Information Processing Apparatus, Information Processing Method, and Program

Abstract

To provide an appropriate service by using data collected from a vehicle.SOLUTION: An information processing device groups a plurality of pieces of location information acquired from a plurality of mobile terminals, and associates the group with one of a plurality of facilities based on a result of comparing the generated group and characteristic data representing a customer-attracting characteristic of each of the plurality of facilities.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a vehicle equipped with an in-vehicle device.

Background Art

[0002] There is a technique for determining to which area a moving object belongs based on the position information of the moving object (see, for example, Patent Document 1). Using such a technique, for example, it becomes possible to make a determination such as "the target vehicle is visiting a shopping center", and it becomes possible to provide an appropriate service according to the position of the moving object.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the development of machine learning, it is considered that the opportunities to utilize the data collected from vehicles will increase more and more in the future.

[0005] The present disclosure aims to provide an appropriate service using the data collected from a vehicle.

Means for Solving the Problems

[0006] One aspect of an embodiment of the present disclosure includes grouping a plurality of pieces of position information acquired from a plurality of mobile terminals, and based on a result of comparing the generated group with characteristic data representing the customer attraction characteristics of each of the plurality of facilities, associating the group with any one of the plurality of facilities. The information processing device has a control unit that executes the above.

[0007] One aspect of an embodiment of the present disclosure includes a step of grouping a plurality of location information obtained from a plurality of mobile terminals, and based on a result of comparing the generated group with characteristic data representing the customer attraction characteristics of each of the plurality of facilities, associating the group with any one of the plurality of facilities.

[0008] Also, as another aspect, there is a program for causing a computer to execute the above method, or a computer-readable storage medium that non-temporarily stores the program.

Advantages of the Invention

[0009] According to the present disclosure, appropriate services can be provided by using the data collected from vehicles.

Brief Description of the Drawings

[0010]

Figure 1

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Mode for Carrying Out the Invention

[0011] There is a technology for determining where a user is visiting based on the location information of the mobile terminal possessed by the user. For example, when the location information of the mobile terminal indicates a specific facility and the user has stayed there for a predetermined time or more, it can be determined that the user is visiting the facility.

[0012] On the other hand, in recent years, there has been a trend to provide services using location information obtained from vehicles. By determining "where a user has visited by car", it becomes possible to provide more useful services. Such services include, for example, services that provide incentives such as coupons and services that determine congestion levels.

[0013] However, the location information transmitted from the vehicle does not always match the facility visited by the user. For example, this is the case when a store and a parking lot are located in separate places. Since the location information transmitted from the vehicle is only the location information corresponding to the parking lot, it may not match the location information of the store. In such a case, there is a risk that it becomes impossible to determine where the user has visited. Therefore, it is necessary to perform a process of associating the location information transmitted from the vehicle with the facility.

[0014] This process will be described. FIG. 1 is a plan view showing the positional relationship between a store and a parking lot. Here, it is assumed that there are four stores, Stores A to D. Also, it is assumed that a parking lot is provided for each store. The black circles represent the positions of the vehicles that transmitted the position information. The dotted lines represent the result of grouping the position information transmitted from the vehicles. The groups can be generated by a known technique such as clustering, for example. Here, it is assumed that four groups, Groups A to D, are generated. By associating each group with Stores A to D within a predetermined distance threshold, it is possible to determine which store each vehicle user is visiting.

[0015] However, when this method is used, a case may occur where the group and the store are not correctly associated. For example, Store B is located at a position where the parking lot is farther away from the other stores. Therefore, when the same distance threshold is used for Store A and Store B, the vehicles belonging to Group B may be associated with Store A. On the other hand, when the distance threshold is increased, for example, there may be an adverse effect that the vehicles belonging to Group C are associated with Store D. That is, it is not appropriate to associate the facility and the parking lot only based on the length of the distance. The information processing apparatus according to the present disclosure solves such a problem.

[0016] An information processing apparatus according to an aspect of the present disclosure has a control unit that executes grouping a plurality of position information acquired from a plurality of mobile terminals, and associating the generated group with any one of the plurality of facilities based on a result of comparing the generated group with characteristic data representing the customer-attracting characteristics of each of the plurality of facilities.

