Information processing device, information processing method, and program

Abstract

To analyze traffic convenience.SOLUTION: An information processing device acquires result data representing movement results by a plurality of users, calculates any of a first evaluation value on convenience when the users move a prescribed section by private vehicles and a second evaluation value on convenience when the users move the prescribed section by public transportation facilities on the basis of at least the result data, and calculates a score indicative of a deviation degree between convenience when the users move by private vehicles and convenience when the users move by the public transportation facilities about a prescribed area on the basis of the first and second evaluation values.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a technology for analyzing transportation convenience. [Background technology]

[0002] There are technologies for improving the convenience of travel. In this regard, for example, Patent Document 1 discloses a system for determining the optimum vehicle to be placed at a car sharing station. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-67975 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure aims to analyze transportation convenience. [Means for solving the problem]

[0005] One aspect of an embodiment of the present disclosure is The information processing device has a control unit that performs the following operations: acquires performance data representing the travel performance of multiple users; calculates, based at least on the performance data, at least one of a first evaluation value regarding the convenience of traveling a specified section by private vehicle and a second evaluation value regarding the convenience of traveling the specified section by public transportation; and calculates, based on the first and second evaluation values, a score indicating the degree of discrepancy between the convenience of traveling by private vehicle and the convenience of traveling by public transportation for a specified area.

[0006] One aspect of an embodiment of the present disclosure is This information processing method includes the steps of: acquiring performance data representing the travel performance of a plurality of users; calculating, based at least on the performance data, at least one of a first evaluation value relating to the convenience of traveling a specified section by private vehicle and a second evaluation value relating to the convenience of traveling the specified section by public transportation; and calculating, based on the first and second evaluation values, a score indicating the degree of discrepancy between the convenience of traveling by private vehicle and the convenience of traveling by public transportation for a specified area.

[0007] Another aspect of the present invention is a program for causing a computer to execute the above-described method, or a computer-readable storage medium that non-transitoryly stores the program. [Effects of the Invention]

[0008] According to the present disclosure, it is possible to analyze the convenience of transportation. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a schematic diagram of a vehicle system according to a first embodiment. [Figure 2] FIG. 2 is a diagram showing components of an in-vehicle device. [Figure 3] 10 is an example of trip data transmitted from an in-vehicle device. [Figure 4] FIG. 2 is a diagram showing components of a server device. [Figure 5] FIG. 4 is a diagram for explaining divided unit areas. [Figure 6] FIG. 10 is a diagram showing the flow of a process for calculating a mobility gap score. [Figure 7A] FIG. 10 is a diagram illustrating a method for calculating a mobility gap score. [Figure 7B] FIG. 10 is a diagram illustrating a method for calculating a mobility gap score. [Figure 8] FIG. 10 is a diagram illustrating a method for calculating a mobility gap score. [Figure 9]FIG. 10 is a diagram illustrating a method for calculating a mobility gap score. [Figure 10] FIG. 10 is a diagram illustrating a method for calculating a mobility gap score. [Figure 11] 10 is a flowchart of a process executed by a server device. [Figure 12] 10 shows an example of weight data according to the second embodiment. [Figure 13] 10 shows an example of processing in step S17 in the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] There is a demand for improving the convenience of travel for users living in specific areas. The convenience of travel can be improved, for example, by shortening the time required to travel from a departure point to a destination or by shortening the time required to start traveling. Examples of measures to improve convenience include operating shared vehicles on demand and establishing new car sharing stations.

[0011] When making such plans, it is necessary to conduct a survey in advance to determine which areas have poor transportation access. However, it is difficult to determine whether the mobility needs of the people living there are being met based solely on information such as the presence or absence of public transportation. It is also difficult to quantitatively assess the extent to which convenience is impaired due to a lack of transportation options. The information processing device according to the present disclosure solves such a problem.

[0012] An information processing device according to a first aspect of the present disclosure has a control unit that performs the following operations: acquires performance data representing travel performance by multiple users; calculates, based at least on the performance data, at least one of a first evaluation value regarding the convenience of traveling a specified section by private vehicle and a second evaluation value regarding the convenience of traveling the specified section by public transportation; and calculates, based on the first and second evaluation values, a score indicating the degree of discrepancy between the convenience of traveling by private vehicle and the convenience of traveling by public transportation for a specified area.

