Route estimation device, route estimation system, and route estimation method

Abstract

To provide a route estimation technique capable of precisely realizing route estimation.SOLUTION: A route estimation device 1 comprises: a transmitting / receiving part 50 which transmits / receives data to / from a user terminal 7; moving means discrimination part 10 which discriminates moving means for each time on the basis of data and moving means properties received from the user terminal 7; a candidate route information acquisition part 20 which extracts an available candidate route; a use probability calculation part 30 which calculates use probability of the candidate route on the basis of comparison between data acquisition properties in the candidate route and data quality received from the user terminal; and a route estimation information creation part 40 which creates route estimation information on the basis of the use probability.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a route estimation device, a route estimation system, and a route estimation method. [Background technology]

[0002] In recent years, reducing CO2 emissions has become a major social goal in order to achieve the Sustainable Development Goals (SDGs). In particular, since transportation has a significant impact on the environment, including air pollution and global warming, reducing CO2 emissions from travel has become an important issue. Public transportation such as trains and buses have lower CO2 emissions per unit of transportation (passenger-kilometer basis) compared to private cars, so it is important for individual users to use public transportation to reduce CO2 emissions from travel. If it were possible to detect the means of transportation (train, bus, car, walking, etc.) from communication information obtained from each user's smartphone, while estimating the energy consumption related to operation and visualizing the amount of CO2 emissions reduction per person for the section and train that each user rode, it would be possible to promote a modal shift for each user and move towards a sustainable social structure. Furthermore, by providing such user trends to public transportation operators (hereinafter referred to as "operators"), the operators will be able to use this trend data to operate their businesses more efficiently, bringing us one step closer to realizing an environmentally friendly society.

[0003] In addition, to calculate CO2 emissions from employee commuting, which is one item of supply chain emissions, it is common to multiply the CO2 emissions per unit of transportation defined for each mode of transportation (private car, airplane, bus, train, etc.) (the amount of CO2 emissions required to transport one person one kilometer) by the distance traveled.

[0004] However, even for the same transportation mode, the environmental impact differs depending on the vehicle's performance, etc. For example, in the case of private cars, it is generally known that electric vehicles emit less CO2 than gasoline-powered vehicles. Even in the railway industry, new vehicles that can reduce CO2 and NOx emissions have a lower environmental impact than conventional vehicles, and the environmental impact per person varies depending on the number of passengers.

[0005] It is possible to calculate the environmental impact in more detail by utilizing the various data held by businesses, but it can be difficult to accurately grasp the travel routes of individual users.

[0006] Patent Document 1 discloses an information processing device that estimates the behavior of a user who travels using a plurality of transportation means whose operation schedules are managed. This information processing device includes a first acquisition means for acquiring operation-related data relating to the public transportation service, the operation data representing the scheduled departure time and scheduled arrival time for each segment, and a second acquisition means for acquiring a planned travel route, which is a collection of multiple segments included in the route along which the user will travel. The system further includes a generation means for periodically acquiring location information from a user terminal carried by the user and generating travel history data including the location information, and an estimation means for estimating, for each segment included in the planned travel route, the means of transportation used by the user and the entry time and exit time of the corresponding segment based on the travel history data and the operation-related data. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] JP 2020-166608 A Summary of the Invention [Problem to be solved by the invention]

[0008] However, in the technology disclosed in Patent Document 1, the planned travel route is divided into sections called segments, and the user's behavior is estimated by comparing the location information from the user terminal with the operation status of public transportation for each segment. Also, for sections where location information cannot be obtained from the user terminal, the user's behavior is estimated by appropriately connecting the last segment for which location information was obtained and the segment for which acquisition of location information has resumed. Therefore, in the technology disclosed in Patent Document 1, in a section where location information from the user terminal cannot be obtained, the location must be simply estimated from the routes of the sections before and after the section. Also, in the technology disclosed in Patent Document 1, when there are multiple routes with the same starting point and destination point, it is difficult to clearly distinguish which route was traveled. Therefore, there are some shortcomings in route estimation for accurately measuring the environmental load caused by travel. Therefore, an object of the present invention is to provide a route estimation technique that can realize route estimation more precisely than ever before. [Means for solving the problem]

[0009] In order to solve the above problems, one representative route estimation method of the present invention includes a transmitter / receiver unit that transmits and receives data to and from a user terminal, a transportation means discrimination unit that discriminates the transportation means for each time based on the data received from the user terminal and the transportation means characteristics, a candidate route information acquisition unit that extracts usable candidate routes, a utilization probability calculation unit that calculates the utilization probability of the candidate route based on a comparison between the data acquisition characteristics of the candidate route and the data quality received from the user terminal, and a route estimation information creation unit that creates route estimation information based on the utilization probability. Effect of the Invention

[0010] According to the present invention, it is possible to provide a route estimation technique that can realize route estimation more precisely than ever before. Problems, configurations and effects other than those described above will become apparent from the following description of the preferred embodiment of the invention. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a schematic diagram of a configuration of a path estimation system. [Diagram 2] FIG. 2 is a diagram showing an example of transportation means determination information. [Diagram 3] FIG. 3 is a diagram showing an example of train running information. [Figure 4] FIG. 4 is a diagram showing a main flow of route estimation. [Diagram 5] FIG. 5 is a diagram showing the travel speed and the means of travel for each acquisition section of location information. [Figure 6] FIG. 6 is a diagram illustrating an example of the candidate route information. [Figure 7] FIG. 7 is a diagram showing an example of boardable train information. [Figure 8] FIG. 8 is a diagram illustrating an example of data acquisition characteristics. [Figure 9] FIG. 9 is a diagram showing an example of the data acquisition characteristics in a graph format. [Figure 10] FIG. 10 is a diagram showing an example of a variation pattern of data acquisition characteristics. [Figure 11] FIG. 11 is a diagram showing a calculation flow of the utilization probability. [Figure 12] FIG. 12 is a diagram illustrating an example of the route estimation information. [Figure 13] FIG. 13 is a diagram showing an example of a display when an estimated route is displayed on a user terminal. [Figure 14] FIG. 14 is a route map showing an example in which there are multiple travel routes and means of transportation between the same departure and arrival points. [Figure 15] FIG. 15 is a diagram illustrating an example of a hardware configuration according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] The technology disclosed herein is a technology that accurately estimates travel routes by combining data emitted by location sensors, motion sensors, environmental sensors, etc. equipped on user devices such as smartphones carried by users with public transportation operation information, usage information for public transportation and the like, usage information for IC cards and the like, past travel information, etc.

[0013] To estimate a travel route, it is reasonable to use location information emitted by the user's device, but the location information of the user's device may be affected by the surrounding environment, resulting in data loss or reduced accuracy. For example, when a train is traveling underground or in a tunnel, GPS signals cannot reach the train, making it impossible to obtain location information. In addition, mobile data communication signals are unstable on a moving train. In particular, data communication is prone to instability on crowded trains, where users are concentrated.