[0017] A mobile terminal is a terminal that moves with a user, and is typically a terminal mounted on a vehicle. The control unit collects a plurality of location information and groups the collected location information according to predetermined parameters. The groups may be generated, for example, by performing clustering. As a result, one or more groups each including one or more mobile terminals can be generated.

[0018] In addition, the control unit associates each of the generated groups with any one of a plurality of facilities. The association can be performed based on characteristic data representing the customer-attracting characteristics of the facilities. Typically, the characteristic data is data representing "at which time period customer attraction is expected" or "at which time period, to what extent customer attraction is expected". The characteristic data can be, for example, the predicted congestion degree by time period, the actual congestion degree by time period, the predicted number of visitors by time period, etc. Such data may be obtained from an external device for each facility. Also, the characteristic data may be data regarding the business hours of the facility.

[0019] The control unit can determine whether the number of mobile terminals in the target group is in line with the customer-attracting characteristics of a specific facility by comparing the number of mobile terminals included in the target group with the characteristic data. For example, in the vicinity of Facility A, if a vehicle group is formed before the business start time of the facility, it can be determined that the vehicle group does not correspond to Facility A. Also, in the vicinity of Facility B, if a vehicle group is formed at the boundary of the business start time of the facility and disappears at the boundary of the business end time, it can be determined that the vehicle group corresponds to Facility B.

[0020] In this way, by using the characteristic data, it becomes possible to more accurately associate the groups with the facilities. Note that the association between the group and the facility may also be determined based on the degree of coincidence between the time-by-time transition of the number of mobile terminals included in the area corresponding to the group and the customer-attracting characteristics of the first facility by time period.

[0021] Furthermore, the control unit may generate a plurality of groups and determine parameters used for clustering so that the ratio of the groups that succeed in association with the facility is the highest among them.

[0022] Hereinafter, specific embodiments of the present disclosure will be described with reference to the drawings. The hardware configuration, module configuration, functional configuration, etc. described in each embodiment are not intended to limit the technical scope of the disclosure only to them unless otherwise specified.

[0023] (First Embodiment) The outline of the vehicle system according to the first embodiment will be described with reference to FIG. 2. The vehicle system according to this embodiment includes a vehicle 10 equipped with an in-vehicle device 100 and a server device 200. A plurality of vehicles (and in-vehicle devices 100) may be included in the vehicle system.

[0024] The vehicle 10 (in-vehicle device 100) is configured to acquire position information and transmit the acquired position information to the server device 200. In this embodiment, the in-vehicle device 100 transmits the position information to the server device 200 at the timing when the vehicle 10 finishes traveling. The timing when traveling ends can be, for example, the timing when the vehicle's travel system is shut down (system power is cut off).

[0025] The server device 200 executes processing for associating position information with facilities based on the position information acquired from a plurality of vehicles (in-vehicle devices 100). Thereby, for example, it becomes possible to make a determination such as "when a certain vehicle finishes traveling and stays within area A, the user of the vehicle is visiting facility B". Also, by providing this information to an external device, the external device can provide an appropriate service to the user of the vehicle.

[0026] Each element constituting the system will be described. Vehicle 10 is a connected car having a communication function with an external network. Vehicle 10 includes an in-vehicle device 100.

[0027] The in-vehicle device 100 is a computer mounted on the vehicle. The in-vehicle device 100 may be a device that provides information to the vehicle occupants (e.g., a car navigation device), or may be other electronic control units (ECUs). Further, the in-vehicle device 100 may be a data communication module (DCM) having a communication function. The in-vehicle device 100 has a function of performing wireless communication with an external network. The in-vehicle device 100 may have a function of downloading traffic information, road map data, music, moving images, etc. by communicating with the external network of the vehicle 10. Further, the in-vehicle device 100 may be a device that can cooperate with a smartphone or the like.

