JP2018018214A - Danger notification device, danger notification system, danger notification method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a danger notification device, a danger notification system, a danger notification method and a program for transmitting risk prediction information on a traffic accident according to a traffic state per road section.SOLUTION: A danger notification device 40 includes: a traffic state index acquisition part 410 for acquiring a traffic state index for a road section from a traffic state database; a danger prediction part 420 for predicting a degree of danger indicating the probability of occurrence of a traffic accident in the road section; and alert information transmission part 450 for transmitting alert information at least either on-vehicle devices mounted in vehicles located in the road section or portable terminals carried by pedestrians when the danger predication part determines that the degree of danger in the road section reaches a specific reference value or higher.SELECTED DRAWING: Figure 8

Description

  The present invention relates to a danger notification device, a danger notification system, a danger notification method, and a program.

As a system for suppressing a collision between a vehicle and a pedestrian, a vehicle based on position information, speed information, and moving direction information acquired from an on-board device mounted on the vehicle and a portable terminal carried by the pedestrian, respectively. In addition, a system is provided that predicts the arrival point of a pedestrian and outputs a warning from an in-vehicle device or a portable terminal when it is determined that the vehicle and the pedestrian may collide (for example, Patent Document 1). .
In addition, a system is provided in which position information acquired from a portable terminal carried by a pedestrian is displayed on the vehicle-mounted device, so that the presence of the pedestrian is notified to the driver of the vehicle (for example, Patent Document 2, Patent). Reference 3).

JP-A-2005-4542 JP 2008-217429 A JP 2007-122735 A

  However, even when the vehicle and the pedestrian are located on the same road, for example, the vehicle and the pedestrian collide in a situation where the traffic volume on the road is high and a situation where the traffic volume is low. Risk may be different. For this reason, when a warning is output from the vehicle-mounted device or the portable terminal based on only the arrival point of the vehicle and the pedestrian, as in the above-described system, the vehicle and the pedestrian have a high risk of collision. There is a possibility that no warning is output or a warning is output despite the low risk of collision between the vehicle and the pedestrian.

  The present invention has been made in view of such problems, and provides a danger notification device, a danger notification system, a danger notification method, and a program for notifying danger prediction information of a traffic accident according to the traffic situation for each road section. provide.

In order to solve the above problems, the present invention employs the following means.
According to an aspect of the present invention, the danger notification device includes a traffic condition index acquisition unit that acquires a traffic condition index of a road section from a traffic condition database, and an occurrence of a traffic accident in the road section based on the traffic condition index. A risk prediction unit for predicting a risk level indicating a probability, and an on-vehicle device mounted on a vehicle located in the road section when the risk prediction unit determines that the risk level of the road section is equal to or higher than a predetermined reference value And an alert information transmitting unit that transmits alert information to at least one of portable terminals carried by pedestrians.
By doing in this way, since the risk prediction unit predicts the risk according to the traffic condition index of the road section, it can improve the prediction accuracy of the risk of each road section. In addition, the alert information transmission unit may send alert information to at least one of the vehicle-mounted device and the mobile terminal located in the road section in which the risk prediction unit has determined that the degree of risk is equal to or higher than a predetermined reference value, thereby calling attention. In addition, it is possible to suppress transmission of unnecessary alert information in a road section in which the risk prediction unit has determined that the degree of risk does not exceed a predetermined reference value.

According to an aspect of the present invention, in the danger notification device according to the aspect described above, the risk prediction unit includes the traffic situation index acquired by the traffic situation index acquisition unit and a probability of a traffic accident with respect to a past traffic situation index. Is predicted based on the risk correlation data in which each road section is associated with each other.
In this way, the danger prediction unit refers to both the current traffic condition index acquired by the traffic condition index acquisition unit and the risk correlation data indicating the probability of occurrence of a traffic accident with respect to the past traffic condition index. As a result, the prediction accuracy of the risk can be further improved.

According to an aspect of the present invention, in the danger notification device according to the above aspect, the alert information transmission unit determines that the risk prediction unit determines that the risk of the road section is equal to or higher than a predetermined reference value, and When a pedestrian exists in the said road area, alert information is transmitted to the said onboard equipment.
By doing in this way, the alert information transmission part can alert appropriately by transmitting alert information to the vehicle-mounted device in a road section where the degree of danger is equal to or higher than a predetermined reference value and a pedestrian is present. In addition, transmission of unnecessary alert information can be suppressed in a road section where there is no pedestrian even if the degree of risk is equal to or higher than a predetermined reference value.

According to one aspect of the present invention, a danger notification system includes a danger notification device according to any one of the above aspects, on-vehicle probe data including position information of an on-vehicle device mounted on each of a plurality of vehicles, and a plurality of Mobile terminal probe data including position information of mobile terminals carried by each of the pedestrians, a probe data collection unit for collecting the mobile terminal probe data, and the mobile terminal probe data based on the mobile terminal probe data. A traffic condition index generation unit that generates a traffic condition index and records it in a traffic condition database.
By doing in this way, a traffic condition index generation part generates a traffic condition index according to the situation of an actual road section based on onboard equipment probe data and portable terminal probe data which a probe data collection part collects sequentially Can do. For this reason, the traffic condition index generation unit can improve the accuracy of the traffic condition index. In addition, the danger notification device can improve the accuracy of predicting the degree of danger of each road section based on such a highly accurate traffic condition index.

