JP2014178299A - Navigation system, navigation method, and navigation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a navigation system capable of preferentially presenting a highly possibly useful spot for each user as a recommendation spot.SOLUTION: A navigation system, which is equipped with a destination recommendation function, comprises: a travel history database 43 which stores data about travel history of a plurality of users between spots in association with the users' movement patterns; a target movement pattern determination unit 26 which determines a target user's movement pattern consistent with the target user's movement tendency; a reference spot setting unit 27 which sets a guide reference spot; a data extraction unit 28 which extracts travel history data T including the guide reference spot associated with the target movement pattern from the travel history database 43; and a recommendation spot presentation unit 29 which presents a recommendation spot determined on the basis of the extracted travel history data T.

Description

  The present invention relates to a navigation system that provides guidance information related to points that can be candidates for a destination. The present invention also relates to such a navigation method and navigation program.

  In the navigation system as described above, based on behavior history data collected from multiple users, a technology that recommends other destinations visited by other users who visited the same destination as recommended destinations (recommendation) Technology) is known. For example, an information providing apparatus described in International Publication No. 2004/075137 (Patent Document 1) records visit histories of a plurality of users, and exists in a section with a lot of user visits based on the recorded visit histories. It is configured to provide information about the facility. Patent Document 1 describes that it is possible to provide useful information even when a user visits an unfamiliar area by presenting a recommended place (recommended point) based on a similar behavior pattern of another person. Has been.

  However, in Patent Document 1, although it is sought to provide information based on “similar behavior patterns of others”, what is actually considered is a destination (several destinations visited by other users). Combination of destinations). That is, the “behavior pattern” in Patent Document 1 is used as a concept representing a simple history of a destination within a reference period (for example, one day). In this sense, the device of Patent Document 1 merely proposes other destinations having a high correlation with the designated destination as recommended points, for example, a user regarding a movement mode between the destinations. It is not intended to provide information that takes into account the behavioral trends (the “behavior pattern” referred to in the present application). For this reason, depending on the user, a place that is not very useful for the user because it does not match the behavior tendency may be suggested as the recommended spot.

International Publication No. 2004/075137

  Therefore, it is desired to realize a navigation system that can preferentially propose points that are likely to be beneficial to each user as recommended points.

  According to the present invention, a navigation system that provides guidance information related to points that can be candidates for a destination is defined by classifying a user's behavior tendency into a plurality of behavior patterns. A movement history database storing movement history data in which movement history is recorded in association with any one of the behavior patterns, and a target that matches the behavior tendency of the target user to be provided with the guidance information from the plurality of behavior patterns A target behavior pattern determination unit that determines a behavior pattern, a reference point setting unit that sets a guide reference point according to a predetermined condition, and the guidance reference point that is associated with the target behavior pattern from the movement history database A data extraction unit that extracts the movement history data including the data before the data extraction unit Based on the movement history data is that a and a recommendation point presenting section for presenting the target user moves frequently a relatively high point as a recommended point between the guide reference point.

  According to this feature configuration, the data extraction unit extracts data associated with an action pattern that matches the action tendency of the target user from the movement history data stored in the movement history database (first extraction). . Further, the data extraction unit extracts data including the guidance reference point set according to a predetermined condition (second extraction). The movement history data finally extracted through both of these includes information on other points visited by other users who match the behavior tendency of the target user when they visited the guide reference point in the past. It will be a thing. Of a plurality of points included in such movement history data, a point having a relatively high movement frequency with respect to the guide reference point is set as a recommended point, and a point that is highly likely to meet the intention of the target user. Can be preferentially proposed as a recommended spot. Therefore, information that is highly likely to be useful can be provided to the target user.

  Here, in association with identification information for identifying each of a plurality of users, a data collection unit that collects action history data that is data of each user's action history, and the action history data collected for each user Based on the behavior pattern determining unit that determines a behavior pattern that matches each behavior tendency of a plurality of users from among the plurality of behavior patterns, the identification information, the behavior pattern determined for each user, It is preferable to further include a database generation unit that associates the movement history data extracted from the action history data with each other and stores them in the movement history database.

  According to this configuration, since the action history data of each of a plurality of users is collected and the movement history data of each user is generated from the collected action history data, the movement history database can be efficiently constructed. Moreover, based on the collected action history data, an action pattern that matches the action tendency of each user can be determined. Based on the identification information assigned to each user, the movement history data for each user can be stored in the movement history database in association with each determined action pattern.

A user database storing home data which is information indicating the home position of each of the plurality of users;
A positional relationship pattern determination unit that determines a target positional relationship pattern that matches a positional relationship between the home position of the target user and the guidance reference point from a plurality of predetermined positional relationship patterns;
When extracting the movement history data from the movement history database, the data extraction unit extracts movement history data of a user whose positional relationship pattern between the home position and the guidance reference point is the same as the target positional relationship pattern. It is preferable to adopt a configuration for extraction.

  According to this configuration, when the movement history data is extracted from the movement history database, the first extraction in which data associated with the behavior pattern that matches the behavior tendency of the target user is extracted and set in accordance with a predetermined condition In addition to the second extraction in which the data including the guided reference point is extracted, the movement history data of the user whose positional relationship pattern between the home position and the guidance reference point is the same as the target positional relationship pattern is extracted. A third extraction is performed. Therefore, the movement history data finally extracted is that other users who match the behavior tendency of the target user and the user whose positional relationship between the home position and the guidance reference point is close to the target user It includes information on other points visited when the reference point was visited in the past. Therefore, it is possible to preferentially propose a point that is more likely to match the target user's intention as a recommended point. As a result, information that is likely to be more useful can be provided to the target user.

  The positional relationship pattern is based on at least one of a relative orientation between a point that can be the home position and a point that can be a guide reference point, and a distance between a point that can be the home position and a point that can be a guide reference point. A configuration classified into a plurality of groups is preferable.

  According to this configuration, a plurality of users whose relative orientations between a point that can be a home position and a point that can be a guidance reference point are classified into the same positional relationship pattern, or a point that can be a home position A plurality of users who are close in distance to a guide reference point are classified into the same positional relationship pattern, or a plurality of users whose relative azimuth and distance are close are in the same positional relationship pattern. Can be classified. Therefore, the positional relationship between the home position and the guidance reference point can be appropriately classified, and appropriate movement history information can be extracted by the data extraction unit. As a result, information that is likely to be more useful can be provided to the target user.

  The behavior history of each user included in the behavior history data includes arrival time at each location, stay time at each location, travel time between two locations, and travel distance between two locations. It is preferable that at least one piece of information is included.

According to this configuration, it is possible to obtain basic information for determining an action pattern that matches each action tendency of a plurality of users.
For example, the behavior history of each user includes information on the arrival time at each point, so that the behavior tendency regarding the start time of a predetermined behavior (for example, sightseeing, dining, shopping, etc.) can be estimated based on the information. In addition, since the behavior history of each user includes information on the staying time at each point, it is possible to infer a behavior tendency regarding the time spent for a predetermined behavior based on the information. In addition, since each user's action history includes information on a movement time or a movement distance between two points, an action tendency related to the width of the action range can be estimated based on the information. By including a plurality of these pieces of information in combination, it is possible to accurately determine an action pattern that matches each action tendency of a plurality of users.

  The reference point setting unit sets the designated destination as the guidance reference point when the destination is specified by the target user, and the recommended point presenting unit sets the recommended point as the next It is preferable to present it as a destination candidate.

  According to this configuration, while assuming that the target user provides guidance regarding the destination that the user wishes to visit, candidates for subsequent visit points are presented from points that are likely to match the intention of the target user. be able to. Therefore, additional useful information can be provided while respecting the intention of the target user.

  The behavior pattern may include at least one of a length of a moving distance between destinations, a length of a staying time at the destination, and a predetermined action start time or end time. The classification based on one is preferable.