[0028] The in-vehicle device 100 can be configured by a general-purpose computer. That is, the in-vehicle device 100 can be configured as a computer having processors such as a CPU and a GPU, a main storage device such as a RAM and a ROM, and auxiliary storage devices such as an EPROM, a hard disk drive, and a removable medium. The auxiliary storage device stores an operating system (OS), various programs, various tables, etc., and by executing the programs stored therein, various functions that meet a predetermined purpose as described later can be realized. However, some or all of the functions may be realized by a hardware circuit such as an ASIC or an FPGA.

[0029] Figure 3 is a diagram showing in detail the components of the in-vehicle device 100 included in the vehicle system according to the present embodiment. The in-vehicle device 100 includes a control unit 101, a storage unit 102, a communication unit 103, a wireless communication unit 104, and a position information acquisition unit 105.

[0030] The control unit 101 is an arithmetic unit that realizes various functions of the in-vehicle device 100 by executing a predetermined program. The control unit 101 may be realized by, for example, a CPU or the like. The control unit 101 is configured to include a data transmission unit 1011 as a functional module. The functional module may be realized by executing the stored program by the CPU.

[0031] The data transmission unit 1011 acquires the position information of its own device via a position information acquisition unit 105 described later at a predetermined timing, and transmits data (vehicle data) including the position information to the server device 200. In the present embodiment, the predetermined timing is the timing when the vehicle's driving system is shut down. In other words, the predetermined timing can be the timing when the vehicle 10 arrives at the destination.

[0032] FIG. 4 is an example of vehicle data. As shown in the figure, the vehicle data includes fields of vehicle ID, date and time information, position information, and status. In the vehicle ID field, an identifier that uniquely identifies the vehicle is stored. In the date and time information field, the date and time when the vehicle data is generated is stored. In the position information field, the position information (for example, latitude and longitude) acquired by the position information acquisition unit 105 is stored. In the status field, data related to the state of the vehicle is stored.

[0033] Note that in this example, the data transmission unit 1011 transmits the vehicle data at the timing when the vehicle's driving system is shut down, but the vehicle data may be transmitted periodically during driving. In this case, information related to the state of the vehicle's driving system may be stored in the status field. Thereby, the server device 200 can identify the timing when the driving system of the vehicle 10 is shut down.

[0034] The storage unit 102 is a means for storing information, and is composed of a storage medium such as a RAM, a magnetic disk, or a flash memory. The storage unit 102 stores various programs executed by the control unit 101, data used by the programs, and the like.

[0035] The communication unit 103 is a communication interface that connects the in-vehicle device 100 to the bus of the in-vehicle network.

[0036] The wireless communication unit 104 includes an antenna and a communication module for performing wireless communication. The antenna is an antenna element that inputs and outputs wireless signals. In this embodiment, the antenna is compatible with mobile communication (for example, mobile communication such as 3G, LTE, 5G, etc.). Note that the antenna may be configured to include a plurality of physical antennas. For example, when performing mobile communication using radio waves in a high-frequency band such as microwaves or millimeter waves, a plurality of antennas may be distributed and arranged in order to improve communication stability. The communication module is a module for performing mobile communication.

[0037] The position information acquisition unit 105 includes a GPS antenna and a positioning module for positioning position information. The GPS antenna is an antenna that receives a positioning signal transmitted from a positioning satellite (also referred to as a GNSS satellite). The positioning module is a module that calculates position information based on the signal received by the GPS antenna.

[0038] Next, the server device 200 will be described. The server device 200 collects position information from the vehicle 10 (in-vehicle device 100), and based on the collected position information, associates the position information with facilities. Specifically, a predetermined facility is associated with an area estimated to be the parking lot of the facility, and the result is output. For example, in the example of FIG. 1, a determination such as "area A corresponds to store A, area B corresponds to store B..." is made, and data recording the correspondence relationship is output.

[0039] FIG. 5 is a diagram showing in detail the components of the server device 200 included in the vehicle system according to the present embodiment.

