JP2016115087A - Program of generating analysis data relevant to driving operation, analysis data generation method and information processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an information processor capable of outputting a generation situation of predetermined driving operation in a graph while associating with a location.SOLUTION: An information processor 101 generates a graph representing a generation rate of a predetermined driving operation in one day in predetermined time segments with respect to a location where a predetermined driving operations are made, which are identified on the basis of travel data of plural vehicles. The predetermined driving operation includes, for example, sudden braking, abrupt steering, turning ON hazard lamp, use of horn and the like. The information processor 101 outputs the generated graph associating with a map which represents a location where predetermined driving operations were made.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an analysis data generation program for driving operation, an analysis data generation method, and an information processing apparatus.

  Conventionally, there is a technique for collecting various information from an on-vehicle device mounted on a vehicle and utilizing it for various services. For example, by collecting information such as vehicle speed and position, detecting the occurrence of sudden braking when avoiding danger, etc., and presenting the number of sudden braking occurrences by day of the week and time zone in a table format , There are things that support the driver's risk prediction.

  As a prior art, for example, in a system including a probe vehicle, an on-vehicle device mounted on the probe vehicle, and a center that receives travel data from the on-vehicle device and generates traffic information, the center is provided for each on-vehicle device. There is a technique for determining a collection interval for transmitting data. There is also a technique for estimating road link traffic information based on VICS (Vehicle Information and Communication System) information and probe information collected for each time span. Also, based on the information on the occurrence of dangerous avoidance behavior and the information on the occurrence of an accident, the risk level of the roads included in the map data is calculated in consideration of the weather, day of the week, time zone, road surface condition, and traffic volume when these occur. There is a technology to set.

JP 2006-146645 A JP 2010-191614 A JP 2012-038006 A

  However, with the conventional technology, it is difficult to present in an easy-to-understand manner the occurrence of driving operations such as sudden braking that the driver takes when avoiding danger. For example, even if the number of occurrences of sudden braking by day of the week or time of day is summarized and presented in a table format, the driver can determine the day of the week or time of day when there is a lot of sudden braking. It will be compared, and it is difficult to judge the occurrence of sudden braking at a glance.

  In one aspect, an object of the present invention is to provide an analysis data generation program, an analysis data generation method, and an information processing apparatus related to a driving operation in which an occurrence state of a predetermined driving operation is graphed and output in association with a place. And

  According to one aspect of the present invention, a graph showing the occurrence rate of the predetermined driving operation in one day in each of the predetermined time segments for a place where the predetermined driving operation specified based on the driving data of a plurality of vehicles is performed. An analysis data generation program, an analysis data generation method, and an information processing apparatus relating to a driving operation are proposed in which the generated graph is output in association with the map indicating the place.

  Further, according to one aspect of the present invention, the designation of an area is accepted, and one or a plurality of places where a predetermined driving operation identified based on traveling data of a plurality of vehicles in the area is identified and identified. In the one or a plurality of places, a graph indicating the daily occurrence of the predetermined driving operation in each of the predetermined time segments is generated, and a map corresponding to the area and the specified one or a plurality of places And an analysis data generation program for driving operation, an analysis data generation method, and an information processing apparatus that output analysis data including the generated graphs corresponding to the one or a plurality of places are proposed.

  According to one aspect of the present invention, there is an effect that the occurrence state of a predetermined driving operation can be graphed and output in association with a place.

FIG. 1 is an explanatory diagram showing an embodiment of an analysis data generation method relating to driving operation. FIG. 2 is an explanatory diagram illustrating a system configuration example of the system 200. FIG. 3 is a block diagram illustrating a hardware configuration example of the analysis data generation apparatus 201. FIG. 4 is a block diagram illustrating a hardware configuration example of the terminal device 202. FIG. 5 is an explanatory diagram showing a specific example of probe data. FIG. 6 is an explanatory diagram showing an example of the contents stored in the sudden brake data DB 230. FIG. 7 is a block diagram illustrating a functional configuration example of the analysis data generation apparatus 201. FIG. 8 is an explanatory diagram showing an example of the stored contents of the sudden braking occurrence frequency table 800 for each day of the week and for each time zone. FIG. 9 is an explanatory diagram showing an example of the stored contents of the sudden braking occurrence frequency table 900 for each traveling direction. FIG. 10 is an explanatory diagram of an example of generating a graph. FIG. 11 is an explanatory diagram illustrating a decoration example for a portion corresponding to the sudden braking frequent occurrence point Ri. FIG. 12 is an explanatory diagram illustrating an arrangement example of icons indicating the sudden braking occurrence direction. FIG. 13 is an explanatory diagram illustrating a screen example of the analysis data screen. FIG. 14 is an explanatory diagram showing a specific example of a poster. FIG. 15 is a flowchart illustrating an example of the analysis data generation processing procedure of the analysis data generation apparatus 201. FIG. 16 is a flowchart illustrating an example of a specific processing procedure of the sudden brake data extraction processing. FIG. 17 is a flowchart illustrating an example of a specific processing procedure of the sudden braking frequent occurrence point specifying process. FIG. 18 is a flowchart illustrating an example of a specific processing procedure of statistical processing. FIG. 19 is a flowchart illustrating an example of a specific processing procedure of the graph generation processing. FIG. 20 is a flowchart illustrating an example of a specific processing procedure of the mapping process. FIG. 21 is a flowchart illustrating an example of a specific processing procedure of the matching process.

  DESCRIPTION OF EMBODIMENTS Embodiments of an analysis data generation program, an analysis data generation method, and an information processing apparatus relating to a driving operation according to the present invention will be described in detail below with reference to the drawings.

(One Example of Analysis Data Generation Method for Driving Operation)
FIG. 1 is an explanatory diagram showing an embodiment of an analysis data generation method relating to driving operation. In FIG. 1, an information processing apparatus 101 is a computer that generates analysis data related to a driving operation.

  Here, the driving operation is an operation performed by a driver of the vehicle, such as sudden braking (rapid stop), sudden steering, lighting of a hazard lamp, or the like. These driving operations are often performed by a driver driving a vehicle to avoid danger.

  For this reason, if the occurrence state of a driving operation such as sudden braking can be presented in an easy-to-understand manner in association with the place of occurrence, it is possible to support the driver's risk prediction. For example, if the driver can grasp in advance the state of occurrence of sudden braking for a place where sudden braking occurs frequently, the driver can be careful when traveling in that place, and can prevent the occurrence of an accident.

  Therefore, in the present embodiment, analysis data generation related to driving operation that makes it possible to easily present the occurrence status of the predetermined driving operation in one day in each of the predetermined time segments for the place where the predetermined driving operation has been performed. A method will be described. Hereinafter, a specific processing example of the information processing apparatus 101 will be described.

  (1) The information processing apparatus 101 indicates the occurrence rate of a predetermined driving operation in one day in each of predetermined time sections for a place where a predetermined driving operation specified based on traveling data of a plurality of vehicles is performed. Generate a graph. Here, the vehicle is, for example, an automobile, a truck, a motorcycle, or the like.

  The travel data is information (so-called probe data) obtained from the vehicle-mounted device mounted on the vehicle, and includes information such as the vehicle speed, position, acceleration, and angular velocity, for example. The travel data may include operation information such as a hazard lamp, a blinker, and a horn operated by the driver, for example.

  The predetermined driving operation is an operation performed for avoiding danger, for example, sudden braking, sudden steering, lighting of a hazard lamp, use of a horn, and the like. The occurrence of the predetermined driving operation can be specified based on the traveling data of the vehicle. The time division is divided by dividing the time axis, and is, for example, a day of the week or a season division described later.

  The degree of occurrence of a predetermined driving operation in a day represents how much the predetermined driving operation has occurred in the day. Specifically, for example, the degree of occurrence of a predetermined driving operation in a day represents the degree of occurrence of a predetermined driving operation for each time period obtained by dividing a day (24 hours) at a predetermined time interval. May be.

  Here, the transition of the degree of occurrence of a predetermined driving operation in one day may be similar even on different days. For example, comparing the changes in the degree of sudden braking in a day by day of the week, from Monday to Friday, even if the number of sudden brakings varies from day to day, the degree of sudden braking in a day Are often similar between days of the week.

  To explain in more detail, from Monday to Friday, there is a similarity in traffic fluctuations during the day, and during the day, the number of times that sudden braking occurs more or less is between days of the week. Tend to be the same. In other words, for Monday through Friday, the occurrence of sudden braking in a day can be expressed in an aggregated manner.

  Similarly, on Saturdays and Sundays, even if there are variations in the number of occurrences of sudden braking from day to day, changes in the degree of occurrence of sudden braking in a day are often similar between days of the week. In other words, for Saturdays and Sundays, the degree of occurrence of sudden braking in a day can be expressed collectively.

  For this reason, the predetermined time segment may be “day of the week segment”, and the day of the week segment may be “Monday through Friday” and “Saturday and Sunday”. In this case, the information processing apparatus 101 generates a graph that collectively shows the degree of occurrence of sudden braking in a day for the pair from Monday to Friday. In addition, the information processing apparatus 101 generates a graph that collectively shows the degree of occurrence of sudden braking in a day for a pair of Saturday and Sunday.