  According to this configuration, the action patterns can be classified according to the width of the action range, for example, based on the length of the moving distance between the destinations. Further, based on the degree of the length of stay at the destination, the behavior pattern can be classified according to the length of time spent for a predetermined behavior, for example. Moreover, based on the start time or end time of specific action, an action pattern can be classified according to the timing which performs specific action, for example. And it becomes possible to determine each action pattern of a some user more appropriately by combining these two or more.

  The technical features of the navigation system according to the present invention having the above-described configurations can also be applied to a navigation method that provides guidance information related to a point that can be a destination candidate and such a navigation program. Therefore, the present invention can also cover such methods and programs.

  In this case, the characteristic configuration of the navigation method is defined by classifying the user's behavior tendency into a plurality of behavior patterns, and moving history data in which movement histories between points of the plurality of users are recorded in any of the behavior patterns. A target behavior pattern determination step for determining a target behavior pattern that matches a behavior tendency of a target user to be provided with the guidance information from among the plurality of behavior patterns, using a movement history database stored in association; A reference point setting step for setting a guidance reference point according to the conditions, a data extraction step for extracting the movement history data associated with the target action pattern and including the guidance reference point from the movement history database, Based on the movement history data extracted in the data extraction step, Movement frequency between the guiding reference point is in that it includes a recommendation location presenting step of presenting to the target user as recommendation point a relatively high point.

  In this case, the feature configuration of the navigation program is defined by classifying the user's behavior tendency into a plurality of behavior patterns, and the movement history data in which movement histories between points of the plurality of users are recorded in any of the behavior patterns. A target behavior pattern determination function that refers to a movement history database stored in association with each other and determines a target behavior pattern that matches a behavior tendency of a target user to be provided with the guidance information from the plurality of behavior patterns; A reference point setting function for setting a guide reference point according to a given condition, and a data extraction function for extracting the movement history data associated with the target behavior pattern and including the guidance reference point from the movement history database; Based on the movement history data extracted by realizing the data extraction function, Serial certain, and recommendation point presentation function to be presented to the target user moves frequently a relatively high point as a recommended point between the guide reference point to the point to be realized on the computer.

  Naturally, these navigation methods and navigation programs can also obtain the effects of the navigation system described above. Furthermore, it is also possible to incorporate some additional techniques mentioned as examples of suitable configurations of the above-described navigation system into these navigation methods and navigation programs. In that case, the effect corresponding to each additional technique can be obtained.

Schematic diagram showing the overall configuration of the navigation system The block diagram which shows schematic structure of the vehicle-mounted terminal device which concerns on 1st embodiment. The block diagram which shows schematic structure of the management server which concerns on 1st embodiment. The figure which shows an example of action history data The figure which shows the data example of action history database The figure which shows an example of matching with each user and an action pattern Diagram showing an example of a behavior pattern determination method The figure which shows the data example of movement history database Conceptual diagram showing the correlation between points ascertained from the movement history database An example of a conceptual diagram showing the degree of correlation between points after the first filtering The other example of the conceptual diagram which shows the correlation degree between points after 1st filtering Schematic diagram showing the recommended point presentation method to the target user Flow chart showing processing procedure of action history database generation Flow chart showing processing procedure for moving history database generation The flowchart which shows the processing procedure of recommendation point presentation The block diagram which shows schematic structure of the vehicle-mounted terminal device which concerns on 2nd embodiment. The block diagram which shows schematic structure of the management server which concerns on 2nd embodiment. The figure which shows an example of matching with each user and a positional relationship pattern The figure which shows an example of the classification of the direction centering on the guidance reference point The flowchart which shows the process sequence of the recommendation spot presentation which concerns on 2nd embodiment.

1. First embodiment 1-1. Configuration of Navigation System An embodiment of the present invention will be described with reference to the drawings. As shown in FIG. 1, a navigation system 1 according to this embodiment includes an in-vehicle terminal device 2 such as a navigation device mounted on a vehicle, and a management server 5 provided to be able to communicate with the plurality of in-vehicle terminal devices 2. It is configured. In this navigation system 1, a plurality of in-vehicle terminal devices 2 are connected to a communication network 3 such as the Internet via a radio base station 4. Wireless communication between the in-vehicle terminal device 2 and the wireless base station 4 can be performed using, for example, a mobile phone network, a wireless LAN (Local Area Network), or the like. The management server 5 is also connected to the communication network 3.

  In the present embodiment, each in-vehicle terminal device 2 generates corresponding user action history data A, and transmits the action history data A to the management server 5 as a kind of probe information. The management server 5 extracts the movement history data T from the action history data A regarding each of a plurality of users collected from the plurality of in-vehicle terminal devices 2. The management server 5 stores the extracted movement history data T in association with each user's behavior pattern (behavior tendency classification result) and distributes the aggregated movement history data T to each in-vehicle terminal device 2. The in-vehicle terminal device 2 provides guidance information G related to a point that can be a destination candidate based on the received movement history data T while performing navigation processing such as route guidance to the destination.

  More specifically, for a target user (a user who is a target for providing guidance information G), any one of points visited by other users who have similar behavior patterns to the target user in the past is recommended. Present as the destination. As will be described below, the navigation system 1 according to the present embodiment is configured to be able to realize a highly convenient destination recommendation function that conforms to the behavior pattern of the target user (behavior tendency related to the movement mode between points). Has been.

  FIG. 2 is a block diagram illustrating a schematic configuration of the in-vehicle terminal device 2. The in-vehicle terminal device 2 includes a local position determination unit 21, a navigation calculation unit 22, an action history data generation unit 23, an update processing unit 24, a communication control unit 25, a target action pattern determination unit 26, a reference point setting unit 27, and data extraction. A unit 28 and a recommended point presentation unit 29 are provided. In each of these function units, a calculation unit for performing various processes on input data is configured by hardware, software (program), or both. The in-vehicle terminal device 2 is connected to a GPS receiver 31, an orientation sensor 32, a distance sensor 33, a calendar timer 34, a communication interface 35, a display input device 36, and an audio output device 37. The in-vehicle terminal device 2 is connected to a map database 41, an action history database 42, and a movement history database 43. These are configured to be able to exchange information with each other.

  FIG. 3 is a block diagram illustrating a schematic configuration of the management server 5. The management server 5 includes a communication control unit 51, a data collection unit 52, an action history database generation unit 53, a history action pattern determination unit 54, and a movement history database generation unit 55. In each of these function units, a calculation unit for performing various processes on the input data is configured by hardware, software (program), or both. The management server 5 is connected to the communication interface 58, the action history database 62, and the movement history database 63. These are configured to be able to exchange information with each other.

  The in-vehicle terminal device 2 and the management server 5 are the same as general-purpose computers such as arithmetic processing devices such as a CPU (Central Processing Unit), storage devices such as a hard disk and an optical disk, and temporary storage devices such as a RAM (Random Access Memory). The hardware configuration is configured.

  Map data M is stored (stored) in the map database 41 provided in the in-vehicle terminal device 2. The map database 41 stores information on the road network constituting the map data M. The map data M includes road network data including a plurality of nodes and a plurality of links corresponding to roads connecting the nodes. Further, the map data M includes link cost information for each link. The link cost is set according to the link length of each link and road attributes (speed limit, road type, etc.). The map data M is referred to by the navigation calculation unit 22 when executing map display processing, route search processing, and the like.

  The map database 41 also stores (stores) facility data F. The facility data F includes coordinate information indicating the position of each facility, attribute information indicating a name, a genre, and the like. Further, the facility data F may further include additional information about business hours, the presence or absence of a parking lot, and the like. The facility data F is referred to by the navigation calculation unit 22 when executing a destination search process or the like.

