Movement means estimation device and movement means estimation method

Abstract

A position information acquisition unit 111 acquires position information pertaining to a plurality of users U-1 to U-n and time information indicating the time at which the position information was obtained. An extraction unit 112 extracts, as a nearby user, a user among the plurality of users U-1 to U-n whose position is close in at least one time zone. An output unit 115 outputs information relating to a movement means of the nearby user.

Description

Transportation means estimation device and transportation means estimation method

[0001] The present disclosure relates to a transportation mode estimation device and a transportation mode estimation method.

[0002] Conventionally, technologies have been developed for analyzing the flow of people (people flow) using mobile devices carried by people. For example, Patent Document 1 below reads location information contained in radio waves received from multiple mobile devices to a fixed positioning device along with time information. The location information received by the fixed positioning device is subjected to a thinning (cleansing process) according to a predetermined rule. Trip data indicating the travel route of the user carrying the mobile device is generated from the cleansed location information. The generated trip data is used to determine the user's means of transportation. The means of transportation is determined by matching the trip data with transportation hubs based on the center coordinates of mesh areas corresponding to the trip data, which are divided into predetermined regions.

[0003] Patent No. 7027605

[0004] As with the conventional technology described above, in order to identify the transportation modes used by people, it is common to compare the route information of the transportation modes with the movement trajectories of the people, and determine that "people used the transportation mode" if the match rate is high. However, there are transportation modes (hereinafter referred to as "unscheduled transportation modes") whose route and / or operating time cannot be identified, such as shuttle buses operated temporarily for events. The conventional technology described above cannot identify users of unscheduled transportation modes.

[0005] An object of the present disclosure is to estimate a user's means of transportation.

[0006] A transportation means estimation device according to a preferred aspect of the present disclosure includes a location information acquisition unit that acquires location information of multiple users and time information indicating the time when the location information was obtained, an extraction unit that extracts users from the multiple users who are located close to each other during at least one time period as nearby users, and an output unit that outputs information regarding the transportation means of the nearby users.

[0007] A preferred aspect of the present disclosure provides a method for estimating means of transportation by acquiring location information of multiple users and time information indicating the time at which the location information was obtained, extracting users from the multiple users who are located close to each other during at least one time period as nearby users, and outputting information regarding the means of transportation of the nearby users.

[0008] According to the present disclosure, it is possible to estimate a user's means of transportation.

[0009] FIG. 1 is a diagram showing the configuration of a system 1 according to an embodiment. FIG. 1 is a schematic diagram showing an example of unfixed transportation. FIG. 2 is a block diagram showing the configuration of a user terminal 20-1. FIG. 3 is a block diagram showing the configuration of a database management device 30. FIG. 4 is a block diagram showing the configuration of a transportation means estimation device 10. FIG. 2 is a schematic diagram showing an example of a location database DB. FIG. 3 is a flowchart showing the processing procedure of a cluster identification process. FIG. 4 is a schematic diagram showing an adjacency matrix MX with similarity D as an element. FIG. 5 is a diagram showing a schematic diagram of a clustering process. FIG. 6 is a diagram showing clustering results in a plurality of time frames. FIG. 7 is a table showing an example of a match rate RC of a user U between clusters. FIG. 8 is a table showing an example of an estimation result of a same-movement cluster. FIG. 9 is a flowchart showing the operation of a processing device 103.

[0010] A. Embodiment A-1. System Configuration FIG. 1 is a diagram showing the configuration of a system 1 according to an embodiment. The system 1 includes a transportation means estimation device 10, a plurality of user terminals 20 (20-1 to 20-n (n is an integer of 2 or more)), and a location information database DB. The user terminals 20-1 to 20-n are each used by a different user U (U-1 to Un). The transportation means estimation device 10 and the user terminals 20-1 to 20-n are connected via a network N.

[0011] The user terminals 20-1 to 20-n are information processing terminals such as smartphones or tablet terminals. In this embodiment, the user terminals 20-1 to 20-n are assumed to be carried and used by the users U-1 to Un. Therefore, the location information of the user terminals 20-1 to 20-n can be considered to be the location information of the users U-1 to Un.

[0012] The database management device manages a location information database DB that records location information of the user terminals 20-1 to 20-n.

[0013] The transportation means estimation device 10 estimates the movement histories of users U-1 to Un based on the location information of user terminals 20-1 to 20-n. In this embodiment, a method is described in which the transportation means estimation device 10 estimates the number of users U among users U-1 to Un who used a predetermined transportation means. The predetermined transportation means is assumed to be an unspecified transportation means for which at least one of the operating route and operating time cannot be specified.