[0014] Furthermore, in areas with complex transportation networks, there are multiple travel routes between the same starting point and destination. For example, it is not uncommon for a train, subway, expressway, and general road to coexist between two stations. Since travel via these four routes has different environmental impacts, it is important to accurately estimate which route and which transportation mode a user used.

[0015] In addition, users do not always travel between departure and arrival points in the shortest time. For example, they may make a detour between the departure and arrival points for shopping or other reasons, or they may choose a detour route based on congestion or other factors.

[0016] Even in such various situations, precise route estimation is required to accurately estimate the environmental load. In the technology disclosed herein, even if the accuracy of the location information of the user terminal is insufficient, the degree of insufficiency of such accuracy is utilized as a piece of route information, and by combining it with other information, precise route estimation is possible.

[0017] The route estimation in the present disclosure may be performed at the end of a day's travel, or may be performed whenever a user changes modes of transportation. The route estimation result may be confirmed each time by giving the user an opportunity to confirm the result at any time, thereby enabling the route to be confirmed accurately.

[0018] Furthermore, if the station and train used by the user can be identified by the route estimation of the present disclosure, the user can settle the usage fee without purchasing a ticket or touching an IC card to the gate. This makes it possible to eliminate tickets and ticket gates. It also makes it possible to realize ticketless at movie theaters and commercial facilities.

[0019] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Note that the present invention is not limited to the embodiment. In addition, in the description of the drawings, the same parts are denoted by the same reference numerals. When there are multiple components having the same or similar functions, they may be described by using the same reference numerals with different subscripts, or when there is no need to distinguish between these multiple components, the subscripts may be omitted. In addition, although terms such as "first," "second," and "third" may be used in the present disclosure to describe various elements or components, it will be understood that these elements or components should not be limited by these terms. These terms are used only to distinguish one element or component from another element or component. Thus, a first element or component discussed below can also be referred to as a second element or component without departing from the teachings of the inventive concept. In order to facilitate understanding of the invention, the position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings.

[0020] In this disclosure, the term "user terminal" includes devices such as smartphones, mobile phones, tablet computers, personal information terminals, notebook computers, wearable computers, and smart watches. These devices can transmit their location information to the outside by communicating with GPS satellites and base stations. The user terminal may also include an accelerometer, a gravity sensor, a gyroscope, a rotation vector sensor, and the like as motion sensors, and transmit measurement results of acceleration in three axial directions and rotational force as data. The user terminal may also include a barometer, an altimeter, a luminometer, and a thermometer as environmental sensors, and transmit sensor data from these sensors. The user terminal may also include a direction sensor and a magnetic sensor as position sensors, and transmit data from these sensors. Furthermore, the location information transmitted by the user terminal may be obtained by combining sensor information such as a GPS and an accelerometer. Furthermore, the connection of the user terminal may be via Bluetooth, BTL, Wi-Fi, Felica, etc. Furthermore, the user terminal may be equipped with a camera and be able to identify its location by reading a QR code (registered trademark). The user's position information for each sampling time obtained from the user terminal may be stored in the storage unit as coordinate position information.

[0021] In this disclosure, "transportation means" refers to the means used by a user for transportation, such as train, bus, car, walking, etc. In addition, a "candidate route" refers to a candidate or option for a route that can be taken when traveling between any section. For example, a walking route, a driving route for a car, a train or bus route, etc. can be a candidate route. When there are multiple travel routes between the same departure point and destination, there are multiple candidate routes.

[0022] "Data acquisition characteristics for a candidate route" refers to the data acquisition characteristics (difficulty of data acquisition) from a user terminal that are set in advance for each candidate route. The data acquisition characteristics can express the difficulty of data acquisition with any parameter. For example, the data acquisition characteristics may be expressed in a dichotomous manner, "possible" and "not possible," or in three levels, "high," "medium," and "low." Furthermore, the strength of the received radio waves and the accuracy of the location data may be expressed numerically (such as a percentage). Furthermore, the "data acquisition characteristics of a candidate route" may be expressed as the acquisition characteristics at any location on the route, or as the acquisition characteristics at any sampling time. For example, in the case of a railway, it is possible to define the data acquisition characteristics by a combination of a station and a time, a combination of a section on the line and a time, a combination of a train and a time, a combination of a train and a station, etc. Furthermore, the location can be classified by an attribute such as above ground or underground.

[0023] Furthermore, the data acquisition characteristics may be expressed as a variation pattern of the data acquisition characteristics for any section in the candidate route or between any time points. In addition, since the data acquisition characteristics may change depending on the season, date and time, weather, etc., the data acquisition characteristics may include information related to time. The data to be acquired is typically coordinate location information transmitted by a user terminal, but may also be data from sensors in other user terminals. It may also be characteristics of communication information by location and time, such as places where radio waves are easy to reach and places where they are difficult to reach.

[0024] The "data acquisition characteristics for a candidate route" can be created according to various route conditions. For example, it can be created from the geography and line attributes of the route. In this case, the data acquisition characteristics can be set based on the geography and line attributes such as subway, tunnel, deep in the mountains, etc. It can also be created from information on operation. In this case, the data acquisition characteristics can be set based on the congestion situation and the running speed of the train. Furthermore, the data acquisition characteristics may be calculated based on the past performance of the user himself or other users, or may be created using artificial intelligence. For example, the data acquisition characteristics may be obtained by accumulating past data acquisition characteristics (difficulty of data acquisition) on the user's own travel route, or the data acquisition characteristics may be obtained by accumulating and averaging data acquisition characteristics (difficulty of data acquisition) at each point and each time obtained from similar user terminals on a large scale. Furthermore, these data may be combined with attribute information such as season, date and time, weather, and above or below ground using artificial intelligence to create a model regarding the difficulty of data acquisition and use it as the data acquisition characteristics.

[0025] "Data quality" refers to the quality of data that the route estimation device receives from the user terminal. For example, this includes data related to the accuracy of location information and radio wave strength that the route estimation device receives from the user terminal. By comparing the fluctuation patterns of this data quality with the data acquisition characteristics of the candidate routes, it becomes possible to precisely determine the route that the user actually used. In addition, when comparing the variation pattern of the "data acquisition characteristics in the candidate route" and the variation pattern of the "quality of data received from the user terminal", these variation patterns do not necessarily need to be the same parameters. For example, the variation pattern of the "data acquisition characteristics in the candidate route" is the difficulty of acquiring data calculated from the geographical situation of the route, while the variation pattern of the "quality of data received from the user terminal" may be data related to the accuracy of location information.

