JPWO2013111506A1 - Course prediction information providing apparatus, course prediction information providing method, and program - Google Patents

Abstract

The present invention provides a route prediction information providing apparatus and the like that can accurately provide information required by a user. A route prediction information providing apparatus 10 includes a travel log storage unit 24 that stores a travel log in advance, an area shape storage unit 25 that stores in advance positions and shapes of a plurality of regions to which a moving body can move, A coverage predictive value calculation unit 21 that calculates, as a coverage predictive value, a value obtained by dividing the route length included in the region in the travel route of the moving body in the travel log by the travel route length of the mobile body in the travel log. And a noise prediction value calculation unit that calculates a noise prediction value 22 that is a prediction value of a ratio at which the information providing application provides inappropriate information for each region, and a first weight value for the coverage prediction value An optimum region selecting unit for selecting a region having a maximum value obtained by subtracting a value obtained by multiplying the noise prediction value by the second weight value from the multiplied value; [Selection] Figure 1

Description

  The present invention relates to a route prediction information providing device, a route prediction information providing method, and a program, and more particularly to a route prediction information providing device and the like that can accurately provide information that a user will need.

  Car navigation systems are already widely used, as can be seen from the questionnaire survey conducted in December 2010 (Non-patent Document 1) that the ownership rate was 52.2% among adult men and women. Moreover, its ownership rate is increasing year by year. And the accuracy and function are improving year by year, and in recent years, car navigation devices that acquire information from a server via a public wireless communication network such as a mobile phone have already become commonplace.

  For example, it is useful for the user, such as the current location, destination, and traffic information and weather information around the planned driving route, or information about gas stations and restaurants in the vicinity of the driving route. There is also a car navigation device having a function of acquiring information considered to be from a server and presenting it to a user. Such a function is called a recommendation function. For example, when the remaining amount of gasoline falls below a certain amount, information on the gas station is presented, and when the continuous driving time exceeds a certain time, information on restaurants and rest areas is presented. Some have a recommendation function that operates according to the situation of the car or the driver.

  However, if there is no input about the destination or the planned travel route from the user, the car navigation device cannot select the data required by the user, so the recommendation function cannot be executed. A large amount of data is stored in the server, most of which is data related to the user's current location, destination, and location unrelated to the planned travel route. That is, it is not practically possible to download and present all data stored in the server regardless of the current location or destination of the user, and it is not information that can be used by the user.

  Patent Document 1 describes a technology that solves such a problem, predicts a user's route even when the user does not input a destination, and executes a recommendation function based on the predicted route. ing. In the technique described in this document, the future course of the mobile body (automobile) is calculated as a prediction target area (course prediction area) based on the current location and the traveling direction, and provided to the car navigation device. Thereby, the car navigation apparatus can select and provide information to the user within the prediction target area.

  In addition to this, there are the following patent documents. Among them, Patent Document 2 discloses a travel plan in which a cell including a planned travel route is extracted from a map divided into a plurality of cells, and information about facilities on the route is acquired to create a travel plan. A creation device is described. Patent Document 3 describes a route prediction device that predicts the route of a car from the past travel history of the car. Patent Document 4 describes a traffic environment prediction device that predicts the course of each automobile in a specific area.

  Patent Document 5 describes a car navigation device capable of guiding a car to an “entertainment route”. Patent Document 6 describes a travel route prediction device that presents to a user link information related to a travel route (for example, information on points to be noted in driving such as railroad crossings).

JP 2003-057049 A Republished WO97 / 06522 JP 2006-284254 A Japanese Patent Laid-Open No. 2008-276552 JP 2009-244160 A JP 2009-270877 A

“Questionnaire Survey on the Use of Car Navigation (5)”, December 2010, My Voice Com Inc., [Search December 5, 2011], Internet <URL: http://www.myvoice.co.jp/biz /surveys/14906/index.html>

  However, in the technique described in Patent Document 1 described above, the course of the automobile is calculated as a “course prediction area” based on the current location and the current traveling direction. The larger the area calculated as the predicted route area, the more information the recommendation function can provide to the user, but conversely there is more information that is not included in the planned travel route within the same area (that is, useless for the user). It will be included. The techniques described in Patent Documents 1 to 6 do not consider such points at all, and thus cannot provide information that the user will need.

  An object of the present invention is to provide a course prediction information providing apparatus, a course prediction information providing method, and a program that can accurately provide information that a user will need.

  In order to achieve the above object, the route prediction information providing apparatus according to the present invention predicts a future route of the mobile body based on a travel log that is a route traveled by the mobile body in the past, and information related to the route. Is a route prediction information providing device that provides a user of the mobile body with a travel log storage unit that stores a travel log in advance, and a region that stores in advance the positions and shapes of a plurality of regions in which the mobile body can move In the travel route of the mobile object in the travel log for each area, the shape storage unit, application execution means for operating application software that provides information related to the position and shape of the area to the user of the mobile object A coverage predictive value calculation unit that calculates a value obtained by dividing the route length included in the region by the total length of the travel route of the moving body in the travel log, and each region On the other hand, a noise prediction value calculation unit that calculates a noise prediction value that is a prediction value of a ratio at which application software specified according to the position and shape of the region provides inappropriate information, and a coverage prediction value As a result of subtracting a value obtained by multiplying the noise prediction value by a second weight value preset for the noise prediction value from a value obtained by multiplying the coverage predictive value by the first weight value preset as An optimum area selecting unit that selects an area having the maximum value as an optimum area and outputs the optimum area to an application execution unit is provided.

  In order to achieve the above object, the route prediction information providing method according to the present invention provides a future course of a moving object based on a traveling log that is a route traveled in the past of the moving object stored in advance in the traveling log storage unit. There is a route prediction information providing apparatus that predicts and provides information related to the route to the user of the mobile object, and a plurality of mobile objects whose shapes and shapes are stored in advance in the region shape storage unit can move For each region, the coverage predictive value calculation unit uses a value obtained by dividing the route length included in the region in the travel route of the mobile object in the travel log by the total length of the travel route of the mobile object in the travel log as the coverage predictive value Calculated noise prediction value that is a predicted value of the ratio at which application software that provides information to the user for each region provides information specified according to the position and shape of the region. A second weight set in advance for the noise prediction value from a value obtained by multiplying the coverage prediction value by the first weight value that is calculated by the noise prediction value calculation unit and set in advance for the coverage prediction value. The optimum region selection unit selects the region where the maximum value obtained by subtracting the value obtained by multiplying the noise prediction value by the value is selected as the optimum region, and outputs this, and information related to the selected optimum region is displayed. Application execution means operates application software to be provided.

