JP2014137682A - Traffic information provision system using location information of mobile terminal - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a traffic information provision system for predicting a hazardous point by analyzing actual travel information of a plurality of users to transmit warning information to respective users.SOLUTION: A traffic information provision system comprises a plurality of mobile terminals for acquiring travel information during a travel, and a host system connected to the mobile terminals through a network. The host system comprises: a database for storing travel information as collection data; hazardous point prediction means for acquiring travel information which includes a latitude and longitude within a predetermined distance from a determination object point of hazardous prediction, from the stored collection data and determining whether the determination object point is a hazardous point on the basis of travel actual data included in the travel information to register the point as a hazardous point; and means for, when receiving travel information from a mobile terminal, determining whether the mobile terminal is travelling just before a hazardous point on the basis of the latitude and longitude included in the received travel information to transmit warning information.

Description

  The present invention relates to a traffic information providing system for a mobile terminal, and by analyzing travel information collected from a plurality of mobile terminals, predicts dangerous points on road traffic and distributes dangerous point information, and also provides safe driving. It relates to a system that also performs trend analysis.

  With the development of vehicles and road traffic networks, it has become easy to move to remote locations. Recently, in a general navigation system, map information is stored in a storage medium such as a memory card, and a map around the current position is displayed on the display screen from the memory card according to the current position detected using a satellite, and the destination is reached. Navigation systems utilizing GPS (Global Positioning System) are becoming popular. FIG. 10 is a block diagram showing an outline of the navigation system.

  In the navigation system of FIG. 10, in order to contribute to the reduction of traffic accidents, information on accident occurrence points 1007 for the whole country is stored in advance in the storage medium 1006 as data. When approaching a registered point, the user is informed in advance that an accident-prone point exists ahead. This accident location information is created based on data released by the National Police Agency once a year. As a result, the user can recognize again that there is a danger, leading to prevention of an accident.

  In addition, a system has been proposed in which a driving tendency of a driver is analyzed using traveling states acquired by various on-vehicle sensors and used for future safe driving.

  For example, Patent Literature 1 discloses a system that records information on a specific behavior of a vehicle or the like on a recording medium and analyzes a driver's operation tendency based on the recorded data. In this system, information obtained by a vehicle is temporarily recorded on a recording medium, and this recording medium is read by an analysis device for analysis. As a result, detailed operation trends for each driver are grasped to prevent accidents from occurring.

  Moreover, in patent document 2, in addition to the technique of patent document 1, even if the change of the weather, the progress of the traveling area, and the like change, safe driving is promptly and finely adapted in time. That is, a system for preventing an accident at a sign stage where the running state changes (overspeed, falling asleep, sudden acceleration / deceleration, meandering, etc.) and monitoring the operation according to the running environment is disclosed.

JP 2000-185676 A JP 2003-048447 A

  The conventional system shown in FIG. 10 is a form in which data on accident-prone points is stored in advance in a mobile terminal. In this case, data that changes every day cannot be reflected, and there is a possibility that old data will be provided to the user.

  In addition, a large amount of data with low utility value for the user, such as information outside the action range, must be stored in the mobile terminal, which may waste hardware resources such as storage devices. is there.

  Further, in the systems disclosed in Patent Document 1 and Patent Document 2, there is an example in which a driver tendency is analyzed from vehicle information of each user and used for safe driving support to individual users. It is not possible to collect statistics on the tendency of multiple drivers or statistics on dangerous spots.

  An object of the present invention is to realize a traffic information providing system capable of predicting a dangerous point by analyzing traveling information based on actual traveling results of a plurality of users and notifying each user of the dangerous point. is there. Furthermore, a system that reduces the amount of data held inside the mobile terminal as much as possible and provides the user with the latest necessary data that is truly necessary is realized.