[0026] <<Route Prediction System>> Next, a configuration example of a route estimation system according to an embodiment of the present disclosure will be described with reference to FIG. Fig. 1 is a schematic diagram of the configuration of a route estimation system. Note that the individual components shown in Fig. 1 include components that are not essential to the invention of the present disclosure. The route estimation system of the present disclosure can be configured from a route estimation device 1, a user terminal 7 connectable to the route estimation device 1 via a network or the like, a route search device 8, and an operation information distribution device 9.

[0027] <Route Estimation Device> The route estimation device 1 can be composed of functional units such as a transportation means discrimination unit 10, a candidate route information acquisition unit 20, a utilization probability calculation unit 30, a route estimation information creation unit 40, and an estimation result correction unit 45, a transmission / reception unit 50, and a memory unit 60 that stores various information and programs. Among these, functional units such as the transportation means discrimination unit 10, the candidate route information acquisition unit 20, the utilization probability calculation unit 30, the route estimation information creation unit 40, and the estimation result correction unit 45 may be implemented in the route estimation device 1 as an API (Application Programming Interface), and these functional units themselves do not necessarily need to be built into the route estimation device 1.

[0028] (Transportation means discrimination unit) The transportation means discrimination unit 10 is a functional unit that discriminates the transportation means of the user based on data received from the user terminal 7 and the transportation means characteristics 61. Here, the transportation means characteristics 61 are data that associates the state of travel, the transportation means used, and their characteristics. For example, the transportation means used can be walking, train, bus, car, bicycle, etc., and the characteristics of each can be the range of travel speed. Specifically, the transportation means characteristics can be defined as the travel speed of 1km / h to 5km / h for walking, and 10km / h to 150km / h for train.

[0029] Therefore, the transportation means discrimination unit 10 can receive, for example, location information from the user terminal 7, calculate the user's travel speed based on the location information, and refer to the transportation means characteristics 61 to discriminate the user's transportation means at the sampling time (this point will be described in more detail in "Step 10" of Figure 4). After determining the user's means of transportation at each time, the transportation means discrimination unit 10 can divide the data for each same means of transportation and classify the means of transportation into public transportation and other means (this point will be described in detail in "Step 20" and "Step 30" of Figure 4).

[0030] (Candidate route information acquisition unit) The candidate route information acquisition unit 20 can acquire public transportation routes that the user may have used by comparing data received from the user terminal 7, such as location information, with public transportation route information 83. In other words, the candidate route information acquisition unit 20 is a functional unit that extracts public transportation routes that can be used by the user based on information obtained from the user terminal.

[0031] (Utilization Probability Calculation Unit) The utilization probability calculation unit 30 is a functional unit that calculates the utilization probability of a candidate route based on the data acquisition characteristics 62 of the candidate route and the data quality received from the user terminal 7. The utilization probability calculation unit 30 may calculate the utilization probability of a candidate route based on the data acquisition characteristics 62 at one point and the data quality received from the user terminal 7, or may calculate the utilization probability of a candidate route based on the data acquisition characteristics 62 at multiple points or an arbitrary section and the data quality received from the user terminal 7. In other words, the utilization probability of a candidate route may be calculated based on a fluctuation pattern of the data acquisition characteristics 62 and data quality in an arbitrary section.

[0032] (Route Estimation Information Creation Unit) The route estimation information generating unit 40 is a functional unit that generates route estimation information based on the utilization probability. For example, the route estimation information creation unit 40 can calculate the user's travel route as route estimation information 69 by linking together candidate routes with the highest probability of use in the section from the start point to the end point of the user's travel.

[0033] (Estimation result correction department) The estimation result correction unit 45 is a functional unit that corrects the route estimation information 69 calculated by the route estimation information creation unit 40. Specifically, when the estimation result of the travel route is disclosed to the user, the estimation result correction unit 45 is a functional unit that corrects the travel route based on the user's input if the estimation result differs from the actual travel. For example, the estimation result correction unit 45 can prompt the user to confirm and correct the route estimation information by displaying a pop-up such as "Are you on the train now?" on the user terminal for the means of transportation currently being used by the user, displaying a pop-up such as "Are you on the train now?" at the timing of getting on / off the train, displaying the contents of the route estimation information 69 at the end of the day, etc. Then, when the user inputs a correction instruction for the route estimation information into the user terminal 7 and transmits it to the route estimation device 1, the estimation result correction unit 45 can correct the route estimation information 69 calculated by the route estimation information creation unit 40.

[0034] Furthermore, the estimation result correction unit 45 may reflect the correction instruction from the user terminal 7 in the transportation means characteristics 61 and the data acquisition characteristics 62 . For example, if the correction instruction from the user terminal 7 is to correct the type of transportation means, it is possible to correct the range of the travel speed defined in the transportation means characteristics 61 so that correct results can be obtained from the next time. Also, if the correction instruction from the user terminal 7 is not about the type of transportation means but about the route or more detailed matters, it is possible to correct the data acquisition characteristics 62 so that correct results can be obtained from the next time. In this way, by adding a route confirmation process by the user, route estimation can be made more reliable, and at the same time, route estimation after a correction instruction is issued can be made more accurate. However, the estimation result correction unit 45 is not an essential requirement of the technology disclosed herein.

[0035] (Storage part) Next, various data stored in the storage unit will be described, although the description of data etc. already explained in the explanation of the functional unit may be simplified or omitted.

[0036] [Transportation characteristics] The transportation means characteristics 61 have already been explained in the transportation means discrimination unit 10, so the explanation will be omitted.

[0037] [Data acquisition characteristics] The data acquisition characteristics 62 are as explained above as “data acquisition characteristics for a candidate route.” Details of the data acquisition characteristics will be described later.

[0038] [Transportation method discrimination criteria] The transportation means discrimination criterion 63 is a criterion for discriminating the possibility of using public transportation means according to the distance between the user's location and a boarding location of the transportation means, etc. For example, it is a criterion that determines the probability for estimating that the transportation means of a station or bus stop is used based on the distance between the coordinate position information 64 acquired from the user terminal 7 and the station or bus stop, etc.

[0039] [Coordinate location information] The coordinate position information 64 is a type of data related to position information, and is data that compiles coordinate position information acquired from the user terminal 7 together with data quality. For example, information in the columns from user ID 201 to accuracy 207 in the transportation means determination information 66 shown in Fig. 2 is coordinate position information, and may include information related to data quality.

[0040] [Train running information] The train running information 65 is running information for transportation means with a set operation plan, such as a railroad or subway, and is information available from the timetable 82 or the operation information distribution device 9. The information stored as the train running information 65 is preferably information on actual train running, and is preferably information obtained from the current day train running information 91. An example of train operation information is shown in FIG.