  The own position determination unit 21 is a functional unit that acquires own position information indicating the current position of the vehicle on which the in-vehicle terminal device 2 is mounted. The own position determination unit 21 is connected to a GPS (Global Positioning System) receiver 31. In the present embodiment, it is further connected to the direction sensor 32 and the distance sensor 33. The own position determination unit 21 performs a calculation for specifying the own position based on outputs from the GPS receiver 31, the direction sensor 32, and the distance sensor 33. When an imaging device such as a camera is mounted on the vehicle, it may be configured to perform self-localization with higher accuracy using an image recognition function.

  The navigation calculation unit 22 is a functional unit that executes various navigation processes such as self-location display, route search from the departure point to the destination, route guidance to the destination, and destination search. The navigation calculation unit 22 generates various images necessary for navigation processing such as a map image and a destination search screen, and performs processing such as displaying on the display input device 36. Further, the navigation calculation unit 22 also performs guidance by voice guidance or the like by the voice output device 37. The display input device 36 may be a device in which a display device such as a liquid crystal display and an input device such as a touch panel are integrated. The audio output device 37 can be constituted by a speaker or the like, for example.

  The behavior history data generation unit 23 is a functional unit that generates behavior history data A that records a behavior history of the in-vehicle terminal device 2 (that is, a user who operates a vehicle in which the in-vehicle terminal device 2 is mounted). The action history data generation unit 23 receives the own position information determined by the own position determination unit 21 and also receives information such as the year, month, day, and time provided from the calendar timer 34, and associates the two and records the action history. Data A is generated. In the present embodiment, the action history data A particularly includes information about the history of a point determined to have stayed for a predetermined time or longer. The action history data A can be information on the location history within a predetermined reference period (one day in this example).

  The stay longer than the predetermined time can be determined based on the fact that the own position indicated by the own position information does not move for a predetermined reference time (for example, 20 minutes). The length of the reference time can be appropriately set in consideration of a length that can be distinguished from a temporary stay for a simple break. When the destination is set by the user, it may be determined uniformly that the destination has stayed for a predetermined time or longer, regardless of the actual staying time. Hereinafter, a point determined to have stayed for a predetermined time or more and a point set as a destination are collectively referred to as a “visit point”. FIG. 4 shows an example of behavior history data A generated by the behavior history data generation unit 23. As shown in this figure, in this embodiment, each user's action history included in the action history data A includes a departure time from home, a return time, a point ID of a visited point, an arrival time at each visited point, and Contains information about the time spent at each visit.

  The action history data A also includes information on the position of each visited point (not shown). The position of each visited point is stored (stored) in advance in the map database 41 in association with the point ID, for example, and can be acquired from the map database 41 based on the point ID. Information on the position of each visit point is acquired as coordinate information specified by, for example, latitude and longitude.

  The action history data A may further include information on a travel time between two visit points and a travel distance between the two visit points. In the present embodiment, these two additional pieces of information are obtained by calculation based on the information on the position of each visit point, the arrival time, and the stay time. Therefore, it may not be included in the action history data A.

  The update processing unit 24 is a functional unit that updates the behavior history database 42 by storing (storing) the behavior history data A generated by the behavior history data generation unit 23 in the behavior history database 42. The update processing unit 24 sequentially stores (stores) the behavior history data A generated by the behavior history data generation unit 23 in the behavior history database 42 and updates the behavior history database 42. The update processing unit 24 also plays a role of updating the movement history database 43 to the latest state when the movement history data T distributed from the management server 5 is received. Details of the movement history data T will be described later. The update of the movement history database 43 may be either a differential update or a full update according to the data format distributed from the management server 5.

  The communication control unit 25 is a functional unit that controls transmission / reception of information to / from the management server 5. The communication control unit 25 performs a process of transmitting the action history data A stored in the action history database 42 to the management server 5 at a predetermined timing. For example, the communication control unit 25 uses the communication interface 35 to transmit the action history data A generated on the day when the user who has boarded the vehicle on which the in-vehicle terminal device 2 is mounted returns home after finishing the action of the day. To the management server 5. As the communication interface 35, a dedicated communication module may be used, or a general-purpose communication device such as a mobile phone terminal may be used. In addition, when a user's action extends over a plurality of consecutive days, the action history data A of the day may be transmitted every time when the user arrives at an intermediate place of stay, or the action history data of each day is collected together when returning home. A may be transmitted. Whether or not the user has returned home can be determined based on the home position set in advance and the information on the own position specified by the own position determination unit 21. The same applies to the determination of whether or not the user has arrived at the accommodation.

  Each in-vehicle terminal device 2 is given a user ID for identifying each of a plurality of users (in-vehicle terminal device 2). When the in-vehicle terminal device 2 (communication control unit 25) transmits the action history data A, the user ID information is also transmitted. In the present embodiment, the user ID corresponds to “identification information” in the present invention.

  In addition, the communication control unit 25 performs processing for requesting the management server 5 to distribute the latest movement history data T. For example, the communication control unit 25 accesses the management server 5 periodically (for example, every week, every month, etc.), and requests distribution of the latest movement history data T at that time. Alternatively, the communication control unit 25 accesses the management server 5 at a timing manually specified by the user, and requests distribution of the latest movement history data T at that time.

  The communication control unit 51 of the management server 5 is a functional unit that controls transmission and reception of information with each in-vehicle terminal device 2. The communication control unit 51 establishes a connection with the in-vehicle terminal device 2 when there is a request for transmission of the action history data A or the distribution of the latest movement history data T from each in-vehicle terminal device 2, and then the communication interface Each data is transmitted and received via 58.

  The data collection unit 52 is a functional unit that collects the action history data A of each user. As described above, when the in-vehicle terminal device 2 transmits the action history data A, the information of the user ID is also transmitted. The data collection unit 52 associates these user IDs with the action history data A, and collects the action history data A of a plurality of users in association with each user ID.

  The behavior history database generation unit 53 is a functional unit that generates and updates the behavior history database 62. The behavior history database generation unit 53 generates and updates the behavior history database 62 by storing (storing) the behavior history data A of a plurality of users collected by the data collection unit 52 in the behavior history database 62. The action history database generation unit 53 stores the action history data A of a plurality of users in the action history database 62 in association with each user ID (see FIG. 5).

  The history behavior pattern determination unit 54 is a functional unit that determines, from a plurality of behavior patterns, a behavior pattern that matches each behavior tendency of a plurality of users, based on the behavior history data A collected for each user. Here, the “behavior pattern” referred to in the present application is a concept representing an action tendency related to a movement mode between visit points and a stay tendency at each visit point. The history behavior pattern determination unit 54 classifies and defines user behavior trends into a plurality of behavior patterns, and alternatively determines a behavior pattern that matches each user behavior trend from among the plurality of defined behavior patterns. To do.

  The behavior pattern is based on at least one of the length of the travel distance between the visited points, the length of the staying time at the visited points, and a predetermined start time or end time of the specific action. It can be classified. In the present embodiment, as an example, behavior patterns are classified based on the distance traveled between the visit points, the stay time at the visit points, the departure time, and the return time (see FIG. 6). In the present embodiment, the entire daily travel behavior corresponds to the “specific behavior” in the present invention.

  The history behavior pattern determination unit 54 evaluates the behavior tendency regarding the movement distance between the visit points by one of “short” / “standard” / “long”. In addition, the behavior tendency regarding the staying time at the visit point is evaluated by any one of “short” / “standard” / “long”. Further, the behavior tendency regarding the departure time is evaluated by any one of “early” / “standard” / “late”. In addition, the behavior tendency regarding the return time is evaluated by any one of “early” / “standard” / “late”.