  According to one aspect of the present invention, the danger notification method includes a traffic condition index acquisition step of acquiring a traffic condition index of a road section from a traffic condition database, and predicts a risk level in the road section based on the traffic condition index. A risk prediction step, and in the risk prediction step, when it is determined that the risk level of the road section is equal to or higher than a predetermined reference value, the vehicle mounted on the vehicle located in the road section and a portable phone carried by a pedestrian An alert information transmitting step of transmitting alert information to at least one of the terminals.

  According to one aspect of the present invention, the program is a computer of the danger notification device, a traffic condition index acquisition unit that acquires a traffic condition index of a road section from a traffic condition database, and based on the traffic condition index, in the road section A risk prediction unit that predicts a risk level, and when the risk prediction unit determines that the risk level of the road section is equal to or higher than a predetermined reference value, an on-board device and a pedestrian mounted on a vehicle located in the road section are portable It is made to function as an alert information transmission part which transmits alert information to at least one of the portable terminals.

  According to the above-described danger notification device, danger notification system, danger notification method, and program, it is possible to notify the risk prediction information of a traffic accident according to the traffic situation for each road section.

It is a figure showing the whole danger notice system composition concerning a 1st embodiment of the present invention. It is a figure which shows the example of the road area which concerns on the 1st Embodiment of this invention. It is a figure which shows the function of the traffic condition parameter | index production | generation part which concerns on the 1st Embodiment of this invention. It is a figure which shows the example of the traffic condition table which concerns on the 1st Embodiment of this invention. It is a 1st figure which shows the processing flow of the traffic condition parameter | index production | generation part which concerns on the 1st Embodiment of this invention. It is a 2nd figure which shows the processing flow of the traffic condition parameter | index production | generation part which concerns on the 1st Embodiment of this invention. It is a 3rd figure which shows the processing flow of the traffic condition parameter | index production | generation part which concerns on the 1st Embodiment of this invention. It is a figure which shows the function structure of the danger notification apparatus which concerns on the 1st Embodiment of this invention. It is a figure which shows the example of the risk correlation data which concern on the 1st Embodiment of this invention. It is a 1st figure which shows the example of the alert information which concerns on the 1st Embodiment of this invention. It is a 2nd figure which shows the example of the alert information which concerns on the 1st Embodiment of this invention. It is a 1st figure which shows the processing flow of the danger notification apparatus which concerns on the 1st Embodiment of this invention. It is a 2nd figure which shows the processing flow of the danger notification apparatus which concerns on the 1st Embodiment of this invention. It is a figure which shows the example of the hardware constitutions of the danger notification apparatus which concerns on the 1st Embodiment of this invention. It is a figure which shows the processing flow of the danger notification apparatus which concerns on the modification of the 1st Embodiment of this invention. It is a figure which shows the example of the risk correlation data which concern on the modification of the 1st Embodiment of this invention.

<First Embodiment>
Hereinafter, the danger notification system 1 according to the first embodiment of the present invention will be described with reference to FIGS.

(Overall configuration of the danger notification system)
FIG. 1 is a diagram showing an overall configuration of a danger notification system according to the first embodiment of the present invention.
As shown in FIG. 1, the danger notification system 1 of the present embodiment includes a probe data collection unit 10, a traffic condition index generation unit 20, and a traffic condition database 30 that are communicably connected via a network NW such as the Internet. And a danger notification device 40.
Note that the probe data collection unit 10, the traffic condition index generation unit 20, the traffic condition database 30, and the danger notification device 40 may be communicably connected via a dedicated line such as a LAN (Local Area Network).

