JP2011081608A - Device and method for estimating dangerous place - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To acquire information regarding a dangerous location of high risk of the occurrence of traffic accidents, even when there is no accumulated information regarding actual traffic accidents having occurred there. <P>SOLUTION: In a device 1 for estimating the dangerous location, position information of a plurality of mobile terminals is acquired by a position information acquisition section 11, each position information where the moving means shown in traffic mode is an automobile and walk is counted for each mesh by a position information counting section 14, and a risk index value showing the risk of each mesh is generated by an index value counting section 15, based on the number of pieces of position information where the traffic mode is an automobile and the number of pieces of position information where the traffic mode is walk, which are counted by each mesh. Since the risk index value is generated, based on the position information serially reflecting user's location, a dangerous location can be estimated in a timely manner, without having to require accumulation of the information regarding traffic accidents. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a dangerous point estimation device and a dangerous point estimation method.

  Conventionally, a system that predicts a place where a traffic accident is likely to occur and notifies the user of the predicted place is known. For example, Patent Document 1 describes a system in which various information related to a traffic accident that has occurred is registered in a database, and the degree of risk is predicted based on the registered traffic accident information.

JP 2006-277165 A

  In the above prior art, since the risk level is predicted based on information on the traffic accident that actually occurred, the risk level cannot be predicted if there is no accumulation of traffic accident information of a certain amount or more. . Also, for example, when a new road is created, there is no information on traffic accidents on the road, so it is impossible to predict the risk of the road.

  Therefore, the present invention has been made in view of the above problems, and even when there is no accumulation of information about an actual traffic accident, information on a dangerous location where the risk of occurrence of the traffic accident is high. It is an object of the present invention to provide a dangerous spot estimation device and a dangerous spot estimation method that can be obtained.

  In order to solve the above-described problem, the dangerous point estimation device of the present invention is based on position information including information indicating the location of the user of the mobile terminal and traffic mode information which is information indicating the type of the moving means of the user. A dangerous point estimation device for estimating a dangerous point with a high risk of occurrence of a traffic accident, comprising position information acquisition means for acquiring a plurality of position information, and a geographical unit area for estimating the dangerous point. Location information indicated by the location information acquired by the location information acquisition unit based on the mesh information acquired by the region information acquisition unit and the mesh information acquired by the region information acquisition unit, which is information related to the mesh that is preliminarily stored. The belonging mesh determining means for determining the belonging mesh that is the mesh to which the position belongs for each position information, and the belonging mesh is determined by the belonging mesh determining means The first position information that is the position information that includes the first movement means that is the first movement means as the traffic mode information, and the second movement that is a movement means different from the first movement means For each of the second position information, which is position information including the means as traffic mode information, position information counting means for counting for each mesh, the number of first position information counted by the position information counting means, and the second position Based on the number of information, an index value totaling unit that generates a risk level index value that is an index value indicating the level of risk for each mesh, and a processing result output unit that outputs the risk level index value generated by the index value totaling unit It is characterized by providing.

  In order to solve the above-mentioned problem, the dangerous point estimation method of the present invention includes position information including information indicating the location of the user of the mobile terminal and traffic mode information which is information indicating the type of the moving means of the user. In accordance with the present invention, there is provided a dangerous point estimation method in a dangerous point estimation device for estimating a dangerous point where a risk of occurrence of a traffic accident is high, and a position information acquisition step for acquiring a plurality of position information, and a dangerous point is estimated. Based on the mesh information acquired in the regional information acquisition step and the regional information acquisition step for acquiring the mesh information stored in advance and the information relating to the mesh defining the geographical unit area of the position information acquisition step Affiliation mesh judgment that determines the affiliation mesh that is the mesh to which the location indicated by the position information belongs for each position information And the first position information that is the position information including the first moving means as the moving means as the traffic mode information, and the number of the position information for which the belonging mesh is determined in the belonging mesh determining step, In each of the second position information, which is the position information including the second moving means that is a moving means different from the one moving means, as the traffic mode information, a position information counting step for counting each mesh, and a position information counting step Based on the counted number of the first position information and the number of the second position information, an index value totaling step for generating a risk index value that is an index value indicating the risk for each mesh, and an index value totaling step And a processing result output step for outputting the obtained risk index value.

  When an application service that uses location information such as a route guidance service is used in a mobile terminal, the location information measured by the GPS device of the mobile terminal is used as information indicating the location of the mobile terminal. . When the mobile terminal uses an application service that uses position information as described above, a server device or the like that provides the application service can collect the position information of the mobile terminal. Therefore, the position information is collected sequentially while reflecting the location of the user. According to the dangerous part estimation device and the dangerous part estimation method of the present invention, the positional information of a plurality of mobile terminals is acquired, and the first positional information and the second positional information related to the user having different moving means are counted for each mesh, An index value indicating the degree of risk for each mesh is generated based on the counted number of first position information and second position information. Since this index value is generated based on location information that reflects the location of the user in sequence, it is possible to estimate dangerous locations in a timely manner without the need to accumulate information related to traffic accidents. It becomes.

