JP2019139462A - Congestive situation estimation method, congestive situation estimation program, and congestive situation estimation system - Google Patents

Abstract

To provide a method of estimating a congestive situation akin to an actual situation on each road.SOLUTION: In a server 20, road division means 220 divides a road (road data) in units of a certain length. Each of road pieces resulting from the division is treated as a unit road that is a processing unit for congestion analysis. Base number-of-people calculation means 230 calculates a base number of people (maximum number of people to be accommodated) per a unit road. In a portable terminal device 10, degree-of-congestion estimation means 110 estimates a degree of congestion at a user's current position on the basis of information on an acceleration. Degree-of-congestion data is transmitted to the server 20. In the server 20, average degree-of-congestion calculation means 250 obtains an average degree of congestion for each advancing direction in relation to each unit road on the basis of the degree-of-congestion data items aggregated for each unit road by data aggregation means 240. Estimated number-of-people calculation means 270 calculates an estimated number of people for each unit road on the basis of the information on the average degree of congestion and the information on the base number of people.SELECTED DRAWING: Figure 4

Description

  The present invention relates to a method for estimating a congestion state of a road on which a person passes based on sensor information obtained by a mobile terminal device.

  In recent years, a lot of application software using a map has been developed in accordance with enhancement of functions and resolution of portable terminal devices such as cellular phones. As one of such application software, application software called “route guidance application” for guiding a user (hereinafter referred to as “user”) of a mobile terminal device to a destination is known. In the route guidance application, a route search from the current location of the user to the destination is performed, and the result is displayed on the screen of the mobile terminal device together with the map. By using such a route guidance application, the user can easily reach the destination even when there is no map at hand.

  In such a route guidance application or a route guidance system composed of a server and a mobile terminal device, estimation of the congestion status (estimation of the degree of congestion of the user's current position, the degree of congestion in a predetermined range, the number of people in a predetermined range, etc.) May be performed. These estimation results may be used, for example, for the purpose of predicting the required time from the current location to the destination, or may be presented to the user as one piece of information regarding route guidance.

  A method for estimating the congestion state is disclosed in, for example, Japanese Patent Application Laid-Open No. 2017-147671 (the applicant is the applicant of the present application). According to the technique disclosed in Japanese Patent Application Laid-Open No. 2017-147671, the degree of congestion of the current position of the user is calculated based on the standard deviation of acceleration obtained by the acceleration sensor of the mobile terminal device. In the server, the average congestion degree for each mesh (one mesh is, for example, a 20 m square area) is calculated based on the congestion degree data sent from each mobile terminal device. Further, the server calculates the estimated number of people for each mesh using the average congestion degree data. With the method described above, it is possible to estimate the congestion state at a low cost without requiring a surveillance camera.

JP 2017-147671 A

  However, according to the conventional method, the average congestion degree and the estimated number of people are calculated for each mesh. For this reason, when a certain road (hereinafter referred to as “target road”) extends over a plurality of meshes, data (congestion degree data) for each position on the target road is stored in a plurality of meshes. Sorted. Then, in each distribution destination mesh, the congestion state is estimated in consideration of congestion degree data on roads other than the target road. Since the estimation of the congestion situation is performed by such a method, it is difficult to grasp the congestion situation close to the actual situation when moving on the road of interest.

The conventional methods have the following problems, and it is required to obtain more practical and high added value results as a result of the congestion analysis.
(A) Since all data used in the congestion analysis is regarded as reliable data, the reliability of the congestion situation obtained as an estimation result is unknown.
(B) Although the flow of people on the road has directionality, the information obtained as an estimation result does not include information on directionality.
(C) Although more accurate estimation results are required for sightseeing spots, etc., congestion analysis is performed in the same way at any place.
(D) In some cases, such as when there is a mesh without a walking user, the estimation result may be missing.

  Therefore, an object of the present invention is to provide a method for estimating a congestion situation close to an actual situation on each road (a road through which people pass). Another object of the present invention is to provide a method for obtaining a congestion analysis result that is more practical and has higher added value than the conventional one.

A first invention is a congestion degree estimation step for estimating a congestion degree based on sensor information of a mobile terminal device;
Congestion degrees of a plurality of mobile terminal devices estimated in the congestion degree estimation step, with a road stored in the road information storage unit or a road piece obtained by dividing the road based on a predetermined rule as a unit road Congestion level aggregation step to aggregate the data of each unit road,
A representative congestion degree calculating step of calculating a representative congestion degree that is a representative value of the congestion degree for each unit road based on the aggregation result in the congestion degree aggregation step.

According to a second invention, in the first invention,
A road piece obtained by dividing a road stored in the road information storage unit with a certain length is used as a unit road.

According to a third invention, in the first invention,
A road piece obtained by dividing the road stored in the road information storage unit at least at any one of a corner, an intersection, and a point where the road width changes is defined as a unit road.

According to a fourth invention, in the first invention,
In the congestion level aggregation step, each congestion level estimated in the congestion level estimation step is associated with one of the two traveling directions,
In the representative congestion degree calculating step, a representative congestion degree for each of the two traveling directions is calculated for each unit road.

According to a fifth invention, in the first invention,
A moving state determination step of determining a state related to movement of the user of the mobile terminal device based on the sensor information;
The method further includes a reliability addition step of adding reliability to the data of the congestion degree estimated in the congestion degree estimation step based on the determination result in the moving state determination step.

According to a sixth invention, in the fifth invention,
A representative reliability calculating step of calculating a representative reliability, which is a representative value of the reliability added in the reliability adding step, for each unit road based on an aggregation result in the congestion degree aggregating step; To do.

According to a seventh invention, in the first invention,
For each unit road, the reliability for obtaining the reliability of the representative congestion degree calculated in the representative congestion degree calculation step based on the number of mobile terminal devices that generate the congestion degree data aggregated in the congestion degree aggregation step The method further includes a degree determination step.

In an eighth aspect based on the first aspect,
The frequency at which the congestion level is estimated in the congestion level estimation step is changed according to the current position of the mobile terminal device.

According to a ninth invention, in the first invention,
The number of types of sensor information used when the congestion level is estimated in the congestion level estimation step is changed according to the current position of the mobile terminal device.

In a tenth aspect based on the first aspect,
And further comprising a representative congestion degree complementation step of complementarily obtaining a representative congestion degree of the information missing road when a unit road whose representative congestion degree has not been calculated in the representative congestion degree calculating step is defined as an information missing road. And

In an eleventh aspect based on the tenth aspect,
In the representative congestion degree complementation step, the representative congestion degree of the information missing road is calculated by an interpolation process using a representative congestion degree for a unit road in the vicinity of the information missing road calculated in the representative congestion degree calculating step. It is characterized by that.

In a twelfth aspect based on the tenth aspect,
In the representative congestion degree complementation step, the representative congestion degree of the information missing road is obtained based on a past representative congestion degree of the information missing road.