  However, depending on the location, the transition of the occurrence of sudden braking in a day on Saturdays and Sundays may be different from any day of the week.

  Therefore, the day of the week classification may be “Monday to Friday”, “Saturday”, and “Sunday”. In this case, the information processing apparatus 101 generates a graph that collectively shows the degree of occurrence of sudden braking in a day for the pair from Monday to Friday. Further, the information processing apparatus 101 generates a graph indicating the degree of occurrence of sudden braking in one day for each of Saturday and Sunday.

  Further, depending on the location, even if the number of occurrences of sudden braking varies from day to day from Monday to Saturday, the transition of the degree of occurrence of sudden braking in a day may be similar between days of the week.

  For this reason, the day of the week classification may be “Monday through Saturday” and “Sunday”. In this case, the information processing apparatus 101 generates a graph that collectively shows the occurrence of sudden braking in a day for the pair from Monday to Saturday. Further, the information processing apparatus 101 generates a graph indicating the degree of occurrence of sudden braking in a day for Sunday.

  In addition, the transition of the degree of occurrence of sudden braking in a day is often similar on holidays and holidays. For this reason, “holiday” or “holiday” may be further included as the day of the week classification.

  Furthermore, depending on the location, if the season is the same, the transition of the degree of occurrence of sudden braking in a day may be similar. For example, in cold regions, roads tend to freeze and snow cover during winter, and the transition of the degree of occurrence of sudden braking in a day is often similar.

  In other words, in the same season, it is possible to collectively express the occurrence of sudden braking in a day. Therefore, the predetermined time segment may be “seasonal segment”, and the season segment may be “spring”, “summer”, “autumn”, and “winter”. The season classification may be at least one of “spring”, “summer”, “autumn”, and “winter”.

  Further, depending on the location, the transition of the degree of occurrence of sudden braking in a day may be similar during a specific period of the year. For example, in tourist spots, during large holidays such as Golden Week, traffic tends to increase with the increase in tourists, and the transition of the occurrence of sudden braking in a day is often similar. . For this reason, the predetermined time segment may be “a specific period of the year”.

  Furthermore, the transition of the degree of occurrence of sudden braking in a day may be similar on a specific day of each month. For example, the 50th day of each month is a day for payment or delivery, and there is a tendency for traffic volume to increase, and the transition of the occurrence of sudden braking in a day is similar. There are many. Therefore, the predetermined time segment may be “a specific day of each month”.

  In addition, the graph showing the degree of occurrence of a predetermined driving operation in a day shows the relationship between the degree of occurrence of the predetermined driving operation and the time and time zone of the day in a graphic form. For example, the graph may display 24 hours in a square shape, or may display 24 hours in a circular shape.

  In the example of FIG. 1, a predetermined driving operation is defined as “sudden braking”, and a place where the predetermined driving operation is performed is defined as “a point X where sudden braking occurs frequently”. Further, the predetermined time segment is “day segment”, and the day segment is “Monday to Friday” and “Saturday and Sunday”.

  In this case, the information processing apparatus 101 generates a graph 110 and a graph 120 for each day of the week division for the point X where frequent sudden braking occurs. Here, the graph 110 shows the degree of occurrence of sudden braking in one day in the group from Monday to Friday. The graph 120 shows the degree of occurrence of sudden braking in one day in a pair of Saturday and Sunday.

  Specifically, the graphs 110 and 120 show the degree of occurrence of sudden braking in each time zone in which 24 hours are displayed in a circle and 24 hours are divided at 6 hour intervals with 0:00 as a reference. Further, in the graph 110, “the time zone from 6 o'clock to 11 o'clock” and “the time zone from 18 o'clock to 23 o'clock”, which have a high frequency of sudden braking, are filled. In the graph 120, “a time period from 18:00 to 23:00”, which has a high frequency of sudden braking, is filled.

  (2) The information processing apparatus 101 outputs the generated graph in association with a map indicating a place where a predetermined driving operation is performed. Here, the map indicating the place where the predetermined driving operation is performed is drawing data in which a predetermined area including the place where the predetermined driving operation is performed on the surface of the earth is reduced and expressed on a plane at a certain rate. .

  In the example of FIG. 1, the information processing apparatus 101 outputs the generated graphs 110 and 120 in association with a map 130 indicating a point X where frequent sudden braking occurs. The map 130 is obtained by reducing an area within a predetermined range from the point X at a certain rate and displaying it on a plane.

  As described above, according to the information processing apparatus 101 according to the embodiment, for a place where the predetermined driving operation is performed, the occurrence degree of the predetermined driving operation in one day in each of the predetermined time segments is graphed, It can be output in association with a map showing the place.

  As a result, it is possible to visually present the occurrence of a predetermined driving operation for each time segment of a certain place in an easy-to-understand manner, and the driver's risk prediction can be supported. For example, according to the graph 110, for the point X shown in the map 130 (in the vicinity of the XX city XX intersection), the time zone from 6 o'clock to 11 o'clock and from 18 o'clock to 23 o'clock from Monday to Friday It can be intuitively determined that the frequency of sudden braking in the time zone of the table is high. In addition, according to the graph 120, the driver has a high frequency of sudden braking at the time zone from 18:00 to 23:00 on Saturday and Sunday at the point X (near the intersection of XX city XX) shown on the map 130. Can be determined intuitively.

  In the above description, the place where the predetermined driving operation is performed is described as an example, but the present invention is not limited to this. For example, the information processing apparatus 101 graphs the degree of occurrence of an accident in one day in each predetermined time segment for a place where accidents frequently occur, and outputs the graph in association with a map indicating the place. Also good. As a result, it is possible to present the situation of occurrence of an accident for each time segment of a place where accidents occur frequently in an easily understandable manner. The location where accidents occur frequently and the degree of accident occurrence can be specified based on, for example, accident information obtained from a fire department or a police station.

(System configuration example of system 200)
Next, a configuration example of the system 200 according to the embodiment will be described. In the following description, the information processing apparatus 101 shown in FIG.

  FIG. 2 is an explanatory diagram illustrating a system configuration example of the system 200. In FIG. 2, a system 200 is configured to include an analysis data generation device 201 and a plurality of terminal devices 202. In the system 200, the analysis data generation device 201 and the plurality of terminal devices 202 are connected via a wired or wireless network 210. Examples of the network 210 include a mobile communication network, the Internet, a LAN (Local Area Network), and a WAN (Wide Area Network).

  The analysis data generation device 201 includes a probe data DB (database) 220, a sudden brake data DB 230, and a map DB 240. The probe data DB 220 stores probe data obtained from the terminal device 202. A specific example of the probe data will be described later with reference to FIG.

  The sudden brake data DB 230 stores sudden brake data. The contents stored in the sudden brake data DB 230 will be described later with reference to FIG. The map DB 240 stores a map. A map is drawing data in which the surface of the earth is reduced at a certain rate and represented on a plane. The probe data DB 220, the sudden brake data DB 230, and the map DB 240 are realized by a storage device such as the memory 302 and the disk 304 of the analysis data generation device 201 shown in FIG.

  The terminal device 202 is a computer that is mounted on the vehicle C and generates probe data including information such as speed, position, acceleration, and angular velocity of the vehicle C and various operation information. Specifically, for example, the terminal device 202 generates probe data of the vehicle C periodically (for example, at intervals of 1 second). Then, the terminal device 202 transmits the generated probe data to the collection device (for example, the analysis data generation device 201) at a predetermined time interval (for example, every 7 minutes).

(Hardware configuration example of analysis data generation apparatus 201)
FIG. 3 is a block diagram illustrating a hardware configuration example of the analysis data generation apparatus 201. In FIG. 3, the analysis data generation apparatus 201 includes a CPU (Central Processing Unit) 301, a memory 302, a disk drive 303, a disk 304, an I / F (Interface) 305, a display 306, and an input device 307. And a printer 308. Each component is connected by a bus 300.

  Here, the CPU 301 governs overall control of the analysis data generation apparatus 201. The memory 302 includes, for example, a ROM (Read Only Memory), a RAM (Random Access Memory), and a flash ROM. Specifically, for example, a flash ROM or ROM stores various programs, and a RAM is used as a work area for the CPU 301. The program stored in the memory 302 is loaded into the CPU 301 to cause the CPU 301 to execute the coded process.

  The disk drive 303 controls reading / writing of data with respect to the disk 304 according to the control of the CPU 301. The disk 304 stores data written under the control of the disk drive 303. Examples of the disk 304 include a magnetic disk and an optical disk.

  The I / F 305 is connected to the network 210 through a communication line, and is connected to another device (for example, the terminal device 202 illustrated in FIG. 2) via the network 210. The I / F 305 controls an internal interface with the network 210 and controls input / output of data from other devices. For example, a modem or a LAN adapter may be employed as the I / F 305.

  A display 306 displays data such as a document, an image, and function information as well as a cursor, an icon, or a tool box. As the display 306, for example, a CRT (Cathode Ray Tube), a liquid crystal display, an organic EL (Electroluminescence) display, or the like can be employed.