  In the risk location estimating apparatus according to the present invention, the index value totaling means is a first index value indicating the number of the first position information based on the number of the first position information counted for each mesh. Is generated for each mesh, and based on the number of second position information counted for each mesh, a second index value indicating the number of the second position information is generated for each mesh, It is preferable to generate the risk index value by multiplying the index value and the second index value.

  In this case, the risk index is obtained by multiplying the first index value generated based on the number of the first position information and the second index value generated based on the number of the second position information. A value is generated. Thereby, since the risk index value reflecting the large amount of the first position information and the large amount of the second position information is generated, it is possible to accurately estimate the dangerous place.

  In the risk location estimating apparatus of the present invention, the position information includes information related to a positioning time that is a time at which the location indicated in the position information is positioned, and the position information aggregation means includes a positioning time included in the position information. It is preferable to weight the number of position information to be counted based on the information regarding

  In this case, for example, the position information can be counted after weighting the number of position information measured in a time zone in which a traffic accident is likely to occur. Therefore, it is possible to obtain a risk index value with higher accuracy.

  Further, in the risk location estimating apparatus of the present invention, the position information totaling unit acquires attribute information of the user of the mobile terminal stored in advance, and based on attribute information about the user of the mobile terminal whose location is indicated by the position information. It is preferable to weight the number of position information to be counted.

  In this case, for example, the position information can be counted after weighting the number of pieces of position information of users such as young people and elderly people, and users who are considered to have low recognition of traffic conditions. Therefore, it is possible to obtain a risk index value with higher accuracy.

  Further, the dangerous point estimation device of the present invention includes position information extraction means, the position information includes information related to the positioning time, which is the time when the location indicated in the position information is located, and the position information extraction means includes: Position information whose positioning time falls within a predetermined time zone is extracted from the position information acquired by the position information acquisition means, and the belonging mesh determination means belongs to the position information extracted by the position information extraction means It is preferable to perform mesh determination.

  In this case, for example, only the position information measured in a time zone where a traffic accident is likely to occur is counted, and a risk index value is generated based on the counted number of position information. As a result, a more useful risk index value can be obtained.

  In the risk location estimating apparatus of the present invention, the attribute information of the user of the mobile terminal stored in advance is acquired, and attribute information related to the user of the mobile terminal whose location is indicated by the position information is predetermined from the acquired position information. It is preferable that a position information extracting unit that extracts position information corresponding to the condition is provided, and the belonging mesh determination unit determines a mesh to which the location belongs, with respect to the position information extracted by the position information extraction unit.

  In this case, for example, only position information of users such as young people and elderly people and users who are considered to have low traffic situation recognition power is counted, and a risk index value is generated based on the counted number of position information. Is done. As a result, a more useful risk index value can be obtained.

  Further, in the dangerous point estimation device of the present invention, the position information acquisition means includes a traffic mode generation means, the position information includes information related to the positioning time, which is the time when the location indicated in the position information is determined, The traffic mode generation means includes location information and positioning time information included in one position information, positioning time of one position information and positioning time adjacent in time series, and included in other position information regarding the same user. It is preferable to determine the moving speed of the user based on the location position and positioning time information, and to generate traffic mode information of one position information based on the determined moving speed.

  In this case, even if the traffic mode information indicating the moving means of the user is not included in the position information, the traffic mode information is obtained based on the user's location and the positioning time included in the position information. Can do.

  Moreover, in the danger location estimation apparatus of this invention, the 1st moving means in the traffic mode information contained in position information is a motor vehicle, and the 2nd moving means is a walk, It is characterized by the above-mentioned.

  In this case, the risk index value is generated on the basis of the position information of the user who uses a car that is highly likely to be involved in a traffic accident as a moving means and the position information of a pedestrian who is a caution target in the traffic environment for the car. The As a result, a more useful risk index value can be obtained.

  According to the danger point estimation device and the danger point estimation method of the present invention, even when there is no accumulation of information about a traffic accident that has actually occurred, information on a dangerous part where the risk of occurrence of the traffic accident is high is obtained. Is possible.

1 is an overall configuration diagram of a system including a dangerous point estimation device. It is a block diagram which shows the functional structure of a dangerous location estimation apparatus. It is a hardware block diagram of a dangerous point estimation apparatus. It is a figure which shows an example of a structure and content of a position information storage part. It is a figure which shows typically the structure of the mesh shown by mesh information. It is a figure which shows concretely the data structure and content of the mesh information memorize | stored in the area information storage part. It is a figure which shows the example of the positional information by which the affiliation mesh was determined and the mesh ID of the affiliation mesh was matched. (A) is a figure which shows the example of the table which memorize | stores the result which the position information totaling part counted position information temporarily, (b) is a table of the table which temporarily memorize | stores the index value calculated from the counted number of position information. It is a figure which shows an example. It is a figure which shows the example of the structure of a user information storage part, and the data stored. It is a figure which shows the example which the process result output part output the danger index value visually on the display etc. It is a flowchart which shows the processing content implemented in a dangerous location estimation apparatus. It is a flowchart which shows the processing content of step S5 in the flowchart of FIG. It is a block diagram which shows the functional structure of the dangerous location estimation apparatus 1 which concerns on 2nd Embodiment. It is a flowchart which shows the processing content implemented with the dangerous location estimation apparatus in 2nd Embodiment. It is a block diagram which shows the functional structure of a traffic mode production | generation part. It is a figure which shows the example of two positional information adjacent on the time series extracted by the traffic mode production | generation part. It is a flowchart which shows the production | generation process of the traffic mode using two positional information adjacent on a time series.