[0040] The server device 200 can be configured by a general-purpose computer. That is, the server device 200 can be configured as a computer having a processor such as a CPU or GPU, a main storage device such as a RAM or ROM, and an auxiliary storage device such as an EPROM, a hard disk drive, or a removable medium. The auxiliary storage device stores an operating system (OS), various programs, various tables, etc. The programs stored therein are loaded into the working area of the main storage device and executed, and by controlling each component through the execution of the programs, various functions that meet a predetermined purpose, as described later, can be realized. However, some or all of the functions may be realized by a hardware circuit such as an ASIC or FPGA.

[0041] The server device 200 is configured to include a control unit 201, a storage unit 202, and a communication unit 203. The control unit 201 is an arithmetic unit that controls the operations performed by 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 include a data collection unit 2011, a group generation unit 2012, and an association unit 2013 as function modules. Each function module may be realized by executing a stored program by the CPU.

[0042] The data collection unit 2011 collects vehicle data from a plurality of vehicles 10 (in-vehicle devices 100) and executes a process of storing the vehicle data as vehicle data 202B in a storage unit 102 described later.

[0043] The group generation unit 2012 groups a plurality of collected location information and generates a plurality of groups. The generation of the groups can be performed using a known method such as clustering. Since the location information is transmitted at the timing when the vehicle 10 arrives at the destination, the group generation unit 2012 can generate a group consisting of a plurality of vehicles parked in a parking lot.

[0044] The association unit 2013 associates the generated groups with the facilities. For example, in the example of FIG. 1, the areas (illustrated by thick frames) included in each group are associated with the facilities. The result of the association may be output in a predetermined format. This enables determination of which facility users the vehicles parked in a predetermined area (area A, area B,... in FIG. 1) belong to.

[0045] 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 programs executed by the control unit 201 and data used by the control programs are expanded. The auxiliary storage device is a device in which programs executed in the control unit 201 and data used by the control programs are stored.

[0046] In addition, facility data 202A and vehicle data 202B are stored in the storage unit 202. The facility data 202A is a set of data regarding a plurality of facilities under the management of the system. FIG. 6(A) is an example of the facility data 202A. As illustrated, the facility data includes fields for a facility ID, location information, and characteristic data. In the facility ID field, an identifier that uniquely identifies the facility is stored. The facility may be a store, a public facility, or other facilities, etc. In the location information field, the location information of the store is stored.

[0047] In the characteristic data field, data regarding the customer attraction characteristics of the facility is stored. Here, the customer attraction characteristics of the facility will be described. In the present embodiment, as the customer attraction characteristics of the facility, data representing "how much customer attraction is expected for each time period" is used. Note that the data regarding the customer attraction characteristics can be, for example, any of the following. (Type 1) Data representing in which time periods there are customers (Type 2) Data representing in which time periods there are no customers (Type 3) Data representing the distribution of visitors (congestion level) throughout the day

[0048] Figure 7(A) is an example of data corresponding to the above (Type 1) and (Type 2). Such data includes, for example, data representing the business hours of the facility. In this example, visitors are expected between 10:00 am and 9:00 pm, and it is shown that there are no visitors during other time periods. Figure 7(B) is an example of data corresponding to the above (Type 3). An example of such data is congestion level data based on past performance. The congestion level can be generated, for example, based on location information transmitted from a plurality of mobile terminals possessed by visitors. Such data may be obtained, for example, from an external device that provides congestion level information. Such data is stored for each facility in the characteristic data field. The usage method of the data will be described later.

[0049] If the customer attraction characteristics of the facility vary depending on the date or day of the week, characteristic data may be defined for each date or day of the week. For example, on the facility's regular holiday, the customer attraction is zero. Figure 6(B) is an example of facility data when characteristic data is defined for each date or day of the week. For example, a plurality of characteristic data may be defined, such as Sunday, Saturday, and weekdays, and appropriate ones may be used.

[0050] Vehicle data 202B is a collection of a plurality of vehicle data transmitted from in-vehicle device 100. The vehicle data 202B stores the plurality of vehicle data described in FIG. 4.

[0051] Communication unit 203 is a communication interface for connecting server device 200 to a network. Communication unit 203 includes, for example, a network interface board or a wireless communication interface for wireless communication.