[0013] The performance data is data representing the travel history of multiple users (typically residents; in this disclosure, this refers to people moving within an area). The performance data preferably includes location information of departure and arrival points. Such data may be transmitted from multiple vehicles, for example, private vehicles. For example, location information of the point where the driving system was started and the point where the driving system was shut down may be received from multiple vehicles. Note that the performance data does not necessarily have to include information about the departure and arrival points, as long as the user's movement can be tracked. In the following description, a combination of a departure point and a destination point will be referred to as a trip.

[0014] The control unit calculates, based on the performance data, at least one of an evaluation value (first evaluation value) regarding the convenience of traveling a specified trip by private vehicle and an evaluation value (second evaluation value) regarding the convenience of traveling the specified trip by public transportation. Convenience can be, for example, the time required for travel. In this case, the first and second evaluation values ​​are values ​​based on the travel time. Note that convenience is not limited to the time required for travel. For example, the evaluation value may also be the effort required for travel, the cost required for travel, the number of transfers, etc.

[0015] Based on the first and second evaluation values, the number of trips traveled by private vehicles is calculated for a particular trip. For example, it is possible to determine the degree of difference between the time required to travel by private vehicle and the time required to travel by public transportation. For example, suppose a trip requires 30 minutes by private vehicle and 60 minutes by public transportation. In this case, it can be said that there is a large discrepancy between the convenience of using a private vehicle and the convenience of using public transportation for that trip. In the following explanation, this discrepancy in convenience is referred to as the mobility gap. Furthermore, the value indicating the degree of discrepancy is referred to as the mobility gap score, or simply the score.

[0016] The mobility gap score can be calculated for each combination of departure and arrival points. In addition, when there are multiple pieces of actual data including multiple trips, multiple pieces of actual data that can be considered to have substantially the same departure and arrival points can be grouped, and a mobility gap score can be calculated for each group. Furthermore, for example, by integrating the mobility gap scores calculated for each group, it is possible to calculate the mobility gap score for an entire area.

[0017] By calculating such scores for each area, it is possible to identify areas where the convenience of using public transportation is lower than that of using a private vehicle (and vice versa), which makes it possible to develop plans to provide new transportation options (for example, by expanding car-sharing stations) to improve the convenience of travel. The private vehicle is typically an automobile, but may also be a small vehicle such as a personal mobility device.

[0018] Specific embodiments of the present disclosure will be described below with reference to the accompanying drawings. Unless otherwise specified, the hardware configuration, module configuration, functional configuration, etc. described in each embodiment are not intended to limit the technical scope of the disclosure to those configurations.

[0019] (First embodiment) An overview of a vehicle system according to a first embodiment will be described with reference to Fig. 1. The vehicle system according to this embodiment includes a vehicle 10 equipped with an on-vehicle device 100, and a server device 200. The system may include multiple vehicles 10 (on-vehicle devices 100).

[0020] The vehicle 10 is a vehicle for collecting data related to movement. The vehicle 10 is typically a private vehicle (i.e., a means of transportation that can start moving at any time). The vehicle 10 may be an autonomous vehicle or a vehicle driven by a driver. The vehicle 10 is configured to be able to wirelessly communicate with the server device 200 via the in-vehicle device 100, and can provide information to the server device 200.

[0021] The server device 200 is a device that evaluates the mobility gap score based on data collected from the vehicle 10. The mobility gap score is a numerical representation of the degree of difference in convenience between transportation modes such as private cars, which allow travel between any distance at any time, and transportation modes such as public transportation, which have fixed operating distances and schedules. For example, suppose that travelling between a certain distance takes 15 minutes by private car and 45 minutes by public transportation. In this case, it can be said that there is a large difference in convenience between the two modes. Similarly, suppose that travelling between another distance takes 15 minutes by private car and 15 minutes by public transportation. In this case, it can be said that there is no difference in convenience between the two modes. The server device 200 can calculate the mobility gap score by calculating the time required to travel a specified section by private car and the time required to travel the same section by public transportation.

[0022] The mobility gap score can be calculated for each combination of departure and arrival points. The server device 200 calculates the mobility gap score for multiple sections of travel history based on data collected from the vehicle 10, which is a private car. The server device 200 also integrates the calculated results to calculate an overall mobility gap score for a specified area. This makes it possible to quantitatively evaluate areas where public transportation is inconvenient.

[0023] In the vehicle system according to this embodiment, a plurality of in-vehicle devices 100 and a server device 200 are interconnected via a network. The network may be, for example, a wide area network (WAN), which is a global public communication network such as the Internet, or another communication network. The network may also include a telephone communication network such as a mobile phone network, or a wireless communication network such as Wi-Fi (registered trademark).