[0014] FIG. 2 is a schematic diagram showing an example of a non-scheduled means of transportation. In this embodiment, the non-scheduled means of transportation is a shuttle bus BS that operates between station ST and stadium SD. The shuttle bus BS does not operate regularly between station ST and stadium SD, but operates, for example, on days when an event is held at stadium SD. For example, the shuttle bus BS operates on a first route from station ST to stadium SD during the time period up to event start time T1, and on a second route from stadium SD to station ST during the time period after event end time T2. The first and second routes may be fixed or may be changed depending on surrounding traffic conditions, etc. The area including the stadium SD, station ST, and the road sections on which the shuttle bus BS may travel is defined as a related area RB.

[0015] A-2. User Terminals 20-1 to 20-n Fig. 3 is a block diagram showing the configuration of user terminal 20-1. Since the configurations of user terminals 20-1 to 20-n are substantially the same, Fig. 3 will be explained using user terminal 20-1 as an example. User terminals 20-1 to 20-n each include a display device 201, an input device 202, a communication device 203, a GPS (Global Positioning System) device 204, a storage device 205, a processing device 206, and a bus 220 that interconnects these devices.

[0016] The display device 201 is a display device (for example, various display panels such as a liquid crystal display panel or an organic EL display panel) that displays information to the outside. The input device 202 is an input device (for example, a keyboard, a mouse, a microphone, a switch, a button, or a sensor) that accepts input from the outside. The display device 201 and the input device 202 may be integrated into one device (for example, a touch panel). The communication device 203 has an interface that can be connected to the network N, and communicates with other devices connected to the network N using wireless communication or wired communication.

[0017] The GPS device 204 receives radio waves from multiple satellites and generates location information indicating the location of the user terminal 20-1 from the received radio waves. The location information may be in any format as long as it can identify the location of the user terminal 20-1. In this embodiment, latitude and longitude are used as the location information.

[0018] The GPS device 204 can generate location information at any frequency (period), but generating location information at a high frequency can easily reduce the remaining battery power of the user terminal 20-1. Therefore, the GPS device 204 generates location information at a frequency set by the user U-1, or at a frequency that does not interfere with tracking the movement history of the user U-1.

[0019] Instead of obtaining location information using the GPS device 204, location information may be obtained by receiving, for example, connection information with a mobile phone base station, connection information with a Wi-Fi (registered trademark) access point, or information transmitted from a beacon transmitter installed on a road, etc.

[0020] The storage device 205 is a recording medium readable by the processing device 206. The storage device 205 includes, for example, a non-volatile memory and a volatile memory. The non-volatile memory is, for example, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), and an electrically erasable programmable read-only memory (EEPROM). The volatile memory is, for example, a random access memory (RAM). The storage device 205 stores a program PG2. The program PG2 is a program for operating the user terminal 20.

[0021] The processing device 206 includes one or more central processing units (CPUs). The one or more CPUs are examples of one or more processors. Each of the processor and the CPU is an example of a computer.

[0022] The processing device 206 reads the program PG2 from the storage device 205. The processing device 206 executes the program PG2 to function as a location information transmission unit 211. The location information transmission unit 211 may be configured by a circuit such as a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array).

[0023] The position information transmission unit 211 transmits the position information generated by the GPS device 204 to the database management device 30 via the communication device 203. As will be described in detail later, the position information transmission unit 211 transmits to the database management device 30 terminal information that associates identification information for identifying the user terminal 20-1, the position information of the user terminal 20-1, and time information indicating the time when the position information was obtained. Note that the time when the position information was obtained is approximately the same as the time when the position information was generated.

[0024] The timing of transmitting the terminal information is arbitrary. For example, the position information transmitting unit 211 may transmit terminal information including the position information each time the GPS device 204 generates the position information. Furthermore, the position information transmitting unit 211 may store the terminal information from the previous terminal information transmission time onward in the storage device 205 until a predetermined terminal information transmission time, and transmit the terminal information stored in the storage device 205 at the terminal information transmission time. Furthermore, the position information included in the terminal information may be all of the position information generated by the GPS device 204, or may be the position information generated by the GPS device 204 thinned out at predetermined intervals. In the latter case, for example, the GPS device 204 generates position information every second, whereas the position information included in the terminal information may be the position information thinned out every 10 seconds.

[0025] A-3. Database Management Device 30 Fig. 4 is a block diagram showing the configuration of the database management device 30. The database management device 30 comprises a communication device 301, a storage device 302, a processing device 303, and a bus 310 that interconnects these devices.

[0026] The communication device 301 has an interface that can be connected to the network N, and communicates with other devices connected to the network N using wireless or wired communication.

[0027] The storage device 302 is a recording medium readable by the processing device 303. The storage device 302 includes, for example, a nonvolatile memory and a volatile memory. The nonvolatile memory is, for example, a ROM, an EPROM, and an EEPROM. The volatile memory is, for example, a RAM. The storage device 302 stores a program PG3 and a position database DB. The program PG3 is a program for operating the database management device 30.