[0052] Note that the configurations shown in FIGS. 3 and 5 are examples, and all or part of the illustrated functions may be executed using a dedicatedly designed circuit. Also, storage or execution of a program may be performed by a combination of a main storage device and an auxiliary storage device other than those illustrated.

[0053] Next, a method for associating the generated group with a facility will be described. FIG. 8 is a schematic diagram for explaining a method for associating a group with a facility. In the present embodiment, as shown in FIG. 8(A), at a predetermined timing, the group generation unit 2012 groups the position information. Then, as shown in FIG. 8(B), the association unit 2013 attempts to associate each generated group with a facility. At this time, the association unit acquires the temporal transition of the number of vehicles included in the group and determines whether this matches the customer attraction characteristics of the facility.

[0054] This will be specifically described. FIGS. 9 and 10 are diagrams showing the relationship between the number of vehicles included in a region corresponding to a certain group and the customer attraction characteristics of a certain facility defined in the facility data. The solid line represents the customer attraction degree corresponding to the facility, and the dotted line represents the transition of the number of vehicles included in the group. In other words, it can be said that the number of vehicles included in the group is a value proportional to the number of vehicles parked in the parking lot.

[0055] FIG. 9 is an example when data regarding the business hours of a facility is used as the facility data. Here, as a result of comparing a certain store A, assume that the result as shown in FIG. 9(A) is obtained. In this example, the number of vehicles starts to increase at the opening of the store and becomes zero at the closing of the store. That is, it can be said that both match. On the other hand, as a result of comparing another store B, assume that the result as shown in FIG. 9(B) is obtained. In this example, vehicles are parked before the opening of the store and do not become zero even after the closing of the store. That is, it can be said that there is a contradiction between the two. Here, when there are two candidate facilities for association (Store A and Store B) for a target group, it can be determined that the group corresponds to Store A.

[0056] FIG. 10 is an example when data regarding the congestion level of a facility is used as facility data. Here, as a result of making a comparison for a certain Store C, assume that a result as shown in FIG. 10(A) is obtained. In this example, the transition of the number of parked vehicles and the transition of the congestion level in the store are synchronized. In such a case, it can be said that both are consistent. On the other hand, as a result of making a comparison for another Store D, assume that a result as shown in FIG. 10(B) is obtained. In this example, the transition of the number of parked vehicles and the transition of the congestion level in the store are not synchronized. For example, while the congestion level of the store peaks from noon to afternoon, the number of parked vehicles peaks before noon and at dusk. In such a case, it can be said that there is a contradiction between the two. Here, when there are two candidate facilities for association (Store C and Store D) for a target group, it can be determined that the group corresponds to Store C.

[0057] Note that which facility the target group corresponds to may be determined based on the degree of agreement between the two. For example, the transitions of the congestion level and the number of vehicles may be normalized to calculate the degree of agreement between the two.

[0058] In this way, the association unit 2013 acquires the temporal transition of the number of vehicles included in the group, collates this with the customer attraction characteristics of a plurality of candidate facilities, and associates them with each other when a degree of agreement equal to or higher than a predetermined value is obtained. By performing this process for each group, the area (parking lot) where the vehicle is parked can be associated with the facility.

[0059] Next, a flowchart of the processing executed by each device will be described. FIG. 11 is a sequence diagram of a process in which the in-vehicle device 100 and the server device 200 transmit and receive vehicle data. The illustrated process is repeatedly executed at a predetermined cycle while the vehicle 10 is running.

[0060] First, in step S11, the data transmission unit 1011 acquires the position information of the host vehicle via the position information acquisition unit 105. In the case of adopting a configuration in which data other than the position information is transmitted to the server device 200, other sensor data may be acquired in this step. In step S12, the data transmission unit 1011 determines whether or not the running of the vehicle 10 has ended. Whether or not the running of the vehicle 10 has ended can be determined based on, for example, data transmitted by an electronic control unit (ECU) that controls the vehicle platform. For example, when data indicating that an operation to cut off the system power of the vehicle 10 has been performed is detected from the in-vehicle network, this step results in an affirmative determination. If this step results in an affirmative determination, the process proceeds to step S13. If this step results in a negative determination, the process returns to step S11.