[0041] [Transportation method determination information] The transportation means determination information 66 is a type of data related to location information, and is information obtained by estimating a transportation means from the coordinate position information 64 and the transportation means characteristics 61. A specific example is as shown in FIG. 2, in which a transportation means 209 is added to the coordinate position information 64 for each time. The method of estimating the means of transportation will be described in detail later.

[0042] [Candidate route information] The candidate route information 67 is information that is obtained by estimating candidate routes of public transportation that the user may use in the candidate route information acquisition unit. For example, in the case of railroads, the routes are extracted as shown in FIG. The method of estimating the candidate route information 67 will be described in detail later.

[0043] [Train information available] The boardable train information 68 is information that specifies available trains by checking the train operation information 65 based on the user's location information and time information against the candidate route information 67. For example, in the case of a railway, the route and train number are extracted as shown in FIG.

[0044] [Route Estimation Information] The route estimation information 69 is a type of data related to position information, and is one of the estimation results of the route estimation device 1. The route estimation information 69 is information in which line position information (line, station, train, utilization probability) is added to the coordinate position information 64 at each time, and the route estimation information may also include the time of boarding and alighting the train and the line position information. The route estimation information 69 may be simplified route estimation information 69' that aggregates information for each mode of transportation. An example of the route estimation information 69 is shown in FIG. 12(a), and an example of the simplified route estimation information 69' is shown in FIG. 12(b). The details of the route estimation information 69 will be described later.

[0045] <User device> The user terminal 7 has a function of transmitting position information 71. Other functions and aspects of the user terminal are as described above.

[0046] <Route search device> The route search device 8 is a device that outputs route search results by inputting a departure point and a destination, and includes a timetable 82 and route information 83, and may include a route search unit 81 as a functional unit. Here, the route information 83 is information that accumulates position information of stations, bus stops, and driving routes, for example, by collecting position information for each section separated by stations, bus stops, and the like. However, if the route information 83 includes information about a combination of routes based on a departure point and a destination and the required time for the routes, the timetable data 82 may not be necessary. Moreover, the route search unit 81 may be included in the route estimation device 1, and in that case, it does not need to be included in the route search device 8. The route search device 8 may be a route search service available on a PC, a smartphone, or the like via the Internet.

[0047] <Train information distribution device> The operation information distribution device 9 is a device provided mainly by a public transport operator, and is a device that provides and distributes data on the operation status of the public transport. The current day train operation information 91 is data relating to the actual operation results, but is not necessarily an essential component. The data of the operation information distribution device 9 may be included in the route estimation device 1 or the route search device 8.

[0048] <<Main flow>> Next, a flow of route estimation according to the present disclosure will be described with reference to FIG. FIG. 4 is a main flow of route estimation. In this disclosure, as shown in FIG. 4, the flow is explained by dividing the means of transportation into public transportation and non-public transportation. This is because, in general, in public transportation, the driving route is determined in advance and it is possible to extract candidate routes, whereas in non-public transportation, the driving route is not determined in advance and it is not possible to obtain candidate routes. For this reason, even if a means of transportation other than public transportation is adopted, if it is possible to obtain candidate routes, it is necessary to note that it may be included in the case of using public transportation that is capable of obtaining candidate routes. Furthermore, this main flow may be processed after a day's worth of travel has been completed and various data has been accumulated in a database or the like, or may be processed in real time during travel. The main flow will be explained below for each step.

[0049] (Step 10) In step 10, as explained in relation to the transportation means discrimination unit 10, transportation means discrimination information 66 shown in FIG. The process of step 10 will be described with reference to FIG. FIG. 5 shows a schematic diagram of how, when location information 71 is acquired along the time on the horizontal axis, the travel speed is calculated for each section in which location information is acquired, and the mode of travel is estimated according to the travel speed. For example, for section 501, it is determined from the transition of the position information that the travel speed is slow 520, and according to the criteria defined in the transportation means characteristics 61, it is estimated that the transportation means for section 501 is walking 530. In step 10, vibration data from an accelerometer obtainable from the user terminal 7 may be used to compare vibrations specific to walking, railroading, etc., and may be used as a factor in determining the mode of transportation. Furthermore, information that is included in the location information and that becomes noise in determining the mode of transportation, such as stopping while waiting for a traffic light or while waiting for an express train to pass, may be appropriately cleansed to determine the mode of transportation.

[0050] (Step 20) Next, in step 20, the data relating to the location information is divided by time for each means of transportation determined in step 10. In the example of Fig. 2, for each means of transportation 209, the data is divided into three pieces of data: walking 210, railroad 230, and walking 250.

[0051] (Step 30) Next, in step 30, for each of the data divided in step 20, it is determined whether the means of transportation is capable of acquiring candidate routes or not. If the means of transportation is determined to be walking, it is classified as a means of transportation that is not capable of acquiring candidate routes, and if it is determined to be public transportation such as a train, subway, or route bus, it is classified as a means of transportation that is capable of acquiring candidate routes. Data classified as a means of transportation for which candidate routes cannot be obtained will be processed in step 70, and data classified as a means of transportation for which candidate routes can be obtained will undergo route selection in steps 40 and onward. The above-mentioned processes from step 10 to step 30 are carried out in the transportation means determination unit 10.

[0052] (Step 40) In step 40, the candidate route information acquisition unit 20 estimates which public transportation system is used as the means of transportation, and outputs this as candidate route information 67. A representative method of estimating the candidate route information 67 is to transmit the coordinates of the start and end points of the divided data and the respective time data for the user's movement data that was divided in step 20 and determined in step 30 to be able to obtain candidate routes to the route search device 8, and perform a route search to estimate the candidate route information 67. Alternatively, the coordinates of the start and end points of the divided data and the respective time data may be collated with a timetable to estimate candidate routes without using the route search device 8. Furthermore, when the accuracy of the position information of the user terminal 7 is low, a search for candidate routes may be performed only on the basis of the required time without using the position information. If information on boardable trains can also be obtained as candidate route information 67 from route search device 8, step 40 can be terminated. However, if only candidate routes can be obtained but the trains cannot be specified, train operation information 65 is acquired and referenced in order to extract boardable trains, and boardable train information 68 is extracted from the candidate routes. For train running information, it is desirable to use the actual train running records for the day, but if no operational delays have occurred, data from the timetable 82 may be used. In addition, delay information distributed to train departure boards at stations (such as "XX line is delayed by approximately XX minutes") may be added to the data of the timetable 82 and used. Examples of the above-described candidate route information 67 and boardable train information 68 will be described with reference to FIG. 6 and FIG. Fig. 6(a) is an example of candidate route information 67 when there is no transfer, and shows that route X is a candidate route. On the other hand, Fig. 6(b) is an example of candidate route information 67 when there is a transfer, and shows that route X and route Z are candidate routes. Figures 7(a) and (b) show that trains a and b are available on line X as an example of a no-transfer trip, while Figure 7(c) shows that trains a on line X and train c on line Z are available as trains when a transfer is required.