In a thirteenth aspect based on the fifth aspect,
A representative congestion degree complementing step of complementarily obtaining a representative congestion degree of the information missing road when defining a unit road whose representative congestion degree was not calculated in the representative congestion degree calculating step as an information missing road;
The calculation of the representative congestion level in the representative congestion level calculation step is performed based on the congestion level data to which the reliability level equal to or higher than a predetermined threshold is added in the reliability level adding step.
The calculation of the representative congestion degree in the representative congestion degree complementing step is performed based on congestion degree data to which a reliability less than a predetermined threshold is added in the reliability addition step.

A fourteenth aspect of the present invention is a congestion situation estimation program executed by the server in a congestion situation estimation system in which a server and a plurality of portable terminal devices are connected via a network,
A congestion degree receiving step for receiving congestion degree data transmitted from each mobile terminal device;
A road stored in the road information storage unit or a road piece obtained by dividing the road based on a predetermined rule is a unit road, and the congestion degree data received in the congestion degree receiving step is obtained for each unit road. A congestion aggregation step to aggregate;
The CPU of the computer uses the memory to execute a representative congestion degree calculation step of calculating a representative congestion degree that is a representative value of the congestion degree for each unit road based on the aggregation result in the congestion degree aggregation step. Features.

A fifteenth aspect of the present invention is a congestion situation estimation system in which a server and a plurality of portable terminal devices are connected via a network.
Each mobile terminal device
A congestion degree estimation means for estimating a congestion degree based on sensor information;
Congestion degree transmission means for transmitting congestion degree data estimated by the congestion degree estimation means to the server,
The server
A congestion degree receiving means for receiving congestion degree data transmitted by the congestion degree transmitting means of each mobile terminal device;
A road information storage unit for storing road information;
The road stored in the road information storage unit or a road segment obtained by dividing the road based on a predetermined rule is used as a unit road, and the congestion degree data received by the congestion degree receiving unit is obtained for each unit road. Congestion degree aggregation means to aggregate,
And a representative congestion degree calculating means for calculating a representative congestion degree that is a representative value of the congestion degree for each unit road based on the result of aggregation by the congestion degree aggregating means.

  According to the first aspect, the congestion degree data estimated by the mobile terminal device is collected for each unit road (a road or a road piece obtained by dividing a road). Based on the result of the aggregation, a representative congestion degree (for example, an average value, a representative value, and a mode value of a plurality of congestion degrees) is calculated for each unit road. In this way, unlike the conventional example in which the congestion analysis is performed for each mesh, the estimation of the congestion status of each unit road is performed based only on the data for each unit road. Therefore, it is possible to estimate the congestion situation close to the actual situation on each road.

  According to the second or third aspect of the invention, the congestion analysis is performed for each road piece obtained by dividing the road, so that it is possible to accurately estimate the local congestion situation.

  According to the fourth aspect, since the calculation of the representative congestion level is performed for each traveling direction, it is possible to grasp the congestion situation in consideration of the flow of people on the road. Thereby, for example, it becomes possible to predict how the congested spot will move in the future.

  According to the fifth to seventh inventions, since the reliability information is added to the congestion degree data or the representative congestion degree data, the reliability of the congestion analysis result can be grasped.

  According to the eighth or ninth aspect, it is possible to estimate the congestion status in an area where the degree of interest of the user is high, such as a sightseeing spot, with higher accuracy than in other areas.

  According to the tenth to thirteenth inventions, even if the congestion analysis result is missing, the missing result is complemented. Thereby, a congestion analysis result without omission is obtained.

  According to the fourteenth aspect, the same effect as in the first aspect can be obtained.

  According to the fifteenth aspect, the same effect as in the first aspect can be obtained.

It is a block diagram which shows the apparatus structure which implement | achieves the route guidance system which concerns on one Embodiment of this invention. In the said embodiment, it is a block diagram which shows the hardware constitutions of a portable terminal device. In the said embodiment, it is a block diagram which shows the hardware constitutions of a server. In the said embodiment, it is a block diagram which shows the detailed function structure of a route guidance system. In the said embodiment, it is a figure which shows the record format of congestion degree data. In the said embodiment, it is a figure for demonstrating a data storage means. In the said embodiment, it is a figure which shows the record format of road data. In the said embodiment, it is a figure which shows the record format of unit road data. In the said embodiment, it is a figure which shows the record format of base number-of-persons data. In the said embodiment, it is a figure for demonstrating the term regarding a advancing direction. In the said embodiment, it is a figure which shows the record format of average congestion degree data. In the said embodiment, it is a figure which shows the record format of estimated number data. In the said embodiment, it is a flowchart which shows the procedure of the congestion analysis process performed with a portable terminal device. In the said embodiment, it is a figure for demonstrating the reason for using the standard deviation of acceleration for estimation of a congestion degree. In the said embodiment, it is a figure which shows an example of the change of the standard deviation of the acceleration when a person is walking. In the said embodiment, it is a figure for demonstrating switching the frequency which estimates a congestion degree according to an area. In the said embodiment, it is a flowchart which shows the schematic procedure of the congestion analysis process performed with a server. In the said embodiment, it is a figure for demonstrating that a road (road data) is divided | segmented by fixed length. In the said embodiment, it is a figure for demonstrating that the road below a fixed length is not divided | segmented. In the said embodiment, it is a flowchart which shows the procedure of the main process of the congestion analysis process performed with a server. In the said embodiment, it is a figure which shows the example of the calculation result of the average congestion degree according to the advancing direction consideration unit road. In the said embodiment, it is a figure for demonstrating a data loss complementation process. It is a figure for demonstrating about dividing | segmenting a road by a corner regarding the 1st modification of the said embodiment. In the 4th modification of the said embodiment, it is a block diagram which shows the detailed function structure of a route guidance system.

  Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings. In the following, “application software” is abbreviated as “application”. In the following, each road (road piece) after a road (road data) prepared in the form of a database or the like is divided according to a predetermined rule is referred to as a “unit road”.

<1. Overall configuration>
FIG. 1 is a block diagram showing a device configuration for realizing a route guidance system according to an embodiment of the present invention. This route guidance system is realized by the server 20 and the plurality of mobile terminal devices 10. The server 20 and the mobile terminal device 10 are connected via a communication line such as the Internet. In the present embodiment, a route guidance application is installed in the mobile terminal device 10 in order to realize this route guidance system. The route guidance application is software for presenting a route from the current location to the destination to the user when the user moves to a certain destination for sightseeing or shopping. The user can activate the route guidance application by a predetermined operation. By installing such a route guidance application in the mobile terminal device 10, even when a user visits a sightseeing place that does not have a sense of land, for example, the user can easily reach the destination by using the route guidance application. It becomes possible.

  By the way, as will be described later, in this route guidance system, estimation of the congestion state of roads through which people pass (the degree of congestion of the current position of the user, the average congestion degree of each unit road according to the traveling direction, and the number of people on each unit road) Estimation) is performed. That is, a congestion situation estimation system is realized by this route guidance system.