[0061] In step S13, the data transmission unit 1011 generates vehicle data and transmits it to the server device 200. As shown in FIG. 4, the vehicle data includes the position information acquired in step S11. In step S14, the server device 200 (data collection unit 2011) receives the vehicle data transmitted from the in-vehicle device 100 and stores it in the storage unit 202. As a result, position information received from a plurality of vehicles 10 is accumulated in the storage unit 202 of the server device 200 at any time.

[0062] Next, the process executed by the server device 200 will be described. FIG. 12 is a flowchart of the process executed by the server device 200. The illustrated process starts from a state where a sufficient amount of vehicle data is stored in the storage unit 202. stored in the storage unit 202.

[0063] First, in step S21, the group generation unit 2012 clusters a plurality of position information included in the collected vehicle data with a predetermined parameter. As a result, a plurality of clusters each including a plurality of position information are generated. Note that when the date of the vehicle data spans multiple days, the process is executed for any one of the dates. This is because if the data of multiple dates is mixed, the transition of the customer acquisition characteristics cannot be correctly determined.

[0064] The processes of steps S22 to S25 are sequentially executed for each of the generated clusters. In step S22, the association unit 2013 acquires the temporal transition of the congestion level for the cluster to be processed. For example, when clustering the position information collected from midnight to midnight of the next day, for each predetermined time slot (e.g., one hour), it is determined how many vehicles are included in the area corresponding to the cluster to be processed. As a result, data as shown by the dotted line in FIG. 9 (hereinafter, transition data) is obtained.

[0065] Next, in step S23, the association unit 2013 extracts candidates for facilities corresponding to the cluster to be processed. In this step, for example, the position representing the cluster is compared with the position information of the facilities included in the facility data 202A, and one or more facilities within a predetermined threshold (e.g., 500 meters) from both are extracted.

[0066] Next, in step S24, the association unit 2013 calculates the degree of coincidence between the transition data corresponding to the target cluster and the customer acquisition characteristics of one or more facilities extracted in step S23. Here, when there are a plurality of extracted facilities, the degree of coincidence is calculated for each facility. The degree of coincidence can be obtained, for example, by a cross-correlation function (CCF) or the like. Note that when the customer acquisition characteristics vary depending on the date or day of the week, the association unit 2013 may select characteristic data that conforms to the date or day of the week as described with reference to FIG. 6(B).

[0067] In step S25, the association unit 2013 associates and stores a facility with a degree of coincidence equal to or higher than a predetermined value and a region corresponding to the cluster. For example, among the plurality of extracted facilities, it is determined that the facility with the highest degree of coincidence is the facility corresponding to the cluster to be processed.

[0068] In this example, the association is made only based on the degree of coincidence, but the facility to be associated may be determined in consideration of other elements. For example, a score may be calculated based on both "(the aforementioned) degree of coincidence" and "the distance between the cluster and the facility", and if there is a facility that has obtained a score equal to or higher than a predetermined value, the facility may be associated. The score may be set to be higher, for example, when the degree of coincidence is higher and the distance between the cluster and the facility is shorter. Also, when the degree of coincidence or the score does not meet a predetermined standard, it may not be necessary to associate the corresponding cluster with a facility. For example, this is the case when "the number of vehicles included in the cluster is extremely small" or "a cluster has been formed even though the facility is closed on a regular holiday". In such a case, it may be determined that the cluster does not correspond to a facility (for example, a coin parking lot or a monthly parking lot).

[0069] By repeating the processes of steps S22 to S25, a facility is associated with each of the plurality of generated clusters.

[0070] In step S26, the ratio of the clusters that have succeeded in the association among the plurality of clusters is determined. Here, when the ratio of the clusters that have succeeded in the association does not satisfy a predetermined value, it can be presumed that the parameters used in the clustering were inappropriate in the first place. For example, if the generated cluster is too small, as shown in Fig. 13(A), even though it is a parking lot of the same facility, multiple clusters may be generated. Also, if the generated cluster is too large, as shown in Fig. 13(B), vehicles parked in multiple facilities may be determined to correspond to the same facility. In either case, the cluster and the facility are not correctly associated.