In order to achieve the above object, the present invention provides the following configurations.
An aspect of the present invention provides traffic information including a plurality of mobile terminals that acquire travel information including travel performance data including position information during travel, and a host system connected to the mobile terminals through a network. The host system is a database that accumulates travel information received from each of the mobile terminals as collected data, and the vicinity of a risk prediction determination point from the collected data accumulated in the database. The travel information including the location information is acquired, and it is determined whether or not the determination target point is a dangerous point based on the driving performance data included in the acquired travel information. A dangerous point prediction means for registering a point as a dangerous point, and position information included in the received traveling information when the traveling information is received from the mobile terminal Based on the means for determining whether or not the mobile terminal is traveling immediately before the registered dangerous point, and when it is determined that the mobile terminal is traveling immediately before the registered dangerous point Means for transmitting caution information to the mobile terminal.

Preferably, the driving information includes driving environment data including weather and light and dark,
When registering the determination target point as a dangerous point, it is registered in association with the data of the driving environment included in the acquired traveling information, and whether or not the mobile terminal is traveling immediately before the dangerous point. At the time of determination, it is further determined whether or not the travel environment data included in the received travel information matches the travel environment data associated with the dangerous location, and the mobile terminal immediately before the dangerous point. The caution information is transmitted when the vehicle is traveling and the data of the traveling environment match.

  Preferably, the travel information stored in the database is stored in association with user information received from the mobile terminal indicating user characteristics and vehicle information indicating vehicle characteristics. The position information included in the travel information acquired from the collected data is acquired, and the speed limit based on the speed limit and traffic regulation of the road corresponding to the acquired position information and the travel performance data included in the travel information. And a level of violation of traffic regulation, and a safe driving tendency analysis that aggregates the determined speed limit and level of traffic regulation violation by user characteristics or vehicle characteristics based on the user information or the vehicle information. Means.

  Preferably, the travel information includes acceleration data, and the safe driving tendency analysis means determines whether the acceleration exceeds a predetermined sudden acceleration or sudden deceleration threshold, and exceeds the threshold. In the case of being present, the data is totaled according to user characteristics and vehicle characteristics based on the user information and the vehicle information.

The effects of the present invention are mainly as follows.
(1) When approaching a dangerous point predicted based on data collected from a plurality of mobile terminals, that is, users, caution information is transmitted to each mobile terminal to promote attention to safe driving. Accidents can be prevented in advance.
(2) It is possible to provide the police with information for identifying and improving a dangerous part with a high probability of occurrence of an accident, and it is also possible to provide information such as a safe driving tendency to an insurer.

  In the present invention, traveling information of mobile terminals of a plurality of users is collected as collected data of a host system connected to a network, and is analyzed by a dangerous point prediction means, thereby predicting a dangerous point and obtaining the latest attention information. Can be provided to users. In addition, since the notification of the dangerous point information is performed by transmitting the corresponding attention information in real time when the user approaches the dangerous point while traveling, the dangerous point information is stored in advance in the mobile terminal. No need for data resources.

FIG. 1 is a system configuration diagram schematically showing an example of an embodiment of the present invention. FIG. 2 is a flowchart showing an outline of the processing flow of the present system. FIG. 3 shows an example of a table that stores information accumulated in a database of collected data in the collection phase. FIG. 4 is a diagram schematically showing a data flow in the safe driving tendency analysis in the analysis phase. FIG. 5A is a flowchart illustrating an example of processing of the safe driving tendency analysis program. FIG. 5B is a flowchart illustrating an example of processing of the safe driving tendency analysis program. FIG. 6 shows an example of a safe driving tendency analysis result table output as a result of the safe driving tendency analysis program. FIG. 7 is a diagram schematically showing the flow of data for risk point prediction analysis in the analysis phase. FIG. 8A is a flowchart illustrating an example of processing of a dangerous point prediction program. FIG. 8B is a flowchart illustrating an example of processing of the dangerous point prediction program. FIG. 9 shows an example of a dangerous point prediction result table output as a result of the dangerous point prediction program. FIG. 10 is a system configuration diagram of the prior art.