  The historical behavior pattern determination unit 54 evaluates each item using a statistical method. FIG. 7 schematically shows an evaluation method for the moving distance between the visited points. As shown in the upper part of FIG. 7, the history behavior pattern determination unit 54 refers to the movement distance included in the plurality of behavior history data A of the user who is paying attention, and calculates the frequency for each category according to the length. calculate. Next, as shown in the middle row, the history behavior pattern determination unit 54 deletes the estimated invalid data that may be irregular from all the data related to the movement distance, and extracts only the other estimated valid data To do. For example, a predetermined number of upper and / or lower data (for example, 5% each) when sorted in order of movement distance can be used as estimated invalid data.

  Next, as shown in the lower part, the history behavior pattern determination unit 54 calculates a representative value having the estimated effective data as a population. Examples of such representative values include an average value, a mode value, a median value, and the like, and in this example, an average value is calculated. Then, the history behavior pattern determination unit 54 compares the calculated representative value with at least one predetermined determination threshold (two in this example) and relates to the movement distance based on the magnitude relationship between them. The behavior tendency is determined by multi-level evaluation (three-level evaluation in this example). In addition, regarding the stay time at the visit point, the departure time, and the return time, the behavior tendency regarding each is determined by the same method.

  The history behavior pattern determination unit 54 classifies the behavior pattern for each user according to the combination of the evaluation results for each item. In this example, the history behavior pattern determination unit 54 performs three-level evaluation for each of the four items, and determines one behavior pattern from a total of 81 patterns defined by a combination thereof. In FIG. 6, for example, the behavior patterns of each user identified by the user IDs “P1” to “P4” are classified into “ABcc”, “BBbb”, “CCbc”, and “AAaa”, respectively. An example is shown.

  The movement history database generation unit 55 is a functional unit that generates and updates the movement history database 63. The movement history database generating unit 55 generates movement history database 63 by extracting movement history data T from action history data A of a plurality of users and storing (storing) this movement history data T in movement history database 63. Update. Here, the movement history data T is data in which movement histories between visiting points of a plurality of users (history of visiting points) are recorded. For example, referring to the action history data A of the “P1” user shown in the forefront of FIG. 5, it can be seen that the “P1” user visited each visit point in the order of D 1 → D 9 → D 2 → D 12. Based on this, the movement history database generating unit 55 moves the movement history data T representing that the “P1” user has moved from D1 to D9, moved from D9 to D2, and moved from D2 to D12, respectively. Is generated (see FIG. 8). The movement history database generation unit 55 similarly generates movement history data T for other users.

  The movement history database generation unit 55 stores the movement history data T for each user in the movement history database 43 in association with any of a plurality of defined behavior patterns. The movement history database generation unit 55 associates the user ID, the behavior pattern determined for each user by the history behavior pattern determination unit 54, and the movement history data T extracted from the behavior history data A with each other. 63. In the present embodiment, the movement history data T is generated as information including a user ID (see FIG. 8), and the behavior pattern of each user is stored in association with the user ID (see FIG. 6). Thereby, the action pattern of each user and the movement history data T are appropriately associated via the user ID. In the present embodiment, the movement history database generation unit 55 corresponds to the “database generation unit” in the present invention. The movement history database generation unit 55 is a functional unit different from the behavior history database generation unit 53 described above, one of which is referred to as a first database generation unit and the other as a second database generation unit. It can also be called.

  The movement history data T of a plurality of users is used to calculate the correlation (the degree of correlation between points) between a plurality of visited points. FIG. 9 conceptually shows the degree of correlation between points ascertained from the movement history data T stored in the movement history database 63. In addition, what is shown here is the correlation between points based on all the user data. In this figure, a line connecting two points indicates that a user moving between the two points has existed significantly in the past. The thickness of each line indicates the degree of correlation, indicating that the correlation becomes higher (the past movement frequency was higher) as the line gets thicker, and the correlation becomes lower (the past movement frequency becomes lower). It was low). For example, in the example of FIG. 9, when attention is paid to the point D1, the point D1 has a very high correlation with the point D9, a high correlation with the point D2, and a certain degree of correlation with the point D4 is recognized. No significant correlation is observed with the point D6. Correlation between other points can be considered similarly. Based on the concept of the degree of correlation between points, the destination recommendation function is realized. This point will be described later.

  The management server 5 (communication control unit 51) distributes the movement history data T in accordance with the distribution request from each in-vehicle terminal device 2. The update processing unit 24 of the in-vehicle terminal device 2 newly generates the movement history database 43 based on the received movement history data T or updates the existing movement history database 43 to the latest state. In this sense, the movement history database 43 of each in-vehicle terminal device 2 is substantially a copy of the movement history database 63 of the management server 5, and the two databases 43 and 63 can be regarded as the same in the present invention. That is, the contents of both databases 43 and 63 are the same except for the deviation of the data contents due to the difference in update frequency. In this way, a movement history database 43 that is a substantial duplicate of the movement history database 63 generated in the management server 5 is generated in each in-vehicle terminal device 2.

  The target behavior pattern determination unit 26 of the in-vehicle terminal device 2 is a functional unit that determines a target behavior pattern that matches the behavior tendency of the target user. The target behavior pattern determination unit 26 alternatively determines the target behavior pattern from the same plurality of behavior patterns defined by the history behavior pattern determination unit 54 of the management server 5. Various modes can be adopted as the target behavior pattern determination method by the target behavior pattern determination unit 26. For example, the target behavior pattern determination unit 26 may determine the target behavior pattern by a statistical method similar to or similar to the history behavior pattern determination unit 54 based on the behavior history data A stored in the behavior history database 42. good. Further, the target behavior pattern determination unit 26 may acquire information on the determination result by the history behavior pattern determination unit 54 for the target user, and determine the target behavior pattern based on the information. Alternatively, the target action pattern may be determined by direct input by the target user manually. These may be changeable according to the accumulation amount of the action history data A for the target user.

  The reference point setting unit 27 is a functional unit that sets a guidance reference point according to a predetermined condition (guidance reference point setting condition). The guidance reference point is a point serving as a reference for determining a point that can be a destination candidate included in the guidance information G. In such a guidance reference point, a destination (including a transit point) designated by the target user, a self-location indicated in the self-location information, and the like can be set. In the present embodiment, the guidance reference point setting condition is a condition regarding whether or not the destination is designated by the target user. When the destination is designated by the target user, the reference point setting unit 27 automatically sets the designated destination as the guidance reference point. When the destination is not specified, the reference point setting unit 27 may automatically set its own position as the guidance reference point. The reference point setting unit 27 may set its own position as the guidance reference point even when the destination is specified, particularly when the target user specifies it.

  The data extraction unit 28 is a functional unit that extracts the movement history data T associated with the target behavior pattern and including the guidance reference point from the movement history database 43. The data extraction unit 28 extracts data associated with the target behavior pattern (the behavior pattern that matches the behavior tendency of the target user) from the movement history data T stored in the movement history database 43. That is, the data extraction unit 28 performs the first filtering on all the movement history data T based on whether or not the movement history data T is another user showing the same behavior tendency as the target user. . Moreover, the data extraction part 28 extracts the data containing the information regarding the set guidance reference | standard point among the movement history data T after the 1st filtering. That is, the data extraction unit 28 performs the second filtering on the movement history data T after the first filtering based on whether or not the guidance reference point currently set for the target user is included.

  The order of these two filtering is not particularly limited. Contrary to the above description, after performing the second filtering based on the guidance reference point, the first filtering based on the behavior pattern may be performed. In any case, the movement history data T that is finally extracted through the two filterings is that other users who have matched with the behavior tendency of the target user visited in conjunction with the previous visit to the guidance reference point. It contains information on the location of

  In addition, the correlation degree between points grasped based on the movement history data T after the first filtering (referred to as “correlation degree between post-processing points”) is based on all user data (see FIG. 9). ) May be different. Further, the degree of correlation between post-processing points may be different between target users having different target behavior patterns. For example, FIG. 10 shows the degree of correlation between post-processing points for a user with a relatively short moving distance between visited points. FIG. 11 shows the degree of correlation between post-processing points for a user who has a relatively long moving distance between visited points. It can be clearly understood that they exhibit completely different forms.