The probe data collecting unit 10 transmits probe data (on-vehicle probe data and portable terminal probe) transmitted from the on-board device A mounted on the vehicle and the portable terminal T carried by the pedestrian at predetermined time intervals (for example, every second). Data).
Specifically, the probe data collection unit 10 collects on-vehicle device probe data transmitted from the on-vehicle device A (on-vehicle devices A1 to An) mounted on each of the plurality of vehicles via the network NW. The probe data collection unit 10 collects portable terminal probe data transmitted via the network NW from the portable terminals T (mobile terminals T1 to Tn) carried by a plurality of pedestrians. Note that an application for transmitting mobile terminal probe data to the mobile terminal T carried by the pedestrian and receiving alert information indicating that “the risk of occurrence of a traffic accident is high” is received in advance from the danger notification device 40. Installed.
The OBE probe data includes “transmission date and time”, “device type”, “device ID”, “position information”, “movement speed”, “alert setting”, and the like. The mobile terminal probe data includes “device type”, “device ID”, “position information”, “alert setting”, and the like.
“Transmission date and time” is information indicating the date and time when the probe data is transmitted from the vehicle-mounted device A or the portable terminal T. The “device type” is information for specifying whether the probe data transmission source is the vehicle-mounted device A or the portable terminal T. The “apparatus ID” is information that can identify the vehicle-mounted device A or the portable terminal T that is the source of probe data. “Position information” is information indicating a point (latitude, longitude, etc.) where a pedestrian carrying the vehicle on which the vehicle-mounted device A is mounted or the portable terminal T at the date and time when the probe data is transmitted. “Movement speed” is information indicating the traveling speed (m / s) of the vehicle on which the vehicle-mounted device A is mounted at the date and time when the probe data is transmitted. “Alert setting” is information indicating whether the vehicle-mounted device A and the mobile terminal T receive alert information (“ON” or “OFF”). The driver of the vehicle can arbitrarily switch whether or not to receive alert information through the vehicle-mounted device A. Further, the pedestrian can arbitrarily switch whether or not to receive the alert information through an application installed in the mobile terminal T.

FIG. 2 is a diagram illustrating an example of a road section according to the first embodiment of the present invention.
Based on the probe data (onboard device probe data and portable terminal probe data) collected by the probe data collection unit 10, the traffic condition index generation unit 20 performs traffic conditions for each road section every predetermined time (for example, every second). Generate indicators. In this embodiment, the traffic condition index generation unit 20 divides a “vehicle traffic condition index” indicating the traffic condition of the vehicle and a “pedestrian traffic condition index” indicating the traffic condition of the pedestrian for each road section. Generate.
As shown in FIG. 2, the road section indicates a region between two adjacent nodes among nodes set at each of a plurality of traffic nodes (intersections and the like) on the road. Each road section is given a “link ID” that can identify the road section. For example, as shown in FIG. 2, a road section indicating a lane heading to the other side has a link ID “B20a” attached to a road section indicating a lane heading to one side, which is an area between two nodes (intersections). A link ID “B20b” is attached to the section. In addition, when the distance between traffic nodes is longer than a predetermined distance (for example, 100 meters), a node may be set for each predetermined distance. In the present embodiment, the traffic condition index generation unit 20 includes “link ID”, “road section position information” indicating the position of the road section (for example, the latitude and longitude of the start point and end point of the road section), and “road section Map data associated with “the length of” in advance.
The traffic condition index (vehicle traffic condition index and pedestrian traffic condition index) is information indicating the traffic condition of the vehicle and the pedestrian in the road section. In the present embodiment, the traffic condition index includes “traffic volume”, “traffic density”, and “average speed”. The “traffic volume” indicates the number of vehicles passing per second (vehicles / second) or the number of pedestrians passing (persons / second) in a road section. “Traffic density” indicates the number of vehicles per meter (vehicles / m) or the number of pedestrians (people / m) in a road section. “Average speed” indicates an average moving speed (m / s) of a vehicle traveling in a road section or a pedestrian traveling.
Moreover, the traffic condition index generation unit 20 is based on the probe data used when calculating the traffic condition index, and information on the road section (link ID) and the vehicle-mounted device A and the mobile terminal T located in the road section ( Data associated with a part of the probe data is added to the traffic situation table D10 (FIG. 4) of the traffic situation database 30 and updated. Details of the traffic situation table D10 will be described later.

  The traffic situation database 30 stores a traffic situation index (vehicle traffic situation index and pedestrian traffic situation index) generated by the traffic situation index generation unit 20 and a traffic situation table D10 shown in FIG.

  The danger notification device 40 predicts a risk level indicating the probability of occurrence of a traffic accident in each road section based on the road condition index for each road section recorded in the traffic situation database 30. In addition, when the danger notification device 40 determines that the “risk degree is high” in a certain road section, the danger notification device 40 indicates “high risk degree” to at least one of the vehicle-mounted device A and the mobile terminal T located in the road section. Send alert information.

(Function of traffic condition index generator)
FIG. 3 is a diagram illustrating the function of the traffic condition index generation unit according to the first embodiment of the present invention.
FIG. 4 is a diagram illustrating an example of a traffic situation table according to the first embodiment of the present invention.
Hereinafter, the function of the traffic condition index generation unit 20 of the present embodiment will be described with reference to FIGS.
As shown in FIG. 3, the traffic condition index generation unit 20 first acquires probe data from the probe data collection unit 10. Then, the traffic condition index generation unit 20 uses a known big data distributed processing technique, a stream data processing technique, etc., and the traffic condition index for each road section based on the probe data collected from the plurality of vehicle-mounted devices A and portable terminals T. Is generated. When generating the traffic condition index, the traffic condition index generating unit 20 records the traffic condition index in the traffic condition database 30 for each road section. Thereby, the traffic condition index generation unit 20 can generate a traffic condition index corresponding to the actual situation for each road section in real time.