[0024] Each element that makes up the system will be explained. The vehicle 10 is a connected car that has a function of communicating with an external network. The vehicle 10 is equipped with an in-vehicle device 100.

[0025] The in-vehicle device 100 is a computer for collecting information. In this embodiment, the in-vehicle device 100 has a module for acquiring location information and transmits data including the acquired location information to the server device 200 at a predetermined timing. The in-vehicle device 100 may be a device that provides information to an occupant of the vehicle 10 (for example, a car navigation device), or may be an electronic control unit (ECU) included in the vehicle 10. The in-vehicle device 100 may also be a data communication module (DCM) having a communication function.

[0026] The in-vehicle device 100 can be configured as a computer having a processor such as a CPU or GPU, a main memory such as a RAM or ROM, and an auxiliary memory such as an EPROM, a hard disk drive, or removable media. The auxiliary memory stores an operating system (OS), various programs, various tables, etc., and by executing the programs stored therein, various functions that match predetermined purposes, as described below, can be realized. However, some or all of the functions may be realized by hardware circuits such as ASICs or FPGAs.

[0027] FIG. 2 is a diagram showing the system configuration of the in-vehicle device 100. As shown in FIG. The in-vehicle device 100 includes a control unit 101 , a storage unit 102 , a communication unit 103 , and a location information acquisition unit 104 .

[0028] The control unit 101 is a calculation unit that executes a predetermined program to realize various functions of the in-vehicle device 100. The control unit 101 may be realized by, for example, a CPU or the like. The control unit 101 is configured to have a data transmission unit 1011 as a functional module. The functional module may be realized by executing a stored program by a CPU.

[0029] The data transmission unit 1011 acquires the location information of its own device via the location information acquisition unit 104 (described later) at a predetermined timing, and transmits data including the location information to the server device 200.

[0030] In this embodiment, the data transmission unit 1011 acquires location information when the vehicle's traveling system is started and when the vehicle's traveling system is shut down. Also, the data transmission unit 1011 transmits data including this information to the server device 200 when the vehicle's traveling system is shut down. In the following description, a combination of a departure point and a destination point is referred to as a trip. That is, the data transmission unit 1011 transmits data for identifying a trip to the server device 200. The data generated by the data transmission unit 1011 is hereinafter referred to as trip data.

[0031] FIG. 3 is an example of trip data. As shown in the figure, the trip data includes a vehicle ID, departure date and time, arrival date and time, location information of the departure point, and location information of the arrival point. The vehicle ID field stores an identifier that uniquely identifies the vehicle. The departure date and time field stores the date and time when the vehicle 10 departs. The arrival date and time field stores the date and time when the vehicle 10 arrives. In the departure point field and the arrival point field, location information (for example, latitude and longitude) acquired by the location information acquisition unit 104 is stored.

[0032] In this example, the data transmission unit 1011 transmits the trip data when the vehicle's driving system is shut down, but the trip data may be transmitted at any timing. In this case, the trip data may include multiple records.

[0033] The storage unit 102 is a memory device including a main storage device and an auxiliary storage device. The auxiliary storage device stores an operating system (OS), various programs, various tables, etc., and by loading the programs stored therein into the main storage device and executing them, various functions that meet predetermined purposes, as will be described later, can be realized. The main memory may include RAM (Random Access Memory) and ROM (Read Only Memory). The auxiliary memory may include EPROM (Erasable Programmable ROM) and hard disk. It may also include a disk drive (HDD, Hard Disk Drive). may include removable media, i.e., portable recording media.

[0034] Furthermore, the memory unit 102 temporarily stores the trip data generated by the control unit 101.

[0035] The communication unit 103 is a wireless communication interface for connecting the in-vehicle device 100 to a network. The communication unit 103 is configured to be able to communicate with the server device 200 according to a communication standard such as mobile communication, wireless LAN, or Bluetooth (registered trademark).

[0036] The location information acquisition unit 104 includes a GPS antenna and a positioning module for determining location information. The GPS antenna is an antenna that receives positioning signals transmitted from positioning satellites (also called GNSS satellites). The positioning module is a module that calculates location information based on the signals received by the GPS antenna.