  In order to achieve the above object, a course prediction information providing program according to the present invention provides a future course of a moving object based on a traveling log that is a route traveled in the past of the moving object stored in advance in a traveling log storage unit. A route prediction information providing device that predicts and provides information related to the route to the user of the mobile object, and the shape and shape are stored in advance in a region shape storage unit in a computer provided in the route prediction information providing device. For each of a plurality of areas in which a moving body can move, the value obtained by dividing the path length included in the area in the travel path of the mobile body in the travel log by the total length of the travel path of the mobile body in the travel log Application software that provides the user with information that is specified according to the position and shape of the area, the procedure for calculating the predicted value, and for each area. A procedure for calculating a noise predicted value that is a predicted value of a ratio to be performed, a noise predicted value set in advance from a value obtained by multiplying the coverage predicted value by a first weight value set in advance for the coverage predicted value A procedure for selecting an area where the value obtained by multiplying the noise prediction value multiplied by the second weight value is subtracted as an optimum area and outputting the optimum area, and related to the selected optimum area And a procedure for operating application software that provides information to be executed.

  As described above, the present invention is configured to calculate the coverage predictive value and the noise predicted value from the past travel log and select the optimum region from the calculated value, and therefore, the accuracy is accurately determined based on the values of the coverage and noise. The user's course can be predicted. Accordingly, it is possible to provide a route prediction information providing apparatus, a route prediction information providing method, and a program having an excellent feature that it is possible to accurately provide information that the user will need.

It is explanatory drawing shown about the more detailed structure of the course prediction information provision means shown in FIG. It is explanatory drawing shown about the structure of the course prediction information provision apparatus which concerns on the 1st Embodiment of this invention. It is explanatory drawing shown about an example of the memory content of the travel log memory | storage part shown to FIGS. It is explanatory drawing shown about an example of the memory content of the area | region shape memory | storage part shown to FIGS. FIG. 5 is an explanatory diagram showing a shape of a region with region ID = “00001” shown in FIG. 4. It is a flowchart shown about operation | movement of the course prediction information provision means shown in FIGS. 1-2, and an application execution means. It is a flowchart shown about the more detailed operation | movement of the coverage predictive value calculation part shown as step S101 of FIG. FIG. 8 is an explanatory diagram illustrating an example of a travel start point, a travel direction, a destination, and a travel prediction region of travel logs that are overlapped in the process illustrated in step S <b> 114 of FIG. 7. It is explanatory drawing shown about the state after superimposing the movement start point of the travel log shown in FIG. It is explanatory drawing which shows the structure of the course prediction information provision apparatus which concerns on the 2nd Embodiment of this invention. It is explanatory drawing shown about an example of the memory content of the area | region shape memory | storage part shown in FIG. It is explanatory drawing which shows the structure of the course prediction information provision apparatus which concerns on the 3rd Embodiment of this invention. It is explanatory drawing shown about the detailed structure of the course prediction information provision means shown in FIG. 12, an application execution means, a content acquisition means, and a content deletion means. It is explanatory drawing shown about an example of the memory content of the content memory | storage part shown by FIGS. It is a flowchart shown about operation | movement of the course prediction information provision apparatus shown in FIGS.

(First embodiment)
Hereinafter, the configuration of an embodiment of the present invention will be described with reference to FIGS.
First, the basic content of the present embodiment will be described, and then more specific content will be described.
The course prediction information providing apparatus 10 according to the present embodiment predicts a future course of the mobile body based on a travel log that is a path traveled by the mobile body in the past, and obtains information related to the course of the mobile body. It is a course prediction information provision apparatus provided with respect to a user. The route prediction information providing apparatus 10 includes a travel log storage unit 24 that stores a travel log in advance, an area shape storage unit 25 that stores in advance the positions and shapes of a plurality of regions in which the mobile body can move, Application execution means 16 that operates application software that provides information related to the shape to the user of the mobile object, and the path length included in the area in the travel route of the mobile object in the travel log for each area A coverage predictive value calculation unit 21 that calculates a value divided by the total length of the travel route of the moving body in the travel log as a coverage predictive value, and an application specified for each region according to the position and shape of the region A noise prediction value calculation unit 22 that calculates a noise prediction value that is a prediction value of a ratio at which software provides inappropriate information, and a coverage prediction value Then, a value obtained by multiplying the noise prediction value by a second weight value preset for the noise prediction value is subtracted from a value obtained by multiplying the coverage prediction value by the first weight value set in advance. And an optimum area selecting unit 23 that selects an area having the maximum value as an optimum area and outputs the optimum area to the application execution means.

  Here, the coverage predictive value calculation unit 21 sets the average value of the coverage predictive values calculated for a plurality of travel logs in the most recently given period in each region as the coverage predictive value for the region. In addition, the noise prediction value calculation unit 22 uses any one or more of the area of the region, the number of data existing in the region, and the total size of the data existing in the region, to determine the noise of the region. Calculate the predicted value.

By providing the above configuration, the route prediction information providing apparatus 10 according to the present embodiment can accurately predict the route of the moving body and provide the user with appropriate information corresponding to the route.
Hereinafter, this will be described in more detail.

  FIG. 2 is an explanatory diagram showing the configuration of the course prediction information providing apparatus 10 according to the first embodiment of the present invention. The course prediction information providing device 10 is mounted on the automobile 1 and has a basic configuration as a computer device. That is, the route prediction information providing apparatus 10 includes a processor 11 that is a main body that executes a computer program, a storage unit 12 that stores data, a travel route recording unit 13 that records a route traveled by the automobile 1 in the past, and a user. Input / output means 14 for receiving an operation command from the computer and presenting a processing result corresponding to the operation command to the user. All of these are connected to a common bus 17 to exchange data inside the apparatus.

  The travel route recording means 13 records a travel route by GPS (Global Positioning System) or the like. The input / output means 14 is built in, for example, the dashboard of the automobile 1 so that a driver or a passenger can input an operation command and can refer to a processing result. The course prediction information providing apparatus 10 can be used in many types of mobile objects such as bicycles, two-wheeled vehicles, and pedestrians, in addition to automobiles. In the present specification, the following description will be made assuming that the moving body on which the route prediction information providing device 10 is mounted is the automobile 1.