  Moreover, the traffic condition index generation unit 20 is based on the probe data used when calculating the traffic condition index, and information on the road section (link ID) and the vehicle-mounted device A and the mobile terminal T located in the road section ( The traffic situation data associated with a part of the probe data is added to the traffic situation table D10 of the traffic situation database 30 and updated. As shown in FIG. 4, in the traffic situation table D10, the “transmission date and time” of each probe data and the “device type”, “device ID” of the vehicle-mounted device A and the portable terminal T that are the transmission sources of each probe data “Position information” and “alert setting” and the “link ID” of the road section where the pedestrian carrying the vehicle on which the vehicle-mounted device A is mounted and the portable terminal T are associated and recorded.

(Processing flow of traffic condition index generation unit)
FIG. 5 is a first diagram illustrating a processing flow of the traffic condition index generation unit according to the first embodiment of the present invention.
Hereinafter, the process flow of the traffic condition index generation unit 20 of the present embodiment will be described with reference to FIG.

  As illustrated in FIG. 5, the traffic condition index generation unit 20 acquires the probe data (on-board device probe data and portable terminal probe data) collected by the probe data collection unit 10 (step S200).

  Next, the traffic condition index generation unit 20 generates a traffic condition index (a vehicle traffic condition index and a pedestrian traffic condition index) for each road section based on the probe data (on-board probe data and portable terminal probe data). At the same time, the traffic situation table D10 is updated (step S201). Details of the traffic condition index generation process and the traffic condition table D10 update process will be described later.

Next, every time the traffic condition index is calculated, the traffic condition index generation unit 20 records and accumulates the generated traffic condition index for each road section and the traffic condition table D10 in the traffic condition database 30 (step S202). ).
As described above, the traffic condition index generation unit 20 repeatedly executes the above process every time the probe data is acquired.

FIG. 6 is a second diagram showing a processing flow of the traffic condition index generation unit according to the first embodiment of the present invention.
FIG. 7 is a third diagram illustrating a processing flow of the traffic condition index generation unit according to the first embodiment of the present invention.
Hereinafter, the process in which the traffic condition index generation unit 20 generates a traffic condition index and the process in which the traffic condition table D10 is updated will be described with reference to FIGS.

First, as shown in FIG. 6, the traffic condition index generation unit 20 includes a road section (hereinafter referred to as a road section where a vehicle is located) in which a vehicle equipped with the vehicle-mounted device A that is a transmission source of each probe data is located, and Then, the process P21 for specifying the road section where the pedestrian carrying the mobile terminal T is located (hereinafter, the road section where the pedestrian is located) is performed.
In the process P21, first, the traffic condition index generation unit 20 reads “position information” from each probe data (vehicle equipment probe data and portable terminal probe data) (step S210).

Next, the traffic condition index generation unit 20 specifies the road section where each vehicle and each pedestrian is located based on the “position information” of each probe data (step S211).
Specifically, the traffic condition index generation unit 20 searches and specifies a road section having “road section position information” including “position information” (latitude and longitude) of the probe data from the map data.

  Next, the traffic condition index generation unit 20 adds the “link ID” of the identified road section to each probe data (step S212).

Next, the traffic condition index generation unit 20 sets the “transmission date / time”, “device type”, “device ID”, and “alert setting” of each probe data, and the “link ID” of the road section added in step S212. Is added to the traffic situation table D10 (FIG. 4) recorded in the traffic situation database 30 and updated (step S213).
As described above, the traffic condition index generation unit 20 repeatedly executes the process P21 each time the probe data is acquired.

Further, as shown in FIG. 7, the traffic condition index generation unit 20 determines each road based on a plurality of probe data ("on-board probe data and portable terminal probe data") to which "link ID" is added by the process P21. Processing P22 for calculating the traffic condition index at each time of the section is performed.
In process P22, first, the traffic condition index generation unit 20 extracts target probe data to be calculated from among a plurality of probe data (step S220).
In the present embodiment, the traffic condition index generation unit 20 generates probe data indicating that it exists in an arbitrary target road section at an arbitrary target time based on the “transmission date and time” and “link ID” of each probe data. Extracted as target probe data for calculation processing. Specifically, the traffic condition index generation unit 20 is probe data having “transmission date and time” within a predetermined time (for example, 1 second) from an arbitrary target time, and is the same as the link ID of an arbitrary target road section. Probe data having “link ID” is extracted as target probe data for the arithmetic processing.