Hereinafter, an embodiment of a traffic information providing system for a mobile terminal to which the present invention is applied will be described.
FIG. 1 is a system configuration diagram schematically showing an example of an embodiment of the present invention. A host system 116 installed in the information accumulation center and a mobile terminal 104 that moves as the user moves are connected via a network 121. The mobile terminals 2, 3 .. n have the same configuration as the mobile terminal 104. The user's means of transportation is not limited to automobiles, but includes walking, bicycles, and motorcycles.

  The mobile terminal 104 is connected to external devices (acceleration sensor 101, wiper ON / OFF detector 102, illuminance sensor 103, etc.), and can travel while acquiring data from the external device at regular intervals. The mobile terminal 104 includes an input device including a screen touch panel 108, an image input device 109, and a voice input device 110 as an input / output device 124, and an output device including an image output device 111 and a voice output device 112. Further, a GPS 105 is provided as a position detection function, and the vehicle can travel while acquiring position information at regular intervals. The plurality of data acquired from the external device and the position information acquired from the GPS constitute travel information described later. Further, by providing a communication function of HTTP 115 as a mobile communication function, it is connected to the network 121.

  The mobile terminal 104 is substantially a computer having a CPU, and implements the functions of the present system by executing the navigation program 113 and the information analysis module 114. Furthermore, a storage medium 122 that is an external storage device is provided, and the storage medium 122 stores data such as map data 106 indicating roads, user information, vehicle type information, travel information 107, facility information 108, and the like.

  The collected data 117 of the host system 116 is a database that accumulates various information (user information, vehicle type information, and traveling information 107) collected from a plurality of mobile terminals 104 and the like. The host system 116 includes dangerous point prediction means 118 that executes dangerous point prediction processing and safe driving tendency analysis means 123 that executes safe driving tendency analysis processing using various information accumulated in the collected data 117. ing. The host system 116 is substantially a computer having a CPU, and realizes the processing functions of the respective means 118 and 123 by executing a dangerous point prediction program and a safe driving tendency analysis program, respectively.

  Data analyzed by each means 118 and 123 is stored in the database as an analysis result 120. The dangerous point information distribution unit 119 distributes the warning information about the dangerous point registered in the analysis result 120 to the mobile terminal 104 via the network 121.

  FIG. 2 is a flowchart showing an outline of the processing flow of the present system. This system is roughly classified into three phases: a data collection phase, an analysis phase, and a distribution phase. In the following description, reference numerals in FIG. 1 may be used.

  First, in the mobile terminal 104, the user sets user information (step 201) and vehicle information (step 202). After starting operation (step 203), the mobile terminal 104 accesses the host system 116. As a result, the host system 116 starts collecting information (step 211). Subsequently, the mobile terminal 104 transmits user information and vehicle information setting information to the host system 116 (step 204). The host system 116 registers the received setting information in the collection data 117 database (step 212).

  The traveling mobile terminal 104 acquires travel information (including travel performance and travel environment data shown in FIG. 3) at regular intervals (step 205). The mobile terminal 104 matches the coordinates, which are position information acquired by the GPS 105, which is one of the traveling information, and the road on the map data 106 (step 206). The coordinates after matching are used as position information of latitude and longitude, and are transmitted to the host system together with travel information other than the position information (step 207). The host system 116 registers the received travel information as collected data in the collected data 117 database (step 213). The travel information is registered in association with user information and vehicle information setting information. This is the collection phase.

  Next, in the host system 116, the information accumulated in the database of the collected data 117 is analyzed by the dangerous point prediction means 116 and the safe driving tendency analysis means 123 about once a month, for example. The analysis result is stored in the analysis result 120 database. (Step 214). This is the analysis phase.