  The processor 11 obtains the travel route of the automobile 1 from the travel route recording means 13 and stores it as a travel log by the operation of the computer program. It functions as a route prediction information providing unit 20 that predicts a region including a point that will be advanced, and an application execution unit 16 that executes application software based on the predicted region.

  Specifically, the application software executed by the application executing means 16 is, for example, car navigation or provision of information (recommendation function) associated therewith. This will also be described later. The storage unit 12 has storage areas such as a travel log storage unit 24 in which the travel route acquisition unit 15 stores a travel log and an area shape storage unit 25 described later.

  FIG. 1 is an explanatory diagram showing a more detailed configuration of the course prediction information providing unit 20 shown in FIG. The course prediction information providing unit 20 includes functions such as an exhaustiveness prediction value calculation unit 21, a noise prediction value calculation unit 22, and an optimum region selection unit 23. The coverage predictive value calculation unit 21 reads the travel log from the travel log storage unit 24, and is a predicted value of the proportion of the travel route included in the route prediction region among the routes traveled after the time when the automobile 1 performs the prediction. A certain comprehensiveness prediction value is calculated.

  The noise prediction value calculation unit 22 predicts the application disadvantage caused by the shape of the region stored in the region shape storage unit 25, that is, the ratio of the application software providing inappropriate information. Calculate a predicted noise value. The optimal region selection unit 23 is configured to generate a plurality of information stored in the region shape storage unit 25 based on the coverage prediction value calculated by the coverage prediction value calculation unit 21 and the noise prediction value calculated by the noise prediction value calculation unit 22. One area is selected from the areas. Details of the functions described above, particularly the details of the concepts of “completeness prediction value” and “noise prediction value” will be described later.

  FIG. 3 is an explanatory diagram showing an example of the contents stored in the travel log storage unit 24 shown in FIGS. The travel log acquired by the travel route acquisition means 15 and stored in the travel log storage unit 24 can be acquired by GPS or the like, and the time 24a and the latitude 24b and longitude 24c of the point where the automobile 1 traveled at that time. It consists of.

  FIG. 4 is an explanatory diagram showing an example of the contents stored in the region shape storage unit 25 shown in FIGS. The “area” in the present embodiment indicates the course prediction result of the moving object provided to the application. In the present embodiment, the region is defined by a fan shape. This “region” can be defined by an arbitrary shape instead of a sector shape, which will be described later.

  The area shape storage unit 25 stores an area ID 25a, a sector radius 25b, a sector central angle 25c, and an X coordinate position 25d and a Y coordinate position 25e of a moving body (automobile) in the sector area. Yes. The X-coordinate position 25d and the Y-coordinate position 25e are positions when the center point of the sector shape in the area is used as the origin.

  FIG. 5 is an explanatory diagram showing the shape of the area of area ID 25a = “00001” shown in FIG. This area ID 25a = “00001” is defined as a fan-shaped area having a radius 25b = “10 km” and a central angle 25c = “60 °”.

  In this area, the moving body is present at the position of (X coordinate position 25d, Y coordinate position 25e) = (0, 3). The unit of position is km. This is because, when the sector-shaped center point of the area of area ID 25a = “00001” is set as the origin, when the course prediction is executed, the moving body exists at a position 3 km away from the origin in the Y-axis direction. It shows that. In FIG. 4, the areas after the area ID 25a = “00002” are also defined in the same manner.

  A more detailed operation of each function of the course prediction information providing unit 20 shown in FIG. 2 will be described. The coverage predictive value calculation unit 21 performs prediction by a mobile object that is a target of course prediction based on the travel log stored in the travel log storage unit 24 and the region definition data stored in the region shape storage unit 25. The coverage predictive value, which is the predicted value of the proportion of the moving path included in the area predicted as the path among the paths moving after the time to be calculated, is calculated corresponding to each area stored in the area shape storage unit 25. To do.

  The noise prediction value calculation unit 22 calculates a noise prediction value, which is a disadvantage for an application determined by the shape of the region stored in the region shape storage unit 25, that is, a prediction value of a ratio at which application software provides inappropriate information, Calculation is performed by a method defined for each application. In general, the larger the area, the smaller the above-mentioned coverage prediction value, but the noise prediction value becomes larger. In other words, the noise prediction value and the coverage prediction value are generally in a trade-off relationship.

  Hereinafter, an example of a noise prediction value calculated by the noise prediction value calculation unit 22 and a calculation method thereof will be described using the above-described recommendation function as an example. According to the predicted range of the future course of the mobile object, the recommendation function predicts that information data corresponding to points in the range will be required for the user in the future and downloads the data from the server. It is a function to present to.

  The disadvantage to the recommendation function can be defined as “amount of data downloaded from the server but not actually recommended to the user”. As described above, since the disadvantage increases as the area of the area increases, it can be considered that there is a certain degree of correlation between the "area of the area" and the "predicted noise value". . The area of this region can also be obtained geometrically from the shape and dimensions of each region stored in the region shape storage unit 25. For example, the area excluding the sea, rivers, forests, etc. is stored in the region shape storage unit 25. It can also be remembered.

  In addition to the area of the region, any numerical value that is considered to have a strong correlation with the disadvantage can be used as the index for calculating the noise prediction value. More specifically, for example, the noise prediction value based on the number of data existing in the area, the total size of those data, or the ratio of the actual travel distance to the total road length in the area. May be calculated.

  The optimum region selection unit 23 is configured to store a plurality of regions stored in the region shape storage unit 25 based on the coverage prediction value calculated by the coverage prediction value calculation unit 21 and the noise prediction value calculated by the noise prediction value calculation unit 22. One area is selected from the areas.

  More specifically, the optimum region selection unit 23 selects a region where the CV value represented by the following formula 1 is maximum. The comprehensiveness prediction value is a numerical value calculated by the comprehensiveness prediction value calculation unit 21, and the noise prediction value is a numerical value calculated by the noise prediction value calculation unit 22. A value obtained by subtracting a value obtained by multiplying the noise predictive value by the weight value Wn from a value obtained by multiplying the comprehensiveness predictive value by the weight value Wm is the CV represented by Equation 1. Here, the weight values Wm and Wn satisfy 0 ≦ Wm ≦ 1 and 0 ≦ Wn ≦ 1.

  Alternatively, the CV shown in the following equation 2 can be used instead of the above equation 1. Here, the weight values Wm and Wn satisfy 0 ≦ Wm ≦ 1 and 0 ≦ Wn ≦ 1.