  An embodiment of a dangerous point estimation apparatus according to the present invention will be described with reference to the drawings. If possible, the same parts are denoted by the same reference numerals, and redundant description is omitted.

(First embodiment)
FIG. 1 is an overall configuration diagram of a system including a dangerous point estimation device according to the first embodiment. As shown in FIG. 1, the dangerous spot estimation device 1 can communicate with the position information storage device 2 and the region information storage device 3 via the network N via the network N. Further, the application service providing apparatus 5 and the mobile terminal 4 can also communicate via the network N.

  Here, prior to the description of the dangerous spot estimation device 1, the position information storage device 2, the regional information storage device 3, and the application service providing device 5 will be described.

  The location information storage device 2 is a device that acquires location information of the mobile terminal 4 from the application service providing device 5 and stores the obtained location information. The position information storage device 2 includes a position information storage unit 20 that is a storage unit for storing position information.

  The regional information storage device 3 is a device that acquires and stores regional information in advance, and includes a regional information storage unit 30 that is a storage unit for storing regional information. The regional information includes mesh information that is information related to a mesh that defines a geographical unit region for estimating a dangerous location, as schematically illustrated in FIG. 5 described later and specifically illustrated in FIG. 6.

  In the present embodiment, the position information storage unit 20 and the region information storage unit 30 are provided in the position information storage device 2 and the region information storage device 3 that can communicate with the dangerous point estimation device 1 via the network N, respectively. However, it may be provided as one functional part of the dangerous point estimation device 1.

  The application service providing device 5 is a device that provides an application service such as a route guidance service to the mobile terminal 4, and is configured by a server device, for example. When the mobile terminal 4 uses an application service such as a route guidance service, position information measured by a GPS device included in the mobile terminal 4 is used as information indicating the location of the mobile terminal 4. The application service providing apparatus 5 can collect the location information of the mobile terminal 4 when the mobile terminal 4 uses an application service that uses the location information as described above.

  Next, the function of the dangerous point estimation device 1 will be described in detail. FIG. 2 is a block diagram showing a functional configuration of the dangerous point estimation apparatus. The risk location estimation device 1 is based on information indicating the location of the user of the mobile terminal 4 and location information including traffic mode information that is information indicating the type of the user's moving means. Is a device for estimating a high-risk point.

  Functionally, the dangerous part estimation apparatus 1 is functionally configured to include a processing request receiving unit 10, a position information acquiring unit 11 (position information acquiring unit), a region information acquiring unit 12 (region information acquiring unit), and a belonging mesh determining unit 13 (belonging mesh). A determination unit), a position information totaling unit 14 (position information totaling unit), an index value totaling unit 15 (index value totaling unit), and a processing result output unit 16 (processing result output unit).

  FIG. 3 is a hardware configuration diagram of the dangerous point estimation apparatus 1. As shown in FIG. 3, the dangerous location estimation apparatus 1 physically includes a CPU 101, a RAM 102 and a ROM 103 which are main storage devices, a communication module 104 which is a data transmission / reception device such as a network card, an auxiliary device such as a hard disk and a flash memory. The computer system includes a storage device 105, an input device 106 such as a keyboard and mouse as input devices, and an output device 107 such as a display. Each function shown in FIG. 2 has a communication module 104, an input device 106, and an output device 107 under the control of the CPU 101 by loading predetermined computer software on the hardware such as the CPU 101 and the RAM 102 shown in FIG. This is realized by reading and writing data in the RAM 102 and the auxiliary storage device 105. Again, with reference to FIG. 2, each function part of the dangerous location estimation apparatus 1 is demonstrated in detail.

  The process request accepting unit 10 is a part that accepts a process request for estimating a dangerous spot for the dangerous spot estimating apparatus 1. The processing request is transmitted, for example, from a terminal device (not shown) that can communicate via the network N to the dangerous point estimation device 1 or input via the input device 106 provided in the dangerous point estimation device 1. It includes information on the area where the dangerous part is desired to be estimated, information on the positioning period of the position information used for estimation, and other processing conditions.

  The position information acquisition unit 11 receives a plurality of pieces of position information that are received by the processing request receiving unit 10 and that correspond to the information regarding the area where the dangerous part is desired to be estimated and the information about the positioning period of the position information used for the estimation. This is a part acquired from the position information storage unit 20 of the information storage device 2. Here, the position information storage unit 20 will be described.

  The location information storage unit 20 is a storage unit that stores location information indicating the location of the user of the mobile terminal. FIG. 4 is a diagram illustrating an example of the configuration and contents of position information stored in the position information storage unit 20. As shown in FIG. 4, the position information storage unit 20 stores user ID, latitude, longitude, date and time, and traffic mode information in association with a point ID for identifying each position information. The user ID is an identifier that identifies the mobile terminal 4 and is unique to the user. The latitude and longitude are information indicating the location of the mobile terminal. The date and time is information indicating the date and positioning time when the location indicated in the position information is determined. The traffic mode is information indicating the type of moving means of the user. When the position information is collected by the application service providing device 5, the type of application used by the mobile terminal 4 and the user when using the application. Acquired based on the input information. Further, as in the third embodiment described later, the traffic mode information may not be included in the position information.