[0028] The processing device 303 includes one or more CPUs. The one or more CPUs are an example of one or more processors. Each of the processor and the CPU is an example of a computer. The processing device 303 functions as a management unit 311 by executing the program PG3. At least a part of the functions of the management unit 311 may be configured by a circuit such as a DSP, an ASIC, a PLD, or an FPGA.

[0029] The management unit 311 manages the location information database DB. More specifically, the management unit 311 records terminal information received from the user terminals 20-1 to 20-n in the location information database DB. In addition, the management unit 311 transmits part of the data included in the location information database DB to the transportation means estimation device 10 based on a request from the transportation means estimation device 10.

[0030] Fig. 6 is a schematic diagram showing an example of a position database DB. The position database DB includes a user ID 401, a latitude 402, a longitude 403, and a timestamp 404. The user ID 401 is identification information that can uniquely identify the user terminals 20-1 to 20-n (users U-1 to Un). The example of Fig. 6 shows part of the terminal information of the user terminal 20 with the user ID 401 "A" (hereinafter referred to as "user terminal 20-A") and the user terminal 20 with the user ID 401 "B" (hereinafter referred to as "user terminal 20-B"), among the user terminals 20-1 to 20-n.

[0031] Latitude 402 indicates the latitude of user terminal 20-A and user terminal 20-B at the time indicated by timestamp 404. Longitude 403 indicates the latitude of user terminal 20-A and user terminal 20-B at the time indicated by timestamp 404. Timestamp 404 indicates the date and time when the values ​​of latitude 402 and longitude 403 were obtained. In FIG. 6, timestamp 404 is given in seconds, but timestamp 404 may also be given in milliseconds, for example.

[0032] The management unit 311 continuously receives terminal information from each of the user terminals 20-1 to 20-n. The management unit 311 also records the terminal information received from the user terminals 20-1 to 20-n in a position database DB. The management unit 311 generates information indicating changes over time in the position information of the user terminals 20-1 to 20-n (users U-1 to Un) by recording the terminal information intermittently transmitted from the user terminals 20-1 to 20-n in the position database DB.

[0033] A-4. Transportation estimation device 10 Fig. 5 is a block diagram showing the configuration of the transportation estimation device 10. The transportation estimation device 10 comprises a communication device 101, a storage device 102, a processing device 103, and a bus 110 that interconnects these devices.

[0034] The communication device 101 has an interface that can be connected to a network N, and communicates with other devices connected to the network N using wireless or wired communication.

[0035] The storage device 102 is a recording medium readable by the processing device 103. The storage device 102 includes, for example, a non-volatile memory and a volatile memory. The non-volatile memory is, for example, a ROM, an EPROM, and an EEPROM. The volatile memory is, for example, a RAM. The storage device 102 stores a program PG1 and a position database DB. The program PG1 is a program for operating the transportation mode estimation device 10. The position database DB will be described in detail later.

[0036] The processing device 103 includes one or more CPUs. The one or more CPUs are an example of one or more processors. Each of the processor and the CPU is an example of a computer. By executing the program PG1, the processing device 103 functions as a position information acquisition unit 111, an extraction unit 112, an operation information acquisition unit 113, a determination unit 114, and an output unit 115. At least some of these functions may be configured by circuits such as a DSP, an ASIC, a PLD, and an FPGA.

[0037] A-5. Details of the Processing Device 103 Next, the location information acquisition unit 111, extraction unit 112, operation information acquisition unit 113, determination unit 114, and output unit 115, which are realized by the processing device 103 executing the program PG1, will be described. The following describes an example in which a user U who is to board a specific bus (hereinafter referred to as a "specific bus") running on a first route among the shuttle buses BS shown in FIG. 2 is identified.

[0038] The location information acquisition unit 111 acquires location information of multiple users U-1 to Un and time information indicating the time when the location information was obtained. More specifically, the location information acquisition unit 111 transmits a request to the database management device 30 to send location information, and receives the location information sent from the database management device 30 in response to the transmission request.

[0039] The extraction unit 112 extracts, as nearby users, users whose locations are nearby during at least one time period from among the multiple users U. Hereinafter, the processing performed by the location information acquisition unit 111 and the extraction unit 112 will be referred to as a "cluster identification processing."

[0040] Fig. 7 is a flowchart showing the processing procedure of the cluster identification process. As will be described later with reference to Fig. 13, the processing device 103 executes the cluster identification process when, for example, an operator inputs analysis conditions and instructs analysis based on the analysis conditions. The flowchart shown in Fig. 7 illustrates processing performed by the processing device 103 functioning as the extraction unit 112 and the position information acquisition unit 111 by executing the program PG1.