[0071] To avoid this problem, it is necessary to identify the parameter that can be most suitably associated. In this embodiment, when the ratio of the clusters successfully associated is below a predetermined value, in step S27, the parameter used for clustering is changed and the process is attempted again. For example, the parameter is changed so that the cluster becomes larger (smaller), and the process is returned to step S21. Thereby, a more appropriate parameter can be obtained. In this example, although at least one parameter that satisfies the predetermined value of the ratio of the clusters successfully associated is identified, multiple parameters may be applied to perform the processes of S21 to S26, and the parameter with the best performance may be identified. That is, the parameter that maximizes the ratio of the clusters successfully associated for all clusters may be searched for.

[0072] When the process ends, the association unit 2013 outputs the processing result. The output may be performed via an input / output unit (for example, a display), via a network, or via a storage medium. Fig. 14 is an example of the output data (result data). In the illustrated example, the association unit 2013 generates and outputs result data including fields of processing date and time, processing target date, area, and facility identifier. In the area field, data for identifying the area corresponding to the cluster (that is, the area estimated to be the parking lot of the facility) is stored. The area can be an area inscribed in the cluster, an area circumscribing the cluster, an area within a predetermined range from the center point of the cluster, etc.

[0073] By using the result data, it becomes possible to accurately identify which facility a vehicle parked at a certain location is using. The result data may be transmitted to an external device that provides services to the vehicle or the user.

[0074] As described above, the server device according to the first embodiment generates a group including parked vehicles by clustering the location information collected from a plurality of vehicles. Further, by comparing the number of vehicles included in the group with the characteristic data, an association with a facility is made for each group. Thereby, it becomes possible to specify the positional relationship between the facility and the parking lot. Also, it becomes possible to accurately identify a vehicle visiting a predetermined facility.

[0075] (Modification of the First Embodiment) In the first embodiment, location information is acquired from the vehicle that has arrived at the destination, but if it is location information transmitted from a mobile terminal, the system can also be applied to other cases. For example, when a navigation application is operating on a mobile terminal, location information may be transmitted from the mobile terminal to the server device 200 at the timing when the guidance ends.

[0076] Also, in the first embodiment, the processing was performed for a single date, but the above-described processing may be performed for a plurality of dates. In this case, result data can be generated for each date. Also, the result data for each date may be integrated to generate one result data.

[0077] When generating result data for each date, a plurality of areas estimated to be the parking lot of a certain facility are generated Therefore, based on these results, the correspondence relationship between the facility and the parking lot may be determined. For example, the above-described processing is performed based on data for seven days, and if the same facility and parking lot are associated for all seven days (or for a predetermined number of days or more), their correspondence relationship may be determined. On the contrary, if there is even one contradiction, such as a cluster being formed on a day when the facility is closed, the correspondence relationship may be discarded. As a result, it becomes possible to accurately identify the parking lot of the facility.

[0078] (Modification example) The above embodiments are merely examples, and the present disclosure can be appropriately modified and implemented without departing from the gist thereof. For example, the processes and means described in the present disclosure can be freely combined and implemented as long as no technical contradiction occurs.

[0079] Also, the processes described as being performed by one device may be shared and executed by a plurality of devices. Alternatively, the processes described as being performed by different devices may be executed by one device. In a computer system, it is possible to flexibly change how each function is realized by a hardware configuration (server configuration).

[0080] The present disclosure can also be realized by supplying a computer program that implements the functions described in the above embodiments to a computer and causing one or more processors included in the computer to read and execute the program. Such a computer program may be provided to the computer by a non-transitory computer-readable storage medium connectable to the system bus of the computer, or may be provided to the computer via a network. The non-transitory computer-readable storage medium includes, for example, any type of disk such as a magnetic disk (floppy (registered trademark) disk, hard disk drive (HDD), etc.), an optical disk (CD-ROM, DVD disk, Blu-ray disk, etc.), a read-only memory (ROM), a random access memory (RAM), an EPROM, an EEPROM, a magnetic card, a flash memory, an optical card, and any type of medium suitable for storing electronic instructions.