  FIG. 6 is a flowchart showing the operations of the course prediction information providing unit 20 and the application execution unit 16 shown in FIGS. First, the coverage predictive value calculation unit 21 reads the travel log stored in the travel log storage unit 24 and the contents of a plurality of regions stored in the region shape storage unit 25, and calculates the coverage predictive value for each region (step) S101).

  Here, the travel log read by the coverage predictive value calculation unit 21 may be all the travel logs stored in the travel log storage unit 24, or a part of the travel logs, for example, “travel within the last three months”. For example, a traveling log that matches a predetermined condition may be selected. Similarly, the area read by the comprehensive predictive value calculation unit 21 may be all areas stored in the area shape storage unit 25, or may be an area designated in advance by the user, such as “a straight line from the current location of the automobile 1. An area that satisfies a predetermined condition such as “within a distance of 100 km or less” may be selected.

  Following this, the noise prediction value calculation unit 22 reads the contents of a plurality of regions stored in the region shape storage unit 25, and calculates a noise prediction value for each region (step S102). The area read by the noise prediction value calculation unit 22 may be the entire area stored in the travel log storage unit 24 or a part of the area, as in the calculation of the comprehensiveness prediction value. However, it is preferable to read the same area as that read in step S101.

  The optimum region selection unit 23 is shown in Equation 1 for a plurality of regions stored in the region shape storage unit 25 based on the coverage predictive value calculated in step S101 and the noise predicted value calculated in step S102. A CV value is calculated, a region where the CV value is maximized is selected as “an optimum course prediction region for the application”, and information about the region is output to the application execution means 16 (step S103).

  The application execution means 16 operates application software such as car navigation in accordance with the information about the optimum course prediction area (step S104). More specifically, the aforementioned recommendation function is operated based on the course prediction area, and information about the area is presented to the user.

  FIG. 7 is a flowchart showing a more detailed operation of the coverage predictive value calculation unit 21 shown as step S101 in FIG. First, the coverage predictive value calculation unit 21 reads the content of an arbitrary region from the regions stored in the region shape storage unit 25 (step S111). Subsequently, the coverage predictive value calculation unit 21 reads the content of the travel log stored in the travel log storage unit 24 (step S112). The number of contents of the travel log read in step S112 may be the number of travel logs stored in the travel log storage unit 24 or less.

  The comprehensive predictive value calculation unit 21 determines the traveling direction of the automobile 1 in each travel log read in step S112 (step S113). Here, the “traveling direction of the automobile 1” may be “a direction connecting points after traveling a certain distance (eg, 1 km) from the travel log travel start point” or “travel log A direction in which the movement start point is connected to the position of the vehicle after a minute time may be set as the traveling direction. Other methods can be used as appropriate.

  Subsequently, the coverage predictive value calculation unit 21 superimposes the movement start point of each travel log, the traveling direction determined in step S113, and the region (progress prediction region) read in step S111 (step S114). FIG. 8 is an explanatory diagram showing an example of the movement start point 51a, the traveling direction 51b, the destination 51c, and the traveling prediction area 52 of the travel log 51 that are superimposed in the process shown in step S114 of FIG. FIG. 9 is an explanatory diagram showing a state after the movement start point 51a and the traveling direction 51b of the travel log shown in FIG.

  In the processing of steps S113 to S114, instead of “the movement start point of each travel log”, “a point where movement of a specific distance from the movement start point of each travel log has been completed” may be used. For example, when the distance from the movement start point of the automobile 1 to the current position is 5 km, in step S114, the point moved 5 km from the movement start point of each travel log is overlapped with the traveling direction and the predicted travel area. You can also.

  The coverage predictive value calculation unit 21 calculates the coverage of each travel log based on the result of superimposing in step S114 (step S115). The completeness here is calculated by the following mathematical formula 3. For example, in FIGS. 8 to 9, when the travel route length is 12 km and the length of the route included in the progress prediction region is 10 km, the coverage of the travel log with respect to the route prediction region is 10 ÷ 12≈0.83. Become.

  Finally, the comprehensiveness predictive value calculation unit 21 calculates an average value (or a representative value such as a median value) of the comprehensiveness of each travel log calculated in step S115, and uses the result as the comprehensiveness predictive value ( Step S116). The coverage predictive value calculation unit 21 performs the process shown in steps S111 to S116 on the region if there is another region for which the coverage predictive value is to be calculated, and performs the processing if it does not remain. The process ends (step S117).

(Overall operation of the first embodiment)
Next, the overall operation of the above embodiment will be described.
The route prediction information providing method according to the present embodiment predicts a future route of the mobile body based on a travel log that is a route traveled in the past of the mobile body stored in advance in the travel log storage unit 24, and the route In the course prediction information providing apparatus 10 that provides information related to the user of the mobile object, the shape and shape of the mobile object whose shape is stored in advance in the area shape storage unit 25 can be moved to a plurality of regions. On the other hand, the coverage predictive value calculation unit calculates, as the coverage predictive value, a value obtained by dividing the route length included in the region in the travel route of the mobile object in the travel log by the total length of the travel route of the mobile object in the travel log ( FIG. 6, step S101), for each region, the predicted value of the ratio at which application software that provides the user with information specified according to the position and shape of the region provides inappropriate information A noise prediction value calculation unit calculates a noise prediction value (step S102 in FIG. 6), and noise prediction is performed from a value obtained by multiplying the coverage prediction value by a first weight value set in advance for the coverage prediction value. The optimum region selection unit selects the region where the value obtained by subtracting the value obtained by multiplying the noise prediction value by the second weight value set in advance for the value as the optimum region is selected as the optimum region, and outputs this (FIG. 6, step S103), the application execution means operates application software that provides information related to the selected optimum region (FIG. 6, step S104).

Here, about each said operation step, this may be programmed so that execution is possible with a computer, and you may make it perform these by the processor 11 of the course prediction information provision apparatus 10 which performs each said step directly. The program may be recorded on a non-temporary recording medium, such as a DVD, a CD, or a flash memory. In this case, the program is read from the recording medium by a computer and executed.
By this operation, this embodiment has the following effects.

  By configuring as described above, the present embodiment has an effect of accurately predicting the future course of the moving body from the travel log and providing the predicted course in an optimum shape for the application software. The reason is that, among the future courses of the mobile body, the coverage predictive value that is the predicted value included in the area with the position of the mobile body as the reference point, and the ratio that the application software provides inappropriate information The noise prediction value that is the prediction value of the image is calculated, one region is selected from a plurality of regions based on the comprehensiveness prediction value and the noise prediction value, and the shape is provided to the application execution means. is there.