Next, the traffic condition index generation unit 20 carries the “moving speed” of the vehicle on which each vehicle-mounted device A included in the target probe data is mounted (hereinafter referred to as “moving speed” of the vehicle) and each portable terminal T. The pedestrian's “movement speed” (hereinafter, “pedestrian's“ movement speed ”) is acquired (step S221).
Specifically, the traffic condition index generation unit 20 first determines whether the transmission source of each probe data is the vehicle-mounted device A based on the “device type” of each probe data included in the target probe data, or the portable terminal T It is judged whether it is.
When the “device type” of the probe data is the vehicle-mounted device A, the “moving speed” of the vehicle is included in the probe data (vehicle-mounted device probe data). For this reason, the traffic condition index generation unit 20 reads and acquires the “movement speed” of the vehicle included in the probe data. In this embodiment, an example in which the “movement speed” of the OBE probe data is recorded in meters per second (m / s) will be described, but in other embodiments, kilometers per hour (km / h). It may be recorded in other units. When the “movement speed” is recorded in other units, the traffic condition index generation unit 20 may acquire the “movement speed” by converting it into meters per second (m / s).
On the other hand, when the “device type” of the probe data is the mobile terminal T, the probe data (mobile terminal probe data) does not include the “movement speed” of the pedestrian. For this reason, the traffic condition index generation unit 20 calculates and acquires the “movement speed” of the pedestrian in units of meters per second (m / s) based on the probe data. For example, the traffic condition index generation unit 20 is the same as the “device ID” of the probe data among the probe data having the “transmission date” of a predetermined time before the “transmission date” of the probe data (for example, 10 seconds before). The probe data having the “device ID” is searched and extracted as reference probe data. The traffic condition index generation unit 20 then calculates the distance from the point indicated by the “position information” of the reference probe data to the point indicated by the “position information” of the probe data, the “transmission date and time” of the reference probe data and the probe data. Based on the time difference (10 seconds), the “movement speed” of the pedestrian related to the probe data is calculated and acquired.
As described above, when the traffic condition index generation unit 20 acquires the “movement speed” of the vehicle and the pedestrian from each probe data included in the target probe data, the traffic condition index generation unit 20 proceeds to the next step.

  Next, the traffic condition index generation unit 20 calculates the “number of vehicles” indicating the number of vehicles equipped with the vehicle-mounted device A located in the target road section based on each probe data included in the target probe data. Then, “the number of pedestrians” indicating the number of pedestrians carrying the portable terminal T located in the target road section is calculated (step S222).

  Next, the traffic condition index generation unit 20 searches and acquires “the length of the target road section” from the map data using the “link ID” of the target road section as a search key (step S223).

Next, the traffic condition index generation unit 20 calculates the traffic volume of the vehicle and the traffic volume of the pedestrian at the target time of the target road section (step S224).
Specifically, first, the traffic condition index generation unit 20 sets the “number of vehicles” calculated in step S222 to N, the “movement speed” of each vehicle acquired in step S221 to v _i (i = 1 to N), The “target road section length” acquired in step S223 is set as K, and the traffic volume q (vehicles / second) of the vehicle is obtained using the following formula (1).

Similarly, the traffic condition index generation unit 20 sets the “number of pedestrians” calculated in step S222 to N, the “movement speed” of each pedestrian acquired in step S221, v _i (i = 1 to N), The “length of the target road section” acquired in step S223 is set as K, and the traffic volume q (person / second) of the pedestrian is obtained using the above formula (1).

Next, the traffic condition index generation unit 20 calculates the traffic density of the vehicle and the traffic density of the pedestrian at the target time in the target road section (step S225).
Specifically, the traffic condition index generation unit 20 uses “the number of vehicles” calculated in step S222 as N and “the length of the target road section” acquired in step S223 as K, and uses the following formula (2). To determine the traffic density k (units / m) of the vehicle.

  Similarly, the traffic condition index generation unit 20 sets the “number of pedestrians” calculated in step S222 to N and the “length of the target road section” acquired in step S223 to K, and uses the above formula (2). To determine the traffic density k (person / m) of the pedestrian.

Next, the traffic condition index generation unit 20 calculates the average speed of the vehicle and the average speed of the pedestrian at the target time in the target road section (step S226).
Specifically, the traffic condition index generation unit 20 sets “the number of vehicles” calculated in step S222 to N, “moving speed” of each vehicle acquired in step S221 to v _i (i = 1 to N), and The average speed v _s (m / s) of the vehicle is obtained using the following equation (3).

Similarly, the traffic condition index generation unit 20 sets the “number of pedestrians” calculated in step S222 to N, and the “movement speed” of each pedestrian acquired in step S221 as v _i (i = 1 to N). , an average speed of the pedestrian with the above equation _{(3) v s (m /} s).

  As described above, the traffic condition index generation unit 20 repeatedly executes the process P22 each time the probe data is acquired, so that the traffic condition index (the vehicle traffic condition index and the pedestrian traffic condition at each time of each road section). The index) is calculated.

(Functional configuration of the danger notification device)
FIG. 8 is a diagram showing a functional configuration of the danger notification device according to the first embodiment of the present invention.
FIG. 9 is a diagram showing an example of risk correlation data according to the first embodiment of the present invention.
FIG. 10 is a first diagram illustrating an example of alert information according to the first embodiment of the present invention.
FIG. 11 is a second diagram illustrating an example of alert information according to the first embodiment of the present invention.
Hereinafter, the functional configuration of the danger notification device 40 of the present embodiment will be described with reference to FIGS.
As shown in FIG. 8, the danger notification device 40 includes a traffic condition index acquisition unit 410, a risk prediction unit 420, a portable terminal data extraction unit 430, an on-vehicle device data extraction unit 440, an alert information transmission unit 450, A recording medium 460.