  Next, the current travel information is collated with the data stored as the analysis result (step 215). It is determined whether or not the current location information (latitude and longitude) received from the mobile terminal is immediately before the location registered as the dangerous point, or in addition to this, the traveling environment such as weather and light and dark It is determined whether or not they match (step 216). Whether or not it is immediately before the dangerous point is determined based on the latitude and longitude included in the travel information, based on whether or not it is within a predetermined distance from the latitude and longitude of the dangerous point. Whether it is approaching or moving away from the danger point can be determined by the traveling direction (direction in which the latitude and longitude change). The determination as to whether or not the driving environments match is made based on whether or not the driving environment data included in the current driving information received from the mobile terminal matches the driving environment data associated with the danger point. To do.

  If it is immediately before the dangerous point, or if it is immediately before the dangerous point and the traveling environment matches, the caution information is transmitted to the mobile terminal 104 (step 217). When the mobile terminal 104 receives the caution information from the host system 116 (step 208), the mobile terminal 104 alerts the output device such as a screen display or sound (step 209). This is the distribution phase.

FIG. 3 shows an example of a table for storing information accumulated in the database of the collected data 117 in the collecting phase.
The user information table 317 stores information indicating user characteristics. The data items include, for example, a user ID 301 that uniquely identifies the user, a user age 302, and a gender 303.

  The vehicle information table 318 stores information indicating the characteristics of the vehicle. As data items, for example, a vehicle ID for identifying vehicle information, a terminal attribute 305 for designating which moving means a user uses a mobile terminal among walking, bicycle, motorcycle, and automobile, and a terminal attribute If the vehicle is an automobile, the vehicle type 306 (extra large, large, medium, normal, light) is included.

  The travel information table 319 stores travel information acquired during travel. As data items, for example, the date and time 307 acquired from the GPS 105 or the clock in the mobile terminal, the user ID 301, the vehicle ID 304, the latitude acquired after matching the coordinates acquired by the GPS with the road on the map, acquired by the GPS 308 Longitude 309 after matching the coordinates with the road on the map, acceleration that can be calculated from coordinates and time change acquired by GPS, or acceleration 310 that can be acquired from an external sensor, calculation from coordinates and time change acquired by GPS Possible speed or speed 311 obtainable from external sensor, cumulative distance 312 calculated from latitude and longitude after matching, running weather 313 obtained from ON / OFF of wiper, travel obtained by illuminance sensor, etc. The lightness and darkness 314 in the middle is stored in the travel information table 319. Here, in the travel information, date 307, latitude 308, longitude 309, acceleration 310, speed 311 and travel distance 312 are referred to as “travel performance” data, and weather 313 and light and dark 314 are data of “travel environment”. It shall be called. The user ID 301 and the vehicle ID 304 are associated with the user information table 317 and the vehicle information table 318.

FIG. 4 is a diagram schematically showing the flow of data for safe driving tendency analysis in the analysis phase.
The data used in the safe driving tendency analysis unit 123 is, for example, user information and vehicle information in the collected data 117 accumulated for about one month, and driving performance data among driving information. The user age 302 and gender 303 in the user information table 317 shown in FIG. 3, the terminal attribute 306 and the vehicle type 305 in the vehicle information table 318, the date and time 307, the latitude 310, the longitude 311, and the acceleration 312 in the travel information table 319. And speed 313. In order to relate these pieces of information, the user ID 301 and the vehicle ID 304 shown in FIG. 3 are used. The collected various information is input to the safe driving tendency analysis program 401. The result of the safe driving tendency analysis program 401 is output to the safe driving tendency analysis result 402. The output safe driving tendency analysis result 402 can be used for providing information to an insurer or the like.

5A and 5B are flowcharts showing an example of processing of the safe driving tendency analysis program 401.
First, for example, user information, vehicle information, and driving results of collected data accumulated for about one month are acquired (step 501). Next, the position information (latitude and longitude) of the running record of the collected data is acquired (step 502). The road type of the road belonging to the acquired position information is acquired (step 504). The speed of the collected data is compared with the speed limit in the road type of the corresponding road (step 505). It is determined whether or not the speed of the collected data exceeds the speed limit (step 506). If it exceeds the speed limit, points relating to the speed limit are deducted (step 507).