  The area information acquisition unit 12 acquires the mesh information of the area from the area information storage unit 30 based on the information regarding the area where the estimation of the dangerous part is desired, which is included in the processing request received by the processing request receiving unit 10. Part. The mesh information is information relating to a mesh that defines a geographical unit area for estimating a dangerous spot. FIG. 5 is a diagram schematically showing the configuration of the mesh indicated by the mesh information. In the example shown in FIG. 5, a certain area where roads R1 to R5 are provided is divided into nine meshes M1 to M9. FIG. 6 is a diagram specifically showing the data structure and contents of the meshes M1 to M9 shown in FIG. As shown in FIG. 6, the data representing the mesh includes information on “latitude”, “longitude”, “latitude interval”, and “longitude interval” associated with “mesh ID” that is information for identifying the mesh. It is out. “Latitude” and “longitude” respectively indicate the latitude (north latitude) and longitude (east longitude) of the southwestern end of the mesh. “Latitude interval” and “longitude sense” indicate the width of the mesh in the latitude direction (north direction) and longitude direction (east direction), respectively.

  Based on the mesh information acquired by the regional information acquisition unit 12, the belonging mesh determination unit 13 determines, for each position information, the belonging mesh that is a mesh to which the location indicated by the position information acquired by the position information acquisition unit 11 belongs. It is a part to judge. FIG. 7 is a diagram illustrating an example of position information in which the belonging mesh is determined and the mesh ID of the belonging mesh is associated. For example, the location indicated by the position information of the point ID “1” is the latitude “35.6” and the longitude “139.6”, so the belonging mesh determination unit 13 refers to the mesh information shown in FIG. Then, the belonging mesh of the position information is determined as the mesh having the mesh ID “M1”, and “M1” is stored in association with the mesh ID of the position information in the position information 20A illustrated in FIG. The affiliation mesh determination unit 13 similarly determines the affiliation mesh for the position information of the point IDs “2” to “5”, and stores the mesh ID in association with each position information.

  The position information totaling unit 14 indicates the number of position information for which the belonging mesh is determined by the belonging mesh determining unit 13, the position information (first position information) where the traffic mode is “automobile”, and the traffic mode is “walking”. Each piece of position information (second position information) is a part that is counted for each mesh. FIG. 8A is an example of a table that temporarily stores the result of counting the position information by the position information totaling unit 14. For example, the position information totaling unit 14 refers to the position information 20A shown in FIG. 7, and the point ID is “1” and “3” as the position information of the mesh ID “M1” and the traffic mode “walking”. Two pieces of position information that are are extracted and counted. Then, the position information totaling unit 14 stores “2” as the “number of walking position information” of the mesh ID “M1” in the table shown in FIG.

  In the present embodiment, the position information in which the traffic mode is “automobile” and “walking” is counted, but the position information of the user of the moving means that is highly likely to be involved in a traffic accident, and the moving means Therefore, if the position information of the user of the moving means that is the object of attention in the traffic environment is counted, the position information is not limited to the position information in which the traffic mode is “car” and “walk”. For example, the position information whose traffic mode is “bicycle” and “walking” may be counted, or the position information whose traffic mode is “automobile” and “bicycle” may be counted respectively.

  Moreover, the position information totalization part 14 can weight the number of the positional information counted based on the information regarding the positioning time contained in position information. When counting the number of pieces of position information for each mesh, the position information totaling unit 14 normally counts the number of pieces of position information related to one piece of position information as “1”. In the case of a band, the number of position information related to the position information is weighted, for example, the number of position information is counted as “2”. Thereby, for example, the position information can be counted after weighting the number of position information measured in a time zone in which a traffic accident is likely to occur. As the time zone for weighting, for example, a dim time zone from 18:00 to 19:00 is set. Further, this time zone may be changed according to the season. By performing weighting in this way, it is possible to obtain a risk index value with higher accuracy.

  Further, the position information totaling unit 14 can weight the number of pieces of position information to be counted based on the attribute information related to the user of the mobile terminal 4 whose position is indicated by the position information. User attribute information is stored in the user information storage unit 40 in advance.

  The user information storage unit 40 is a storage unit that stores in advance the attribute information of the user of the mobile terminal 4, and may be provided in a server device that can communicate with the dangerous point estimation device 1 via the network N. It is good also as being provided as one function part of the danger location estimation apparatus 1. FIG. FIG. 9 is a diagram illustrating a configuration of the user information storage unit 40 and an example of stored data. In the example illustrated in FIG. 9, the user information storage unit 40 is associated with the “user ID” that is the identifier of the user of the mobile terminal 4, and “name”, “sex”, “birth date”, “mobile terminal model” And “identification documents” are stored. Among the user attribute information, “name”, “gender”, and “birth date” are information indicating the user name, gender, and date of birth, respectively, and “mobile terminal model” is the model of the mobile terminal 4 The “identity confirmation document” indicates a document presented by the user as the identity confirmation document when contracting with the mobile terminal 4.