  The input device 307 has keys for inputting characters, numbers, various instructions, and the like, and inputs data. The input device 307 may be a keyboard or a mouse, or may be a touch panel type input pad or a numeric keypad.

  The printer 308 prints image data and document data. As the printer 308, for example, a laser printer or an ink jet printer can be employed.

  Note that the analysis data generation device 201 may not include the display 306, the input device 307, the printer 308, and the like among the above-described components. The analysis data generation apparatus 201 may include, for example, an SSD (Solid State Drive), a scanner, and the like in addition to the above-described components.

(Hardware configuration example of terminal device 202)
FIG. 4 is a block diagram illustrating a hardware configuration example of the terminal device 202. In FIG. 4, the terminal device 202 includes a CPU 401, a memory 402, an I / F 403, a GPS (Global Positioning System) unit 404, and various sensors 405. Each component is connected by a bus 400.

  Here, the CPU 401 governs overall control of the terminal device 202. The memory 402 includes, for example, a ROM, a RAM, a flash ROM, and the like. Specifically, for example, a flash ROM or ROM stores various programs, and the RAM is used as a work area of the CPU 401. The program stored in the memory 402 is loaded on the CPU 401 to cause the CPU 401 to execute the coded process.

  The I / F 403 is connected to the network 210 via a communication line, and is connected to another device (for example, the analysis data generation device 201 illustrated in FIG. 2) via the network 210. The I / F 403 controls an internal interface with the network 210 and controls input / output of data from other devices.

  The GPS unit 404 receives radio waves from GPS satellites and outputs position information indicating the position of the vehicle C. The position information of the vehicle C is information specifying one point on the earth such as latitude / longitude and altitude. The vehicle position information may be corrected using output values of various sensors 405.

  Various sensors 405 output information (for example, date, speed, acceleration, angular velocity, etc.) for determining the position and behavior of the vehicle C. The output values of the various sensors 405 are used for the correction of the position of the vehicle C (terminal device 202) by the CPU 401 and the measurement of the amount of change in speed and direction.

  The various sensors 405 may include, for example, a sensor that detects each operation of the vehicle C by the driver. Specifically, for example, the various sensors 405 detect steering wheel operation, hazard lamp operation, horn operation, winker operation, opening / closing operation of the door of the vehicle C, ON / OFF operation of the engine of the vehicle C, and the like. Good.

(Specific example of probe data)
Next, a specific example of probe data obtained from the terminal device 202 will be described.

  FIG. 5 is an explanatory diagram showing a specific example of probe data. In FIG. 5, the probe data 500 includes a vehicle ID, a date and time, a position, and a speed. Here, the vehicle ID is an identifier for identifying the vehicle C on which the terminal device 202 is mounted. The date and time is the date and time when the position information of the vehicle C (terminal device 202) is acquired by the GPS unit 404 (see FIG. 4).

  The position is the position of the vehicle C specified from the position information acquired by the GPS unit 404. Here, the position is represented by latitude and longitude. The speed is the speed of the vehicle C at the position of the vehicle C specified from the position information acquired by the GPS unit 404.

  In addition to the information described above, the probe data 500 includes, for example, the acceleration, angular velocity, and operation of the vehicle C performed by the driver (for example, steering wheel operation, hazard lamp operation, horn operation, blinker operation, etc.) ) Operation information may be included.

(Contents stored in the sudden brake data DB 230)
FIG. 6 is an explanatory diagram showing an example of the contents stored in the sudden brake data DB 230. In FIG. 6, the sudden brake data DB 230 has fields of vehicle ID, date / time, position, speed, and traveling direction, and by setting information in each field, sudden brake data (for example, sudden brake data 600-1, 600-2) is stored as a record.

  Here, the vehicle ID is an identifier for identifying the vehicle C on which the terminal device 202 is mounted. The date and time is the date and time when the vehicle C suddenly braked. The position is the position of the point where sudden braking has occurred in the vehicle C. The speed is a speed when a sudden brake is generated in the vehicle C. The traveling direction is the traveling direction when sudden braking occurs in the vehicle C.

  In addition, although mentioned later for details, the information of each field in the sudden brake data DB230 is specified based on the probe data from the terminal device 202 mounted in the vehicle C.

(Functional configuration example of the analysis data generation device 201)
FIG. 7 is a block diagram illustrating a functional configuration example of the analysis data generation apparatus 201. In FIG. 7, the analysis data generation apparatus 201 includes an acquisition unit 701, a reception unit 702, an extraction unit 703, a specification unit 704, a statistics unit 705, a generation unit 706, and an output unit 707. is there. The acquisition unit 701 to the output unit 707 are functions serving as control units. Specifically, for example, by causing the CPU 301 to execute a program stored in a storage device such as the memory 302 and the disk 304 illustrated in FIG. Alternatively, the function is realized by the I / F 305. The processing result of each functional unit is stored in a storage device such as the memory 302 and the disk 304, for example.

  The acquisition unit 701 acquires probe data for a plurality of vehicles C. Here, the probe data is travel data obtained from the terminal device 202 mounted on the vehicle C, for example, the probe data 500 shown in FIG. Specifically, for example, the acquisition unit 701 acquires probe data of a plurality of vehicles C by receiving probe data from the terminal device 202 mounted on each vehicle C.

  For example, the acquisition unit 701 may acquire probe data for a plurality of vehicles C from another device that collects probe data for the plurality of vehicles C. Further, for example, the acquisition unit 701 may acquire probe data of a plurality of vehicles C by a user operation input using the input device 307 illustrated in FIG. The acquired probe data is stored in, for example, the probe data DB 220 (see FIG. 2).

  The accepting unit 702 accepts designation of a target area. Here, the target area is an area to be analyzed for probe data. The target area is specified, for example, by country, prefecture, or city. Further, the target area may be any area designated on the map, for example.

  Specifically, for example, the accepting unit 702 accepts designation of a target area by a user operation input using the input device 307. In addition, for example, the reception unit 702 may receive designation of a target area from another device.

  The extraction unit 703 extracts probe data related to a predetermined driving operation from the probe data DB 220. Here, the predetermined driving operation is an operation performed for avoiding danger, for example, sudden braking, sudden steering, lighting of a hazard lamp, use of a horn, and the like.

  In the following description, “sudden braking” will be described as an example of the predetermined driving operation.

  Specifically, for example, the extraction unit 703 extracts the probe data of the designated target area from the probe data DB 220. Next, the extraction unit 703 searches the probe data of the target area for the first and second probe data of the same vehicle that is continuous in time series. However, the date and time indicated by the first probe data is assumed to be earlier than the date and time indicated by the second probe data.

  Then, the extraction unit 703 determines whether or not a deceleration exceeding a predetermined speed α per second has occurred based on the speeds of the first and second probe data. The predetermined speed α is set to a value that can be determined that sudden braking has occurred in the vehicle C, for example, about 10 km if the vehicle decelerates at a predetermined speed α or more per second.

  Here, if the deceleration more than the predetermined speed (alpha) has generate | occur | produced per second, the extraction part 703 will calculate the advancing direction of the vehicle C based on the position of 1st and 2nd probe data. Here, as the traveling direction of the vehicle C, the direction of “north”, “northeast”, “east”, “southeast”, “south”, “southwest”, “west”, “northwest” is calculated. Is assumed.

  Then, the extraction unit 703 associates the second probe data (or first probe data) with the calculated traveling direction and stores them in the sudden brake data DB 230 (see FIG. 6). Thereby, the date, time, position, speed, and traveling direction when sudden braking occurs in the vehicle C can be stored as sudden braking data.

  When the predetermined driving operation includes “steep steering wheel”, “lighting of a hazard lamp”, “use of a horn”, and the like, the extraction unit 703, for example, based on operation information included in probe data , Probe data relating to driving operations such as “steep steering wheel”, “lighting of hazard lamp”, “use of horn”, and the like can be extracted.

  The identifying unit 704 identifies a point where sudden braking (predetermined driving operation) occurs frequently. Specifically, for example, the identifying unit 704 refers to the sudden brake data DB 230 and identifies an area where sudden braking frequently occurs from a plurality of areas divided by dividing the target area. Each region is, for example, a rectangular region having a length × width of 30 [m] × 70 [m].

  In the following description, a plurality of regions divided by dividing the target area are denoted as “regions R1 to Rn” (n: a natural number of 2 or more), and any region among the regions R1 to Rn is denoted as “region Ri”. In some cases (i = 1, 2,..., N).

  Specifically, for example, the identifying unit 704 first extracts the sudden brake data of the region Ri from the sudden brake data DB 230. The sudden brake data in the region Ri is that in which the position indicated by the sudden brake data is in the region Ri. Next, the specifying unit 704 calculates the number of occurrences of sudden braking Pi by counting the number of extracted sudden braking data.

  Further, the identifying unit 704 calculates the number of sudden brakes Qi by counting the number of sudden brake data with different vehicle IDs in the extracted sudden brake data. Then, the specifying unit 704 determines whether or not the region Ri is a sudden braking frequent occurrence point based on the calculated number of occurrences Pi and the number of occurrences Qi.