[0053] (Step 50) In step 50 , the utilization probability calculation unit 30 calculates the utilization probability for the trains included in the boardable train information 68 extracted in step 40 . In describing the process content in step 50, first, the data acquisition characteristic 62 will be described with reference to FIGS.

[0054] <Details of data acquisition characteristics> A specific example of the data acquisition characteristic 62 will be described below with reference to FIG. Figure 8 shows examples of data acquisition characteristics. Figure 8(a) shows data acquisition characteristics for points (observation points) on a candidate route as acquisition difficulty. FIG. 8(a) shows that the acquisition difficulty, which is a data acquisition characteristic at station A from time 9:00 to time 10:00, is X1%, and the acquisition difficulty, which is a data acquisition characteristic at station B from time 9:00 to time 10:00, is X2%. In the example of Fig. 8(a), a station is shown as an example of a location, but the location to be selected is not limited to a station or a bus stop, and any location can be used. For example, the location may be defined by area within a station, such as a platform, ticket gate, exit, or commercial facility. In addition, in the example of FIG. 8(a), the acquisition difficulty is limited to a specific time interval, but it is not necessary to attach a time interval requirement, and it is not necessary to limit the time. Even if a time limit is attached, it may be limited by a day of the week, a season, a year and a month, etc. In this manner, in the present disclosure, the insufficient accuracy of the location information is regarded as information on data quality. Therefore, even if the accuracy of the location information received from the user terminal is insufficient, the data quality can be utilized as information for confirming the user's location by comparing it with the data acquisition characteristics set in advance for points on the candidate route.

[0055] Figure 8(b) shows the data acquisition characteristics as acquisition difficulty for a section including multiple observation points on a candidate route. Figure 8(b) shows that the acquisition difficulty, which is the data acquisition characteristic, from Station A to Station B from 9:00 to 10:00 is Y1%, and the acquisition difficulty, which is the data acquisition characteristic, from Station B to Station C from 9:00 to 10:00 is Y2%. In the example of Fig. 8(b), stations are used as the section, but the section is not limited to the section between stations, and any section between two points can be used. For example, a section may be defined as a section between multiple stations, taking into account cases where an express train does not stop at any stations along the way, or where a train stops at stations along the way but does not affect the difficulty. Furthermore, the start and end stations may be defined in kilometers. In addition, in the example of FIG. 8(b), the acquisition difficulty is limited to a specific time interval, but the time interval requirement does not necessarily have to be attached. Even if a time restriction is attached, the time setting can be selected arbitrarily. In this way, by presetting data acquisition characteristics for sections on a candidate route, the user's location can be determined more accurately by comparing the data acquisition characteristics for the section with the data quality, compared to comparing only one point.

[0056] Figure 8(c) shows the data acquisition characteristics for each means of transportation as acquisition difficulty. Figure 8(c) shows that for train a, which is an example of a means of transportation, the acquisition difficulty, which is the data acquisition characteristic, for the running section from station A to station B is Z1%, and for train b, the data acquisition characteristic for the running section from station B to station C is Z2%. In addition, for trains, the information may be defined by train type, such as new train type / express train, or by train type. In the example of Fig. 8(c), a train is used as a means of transportation, but the means of transportation is not limited to trains and any means of transportation can be used. In the example of Fig. 8(c), the train is shown with a limited running section, but the running section does not necessarily have to be limited. In addition, it is also possible to limit the running section by using the running time. In this way, by setting data acquisition characteristics for a means of transportation in advance, the user's location can be confirmed with greater accuracy, taking into account differences in data acquisition characteristics due to differences in driving speed and vehicle environment, even in the same location or section.

[0057] Next, with reference to Fig. 9, the data acquisition characteristics around the point or time at which the data acquisition characteristics change will be described. Fig. 9 is an example showing the data acquisition characteristics of candidate route 1 in a graph format, with the horizontal axis representing time or point and the vertical axis representing the difficulty of data acquisition. An upward direction H on the vertical axis indicates a high degree of difficulty in data acquisition, and a downward direction L on the opposite side indicates a low degree of difficulty in data acquisition. It shows that the data acquisition characteristics change significantly at the time or point X on the horizontal axis. In such a case, if data acquisition characteristics are set at times or points (observation points) α and β before and after X, this can be used as a strong clue for identifying candidate route 1, and the user's location can be confirmed with higher accuracy based on the difference in data acquisition characteristics. Moreover, the amount of data to be set for the data acquisition characteristics can be reduced, resulting in a route estimation device with a low processing load.

[0058] Next, with reference to FIG. 10, a case where a variation pattern of data acquisition characteristics at a plurality of observation points in a section or between times of a candidate route is used will be described. Figures 10(a) and (b) are examples of graphs showing data acquisition characteristics, with the horizontal axis representing time or location and the vertical axis representing the difficulty of acquiring data, similar to Figure 9. An upward direction H on the vertical axis indicates a high degree of difficulty in acquiring data, and a downward direction L on the opposite side indicates a low degree of difficulty in acquiring data. FIG. 10(c) shows the data quality received from the user terminal in a similar format, with L on the upper side of the vertical axis indicating low data quality and H on the lower side indicating high data quality. Fig. 10(a) shows the pattern of change in data acquisition characteristics for candidate route 2, and it can be seen that the data acquisition difficulty is low in the first half of the route or between times, and becomes high from the middle of the route onwards. On the other hand, Fig. 10(b) shows the pattern of change in data acquisition characteristics for candidate route 3, and it can be seen that the data acquisition difficulty becomes low at the middle of the route or between times. Such a pattern of change in the data acquisition characteristics can be set for any interval or time period. When such data acquisition characteristics of a candidate route are set, if the data quality received from the user terminal exhibits the pattern shown in Figure 10(c), it can be seen that the fluctuation pattern is such that the data quality becomes good at a midpoint or time. In other words, even if the section or time period is difficult to acquire data, the user's travel route can be determined to be candidate route 3 by using the correlation between the data acquisition characteristics of the candidate route and the fluctuation pattern of the data quality received from the user terminal. The correlation between the data acquisition characteristics of the candidate route and the fluctuation pattern of the data quality received from the user terminal can be determined using a common method such as a cross-correlation function.

[0059] <Details of Step 50> Next, the use probability calculation process of step 50 performed by the use probability calculation section 30 will be described with reference to FIG. Here, an example is described in which the fluctuation pattern of data acquisition characteristics is used in the calculation process of utilization probability, but the fluctuation pattern of data acquisition characteristics does not necessarily have to be used in the calculation process of utilization probability. For example, data acquisition characteristics at locations, sections, trains, and before and after locations or times at which data acquisition characteristics change, as described in Figures 8 and 9, can also be used.