(Second Embodiment)
In the second embodiment of the present invention, the shape of each region stored in the region shape storage unit 225 is a rectangular shape in the configuration of the first embodiment. Also with this configuration, the same effect as described in the first embodiment can be obtained.
Hereinafter, this will be described in more detail.

  FIG. 10 is an explanatory diagram showing the configuration of the course prediction information providing apparatus 210 according to the second embodiment of the present invention. Since the course prediction information providing apparatus 210 has almost the same configuration as the course prediction information providing apparatus 10 according to the first embodiment, the same elements are referred to by the same names and reference numbers.

  More specifically, in the course prediction information providing apparatus 210, the area shape storage unit 25 of the course prediction information providing apparatus 10 according to the first embodiment is simply replaced with another area shape storage unit 225. The others are all the same as the route prediction information providing apparatus 10.

  FIG. 11 is an explanatory diagram showing an example of the contents stored in the region shape storage unit 225 shown in FIG. In the first embodiment described above, the area shape storage unit 25 stores data defining the shape of a fan-shaped area. However, the area shape storage unit 225 of this embodiment stores a rectangular shape as an area. ing. A direction parallel to the traveling direction of the automobile 1 is a side A and a direction perpendicular to the side B is a side B. The region shape storage unit 225 stores a region ID 225a of each region, a side A that is the length of each side A and side B. The length 225b and the side B length 225c, the X coordinate position 225d and the Y coordinate position 225e of the moving body (automobile) in the area are stored.

  The area shape storage unit 225 may be configured to store an arbitrary shape such as a circle, a triangle, a rhombus, etc. in addition to such a rectangle. Further, a plurality of types of shapes may be stored together with each shape type. Of course, there is no particular change in the processing contents in other components of the route prediction information providing apparatus 210 regardless of the shape of each region.

(Third embodiment)
In the third embodiment of the present invention, in addition to the configuration of the first embodiment, content data corresponding to the selected optimum region is acquired from the external device 330 and stored in the content storage unit 326 provided in advance. It has content acquisition means 318 for storing, and content deletion means 319 for deleting content that does not correspond to the selected optimum area from the content storage unit.

  Further, the optimum region selection unit 323 uses the numerical value of the minimum coverage prediction value that is the minimum value of the coverage prediction value provided in advance from the application execution unit 316 and the maximum noise prediction value that is the maximum value of the noise prediction value. Then, the second weight value is calculated from a value obtained by multiplying the coverage predictive value by the first weight value in a range in which the coverage predictive value is larger than the minimum predictive coverage value and the noise predicted value is smaller than the maximum noise predictive value. It is assumed that a region having the maximum value obtained by subtracting the value obtained by multiplying the predicted noise value by the noise is selected as an optimal region and output to the application execution means.

Even with this configuration, in addition to obtaining the same effect as described in the first embodiment, it is possible to reduce the amount of communication required for content acquisition, which is more suitable for mounting on a mobile object. Can be. In addition, since the application execution unit preliminarily designates the minimum coverage predictive value and the maximum noise predictive value, it is possible to further reduce the proportion of inappropriate information provision.
Hereinafter, this will be described in more detail.

  FIG. 12 is an explanatory diagram showing the configuration of the course prediction information providing apparatus 310 according to the third embodiment of the present invention. Since the course prediction information providing apparatus 310 has almost the same configuration as the course prediction information providing apparatus 10 according to the first embodiment, the same elements are referred to by the same names and reference numbers.

  More specifically, the route prediction information providing device 310 is hardware-related data in addition to the elements of the first embodiment shown in FIG. 1 and data with the external device 330 via the wireless public communication network. A communication means 317 for performing communication is added. The external device 330 includes a communication unit 331 and a storage unit 332, and reads data corresponding to the transmission request from the storage unit 332 according to the transmission request received from the route prediction information providing device 310 via the communication unit 331. Send back.

  Then, the processor 11 of the route prediction information providing apparatus 310 provides the route prediction information providing unit 15 that is the same as that in the first embodiment and the route prediction information that is different from that in the first embodiment, by operating the computer program. In addition to the means 320 (coverage predictive value calculating unit 21, noise predicted value calculating unit 22, optimum region selecting unit 323) and application executing means 316, it functions as a content acquiring means 318 and a content deleting means 319. Among these, the comprehensiveness prediction value calculation unit 21 and the noise prediction value calculation unit 22 are the same as those in the first embodiment.

  In the storage unit 12, a storage area as a content storage unit 326 is secured in addition to the travel log storage unit 24 and the region shape storage unit 25 that are the same as those in the first embodiment.

  FIG. 13 is an explanatory diagram showing more detailed configurations of the course prediction information providing unit 320, the application execution unit 316, the content acquisition unit 318, and the content deletion unit 319 shown in FIG.

  The application execution unit 316 operates application software such as car navigation described above based on the area acquired from the optimum area selection unit 323. In addition, the application execution unit 316 sends the minimum coverage prediction value and the maximum noise prediction value defined by the application software to the optimal region selection unit 323, and the optimal region selection unit 323 uses the region as a basis. It has a function of selecting one of a plurality of areas stored in the shape storage unit 25.

  Then, the optimum region selection unit 323 selects one region based on the minimum coverage prediction value acquired from the application execution unit 316. More specifically, a region where the coverage predictive value is larger than the minimum coverage predictive value defined by the application software and the noise predictive value is the smallest is selected.

  In addition, the optimum region selection unit 323 selects one region based on the maximum noise prediction value acquired from the application execution unit 316. More specifically, a route prediction region having a noise prediction value smaller than the maximum noise prediction value defined by the application software and having the largest comprehensiveness prediction value is selected.

  The content deletion unit 319 acquires the area selected by the optimal area selection unit 323 from the optimal area selection unit 23 and is not related to the area acquired from the optimal area selection unit 23 among the content data stored in the content storage unit 326. Delete the data. Here, the content deletion unit 319 may operate so as to delete some but not all of the data not related to the area acquired from the optimum area selection unit 323.

  The content acquisition unit 318 acquires the region selected by the optimal region selection unit 23 from the optimal region selection unit 23, acquires content data related to the region from the external device 330 via the communication unit 317, and stores the content data. Stored in the unit 326. Here, the content acquisition unit 318 may operate so as to acquire some but not all of the data not related to the region acquired from the optimal region selection unit 323.

  The communication unit 317 performs data communication with the external device 330. The content storage unit 326 stores content data used by application software executed by the application execution unit 316 together with position data represented by latitude and longitude.