[0037] Next, the configuration of the server device 200 will be described. The server device 200 can be configured as a computer having a processor such as a CPU or GPU, a main memory such as a RAM or ROM, and an auxiliary memory such as an EPROM, a hard disk drive, or removable media. The auxiliary memory stores an operating system (OS), various programs, various tables, etc., and by executing the programs stored therein, various functions that match a predetermined purpose, as will be described later, can be realized. However, some or all of the functions may be realized by hardware circuits such as ASIC or FPGA. Note that the server device 200 can also be configured as a single computer. Alternatively, it may be configured by a plurality of computers that cooperate with each other.

[0038] 4 is a diagram showing the system configuration of the server device 200. The server device 200 includes a control unit 201, a storage unit 202, a communication unit 203, and an input / output unit 204.

[0039] The control unit 201 is an arithmetic unit that controls the server device 200. The control unit 201 can be realized by an arithmetic processing unit such as a CPU. The control unit 201 is configured to have three functional modules: a data collection unit 2011, a score calculation unit 2012, and a route search unit 2013. Each functional module may be realized by a CPU executing a program stored in an auxiliary storage means.

[0040] The data collection unit 2011 collects trip data transmitted from a plurality of vehicles 10 (on-vehicle devices 100) and stores the data in the storage unit 202, which will be described later.

[0041] The score calculation unit 2012 calculates a mobility gap score based on multiple trip data stored in the storage unit 202. Specifically, for each section indicated by the multiple trip data, the score calculation unit 2012 calculates the average travel time between a private car and public transportation. The ratio of the calculated average travel times is then used as the mobility gap score for that section. The mobility gap scores calculated for each section are then integrated to calculate the mobility gap score for a given area. The specific method will be described later.

[0042] To calculate the mobility gap score for a certain section, the travel time required to travel that section by private car and the travel time required to travel that section by public transportation are required. The travel time required to travel that section by private car indicated in the trip data is indicated in the trip data. On the other hand, the travel time required to travel that section by public transportation must be calculated using a route search service or the like. The route search unit 2013 is configured to be able to execute a route search service using public transportation, and in response to a route search request, finds the route and travel time required to travel a specified section by public transportation.

[0043] The storage unit 202 is configured to include a main storage device and an auxiliary storage device. The main storage device is a memory in which the programs executed by the control unit 201 and the data used by the control programs are expanded. The auxiliary storage device is a device in which the programs executed by the control unit 201 and the data used by the control programs are stored. The storage unit 202 stores trip data 202A and map data 202B.

[0044] The trip data 202A is a collection of a plurality of trip data transmitted from the in-vehicle device 100. The map data 202B is map data of an area in which the vehicle 10 can travel. In this embodiment, the map data 202B is divided into a plurality of unit areas as shown in Fig. 5. The unit areas may be divided by a grid, or may be divided based on geographical features. Furthermore, the unit areas may be divided by administrative divisions or buildings.

[0045] The communication unit 203 is a communication interface for connecting the server device 200 to a network. The communication unit 203 is, for example, a network interface board or a wireless communication The wireless communication circuit is configured to include a wireless communication circuit for the wireless communication.

[0046] The input / output unit 204 is a means for accepting input operations performed by the device user and presenting information. In this embodiment, it is made up of a single touch panel display. That is, it is made up of a liquid crystal display and its control means, and a touch panel and its control means.

[0047] 2 and 4 are merely examples, and all or part of the illustrated functions may be performed using dedicated circuits. Furthermore, programs may be stored or executed using a combination of a main memory device and an auxiliary memory device other than those illustrated.

[0048] Here, the flow of the processes executed by the data collection unit 2011, score calculation unit 2012, and route search unit 2013 will be described in more detail.

[0049] FIG. 6 is a diagram showing the flow of a process for calculating a mobility gap score based on trip data received from the vehicle 10. When the data collection unit 2011 receives the trip data from the in-vehicle device 100, it stores the trip data in the trip data 202A. The score calculation unit 2012 receives a period and area specification from the device user, acquires data on multiple trips that occurred in the area during that period, and calculates a mobility gap score corresponding to the area based on this data.

[0050] Next, a method for calculating the mobility gap score performed by the score calculation unit 2012 will be described. 7A is a diagram showing the relationship between departure points and destination points indicated by a plurality of trip data. As shown in the figure, each of the plurality of trip data has a different combination of departure point and destination point. First, the score calculation unit 2012 extracts, from the plurality of trip data, trip data that starts from a point within a target area for which a mobility gap score is to be calculated (hereinafter, target area).