[0060] (Step 51) In step 51, it is determined whether the position of the user terminal 7 can be measured with high accuracy at all sampling times in the section where the candidate route was extracted in step 40, and screening is performed to determine whether to perform a comparison process with the data quality. If it is determined that the position of the user terminal 7 can be measured with high accuracy at all sampling times, the process proceeds to steps 52 and 53. If the position of the user terminal 7 can be measured with high accuracy at all sampling times, as will be described later, it is considered possible to calculate the utilization probability with a high degree of accuracy without comparing the data acquisition characteristics with the quality of the data received from the user terminal. For this reason, the comparison process with the data quality is not performed. This avoids the process of comparing the data acquisition characteristics with the quality of the data received from the user terminal, and reduces the calculation load on the route estimation device.

[0061] (Step 52) In step 52, variation patterns of data acquisition characteristics as shown in Figures 10(a) and (b) are extracted for the candidate routes extracted in step 40. It is desirable to set in advance variation patterns of data characteristics for candidate routes for all routes on which the position of the user terminal 7 may not be measured with high accuracy.

[0062] (Step 53) Next, in step 53, a fluctuation pattern of the quality of data received from the user terminal as shown in FIG. 10(c) is created. Incidentally, steps 52 and 53 may be performed simultaneously in parallel, or step 52 may be performed after step 53.

[0063] (Step 54) In step 54, the degree of agreement between the variation pattern of the data acquisition characteristics for each candidate route extracted in step 52 and the variation pattern of the data quality created in step 53 is calculated. The degree of coincidence may be calculated using a cross-correlation function or the like, or the similarity of the signals may be used as the degree of coincidence, or a new index may be created taking into account the position of the peak, etc. Then, the utilization probability of the candidate route may be calculated from the degree of coincidence calculated in step 54 .

[0064] (Step 55) In step 55, the utilization probability may be calculated by taking into account the degree of coincidence between the variation pattern of the data acquisition characteristics of the candidate route and the variation pattern of the data quality calculated in step 54, as well as the distance between the position of the candidate route and the position obtained from the user terminal. Specifically, first, the distance between the position information of the candidate route that was the basis for setting the variation pattern in the candidate route and the position that was the basis for the corresponding data quality, that is, the position information obtained from the user terminal, is calculated.The smaller this distance is, the stronger the relationship between the candidate route and the data quality can be assumed to be.For example, if the degree of agreement between the variation pattern of the data acquisition characteristics of the candidate route calculated in step 54 and the variation pattern of the data quality created in step 53 is calculated as a correlation coefficient of 0.8, and the distance between the position of the candidate route and the position obtained from the user terminal is 90 m, the utilization probability may be calculated by the following formula. Probability of use = 0.8 × a + (1 / 90) × b (1) where a and b are arbitrary weighting coefficients. In addition, when taking into account the distance relationship between the position of the candidate route and the position obtained from the user terminal, it is possible to further improve the accuracy of the utilization probability by selecting a point where the position information can be determined with as high an accuracy as possible.

[0065] (Step 56) If position data from the user terminal 7 can be received with high accuracy at all sampling times or positions of the candidate route, the above-mentioned comparison of variation patterns is not performed, and in step 56, the distance between each candidate route at any time and the position indicated by the position data from the user terminal 7 is calculated.

[0066] (Step 57) In step 57, the utilization probability is defined so that the utilization probability increases as the distance between the position of each candidate route at any time and the position obtained from the user terminal 7 becomes shorter, and the utilization probability is calculated. For example, the reciprocal of the distance can be applied to the utilization probability. Also, the distance may be calculated at multiple times to calculate the utilization probability for the entire route.

[0067] (Step 58) In step 58, if the utilization probability in step 55 or step 57 does not reach a certain value or more, it is determined that there is a high possibility that the means of transportation used by the user is other than public transportation. This concludes the detailed explanation of step 50, and below, the explanation will resume from step 60 onwards in the main flow of FIG.

[0068] (Step 60) In step 60, a presumed route is selected based on the utilization probability as a route in the case where it is possible to obtain a candidate route. Usually, the candidate route with the highest utilization probability is extracted from the candidate routes and selected as the presumed route.

[0069] (Step 70) In step 70, the route estimation information creation unit 40 creates route estimation information. For a mode of transportation for which a candidate route could not be acquired, route estimation information is created in a state in which a candidate route cannot be acquired, based on the position information received from the user terminal 7. Specifically, a transportation means is selected according to the transportation means characteristics 61, and route estimation information 69 is created. On the other hand, if a route can be selected from the candidate routes in step 60, that route is adopted and route estimation information 69 is created.

[0070] (Details of route estimation information 69) Next, details of the route estimation information 69 will be described with reference to Fig. 12. Fig. 12(a) is an example of the route estimation information 69, which is information in which line position information (line, station, train, utilization probability) is added to the coordinate position information 64 at each time. The route estimation information 69 also includes the times of boarding and alighting the train and the line position information. FIG. 12(b) is an example of simplified route estimation information 69' in which information is aggregated for each mode of transportation in the route estimation information 69. The route estimation information 69 and simplified route estimation information 69' need only include information such as coordinate positions at each time, routes and stations, means of transportation, and utilization probability, and do not necessarily have to have the arrangement or content shown in FIG. To create the route estimation information 69 shown in Fig. 12, first, line position information (line, station, train, and usage probability) is added to the coordinate position information 64 at each time. That is, data excluding lines 1205 and 1211 in Fig. 12(a) is created. If there is an error in the means of movement at this stage, a correction process can be carried out, as will be described later. If no corrections are required, the next step is to add the time and route position information for the timing of boarding and alighting trains and buses, that is, rows 1205 and 1211, to obtain route estimation information 69 shown in Figure 12(a).

[0071] Moreover, the simplified route estimation information 69' is information obtained by aggregating information for each mode of transportation from the data of the route estimation information 69. In the example of Fig. 12, the data in rows 1201 and 1202, rows 1205 to 1211, and rows 1213 and 1215 in Fig. 12(a), which have consecutive modes of transportation, are aggregated, and columns for the start position and end position are provided to combine the data into one row, which is the simplified route estimation information 69' in Fig. 12(b). By calculating the travel route in a simple manner in this way, it becomes possible to easily display information about the travel route on the user terminal in an easy-to-read format.

[0072] Next, a display example of an estimated route displayed on the user terminal 7 will be described with reference to Fig. 13. Fig. 13(a) is an example of an estimated route display displayed on the user terminal 7 at the stage when the route estimation information 69 and the simplified route estimation information 69' are created. The display screen of the user terminal 7 can display various methods, but by presenting information on the departure point, boarding point, details of the transportation means, drop-off point, and arrival point together with time information, the user can easily check his / her own travel route. In addition, by displaying information related to the CO2 emissions of the transportation means, it becomes possible to visualize the amount of CO2 emissions reduction.