  More specifically, for example, the specifying unit 704 determines that the region Ri is a sudden braking frequent occurrence point if the number of occurrences Pi is equal to or greater than β times the number of occurrences Qi (for example, β = 5). As a result, the sudden braking is not frequent in the specific vehicle C (for example, the vehicle C having a low driving ability of the driver), but the region Ri where the sudden braking is frequently occurring in the plurality of vehicles C is suddenly braked. It can be identified as a frequent occurrence point.

  In the above description, the specifying unit 704 determines whether or not the region Ri is a sudden braking frequent occurrence point based on the number of occurrences Pi and the number of occurrences Qi, but is not limited thereto. For example, the specifying unit 704 may determine that the region Ri is a sudden braking frequent occurrence point if the number of occurrences Pi is equal to or greater than a predetermined number. Further, for example, the specifying unit 704 may accept designation of a region Ri in which sudden braking frequently occurs by a user operation input using the input device 307.

  In the following description, the region Ri that is identified as a point where sudden braking frequently occurs may be referred to as “severe braking frequent point Ri”.

  The statistics unit 705 calculates the number of occurrences of sudden braking at each day of the week / time zone of the sudden braking frequent occurrence point Ri. Specifically, for example, the statistics unit 705 classifies the data into the sudden brake data for each day of the week from Sunday to Saturday based on the date and time of the sudden brake data at the sudden braking frequent occurrence point Ri. Next, the statistics unit 705 classifies the sudden brake data classified by day of the week into sudden brake data classified by time of day.

  The time zone for classifying the sudden brake data can be arbitrarily set. Here, four time zones (24 hours divided by 6 hours from 0 o'clock) (time zone from 0 o'clock to 5 o'clock, time zone from 6 o'clock to 11 o'clock, and 12 o'clock to 17 o'clock) The time zone of the table, the time zone from 18:00 to 23:00) will be described as an example.

  Then, the statistics unit 705 calculates the number of occurrences of sudden braking for each day of the week / time of the sudden braking frequent occurrence point Ri by counting the classified sudden braking data for each day of the week / time of day. The calculated number of occurrences of sudden braking for each day of the week / time zone at the sudden braking frequent occurrence point Ri is stored in, for example, the sudden braking occurrence frequency table 800 for each day of the week / time zone shown in FIG.

  The sudden braking occurrence frequency table 800 for each day of the week / time zone is realized by a storage device such as the memory 302 and the disk 304, for example. Here, the contents stored in the sudden braking occurrence frequency table 800 for each day of the week and each time zone will be described.

  FIG. 8 is an explanatory diagram showing an example of the contents stored in the sudden braking occurrence frequency table 800 for each day of the week and for each time zone. In FIG. 8, a sudden brake occurrence frequency table 800 for each day of the week and for each time zone includes occurrence frequency data (for example, occurrence frequency data 800- 1,800-2) is stored.

  Here, the region ID is an identifier for identifying a region Ri that is a sudden braking frequent occurrence point. For example, the occurrence count data 800-1 indicates the number of occurrences of sudden braking by day of the week / time zone of the sudden braking frequent occurrence point R1. As an example, taking the time zone from 0 o'clock to 5 o'clock on Sunday as an example, the number of occurrences of sudden braking at the sudden braking frequent occurrence point R1 is “1”.

  Returning to the description of FIG. 7, the statistics unit 705 may calculate the number of occurrences of sudden braking for each traveling direction of the sudden braking frequent occurrence point Ri. Specifically, for example, the statistical unit 705 performs sudden braking according to traveling directions of north, northeast, east, southeast, south, southwest, west, and northwest based on the traveling direction of the sudden braking data at the sudden braking frequent point Ri. Classify into data.

  Then, the statistical unit 705 calculates the number of occurrences of sudden braking for each traveling direction of the sudden braking frequent occurrence point Ri by counting the classified sudden braking data for each traveling direction. The calculated number of occurrences of sudden braking for each traveling direction at the sudden braking frequent occurrence point Ri is stored, for example, in a sudden braking occurrence number table 900 for each traveling direction shown in FIG.

  The sudden braking occurrence frequency table 900 for each traveling direction is realized by a storage device such as the memory 302 and the disk 304, for example. Here, the contents stored in the sudden braking occurrence count table 900 for each traveling direction will be described.

  FIG. 9 is an explanatory diagram showing an example of the stored contents of the sudden braking occurrence frequency table 900 for each traveling direction. In FIG. 9, the sudden braking occurrence count table 900 for each traveling direction is generated number data indicating the number of sudden braking occurrences for each traveling direction of the sudden braking frequent point Ri (for example, occurrence number data 900-1 and 900-2). Remember.

  For example, the occurrence frequency data 900-1 indicates the number of occurrences of sudden braking in the traveling direction of north, northeast, east, southeast, south, southwest, west, and northwest of the sudden braking frequent occurrence point R1. As an example, taking the traveling direction “north” as an example, the number of occurrences of sudden braking at the sudden braking frequent occurrence point R1 is “0”.

  Returning to the description of FIG. 7, the generation unit 706 generates a graph indicating the occurrence of sudden braking in one day in each of the predetermined day of week divisions for the sudden braking frequent occurrence point Ri. Specifically, for example, the generation unit 706 generates, for a predetermined day of the week, the number of occurrences of sudden braking in a plurality of time zones obtained by dividing a day (24 hours) at a predetermined time interval (for example, 6 hours). Produces a graph showing highlighted time zones with relatively high.

  Here, an example of generating a graph indicating the occurrence of sudden braking in one day will be described with reference to FIG. Here, the sudden brake frequent occurrence point Ri is set as “sudden brake frequent occurrence point R1”, and the predetermined day of the week classification is “Monday to Friday group”, “Saturday” and “Sunday”.

  FIG. 10 is an explanatory diagram of an example of generating a graph. In FIG. 10, (10-1) the generation unit 706 reads out templates 1001 to 1003 corresponding to “Monday to Friday”, “Saturday”, and “Sunday”, respectively. Graphs 1001 to 1003 are pie charts in which 24 hours are displayed in a circle, and 24 hours are divided at intervals of 6 hours on the basis of 0:00.

  At this point, the graphs 1001 to 1003 are not decorated. In addition, graph models (for example, models of graphs 1001 to 1003) are stored in a storage device such as the memory 302 and the disk 304, for example.

  (10-2) The generating unit 706 refers to the sudden braking occurrence frequency table 800 (see FIG. 8) for each day of the week and for each time zone, and determines the day of the week and the time zone where the number of sudden braking occurrences is the first (maximum) Identify. Here, it is assumed that “Monday from 6 o'clock to 11 o'clock” is specified as the day of the week / time zone in which the number of occurrences of sudden braking is the first.

  In addition, when there are a plurality of day / time zones in which the number of occurrences of sudden braking is the highest, the generation unit 706 identifies the plurality of days / time zones as days of the week / time zones in which the number of occurrences of sudden braking is first. You may decide to do it.

  Next, the generating unit 706 determines whether the identified “Monday from 6 o'clock to 11 o'clock” belongs to “Day group from Monday to Friday”, “Saturday”, and “Sunday”. Here, the generation unit 706 determines that the identified “Monday from 6 o'clock to 11 o'clock” belongs to “Monday to Friday”.

  Then, the generation unit 706 performs predetermined decoration on the corresponding time zone of the graph corresponding to the determined day of the week according to the time zone in which the number of sudden braking occurrences is the first. Specifically, for example, the generation unit 706 fills the corresponding time zone “from 6 o'clock to 11 o'clock” of the graph 1001 corresponding to the determined “Monday to Friday group”.

  Here, the generation unit 706 fills the corresponding time zone “from 6 o'clock to 11 o'clock” in the graph 1001, but is not limited thereto. For example, the generation unit 706 may shade the corresponding time zone “from 6 o'clock to 11 o'clock” in the graph 1001. Further, for example, the generation unit 706 may highlight the number of occurrences of sudden braking in the corresponding time zone “from 6 o'clock to 11 o'clock” in the graph 1001.

  (10-3) The generation unit 706 refers to the sudden braking occurrence count table 800 for each day of the week and for each time zone, and identifies the day of the week / time zone in which the number of sudden braking occurrences is second. Here, it is assumed that “Monday from 12 o'clock to 17 o'clock” is specified as the day of the week / time zone in which the number of occurrences of sudden braking is second.

  Next, the generation unit 706 determines whether the specified “Monday from 12:00 to 17:00” belongs to “Day group from Monday to Friday”, “Saturday”, or “Sunday”. Here, the generation unit 706 determines that the identified “Monday from 12:00 to 17:00” belongs to “Monday to Friday”.

  Then, the generation unit 706 performs predetermined decoration on the corresponding time zone of the graph corresponding to the determined day segment according to the time zone in which the number of occurrences of sudden braking is second. Specifically, for example, the generation unit 706 fills the corresponding time zone “12:00 to 17:00” of the graph 1001 corresponding to the determined “Monday to Friday group”.

  (10-4) The generation unit 706 refers to the sudden braking occurrence count table 800 for each day of the week / time zone, and identifies the day of the week / time zone in which the number of sudden braking occurrences is third. Here, it is assumed that “Saturday from 6 o'clock to 11 o'clock” is specified as the day of the week / time zone in which the number of occurrences of sudden braking is third.