  For example, when attention is paid to the point D9, when all user data is considered, the degree of correlation with the point D1 is the highest (see FIG. 9). On the other hand, when filtering is performed for a user with a relatively short moving distance, the degree of correlation with the point D1 is almost lost and the degree of correlation with the point D8 is the highest (see FIG. 10). In addition, when filtering is performed for a user with a relatively long moving distance, the degree of correlation with the point D1 is low and the degree of correlation with the point D2 is the highest (see FIG. 11). As described above, in the present embodiment, the degree of correlation between post-processing points that is highly likely to match the behavior tendency regarding the movement mode between the visited points is determined for each target user according to the target behavior pattern.

  The recommended point presentation unit 29 is a functional unit that presents a recommended point R, which is a point recommended as a destination candidate, to the target user. The recommended spot presenting unit 29 determines the recommended spot R based on the movement history data T extracted by the data extracting unit 28 and presents it to the target user. As described above, the movement history data T extracted by the data extraction unit 28 is obtained from other points that have been visited when another user who matches the behavior tendency of the target user visited the guidance reference point in the past. Contains information. Therefore, the recommended point presentation unit 29 determines a recommended point R as a recommended point R that is included in the movement history data T that is finally extracted through two filterings and that has a relatively high movement frequency with respect to the guidance reference point. To do. In other words, the recommended point presentation unit 29 determines a recommended point R that has a relatively high degree of correlation between post-processing points based on the guide reference point.

  The recommended point presentation unit 29 determines at least one point including the point having the highest movement frequency with respect to the guidance reference point as the recommended point R. Then, the recommended point presentation unit 29 presents the determined recommended point R as a candidate for a destination next to the currently set destination. For example, the guidance information G to that effect is displayed in a guidance frame set in the display area of the display input device 36 or uttered from the voice output device 37. FIG. 12 shows an example in which one point having the highest movement frequency with respect to the guide reference point is displayed and voice-guided as a recommended point R (next destination candidate).

1-2. Navigation Process Procedure A navigation process procedure (navigation method) executed in the navigation system 1 according to the present embodiment will be described. The procedure of the navigation process described below is executed by hardware, software (program), or both constituting each functional unit of the navigation system 1. When each functional unit is configured by a program, the arithmetic processing device included in the navigation system 1 operates as a computer (for realizing each function) that executes the program that configures each functional unit described above.

  FIG. 13 is a flowchart showing a procedure for generating the action history database 42 in each in-vehicle terminal device 2. In the action history database generation process, first, the date of the day and the departure time of the home (or the previous night's accommodation) are recorded (step # 01). It is determined whether or not the own position indicated in the own position information is located outside the living area for the user (# 02). Note that the range of the living area is set in advance for each user based on the home position. If it is determined that it is out of the living area (# 02: Yes), then the presence of a visit point (a point set as a destination or a point determined to have stayed for a predetermined time or more) is confirmed (# 03). If it is determined that there is a visit point (# 03: Yes), the point ID, arrival time, and stay time for the visit point are recorded (# 04). The processes of steps # 03 and # 04 are repeatedly executed until the user arrives at his / her home (or staying place of the day). When the return is determined (# 05: Yes), the return time is recorded (# 06). As described above, the action history data A of the day is generated, and the action history database 42 is generated (updated) based on the action history data A (# 07).

  FIG. 14 is a flowchart showing the procedure of the generation process of the movement history database 63 in the management server 5. In the movement history database generation process, first, action history data A for each user is collected from each in-vehicle terminal device 2 (# 11). Based on the collected action history data A, an action history database 62 including action history data A of all users is generated (# 12). At this time, the action history data A is stored in a state associated with the user ID. The behavior history data A for each user is extracted based on the user ID (# 13), and the behavior pattern for each user is determined by applying a statistical method to these (# 14). Further, the movement history data T is extracted from the action history data A (# 15). The action pattern determination and the movement history data T extraction may be performed in the reverse order. Based on the extracted movement history data T, a movement history database 63 including movement history data T for all users is generated (# 16). At this time, the movement history data T is stored in a state associated with the user ID and each behavior pattern. Then, upon receiving a distribution request from each in-vehicle terminal device 2 (# 17: Yes), the movement history data T is distributed (# 18).

  FIG. 15 is a flowchart showing the procedure of the recommended point R presentation process in each in-vehicle terminal device 2. In the recommended point presentation process, first, an action pattern (target action pattern) of the target user is determined (# 21), and a guide reference point is set (# 22). These may be performed in the reverse order. Next, data associated with the target behavior pattern (the behavior pattern that matches the behavior tendency of the target user) is extracted from all the movement history data T (# 23). Subsequently, data including information related to the guidance reference point is extracted from the movement history data T extracted in step # 23 (# 24). Then, a point that is included in the movement history data T after the two extraction processes of steps # 23 and # 24 and that has a relatively high movement frequency with respect to the guide reference point is determined as the recommended point R ( # 25). The recommended point R is presented to the target user as a next destination candidate (# 26).

  As described above, the navigation system 1 and the navigation method according to the present embodiment are characterized in that data after filtering based on an action pattern (first filtering) is used when realizing a destination recommendation function. . Thereby, the destination recommendation function in which each user's behavior tendency regarding the movement mode between the visit points is appropriately considered is realized. That is, for each user, it is possible to preferentially propose a recommended spot R as a spot that is likely to match the behavior tendency according to the behavior pattern. Therefore, even when the user visits an unfamiliar area, it is possible to provide useful information to each user.

2. Second embodiment 2-1. Configuration of Navigation System In the first embodiment described above, the target user (the user who is the target for providing the guidance information G) is a point that has been visited by other users who have similar behavior patterns to the target user in the past. A configuration that presents either as a recommended destination has been described as an example. In the present embodiment, a point visited by a user who has another behavior pattern similar to that of the target user and whose positional relationship between the home position and the guidance reference point is close to that of the target user. One of these is presented as a recommended destination. As a result, it is possible to preferentially propose a point that is more likely to match the intention of the target user as a recommended point.

  The in-vehicle terminal device 102 according to the present embodiment includes the data extraction unit 71, the update processing unit 72, the user database 73, the positional relationship pattern determination unit 74, and the recommended spot presentation unit 75. This is different from the configuration of the in-vehicle terminal device 2 of the embodiment. The management server 105 of this embodiment is different from the configuration of the management server 5 of the first embodiment in that it includes a user database 64 and a data collection unit 65. The data extraction unit 71, the update processing unit 72, the recommended spot presentation unit 75, and the data collection unit 65 are the data extraction unit 28, the update processing unit 24, the recommended spot presentation unit 29, and the data collection unit in the first embodiment. The same name as 52, but the content of the process is different. Hereinafter, the configuration different from the configuration of the first embodiment will be mainly described. In addition, about the point which is not demonstrated especially, it is set as the structure similar to said 1st embodiment.

  The user database 73 provided in the in-vehicle terminal device 102 stores (stores), for each user, user information that is information about various users including home data K that is information indicating the home positions of a plurality of users. Has been. The home data K is information for specifying the approximate location of the user's home location, and is, for example, a home address, a telephone number, a postal code, or the like. In the present embodiment, at the time of initial setting immediately after purchase of the in-vehicle terminal device 102 (the vehicle in which the in-vehicle terminal device 102 is mounted), the user can set the home address, home telephone number, etc. via the display input device 36 or the like. By performing a user information registration operation, user information including the user's home data K is stored in the user database 73. Specifically, when the registration operation is performed, the update processing unit 72 generates a user database 73, stores the registered user information, and transmits the user information to the management server 5 via the communication control unit 25. . In addition to the home data K, the user information may include various types of information such as places where the user frequently visits and favorite points.