  The traffic condition index acquisition unit 410 acquires a traffic condition index for each road section from the traffic condition database 30 via the network NW. In the present embodiment, the traffic condition index acquisition unit 410 acquires only the vehicle traffic condition index from among the traffic condition indices recorded in the traffic condition database 30.

The risk prediction unit 420 is based on the vehicle traffic condition index for each road section acquired by the traffic condition index acquisition unit 410 and the risk degree correlation data D20 for each road section recorded in the recording medium 460 in advance (FIG. 9). The degree of risk indicating the probability of occurrence of a traffic accident on each road section is predicted.
The danger prediction unit 420 refers to the map data recorded in advance in the recording medium 460, and there is a possibility that the vehicle and the pedestrian collide, such as a road section having a dedicated sidewalk or a road section having only a dedicated motorway. The risk level may not be predicted for low road sections. By doing in this way, while the risk prediction part 420 can reduce the process which predicts a danger level, it can suppress that the alert information transmission part 450 (after-mentioned) transmits an unnecessary alert.

The risk correlation data D20 is data indicating the correlation between each of the traffic volume, traffic density and average speed of the vehicle and the risk indicating the probability of occurrence of a traffic accident for each road section. As shown in FIG. 9, each risk degree correlation data D20 has “traffic volume-risk degree correlation data D20a”, “traffic density-risk degree correlation data D20b”, and “average speed-risk degree correlation data D20c”. ing.
In the “traffic volume-risk degree correlation data D20a”, the horizontal axis indicates the traffic volume (vehicles / second) of the vehicle, and the vertical axis indicates the risk level (%) with respect to the traffic volume of the vehicle. The degree of risk for vehicle traffic is calculated as the probability of occurrence of traffic accidents (%) with respect to vehicle traffic by statistically counting the number of traffic accidents with respect to traffic among the vehicle traffic indicators in the past road sections. Data.
In the “traffic density-risk degree correlation data D20b”, the horizontal axis indicates the traffic density (vehicles / m) of the vehicle, and the vertical axis indicates the risk (%) with respect to the traffic density of the vehicle. The degree of risk for traffic density of vehicles is expressed as the probability of occurrence of traffic accidents (%) against the traffic density of vehicles by statistically analyzing the number of traffic accidents with respect to traffic density among the vehicle traffic indicators in the past road sections. Data.
In the “average speed-risk degree correlation data D20c”, the horizontal axis indicates the average speed (m / s) of the vehicle, and the vertical axis indicates the degree of risk (%) with respect to the average speed of the vehicle. The degree of danger for the average speed of the vehicle is expressed as the probability of occurrence of traffic accidents (%) with respect to the average speed of the vehicle by statistically counting the number of traffic accidents with respect to the average speed among the vehicle traffic indicators in each past road section. Data.

In the present embodiment, the risk prediction unit 420 determines that “the risk is high” when the risk of the risk correlation data D20 is equal to or greater than a predetermined reference value r1. Specifically, as shown in FIG. 9, in the road section B10a whose link ID is “B10a”, the reference value r1 is set to 10%, for example. In this case, the risk prediction unit 420 has a value in which the traffic level, the traffic density, and the average speed of the vehicle traffic condition index of the road section B10a all have a risk level equal to or higher than the reference value r1 (10%). , “Danger is high”. In addition, the risk prediction unit 420, when at least one of the traffic volume, the traffic density, and the average speed of the vehicle traffic condition index of the road section B10a has a value that the danger level is less than the reference value r1 (10%), Judge that the risk is low.
The predetermined reference value r1 may be set to the same value for a plurality of road sections, or may be set to a different value for each road section. The predetermined reference value r1 is set to a different value for each of “traffic volume-risk degree correlation data D20a”, “traffic density-risk degree correlation data D20b”, and “average speed-risk degree correlation data D20c”. It may be.
Further, in the present embodiment, the danger prediction unit 420 determines that the road is present when there is a road section in which all of the traffic volume, traffic density, and average speed of the vehicle have risk values that are greater than or equal to the reference value r1. An example in which a section is determined to be “high risk” will be described, but is not limited thereto. In another embodiment, the risk prediction unit 420 includes a road section in which at least one of the traffic volume, traffic density, and average speed of the vehicle has a value that is a risk level equal to or higher than the reference value r1. The road section may be determined to be “high risk”.