  FIG. 14 is an explanatory diagram illustrating an example of the contents stored in the content storage unit 326 illustrated in FIGS. The content storage unit 326 includes a content ID 326a that uniquely identifies each content, a content acquisition time 326b that indicates the time when the content was acquired from the external device 330, a latitude 326c and a longitude 326d of a location corresponding to the content, and a content body Content data such as 326e is stored.

  Of course, the data structure of the content storage unit 326 is not limited to the above example. For example, data of items other than the above may be stored. The content main body 326e is not limited to text data, and may be a still image, a moving image, audio, or a combination of a plurality of types of data (for example, text + moving image + sound). Good.

  FIG. 15 is a flowchart showing the operation of the course prediction information providing apparatus 310 shown in FIGS. In this flowchart, first, the application execution unit 316 sends numerical values of the minimum coverage predictive value and the maximum noise predicted value in advance to the optimum region selecting unit 323 according to the type of application to be operated (step S400).

  Here, the minimum coverage predictive value and the maximum noise predicted value may be determined according to the type of application that the application execution unit 316 operates, and even if they are the same application, It may be determined according to the characteristics of a certain area (for example, whether it is a city center or a suburb). Of course, the minimum coverage prediction value and the maximum noise prediction value may be determined based on other criteria.

  Steps S101 to S102 are the same as the operation of the route prediction information providing apparatus 10 according to the first embodiment shown in FIG. In the subsequent step S403, as in step S103 of FIG. 6, the optimum region selection unit 323 selects the optimum course prediction region for the application. “Large” and “the predicted noise value is greater than the maximum predicted noise value”.

  For the optimum course prediction region selected and output by the optimum region selection unit 323, the application execution unit 316 performs step S404 described later, the content deletion unit 319 performs step S405 described later, and the content acquisition unit 318 performs step S406 described later. Perform ~ 7. Since each of these is executed separately, it need not be executed in the order of the numbers.

  In step S404, the application execution unit 316 acquires one area selected by the optimal area selection unit 323 in step S103, and executes various applications based on the acquired areas.

  In step S405, the content deletion unit 319 obtains one area selected by the optimum area selection unit 323 in step S103 from the optimum area selection unit 323, and the optimum area selection unit 323 out of the content data stored in the content storage unit 326. Delete data that is not related to the acquired area.

  In step S406, the content acquisition unit 318 acquires one area selected by the optimal area selection unit 323 in step S103 from the optimal area selection unit 323, and relates to the area acquired from the optimal area selection unit 323 via the communication unit 317. The acquired content data is acquired from the external device 330. Subsequent to step S406, the content acquisition unit 318 stores the content data acquired from the external device 330 in the content storage unit 326 (step S407).

  It is not always necessary to execute all the operations shown in step S404 and subsequent steps, and one or two steps may be executed as necessary. For example, when the storage capacity of the content storage unit 326 has a margin, step S405 may not be executed (content data is not deleted). In addition, when content data related to a region newly acquired from the optimum region selection unit 323 is already stored in the content storage unit 326, steps S406 to S406 are not performed (the acquired content data is duplicated). It is also possible not to get it. In this way, the amount of communication required for content acquisition can be reduced.

  Furthermore, when step S405 is executed, content data that has passed a predetermined period (for example, one year or more) from the content acquisition time 326b stored in the content storage unit 326 is preferentially deleted. Good. When the content data and the travel log are matched, and a trace of traveling in the vicinity of the place (latitude 326c and longitude 326d) corresponding to the content data is not found within a predetermined period (for example, within one year) The content data may be deleted.

  This embodiment is configured as described above, predicts a region where the user's disadvantage when executing the application is below a certain level and the user's benefit when executing the application is as large as possible, and the result Can be provided to the application. The reason is that the optimum region selection unit 323 acquires the maximum noise predicted value from the application execution unit 316, and selects the course predicted region having the largest comprehensiveness predicted value that is smaller than the maximum noise predicted value.

  Furthermore, the present embodiment predicts a region where the user's profit at the time of executing the application becomes a certain level and the user's disadvantage at the time of executing the application is minimized, and the result can be provided to the application. Play. The reason is that the optimal region selection unit 323 acquires the minimum coverage predictive value from the application execution unit 316 and selects a region that is larger than the minimum coverage predictive value and has the smallest noise predicted value.

  Furthermore, the present embodiment has an effect that the application execution means 316 can execute the application so that the user's profit generated by executing the application is increased. The reason is that the application execution means 16 executes the application based on the course prediction information provided by the optimum region selection unit 323.

  Further, the present embodiment has an effect that the content deleting unit 319 can delete the content so that the user's disadvantage caused by the deletion of the content is reduced. The reason is that the content deletion means 319 deletes the content based on the route prediction information provided by the optimum region selection unit 323, so that data that the user does not need in the future can be deleted preferentially.

  Furthermore, this embodiment has an effect that the content acquisition unit 318 can acquire the content so that the benefit of the user that increases with the acquisition of the content is increased. This is because the content acquisition unit 318 acquires content based on the course prediction information provided by the optimal region selection unit 323, so that data required by the user in the future can be acquired preferentially.

  The present invention has been described with reference to the specific embodiments shown in the drawings. However, the present invention is not limited to the embodiments shown in the drawings, and any known hitherto provided that the effects of the present invention are achieved. Even if it is a structure, it is employable.

  Regarding the embodiment described above, the main points of the new technical contents are summarized as follows. In addition, although part or all of the said embodiment is summarized as follows as a novel technique, this invention is not necessarily limited to this.

(Additional remark 1) The course prediction information which predicts the future course of the said mobile body based on the travel log which is the path | route which the mobile body moved in the past, and provides the user of the said mobile body with the information relevant to the course A providing device,
A travel log storage unit for storing the travel log in advance;
A region shape storage unit that stores in advance the positions and shapes of a plurality of regions in which the movable body can move;
Application execution means for operating application software that provides information related to the position and shape of the region to the user of the mobile body;
For each region, a value obtained by dividing the route length included in the region in the travel route in the travel log by the total length of the travel route in the travel log is calculated as a comprehensiveness prediction value. A comprehensive predictive value calculation unit;
A noise prediction value calculation unit that calculates a noise prediction value that is a prediction value of a ratio at which the application software specified according to the position and shape of the region provides inappropriate information for each region;
From a value obtained by multiplying the coverage predictive value by a first weight value preset for the coverage predictive value, the noise predictive value is set to a second weight value preset for the noise predictive value. A route prediction information provision comprising: an optimum region selecting unit that selects a region having a maximum value obtained by subtracting a value obtained by multiplying the value as an optimum region and outputs the selected region to the application executing unit. apparatus.