[0051] Next, the score calculation unit 2012 associates the departure point and arrival point indicated by the extracted multiple trip data with one of multiple unit areas (see FIG. 5) defined by the map data 202B. Then, trips that depart from and arrive at the same unit area are grouped together. Herein, the unit area corresponding to the departure point is referred to as the departure area, and the unit area corresponding to the arrival point is referred to as the arrival area. Hereinafter, the combination of the departure area and the arrival area is referred to as OD (Origin-Destination). As a result, multiple trips are grouped as shown in Fig. 7B. Multiple trips classified into the same group are trips that can be considered to have the same departure and arrival points.

[0052] Next, an evaluation value is calculated for each of the multiple ODs. Figure 8 shows an example of calculating an evaluation value for a combination of area A1 as the departure area and area B1 as the arrival area. The score calculation unit 2012 first calculates the average travel time required to travel from the departure area to the destination area by car. The average travel time is obtained by averaging the travel times recorded in multiple trip data. The result is used as the first evaluation value.

[0053] Next, the score calculation unit 2012 calculates the average time required to travel from the departure area to the destination area by public transportation. This result is the second evaluation value. The departure time indicated in the trip data may be used as the departure time when searching for a route. For example, if there are five trips in the trip data, route searches may be performed five times assuming that the user departed at the same time, and the average of the obtained travel times may be calculated.

[0054] Next, the score calculation unit 2012 obtains a value obtained by dividing the first evaluation value by the second evaluation value, and sets the obtained value as a third evaluation value. The third evaluation value is greater than 1, indicating a higher convenience of public transportation, and is smaller than 1, indicating a higher convenience of private cars. Note that the third evaluation value may be calculated by any method other than the above, as long as it represents the ratio between the first evaluation value and the second evaluation value. For example, the third evaluation value may be calculated by dividing the second evaluation value by the first evaluation value. The score calculation unit 2012 executes the process shown in Fig. 8 for all OD combinations. As a result, a third evaluation value is obtained for each combination of departure area and arrival area, as shown in Fig. 9.

[0055] Next, the score calculation unit 2012 integrates the third evaluation values ​​thus obtained to calculate a mobility gap score corresponding to the specified area. FIG. 10 is a diagram illustrating the process of calculating the mobility gap score corresponding to a specified area. As described above, the multiple trip data to be processed all start within the specified area. Therefore, the mobility gap score corresponding to the specified area can be calculated by integrating the third evaluation values ​​(shown by the dotted line) calculated for each trip. The third evaluation values ​​may be integrated by averaging them.

[0056] Fig. 11 is a flowchart of the process described with reference to Fig. 7A to Fig. 10. The process shown in the figure is started by an operation by the user of the device. First, in step S11, the designation of an area (target area) for which the mobility gap score is to be calculated is accepted. The target area may be designated on a map or by other methods. For example, the target area may be set by designating an administrative division or a building name. Next, in step S12, trip data whose starting point is a point within the target area is extracted from the plurality of stored trip data.

[0057] Next, in step S13, the departure point included in the extracted trip data is associated with the unit area defined by map data 202B to form a departure area. Also, the arrival point included in the trip data is associated with the unit area defined by map data 202B to form a destination area. This generates a combination of departure area and destination area.

[0058] The processing of steps S14 to S16 is executed for each combination of departure area and arrival area (OD pair). First, in step S14, the average travel time for traveling the relevant OD by private car is calculated. The travel time can be determined from the date and time recorded in the trip data. This gives a first evaluation value. Next, in step S15, the average travel time when traveling the corresponding OD by public transportation is calculated. The travel time can be obtained by using a route search service provided by the route search unit 2013. This results in a second evaluation value. Note that the departure date and time recorded in the trip data may be used as the date and time when the travel begins. Next, in step S16, the first evaluation value is divided by the second evaluation value to obtain a third evaluation value. The acquired third evaluation value is stored in association with each OD pair, as shown in FIG.

[0059] In step S17, the obtained third evaluation values ​​are integrated to obtain a mobility gap score corresponding to the target area. The score obtained here is output via the input / output unit 204.

[0060] As described above, the vehicle system according to the first embodiment calculates the average travel time required to travel a specified section by private vehicle and the average travel time required to travel the same section by public transportation, based on the trip data transmitted from the vehicle 10. Based on this, a score is calculated that indicates the degree of discrepancy between the convenience of traveling by private vehicle and the convenience of traveling by public transportation for a specified area. This makes it possible to quantify for each area how inconvenient it is to travel by public transport compared to using a private car.

[0061] (Second embodiment) In the first embodiment, in step S12, all trip data originating from within the target area is acquired. However, the destinations of people in their daily lives can vary greatly depending on gender, age, occupation, etc. Therefore, the trip data may be filtered based on the user's attributes.