Explanation of reference numerals

[0081] 10 ··· Vehicle 100 ·· In-vehicle device 200 ·· Server device 101, 201 ··· Control unit 102, 202 ··· Memory unit 103, 203 ··· Communication unit 104 ··· Wireless communication unit 105 ··· Location information acquisition unit

Claims

1. Grouping a plurality of pieces of location information acquired from a plurality of mobile terminals into a plurality of groups by clustering using a predetermined parameter; Based on the result of comparing the generated groups with characteristic data representing the customer attraction characteristics of each of the plurality of facilities, associating each of the plurality of groups with any one of the plurality of facilities; having a control unit that executes; The control unit determines whether the ratio of the groups that have been successfully associated among the plurality of groups exceeds a predetermined value; When the ratio of the groups that have been successfully associated is below the predetermined value, the parameter is changed so that the size of the group changes. An information processing apparatus.

2. The control unit performs the association based on the degree of coincidence between the number of mobile terminals included in the group and the customer attraction characteristics of each of the plurality of facilities. The information processing apparatus according to claim 1.

3. The characteristic data is data representing the customer attraction characteristics of each of the plurality of facilities by time zone. The information processing apparatus according to claim 2.

4. The characteristic data is data regarding the business hours of each of the plurality of facilities. The information processing apparatus according to claim 3.

5. The characteristic data is data regarding the congestion status of each of the plurality of facilities by time zone. The information processing apparatus according to claim 3.

6. The control unit acquires the characteristic data from an external device. The information processing apparatus according to any one of claims 3 to 5.

7. The control unit performs the association based on the change over time in the number of mobile terminals included in the area corresponding to the group and the degree of coincidence with the customer attraction characteristics for each of the plurality of facilities by time zone. The information processing apparatus according to any one of claims 3 to 6.

8. The control unit outputs a combination of the area corresponding to the group and the facility associated with the area. The information processing apparatus according to any one of claims 1 to 7.

9. The control unit acquires the position information from the plurality of mobile terminals mounted on the plurality of vehicles. The information processing apparatus according to any one of claims 1 to 8.

10. The position information indicates the position where the driving system of the vehicle has stopped. The information processing apparatus according to claim 9.

11. An information processing apparatus grouping a plurality of pieces of position information acquired from a plurality of mobile terminals into a plurality of groups by clustering using a predetermined parameter; associating each of the plurality of groups with any one of the plurality of facilities based on a result of comparing the generated groups with characteristic data representing customer attraction characteristics for each of the plurality of facilities; determining whether a ratio of the groups that have succeeded in the association among the plurality of groups exceeds a predetermined value; when the ratio of the groups that have succeeded in the association is less than the predetermined value, changing the parameter so that the size of the group changes; An information processing method for executing the above.

12. The information processing apparatus performs the association based on a degree of coincidence between the number of mobile terminals included in the group and the customer attraction characteristics for each of the plurality of facilities. The information processing method according to claim 11.

13. The characteristic data is data representing the customer attraction characteristics for each of the plurality of facilities by time band. The information processing method according to claim 12.

14. The information processing apparatus performs the association based on the change over time of the number of mobile terminals included in the area corresponding to the group and the degree of coincidence with the customer attraction characteristics for each of the plurality of facilities by time band. The information processing method according to claim 13.

15. The information processing apparatus outputs a combination of the area corresponding to the group and the facilities associated with the area. The information processing method according to any one of claims 11 to 14.

16. The information processing apparatus acquires the position information from the plurality of mobile terminals mounted on the plurality of vehicles. The information processing method according to any one of claims 11 to 15.

17. The position information indicates the position where the driving system of the vehicle has stopped operating. The information processing method according to claim 16.

18. A program for causing a computer to execute the information processing method according to any one of claims 11 to 17.

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