travel section. For example, the calculation unit 133 calculates the predicted travel time for each travel section based on the correction coefficient for each travel section. Subsequently, the calculation unit 133 calculates the overall predicted travel time by adding up the predicted travel times for each travel section.

[0053] In the above example, the determination unit 132 determines the correction coefficient for each travel section obtained by dividing the travel distance of the target vehicle into sections of a predetermined distance as the correction coefficient for each travel distance. However, the correction coefficient for each travel distance is not limited to this. For example, the determination unit 132 determines the correction coefficient for each travel distance based on the actual required time for each travel distance of the target vehicle. For example, the determination unit 132 determines the correction coefficients for a plurality of different travel distances based on the actual required times for a plurality of different travel distances such as 5 km, 10 km, 15 km,....

[0054] Also, the determination unit 132 determines the correction coefficient for each category of facilities based on the actual required time for each category of facilities located within a predetermined range from the destination of the travel route. For example, the determination unit 132 identifies the category of facilities (e.g., hospital, airport, or commercial facility, etc.) located within a predetermined range from the destination of the travel route based on the map information including the travel route. Subsequently, the determination unit 132 determines the correction coefficient for each category of the identified facilities based on the actual required time for each category of the identified facilities. The calculation unit 133 calculates the predicted travel time based on the correction coefficient for each category of facilities.

[0055] Further, the determination unit 132 determines a correction coefficient for each number of passengers based on the actual required time for each number of passengers in the target vehicle. For example, the acquisition unit 131 acquires in advance the actual information including the number of passengers in the target vehicle and stores it in the storage unit 120. The determination unit 132 refers to the number of passengers in the target vehicle included in the actual information and determines a correction coefficient for each number of passengers based on the actual required time for each number of passengers in the target vehicle. The calculation unit 133 calculates the predicted travel time based on the correction coefficient for each number of passengers.

[0056] Further, the determination unit 132 determines a correction coefficient for each passenger attribute based on the actual required time for each passenger attribute in the target vehicle. For example, the acquisition unit 131 acquires in advance the actual information including the attribute information regarding the attributes of the passengers in the target vehicle and stores it in the storage unit 120. The determination unit 132 refers to the passenger attribute information of the target vehicle included in the actual information and determines a correction coefficient for each number of passengers based on the actual required time for each passenger attribute in the target vehicle. The calculation unit 133 calculates the predicted travel time based on the correction coefficient for each passenger attribute.

[0057] Further, the determination unit 132 determines a correction coefficient for each number of boarding / alighting points based on the number of boarding / alighting points where the passengers in the target vehicle board and alight. For example, the acquisition unit 131 acquires in advance the actual information including the number of boarding / alighting points where the passengers in the target vehicle board and alight and stores it in the storage unit 120. The determination unit 132 refers to the number of boarding / alighting points of the target vehicle included in the actual information and determines a correction coefficient for each number of passengers based on the actual required time for each number of boarding / alighting points of the target vehicle. The calculation unit 133 calculates the predicted travel time based on the correction coefficient for each number of boarding / alighting points.

[0058] 〔5. Effect〕 As described above, the information processing apparatus 100 according to the embodiment includes an acquisition unit 131, a determination unit 132, and a calculation unit 133. The acquisition unit 131 acquires a predicted travel time calculated by a route search service that calculates a predicted travel time required for a vehicle to travel along a travel route from a departure point to a destination. The determination unit 132 determines a correction coefficient used when calculating a predicted travel time predicted to be required for the target vehicle to actually move along a movement route corresponding to the travel route, based on the actual required time, which is the time required for the target vehicle used in the on-demand vehicle dispatch service to actually move along the movement route. The calculation unit 133 calculates a predicted travel time based on the predicted travel time and the correction coefficient.

[0059] Thereby, the information processing apparatus 100 can calculate a predicted travel time based on the correction coefficient determined based on the actual required time, so that the prediction accuracy of the predicted travel time predicted to be required for the target vehicle used in the on-demand vehicle dispatch service to move along the movement route can be improved.

[0060] Also, the determination unit 132 determines the correction coefficient so as to reduce the magnitude of the difference between the predicted travel time and the actual required time.

[0061] Thereby, the information processing apparatus 100 can calculate a predicted travel time based on the correction coefficient determined so as to reduce the magnitude of the difference between the predicted travel time and the actual required time, so that the prediction accuracy of the predicted travel time can be improved.

[0062] Also, the determination unit 132 determines a correction coefficient for each region based on the actual required time for each region related to the movement route. The calculation unit 133 calculates a predicted travel time based on the correction coefficient for each region.