  Next, the generation unit 706 determines whether the identified “Saturday from 6 o'clock to 11 o'clock” belongs to “Day group from Monday to Friday”, “Saturday”, or “Sunday”. Here, the generation unit 706 determines that the identified “Saturday 6 o'clock to 11 o'clock” belongs to “Saturday”.

  Then, the generation unit 706 performs predetermined decoration on the corresponding time zone of the graph corresponding to the determined day segment according to the time zone in which the number of occurrences of sudden braking is third. Specifically, for example, the generation unit 706 fills the corresponding time zone “from 6 o'clock to 11 o'clock” in the graph 1002 corresponding to the determined “Saturday”.

  Thereby, the time slot | zone corresponding to each graph 1001-1003 can be filled according to the time slot | zone with a high occurrence frequency of sudden braking.

  Note that the generation unit 706 sets the color (or shading) for painting the top third day of the week / time zone with the most frequent occurrences of sudden braking to a different color (for example, the darker the higher the order). You may decide to do it. Here, the top third day of the week / time zone in which the number of occurrences of sudden braking is large is specified. However, it is possible to arbitrarily set the top day of the week / time zone. In addition, here, the number of occurrences of sudden braking is relatively evaluated, but the present invention is not limited to this. For example, the generation unit 706 may specify a day / time period that is equal to or greater than a predetermined threshold as a day / time period in which the number of occurrences of sudden braking is large.

  Returning to the description of FIG. 7, the generation unit 706 acquires a map of the target area from the map DB 240 (see FIG. 2). And the production | generation part 706 carries out predetermined decoration in the location corresponding to the sudden brake frequent occurrence point Ri on the map of the acquired object area. Specifically, for example, the generation unit 706 may place an icon indicating that sudden braking is frequently occurring at a location corresponding to the sudden braking frequent point Ri on the map of the target area.

  Here, with reference to FIG. 11, a decoration example for a portion corresponding to the sudden braking frequent occurrence point Ri will be described. Here, the target area is “XX prefecture”, and among the areas R1 to Rn divided by dividing the target area “XX prefecture”, there are four areas R1, R5, R10, and R20 as frequent braking points. Suppose that it was identified.

  FIG. 11 is an explanatory diagram illustrating a decoration example for a portion corresponding to the sudden braking frequent occurrence point Ri. In FIG. 11, first, the generation unit 706 acquires a map 1101 of the target area “XX prefecture” from the map DB 240. In FIG. 11, a part of the target area “XX prefecture” is extracted and displayed.

  Then, the generation unit 706 arranges an icon indicating that sudden braking is frequently occurring at locations corresponding to the sudden braking frequent occurrence points R1, R5, R10, and R20 on the map 1101 of the target area “XX prefecture”. . In the example of FIG. 11, frames 1111 to 1114 are arranged at locations corresponding to the sudden braking frequent occurrence points R1, R5, R10, and R20.

  The number “1” in FIG. 11 corresponds to the sudden braking frequent occurrence point R1. Further, the number “2” in FIG. 11 corresponds to the sudden braking frequent occurrence point R5. Further, the number “3” in FIG. 11 corresponds to the sudden braking frequent occurrence point R10. Further, the number “4” in FIG. 11 corresponds to the sudden braking frequent occurrence point R20.

  Frames 1111 to 1114 are icons indicating that sudden braking is frequently occurring. For example, the frames 1111 to 1114 are arranged such that the centers of the frames 1111 to 1114 coincide with the centers of the sudden braking frequent occurrence points R1, R5, R10, and R20. The sizes of the frames 1111 to 1114 can be arbitrarily set.

  As a result, the decoration corresponding to the sudden braking frequent occurrence points R1, R5, R10, and R20 on the map 1101 of the target area “XX prefecture” can be identified as a place where sudden braking frequently occurs. Can be applied.

  Returning to the description of FIG. 7, the generation unit 706 may specify the sudden braking occurrence direction of the sudden braking frequent occurrence point Ri with reference to the sudden braking occurrence count table 900 (see FIG. 9) for each traveling direction. . Here, the sudden braking direction is the traveling direction of the vehicle C when sudden braking occurs.

  For example, the sudden brake frequent occurrence point Ri is set as “the sudden brake frequent occurrence point R1”. In this case, for example, the generation unit 706 refers to the sudden braking occurrence count table 900 for each traveling direction, and determines the traveling direction “west” where the number of sudden braking occurrences at the sudden braking frequent occurrence point R1 is the largest. You may decide to specify as the sudden brake generation direction of the point R1.

  As a result, the traveling direction in which the most sudden braking occurs at the sudden braking frequent point R1 can be specified as the sudden braking occurrence direction at the sudden braking frequent point R1.

  Further, the generation unit 706 may arrange an icon indicating the specified sudden brake occurrence direction at a location corresponding to the sudden braking frequent occurrence point Ri on the map of the target area.

  Here, with reference to FIG. 12, an example of the arrangement of icons indicating the sudden braking occurrence direction will be described. Here, taking an example of the sudden braking frequent occurrence point R1 in the target area “○ × prefecture”, an example of the arrangement of icons indicating the sudden braking occurrence direction will be described.

  FIG. 12 is an explanatory diagram illustrating an arrangement example of icons indicating the sudden braking occurrence direction. In FIG. 12, the (12-1) generation unit 706 extracts a map of a predetermined range including the sudden braking frequent occurrence point R1 from the map of the target area “XX prefecture” (for example, the map 1101 shown in FIG. 11). .

  Here, the predetermined range can be arbitrarily set. For example, the vertical × horizontal is set to a range of about 200 [m] × 100 [m]. In the following description, the predetermined range including the sudden braking frequent occurrence point Ri may be referred to as “sudden braking frequent occurrence area Ai”. In the example of FIG. 12, a map 1201 of the sudden braking frequent occurrence area A1 including the sudden braking frequent occurrence point R1 is extracted.

  (12-2) The generation unit 706 arranges an icon 1210 indicating the sudden braking occurrence direction “west” at a location corresponding to the sudden braking frequent occurrence point R1 on the map 1201 of the sudden braking frequent occurrence area A1. An icon 1210 is a west-facing arrow that is the direction of sudden braking. Note that the arrangement position of the icon 1210 can be arbitrarily set, and is set to any position within the frame 1111 (for example, the center, the upper center, and the lower center), for example. In the example of FIG. 12, the icon 1210 is arranged at the upper center in the frame 1111.

  As a result, the icon 1210 indicating the traveling direction in which the most sudden braking is occurring at the sudden braking frequent point R1 can be displayed at the location corresponding to the sudden braking frequent point R1 on the map 1201 of the sudden braking frequent area A1. it can.

  Returning to the description of FIG. 7, the output unit 707 outputs the generated graph in association with a map indicating a place where sudden braking (predetermined driving operation) is performed. Specifically, for example, the output unit 707 indicates a sudden braking frequent occurrence point Ri, a graph generated for the sudden braking frequent occurrence point Ri and indicating the daily occurrence of sudden braking in each of the predetermined day-of-week segments. Output in association with the map.

  The output format of the output unit 707 includes, for example, storage in a storage device such as the memory 302 and the disk 304, display on the display 306, print output to the printer 308, transmission to an external device via the I / F 305, and the like.

  More specifically, for example, the output unit 707 displays the graphs 1001 to 1003 (see (10-4) in FIG. 10) generated for the sudden braking frequent occurrence point R1 and the map 1201 (see FIG. 12) of the sudden braking frequent occurrence area A1. (See (12-2)) may be displayed on the display 306.

  Here, a screen example of an analysis data screen that displays a graph and a map for the sudden braking frequent occurrence point Ri in association with each other will be described with reference to FIG.

  FIG. 13 is an explanatory diagram illustrating a screen example of the analysis data screen. In FIG. 13, the analysis data screen 1300 includes graphs 1001 to 1003 generated for the sudden braking frequent occurrence point R1 (Δ △ City eastern part 5 (near the west intersection)), and a map 1201 of the sudden braking frequent occurrence area A1. This is a screen for displaying in association with each other.

  When the frame 1111 is selected on the analysis data screen 1300 by a user operation input using the input device 307, the arrangement position of the frame 1111 can be adjusted. When the icon 1210 is selected on the analysis data screen 1300, the arrangement position and orientation of the icon 1210 can be adjusted.

  In the example of FIG. 13, the arrangement position and orientation of the icon 1210 are adjusted to a different arrangement position and orientation from the example shown in FIG. In this way, by adjusting the arrangement position and orientation of the icon 1210 in accordance with the position and shape of the road on the map 1201, the sudden braking direction can be displayed more easily.

  Returning to the description of FIG. 7, the output unit 707 outputs a map corresponding to the target area, one or a plurality of places identified as sudden braking frequent occurrence points, and each graph corresponding to the generated one or more places. You may decide to output the analysis data containing.