[0073] 13(b), the user terminal 7 can also display a correction icon for when the user corrects the estimated route. When the correction icon is selected, the estimated result correction unit 45 corrects the estimated result, but such a function is not a required configuration in the present disclosure.

[0074] <Effects> Next, with reference to FIG. 14, the effect of determining a user's movement route using the correlation between the variation pattern of data acquisition characteristics and the variation pattern of the quality of data received from a user terminal will be described. FIG. 14 is a route map showing an example in which there are multiple travel routes and means of travel between the same departure point and destination. For example, if candidate route 2 and candidate route 3 exist close to each other and are almost parallel, and the data quality received from the user terminal is not high, it is difficult to identify which of these routes the user used based on the position data alone. However, if the variation patterns of the data acquisition characteristics are different, as explained using FIG. 10, it is possible to identify the user's travel route with higher accuracy by identifying candidate route 2 and candidate route 3 using the difference in the variation patterns. For example, in sections in the center of a large city where trains and subways run parallel to each other, or where trains and route buses run parallel to each other, it is often difficult to obtain location information of a user terminal with sufficient accuracy. However, by using the differences in the fluctuation patterns of the data acquisition characteristics as disclosed herein, it is possible to distinguish between each travel route with high accuracy and realize precise route estimation.

[0075] <Hardware configuration example> Next, with reference to FIG. 15, an example of a hardware configuration for implementing an embodiment of the present disclosure will be described. A hardware configuration example according to an embodiment of the present disclosure may be configured with any appropriate computing system. The main components of the hardware constituting the route estimation system are a user terminal 7 connectable to a route estimation device 1 via a network 2, a route search device 8, and an operation information distribution device 9. The hardware configuration may also include an application server (not shown). The path estimation device 1 includes a storage device 101, a memory 110, a CPU 120, a UI device 130, and a communication device 140. These components can be interconnected via a bus 150.

[0076] The storage device 101 can be configured with an appropriate non-volatile storage element such as an SSD (Solid State Drive) or a hard disk drive, and can store a program 102 and data 103. Moreover, the memory 110 can be composed of a volatile storage element such as a RAM. The CPU 120 may be equipped with one or more CPUs, or may be a single CPU system. The CPU 120 executes the program 102 stored in the storage device 101 by, for example, reading the program 102 into the memory 110, and performs overall control of the device itself as well as various judgments, calculations, and control processes.

[0077] UI devices 130 may include user output devices, such as a video display, speakers, television, etc., and user I / O devices, such as user input devices, such as a keyboard, mouse, keypad, touchpad, trackball, buttons, light pen, or other pointing device.

[0078] The network 2 is a path that allows connection and communication between the user terminal 7, the route search device 8, and the operation information distribution device 9 via the communication device 140 of the route estimation device 1.

[0079] Individual users can receive the route estimation information 69 using user terminals 7 . In addition, the business operator may also be able to receive the user's route estimation information 69 via an application server or the like.

[0080] In addition to the above, the present disclosure also discloses the following aspects. (Aspect 1) A route estimation device that estimates a route traveled by a user, A transceiver capable of transmitting and receiving data to and from a user terminal; A transportation means discrimination unit that discriminates a transportation means for each time based on data received from the user terminal and transportation means characteristics; a candidate route information acquisition unit that extracts available candidate routes; a utilization probability calculation unit that calculates a utilization probability of the candidate route based on a comparison between data acquisition characteristics of the candidate route and data quality received from the user terminal; a route estimation information generating unit that generates route estimation information based on the utilization probability; A route estimation device comprising:

[0081] (Aspect 2) In the route estimation device according to aspect 1, The data acquisition characteristics are data acquisition characteristics for an arbitrary section or an arbitrary time period set for each arbitrary means of transportation, and the received data quality is data quality corresponding to the section or the time period. Path estimation device.

[0082] (Aspect 3) In the route estimation device according to aspect 1 or 2, The data acquisition characteristics include data characteristics before and after a point or time at which the data acquisition characteristics change, and the received data quality also includes data quality before and after a point or time at which the data acquisition characteristics change. Route estimation device.

[0083] (Aspect 4) In the route estimation device according to any one of aspects 1 to 3, The data acquisition characteristic is a variation pattern of the data acquisition characteristic in an arbitrary section or time period, and the received data quality is also a data quality corresponding to the section or time period. Route estimation device.

[0084] (Aspect 5) In the route estimation device according to any one of aspects 1 to 4, The utilization probability calculation unit, when it is determined that the position of the user terminal has not been measured with high accuracy at all sampling times, compares the data acquisition characteristics with the quality of data received from the user terminal. Route estimation device.

[0085] (Aspect 6) In the route estimation device according to any one of aspects 1 to 5, The transportation means discrimination unit calculates a user's moving speed based on the position information received from the user terminal, discriminates a transportation means according to the moving speed, divides data related to the position information for each of the transportation means, discriminates whether the transportation means is capable of acquiring a candidate route or not, and divides data related to the position information for each of the discriminated transportation means. Route estimation device.

[0086] (Aspect 7) In the route estimation device according to any one of aspects 1 to 6, The candidate route information acquisition unit compares the data on the location information for each transportation means that is determined by the transportation means determination unit as a transportation means capable of acquiring the candidate route with route information of public transportation, and extracts the candidate route. Route estimation device.

[0087] (Aspect 8) In the route estimation device according to any one of aspects 1 to 7, The route estimation device includes an estimation result correction unit, The estimation result correction unit corrects the route estimation information when receiving an instruction to correct the route estimation information from a user terminal. Route estimation device.

[0088] (Aspect 9) In the route estimation device according to any one of aspects 1 to 8, The estimation result correction unit corrects the transportation means characteristics and / or the data acquisition characteristics when receiving an instruction to correct the route estimation information from the user terminal. Route estimation device.

[0089] (Aspect 10) A route estimation system including a route estimation device according to any one of aspects 1 to 9, a user terminal connected to the route estimation device via a network, and a route search device, the user terminal is capable of transmitting at least location information to the route estimation device; The route search device is capable of performing a route search in response to a request from the route estimation device and transmitting a search result to the route estimation device. Path estimation system.

[0090] (Aspect 11) In the route estimation device according to any one of aspects 1 to 9, The transportation means discrimination unit discriminates the transportation means at each time based on the data received from the user terminal and the transportation means characteristics, and divides the data related to the location information for each transportation means; the candidate route information acquisition unit performing a route search and extracting candidate routes; a step of calculating a utilization probability of the candidate route based on a comparison between data acquisition characteristics of the candidate route and data quality received from the user terminal by the utilization probability calculation unit; and a step of selecting an estimated route based on the utilization probability; A path estimation method comprising:

[0091] (Aspect 12) In the route estimation method according to aspect 11, The step of calculating a utilization probability of the candidate route includes: extracting a variation pattern of the data acquisition characteristics; creating a variation pattern of the quality of data received from a user terminal; calculating a degree of agreement between a variation pattern of the data acquisition characteristics and a variation pattern of the data quality; A path estimation method comprising:

[0092] (Aspect 13) In the route estimation method according to aspect 11 or 12, The step of calculating the utilization probability includes a step of taking into account a distance between a position of the candidate route and a position obtained from a user terminal. Route estimation methods.