  When counting the number of pieces of position information for each mesh, the position information totaling unit 14 usually counts the number of pieces of position information related to one piece of position information as “1”. If the user is older than a certain date and the user is an elderly person, or if the user's mobile terminal model related to the location information is a model for elderly people, The position information number can be weighted so as to be counted as a number larger than “1”.

  In addition, when the position information totaling unit 14 counts the number of pieces of position information for each mesh, if the user's date of birth related to the position information is after a certain date, and the user is a younger generation, When the user's mobile terminal model related to the location information is a model for young people, and when the user's identity confirmation document related to the location information is not a "vehicle driving license", the user's location information Can be weighted. By performing weighting as described above, it is possible to obtain a risk index value with higher accuracy.

  The index value totalization unit 15 calculates the risk level for each mesh based on the number of position information whose traffic mode is “automobile” and the number of position information whose traffic mode is “walk” counted by the position information totalization unit 14. This is a part that generates a risk index value that is an index value indicating. Specifically, the index value totaling unit 15 calculates an index value (first index value) indicating the number of the position information for each mesh based on the number of position information whose traffic mode is “automobile”. Based on the number of pieces of position information with the traffic mode “walking”, an index value (second index value) indicating the number of pieces of the position information is generated for each mesh, and the traffic mode is “automobile”. The risk index value is generated by multiplying the index value of the position information that is and the index value of the position information whose traffic mode is “walking”. Next, generation of index values by the index value totaling unit 15 will be described with reference to FIG.

  The index value totaling unit 15 divides the position information number CN1 of each car by the maximum value ("5" in the example of FIG. 8A) of the car position information number CN1 shown in FIG. Thus, an index value is calculated such that the number of pieces of position information is in the range of 0 to 1. The index value calculated in this way is shown in the index value CN2 of the automobile in FIG. Similarly, the index value totaling unit 15 uses the maximum value (“5” in the example of FIG. 8A) of the walking position information number WN1 shown in FIG. The index value is calculated such that the number of pieces of position information is a value in the range of 0 to 1. The index value calculated in this way is indicated by the walking index value WN2 in FIG. Then, the index value totaling unit 15 multiplies the vehicle index value CN2 and the walking index value WN2 for each mesh ID to generate the risk index value DN. Thus, since the risk index value is generated by the index value totaling unit 15, it is possible to accurately estimate the dangerous spot.

  The processing result output unit 16 is a part that outputs the risk index value generated by the index value totaling unit 15. For example, the processing result output unit 16 returns a risk index value as information on the dangerous part to a terminal device (not shown) that has transmitted a processing request to the dangerous part estimation apparatus 1 or the dangerous part estimation apparatus 1. For example, the risk index value is displayed as information on the dangerous part on the output device 107 such as a display. FIG. 10 is a diagram illustrating an example in which the processing result output unit 16 visually outputs a risk index value to a display or the like. In FIG. 10, a mesh having a risk index value of 0.8 or more is displayed by a dot pattern, a mesh having a risk index value of 0.4 or more and less than 0.8 is displayed by a right-downward oblique line pattern, This is a display example in which a mesh that is less than 4 is displayed as a left-slanting diagonal line pattern, and a mesh that is 0 or more and less than 0.2 is displayed as a white pattern. For example, since the risk index value DN of the mesh ID “M5” is “1.00” (see FIG. 8B), the mesh M5 is displayed by a dot pattern in the display example shown in FIG. .

  Next, with reference to FIG. 11 and FIG. 12, the operation of the dangerous part estimation device 1 in the dangerous part estimation method of the present embodiment will be described. FIG. 11 is a flowchart showing the processing contents executed in the dangerous spot estimation apparatus 1. FIG. 12 is a flowchart showing the processing content of step S5 in the flowchart of FIG.

  First, the process request receiving unit 10 receives a process request for estimating a dangerous spot for the dangerous spot estimating apparatus 1 (S1). This processing request includes information related to the area where the dangerous location is desired to be estimated. Subsequently, the position information acquisition unit 11 acquires position information corresponding to the area from the position information storage unit 20 based on the information regarding the area included in the processing request received in Step 1 (S2, position information acquisition). Step). Further, the area information acquisition unit 12 acquires the mesh information of the area from the area information storage unit 30 based on the information regarding the area where the estimation of the dangerous part is desired, which is included in the processing request received in Step 1 ( S2, area information acquisition step).

  Next, the affiliation mesh determination unit 13 determines, for each position information, the affiliation mesh of the position information acquired by the position information acquisition unit 11 based on the mesh information acquired by the region information acquisition unit 12, and determines the determined affiliation. A mesh is added to each position information (S3, belonging mesh determination step).

  Subsequently, the position information totaling unit 14 indicates the number of pieces of position information for which the belonging mesh is determined by the belonging mesh determining unit 13, the position information whose traffic mode is “automobile”, and the position information whose traffic mode is “walking”. Is counted for each mesh (S4, position information counting step). The position information totaling unit 14 counts the position information in step S4 based on the information on the positioning time included in the position information and the attribute information on the user of the mobile terminal 4 whose location is indicated by the position information. Weighting can be performed on the number of pieces of positional information.