[0041] First, the processing device 103 sets a related area RB (step S100). Setting the related area RB may mean that the processing device 103 determines the related area RB based on the shapes of the station ST, the stadium SD, and the surrounding roads, or may mean that the processing device 103 receives the setting of the related area RB from an operator (not shown) as part of the analysis conditions. The range of the related area RB may be specified, for example, by polygon coordinates.

[0042] Next, the processing device 103 identifies the position information included in the related region RB (step S102). Identifying the position information included in the related region RB may mean, for example, converting the related region RB specified by polygon coordinates into position information expressed by latitude and longitude.

[0043] Next, the processing device 103 sets an analysis start time Ts, which is the start time of the time period to be analyzed, and an analysis end time Te, which is the end time of the time period to be analyzed (step S104). Specifically, for example, the analysis start time Ts is set to a first predetermined time (e.g., 3 hours) before the event start time T1, and the analysis end time Te is set to a second predetermined time (e.g., 30 minutes) after the event start time T1. The analysis start time Ts and the analysis end time Te may be set automatically by the extraction unit 112 based on the event start time T1, for example, or may be input by an operator.

[0044] The processing device 103 also sets a time frame width H and a shift time M for the analysis (step S106). As will be described in detail later, the time frame width H is the length (width) of the time frame for aggregating location information when clustering user terminals 20 located close to each other. The shift time M is the length (width) of the time by which the time frame width H is shifted. The analysis time frame width H and shift time M may be automatically set by the extraction unit 112 based on, for example, the processing load of the processing device 103, or may be input by an operator. In this embodiment, the time frame width H is set to 5 minutes, and the shift time M is set to 2.5 minutes.

[0045] The processing device 103 sets L = 0 as an initial value (step S108) and calculates the time frame start time Tp = Ts + M * L (step S110). Until the time frame start time Tp reaches the analysis end time Te (Tp < Te, step S112: NO), the processing device 103 functions as the location information acquisition unit 111 and acquires, from the database management device 30, the terminal information of the user U located in the related area RB during the time frame from the time frame start time Tp to the time frame end time Tp + N (step S114). Due to the limited character limit, this is expressed as "acquiring terminal information from Tp to Tp + N" in the figure.

[0046] More specifically, the processing device 103 transmits a transmission request including the time frame start time Tp, the time frame end time Tp+N, and the location information of the related area RB to the database management device 30. The database management device 30 identifies the user terminals 20-1 to 20-n that are located in the related area RB between the time frame start time Tp and the time frame end time Tp+N, and transmits the terminal information of the user terminals 20-1 to 20-n from the time frame start time Tp to the time frame end time Tp+N to the transportation mode estimation device 10.

[0047] The processing device 103 functions as the extraction unit 112 and performs clustering on the terminal information extracted in step S114 (step S116). Details of clustering will be described later. Thereafter, the processing device 103 assigns L+1 to L (step S118) and returns the process to step S110.

[0048] When the time frame start time Tp reaches the analysis end time Te (Tp≧Te, step S112: YES), the processing device 103 functions as the extraction unit 112, compares the user configurations of clusters in adjacent time frames, and calculates the match rate RC of user U between the clusters (step S120). The processing device 103 functions as the extraction unit 112, and identifies clusters where the match rate RC of user U is equal to or greater than a predetermined value as clusters composed of users U using the same means of transportation (same-travel clusters) (step S122), and ends the processing of this flowchart. Details of steps S120 and S122 will be described later.

[0049] [Step S116: Clustering] Details of the clustering shown in step S116 of Fig. 7 will be described. As described above, in step S114, the location information acquisition unit 111 acquires terminal information from the time frame start time Tp to the time frame end time Tp+N of user terminals 20 located in the related region RB during the time frame from the time frame start time Tp to the time frame end time Tp+N. Here, it is assumed that the time frame start time Tp is 8:00:00 AM (8:00:00) and the time frame end time Tp+N is 8:04:59 AM.

[0050] The extraction unit 112 performs clustering on the acquired time-series terminal information. Specifically, the extraction unit 112 calculates the similarity D shown in the following formula (1) using, for example, the Euclidean distance between two users U (more precisely, two user terminals 20) at the same time. In the following formula (1), α represents the first user U of the two users U to be analyzed (hereinafter referred to as "user α"), and β represents the second user U of the two users U to be analyzed (hereinafter referred to as "user β"). Furthermore, xαt represents the latitude of user α at time t, xβt represents the latitude of user β at time t, yαt represents the longitude of user α at time t, and yβt represents the longitude of user β at time t. Furthermore, ε is a small value to prevent division by zero. In this embodiment, for example, ε = 0.01.