[0062] The second embodiment is an embodiment in which trip data is filtered using attributes of the user who made the trip. In the second embodiment, user attributes are associated with the trip data. The user attributes may be assigned by the in-vehicle device 100. In this case, a field for storing the user attributes is added to the trip data. The user attributes may also be assigned by the server device 200. In this case, a user identifier may be included in the trip data, and the server device 200 may assign the user attributes based on the identifier.

[0063] In the second embodiment, the specification of target user attributes is accepted in step S11, and trip data having the specified user attributes is extracted in step S12. This makes it possible to obtain a mobility gap score corresponding to a user having a specific attribute, such as age or gender (for example, "male aged 65 or older").

[0064] (Third embodiment) In the first embodiment, the third evaluation value is unconditionally integrated in step S17. However, the value of travel may differ depending on the person's attributes and destination. For example, in an area with a large elderly population, travel to a hospital may be more important than travel to other destinations. Also, in an area with a large student population, travel to an educational institution may be more important than travel to other destinations. In order to address this, the third embodiment is an embodiment in which weights are set when integrating the third evaluation values ​​depending on the type of facility at the destination (arrival point).

[0065] In the third embodiment, the server device 200 stores data (weight data) that defines weights used when integrating the third evaluation values. Fig. 12 shows an example of weight data. The weight data shown in the figure is data that associates attributes of facilities in the destination area with weights. For example, in the example shown in the figure, large weights are assigned to trips to educational institutions and trips to medical institutions.

[0066] FIG. 13 shows in detail the process executed in step S17 in the third embodiment. 1 is a flowchart. First, in step S171, a weight is determined for each of the multiple OD pairs associated with the third evaluation value. In this step, facilities included in the arrival area are determined based on the map data 202B, and if the facility is defined in the weight data, a corresponding weight is assigned. If multiple facilities apply, any of the weights may be used, or the largest weight may be used. If none of the facilities included in the arrival area are defined in the weight data, the weight is 1.0. This process is performed for all OD pairs. Then, in step S172, a weighted average is calculated using the determined weights to integrate the plurality of third evaluation values.

[0067] As described above, according to the third embodiment, the weight is determined based on the importance of the movement, so that the mobility gap score can be calculated more appropriately.

[0068] (Modification of the third embodiment) In the third embodiment, the weights are determined based on the attributes of the facilities included in the arrival destination area, but the weights may be determined based on other factors. For example, the weight may be determined based on the magnitude of demand for the facility. The magnitude of demand for the facility may be determined, for example, based on the number of people who visited the facility during a predetermined period in the past. This determination may be made using past trip data. For example, if the number of people who moved between an OD pair is greater than between other OD pairs, a higher weight may be assigned to the OD. In other words, an OD pair with more movement between the OD pairs may be considered more important.

[0069] In the third embodiment, the weight data is common, but the weight data may be used differently depending on the characteristics of the target area. For example, in an area where many elderly people live, a larger weight may be assigned to travel to medical institutions, and in an area where many children live, a larger weight may be assigned to travel to nurseries or schools. The weight data to be used for each area may be determined by the system or specified by the user of the device.

[0070] Weighting data may also be defined for each person's attribute. For example, when filtering trip data based on user attributes, weighting data corresponding to the specified user attributes may be obtained and used.

[0071] (Fourth embodiment) In the first to third embodiments, the mobility gap score is calculated using trip data transmitted from a vehicle. However, as long as the user's departure and arrival locations can be determined, it is not necessary to use data transmitted from the vehicle. For example, the user's departure and arrival locations, departure time, and arrival time may be determined based on location information transmitted from a mobile device carried by the user. Alternatively, the user's departure and arrival locations, departure time, and arrival time may be determined from the user's transportation card usage record, electronic payment record, or the like. Alternatively, the server device 200 may generate trip data based on these data.

[0072] In this embodiment, since there is no data related to travel by private car, the required time for calculating the first evaluation value cannot be obtained from the trip data. Therefore, the route search unit 2013 may calculate the route and required time for travel by private car.

[0073] (Other variations) The above-described embodiment is merely an example, and the present disclosure can be modified and implemented as appropriate within the scope that does not deviate from the gist of the disclosure. For example, the processes and means described in this disclosure can be freely combined and implemented as long as no technical contradiction occurs.