<2. Hardware configuration>
FIG. 2 is a block diagram illustrating a hardware configuration of the mobile terminal device 10. The mobile terminal device 10 includes a CPU 11, a flash ROM 12, a RAM 13, a communication control unit 14, a video photographing unit (camera) 15, an input operation unit 16, a display unit 17, a GPS sensor 18a, an acceleration sensor 18b, and a geomagnetic sensor 18c. ing. The CPU 11 performs various arithmetic processes and the like in order to control the entire mobile terminal device 10. The flash ROM 12 is a writable nonvolatile memory, and stores various programs and various data that should be retained even when the power of the mobile terminal device 10 is turned off. The RAM 13 is a writable volatile memory, and temporarily stores an executing program, data, and the like. The communication control unit 14 performs control of data transmission to the outside and control of data reception from the outside. The video shooting unit (camera) 15 shoots a landscape seen from the current position based on an operation by the user. The input operation unit 16 is a touch panel, for example, and accepts an input operation by a user. The display unit 17 displays an image based on a command from the CPU 11. The GPS sensor 18a acquires position information (latitude / longitude information) for specifying the current position of the user based on radio waves received from GPS satellites. The acceleration sensor 18 b measures acceleration based on the movement of the mobile terminal device 10. The geomagnetic sensor 18c acquires azimuth information indicating the azimuth in which the mobile terminal device 10 is facing (for example, the azimuth in which the display unit 17 is facing).

  In the mobile terminal device 10, a route guidance program for realizing a route guidance application is stored in the flash ROM 12. When the user gives an instruction to start the route guidance application, the route guidance program stored in the flash ROM 12 is read into the RAM 13, and the CPU 11 executes the route guidance program read into the RAM 13, thereby causing the route guidance. App functionality is provided to users.

  FIG. 3 is a block diagram illustrating a hardware configuration of the server 20. The server 20 includes a CPU 21, ROM 22, RAM 23, auxiliary storage device 24, communication control unit 25, input operation unit 26, and display unit 27. The CPU 21 performs various arithmetic processes and the like to control the entire server 20. The ROM 22 is a read-only memory and stores, for example, an initial program to be executed by the CPU 21 when the server 20 is activated. The RAM 23 is a writable volatile memory, and temporarily stores programs and data being executed. The auxiliary storage device 24 is a magnetic disk device or the like, and stores various programs and various data that should be retained even when the server 20 is powered off. The communication control unit 25 performs control of data transmission to the outside and control of data reception from the outside. The input operation unit 26 is a keyboard or a mouse, for example, and accepts an input operation by an operator. The display unit 27 displays an image based on a command from the CPU 21.

  The server 20 is installed with a congestion situation estimation program that estimates the average degree of congestion of each unit road in the traveling direction and the number of people on each unit road. In the server 20, the congestion status estimation program is stored in the ROM 22 or the auxiliary storage device 24. When the server 20 is activated, the congestion state estimation program stored in the ROM 22 or the auxiliary storage device 24 is read into the RAM 23, and the CPU 21 executes the congestion state estimation program read into the RAM 23. Thereby, as will be described later, the congestion state is estimated using data (congestion degree data) sent from the mobile terminal device 10.

<3. Functional configuration>
FIG. 4 is a block diagram showing a detailed functional configuration of the route guidance system. As described above, this route guidance system is realized by the mobile terminal device 10 and the server 20. The mobile terminal device 10 includes an acceleration measuring unit 100, a congestion degree estimating unit 110, a current position detecting unit 120, a traveling direction estimating unit 130, a walking determining unit 140, a walking determination result use reliability determining unit 150, a data transmitting unit 160, data Receiving means 170, route searching means 180, and result display means 190 are provided. The server 20 includes a data storage unit 200, a data receiving unit 210, a road dividing unit 220, a base number calculating unit 230, a data aggregating unit 240, an average congestion degree calculating unit 250, a user number utilization reliability determining unit 260, and an estimated number calculating unit. 270, data loss complementing means 280, and data transmitting means 290.

<3.1 Operations of Components of Portable Terminal Device>
The operation of each component of the mobile terminal device 10 will be described. The acceleration measuring unit 100 measures acceleration based on the movement of the mobile terminal device 10 caused by the user's movement. The acceleration measuring means 100 is realized by an acceleration sensor 18b (see FIG. 2) as hardware. The acceleration measurement by the acceleration measuring means 100 is performed every 70 milliseconds, for example. The congestion level estimation unit 110 estimates the congestion level of the current position of the user based on the acceleration measured by the acceleration measurement unit 100. A detailed description of the congestion degree estimation method will be given later.

  The current position detection unit 120 detects the current position of the user based on the radio wave received from the GPS satellite. The current position detection means 120 is realized by a GPS sensor 18a (see FIG. 2) as hardware. The traveling direction estimation unit 130 estimates the traveling direction of the user based on the change in the current position detected by the current position detection unit 120. The traveling direction may be estimated based on the direction information obtained by the geomagnetic sensor 18c.

  The walking determination unit 140 determines a state (movement state) related to the movement of the user based on a change per unit time of the current position detected by the current position detection unit 120 (that is, a movement distance of the user per unit time). To do. In this embodiment, it is assumed that the moving state is determined from four states of “walking”, “running”, “sightseeing behavior”, and “stop”. The determination of the movement state by the determination unit 140 is referred to as “walking determination”. The walking determination result use reliability determination means 150 determines the reliability of the congestion degree estimated by the congestion degree estimation means 110 based on the movement state as a result of the walking determination. The reliability is a value representing the relative height of data reliability.

  The data transmission unit 160 includes information on the degree of congestion estimated by the congestion degree estimation unit 110, information on the direction of travel estimated by the direction of travel estimation estimation unit 130, and the reliability determined by the walking determination result use reliability determination unit 150. The congestion degree data having a record format as shown in FIG. The data transmission means 160 implements a congestion degree transmission means. Transmission of the congestion degree data by the data transmission means 160 is performed, for example, every 5 minutes.

  The data receiving unit 170 receives the congestion status data sent from the server 20. The route search unit 180 receives a destination input by the user and searches for a route from the current position of the user to the destination. The result display unit 190 displays information based on the result of the route search by the route search unit 180 and the congestion status data sent from the server 20 on the display unit 17.

<3.2 Operations of server components>
Next, the operation of each component of the server 20 will be described. As shown in FIG. 6, the data storage means 200 holds a road database 202, a unit road database 204, a base number database 206, and a congestion degree database 208. This data storage means 200 is realized by an auxiliary storage device 24 (see FIG. 3) as hardware. The road database 202 schematically stores road data having a record format as shown in FIG. In the present embodiment, a road information storage unit is realized by the road database 202. Here, it is assumed that road data within a target range for performing congestion analysis is stored in the road database 202 in advance. The unit road database 204 typically stores unit road data having a record format as shown in FIG. The base number database 206 stores base number data having a record format as shown in FIG. 9, for example. In addition, the base number of people here is the maximum number of people that can indicate how many people can exist at one point on each unit road. The congestion degree database 208 stores the above-described congestion degree data.