  The data collection unit 65 of the management server 105 has a function of collecting user information (including home data K) of each user in addition to a function of collecting the action history data A of each user. Further, when collecting the user information of each user, the management server 105 collects the user ID transmitted together with the user information in association with each other. The user information of a plurality of users collected by the data collection unit 65 is stored (stored) in a format in which personal information cannot be specified in the user database 64 generated and updated by a user database generation unit (not shown). .

  The management server 105 distributes user information of all users stored in the user database 64 in a format in which personal information cannot be specified in accordance with a distribution request from each in-vehicle terminal device 102. When receiving the user information from the management server 105, the update processing unit 72 of the in-vehicle terminal device 102 updates the user database 73 to the latest state based on the received user information. When the registered home address or the like is changed, each in-vehicle terminal device 102 (each user) transmits the changed information together with the user ID to the management server 105, and the management server 105 User information (including home data K) stored in the user database 64 is updated based on the user ID. The management server 105 then updates the updated user database 64 with respect to all the in-vehicle terminal devices 102 in which user information is registered, the in-vehicle terminal devices 102 in which the user information distribution request has been made even once, and the like. Information is distributed (here, only difference information is distributed). That is, the user database 73 is substantially a copy of the user database 64.

  The positional relationship pattern determination unit 74 is a functional unit that determines a target positional relationship pattern that matches the positional relationship between the home position of the target user and the guidance reference point from a plurality of predetermined positional relationship patterns. In the present embodiment, the positional relationship pattern determination unit 74, when the guide reference point is set by the reference point setting unit 27, from the guide reference point based on the guide reference point and the home data K of the target user. The distance to the home position and the relative orientation between the guidance reference point and the home position are calculated. Then, the positional relationship pattern determination unit 74 refers to a plurality of predetermined positional relationship patterns (hereinafter referred to as reference positional relationship patterns), selects a positional relationship pattern that matches the positional relationship of the target user, and selects the target position. Decide on a relationship pattern. Specifically, the positional relationship pattern determination unit 74 uses the position coordinates of the guidance reference point set by the reference point setting unit 27, the home address (home data K) of the target user stored in the user database 73, and the like. The distance of the shortest route connecting the two positions is calculated from the identified position coordinates of the home, and the relative orientation of the target user's home around the guidance reference point is calculated. Then, the positional relationship pattern determination unit 74 refers to a predetermined reference positional relationship pattern, selects a pattern that matches the positional relationship of the target user, and determines the target positional relationship pattern as the target positional relationship pattern. The positional relationship pattern determination unit 74 is configured to calculate a linear distance between the position coordinates instead of calculating the distance of the shortest route connecting the position coordinates of the guidance reference point and the position coordinates of the target user's home. May be. Further, the positional relationship pattern determination unit 74 may be configured to calculate the relative orientation of the guidance reference point centered on the home position instead of the relative orientation of the home position centered on the guidance reference point.

  In addition, when the guide reference point is set by the reference point setting unit 27, the positional relationship pattern determination unit 74 determines the target position relationship pattern when extracting the movement history data T of another user by the data extraction unit 71. Similarly to the determination, each positional relationship pattern that matches the positional relationship between the home position of another user and the guidance reference point is determined.

  Here, the positional relationship pattern is defined by classifying the positional relationship between two points into a plurality based on one or more indices. In the present embodiment, the positional relationship patterns classified into a plurality in advance are stored (stored) in a storage device such as the user database 73 as a reference positional relationship pattern so that they can be referred to by other functional units. The positional relationship pattern in the present embodiment includes at least one of a relative orientation between a point that can be a home position and a point that can be a guidance reference point, and a distance between the point that can be the home position and a point that can be a guidance reference point. Based on multiple categories. In the following, description will be given using an example in which the positional relationship patterns are classified into a plurality based on both the relative azimuth and the distance. In this embodiment, specifically, as a classification based on the relative orientation as one index, as shown in FIG. 19, centering on one point between the two points, eight directions (east, west, South, North, Southeast, Southwest, Northeast, Northwest). In addition, as a classification based on distance as another index, it is classified into any one of the distance categories divided into “far”, “standard”, and “close” compared with a predetermined reference distance X. Is done. Specifically, for example, a straight reference distance X (a straight line connecting the two points defined in advance with one point out of two points that can be a home position and a guide reference point as a reference point) A distance shorter than the distance) is set, the range from the reference point to the reference distance X + Y (Y is an arbitrary value smaller than X) and the reference distance X−Y is “standard”, and the reference distance from the reference point A range less than XY is classified as “close”, and a range larger than the reference distance X + Y from the reference point is classified as “far”. The reference positional relationship pattern is a pattern classified according to the combination of the classification results of these azimuth divisions and distance divisions. In this embodiment, as shown in FIGS. 18 and 19, A to D, Ab, Ad, Cb, and Cd are assigned to each azimuth section, and the above-mentioned distance sections are “far” and “standard”. , “Close” are assigned α, β, and γ, respectively. As a positional relationship pattern that is a combination of these, “Bβ” is assigned to the classification in which the relative direction is “east” and the distance is “standard”, the relative direction is “southeast”, and the distance is “Cbγ” is assigned to the classification in which “is close”.

  The data extraction unit 71 is the movement history data T that is associated with the target action pattern and includes the guidance reference point from the movement history database 43, and the positional relationship pattern between the home position and the guidance reference point is the target position. It is a functional unit that extracts user movement history data T that is the same as the relationship pattern. In the present embodiment, first, the data extraction unit 71 extracts movement history data T including information on the set guidance reference point from the movement history data T stored in the movement history database 43. That is, the data extraction unit 71 performs the first filtering on all the movement history data T based on whether or not the guidance reference point currently set for the target user is included. Then, the data extraction unit 71 extracts the movement history data T associated with the target behavior pattern (the behavior pattern that matches the behavior tendency of the target user) from the movement history data T after the first filtering. That is, the data extraction unit 71 uses the movement history data T after the first filtering as a target, based on whether or not the movement history data T is the other user's movement history data T showing the same behavior tendency as the target user. Perform filtering. Further, in the present embodiment, the data extraction unit 71 is a target whose positional relationship pattern between the home position and the guidance reference point is determined by the positional relationship pattern determination unit 74 in the movement history data T after the second filtering. User movement history data T that is the same as the positional relationship pattern is extracted. That is, the data extraction unit 71 uses the movement history data T after the second filtering as a target, based on whether or not the movement history data T is a user having the same positional relationship pattern as the target positional relationship pattern. 3 filtering is performed.

  Specifically, in the third filtering, the positional relationship pattern determination unit 74 sets the user's home position and guidance reference point for each user related to all the movement history data T after the second filtering. A positional relationship pattern that matches the positional relationship is determined. And the data extraction part 71 specifies the user who has the same positional relationship pattern as the target positional relationship pattern from among them, and based on the user ID of the specified user, the movement history data T after the second filtering Then, data including information on the identified user is extracted.

  The order of the first to third filtering is not particularly limited. For example, the first and second filtering may be performed after the third filtering. In any case, the movement history data T finally extracted through the three filterings is another user that matches the behavior tendency of the target user, and the positional relationship between the home position and the guidance reference point is the target user. When the user who has a close relationship with the information visits the guidance reference point in the past, information on other points visited is included.

  The recommended spot presentation unit 75 is a functional unit having the same function as the recommended spot presentation unit 29 in the first embodiment. In the present embodiment, the recommended spot presentation unit 75 has a function of determining a recommended spot based on the movement history data T extracted by the data extraction unit 71 and presenting it to the target user.