(Supplementary Note 2) The comprehensiveness predictive value calculation unit calculates an average value of the comprehensiveness predictive values calculated for a plurality of the travel logs in the most recent preset period in each region as the comprehensiveness for the region. The course prediction information providing apparatus according to appendix 1, wherein a predicted value is used.

(Additional remark 3) The said noise estimated value calculation part uses the said area | region using any one or more among the area of the said area | region, the number of the data which exist in the said area | region, and the total size of the data which exist in the said area | region. The route prediction information providing apparatus according to claim 1, wherein the predicted noise value is calculated.

(Additional remark 4) The content acquisition means which acquires the content data applicable to the selected said optimal area | region from an external device, and memorize | stores it in the content storage part with which it prepared previously, The additional description 1 characterized by the above-mentioned. A route prediction information providing device.

(Additional remark 5) The course prediction information provision apparatus of Additional remark 4 characterized by having a content deletion means to delete the content which does not correspond to the selected said optimal area | region from the said content storage part.

(Supplementary Note 6) The optimum coverage selection unit is provided with a minimum coverage prediction value that is a minimum value of the coverage prediction value provided in advance from the application execution unit, and a maximum noise prediction value that is a maximum value of the noise prediction value. The first weight value is used as the comprehensive predictive value in a range where the comprehensive predictive value is greater than the minimum predictive predictive value and the noise predictive value is smaller than the maximum predictive noise value. A region having the maximum value obtained by subtracting the value obtained by multiplying the noise prediction value by the second weight value from the value multiplied by is selected as the optimum region and is output to the application execution means. The route prediction information providing device according to appendix 1, characterized by having a function of:

(Supplementary Note 7) Based on a travel log that is a route traveled in the past by the mobile object stored in advance in the travel log storage unit, a future course of the mobile object is predicted, and information related to the course is stored in the mobile object. In the course prediction information providing device provided to the user,
For each of a plurality of areas in which the mobile body whose shape and shape are stored in advance in the area shape storage unit can move, the travel log includes the path length included in the travel path of the mobile body in the travel log. The coverage predictive value calculation unit calculates a value divided by the total length of the travel route of the mobile body as a coverage predictive value,
For each region, a noise prediction value is a noise prediction value that is a prediction value of a ratio at which application software that provides the user with information specified according to the position and shape of the region provides inappropriate information. The calculation unit calculates,
From a value obtained by multiplying the coverage predictive value by a first weight value preset for the coverage predictive value, the noise predictive value is set to a second weight value preset for the noise predictive value. The optimal region selection unit selects the region where the value obtained by subtracting the value multiplied by is the optimal region and outputs this,
A course prediction information providing method, wherein an application execution unit operates the application software that provides information related to the selected optimum region.

(Supplementary Note 8) Based on a travel log that is a route traveled in the past by the mobile object stored in advance in the travel log storage unit, a future course of the mobile object is predicted, and information related to the route is stored in the mobile object. In the course prediction information providing device provided to the user,
In the computer provided in the course prediction information providing device,
For each of a plurality of areas in which the mobile body whose shape and shape are stored in advance in the area shape storage unit can move, the travel log includes the path length included in the travel path of the mobile body in the travel log. A procedure for calculating a value divided by the total length of the travel route of the moving body as a comprehensiveness prediction value,
A procedure for calculating a noise prediction value, which is a prediction value of a ratio at which the application software that provides the user with information specified according to the position and shape of the region provides inappropriate information for each region ,
From a value obtained by multiplying the coverage predictive value by a first weight value preset for the coverage predictive value, the noise predictive value is set to a second weight value preset for the noise predictive value. A procedure for selecting the area where the value obtained by subtracting the value multiplied by is the optimum area and outputting it,
And a course prediction information providing program for executing a procedure for operating the application software that provides information related to the selected optimum region.

  This application claims the priority on the basis of Japanese application Japanese Patent Application No. 2012-012721 for which it applied on January 25, 2012, and takes in those the indications of all here.

  The present invention can be used in a device that executes application software for using geographic information that can be mounted on a moving body such as a car, a bicycle, and a two-wheeled vehicle such as a car navigation device. Or as a thing which a pedestrian can use, it may be provided with forms, such as application software performed with portable devices, such as a mobile phone and a smart phone, for example. Moreover, you may perform processes, such as course prediction, not by what is mounted in a mobile body but by the fixed server computer.

DESCRIPTION OF SYMBOLS 1 Car 10, 210, 310 Course prediction information provision apparatus 11 Processor 12, 332 Storage means 13 Travel route recording means 14 Input / output means 15 Travel route acquisition means 16, 316 Application execution means 17 Common bus 20, 320 Course prediction information provision means DESCRIPTION OF SYMBOLS 21 Comprehensiveness prediction value calculation part 22 Noise prediction value calculation part 23,323 Optimal area selection part 24 Travel log memory | storage part 25,225 Area shape memory | storage part 51 Travel log 52 Progress prediction area 317,331 Communication means 318 Content acquisition means 319 Content Deletion unit 326 content storage unit 330 external device

In order to achieve the above object, the route prediction information providing apparatus according to the present invention predicts a future route of the mobile body based on a travel log that is a route traveled by the mobile body in the past, and information related to the route. Is a route prediction information providing device that provides information to a user of the moving object, and provides application information that provides the user of the moving object with information related to the position and shape of a region where the moving object can move. For each area, the value obtained by dividing the path length included in the travel path of the moving body in the travel log by the total length of the travel path of the mobile body in the travel log is calculated as the coverage predictive value. The coverage predictive value calculation unit that performs the application and the application software that is specified according to the position and shape of the area provide inappropriate information for each area. The noise prediction value is calculated from a noise prediction value calculation unit that calculates a noise prediction value that is a combined prediction value, and a value obtained by multiplying the coverage prediction value by a first weight value set in advance for the coverage prediction value. Optimal region selection for selecting a region having the maximum value obtained by subtracting a value obtained by multiplying a noise prediction value by a second weight value set in advance as an object as an optimum region and outputting the selected region to the application execution means And a section.