[0051] Furthermore, although the position information of two users U at the same time is compared in the following formula (1), the time when the position information was recorded may differ depending on the user U. In this case, the extraction unit 112 may perform missing value interpolation or resampling.

[0052] When the value of D(α, β) is equal to or less than η (D(α, β)≦η), D(α, β) may be approximated to 0 (D(α, β)=0). η is a small value for simplifying unnecessary calculations. In this embodiment, η is set to 0.01, for example.

[0053]

[0054] FIG. 8 is a schematic diagram showing an adjacency matrix MX having similarity D as an element. The extraction unit 112 creates the adjacency matrix MX shown in FIG. 8 using similarity D. The processing device 103 then classifies the users U into clusters using spectral clustering. FIG. 9 is a diagram schematically showing the clustering process. In the left diagram F1 of FIG. 9, users U-A to U-F are mapped based on the similarity D between the users U. In FIG. 9, for example, user U-A is indicated by a circle with an "A" written on it. Then, as shown in the right diagram F2 of FIG. 9, users U located close to each other on the map are classified into the same cluster CL (CL1-1 to CL1-3). For example, in FIG. 9, users U-A, UB, UD, and UE are classified into cluster CL1-1, user U-F is classified into cluster CL1-2, and user U-C is classified into cluster CL1-3.

[0055] 9, a single user U, such as user U-F, is classified as a cluster, but a predetermined number or more of users U (the predetermined number is an integer of 2 or more) may be recognized as a cluster. Cluster CL1-N (N is an integer from 1 to 3) indicates that this is the Nth cluster in the first time slot (8:00:00 to 8:04:59).

[0056] [Step S120: Calculation of Match Rate RC of User U Between Clusters] Next, the calculation of the match rate RC of user U between clusters shown in step S120 of Fig. 7 will be described. As described above, the extraction unit 112 repeats the above clustering while shifting the time frame by the shift time M (corresponding to steps S110 to S118 of Fig. 7). Then, when the time frame start time Tp is equal to or greater than the analysis end time Te (step S112: YES), the process proceeds to calculation of the match rate RC of user U between clusters shown in step S120.

[0057] 10 is a diagram showing clustering results for multiple time frames. Fig. 10 shows clustering results for the first time frame (8:00:00 to 8:04:59), the second time frame (8:02:30 to 8:07:29), and the third time frame (8:05:00 to 8:09:59). For example, user U-A belongs to cluster CL1-1 in the first time frame, cluster CL2-1 in the second time frame, and cluster CL3-1 in the third time frame.

[0058] The extraction unit 112 calculates the match rate RC of user U between clusters in adjacent time frames. The match rate RC of user U between clusters is expressed, for example, by the following formula (2). In the following formula (2), "X and Y" is the number of users U who belong to cluster X and also belong to cluster Y, and "X or Y" is the number of users U who belong to at least either cluster X or cluster Y. Cluster X and cluster Y are clusters formed in adjacent time frames.

[0059]

[0060] FIG. 11 is a table showing an example of the match rate RC of a user U between clusters. FIG. 11 shows the match rate RC between a cluster occurring in a first time slot and a cluster occurring in a second time slot. For example, the match rate RC between cluster CL1-1 occurring in the first time slot and cluster CL2-1 occurring in the second time slot is 0.75. Therefore, three-quarters of the users U constituting cluster CL1-1 and cluster CL2-1 match. Furthermore, for example, the match rate RC between cluster CL1-1 occurring in the first time slot and cluster CL2-2 occurring in the second time slot is 0. Therefore, the users U constituting cluster CL1-1 and cluster CL2-2 do not match at all.

[0061] The extraction unit 112 calculates the match rate RC of the user U between clusters in subsequent time frames, for example, the second time frame and the third time frame, the third time frame and the fourth time frame (8:07:30 to 8:12:29), etc. Then, the extraction unit 112 estimates that clusters with a match rate RC equal to or greater than a predetermined value are clusters made up of approximately the same user U, and that they have continued to travel by the same means of transportation.

[0062] FIG. 12 is a table showing an example of the estimation results of a same-travel cluster. Cluster CL1-1, which occurred in the first time frame, has a high match rate RC for user U with cluster CL2-1 in the second time frame and cluster CL3-1 in the third time frame. Therefore, it can be estimated that users U constituting clusters CL1-1, CL2-1, and CL3-1 traveled by the same means of transportation from the first time frame to the third time frame. In this way, a cluster estimated to have traveled by the same means of transportation during at least a portion of a time period (time frame) is referred to as a "same-travel cluster." The means of transportation used by users U constituting clusters CL1-1, CL2-1, and CL3-1 from the first time frame to the third time frame is referred to as transportation means M1. Furthermore, clusters CL1-1, CL2-1, and CL3-1 are referred to as a same-travel cluster CL-M1.