The recording medium 460 may further record in advance risk correlation data D20 that is different for each month, each day of the week, and each time zone. In this case, the risk level correlation data D20 is referred to based on the month, day of the week, and time zone when the risk prediction unit 420 predicts the risk level. Thus, for example, even in road sections having different degrees of risk between daytime and nighttime, the risk prediction unit 420 refers to the risk level correlation data D20 that is different based on the time zone in which the risk level is predicted. The accuracy of predicting can be improved.
In the present embodiment, an example has been described in which the number of traffic accidents for each of the past road sections, the traffic density, and the average speed is statistically calculated to determine the degree of risk. It will never be done. In addition to the number of traffic accidents (actual traffic accidents), the number of traffic accidents that are likely to occur (for example, the “moving speed” of OBE probe data is rapidly reduced in a short time. The number of cases where data indicating braking is acquired) may be statistically set.

  Then, the risk prediction unit 420 outputs the road section determined to be “high risk” to the portable terminal data extraction unit 430 as a dangerous road section. Specifically, the danger prediction unit 420 outputs the “link ID” of the dangerous road section to the mobile terminal data extraction unit 430 and the vehicle-mounted device data extraction unit 440.

The mobile terminal data extraction unit 430 extracts data of the mobile terminal T carried by a pedestrian traveling in the dangerous road section from the traffic situation table D10 of the traffic situation database 30.
Specifically, the mobile terminal data extraction unit 430 extracts data of the mobile terminal T having the “link ID” of the dangerous road section (data whose “device type” is “mobile terminal”). The mobile terminal data extraction unit 430 further extracts data of the mobile terminal T whose “alert setting” is “ON” from the extracted data of the mobile terminal T.
When the mobile terminal data extraction unit 430 extracts one or more data of the mobile terminal T having the “link ID” of the dangerous road section and the “alert setting” being “ON”, the extracted mobile terminal The T data is output to the alert information transmission unit 450 for each dangerous road section.

The onboard equipment data extraction unit 440 extracts the data of the onboard equipment A mounted on the vehicle running on the dangerous road section where the pedestrian is present from the traffic situation table D <b> 10 of the traffic situation database 30.
Specifically, the vehicle-mounted device data extraction unit 440 first identifies a dangerous road section where a pedestrian exists among the dangerous road sections output from the danger prediction unit 420. For example, the vehicle-mounted device data extraction unit 440 receives from the portable terminal data extraction unit 430 “hazardous road section where there is a pedestrian (data of the portable terminal T) whose“ alert setting ”of the portable terminal T is“ ON ”. By acquiring the “link ID”, such a dangerous road section is specified. And the onboard equipment data extraction part 440 extracts the data (data whose "equipment classification" is "onboard equipment") of the onboard equipment A which has the same "link ID" as the "link ID" of the specified dangerous road section. To do. Moreover, the onboard equipment data extraction part 440 further extracts the data of the onboard equipment A whose "alert setting" is "ON" among the extracted data of the onboard equipment A.
As described above, the on-vehicle device data extraction unit 440 has at least one data of the on-vehicle device A having “link ID” of the dangerous road section where the pedestrian exists and having “alert setting” “ON”. When extracted, the data of the onboard equipment A extracted is output to the alert information transmission unit 450 for each dangerous road section.

The alert information transmission unit 450 transmits the network NW to at least one of the mobile terminal T and the vehicle-mounted device A based on the data of the mobile terminal T and the vehicle-mounted device A extracted by the mobile terminal data extraction unit 430 and the vehicle-mounted device data extraction unit 440. Send alert information via.
The alert information includes at least one of text, an image, a voice, and the like indicating that the risk of occurrence of a traffic accident is high. The alert information may include “position information” of the mobile terminal T and the vehicle-mounted device A.
Note that the alert information transmission unit 450 may transmit different alert information between the mobile terminal T and the vehicle-mounted device A according to the functions of the mobile terminal T and the vehicle-mounted device. For example, the alert information transmission unit 450 may transmit, as alert information, a command for operating the vibration to the portable terminal T having a vibration function. In addition, when the mobile terminal T is outputting sound such as music, the alert information transmitting unit 450 may transmit a command for interrupting the sound and outputting sound for alert information as alert information.

Specifically, the alert information transmission unit 450 sets the same “device ID” as the “device ID” based on the “device ID” included in the data of the mobile terminal T extracted by the mobile terminal data extraction unit 430. The alert information is transmitted to the portable terminal T having the information.
When receiving the alert information, the portable terminal T outputs the alert information as visual information such as text and images as shown in FIG. Further, the mobile terminal T may output alert information as audio information from a speaker, or may notify a pedestrian that the alert information has been received by vibration or the like. Furthermore, when “position information” of the vehicle-mounted device A is included in the alert information, the position of the vehicle-mounted device A may be indicated by an image, a map, a voice, or the like. By making such a notification, it is possible to make a pedestrian carrying the mobile terminal T recognize that “the risk level is high” in the road section where the pedestrian is currently passing.
Moreover, the alert information transmission part 450 is based on "equipment ID" contained in the data of the onboard equipment A which the onboard equipment data extraction part 440 extracted, and onboard equipment which has the same "equipment ID" as the said "equipment ID" Send alert information to A.
Upon receiving the alert information, the vehicle-mounted device A outputs the alert information as visual information such as text and images as shown in FIG. 11, for example. Moreover, you may make it the onboard equipment A output alert information as audio | voice information from a speaker. Furthermore, when the “location information” of the mobile terminal T is included in the alert information, the position of the mobile terminal T may be indicated by an image, a map, a voice, or the like. By giving such a notification, the driver of the vehicle on which the vehicle-mounted device A is mounted has “high risk” of the road section that is currently traveling, and the road section that is currently traveling. Notify that there is a pedestrian whose “alert setting” is set to “ON”.
In addition, in this embodiment, although the alert information transmission part 450 demonstrated the example which transmits alert information to the portable terminal T and the onboard equipment A, it is not restricted to this. The alert information transmitting unit 450 may transmit alert information only to the mobile terminal T, or may transmit alert information only to the vehicle-mounted device A.