  Specifically, for example, the output unit 707 displays a map corresponding to the target area “XX prefecture”, sudden braking frequent occurrence points R1, R5, R10, R20, and sudden braking frequent occurrence points R1, R5, R10, R20. Analysis data including each corresponding graph may be printed out to the printer 308.

  Thereby, for example, in the target area “XX prefecture”, it is possible to generate a poster or a leaflet indicating a point where sudden braking frequently occurs.

  FIG. 14 is an explanatory diagram showing a specific example of a poster. In FIG. 14, a poster 1400 indicates a point where sudden braking frequently occurs in the XX prefecture (target area). The poster 1400 displays a map 1410 corresponding to the target area “XX prefecture” and an enlarged central part 1420 showing the central part CP of the target area “XX prefecture” in an enlarged manner.

  The poster 1400 also includes graphs corresponding to the sudden brake frequent occurrence points R1, R5, R10, and R20, and the sudden brake frequent occurrence areas A1, A5, A10, including the sudden brake frequent occurrence points R1, R5, R10, and R20. A map of A20 is displayed in association with each other.

  For the sake of illustration, FIG. 14 shows graphs 1431 to 1433 (corresponding to graphs 1001 to 1003 shown in FIG. 13) corresponding to sudden braking frequent occurrence point R1 and map 1440 of sudden braking frequent occurrence area A1 (shown in FIG. 13). Corresponding to the map 1201).

  According to poster 1400, it is possible to visually indicate the occurrence of sudden braking for each day of the week at points where sudden braking frequently occurs in the prefecture, and support the driver's risk prediction. Can do.

  For example, according to the graph 1431, the driver has a sudden change in the time zone from 6:00 to 11:00 and from 12:00 to 17:00 in the vicinity of XX West intersection shown on the map 1440. It can be intuitively determined that the occurrence frequency of the brake is high. Further, according to the graph 1432, the driver intuitively determines that the frequency of sudden braking is high in the time zone from 6 o'clock to 11 o'clock on Saturday around the XX west intersection indicated by the map 1440. be able to.

  In addition, on the upper portion of the map 1440, the number “1” written at a location corresponding to the sudden braking frequent occurrence point R1 on the enlarged central portion 1420 is written. For this reason, the driver can easily grasp which position on the enlarged central portion 1420 corresponds to the location indicated by the map 1440.

  In addition, by presenting the poster 1400 in an office such as a transportation company or a break room, it can be used for in-house safety guidance, education, operation planning, and the like. In addition, by presenting the poster 1400 in a service area on an expressway, it is possible to support a general driver's risk prediction.

(Analysis data generation processing procedure of the analysis data generation apparatus 201)
Next, the analysis data generation processing procedure of the analysis data generation apparatus 201 will be described.

  FIG. 15 is a flowchart illustrating an example of the analysis data generation processing procedure of the analysis data generation apparatus 201. In the flowchart of FIG. 15, first, the analysis data generation apparatus 201 determines whether designation of a target area has been received (step S1501).

  Here, the analysis data generation device 201 waits to receive the designation of the target area (step S1501: No). When the analysis data generating apparatus 201 receives the designation of the target area (step S1501: Yes), the analysis data generating apparatus 201 acquires a map of the target area from the map DB 240 (step S1502).

  Next, the analysis data generation device 201 executes a sudden brake data extraction process for extracting sudden brake data of the target area (step S1503). A specific processing procedure of the sudden brake data extraction processing will be described later with reference to FIG.

  Next, the analysis data generation device 201 executes a sudden braking frequent occurrence point identification process for identifying sudden braking frequent occurrence points in the target area (step S1504). A specific processing procedure for the sudden braking frequent occurrence point specifying process will be described later with reference to FIG.

  Next, the analysis data generation device 201 selects an unselected sudden braking frequent point Ri among the sudden braking frequent points in the identified target area (step S1505). Then, the analysis data generation device 201 performs statistical processing of the sudden brake data at the sudden brake frequent occurrence point Ri (step S1506). Note that a specific processing procedure of the statistical processing will be described later with reference to FIG.

  Next, the analysis data generation device 201 executes graph generation processing for generating a graph indicating the daily occurrence of sudden braking in each of the predetermined day of the week for the sudden braking frequent occurrence point Ri (step S1507). A specific processing procedure of the graph generation processing will be described later with reference to FIG.

  Next, the analysis data generation device 201 executes a mapping process for performing a predetermined decoration on a location corresponding to the sudden braking frequent occurrence point Ri on the map of the target area (step S1508). A specific processing procedure of the mapping process will be described later with reference to FIG.

  Next, the analysis data generation device 201 executes a matching process for associating the graph of the sudden brake frequent occurrence point Ri with the map of the sudden brake frequent occurrence area Ai including the sudden brake frequent occurrence point Ri (step S1509). A specific processing procedure of the matching process will be described later with reference to FIG.

  Then, the analysis data generating apparatus 201 outputs a graph and a map for the associated sudden braking frequent occurrence point Ri (step S1510). Next, the analysis data generation device 201 determines whether or not there is an unselected sudden braking frequent point among the sudden braking frequent points in the specified target area (step S1511).

  Here, when there is an unselected sudden braking frequent occurrence point (step S1511: Yes), the analysis data generating apparatus 201 returns to step S1505. On the other hand, when there is no unselected sudden braking frequent occurrence point (step S1511: No), the analysis data generation device 201 ends a series of processes according to this flowchart.

  Thereby, the graph and map about the sudden braking frequent occurrence point Ri of a target area can be matched and output.

<Sudden brake data extraction process>
Next, a specific processing procedure of the sudden brake data extraction process shown in step S1503 of FIG. 15 will be described.

  FIG. 16 is a flowchart illustrating an example of a specific processing procedure of the sudden brake data extraction processing. In the flowchart of FIG. 16, first, the analysis data generation device 201 extracts the probe data of the target area from the probe data DB 220 (step S1601).

  Next, the analysis data generation apparatus 201 selects unselected probe data in order from the latest date and time, from the extracted probe data of the target area (step S1602). Then, the analysis data generation device 201 searches the probe data in the target area for the immediately previous probe data having the same vehicle ID as the selected probe data (step S1603).

  Next, the analysis data generation device 201 determines whether or not the previous probe data has been searched (step S1604). Here, when the immediately preceding probe data is not searched (step S1604: No), the analysis data generation device 201 proceeds to step S1608.

  On the other hand, when the previous probe data is searched (step S1604: Yes), the analysis data generating apparatus 201 reduces the speed of the selected probe data by 10 [km / sec] or more from the speed of the previous probe data. It is determined whether or not there is (step S1605). Here, when the vehicle is not decelerated by 10 [km / sec] or more (step S1605: No), the analysis data generating apparatus 201 proceeds to step S1608.

  On the other hand, when the vehicle is decelerated by 10 [km / sec] or more (step S1605: Yes), the analysis data generation device 201 advances the vehicle C based on the position of the selected probe data and the position of the previous probe data. The direction is calculated (step S1606). Then, the analysis data generation device 201 stores the selected probe data and the calculated traveling direction of the vehicle C in the sudden brake data DB 230 as sudden brake data (step S1607).

  Next, the analysis data generation apparatus 201 determines whether there is unselected probe data that has not been selected from the probe data in the target area (step S1608). If there is unselected probe data (step S1608: YES), the analysis data generating apparatus 201 returns to step S1602.

  On the other hand, when there is no unselected probe data (step S1608: No), the analysis data generation device 201 returns to the step that called the sudden brake data extraction process. Thereby, the sudden brake data of the target area can be extracted.

<Sudden brake frequent occurrence point identification processing procedure>
Next, a specific processing procedure of the sudden braking frequent occurrence point specifying process shown in step S1504 of FIG. 15 will be described.

  FIG. 17 is a flowchart illustrating an example of a specific processing procedure of the sudden braking frequent occurrence point specifying process. In the flowchart of FIG. 17, first, the analysis data generation device 201 divides the map of the target area into regions R1 to Rn (step S1701). Next, the analysis data generating apparatus 201 sets “i” in the region Ri to “i = 1” (step S1702), and selects the region Ri from the regions R1 to Rn (step S1703).

  Then, the analysis data generation device 201 extracts the sudden brake data of the region Ri from the sudden brake data DB 230 (step S1704). Next, the analysis data generation device 201 calculates the number of sudden braking occurrences Pi by counting the number of data of the sudden braking data in the extracted region Ri (step S1705).

  Next, the analysis data generation device 201 calculates the number of sudden braking occurrences Qi by counting the number of sudden braking data with different vehicle IDs among the sudden braking data of the extracted region Ri (step S1706). . Then, the analysis data generating apparatus 201 determines whether or not the calculated number of occurrences Pi is five times or more the number of occurrences Qi (step S1707).

  If the number of occurrences Pi is less than five times the number of occurrences Qi (step S1707: No), the analysis data generating apparatus 201 proceeds to step S1709. On the other hand, when the number of occurrences Pi is five times or more of the number of occurrences Qi (step S1707: Yes), the analysis data generation device 201 sets the region Ri as a sudden braking frequent occurrence point (step S1708).