  The data receiving unit 210 receives congestion degree data transmitted from the mobile terminal device 10. The congestion degree data is stored in the congestion degree database 208. The data receiving unit 210 implements a congestion degree receiving unit.

  The road dividing means 220 performs a road dividing process of dividing a road (road data) stored in the road database 202 according to a predetermined rule. A road piece obtained by this road division processing becomes a unit road (unit road data) which is a processing unit in the congestion analysis. The unit road data is stored in the unit road database 204.

  By the way, in this embodiment, as will be described later, the calculation of the degree of congestion (average congestion degree) for each unit road is performed for each traveling direction. In this regard, the unit road data includes information on the coordinates of the start point and the coordinates of the end point, and the user's traveling direction on the unit road is a direction from the start point to the end point and a direction from the end point to the start point. There is. Therefore, in this specification, as shown in FIG. 10, the direction from the start point to the end point is referred to as “first direction”, and the direction from the end point to the start point is referred to as “second direction”. Further, for convenience of explanation, the term “traveling direction considering unit road” is used as a term representing one unit road in consideration of the traveling direction. Therefore, one unit road includes two traveling direction consideration unit roads.

  The base number calculating means 230 calculates the base number for each unit road. Information about the base number calculated by the base number calculating unit 230 is stored in the base number database 206 as base number data.

  Based on the unit road data stored in the unit road database 204, the data aggregating unit 240 aggregates the congestion degree data accumulated in the congestion degree database 208 within a predetermined time for each unit road. As described above, the data aggregating unit 240 aggregates the congestion degree data for each unit road, so that the average congestion degree calculating unit 250, the user number use reliability determining unit 260, and the estimated number calculating unit 270 perform processing for each unit road. Can be done.

  The average congestion degree calculation unit 250 calculates an average congestion degree, which is an average value of the congestion degree, for each traveling direction consideration unit road based on the aggregation result by the data aggregation unit 240. The number-of-users use reliability determination unit 260 determines the reliability of the average congestion degree calculated by the average congestion degree calculation unit 250 based on the aggregation result by the data aggregation unit 240. From the above, the average congestion degree calculation means 250 and the user number use reliability determination means 260 generate average congestion degree data having a record format as shown in FIG. 11, for example.

  The estimated number of persons calculation means 270 calculates the estimated number of persons for each unit road based on the average congestion degree data and the base number of persons data. Thereby, for example, estimated number of people data having a record format as shown in FIG. 12 is generated.

  The data loss complementing means 280 is a process for complementing data on unit roads for which the average congestion degree and the estimated number of people have not been calculated because the congestion degree data does not exist (hereinafter referred to as “data loss complementation process”). )I do. A detailed description of the data loss complementing process will be described later. The data transmission means 290 transmits the average congestion degree data and the estimated number of people data to the portable terminal device 10 as congestion status data after the data loss complementation processing.

<4. Congestion analysis processing>
In the route guidance system according to the present embodiment, a process of analyzing a congestion state of a road on which a person passes (hereinafter referred to as “congestion analysis process”) is performed. The congestion analysis process is divided into a process performed in each mobile terminal device 10 and a process performed in the server 20. In each mobile terminal device 10, the degree of congestion of the current position of the user is estimated using only data obtained by each mobile terminal device 10. Congestion degree data obtained as a result of estimating the degree of congestion in each mobile terminal device 10 is transmitted to the server 20. In the server 20, based on the congestion degree data transmitted from each mobile terminal device 10, estimation of the average congestion degree for each traveling unit road in consideration of the traveling direction and estimation of the number of persons for each unit road are performed. Hereinafter, each of the process performed by the portable terminal device 10 and the process performed by the server 20 will be described in detail.

<Processing performed in 4.1 mobile terminal device>
FIG. 13 is a flowchart illustrating the procedure of the congestion analysis process performed in the mobile terminal device 10. When the route guidance application is activated on the mobile terminal device 10, first, calibration processing is performed to acquire data necessary for normalization in step S130 described later (step S110). A detailed description of the calibration process will be described later.

  After the calibration process is completed, walking determination is performed by the walking determination unit 140 (step S120). That is, the movement state of the user of the mobile terminal device 10 is determined. As described above, in this embodiment, this determination is made based on the change of the current position per unit time, and the movement state is “walking”, “running”, “tourism behavior”, and “stop”. It is determined from the four states.

  Next, the congestion level estimation unit 110 estimates the congestion level of the current position of the user (step S130). In step S130, first, the standard deviation of acceleration for the latest 10 seconds is calculated. Here, the reason why the standard deviation of acceleration is used for estimating the degree of congestion will be described with reference to FIG. FIG. 14 is a diagram illustrating an example of a change in acceleration when a person is walking. When a person is walking in a crowded place, the stride is reduced and the overall movement is reduced, so that the variation in acceleration is reduced as indicated by reference numeral 33 in FIG. On the other hand, when a person is walking in a vacant place, the stride is increased and the overall movement is increased, so that the variation in acceleration increases as indicated by reference numeral 34 in FIG. Thus, the variation in acceleration changes depending on the degree of congestion. Therefore, the congestion degree is estimated using a standard deviation (acceleration) serving as an index of variation in acceleration.

  FIG. 15 is a diagram illustrating an example of a change in the standard deviation of acceleration when a person is walking. In the time zone indicated by reference numeral 35 in FIG. 15, the standard deviation of the acceleration is a relatively large value. During this time, people are thought to be walking in relatively free places. On the other hand, the standard deviation of the acceleration is a relatively small value in the time zone indicated by reference numeral 36 in FIG. During this time, people are thought to be walking in relatively crowded places.

  By the way, because people have individuality, there are differences in how they walk. For this reason, even if the standard deviation of acceleration is the same for data of two users, the degree of congestion is not always the same. In particular, it is considered that the characteristics of walking by a person appear when walking in a vacant place. Therefore, it is considered that the maximum value of the standard deviation of acceleration differs depending on the person. It should be noted that the standard deviation of acceleration is considered to be almost zero for most people when it is so crowded that it cannot move. Therefore, in the present embodiment, normalization is performed so that the maximum value of the standard deviation of the acceleration is 1.0 for each mobile terminal device 10 in order to absorb the difference in walking by people. Calibration processing (step S110) is performed for this normalization.

  In the calibration process, a standard deviation of acceleration is acquired every predetermined period (for example, 10 minutes), for example, every 10 seconds after the route guidance application is activated. The maximum value among the standard deviations acquired during the predetermined period is set to a value corresponding to 1.0 after normalization. For example, it is assumed that the maximum value of the standard deviation of acceleration is 0.7 as a result of the calibration process. In this case, normalization is performed so that a value in the range from 0.0 to 0.7 becomes a value in the range from 0.0 to 1.0.