  Next, the index value totaling unit 15 counts each mesh based on the number of position information whose traffic mode is “automobile” and the number of position information whose traffic mode is “walk” counted by the position information totaling unit 14. A risk index value that is an index value indicating the risk level of the current risk is generated (S5, index value aggregation step).

  More specifically, with reference to FIG. 12, the index value totaling unit 15 extracts the maximum number of pieces of position information from the number of pieces of position information WN1 counted for each mesh (S50). Subsequently, the index value totaling unit 15 divides each walking position information number WN1 by the number of position information extracted in step S50, so that each index value becomes a value in the range of 0 to 1. Is indexed by calculating (S51).

  The index value totaling unit 15 extracts the maximum number of pieces of position information from the number of pieces of position information CN1 of the automobile counted for each mesh (S52). Subsequently, the index value totaling unit 15 divides the number of position information CN1 of each car by the number of position information extracted in step S52, and the index value is such that each position information number is a value in the range of 0 to 1. Is indexed by calculating (S53).

  Then, the index value totaling unit 15 multiplies the index value WN2 of the walking position information and the index value CN2 of the car position information for each mesh ID, and generates the risk index value DN (S54).

  Referring to FIG. 11 again, the processing result output unit 16 outputs the risk index value generated by the index value totaling unit 15 (S6, processing result output step). In this way, the process of this embodiment is complete | finished.

  In the dangerous point estimation device 1 of the first embodiment described above, the position information of a plurality of mobile terminals is acquired by the position information acquisition unit 11, and each of the position information where the moving means indicated in the traffic mode is a car and a walk is included. A risk index indicating the degree of risk for each mesh based on the number of position information in which the traffic mode is a car and the number of position information in which the traffic mode is walking is counted for each mesh by the position information totaling unit 14. A value is generated by the index value totaling unit 15. Since this risk index value is generated based on location information that sequentially reflects the location of the user, it is not necessary to accumulate information on traffic accidents, and the risk location is estimated in a timely manner. Is possible.

(Second Embodiment)
Next, the dangerous spot estimation apparatus 1 according to the second embodiment will be described. FIG. 13 is a block diagram showing a functional configuration of the dangerous spot estimation apparatus 1 according to the second embodiment. The dangerous point estimation apparatus 1 according to the second embodiment is different from the first embodiment in that it further includes a position information extraction unit 17 (position information extraction means).

  The position information extraction unit 17 is a part that extracts position information corresponding to a predetermined condition from the plurality of position information acquired by the position information acquisition unit 11 and supplies the extracted position information to the belonging mesh determination unit 13. The affiliation mesh determination unit 13 determines the mesh to which the location belongs to the position information extracted by the position information extraction unit 17.

  For example, the position information extraction unit 17 can extract position information whose positioning time corresponds to a predetermined time zone from the position information acquired by the position information acquisition unit 11. The position information extraction unit 17 can set, for example, 18:00 to 19:00, which is a dim time zone and is likely to cause a traffic accident, as the predetermined time zone. Further, this time zone may be changed according to the season. Thereby, only the positional information measured in the time zone where a traffic accident is likely to occur is counted by the positional information totaling unit 14, and a risk index value is generated based on the counted number of positional information. This makes it possible to obtain a more useful risk index value.

  In addition, the location information extraction unit 17 acquires user attribute information of the user of the mobile terminal 4 stored in advance from the user information storage unit 40, and the user of the mobile terminal whose location is indicated by the location information from the acquired location information. It is possible to extract position information in which the attribute information relating to the predetermined condition is met. The configuration of the user information storage unit 40 is the same as that described with reference to FIG. 9 in the first embodiment.

  The position information extraction unit 17 may extract the position information from the position information acquired by the position information acquisition unit 11 when, for example, the user's date of birth related to the position information is before a certain date. it can. Thereby, it becomes possible to extract the positional information of the elderly user. The position information extraction unit 17 extracts the position information from the position information acquired by the position information acquisition unit 11 when the user's mobile terminal model related to the position information is a model for elderly people, for example. be able to. Also in this case, it becomes possible to extract the position information of the elderly users.

  For example, the position information extraction unit 17 may extract the position information from the position information acquired by the position information acquisition unit 11 when the date of birth of the user related to the position information is after a certain date. it can. Thereby, it becomes possible to extract the position information of the young user. The location information extraction unit 17 extracts the location information from the location information acquired by the location information acquisition unit 11 when the user's mobile terminal model related to the location information is a model for young people, for example. be able to. Also in this case, it becomes possible to extract the position information of the young user.

  In addition, the position information extraction unit 17 extracts the position information from the position information acquired by the position information acquisition unit 11 when the user identification document related to the position information is not a “vehicle driving license”. Can do. As a result, it is possible to extract the location information of the user who does not have a car driver's license and seems to have a low awareness of traffic conditions.

  As described above, in the present embodiment, only the position information of users such as young people and elderly people and users who are considered to have low traffic situation recognition power is counted by the position information totaling unit 14, and the counted position information. A risk index value is generated based on the number. This makes it possible to obtain a more useful risk index value.

  Then, with reference to FIG. 14, operation | movement of the dangerous location estimation apparatus 1 in the dangerous location estimation method of 2nd Embodiment is demonstrated. FIG. 14 is a flowchart showing the contents of processing performed in the dangerous spot estimation apparatus 1.