  Next, the analysis data generating apparatus 201 increments “i” in the region Ri (step S1709), and determines whether “i” is larger than “n” (step S1710). If “i” is equal to or less than “n” (step S1710: NO), the analysis data generating apparatus 201 returns to step S1703.

  On the other hand, when “i” is larger than “n” (step S1710: Yes), the analysis data generating apparatus 201 returns to the step that called the sudden braking frequent occurrence point specifying process. Thereby, the sudden braking frequent occurrence point Ri in a target area can be specified.

<Statistical processing procedure>
Next, a specific processing procedure of the statistical processing shown in step S1506 in FIG. 15 will be described.

  FIG. 18 is a flowchart illustrating an example of a specific processing procedure of statistical processing. In the flowchart of FIG. 18, first, the analysis data generation device 201 calculates the number of occurrences of sudden braking for each day of the week and for each time zone of the sudden braking frequent point Ri based on the date and time of the sudden braking data at the sudden braking frequent point Ri. (Step S1801).

  Then, the analysis data generating apparatus 201 stores the calculated number of occurrences of sudden braking by day of the week / time of the sudden braking frequent occurrence point Ri in the number of sudden braking occurrences table 800 by day of the week / time of day (step S1802). ).

  Next, the analysis data generation apparatus 201 calculates the number of occurrences of sudden braking for each traveling direction of the sudden braking frequent point Ri based on the traveling direction of the sudden braking data at the sudden braking frequent point Ri (step S1803).

  Then, the analysis data generating apparatus 201 stores the calculated number of sudden brakings by the traveling direction of the sudden braking frequent points Ri in the traveling direction sudden braking number-of-times table 900 (step S1804), and performs statistical processing. Return to the calling step.

  As a result, the number of occurrences of sudden braking for each day of the week / time zone at the sudden braking frequent occurrence point Ri and the number of occurrences of sudden braking for each traveling direction can be calculated.

<Graph generation procedure>
Next, a specific processing procedure of the graph generation processing shown in step S1507 in FIG. 15 will be described.

  FIG. 19 is a flowchart illustrating an example of a specific processing procedure of the graph generation processing. In the flowchart of FIG. 19, first, the analysis data generation apparatus 201 reads a graph (model) corresponding to each predetermined day section from a storage device such as the memory 302 and the disk 304 (step S1901).

  Here, an example will be described in which the predetermined day classification is “Monday to Friday”, “Saturday”, and “Sunday”.

  Next, the analysis data generation device 201 refers to the sudden braking occurrence count table 800 for each day of the week and for each time zone, and identifies the day of the week / time zone in which the number of sudden braking occurrences is the first (step S1902). Next, the analysis data generation apparatus 201 determines whether the identified first day of the week / time zone belongs to a day of the week classification “Monday through Friday”, “Saturday”, or “Sunday” (step S1903). ). Then, the analysis data generation apparatus 201 paints out the corresponding time zone of the determined day of the week segment graph (step S1904). The corresponding time zone is the time zone of the first day of the week / time zone.

  Next, the analysis data generation device 201 refers to the sudden brake occurrence count table 800 for each day of the week / time zone, and identifies the day / time zone for which the number of sudden brake occurrences is second (step S1905). Next, the analysis data generating apparatus 201 determines whether the specified second day of the week / time zone belongs to any of the day classifications “Monday to Friday”, “Saturday”, and “Sunday” (step S1906). ). Then, the analysis data generation apparatus 201 paints out the corresponding time zone of the determined graph of the corresponding day of the week (step S1907). The corresponding time zone is the second day of the week / time zone. If the corresponding time zone is already painted, the process of step S1907 can be omitted.

  Next, the analysis data generation device 201 refers to the sudden braking occurrence count table 800 for each day of the week and for each time zone, and identifies the day of the week / time zone in which the number of sudden braking occurrences is third (step S1908). Next, the analysis data generating apparatus 201 determines whether the specified third day of the week / time zone belongs to “Monday to Friday”, “Saturday”, or “Sunday” (Step S1909). ). Then, the analysis data generating apparatus 201 fills the corresponding time zone of the determined day of the week segment graph (step S1910), and returns to the step of calling the graph generation processing. The corresponding time zone is the time zone of the third day of the week / time zone. Further, when the corresponding time zone has already been painted, the process of step S1910 can be omitted.

  As a result, it is possible to generate a graph indicating the daily occurrence of sudden braking in each of the predetermined day of the week divisions for the sudden braking frequent point Ri.

<Mapping procedure>
Next, a specific processing procedure of the mapping process shown in step S1508 of FIG. 15 will be described.

  FIG. 20 is a flowchart illustrating an example of a specific processing procedure of the mapping process. In the flowchart of FIG. 20, first, the analysis data generation device 201 specifies a location corresponding to the sudden braking frequent occurrence point Ri on the map of the target area (step S2001).

  Then, the analysis data generating apparatus 201 arranges an icon indicating that sudden braking is frequently occurring at a location corresponding to the specified sudden braking frequent occurrence point Ri (step S2002), and the step of calling the mapping process is performed. Return. Thereby, decoration can be given to the location corresponding to the sudden braking frequent occurrence point Ri on the map of the target area.

<Matching procedure>
Next, a specific processing procedure of the matching process shown in step S1509 in FIG. 15 will be described.

  FIG. 21 is a flowchart illustrating an example of a specific processing procedure of the matching process. In the flowchart of FIG. 21, first, the analysis data generation device 201 extracts a map of the sudden braking frequent area Ai including the sudden braking frequent spot Ri from the map of the target area (step S2101).

  Then, the analysis data generating apparatus 201 refers to the sudden braking occurrence count table 900 for each traveling direction, and identifies the sudden braking occurrence direction at the sudden braking frequent occurrence point Ri (step S2102). Next, the analysis data generation device 201 arranges an icon indicating the identified sudden braking occurrence direction at a location corresponding to the sudden braking frequent occurrence point Ri on the map of the sudden braking frequent occurrence area Ai (step S2103).

  Then, the analysis data generating apparatus 201 associates the graph of the sudden braking frequent occurrence point Ri with the map of the sudden braking frequent occurrence area Ai (step S2104), and returns to the step of calling the matching process. Thereby, the graph of sudden braking frequent occurrence point Ri and the map of sudden braking frequent occurrence area Ai including sudden braking frequent occurrence point Ri can be matched.

  As described above, according to the analysis data generating apparatus 201 according to the embodiment, sudden braking (or sudden steering, lighting of a hazard lamp, use of a horn, etc.) is performed based on probe data of a plurality of vehicles C. The made area Ri can be specified. As a result, it is possible to specify a region Ri (for example, sudden braking frequent occurrence point Ri) where the driver has suddenly braked to avoid danger or the like.

  Further, according to the analysis data generation device 201, it is possible to generate a graph indicating the daily occurrence of sudden braking in each predetermined day of the week for the region Ri where sudden braking has been performed. Further, according to the analysis data generation device 201, the generated graph can be output in association with the map indicating the region Ri. As a result, it is possible to visually present the occurrence of sudden braking for each day of the week in the region Ri (for example, sudden braking frequent points Ri), and it is possible to assist the driver in predicting danger.

  Further, according to the analysis data generating apparatus 201, the predetermined day of the week classification is divided into a group from Monday to Friday, a group from Saturday to Sunday, a group from Monday to Saturday, and a group from Sunday or Monday to Friday. , Saturday and Sunday. As a result, with respect to the days of the week when the transition of the degree of occurrence of sudden braking in a day is similar, the degree of occurrence of sudden braking in a day can be expressed in a single graph. For this reason, the visibility when the driver confirms the occurrence of sudden braking can be enhanced.

  Further, according to the analysis data generation device 201, the predetermined day of the week division can be a holiday or a holiday. As a result, with respect to holidays and holidays that have similar transitions in the degree of occurrence of sudden braking in a day, the degree of occurrence of sudden braking in a day can be expressed in a single graph.

  Further, according to the analysis data generation device 201, a graph in which 24 hours are displayed as a square or a circle can be generated as a graph indicating the degree of occurrence of sudden braking in one day. Thereby, it is possible to present the relationship between the time zone of the day and the degree of occurrence of sudden braking in the time zone in an easily understandable manner.

  Further, according to the analysis data generation device 201, it is possible to output with a predetermined decoration in the time zone corresponding to the graph according to the time zone in which the occurrence frequency of sudden braking is high. This makes it easy to intuitively determine the day of the week and the time of day when the frequency of sudden braking is high.

  Moreover, according to the analysis data generation device 201, it is possible to output the decoration on the map indicating the region Ri with a predetermined decoration. As a result, it is possible to determine on the map where the sudden braking has been performed.

  Further, according to the analysis data generation device 201, the analysis data including a map corresponding to the target area, one or a plurality of places where the sudden braking is performed, and each graph corresponding to the generated one or a plurality of places is output. can do. As a result, for example, as shown in a poster 1400 (see FIG. 14), it is possible to visually present the occurrence of sudden braking for each day of the week for a plurality of sudden braking points in the target area. .