2-2. Procedure of Navigation Process The procedure of the recommended spot presentation process in each in-vehicle terminal device 102 will be described using the flowchart shown in FIG. FIG. 20 is a flowchart showing the procedure of the recommended point R presentation process in each in-vehicle terminal device 102. In the recommended spot presentation process, first, an action pattern (target action pattern) of the target user is determined (# 31), and a guidance reference point is set (# 32). These may be performed in the reverse order. Then, based on the guidance reference point set in step # 32 and the home data K of the target user, the target position relationship pattern is determined (# 33). Next, data including information related to the guidance reference point is extracted from all the movement history data T (# 34). Subsequently, data associated with the target behavior pattern (the behavior pattern that matches the behavior tendency of the target user) is extracted from the movement history data T extracted at step # 34 (# 35). Further, user data having a positional relationship pattern that matches the target positional relationship pattern determined in step # 33 is extracted from the movement history data T extracted in step # 35 (# 36). A point that is included in the movement history data T that has undergone the three extraction processes of Step # 34, Step # 35, and Step # 36 and that has a relatively high movement frequency with respect to the guide reference point is set as the recommended point R. Is determined (# 37). This recommended point R is presented to the target user as a next destination candidate (# 38). Note that the three extraction processes of step # 34, step # 35, and step # 36 may be executed in a different order from the above.

3. Other Embodiments Finally, other embodiments of the navigation system according to the present invention will be described. Note that the configurations disclosed in the following embodiments can be applied in combination with the configurations disclosed in other embodiments as long as no contradiction arises.

(1) In the first embodiment, the recommended point presenting unit 29 has described an example in which the recommended point R is determined as the recommended point R and is presented to the target user with the highest frequency of movement from the guidance reference point. . However, the embodiment of the present invention is not limited to this. The recommended point presentation unit 29 may present a plurality of points including the point having the highest movement frequency with respect to the guidance reference point as the recommended points R. For example, when sorting is performed based on the movement frequency between the guidance reference points, a plurality of points included on the higher movement frequency side may be presented as the recommended points R.

(2) In the first embodiment, the recommended point presentation unit 29 does not ask the moving direction (direction) between two points including the guidance reference point, and the movement frequency between the guidance reference point is relative. An example in which a high spot is determined as the recommended spot R and presented to the target user has been described. However, the embodiment of the present invention is not limited to this. For example, the recommended point presenting unit 29 may present a recommended point R as a recommended point R based on the moving direction (direction) between the two points as the recommended point R. .

(3) In the first embodiment described above, when extracting the movement history data T from the action history data A, the example in which only the movement between the directly moved points is extracted has been described. However, the embodiment of the present invention is not limited to this. For example, indirect movement between points excluding the waypoints along the way may be extracted (D1 → D2, D1 → D12, etc. in the example of FIG. 5). In this case, the movement history data T related to movement between indirect points may be weighted smaller than the movement history data T related to movement between direct points. That is, when counting the movement frequency between points, the movement between points may be counted with a value smaller than “1”.

(4) In the first embodiment, the example has been described in which the recommended spot presentation unit 29 presents the determined recommended spot R as a candidate for the next destination after the currently set destination. However, the embodiment of the present invention is not limited to this. For example, the recommended spot presentation unit 29 may present the determined recommended spot R as a stopover candidate until the destination that is currently set is reached.

(5) In the first embodiment, the example in which the entire day of travel behavior is defined as “specific behavior” and the behavior patterns of each user are classified based on the departure time and the return home time has been described. However, the embodiment of the present invention is not limited to this. For example, even if a specific event (for example, a meal) in one day's behavior is defined as “specific behavior”, the behavior pattern of each user is classified based on at least one of start time and end time such as lunch or dinner good.

(6) In the first embodiment, the three-stage evaluation is performed for the travel distance between the visit points, the stay time at the visit points, the departure time, and the return time, and the action patterns are classified based on the combinations thereof. An example has been described. However, the embodiment of the present invention is not limited to this. The items that are the basis of the behavior pattern classification and the evaluation method of each item can be arbitrary. The classification basic items may be fewer or more than the example in the above embodiment. Regarding the evaluation method, it may be coarser or further subdivided as compared to the example in the above embodiment.

(7) In the first embodiment, when evaluating each item for behavior pattern classification, representative values having the estimated effective data remaining after deleting the estimated invalid data from all the data as the population An example of calculating and determining a behavior tendency based on the representative value has been described. However, the embodiment of the present invention is not limited to this. For example, it is possible to calculate a representative value using all data including data that may be irregular as a population, and determine a behavior tendency based on the representative value.

(8) In the first embodiment, the example in which the action history of the target user is reflected when the management server 5 generates (updates) the movement history database 63 has been described. However, the embodiment of the present invention is not limited to this. For example, the in-vehicle terminal device 2 corresponding to the target user may be configured not to transmit the action history data A to the management server 5. And the vehicle-mounted terminal device 2 is comprised so that a destination recommendation function may be implement | achieved using the movement history data T produced | generated based on the action history data A collected exclusively from the other vehicle-mounted terminal device 2. FIG. Also good. In this case, the movement history data T may be distributed from the management server 5 or may be stored (stored) in advance in the in-vehicle terminal device 2.

(9) In the first embodiment, the in-vehicle terminal device 2 includes the data extraction unit 28 and the recommended spot presentation unit 29, and the in-vehicle terminal device 2 side performs data extraction processing, recommended spot determination processing, and recommended spot presentation processing. The example where is executed has been described. However, the embodiment of the present invention is not limited to this. For example, the data extraction process and the recommended spot determination process among these may be executed on the management server 5 side. In this case, the management server 5 includes a data extraction unit and a recommended spot determination unit, and the recommended spot R is determined by these functional units in the same manner as in the above embodiment. Information on the determined recommended spot R is transmitted to the in-vehicle terminal device 2 of the target user, and the recommended spot R is presented as a destination candidate.

(10) In the first embodiment, the example in which the aggregated movement history data T is directly distributed via the communication network 3 between the management server 5 and the in-vehicle terminal device 2 has been described. However, the embodiment of the present invention is not limited to this. The movement history data T may be distributed through the user's hand. For example, each user who has downloaded the movement history data T distributed via the communication network 3 using a personal computer at home or the like transfers the acquired movement history data T to the in-vehicle terminal device 2 using a medium such as a flash memory. May be. Alternatively, each user may transfer the movement history data T distributed in a state of being stored (stored) in a medium such as a flash memory or an optical disk to the in-vehicle terminal device 2.

(11) In the first embodiment described above, an example in which the in-vehicle terminal device 2 configuring the navigation system 1 is a stationary navigation device fixed to the vehicle has been described. However, the embodiment of the present invention is not limited to this. The vehicle-mounted terminal device 2 should just be mounted in the vehicle with the user at least, and does not necessarily need to be fixed to the vehicle. Examples of such a non-installation type navigation device include a PND (Portable Navigation Device), a multi-function mobile phone equipped with a predetermined navigation application and the like. In addition, when using these apparatuses, it does not necessarily need to be mounted on the vehicle. That is, the navigation system 1 according to the present embodiment may be configured by a mobile terminal device having a navigation function and a management server 5 provided to be able to communicate with a plurality of mobile terminal devices.

(12) In the second embodiment, when the positional relationship pattern determination unit 74 determines the positional relationship pattern of the target user and other users, the guidance reference point and the home position of each user (including the target user) It has been described as a configuration that is determined based on the coordinates. However, the embodiment of the present invention is not limited to this. For example, instead of the location coordinates of each user's home, the administrative divisions and other areas such as prefectures and cities where each user's home is located are identified as the home surrounding area from the registered home phone number or postal code. The position relationship pattern may be determined using the representative point of the area around the home (for example, the position of an administrative agency such as a prefectural office or city hall or the center of gravity when the area is regarded as a plane) as the home position. In addition, since the area where the user's movement history points are gathered is highly likely to be within the user's living area, the movement history points are gathered if the home address or the like is not registered by the user. The configuration may be such that the positional relationship pattern is determined with the representative point (such as the average position) of the area as the home position.