[0074] In the embodiment, trip data with a target area as a departure point is extracted from multiple trip data, but trip data with a target area as a destination may also be extracted, thereby making it possible to determine the mobility gap corresponding to traffic heading to the target area. By extracting trips that start from the target area, it is possible to obtain information about, for example, "places that are difficult for people without a car to live in." Also, by extracting trips that end in the target area, it is possible to obtain information about, for example, "places that are difficult to reach by means other than a car."

[0075] In addition, in the description of the embodiment, the target area is specified by the user of the device, but the target area may also be set automatically. For example, the process of calculating the mobility gap score may be repeated with each of the unit areas shown in FIG. 5 as the target area. This allows a heat map of the mobility gap score to be generated. In other words, it is possible to visualize which areas require additional transportation means.

[0076] In addition, in the description of the embodiment, the required time is used as the first evaluation value and the second evaluation value. However, the first evaluation value may be calculated using an index other than the required time, as long as the first evaluation value increases the more inconvenient it is to travel by private car. Such indexes may include travel costs (tolls, fuel costs, etc.) and variations in required time (probability of not arriving on schedule, etc.). Similarly, the second evaluation value may also be calculated using an index other than the required time, as long as the second evaluation value increases the more inconvenient it is to travel by public transportation. Such indexes may include travel costs (fares), the number of transfers, waiting times incurred during transfers, and variations in waiting times. The first and second evaluation values ​​may be calculated by calculating these multiple values ​​using a predetermined method.

[0077] Furthermore, a process described as being performed by one device may be shared and executed by multiple devices. Alternatively, a process described as being performed by different devices may be executed by a single device. In a computer system, the hardware configuration (server configuration) by which each function is realized can be flexibly changed.

[0078] The present disclosure can also be realized by providing a computer program implementing the functions described in the above embodiments to a computer, and having one or more processors in the computer read and execute the program. Such a computer program may be provided to the computer via a non-transitory computer-readable storage medium connectable to the computer's system bus or via a network. Non-transitory computer-readable storage media include, for example, any type of disk, such as a magnetic disk (e.g., a floppy disk, a hard disk drive (HDD), etc.), an optical disk (e.g., a CD-ROM, a DVD disk, a Blu-ray disk), a read-only memory (ROM), a random access memory (RAM), an EPROM, an EEPROM, a magnetic card, a flash memory, an optical card, or any type of medium suitable for storing electronic instructions. [Explanation of symbols]

[0079] 10. Vehicle 100...In-vehicle equipment 200 Server device 101,201 Control unit 102,202...Storage section 103,203···Communications Department 204...Input / output section 104...Location information acquisition unit

Claims

1. Designating a target area for calculating a mobility gap score that indicates the degree of discrepancy between the convenience of traveling by private vehicle and the convenience of traveling by public transportation; acquiring performance data representing travel performance by a plurality of users; extracting, from the performance data, performance data from a departure point to a destination, where either the departure point or the destination belongs to the target area; setting the area to which the departure point of the extracted performance data belongs as a departure point area among a plurality of predefined areas; setting the area to which the destination of the extracted performance data belongs, among the plurality of predefined areas, as the destination area; calculating, for each combination of the departure area and the arrival area, a first evaluation value regarding the convenience of traveling a predetermined section by private vehicle and a second evaluation value regarding the convenience of traveling a predetermined section by public transportation, based on the extracted performance data; calculating a third evaluation value, which is a ratio of the first evaluation value to the second evaluation value, for each combination of the departure area and the arrival area; When performance data in which the departure point belongs to the target area is extracted in the extraction of the performance data, the third evaluation value calculated for each combination is integrated based on the performance data in which the departure point belongs to the target area to calculate the mobility gap score corresponding to traffic departing from the target area; and When performance data in which the destination belongs to the target area is extracted in the extraction of the performance data, calculating the mobility gap score corresponding to traffic heading to the target area by integrating the third evaluation values ​​calculated for each of the combinations, the third evaluation values ​​being calculated based on performance data in which the destination belongs to the target area; a control unit that executes the following: Information processing device.

2. The performance data includes location information of a point where the driving system of the private vehicle is started and location information of a point where the driving system of the first vehicle is shut down. The information processing device according to claim 1 .

3. the first evaluation value is an average value of travel times required for the plurality of users to travel by private vehicle from the departure point to the destination indicated by the performance data; The information processing device according to claim 1 .

4. The second evaluation value is an average value of the travel time required to travel from the departure point to the arrival point indicated by the performance data by public transportation. The information processing device according to claim 1 .