In step S130, the degree of congestion is calculated based on the normalized standard deviation obtained as described above. In the present embodiment, the degree of congestion is represented by a value from 0 to 1. The degree of congestion increases as the value of the congestion degree approaches 1. By the way, as described above, the standard deviation of acceleration decreases when walking in a crowded place, and increases when walking in a vacant place. Therefore, in step S130, the congestion degree is calculated such that the congestion degree approaches 0 as the standard deviation of acceleration approaches 1, and the congestion degree approaches 1 as the standard deviation of acceleration approaches 0. More specifically, the degree of congestion is calculated by the following equation (1).
Congestion level = 1-standard deviation (1)

  Here, an example in which acceleration information is used for estimating the degree of congestion has been described as sensor information. However, the present invention is not limited to this, for example, information on angular velocity obtained by a gyro sensor (not shown) or a geomagnetic sensor. The direction information obtained by 18c can also be adopted as sensor information used for estimating the degree of congestion.

  After the estimation of the congestion level, the walking determination result use reliability determining unit 150 determines the reliability of the congestion level estimated in step S130 based on the result of the walking determination in step S120 (step S140). For example, when the movement state is determined as “walking” or “running”, the reliability of the congestion level is set to “1”, and when the movement state is determined as “tourism behavior”, the congestion level is determined. Is determined to be “0.5”, and when the movement state is determined to be “stop”, the reliability of the congestion level is determined to be “0”. Regarding the reliability, the maximum value is 1 and the minimum value is 0.

  Next, the traveling direction estimation unit 130 estimates the traveling direction of the user based on the change in the current position (step S150). The traveling direction is represented by an angle, for example, with the true north direction as a reference (0 degree) from the current position. By performing the processing of steps S130 to S150 as described above, congestion degree data as shown in FIG. 5 is generated.

  Thereafter, it is determined whether or not a predetermined time (for example, 5 minutes) has elapsed (step S160). As a result, if the predetermined time has elapsed, the process proceeds to step S170, and if the predetermined time has not elapsed, the process returns to step S120.

  In step S <b> 170, the data transmission unit 160 transmits the congestion degree data accumulated during a predetermined time from the mobile terminal device 10 to the server 20. However, if no congestion level data is accumulated, the congestion level data is not transmitted to the server 20. In the present embodiment, only the data whose reliability is set to “1” among the generated congestion degree data is transmitted to the server 20. As a result, the server 20 performs the congestion analysis process using only reliable data. After the congestion degree data is transmitted to the server 20, the process returns to step S120. Thereafter, the processes in steps S120 to S170 are repeated until the route guidance application is terminated.

  By the way, in this embodiment, the frequency which estimates a congestion degree is made higher in the area where a highly accurate congestion analysis result is calculated | required, such as a sightseeing spot, than other areas. In order to realize this, the server 20 includes an area where the congestion degree is estimated with a relatively high frequency (hereinafter referred to as “high accuracy area”) and an area where the congestion degree is estimated with a relatively low frequency (hereinafter referred to as “high accuracy area”). , "Normal area") is prepared. For example, when the areas within the thick dotted lines denoted by reference numerals 41 and 42 in FIG. 16 are high-precision areas, data for specifying these areas is prepared in the server 20. Then, based on position information (latitude / longitude information) (see FIG. 5) included in the congestion degree data sent from the mobile terminal device 10 to the server 20, when the user moves from the high-precision area to the normal area, When moving from the normal area to the high accuracy area, the server 20 transmits a signal instructing the mobile terminal device 10 to switch the frequency. Thereby, in the portable terminal device 10, the frequency which estimates congestion degree is switched. Specifically, when the user is located in the high accuracy area, the processes in steps S120 to S160 are performed more frequently than when the user is located in the normal area. In addition, although the example which divides | segments the whole area | region into two types of areas (high precision area and normal area) was shown here, the whole area | region is divided | segmented into three or more types of areas, and the congestion degree in the portable terminal device 10 is shown. The estimation frequency may be switched in three or more stages.

  In the present embodiment, the moving state determination step is realized by step S120, the congestion degree estimation step is realized by step S130, and the reliability addition step is realized by step S140.

<4.2 Processing performed on the server>
<4.2.1 Overview>
FIG. 17 is a flowchart illustrating a schematic procedure of the congestion analysis process performed by the server 20. In the server 20, first, road division processing is performed by the road dividing means 220 (step S210). In the present embodiment, roads (road data) stored in the road database 202 are divided by a certain length as shown in FIG. For example, when it is determined to divide a road every 20 m, a road having a length of 90 m is divided into four 20 m unit roads and one 10 m unit road. In addition, about the road below a fixed length, it is not divided | segmented (refer FIG. 19). That is, for a road having a certain length or less, the road is treated as a unit road as it is. As described above, in step S210, the roads (road data) stored in the road database 202 are subjected to road division processing, whereby unit roads composed of unit road data serving as a processing unit for congestion analysis. A database 204 is generated.

  After the road division processing is completed, the base number calculating unit 230 calculates the base number for each unit road (step S220). In this regard, as shown in FIG. 8, the unit road data includes coordinate information and road width information. Therefore, the area that can be passed for each unit road can be obtained. The base number of people for each unit road is calculated by multiplying the passable area by a predetermined coefficient. As described above, in step S220, based on the unit road data stored in the unit road database 204, the base number database 206 including the base number data as shown in FIG. 9 is generated.

  Note that the processing in step S210 and step S220 is performed only when the road data stored in the road database 202 is changed, or is performed at a relatively long span interval (time interval). At normal times, only the processes of the following steps S230 and S240 are performed.

  In step S <b> 230, the data reception unit 210 receives the congestion degree data transmitted from each mobile terminal device 10. The congestion level receiving step is realized by this step S230. As described above, the congestion degree data received by the data receiving unit 210 is stored in the congestion degree database 208. In step S240, the main process of the congestion analysis process is performed. A detailed description of this main process will be given later.

  In FIG. 17, the process of step S230 and the process of step S240 are illustrated to be performed sequentially, but in practice, the process of step S240 is performed every predetermined time, and the process of step S240 is performed. Even during the period of time, the process of step S230 is performed each time congestion level data is sent from the mobile terminal device 10.

<4.2.2 Main processing of congestion analysis processing>
FIG. 20 is a flowchart showing the procedure of the main process of the congestion analysis process. Although the frequency with which this main process is performed is not particularly limited, it is assumed here that this main process is performed every 15 minutes. In the flow shown in FIG. 20, the processes of steps S310 to S340 are performed for each unit road, and the processes of steps S320 to S330 among the processes of steps S310 to S340 are performed for each traveling direction. That is, in one loop of steps S310 to S340, one unit road is a processing target, and in one loop of steps S320 to S330, one traveling direction consideration unit road is a processing target. Based on the above points, detailed processing contents in each step will be described.