[0063] Similarly, it can be estimated that users U who make up clusters CL2-3 and CL3-4 used transportation mode M2 ​​from the second time slot to the third time slot. Therefore, clusters CL2-3 and CL3-4 are designated as the same-movement cluster CL-M2. Users U who make up the same-movement cluster correspond to the nearby users described above. Note that in Figure 12, the number of people shown below each cluster indicates the number of users U who make up that cluster.

[0064] 5 , the operation information acquisition unit 113 acquires operation information of transportation facilities operating near the location of the nearby user. The operation information may be, for example, information indicating the operation record of a specific transportation facility (e.g., a specific service of the shuttle bus BS described above). The operation information may include, for example, at least one of information indicating the transportation facility's operating route, information indicating the transportation facility's operating hours, information indicating the transportation facility's capacity, and information indicating the transportation facility's name.

[0065] The operation information acquisition unit 113 may acquire operation information input by an operator to the transportation mode estimation device 10. The operation information acquisition unit 113 may also acquire operation information published on the Internet via the network N, for example.

[0066] The determination unit 114 determines whether a nearby user has used public transportation based on the operation information. It can also be said that the determination unit 114 determines the public transportation used by the nearby user based on the operation information. In this embodiment, the determination unit 114 compares the public transportation operation information with the nearby users extracted by the extraction unit 112 using a heuristic algorithm, and identifies whether the nearby user has used public transportation.

[0067] Specifically, for example, the following information 1 and information 2 are assumed to be obtained as operation information for a specific flight. Information 1: The specific flight operated between 8:00 and 8:10. Information 2: The capacity of the specific flight was 60 people.

[0068] Referring to the estimation results shown in FIG. 12, it is the same movement clusters CL-M1 and CL-M2 that occur between 8:00 and 8:10. Therefore, it is the same movement clusters CL-M1 and CL-M2 that satisfy the conditions of information 1. Furthermore, the number of people in the same movement cluster CL-M1 is around 200, and the number of people in the same movement cluster CL-M2 is around 30. Therefore, it is the same movement cluster CL-M2 that satisfies the conditions of information 2. From the above, the determination unit 114 determines that the user U who belongs to the same movement cluster CL-M2 has used the specified flight. In other words, the determination unit 114 determines that the user U who belongs to the same movement cluster CL-M2 has used the specified flight.

[0069] The information 1 indicates the operating time period of a specific flight. The determination unit 114 determines that a user U belonging to the same movement cluster has used the specific flight if the time period during which the same movement cluster occurs is included in the operating time period. The specific flight is an example of a means of transportation, and the time period during which the same movement cluster occurs is an example of at least one time period.

[0070] Furthermore, the information 2 is information indicating the capacity of a specific flight. The determination unit 114 determines that a user U belonging to the same movement cluster has used a specific flight when the absolute value of the difference between the number of people in the same movement cluster and the capacity is equal to or less than a predetermined number. Note that the capacity of a transportation facility includes seat capacity, service capacity, safety officer capacity, etc. If the capacity strictly indicates the upper limit of the number of passengers, the determination unit 114 may determine that a user U belonging to the same movement cluster has used a specific flight when the number of people in the same movement cluster is equal to or less than the capacity.

[0071] The operation information may be information indicating the operation route of a specific flight. In this case, the determination unit 114 determines that a user U belonging to the same movement cluster has used a specific flight when the degree of coincidence between the movement route (transition of location information) of the user U belonging to the same movement cluster and the operation route is equal to or greater than a predetermined value.

[0072] The operation information may also be information indicating the number of users of a specific flight. In this case, the determination unit 114 determines that a user U belonging to the same movement cluster has used the specific flight when the difference between the number of users of the specific flight and the number of users of the specific flight is equal to or less than a predetermined number.

[0073] The determination unit 114 may determine that the means of travel of the same movement cluster is not public transportation, for example. Specifically, the determination unit 114 may determine that the means of travel of the same movement cluster is walking or a horizontal escalator (a so-called moving walkway), for example.

[0074] The output unit 115 outputs information about the nearby user's means of transportation. For example, when it is determined that the nearby user has used public transportation, the output unit 115 outputs information about the public transportation as information about the nearby user's means of transportation. The information about the public transportation is, for example, the name of the public transportation (including the flight number, etc.). Furthermore, the output unit 115 may output, for example, the number of nearby users who have used the public transportation, the time required to reach the destination from the departure point using the public transportation, etc. as information about the nearby user's means of transportation.