[0063] Thereby, the information processing apparatus 100 can calculate a predicted travel time based on the correction coefficient determined based on the actual required time for each region related to the movement route, so that the prediction accuracy of the predicted travel time can be improved.

[0064] Further, the determination unit 132 determines a correction coefficient for each grid area based on the actual required time for each grid area obtained by dividing the area related to the movement route into a grid pattern. The calculation unit 133 calculates a predicted travel time based on the correction coefficient for each grid area.

[0065] As a result, the information processing apparatus 100 can calculate a predicted travel time based on the correction coefficient determined based on the actual required time for each grid area obtained by dividing the area related to the movement route into a grid pattern, so that the prediction accuracy of the predicted travel time can be improved.

[0066] Further, the determination unit 132 determines a correction coefficient for each travel distance based on the actual required time for each travel distance of the target vehicle. The calculation unit 133 calculates a predicted travel time based on the correction coefficient for each travel distance.

[0067] As a result, the information processing apparatus 100 can calculate a predicted travel time based on the correction coefficient determined based on the actual required time for each travel distance of the target vehicle, so that the prediction accuracy of the predicted travel time can be improved.

[0068] Further, the determination unit 132 determines a correction coefficient for each movement section based on the actual required time for each movement section obtained by dividing the travel distance of the target vehicle into predetermined distances. The calculation unit 133 calculates a predicted travel time based on the correction coefficient for each movement section.

[0069] As a result, the information processing apparatus 100 can calculate a predicted travel time based on the correction coefficient determined based on the actual required time for each movement section obtained by dividing the travel distance of the target vehicle into predetermined distances, so that the prediction accuracy of the predicted travel time can be improved.

[0070] Further, the determination unit 132 determines a correction coefficient for each category of facilities located within a predetermined range from the destination of the movement route based on the actual required time for each category of facilities. The calculation unit 133 calculates a predicted travel time based on the correction coefficient for each category of facilities.

[0071] As a result, the information processing apparatus 100 can calculate a predicted travel time based on a correction coefficient determined based on the actual required time for each category of facilities located within a predetermined range from the destination of the travel route, so that the prediction accuracy of the predicted travel time can be improved.

[0072] In addition, the determination unit 132 determines a correction coefficient for each number of passengers based on the actual required time for each number of passengers in the target vehicle. The calculation unit 133 calculates a predicted travel time based on the correction coefficient for each number of passengers.

[0073] As a result, the information processing apparatus 100 can calculate a predicted travel time based on a correction coefficient determined based on the actual required time for each number of passengers in the target vehicle, so that the prediction accuracy of the predicted travel time can be improved.

[0074] In addition, the determination unit 132 determines a correction coefficient for each attribute of passengers based on the actual required time for each attribute of passengers in the target vehicle. The calculation unit 133 calculates a predicted travel time based on the correction coefficient for each attribute of passengers.

[0075] As a result, the information processing apparatus 100 can calculate a predicted travel time based on a correction coefficient determined based on the actual required time for each attribute of passengers in the target vehicle, so that the prediction accuracy of the predicted travel time can be improved.

[0076] In addition, the determination unit 132 determines a correction coefficient for each number of boarding / alighting points based on the number of boarding / alighting points where the passengers of the target vehicle board and alight. The calculation unit 133 calculates a predicted travel time based on the correction coefficient for each number of boarding / alighting points.

[0077] As a result, the information processing apparatus 100 can calculate a predicted travel time based on a correction coefficient determined based on the number of boarding / alighting points where the passengers of the target vehicle board and alight, so that the prediction accuracy of the predicted travel time can be improved.

[0078] 〔6. Hardware Configuration〕 Further, the information processing apparatus 100 according to the above-described embodiments is realized by a computer 1000 having a configuration as shown in FIG. 8, for example. FIG. 8 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing apparatus. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0079] The CPU 1100 operates based on programs stored in the ROM 1300 or the HDD 1400, and controls each part. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, programs dependent on the hardware of the computer 1000, and the like.

[0080] The HDD 1400 stores programs executed by the CPU 1100, data used by such programs, and the like. The communication interface 1500 receives data from other devices via a predetermined communication network and sends it to the CPU 1100, and sends data generated by the CPU 1100 to other devices via a predetermined communication network.

[0081] The CPU 1100 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input devices via the input / output interface 1600. Further, the CPU 1100 outputs the generated data to the output devices via the input / output interface 1600. Note that an MPU (Micro Processing Unit) or a GPU (Graphics Processing Unit) may be used instead of the CPU 1100.

[0082] The media interface 1700 reads the program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads such a program from the recording medium 1800 onto the RAM 1200 via the media interface 1700 and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc), PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory, etc.