To achieve the above object, course prediction information providing method according to the present invention, the route by predicting the future path of the mobile body based on the travel log is a path that has moved past the previously stored mobile In the course prediction information providing apparatus that provides information related to the user to the user of the mobile object, movement in the travel log with respect to each of a plurality of areas where the mobile object whose position and shape are stored in advance can move The coverage predictive value calculation unit calculates, as the coverage predictive value, a value obtained by dividing the route length included in the region in the travel route of the body by the total length of the travel route of the mobile object in the travel log. A noise prediction value calculation unit calculates a noise prediction value that is a prediction value of a ratio at which application software that provides information specified according to the position and shape of the region to the user provides inappropriate information, and A value obtained by multiplying the noise prediction value by a second weight value preset for the noise prediction value from a value obtained by multiplying the coverage prediction value by the first weight value preset for the noise prediction value. The application execution means operates the application software that provides the information related to the selected optimum area by the optimum area selection unit selecting the area where the value obtained by subtraction is the maximum as the optimum area and outputting it. It is characterized by making it.

In order to achieve the above object, the route prediction information providing program according to the present invention predicts a future route of the moving body based on a travel log that is a route of the moving body that has been stored in the past, and the route. In the course prediction information providing apparatus that provides information related to the user of the mobile body, a plurality of mobile bodies whose positions and shapes are stored in advance in a computer provided in the course prediction information provision apparatus can move. For each area, a procedure for calculating a value obtained by dividing the path length included in the area in the travel route of the moving body in the travel log by the total length of the travel path of the mobile body in the travel log as a comprehensiveness prediction value. On the other hand, the noise prediction, which is a predicted value of the rate at which application software that provides information specified according to the position and shape of the region to the user provides inappropriate information. A procedure for calculating a value, a value obtained by multiplying a coverage predictive value by a first weight value preset for a coverage predictive value as a target, and a second weight value preset for a noise predicted value as noise Select the area that maximizes the value obtained by subtracting the value obtained by multiplying the predicted value as the optimum area and output it, and run application software that provides information related to the selected optimum area It is characterized in that the procedure to be executed is executed.

Claims (8)

A route prediction information providing device that predicts a future course of a mobile body based on a travel log that is a route traveled by the mobile body in the past and provides information related to the course to a user of the mobile body. And
A travel log storage unit for storing the travel log in advance;
A region shape storage unit that stores in advance the positions and shapes of a plurality of regions in which the movable body can move;
Application execution means for operating application software that provides information related to the position and shape of the region to the user of the mobile body;
For each region, a value obtained by dividing the route length included in the region in the travel route in the travel log by the total length of the travel route in the travel log is calculated as a comprehensiveness prediction value. A comprehensive predictive value calculation unit;
A noise prediction value calculation unit that calculates a noise prediction value that is a prediction value of a ratio at which the application software specified according to the position and shape of the region provides inappropriate information for each region;
From a value obtained by multiplying the coverage predictive value by a first weight value preset for the coverage predictive value, the noise predictive value is set to a second weight value preset for the noise predictive value. A route prediction information provision comprising: an optimum region selecting unit that selects a region having a maximum value obtained by subtracting a value obtained by multiplying the value as an optimum region and outputs the selected region to the application executing unit. apparatus.   The comprehensive predictive value calculation unit calculates an average value of the comprehensive predictive values calculated for the plurality of travel logs in the preset period nearest to each region as the comprehensive predictive value for the region. The course prediction information providing apparatus according to claim 1, wherein   The noise prediction value calculation unit uses the one or more of the area of the region, the number of data existing in the region, and the total size of data existing in the region to predict the noise of the region. The route prediction information providing apparatus according to claim 1, wherein a value is calculated.   The route prediction information according to claim 1, further comprising content acquisition means for acquiring content data corresponding to the selected optimum area from an external device and storing the content data in a content storage unit provided in advance. Providing device.   5. The course prediction information providing apparatus according to claim 4, further comprising content deletion means for deleting content that does not correspond to the selected optimal area from the content storage unit.   The optimum region selection unit uses a numerical value of a minimum coverage prediction value that is a minimum value of the coverage prediction value provided in advance from the application execution unit and a maximum noise prediction value that is a maximum value of the noise prediction value. Then, a value obtained by multiplying the comprehensiveness prediction value by the first weight value in a range where the comprehensiveness prediction value is larger than the minimum comprehensiveness prediction value and the noise prediction value is smaller than the maximum noise prediction value. From which a value obtained by subtracting a value obtained by multiplying the noise prediction value by the second weight value is selected as the optimum region, and this is output to the application execution means The course prediction information providing apparatus according to claim 1, wherein A future course of the mobile body is predicted based on a travel log that is a path traveled in the past by the mobile body stored in advance in the travel log storage unit, and information related to the course is given to the user of the mobile body. In the provided route prediction information providing device,
For each of a plurality of areas in which the mobile body whose shape and shape are stored in advance in the area shape storage unit can move, the travel log includes the path length included in the travel path of the mobile body in the travel log. The coverage predictive value calculation unit calculates a value divided by the total length of the travel route of the mobile body as a coverage predictive value,
For each region, a noise prediction value is a noise prediction value that is a prediction value of a ratio at which application software that provides the user with information specified according to the position and shape of the region provides inappropriate information. The calculation unit calculates,
From a value obtained by multiplying the coverage predictive value by a first weight value preset for the coverage predictive value, the noise predictive value is set to a second weight value preset for the noise predictive value. The optimal region selection unit selects the region where the value obtained by subtracting the value multiplied by is the optimal region and outputs this,
A course prediction information providing method, wherein an application execution unit operates the application software that provides information related to the selected optimum region. A future course of the mobile body is predicted based on a travel log that is a path traveled in the past by the mobile body stored in advance in the travel log storage unit, and information related to the course is given to the user of the mobile body. In the provided route prediction information providing device,
In the computer provided in the course prediction information providing device,
For each of a plurality of areas in which the mobile body whose shape and shape are stored in advance in the area shape storage unit can move, the travel log includes the path length included in the travel path of the mobile body in the travel log. A procedure for calculating a value divided by the total length of the travel route of the moving body as a comprehensiveness prediction value,
A procedure for calculating a noise prediction value, which is a prediction value of a ratio at which the application software that provides the user with information specified according to the position and shape of the region provides inappropriate information for each region ,
From a value obtained by multiplying the coverage predictive value by a first weight value preset for the coverage predictive value, the noise predictive value is set to a second weight value preset for the noise predictive value. A procedure for selecting the area where the value obtained by subtracting the value multiplied by is the optimum area and outputting it,
And a course prediction information providing program for executing a procedure for operating the application software that provides information related to the selected optimum region.

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