[0075] The output unit 115 outputs information about the transportation means of nearby users to, for example, a display device (not shown) provided in the transportation means estimation device 10. The output unit 115 may also transmit information about the transportation means of nearby users to another information processing device via the network N. The information about the transportation means of nearby users output by the output unit 115 is used, for example, for considering how to manage an event at the stadium SD, or how to respond when an event is held at the station ST, etc.

[0076] A-6. Flowchart Fig. 13 is a flowchart showing the operation of the processing device 103. The processing device 103 waits until the operator inputs analysis conditions and instructs analysis based on the analysis conditions (step S100: NO). When the operator inputs analysis conditions and instructs analysis based on the analysis conditions (step S100: YES), the processing device 103 functions as the extraction unit 112 and the position information acquisition unit 111 and executes the cluster identification process (step S103). Details of the cluster identification process are as described using Fig. 7 etc.

[0077] Next, the processing device 103 functions as the operation information acquisition unit 113 and acquires operation information of the transportation means (step S104). The processing device 103 functions as the determination unit 114 and determines whether or not a nearby user has used the transportation means (step S105). The processing device 103 functions as the output unit 115 and outputs information about the means of transportation (step S106). Thereafter, the processing device 103 returns to the process of step S100 and repeats the subsequent processes.

[0078] A-7. Summary of the embodiment As described above, the transportation mode estimation device 10 according to the embodiment extracts, from among multiple users U-1 to U-n, users U who are located close to each other during at least one time period as nearby users, and outputs information about the transportation modes of the nearby users. This makes it possible to obtain information about the movements of users U who travel in groups, and to efficiently analyze the flow of people when a large number of users U are traveling, for example, during an event.

[0079] Furthermore, the transportation means estimation device 10 acquires operation information of transportation means operating near the location of the nearby user and determines whether the nearby user has used the transportation means. Thus, it is possible to specifically identify the nearby user's transportation means and analyze the usage of irregular transportation means, such as special shuttle buses BS, for which usage records are difficult to keep.

[0080] Furthermore, the transportation mode estimation device 10 determines the same movement cluster using a specific transportation mode using a heuristic method. Since irregular transportation modes have many uncertain factors compared to regular transportation modes, the accuracy of the determination can be improved compared to, for example, performing the above determination using a systematic method.

[0081] B: Modifications Modifications of the above-described embodiment are shown below. Two or more of the following modifications may be arbitrarily selected and combined as long as they are not mutually contradictory.

[0082] B1: First Modification In the above-described embodiment, the location information database DB is managed by the database management device 30. This is not limiting, and for example, the location information database DB may be managed by the transportation means estimation device 10. In this case, the transportation means estimation device 10 stores the location information database DB in the storage device 102. The user terminals 20-1 to 20-n transmit terminal information to the transportation means estimation device 10. The processing device 103 of the transportation means estimation device 10 functions as a management unit 311 and stores the received terminal information in the location information database DB.

[0083] B2: Second Modification In the embodiment described above, the location information acquisition unit 111 acquires, in step S114, the terminal information of the user U located in the related area RB during the time frame from the time frame start time Tp to the time frame end time Tp+N. In this case, the database management device 30 needs to refer to the location information of all users U-1 to U-n during the time frame from the time frame start time Tp to the time frame end time Tp+N, and determine whether they are located in the related area RB.

[0084] Without being limited to this, for example, it is also possible to narrow down in advance the users U who are likely to be located in the related area RB during the time period to be analyzed. For example, it is predicted that the users U who will be riding the shuttle bus BS will be participating in an event at the stadium SD. Therefore, for example, it is possible to extract users U who will be located at the stadium SD during the time when an event is being held at the stadium SD (which may be a time period or a single time), and perform clustering among the extracted users U. According to the second modification, it is possible to reduce the number of users U whose location information needs to be matched, thereby reducing the processing load.

[0085] B3: Third Modification In the above-described embodiment, the output unit 115 outputs information about public transportation as information about the nearby user's means of transportation. However, the output unit 115 may output information that is not limited to public transportation, such as the travel route, number of people, or travel time (e.g., the time required to reach the stadium SD from station ST) of the same travel cluster, as information about the nearby user's means of transportation. According to the third modification, information about user U's travel can be obtained even when user U does not use public transportation, or when operation information cannot be obtained and it cannot be determined whether or not user U used public transportation.

[0086] C: Others (1) In the above-described embodiments, ROM, RAM, etc. are given as examples of storage devices 102, 205, and 302. However, storage devices 102, 205, and 302 may also be flexible disks, magneto-optical disks (e.g., compact disks, digital versatile disks, Blu-ray (registered trademark) disks), smart cards, flash memory devices (e.g., cards, sticks, key drives), CD-ROMs (Compact Disc-ROMs), registers, removable disks, hard disks, floppy (registered trademark) disks, magnetic strips, databases, servers, or other suitable storage media.