[0083] For example, when the computer 1000 functions as the information processing apparatus 100, the CPU 1100 of the computer 1000 realizes the functions of the control unit 130 by executing the program loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800. As another example, these programs may be acquired from other devices via a predetermined communication network.

[0084] As described above, some of the embodiments of the present application have been described in detail with reference to the drawings. However, these are merely examples, and the present invention can be implemented in other forms with various modifications and improvements based on the knowledge of those skilled in the art, including the aspects described in the column of the disclosure of the invention.

[0085] 〔7. Others〕 Also, among the respective processes described in the above embodiments and modification examples, all or part of the processes described as being automatically performed can be manually performed, or all or part of the processes described as being manually performed can be automatically performed by a known method. In addition, regarding the processing procedures, specific names, and information including various data and parameters shown in the above documents and drawings, they can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

[0086] In addition, each component of each illustrated device is functionally conceptual and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of it can be functionally or physically distributed and integrated in any unit according to various loads, usage situations, etc.

[0087] Also, the above-described embodiments and modifications can be appropriately combined as long as the processing contents do not conflict.

Description of Reference Numerals

[0088] 100 Information Processing Device 110 Communication Unit 120 Storage Unit 130 Control Unit 131 Acquisition Unit 132 Decision Unit 133 Calculation Unit 134 Provision Unit

Claims

1. An acquisition unit that acquires the predicted travel time calculated by a route search service that calculates the predicted travel time required to travel the travel route of a vehicle from a departure point to a destination; A determination unit that determines a correction coefficient used when calculating a predicted travel time predicted to be required for the target vehicle to move along the travel route, based on the actual travel time required for the target vehicle to actually move along the travel route corresponding to the travel route, which is used in an on-demand vehicle dispatch service; A calculation unit that calculates the predicted travel time based on the predicted travel time and the correction coefficient; An information processing apparatus comprising:

2. The determination unit: Determines the correction coefficient so as to reduce the magnitude of the difference between the predicted travel time and the actual travel time The information processing apparatus according to claim 1.

3. The determination unit: Determines the correction coefficient for each region based on the actual travel time for each region related to the travel route, The calculation unit: Calculates the predicted travel time based on the correction coefficient for each region The information processing apparatus according to claim 1.

4. The determination unit: Determines the correction coefficient for each grid region based on the actual travel time for each grid region obtained by dividing the region related to the travel route into a grid, The calculation unit: Calculates the predicted travel time based on the correction coefficient for each grid region The information processing apparatus according to claim 3.

5. The determination unit: Determines the correction coefficient for each travel distance based on the actual travel time for each travel distance of the target vehicle, The calculation unit: Calculate the predicted travel time based on the correction coefficient for each of the travel distances. The information processing apparatus according to claim 1.

6. The determination unit determines the correction coefficient for each of the travel intervals based on the actual required time for each of the travel intervals obtained by dividing the travel distance of the target vehicle into travel intervals of a predetermined distance, The calculation unit calculates the predicted travel time based on the correction coefficient for each of the travel intervals. The information processing apparatus according to claim 1.

7. The determination unit determines the correction coefficient for each category of facilities located within a predetermined range from the destination of the travel route based on the actual required time for each category of facilities, The calculation unit calculates the predicted travel time based on the correction coefficient for each category of facilities. The information processing apparatus according to claim 1.

8. The determination unit determines the correction coefficient for each number of passengers of the target vehicle based on the actual required time for each number of passengers, The calculation unit calculates the predicted travel time based on the correction coefficient for each number of passengers. The information processing apparatus according to claim 1.

9. The determination unit determines the correction coefficient for each attribute of the passengers of the target vehicle based on the actual required time for each attribute of the passengers, The calculation unit calculates the predicted travel time based on the correction coefficient for each attribute of the passengers. The information processing apparatus according to claim 1.

10. The determination unit Based on the number of boarding and alighting points for passengers of the target vehicle, determine the correction coefficient for each number of boarding and alighting points. The calculation unit Calculate the predicted travel time based on the correction coefficient for each number of boarding and alighting points. The information processing apparatus according to claim 1.

11. An information processing method realized by a program executed by an information processing apparatus, comprising: An acquisition step of acquiring the predicted travel time calculated by a route search service that calculates a predicted travel time required to travel a driving route of a vehicle from a departure point to a destination; A determination step of determining a correction coefficient used when calculating a predicted travel time predicted to be required for the target vehicle to move along the movement route based on the actual required time, which is the time required for the target vehicle used in an on-demand vehicle dispatch service to actually move along the movement route corresponding to the driving route; A calculation step of calculating the predicted travel time based on the predicted travel time and the correction coefficient; An information processing method including the above.

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