  Next, the traffic regulation information of the corresponding road is acquired (step 508), and the travel locus based on the position information (latitude and longitude) is compared with the traffic regulation information (step 509). A right / left turn prohibition or one-way traffic regulation violation is determined (step 510). If there is a violation, points relating to traffic regulation are deducted (step 511).

  Next, the acceleration of the collected data is compared with a preset rapid acceleration threshold value (step 512), and it is determined whether the acceleration exceeds the rapid acceleration threshold value (step 513). If it has exceeded, points for sudden acceleration are deducted (step 514). Further, it is determined whether or not the sudden deceleration threshold is exceeded (step 515). If so, points for sudden deceleration are deducted (step 516). When all the data has been analyzed, the process ends (step 517).

FIG. 6 shows an example of a table of safe driving tendency analysis results 402 output as a result of the safe driving tendency analysis program 401.
The table of the safe driving tendency analysis result 402 shows the average value of the points and the mileage 610 that are 100 points from four viewpoints (restriction speed 606, sudden acceleration 607, sudden deceleration 608, traffic regulation 609), age 601, sex 602, the terminal attribute 603, and the vehicle type 605 are totaled and the average value is stored. As described above, the determination results of the speed limit and traffic regulation violation level and the sudden acceleration / deceleration level are tabulated based on the user information and the vehicle information, so that the safety according to the user characteristics and the vehicle characteristics is obtained. Driving tendency can be obtained.

FIG. 7 is a diagram schematically showing the flow of data for risk point prediction analysis in the analysis phase.
The data used in the danger point prediction means 118 is, for example, travel information (data on both travel environment and travel performance) in the collected data 117 accumulated for about one month. The travel environment is the weather 313 and the brightness 314 of the travel information table 319 shown in FIG. 3, and the travel results are the date and time 307, the latitude 310, the longitude 311, the acceleration 312 and the speed 313 of the travel information table 319 shown in FIG. It is. The collected various data is input to the danger point prediction program 701. The result of the dangerous point prediction program 701 is output to the dangerous point prediction result 702. The output dangerous point prediction result 702 can be used as information provision to a police station or the like. Also, it is distributed to the mobile terminal 104 via the network 121.

8A and 8B are flowcharts illustrating an example of processing of the dangerous point prediction program 701.
Here, as an example, the determination target point of the dangerous point prediction is a road with three or more lanes and an intersection. The position information (latitude and longitude) of the determination target point is previously picked up from the map, and the driving performance and the driving environment near the determination target point are acquired from the collected data accumulated for about one month. (Step 801). Whether or not it is in the vicinity of the determination target point is determined based on the latitude and longitude included in the traveling performance based on whether or not it is within a predetermined distance from the latitude and longitude of the determination target point.

  Next, the speed of the collected data is compared with the speed limit in the road type of the corresponding road (step 802). It is determined whether or not the speed of the collected data exceeds the speed limit (step 803), and if it exceeds, a dangerous point is given to that point. (Step 804). Next, the traffic regulation information of the road is acquired (step 805), and the travel locus based on the latitude and longitude of the collected data is compared with the traffic regulation information (step 806). A right / left turn prohibition or a one-way traffic regulation violation is determined (step 807). If there is a violation, a risk point is assigned to the determination target point (step 804). Next, the acceleration of the collected data is compared with a preset acceleration threshold (step 808), and it is determined whether the acceleration exceeds the acceleration threshold (step 809). If it has exceeded, a risk point is assigned to the determination target point (step 804).

  Further, it is determined whether or not the acceleration exceeds a sudden deceleration threshold (step 810). If it has exceeded, a risk point is assigned to the determination target point (step 804). Next, it is determined whether or not there is a lane change based on the travel locus (step 811). If there is a lane change, a danger point is given to the determination target point (step 804). When the analysis of all the collected data is completed (step 816), the risk points at the respective determination target points are totaled, and it is determined whether or not the risk points have exceeded a preset threshold value (step 812). If the dangerous point exceeds the threshold, the latitude and longitude of the determination target point are registered as a dangerous point in the dangerous point information table (step 813).