  The processing contents executed in steps S11 to S12 are the same as the processing contents shown in steps S1 to S2 in the flowchart of FIG. Subsequently, the position information extraction unit 17 extracts position information corresponding to a predetermined condition from the plurality of position information acquired by the position information acquisition unit 11 (S13). The position information extracted in step S13 is used for belonging mesh determination processing by the belonging mesh determination unit 13. The processing contents executed in steps S14 to S17 are the same as the processing contents shown in steps S3 to S6 in the flowchart of FIG. In this way, the process of this embodiment is complete | finished.

  In the danger point estimation device 1 of the second embodiment described above, it is considered that the position information measured in a time zone where a traffic accident is likely to occur, or a user such as a younger group or an elderly group, or the ability to recognize traffic conditions is low. Only the position information of the user is counted, and a risk index value is generated based on the counted number of position information. Thereby, since the risk index value is generated based on the position information that matches the predetermined condition, it is possible to obtain a more useful risk index value.

(Third embodiment)
Next, the dangerous spot estimation apparatus 1 according to the third embodiment will be described. The danger point estimation device 1 of the third embodiment is characterized in that the position information acquisition unit 11 in the functional block diagrams of FIG. 2 (first embodiment) and FIG. 13 (second embodiment) includes a traffic mode generation unit 11A. It is said. FIG. 15 is a functional block diagram of the traffic mode generation unit 11A.

  The traffic mode generation unit 11A includes the location information and positioning time information included in the one location information, the positioning time of the one location information and the positioning time adjacent in time series, and other location information related to the same user. This is a part that obtains the moving speed of the user based on the included location information and positioning time information, and generates traffic mode information of one position information based on the obtained moving speed. And a moving means determination unit 11C. The traffic mode generation processing by the traffic mode generation unit 11A will be specifically described below.

  First, the traffic mode generation unit 11A extracts one position information that does not include the traffic mode information, and another position information that includes the position information that is adjacent to the one position information in time series and that is related to the same user. . An example of the two pieces of position information 20B extracted in this way is shown in FIG. FIG. 17 is a flowchart showing a traffic mode generation process using the extracted position information.

  Subsequently, the moving speed calculation unit 11B obtains the distance between the two pieces of position information from the latitude and longitude information included in the extracted two pieces of position information, and obtains the time difference from the positioning time of the two pieces of position information. The moving speed V between the position information is calculated by dividing the distance between the position information by the time difference (S201). Assuming that the extracted position information is one “section”, the position information with the earlier positioning time (upstream in the time series) of the two position information is referred to as the “start point” and the later one ( Position information on the downstream side in the time series is referred to as “end point”.

  Then, the movement method determination unit 11C determines whether or not the calculated movement speed V is less than a predetermined reference speed V1 for walking determination (S202), and if the movement speed V is less than the reference speed V1. For example, the moving method between the position information is determined as “walking” (step S203).

  If the moving speed V is not less than the reference speed V1, the moving method determination unit 11C determines whether or not the moving speed V is equal to or higher than the reference speed V1 and less than a predetermined reference speed V2 for bicycle determination (S204). If the moving speed V is not less than the reference speed V1 and less than the reference speed V2, the moving method between the position information is determined as “bicycle” (S205).

  When the moving speed V is equal to or higher than the reference speed V2, the moving method determination unit 11C determines whether or not at least one of the start point and the end point is located on the train line in light of the train line map data stored in advance. (S206) If at least one of the start point and the end point is located on the train route, the movement method between the position information is determined as “train” (S207). In addition, 11 C of movement method determination parts shall memorize | store the route map data of public transport beforehand.

  If a negative determination is made in step S206, the movement method determination unit 11C determines whether at least one of the start point and the end point is located on the bus route in light of the bus route map data stored in advance (S208). ) If at least one of the start point and the end point is located on the bus route, the movement method between the position information is determined as “bus” (S209). On the other hand, if a negative determination is made in step S208, the moving method between the target point data is determined to be “automobile” (step S210).

  Then, the movement method determination unit 11C stores the movement method information between the position information obtained by the determination in the position information 20B as the traffic mode information of the position information of “start point” or “end point” (step S211). Here, whether the movement mode information between the “start point” and the “end point” is set as the traffic mode of the position information of “start point” or “end point” is a design item appropriately set. Even if the traffic mode information is not included in the position information, the traffic mode information can be obtained based on the location and positioning time of the user included in the position information by the traffic mode generation process described above. It becomes possible.

  The present invention has been described in detail based on the embodiments. However, the present invention is not limited to the above embodiment. The present invention can be variously modified without departing from the gist thereof.

  For example, in the first embodiment, weighting performed when the position information counting unit 14 counts position information can be omitted. On the other hand, the weighting performed based on the positioning time and the weighting performed based on the user attribute information may be performed in an overlapping manner.

  In the second embodiment, in the position information extraction process by the position information extraction unit 17, the extraction process based on the positioning time and the extraction process based on the user attribute information and the like may be performed in duplicate.