5. and combining the third evaluation values ​​by assigning weights to the third evaluation values ​​according to the arrival destination area and then combining the third evaluation values. The information processing device according to claim 1 .

6. The weight is determined based on the type of facility included in the arrival area. The information processing device according to claim 5 .

7. The information processing device according to claim 5 , wherein the weight is determined based on the magnitude of demand for facilities included in the arrival destination area.

8. acquiring the performance data by acquiring the performance data corresponding to a user having a predetermined attribute; The information processing device according to claim 1 .

9. 1. A computer-implemented information processing method, comprising: Designating a target area for calculating a mobility gap score that indicates the degree of discrepancy between the convenience of traveling by private vehicle and the convenience of traveling by public transportation; acquiring performance data representing travel performance by a plurality of users; extracting, from the performance data, performance data from a departure point to a destination, where either the departure point or the destination belongs to the target area; setting the area to which the departure point of the extracted performance data belongs as a departure point area among a plurality of predefined areas; setting the area to which the destination of the extracted performance data belongs, among the plurality of predefined areas, as the destination area; calculating, for each combination of the departure area and the arrival area, a first evaluation value regarding the convenience of traveling a predetermined section by private vehicle and a second evaluation value regarding the convenience of traveling a predetermined section by public transportation, based on the extracted performance data; calculating a third evaluation value, which is a ratio of the first evaluation value to the second evaluation value, for each combination of the departure area and the arrival area; When performance data in which the departure point belongs to the target area is extracted in the extraction of the performance data, the third evaluation value calculated for each combination is integrated based on the performance data in which the departure point belongs to the target area to calculate the mobility gap score corresponding to traffic departing from the target area; and When performance data in which the destination belongs to the target area is extracted in the extraction of the performance data, calculating the mobility gap score corresponding to traffic heading to the target area by integrating the third evaluation values ​​calculated for each of the combinations, the third evaluation values ​​being calculated based on performance data in which the destination belongs to the target area; Including, Information processing methods.

10. The performance data includes location information of a point where the driving system of the private vehicle is started and location information of a point where the driving system of the first vehicle is shut down. The information processing method according to claim 9.

11. the first evaluation value is an average value of travel times required for the plurality of users to travel by private vehicle from the departure point to the destination indicated by the performance data; The information processing method according to claim 9.

12. The second evaluation value is an average value of the travel time required to travel from the departure point to the arrival point indicated by the performance data by public transportation. The information processing method according to claim 9.

13. and combining the third evaluation values ​​by assigning weights to the third evaluation values ​​according to the arrival destination area and then combining the third evaluation values. The information processing method according to any one of claims 9 to 12.

14. The weight is determined based on the type of facility included in the arrival area. The information processing method according to claim 13.

15. The information processing method according to claim 13 , wherein the weight is determined based on the magnitude of demand for facilities included in the arrival destination area.

16. acquiring the performance data by acquiring the performance data corresponding to a user having a predetermined attribute; 16. The information processing method according to any one of claims 9 to 15.

17. A program for causing a computer to execute an information processing method, The information processing method includes: Designating a target area for calculating a mobility gap score that indicates the degree of discrepancy between the convenience of traveling by private vehicle and the convenience of traveling by public transportation; acquiring performance data representing travel performance by a plurality of users; extracting, from the performance data, performance data from a departure point to a destination, where either the departure point or the destination belongs to the target area; setting the area to which the departure point of the extracted performance data belongs as a departure point area among a plurality of predefined areas; setting the area to which the destination of the extracted performance data belongs, among the plurality of predefined areas, as the destination area; calculating, for each combination of the departure area and the arrival area, a first evaluation value regarding the convenience of traveling a predetermined section by private vehicle and a second evaluation value regarding the convenience of traveling a predetermined section by public transportation, based on the extracted performance data; calculating a third evaluation value, which is a ratio of the first evaluation value to the second evaluation value, for each combination of the departure area and the arrival area; When performance data in which the departure point belongs to the target area is extracted in the extraction of the performance data, the mobility gap score corresponding to traffic departing from the target area is calculated by integrating the third evaluation values ​​calculated for each combination, the third evaluation values ​​being calculated based on performance data in which the departure point belongs to the target area. and When performance data in which the destination belongs to the target area is extracted in the extraction of the performance data, calculating the mobility gap score corresponding to traffic heading to the target area by integrating the third evaluation values ​​calculated for each of the combinations, the third evaluation values ​​being calculated based on performance data in which the destination belongs to the target area; Including, program.

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