  Note that the analysis data generation method related to the driving operation described in the present embodiment can be realized by executing a program prepared in advance on a computer such as a personal computer or a workstation. The analysis data generation program related to the driving operation is recorded on a computer-readable recording medium such as a hard disk, a flexible disk, a CD-ROM, an MO, and a DVD, and is executed by being read from the recording medium by the computer. Further, the analysis data generation program related to the main operation may be distributed through a network such as the Internet.

  The following additional notes are disclosed with respect to the embodiment described above.

(Appendix 1) Generating a graph showing the occurrence of the predetermined driving operation in one day in each of the predetermined time segments for a place where the predetermined driving operation specified based on the traveling data of a plurality of vehicles is performed,
Outputting the generated graph in association with a map indicating the location;
An analysis data generation program relating to a driving operation, characterized by causing a computer to execute processing.

(Additional remark 2) The said time division is a day-of-week division, The analysis data generation program regarding the driving operation of Additional remark 1 characterized by the above-mentioned.

(Supplementary Note 3) The day of the week classification is a group from Monday to Friday and a group of Saturday and Sunday, or a group of Monday to Saturday and Sunday, or a group of Monday to Friday, The analysis data generation program relating to the driving operation according to Supplementary Note 2, wherein the program is Saturday and Sunday.

(Additional remark 4) The said day-of-week division | segmentation further contains a holiday or a holiday, The analysis data generation program regarding the driving operation of Additional remark 3 characterized by the above-mentioned.

(Additional remark 5) The said graph displays 24 hours by a square or a circle, The analysis data generation program regarding the driving | operation operation as described in any one of Additional remark 1-4 characterized by the above-mentioned.

(Appendix 6)
6. The driving operation according to appendix 5, wherein a process is performed in which a predetermined decoration is output in a corresponding time zone of the graph according to a time zone in which the occurrence frequency of the predetermined driving operation is high. Analysis data generation program.

(Appendix 7)
The analysis data generation related to the driving operation according to any one of appendices 1 to 6, wherein the processing is executed by outputting a predetermined decoration at a place where the predetermined driving operation is performed on the map. program.

(Additional remark 8) The said time division is a seasonal division, The analysis data generation program regarding the driving operation of Additional remark 1 characterized by the above-mentioned.

(Appendix 9) Accepting designation of area,
Identifying one or a plurality of places where a predetermined driving operation identified based on traveling data of a plurality of vehicles in the area is performed;
Generating a graph indicating the occurrence of the predetermined driving operation in one day in each of the predetermined time segments at the identified one or a plurality of locations;
Outputting analysis data including a map corresponding to the area, the identified one or more locations, and each graph corresponding to the generated one or more locations,
An analysis data generation program relating to a driving operation, characterized by causing a computer to execute processing.

(Supplementary note 10) The analysis data generation program relating to the driving operation according to supplementary note 9, wherein the time division is a day division.

(Supplementary note 11)
The analysis data generation program relating to the driving operation according to appendix 9 or 10, wherein a process of outputting a predetermined decoration on the map corresponding to the area with predetermined decoration is executed.

(Supplementary note 12)
The driving operation according to appendix 11, wherein when there are a plurality of specified places, a process is executed on a map corresponding to the area, with a predetermined decoration that can identify the plurality of places being output. Analysis data generation program for.

(Supplementary note 13) Generate a graph showing the occurrence of the predetermined driving operation in one day in each of the predetermined time segments for the place where the predetermined driving operation specified based on the traveling data of a plurality of vehicles is performed,
Outputting the generated graph in association with a map indicating the location;
An analysis data generation method relating to a driving operation, characterized in that the processing is executed by a computer.

(Additional remark 14) The poster produced | generated by the analysis data production | generation method regarding the driving | operation operation of Additional remark 13.

(Appendix 15) Accepting designation of area,
Identifying one or a plurality of places where a predetermined driving operation identified based on traveling data of a plurality of vehicles in the area is performed;
Generating a graph indicating the occurrence of the predetermined driving operation in one day in each of the predetermined time segments at the identified one or a plurality of locations;
Outputting analysis data including a map corresponding to the area, the identified one or more locations, and each graph corresponding to the generated one or more locations,
An analysis data generation method relating to a driving operation, characterized in that the processing is executed by a computer.

(Supplementary Note 16) Generate and generate a graph showing the occurrence of the predetermined driving operation in one day in each of the predetermined time segments for the place where the predetermined driving operation specified based on the driving data of a plurality of vehicles is performed A control unit that outputs the graph in association with a map indicating the location;
An information processing apparatus comprising:

(Supplementary Note 17) Accepting designation of area,
Identifying one or a plurality of places where a predetermined driving operation identified based on traveling data of a plurality of vehicles in the area is performed;
Generating a graph indicating the occurrence of the predetermined driving operation in one day in each of the predetermined time segments at the identified one or a plurality of locations;
Outputting analysis data including a map corresponding to the area, the identified one or more locations, and each graph corresponding to the generated one or more locations,
An information processing apparatus having a control unit.

101 Information Processing Device 200 System 201 Analysis Data Generation Device 202 Terminal Device 220 Probe Data DB
230 Sudden brake data DB
240 Map DB
500 Probe data 701 Acquisition unit 702 Reception unit 703 Extraction unit 704 Identification unit 705 Statistics unit 706 Generation unit 707 Output unit 800 Rapid brake occurrence count table by day of week / time zone 900 Rapid brake occurrence count table by travel direction 1400 Poster

Claims (16)

Generating a graph indicating the occurrence of the predetermined driving operation in one day in each of the predetermined time segments for a place where the predetermined driving operation specified based on the driving data of a plurality of vehicles is performed;
Outputting the generated graph in association with a map indicating the location;
An analysis data generation program relating to a driving operation, characterized by causing a computer to execute processing.   The said time division is a day of week division, The analysis data generation program regarding the driving operation of Claim 1 characterized by the above-mentioned.   The day of the week classification is a pair of Monday to Friday and a pair of Saturday and Sunday, or a pair of Monday to Saturday and Sunday, or a pair of Monday to Friday, Saturday and Sunday. The analysis data generation program relating to the driving operation according to claim 2, wherein:   The analysis data generation program for driving operation according to claim 3, wherein the day of the week classification further includes a holiday or a holiday.   The analysis data generation program relating to the driving operation according to any one of claims 1 to 4, wherein the graph displays 24 hours as a square or a circle. In the computer,
6. The driving operation according to claim 5, wherein a process of outputting a predetermined decoration in a corresponding time zone of the graph is output according to a time zone in which the frequency of the predetermined driving operation is high. Analysis data generation program for. In the computer,
The analysis data relating to the driving operation according to any one of claims 1 to 6, wherein a process of outputting a predetermined decoration at a place where the predetermined driving operation is performed on the map is executed. Generation program. Accept area specification,
Identifying one or a plurality of places where a predetermined driving operation identified based on traveling data of a plurality of vehicles in the area is performed;
Generating a graph indicating the occurrence of the predetermined driving operation in one day in each of the predetermined time segments at the identified one or a plurality of locations;
Outputting analysis data including a map corresponding to the area, the identified one or more locations, and each graph corresponding to the generated one or more locations,
An analysis data generation program relating to a driving operation, characterized by causing a computer to execute processing.   The said time division is a day division, The analysis data generation program regarding the driving operation of Claim 8 characterized by the above-mentioned. In the computer,
The analysis data generation program for driving operation according to claim 8 or 9, wherein a process of outputting predetermined decoration on the one or a plurality of places on the map corresponding to the area is executed. In the computer,
The driving according to claim 10, wherein when there are a plurality of specified locations, a process of outputting predetermined decorations that can identify the plurality of locations on a map corresponding to the area is executed. Analysis data generation program for operation. Generating a graph indicating the occurrence of the predetermined driving operation in one day in each of the predetermined time segments for a place where the predetermined driving operation specified based on the driving data of a plurality of vehicles is performed;
Outputting the generated graph in association with a map indicating the location;
An analysis data generation method relating to a driving operation, characterized in that the processing is executed by a computer.   The poster produced | generated by the analysis data production | generation method regarding the driving operation of Claim 12. Accept area specification,
Identifying one or a plurality of places where a predetermined driving operation identified based on traveling data of a plurality of vehicles in the area is performed;
Generating a graph indicating the occurrence of the predetermined driving operation in one day in each of the predetermined time segments at the identified one or a plurality of locations;
Outputting analysis data including a map corresponding to the area, the identified one or more locations, and each graph corresponding to the generated one or more locations,
An analysis data generation method relating to a driving operation, characterized in that the processing is executed by a computer. Generate a graph showing the daily occurrence of the predetermined driving operation in each of the predetermined time segments for the place where the predetermined driving operation specified based on the traveling data of a plurality of vehicles is made, and the generated graph , A control unit that outputs in association with a map indicating the place,
An information processing apparatus comprising: Accept area specification,
Identifying one or a plurality of places where a predetermined driving operation identified based on traveling data of a plurality of vehicles in the area is performed;
Generating a graph indicating the occurrence of the predetermined driving operation in one day in each of the predetermined time segments at the identified one or a plurality of locations;
Outputting analysis data including a map corresponding to the area, the identified one or more locations, and each graph corresponding to the generated one or more locations,
An information processing apparatus having a control unit.

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