[0087] (2) In the above-described embodiments, the described information, signals, etc. may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0088] (3) In the above-described embodiment, input and output information may be stored in a specific location (for example, a memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be transmitted to another device.

[0089] (4) In the above-described embodiment, the determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a comparison of numerical values ​​(e.g., a comparison with a predetermined value).

[0090] (5) The order of the process procedures, sequences, flowcharts, etc. illustrated in the above-described embodiments may be rearranged unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0091] (6) Each function illustrated in Figures 3 to 5 is realized by any combination of hardware and / or software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (e.g., wired, wireless, etc.) and these multiple devices. A functional block may be realized by combining software with the single device or the multiple devices.

[0092] (7) The programs exemplified in the above-described embodiments should be broadly construed to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., regardless of whether they are called software, firmware, middleware, microcode, hardware description language, or by other names.

[0093] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0094] (8) In each of the foregoing embodiments, the terms "system" and "network" are used interchangeably.

[0095] (9) The information, parameters, etc. described in this disclosure may be expressed using absolute values, relative values ​​from a predetermined value, or corresponding other information.

[0096] (10) In the above-described embodiments, the portable device may be a mobile station (MS). Those skilled in the art may also refer to a mobile station as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other appropriate terminology. In this disclosure, the terms "mobile station," "user terminal," "user equipment (UE)," "terminal," etc. may be used interchangeably.

[0097] (11) In the above-described embodiments, the terms "connected" and "coupled," or any variations thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using at least one of one or more electric wires, cables, and printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0098] (12) In the above embodiments, the phrase "based on" does not mean "based only on," unless otherwise specified. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0099] (13) As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining something as "determining" or "determining," and the like. Furthermore, "judgment" and "decision" may include regarding receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, and accessing (e.g., accessing data in memory) as having been "judgment" or "decision." Furthermore, "judgment" and "decision" may include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judgment" or "decision." In other words, "judgment" and "decision" may include regarding some action as having been "judgment" or "decision." Furthermore, "judgment" may be interpreted as "assuming," "expecting," "considering," etc.

[0100] (14) In the above embodiments, when "include," "including," and variations thereof are used, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, the term "or," as used in this disclosure, is not intended to be an exclusive or.

[0101] (15) In this disclosure, where articles are added by translation, such as a, an, and the in English, this disclosure may include the noun following these articles being plural.

[0102] (16) In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combined" may also be interpreted in the same way as "different."

[0103] (17) The aspects / embodiments described in this disclosure may be used alone, in combination, or switched depending on the implementation. Notification of predetermined information (e.g., notification that "X is true") is not limited to being explicit, but may be implicit (e.g., not notifying the predetermined information).

[0104] 1...system, 10...transportation means estimation device, 20 (20-1 to 20-n)...user terminal, 30...database management device, 101...communication device, 102...storage device, 103...processing device, 111...location information acquisition unit, 112...extraction unit, 113...operation information acquisition unit, 114...determination unit, 115...output unit, N...network, U (U-1 to Un)...user

Claims

1. A transportation means estimation device comprising: a location information acquisition unit that acquires location information of multiple users and time information indicating the time when the location information was obtained; an extraction unit that extracts users from the multiple users who are located close to each other during at least one time period as nearby users; and an output unit that outputs information regarding the transportation means of the nearby users.

2. A transportation means estimation device as described in claim 1, further comprising: an operation information acquisition unit that acquires operation information of transportation means operating near the location of the nearby user; and a determination unit that determines whether the nearby user has used the transportation means based on the operation information, wherein the output unit outputs information about the transportation means as information about the nearby user's transportation means when it is determined that the nearby user has used the transportation means.

3. The transportation means estimation device according to claim 2, wherein the operation information includes information indicating the operating hours of the transportation means, and the determination unit determines that the nearby user has used the transportation means if the at least one time period is included in the operating hours.

4. The transportation means estimation device according to claim 2, wherein the operation information includes information indicating the capacity of the transportation means, and the determination unit determines that the nearby user has used the transportation means when the absolute value of the difference between the number of nearby users and the capacity is less than or equal to a predetermined number.

5. The transportation means estimation device according to claim 2, wherein the operation information includes information indicating the operation route of the transportation means, and the determination unit determines that the nearby user has used the transportation means if the degree of coincidence between the movement route of the nearby user and the operation route during the at least one time period is equal to or greater than a predetermined value.

6. A method for estimating means of transportation, comprising: acquiring location information of multiple users and time information indicating the time when the location information was obtained; extracting, from among the multiple users, users whose locations are close to each other during at least one time period, as nearby users; and outputting information regarding the means of transportation of the nearby users.

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