  In step S310, the data aggregation means 240 stores the congestion degree data stored in the congestion degree database 208 in the latest 15 minutes (that is, the congestion degree data sent from the mobile terminal device 10 to the server 20 in the latest 15 minutes). Congestion degree data on the unit road to be processed is extracted from the inside. In this regard, as shown in FIG. 5, the congestion degree data sent from the mobile terminal device 10 includes latitude / longitude information. Further, as shown in FIG. 8, the unit road data includes coordinate information and road width information. From the above, it is possible to specify which unit road each congestion degree data is related to. Therefore, it is possible to extract congestion degree data for the unit road to be processed from the congestion degree database 208. As described above, in step S310, the congestion degree data for the unit roads to be processed is collected. Whether the congestion degree data is data related to the first direction based on the information on the traveling direction included in the congestion degree data and the information on the coordinates of the unit road data associated with the congestion degree data. It is possible to specify whether the data is related to the direction. Accordingly, in step S310, each congestion degree data is associated with one of the two traveling directions (first direction, second direction). Thereby, it becomes possible to perform the process according to the traveling direction for each unit road.

  In step S320, the average congestion degree calculation means 250 is based on the data on the traveling direction of the processing target in the congestion degree data extracted in step S310 (that is, the congestion degree data on the processing target traveling direction road). The average congestion degree is calculated. For example, if three congestion degree data of “congestion degree = 0.1”, “congestion degree = 0.15”, and “congestion degree = 0.35” are accumulated for a unit road in consideration of the traveling direction. The average degree of congestion for the traveling direction consideration unit road is 0.2. In this way, information on the average congestion degree for each unit road in consideration of the traveling direction can be obtained as schematically shown in FIG. Note that, in the present embodiment, the average congestion degree is not calculated for the traveling direction consideration unit road for which no congestion degree data exists. In FIG. 21, the travel direction consideration unit road in which the value of the average congestion degree is not described is the travel direction consideration unit road in which the average congestion degree is not calculated.

  Incidentally, as described above, in the present embodiment, only the congestion degree data whose reliability is set to “1” is transmitted from the mobile terminal device 10 to the server 20. Therefore, in this embodiment, in step S320, the average congestion degree is calculated using only reliable congestion degree data.

  In step S330, the reliability of the average congestion degree calculated in step S320 is determined by the user number use reliability determination unit 260 based on the number of users of the mobile terminal device 10. In this regard, a large amount of congestion data can be transmitted from each mobile terminal device 10 in the last 15 minutes. However, as shown in FIG. 5, the congestion data includes time information and coordinate information (latitude / longitude). Information) is included, and by considering the continuity of changes in time and coordinates, the congestion degree data aggregated as data on the unit road in consideration of the traveling direction to be processed is obtained from how many mobile terminal devices 10 It is possible to estimate whether the data is congestion data that has been sent. That is, the number of users involved in the calculation of the average congestion degree in step S320 can be estimated. In step S330, the reliability is determined according to the estimation result. At that time, the greater the number of users, the higher the reliability. For example, if the number of users is 3 or more, the reliability is set to “1”, if the number of users is 2, the reliability is set to “0.6”, and the number of users is 1 If so, the reliability is set to “0.3”. Note that the reliability determination method described here is an example, and the reliability may be determined based on the number of users by another method.

  The processes of steps S320 to S330 are performed for each of the first direction and the second direction with respect to the unit road to be processed. Therefore, as shown in FIG. 11, the average congestion degree data includes information on the average congestion degree and reliability for each of the first direction and the second direction. When the processes in steps S320 to S330 are completed for both the first direction and the second direction, the main process proceeds to step S340.

In step S340, the estimated number of persons for the unit road to be processed is calculated by the estimated number of persons calculation means 270. As described above, the estimated number of people is calculated based on the average congestion degree data and the base number of people data. As shown in FIG. 11, the average congestion degree data includes information on the average congestion degree in each of the first direction and the second direction, but when calculating the estimated number of people, the first direction and the second direction. The average value of the average congestion degree in both directions is used. Specifically, the estimated number of people is calculated by multiplying the average value of the average congestion degree in the first direction and the average congestion degree in the second direction calculated in step S320 by the base number of people for the unit road to be processed. The That is, regarding the unit road to be processed, when the average congestion degree in the first direction is represented as AVE1, the average congestion degree in the second direction is represented as AVE2, and the base number of persons is represented as BN, the estimated number of persons EN is expressed by the following formula ( 2).
EN = ((AVE1 + AVE2) / 2) × BN (2)
However, the estimated number of persons may be calculated by an expression other than the above expression (2).

  When the processes in steps S310 to S340 are completed for all unit roads, the main process proceeds to step S350. In step S350, the data loss complementing process is performed by the data loss complementing means 280. As described above, in the present embodiment, only the congestion degree data whose reliability is set to “1” is transmitted from the mobile terminal device 10 to the server 20. Further, a unit road having a short length may be generated by the road division process (step S210 in FIG. 17). For the above reasons, there may be a unit road in which the average congestion degree is not calculated and a unit road in which the estimated number of persons is not calculated.

  Therefore, the lack of congestion analysis result is complemented by the data loss complementing process in step S350. If the unit road whose traveling direction is not calculated in step S320 or the unit road in which the estimated number of people is not calculated in step S340 is defined as “information missing road”, a value (average congestion degree or Specifically, the estimated number of persons) is calculated by an interpolation process using values (values obtained in step S320 and step S340) for unit roads in the vicinity of the information missing road. As the interpolation processing method, for example, linear interpolation or spline interpolation can be employed.

  For example, as shown in FIG. 22, it is assumed that the average congestion degree for one unit road R3 among the five unit roads R1 to R5 is not calculated when focusing on the first direction. In such a case, for example, the average congestion degree of the unit road R3 can be calculated by linear interpolation using the data of the unit road R2 and the data of the unit road R4. Further, for example, the average congestion degree for the unit road R3 can be calculated by spline interpolation using the data of the unit roads R1, R2, R4, and R5.

  As for the reliability of the complemented data, a certain relatively low value, the lowest value of the reliability of the data used for the interpolation process, or the average value of the reliability of the data used for the interpolation process, etc. Should be adopted.

  If past congestion analysis results are retained, instead of the above-described interpolation processing, missing of the latest congestion analysis results is complemented based on the values of past congestion analysis results for information-deficient roads. Anyway. At that time, for example, it is preferable to ensure the reliability of the congestion analysis result by adopting the value of the past data of the same day of the week and the same time as the data to be complemented as the value after complementation.

  After the completion of the data loss complementing process, the data transmission means 290 transmits the average congestion degree data and the estimated number of people data to the portable terminal device 10 as congestion status data (step S360). Thereby, the main process of the congestion analysis process ends.

  By the way, when the congestion status data is transmitted to the mobile terminal device 10, the mobile terminal device 10 displays, for example, information indicating the congestion status on a map screen on which a route search result or the like is shown as necessary. The In this regard, information based on the average congestion degree data with low reliability may not be displayed on the screen of the mobile terminal device 10. Further, the average congestion degree data with low reliability may not be transmitted from the server 20 to the mobile terminal device 10.

  In the present embodiment, the congestion degree aggregation step is realized by step S310, the representative congestion degree calculation step is realized by step S320, the reliability determination step is realized by step S330, and the representative congestion degree complementation step is realized by step S350. Has been.