(13) In the second embodiment, the positional relationship pattern includes a relative orientation between a point that can be a home position and a point that can be a guide reference point, and a point that can be the home position and a guide reference point. The description has been made using an example classified into a plurality based on both the distances to and from. However, the embodiment of the present invention is not limited to this. For example, classification based only on the relative orientation of a point that can be a home position and a point that can be a guide reference point, a classification based only on the distance between a point that can be a home position and a point that can be a guide reference point, etc. It is also possible to classify based on the indicators.

(14) In the second embodiment, as the classification based on the relative orientation as one index, any orientation in which the position relation pattern is divided into 8 orientations centering on one point between the two locations. Explained the structure classified into categories. However, the embodiment of the present invention is not limited to this. For example, it is good also as a structure classified into one of the azimuth | direction divisions divided into four directions of east, west, south, and north, and it is good also as a structure classified into one of the azimuth | direction divisions divided into 32 directions. Further, in the second embodiment, the positional relationship pattern is classified into three as “distance”, “standard”, and “close” as compared with the predetermined reference distance X as the classification based on the distance. Although the configuration classified into one of the distance categories has been described, the configuration classified into one of the distance categories classified as “far” or “near”, “far”, “somewhat far” , “Slightly close”, “close”, and the like may be classified into any of the four distance categories. Moreover, the structure classified into any distance division divided into five or more may be sufficient.

(15) In the second embodiment, the data extraction unit 71 is configured to identify a user having a positional relationship pattern that matches the target positional relationship pattern and extract the movement history data T of the user. However, the embodiment of the present invention is not limited to this. For example, if the data extraction unit 71 cannot identify a user having a positional relationship pattern that matches the target positional relationship pattern and cannot be identified, the movement history data of the user having a positional relationship pattern similar to the target positional relationship pattern T may be extracted. Here, the positional relationship pattern similar to the target positional relationship pattern is the case where the classification based on the relative orientation matches but the classification based on the distance does not match, or the classification based on the distance matches, but the classification based on the relative orientation is The case where it classify | categorizes into the azimuth | direction adjacent to each other without matching is included. Specifically, when the target user is “P3”, the target positional relationship pattern is “Cdα” as shown in FIG. If there is no user having the positional relationship pattern “Cdα” from the positional relationship pattern information for each user stored in the user database 73, the classification based on the distance is “α” and “γ”. Although it is different, “P5” having a positional relationship pattern of “Cdγ” that is common in the relative orientation “Cd” is extracted, and movement history data T of a user having a positional relationship pattern that matches the target positional relationship pattern is extracted. It can be set as the structure to do.

(16) Regarding other configurations as well, the embodiments disclosed herein are illustrative in all respects, and embodiments of the present invention are not limited thereto. In other words, configurations that are not described in the claims of the present application can be modified as appropriate without departing from the object of the present invention.

  The present invention can be used in a navigation system capable of executing a destination recommendation function.

1: Navigation system 26: Target behavior pattern determination unit 27: Reference point setting unit 28: Data extraction unit 29: Recommended point presentation unit 42: Behavior history database 43: Movement history database 52: Data collection unit 54: History behavior pattern determination unit 55: Movement history database generation unit (database generation unit)
62: Action history database 63: Movement history database 64: User database 65: Data collection unit 71: Data extraction unit 73: User database 74: Position relationship pattern determination unit A: Action history data K: Home data T: Movement history data G : Guidance information

Claims (9)

A navigation system that provides guidance information related to points that can be candidates for a destination,
A movement history database that classifies and defines user behavior trends into a plurality of behavior patterns, stores movement history data in which movement histories between points of a plurality of users are recorded in association with any of the behavior patterns, and
A target behavior pattern determination unit that determines a target behavior pattern that matches a behavior tendency of a target user to be provided with the guidance information from the plurality of behavior patterns;
A reference point setting unit that sets a guide reference point according to a predetermined condition;
A data extraction unit that extracts the movement history data associated with the target behavior pattern and including the guidance reference point from the movement history database;
Based on the movement history data extracted by the data extraction unit, a recommended point presenting unit that presents to the target user as a recommended point a point having a relatively high movement frequency with the guide reference point;
A navigation system comprising: A data collection unit that collects behavior history data that is data of behavior history of each user in association with identification information for identifying each of a plurality of users;
Based on the behavior history data collected for each user, a history behavior pattern determination unit that determines a behavior pattern that matches each behavior tendency of a plurality of users from among the plurality of behavior patterns;
A database generating unit that associates the identification information, the behavior pattern determined for each user, and the movement history data extracted from the behavior history data with each other, and stores it in the movement history database;
The navigation system according to claim 1, further comprising: A user database storing home data which is information indicating the home position of each of the plurality of users;
A positional relationship pattern determination unit that determines a target positional relationship pattern that matches a positional relationship between the home position of the target user and the guidance reference point from a plurality of predetermined positional relationship patterns;
When extracting the movement history data from the movement history database, the data extraction unit extracts movement history data of a user whose positional relationship pattern between the home position and the guidance reference point is the same as the target positional relationship pattern. The navigation system according to claim 1 or 2, wherein the navigation system is extracted.   The positional relationship pattern is based on at least one of a relative orientation between a point that can be the home position and a point that can be a guide reference point, and a distance between a point that can be the home position and a point that can be a guide reference point. The navigation system according to claim 3, which is classified into a plurality of categories.   Each user's action history included in the action history data includes at least one of an arrival time at each point, a staying time at each point, a moving time between two points, and a moving distance between two points. The navigation system according to any one of claims 2 to 4, comprising one piece of information. When the destination is designated by the target user, the reference point setting unit sets the designated destination as the guidance reference point,
The navigation system according to any one of claims 1 to 5, wherein the recommended spot presentation unit presents the recommended spot as a candidate for a next destination.   The behavior pattern includes at least one of a length of a moving distance between destinations, a length of a staying time at a destination, and a predetermined action start time or end time. The navigation system according to any one of claims 1 to 6, wherein the navigation system is based on classification. A navigation method for providing guidance information related to a point that can be a destination candidate,
By classifying and defining user behavior trends into a plurality of behavior patterns, using a movement history database storing movement history data in which movement histories between points of a plurality of users are recorded in association with any of the behavior patterns,
A target behavior pattern determination step for determining a target behavior pattern that matches a behavior tendency of a target user to be provided with the guidance information from the plurality of behavior patterns;
A reference point setting step for setting a guide reference point according to a predetermined condition;
A data extraction step for extracting the movement history data associated with the target behavior pattern and including the guidance reference point from the movement history database;
Based on the movement history data extracted in the data extraction step, a recommended point presenting step for presenting the target user with a point having a relatively high movement frequency with the guide reference point as a recommended point;
Navigation method including. A navigation program that provides guidance information related to points that can be destination candidates,
A user's behavior tendency is classified and defined into a plurality of behavior patterns, referring to a movement history database storing movement history data in which movement histories between points of a plurality of users are recorded in association with any of the behavior patterns,
A target behavior pattern determination function for determining a target behavior pattern that matches a behavior tendency of a target user to be provided with the guidance information from the plurality of behavior patterns;
A reference point setting function for setting a guide reference point according to predetermined conditions;
A data extraction function for extracting the movement history data associated with the target behavior pattern and including the guidance reference point from the movement history database;
Based on the movement history data extracted by the realization of the data extraction function, a recommended point presenting function for presenting the target user with a point having a relatively high movement frequency with the guide reference point as a recommended point;
A navigation program that enables computers to realize this.

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