  Further, the weather and lightness and darkness of the travel environment of the collected data in the vicinity of the determination target point registered in the dangerous point information table are totaled (step 814), and the most frequent weather and light and dark in the totaled result are stored in the dangerous point information table. (Step 815). When the aggregation of the risk points is completed for all the determination target points, the process is terminated (step 817). Thus, the location information of the dangerous point is registered in association with the traveling environment at that point.

  FIG. 9 shows an example of a table of dangerous point prediction results 702 output as a result of the dangerous point prediction program 701.

The table of risk point prediction results 702 is composed of an ID 901 that uniquely identifies the risk point, a risk point certification date 902, a latitude 903, a longitude 904, weather 905, and light and dark 906.
Finally, the third distribution phase will be described. In the distribution phase, the travel information and the dangerous point information 702 stored as a result of the dangerous point prediction program 701 are collated. If the current driving information is immediately before the location registered as the dangerous point, and the driving environment such as weather and light and dark stored in the dangerous point information matches the current driving environment, a caution is transmitted. When the mobile terminal receives attention, the mobile terminal alerts the user with a screen display or voice.

101: Acceleration sensor, 102: Wiper ON / OFF detector, 103: Illuminance sensor, 104: Mobile terminal, 105: GPS, 107: User information / vehicle information / running information, 116: Host system, 117: Collected data 118: Hazardous point prediction means, 119: Hazardous point information distribution means, 120: Analysis result, 121: Network, 122: Storage medium, 123: Safe driving tendency analysis means, 124: Input / output device

Claims (4)

A traffic information providing system comprising a plurality of mobile terminals that acquire travel information including travel performance data including location information during travel, and a host system connected to the mobile terminals via a network, The host system
A database that accumulates travel information received from each of the mobile terminals as collected data;
From the collected data stored in the database, travel information including position information in the vicinity of the risk prediction determination target point is acquired, and the determination target point is dangerous based on the travel performance data included in the acquired travel information. A risk point prediction means for registering the determination target point as a dangerous point if it is determined as a dangerous point,
Means for determining whether or not the mobile terminal is traveling immediately before a registered danger point based on position information included in the received travel information when the travel information is received from the mobile terminal; A traffic information providing system comprising: means for transmitting attention information to the mobile terminal when it is determined that the mobile terminal is traveling immediately before the registered danger point. The driving information includes data on driving environment including weather and light and dark,
When registering the determination target point as a dangerous point, register in association with the data of the travel environment included in the acquired travel information, and
When determining whether or not the mobile terminal is traveling immediately before the dangerous point, whether or not the traveling environment data included in the received traveling information matches the traveling environment data associated with the dangerous point The traffic information according to claim 1, further comprising: determining whether the mobile terminal is traveling immediately before the dangerous point and the data of the traveling environment match, and transmitting the caution information. Offer system. The travel information stored in the database is stored in association with user information indicating user characteristics and vehicle information indicating vehicle characteristics received from the mobile terminal,
Based on the road speed limit and traffic regulation corresponding to the acquired position information and the driving performance data included in the driving information, the position information included in the acquired driving information is acquired from the accumulated collected data. Safety to determine the speed limit and traffic regulation violation level, and to aggregate the determined speed limit and traffic regulation violation level by user characteristics or vehicle characteristics based on the user information or vehicle information The traffic information providing system according to claim 1, further comprising driving tendency analysis means. The travel information includes acceleration data,
The safe driving tendency analysis means determines whether or not the acceleration exceeds a predetermined sudden acceleration or sudden deceleration threshold. If the acceleration exceeds the threshold, the safe driving tendency analysis means The traffic information providing system according to claim 3, wherein the traffic information is totaled according to characteristics and vehicle characteristics.

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