  Here, the example in which the average congestion degree is obtained in step S320 as a representative value (representative value) representing a plurality of congestion degrees included in the congestion degree data extracted in step S310 has been described. Instead, the median value of the plurality of congestion levels or the mode value of the plurality of congestion levels may be obtained in step S320.

<5. Effect>
According to the present embodiment, data on the degree of congestion (congestion degree at the current position of the user) estimated by the mobile terminal device is aggregated for each unit road obtained by dividing the road (road data) by a certain length. Then, based on the result of the aggregation, the average congestion degree for each unit road for each traveling direction and the estimated number of persons for each unit road are calculated. In this way, unlike the conventional example in which the congestion analysis is performed for each mesh, the estimation of the congestion status of each unit road is performed based only on the data for each unit road. Therefore, it is possible to estimate a congestion situation close to the actual situation on each road (a road through which people pass). In addition, as described above, the unit road is obtained by dividing the road by a certain length, and processing is performed for each unit road, so that it is possible to accurately estimate the local congestion situation. Become.

  In addition, since the reliability information is added to the data of the congestion degree and the average congestion degree, it is possible to grasp the reliability of the congestion analysis result. In addition, since the average degree of congestion is calculated for each direction of travel, it is possible to grasp the congestion situation in consideration of the flow of people on the road, for example, it is possible to predict how the congestion point will move in the future Become. Further, in the area set as the high accuracy area, the estimation of the degree of congestion in the mobile terminal device is more frequently performed than in other areas. For this reason, for example, it is possible to estimate the congestion state in an area where the user has a high degree of interest, such as a sightseeing spot, with higher accuracy than in other areas. Furthermore, when a congestion analysis result is missing, the result is complemented by interpolation processing using data of a unit road in the vicinity of a unit road (information missing road) where the result is missing. That is, a congestion analysis result without omission is obtained. Here, in the present embodiment, the degree of congestion is calculated based on the sensor information obtained by the mobile terminal device 10, so that it is not necessary to provide a device such as a monitoring camera for performing the congestion analysis. As described above, according to the present embodiment, it is possible to obtain a congestion analysis result that is more practical and has higher added value than the conventional one, without requiring a monitoring camera.

<6. Modification>
Hereinafter, modifications of the embodiment will be described.

<6.1 Modification regarding road division (first modification)>
In the above embodiment, the road is divided into a certain length by the road dividing means 220. However, the present invention is not limited to this. For example, as shown in FIG. 23, the road may be divided at a corner, or the road may be divided at an intersection. Alternatively, the road may be divided at a point where the road width changes using the road width information. Furthermore, as long as mesh information is held, the road may be divided at the boundary of the mesh. Furthermore, the road may be divided according to a combination of these rules. For example, after dividing a road at a corner, a road having a length greater than a certain threshold may be divided into a certain length, or the road may be divided at both an intersection and a point where the road width changes. Anyway.

<6.2 Modification of Unit Road (Second Modification)>
In the above-described embodiment, the congestion analysis process is performed using a road piece obtained by dividing a road (road data) stored in the road database 202 by a certain length as a unit road. However, the present invention is not limited to this, and the congestion analysis processing may be performed using the road itself stored in the road database 202 as a unit road. However, when a road (road data) having a significantly long length is stored in the road database 202, it is difficult to grasp the local congestion status of the road. From the viewpoint of accurately estimating the congestion, it is preferable to perform the congestion analysis process using the road piece obtained by the road division process as described above as a unit road.

  In this modification, the server 20 is not provided with the road dividing means 220 and the unit road database 204 shown in FIG. Further, the base number calculating means 230 calculates the base number for each road based on the road data stored in the road database 202.

<6.3 Modified Example Regarding Data Loss Complementary Processing (Third Modified Example)>
In the above embodiment, only the data whose reliability is set to “1” among the congestion degree data generated by the mobile terminal device 10 is sent to the server 20. That is, the calculation of the average congestion degree in the server 20 is performed using only the congestion degree data whose reliability is set to “1”. When there is a gap in the congestion analysis result, data loss complementation processing by interpolation processing has been performed. However, this invention is not limited to this, The structure described below can also be employ | adopted.

  In this modification, congestion data other than the congestion data whose reliability is set to “1” is also transmitted from the mobile terminal device 10 to the server 20. However, congestion level data having a reliability level equal to or lower than a predetermined threshold may be excluded from transmission targets. Under such a premise, in step S320 described above, the average congestion degree is calculated using only the congestion degree data whose reliability is set to “1”, as in the above embodiment. When the congestion analysis result is lost, the average congestion degree and the estimated number of persons are calculated using the congestion degree data other than the congestion degree data whose reliability is set to “1” in the above-described step S350. . As described above, for the unit roads in which the congestion analysis result is missing and the unit roads in consideration of the traveling direction, the data loss complementing process is performed using data with low reliability.

<6.4 Modification of Average Congestion Level and Determination of Reliability (Fourth Modification)>
In the above embodiment, the calculation of the average congestion degree in the server 20 is performed using only the congestion degree data whose reliability is set to “1”. The reliability is determined according to the number of users involved in the calculation of the average congestion degree. However, this invention is not limited to this, The structure described below can also be employ | adopted.

  In this modified example, as in the third modified example, congestion level data other than the congestion level data whose reliability is set to “1” is also transmitted from the mobile terminal device 10 to the server 20. In step S320 described above, unlike the above-described embodiment, the average congestion degree is calculated by using all the reliability degree congestion data or the reliability degree congestion data not less than a predetermined threshold (a plurality of congestion degrees). Or the median of multiple congestion levels may be determined). In the present modification, the server 20 is provided with an average reliability calculation means 265 instead of the user number use reliability determination means 260 in the above embodiment (see FIG. 24). In step S330 described above, The average reliability calculation means 265 calculates an average value (average reliability) of the reliability of the congestion data used for calculating the average congestion in step S320 (a median of a plurality of reliability or a plurality of reliability values). The mode of reliability may be determined). In this regard, as shown in FIG. 5, since the congestion degree data includes reliability information, the average reliability can be calculated in the same manner as the calculation of the average congestion in step S320. In the present modification, the average reliability calculated in this way is information on the reliability constituting the average congestion degree data (see FIG. 11). In addition, when omissions occur in the congestion analysis result, the same data loss complementing process as in the above embodiment can be performed.

<6.5 Modified Example (Fifth Modified Example) Regarding a Method for Increasing the Accuracy of Congestion Analysis in a Specific Area>
In the above embodiment, the frequency of estimating the congestion degree is switched between the high accuracy area and the normal area. However, the present invention is not limited to this, and the degree of congestion in the high-precision area may be increased by estimating the degree of congestion in the high-precision area using a plurality of types of sensor information. For example, in a normal area, the degree of congestion is estimated using only acceleration information, and in a high-precision area, the degree of congestion is estimated using acceleration information and angular velocity information. As described above, the number of types of sensor information used when estimation of the degree of congestion is performed in the mobile terminal device 10 (step S130 in FIG. 13) may be changed according to the current position of the mobile terminal device 10.