[0093] (Aspect 14) In the route estimation method according to any one of aspects 11 to 13, The utilization probability calculation unit judges whether or not the position of the user terminal can be measured with high accuracy at all sampling times, and if the position of the user terminal can be measured with high accuracy at all sampling times, does not compare the data acquisition characteristics with the data quality, and otherwise compares the data acquisition characteristics with the data quality. Route estimation methods.

[0094] (Aspect 15) In the route estimation method according to any one of aspects 11 to 14, A route estimation method comprising the steps of: correcting the route estimation information when an instruction to correct the route estimation information is received from a user terminal.

[0095] Although the embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and various modifications are possible without departing from the gist of the present invention. For example, the utilization probability may be calculated based on the user's past travel history in addition to the fluctuation pattern of the data acquisition characteristics. Specifically, the utilization probability of a route or place may be set high by taking into account information such as a route that the user uses frequently, a store that the user frequently stops at, or a detour. In addition, when calculating the degree of difficulty of obtaining data, the degree of difficulty of obtaining data for each location and time may be estimated from people flow data obtained from smartphone location information and reflected in the degree of difficulty of obtaining data, or the degree of difficulty of obtaining data may be estimated using artificial intelligence, etc. In this disclosure, the term "functional unit" may be read as "means." can. [Explanation of symbols]

[0096] 1: Route estimation device 7: User terminal 8: Route search device 9: Traffic information distribution device 10: Means of transportation discrimination unit 20: Candidate route information acquisition unit 30: Utilization probability calculation unit 40: Route estimation information creation unit 45: Estimation result correction part 50: Transmitter / receiver 60: Storage section 61: Transportation characteristics 62: Data Acquisition Characteristics 63: Travel method discrimination criteria 64: Coordinate location information 65:Train operation information 66: Transportation method determination information 67: Candidate route information 68:Train information available for boarding 69: Route estimation information 71: Location information 81: Route search section 82: Timetable 83: Route information 91: Today's train operation information

Claims

1. A route estimation device that estimates a route traveled by a user, A transceiver capable of transmitting and receiving data to and from a user terminal; A transportation means discrimination unit that discriminates a transportation means for each time based on data received from the user terminal and transportation means characteristics; a candidate route information acquisition unit that extracts available candidate routes; a utilization probability calculation unit that calculates a utilization probability of the candidate route based on a comparison between data acquisition characteristics of the candidate route and data quality received from the user terminal; a route estimation information generating unit that generates route estimation information based on the utilization probability; A route estimation device comprising:

2. The route estimation device according to claim 1 , The data acquisition characteristics are data acquisition characteristics for any section or any time period set for each of any means of transportation, and the data quality is data quality corresponding to the section or the time period. Path estimation device.

3. 3. The route estimation device according to claim 1, The data acquisition characteristics include data characteristics before and after a point or time at which the data acquisition characteristics change, and the data quality also includes data quality before and after a point or time at which the data acquisition characteristics change. Path estimation device.

4. 3. The route estimation device according to claim 1, The data acquisition characteristic is a variation pattern of the data acquisition characteristic in an arbitrary section or time period, and the data quality is also data quality corresponding to the section or time period. Path estimation device.

5. 3. The route estimation device according to claim 1, The utilization probability calculation unit compares the data acquisition characteristics with the quality of the received data when it is determined that the position of the user terminal has not been measured with high accuracy at all sampling times. Path estimation device.

6. 3. The route estimation device according to claim 1, The transportation means discrimination unit calculates a user's moving speed based on the position information received from the user terminal, discriminates a transportation means according to the moving speed, divides data related to the position information for each of the transportation means, discriminates whether the transportation means is capable of acquiring a candidate route or not, and divides data related to the position information for each of the discriminated transportation means. Path estimation device.

7. 7. The route estimation device according to claim 6, The candidate route information acquisition unit compares the data on the location information for each transportation means that is determined by the transportation means determination unit as a transportation means capable of acquiring the candidate route with route information of public transportation, and extracts the candidate route. Path estimation device.

8. 3. The route estimation device according to claim 1, The route estimation device includes an estimation result correction unit, The estimation result correction unit corrects the route estimation information when receiving an instruction to correct the route estimation information from a user terminal. Path estimation device.

9. 9. The route estimation device according to claim 8, The estimation result correction unit corrects the transportation means characteristics and / or the data acquisition characteristics when receiving an instruction to correct the route estimation information from the user terminal. Path estimation device.

10. A route estimation system comprising the route estimation device according to claim 1 or 2, a user terminal connected to the route estimation device via a network, and a route search device, the user terminal is capable of transmitting at least location information to the route estimation device; The route search device is capable of performing a route search in response to a request from the route estimation device and transmitting a search result to the route estimation device. Path estimation system.

11. 3. The route estimation device according to claim 1, The transportation means discrimination unit discriminates the transportation means at each time based on the data received from the user terminal and the transportation means characteristics, and divides the data related to the location information for each transportation means; the candidate route information acquisition unit performing a route search and extracting candidate routes; a step of calculating a utilization probability of the candidate route based on a comparison between data acquisition characteristics of the candidate route and data quality received from the user terminal by the utilization probability calculation unit; and a step of selecting an estimated route based on the utilization probability; A path estimation method comprising:

12. The route estimation method according to claim 11, The step of calculating a utilization probability of the candidate route includes: extracting a variation pattern of the data acquisition characteristics; creating a variation pattern of the quality of data received from a user terminal; calculating a degree of agreement between a variation pattern of the data acquisition characteristics and a variation pattern of the data quality; A path estimation method comprising:

13. The route estimation method according to claim 12, The step of calculating the utilization probability includes a step of taking into account a distance between a position of the candidate route and a position obtained from a user terminal. Route estimation methods.

14. The route estimation method according to claim 11, The utilization probability calculation unit judges whether or not the position of the user terminal can be measured with high accuracy at all sampling times, and if the position of the user terminal can be measured with high accuracy at all sampling times, does not compare the data acquisition characteristics with the data quality, and otherwise compares the data acquisition characteristics with the data quality. Route estimation methods.

15. The route estimation method according to claim 11, A route estimation method comprising the steps of: correcting the route estimation information when an instruction to correct the route estimation information is received from a user terminal.

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