DESCRIPTION OF SYMBOLS 1 ... Risk location estimation apparatus, 2 ... Location information storage device, 3 ... Area information storage device, 4 ... Mobile terminal, 5 ... Application service provision apparatus, 10 ... Processing request reception part, 11 ... Location information acquisition part, 11A ... Traffic Mode generation unit, 11B ... movement speed calculation unit, 11C ... movement means determination unit, 12 ... area information acquisition unit, 13 ... belonging mesh determination unit, 14 ... location information aggregation unit, 15 ... index value aggregation unit, 16 ... processing result Output unit, 17 ... position information extraction unit, 20 ... position information storage unit, 20A ... position information, 20B ... position information, 30 ... region information storage unit, 40 ... user information storage unit.

Claims (9)

A risk that estimates a dangerous location with a high risk of occurrence of a traffic accident based on information indicating the location of the user of the mobile terminal and location information including traffic mode information that is information indicating the type of the user's moving means A location estimation device,
Position information acquisition means for acquiring a plurality of the position information;
Area information acquisition means for acquiring mesh information stored in advance and information relating to a mesh defining a geographical unit area for estimating the dangerous place;
Based on the mesh information acquired by the area information acquisition means, the belonging mesh that is the mesh to which the location indicated by the position information acquired by the position information acquisition means belongs is determined for each position information Mesh determination means;
The first position information which is position information including the first movement means which is the one movement means as the traffic mode information, and the number of pieces of position information where the belonging mesh is determined by the belonging mesh determination means, and Position information totaling means for counting each mesh for each of the second position information, which is position information including the second moving means that is the moving means different from the first moving means, as the traffic mode information;
Based on the number of first position information and the number of second position information counted by the position information totaling unit, an index value totaling unit that generates a risk index value that is an index value indicating the risk for each mesh. When,
A risk location estimation apparatus comprising: a processing result output unit that outputs the risk index value generated by the index value totaling unit. The index value aggregation means includes
Based on the number of the first position information counted for each mesh, a first index value indicating the number of the first position information is generated for each mesh, and counted for each mesh. Based on the number of the second position information, a second index value indicating the number of the second position information is generated for each mesh, and the first index value, the second index value, The risk point estimation device according to claim 1, wherein the risk index value is generated by multiplying by. The position information includes information on a positioning time that is a time at which the location indicated in the position information is measured,
The position information counting means weights the number of the position information to be counted based on information on the positioning time included in the position information.
The danger point estimation apparatus according to claim 1 or 2, wherein The location information counting means acquires the attribute information of the user of the mobile terminal stored in advance, and the location counted based on the attribute information related to the user of the mobile terminal whose location is indicated by the location information The risk location estimation apparatus according to any one of claims 1 to 3, wherein the number of information is weighted. Comprising location information extraction means,
The position information includes information on a positioning time that is a time at which the location indicated in the position information is measured,
The position information extraction unit extracts the position information in which the positioning time corresponds to a predetermined time zone from the position information acquired by the position information acquisition unit,
The belonging mesh determination means determines the mesh to which the location belongs, with respect to the position information extracted by the position information extraction means.
The danger point estimation apparatus according to claim 1 or 2, wherein The attribute information of the user of the mobile terminal that is stored in advance is acquired, and the attribute information related to the user of the mobile terminal whose location is indicated by the position information from the acquired position information satisfies a predetermined condition A position information extracting means for extracting information;
The belonging mesh determination means determines the mesh to which the location belongs, with respect to the position information extracted by the position information extraction means.
The dangerous point estimation apparatus according to claim 1, 2, or 5. The position information acquisition means includes a traffic mode generation means,
The position information includes information on a positioning time that is a time at which the location indicated in the position information is measured,
The traffic mode generation means includes the location information and the positioning time information included in one position information, and the positioning time adjacent to the positioning time of the one position information in time series and relates to the same user Based on the location position and the positioning time information included in other position information, the moving speed of the user is obtained, and traffic mode information of the one position information is generated based on the obtained moving speed.
The dangerous point estimation apparatus according to any one of claims 1 to 6, characterized in that:   The danger location estimation apparatus according to claim 1, wherein the first moving means in the traffic mode information included in the position information is a car, and the second moving means is a walk. A risk that estimates a dangerous location with a high risk of occurrence of a traffic accident based on information indicating the location of the user of the mobile terminal and location information including traffic mode information that is information indicating the type of the user's moving means A risk location estimation method in a location estimation apparatus,
A position information acquisition step of acquiring a plurality of the position information;
An area information acquisition step for acquiring mesh information stored in advance, which is information related to a mesh that defines a geographical unit area for estimating the dangerous place;
Based on the mesh information acquired in the region information acquisition step, a belonging mesh that is the mesh to which the location indicated by the position information acquired in the position information acquisition step belongs is determined for each position information. Belonging mesh determination step;
The first position information which is position information including the first moving means as the moving means as the traffic mode information, the number of position information where the belonging mesh is determined in the belonging mesh determining step, and A position information counting step for counting each mesh for each of the second position information, which is position information including the second moving means, which is the moving means different from the first moving means, as the traffic mode information;
An index value counting step for generating a risk index value that is an index value indicating the risk for each mesh based on the number of first position information and the number of second position information counted in the position information counting step When,
A risk location estimation method comprising: a process result output step of outputting the risk index value generated in the index value aggregation step.

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