<6.6 Modified Example Regarding Walking Determination and Reliability Determination (Sixth Modified Example)>
In the above embodiment, determination of the user's moving state (walking determination) is performed based on the change per unit time of the current position detected by the GPS sensor 18a, and the reliability is determined according to the moving state. . However, the present invention is not limited to this. For example, the walking determination may be performed based on the difference per unit time of the azimuth angle obtained by the geomagnetic sensor 18c or the difference per unit time of the angular velocity obtained by the gyro sensor (not shown). Further, for example, walking determination and reliability based on results (data of output intensity corresponding to each combination of time and frequency) obtained by performing frequency analysis (wavelet transform, Fourier transform, etc.) on acceleration information. The degree may be determined.

<7. Other>
In the above embodiment, the route guidance system has been described as an example, but the present invention is not limited to this. The congestion situation can be estimated by a system similar to the route guidance system, and the estimation result can be used for various purposes. For example, the congestion status information can be used for real-time changes in the arrangement of guards during an event (fireworks display, concert, various sports competitions, etc.).

  In addition, a configuration in which the above-described embodiment and each of the above-described modified examples are appropriately combined so as not to cause inconsistencies is also included in the scope of the present invention.

DESCRIPTION OF SYMBOLS 10 ... Portable terminal device 20 ... Server 110 ... Acceleration measurement means 110 ... Congestion degree estimation means 120 ... Current position detection means 130 ... Traveling direction estimation means 140 ... Walk determination means 150 ... Walk determination result utilization reliability determination means 160 ... (Portable) Data transmission means 170 on the terminal device side 170 Data reception means on the mobile terminal device side 180 Route search means 190 Result display means 200 Data storage means 210 Data reception means on the server side 220 Road division means 230 ... Base number calculating means 240 ... Data aggregating means 250 ... Average congestion degree calculating means 260 ... User number use reliability determining means 265 ... Average reliability calculating means 270 ... Estimated number of persons calculating means 280 ... Data loss complementing means 290 ... (Server Data transmission means

Claims (15)

A congestion degree estimation step for estimating a congestion degree based on sensor information of the mobile terminal device;
Congestion degrees of a plurality of mobile terminal devices estimated in the congestion degree estimation step, with a road stored in the road information storage unit or a road piece obtained by dividing the road based on a predetermined rule as a unit road Congestion level aggregation step to aggregate the data of each unit road,
And a representative congestion degree calculating step of calculating a representative congestion degree that is a representative value of the congestion degree for each unit road based on the aggregation result in the congestion degree aggregation step.   The congestion state estimation method according to claim 1, wherein a road piece obtained by dividing a road stored in the road information storage unit with a certain length is used as a unit road.   The road piece obtained by dividing the road stored in the road information storage unit into at least one of a corner, an intersection, and a point where the road width changes is defined as a unit road. The congestion state estimation method according to 1. In the congestion level aggregation step, each congestion level estimated in the congestion level estimation step is associated with one of the two traveling directions,
2. The congestion state estimation method according to claim 1, wherein the representative congestion degree calculating step calculates a representative congestion degree for each of the two traveling directions for each unit road. A moving state determination step of determining a state related to movement of the user of the mobile terminal device based on the sensor information;
The method according to claim 1, further comprising a reliability addition step of adding reliability to the congestion degree data estimated in the congestion degree estimation step based on a determination result in the moving state determination step. The congestion state estimation method described.   A representative reliability calculating step of calculating a representative reliability, which is a representative value of the reliability added in the reliability adding step, for each unit road based on an aggregation result in the congestion degree aggregating step; The congestion status estimation method according to claim 5.   For each unit road, the reliability for obtaining the reliability of the representative congestion degree calculated in the representative congestion degree calculation step based on the number of mobile terminal devices that generate the congestion degree data aggregated in the congestion degree aggregation step The congestion state estimation method according to claim 1, further comprising a degree determination step.   The congestion state estimation method according to claim 1, wherein a frequency at which the congestion degree is estimated in the congestion degree estimation step is changed according to a current position of the mobile terminal device.   The congestion state estimation according to claim 1, wherein the number of types of sensor information used when the congestion degree is estimated in the congestion degree estimation step is changed according to a current position of the mobile terminal device. Method.   And further comprising a representative congestion degree complementation step of complementarily obtaining a representative congestion degree of the information missing road when a unit road whose representative congestion degree has not been calculated in the representative congestion degree calculating step is defined as an information missing road. The congestion state estimation method according to claim 1.   In the representative congestion degree complementation step, the representative congestion degree of the information missing road is calculated by an interpolation process using a representative congestion degree for a unit road in the vicinity of the information missing road calculated in the representative congestion degree calculating step. The congestion state estimation method according to claim 10, wherein:   11. The congestion state estimation method according to claim 10, wherein in the representative congestion degree complementing step, a representative congestion degree of the information missing road is obtained based on a past representative congestion degree of the information missing road. A representative congestion degree complementing step of complementarily obtaining a representative congestion degree of the information missing road when defining a unit road whose representative congestion degree was not calculated in the representative congestion degree calculating step as an information missing road;
The calculation of the representative congestion level in the representative congestion level calculation step is performed based on the congestion level data to which the reliability level equal to or higher than a predetermined threshold is added in the reliability level adding step.
6. The calculation of the representative congestion degree in the representative congestion degree complementing step is performed based on congestion degree data to which a reliability less than a predetermined threshold is added in the reliability addition step. The congestion situation estimation method described in 1. A congestion situation estimation program executed by the server in a congestion situation estimation system in which a server and a plurality of portable terminal devices are connected via a network,
A congestion degree receiving step for receiving congestion degree data transmitted from each mobile terminal device;
A road stored in the road information storage unit or a road piece obtained by dividing the road based on a predetermined rule is a unit road, and the congestion degree data received in the congestion degree receiving step is obtained for each unit road. A congestion aggregation step to aggregate;
The CPU of the computer uses the memory to execute a representative congestion degree calculation step of calculating a representative congestion degree that is a representative value of the congestion degree for each unit road based on the aggregation result in the congestion degree aggregation step. A featured congestion situation estimation program. A congestion situation estimation system in which a server and a plurality of portable terminal devices are connected via a network,
Each mobile terminal device
A congestion degree estimation means for estimating a congestion degree based on sensor information;
Congestion degree transmission means for transmitting congestion degree data estimated by the congestion degree estimation means to the server,
The server
A congestion degree receiving means for receiving congestion degree data transmitted by the congestion degree transmitting means of each mobile terminal device;
A road information storage unit for storing road information;
The road stored in the road information storage unit or a road segment obtained by dividing the road based on a predetermined rule is used as a unit road, and the congestion degree data received by the congestion degree receiving unit is obtained for each unit road. Congestion degree aggregation means to aggregate,
A congestion condition estimation system comprising: a representative congestion degree calculation unit that calculates a representative congestion degree that is a representative value of the congestion degree for each unit road based on an aggregation result by the congestion degree aggregation unit.

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