  In the recording medium 460, risk correlation data D20 and map data are recorded in advance.

(Danger notification device processing flow)
FIG. 12 is a first diagram showing a processing flow of the danger notification device according to the first embodiment of the present invention.
FIG. 13 is a second diagram showing a processing flow of the danger notification device according to the first embodiment of the present invention.
Hereinafter, the processing flow of the danger notification device 40 of the present embodiment will be described with reference to FIGS.

First, as shown in FIG. 12, the danger notification device 40 performs a process P40 for predicting a degree of danger indicating the probability of occurrence of a traffic accident in each road section.
In the process P40, first, the traffic condition index acquisition unit 410 selects a road section for which the degree of risk is predicted as the “target road section” (step S400).
For example, the traffic condition index acquisition unit 410 first selects the road section having the smallest link ID as the target road section. In the example of FIG. 2, the traffic condition index acquisition unit 410 selects the road section B10a having the smallest value “B10a” among the plurality of link IDs as the target road section.

  Next, the traffic condition index acquisition unit 410 acquires the vehicle traffic condition index of the target road section from the traffic condition database 30 via the network NW (step S401). Specifically, the traffic condition index acquisition unit 410 searches and acquires a vehicle traffic condition index having the same link ID as the link ID of the target road section from among the plurality of vehicle traffic information indices from the traffic condition database 30. To do.

  Next, the risk prediction unit 420 acquires the risk correlation data D20 of the target road section from the recording medium 460 (step S402). Specifically, the risk prediction unit 420 obtains risk correlation data D20 having the same “link ID” as the “link ID” of the target road section from the plurality of risk correlation data D20 recorded in the recording medium 460. Search and get.

Next, based on the vehicle traffic condition index of the target road section and the risk correlation data D20 of the target road section, the risk prediction unit 420 has the “risk with respect to traffic volume” of the target road section equal to or greater than the reference value r1. It is determined whether or not there is (step S403).
In the risk level correlation data D20 (traffic volume-risk level correlation data D20a) shown in FIG. 9, the reference value r1 of the “risk level with respect to traffic volume” of the road section B10a that is the target road section is set to 10%, for example. Yes. In addition, the range of the traffic value in which the degree of risk is the reference value r1 (10%) or more is “q1 or more and less than q2”.
For this reason, when the “traffic volume” of the vehicle traffic condition index of the road section B10a is included in the range of “q1 or more and less than q2”, the risk prediction unit 420 determines that “the risk level for the traffic volume of the road section B10a is the reference value r1. Is determined ”(step S403: YES), the process proceeds to the next step S404.
On the other hand, when the “traffic volume” included in the vehicle traffic condition index in the road section B10a is not included in the range of “q1 or more and less than q2”, the risk prediction unit 420 determines that “the degree of risk for the traffic volume in the road section B10a is It is determined that it is less than the reference value r1 (step S403: NO), and the process proceeds to step S407.

Next, based on the vehicle traffic condition index of the target road section and the risk correlation data D20 of the target road section, the risk prediction unit 420 has a “risk with respect to traffic density” of the target road section equal to or higher than the reference value r1. It is determined whether or not there is (step S404).
In the risk degree correlation data D20 (traffic density-risk degree correlation data D20b) shown in FIG. 9, the reference value r1 of the “risk with respect to traffic density” of the road section B10a that is the target road section is set to 10%, for example. Yes. In addition, the range of the traffic density value in which the degree of risk is the reference value r1 (10%) or more is “k1 or more and less than k2”.
For this reason, when the “traffic density” of the vehicle traffic condition index of the road section B10a is included in the range of “k1 or more and less than k2”, the risk prediction unit 420 indicates that the “risk degree with respect to the traffic density of the road section B10a is the reference value r1. It is determined that the above is true (step S404: YES), and the process proceeds to the next step S405.
On the other hand, when the “traffic density” of the vehicle traffic condition index of the road section B10a is not included in the range of “k1 or more and less than k2”, the risk prediction unit 420 determines that the “risk degree with respect to the traffic density of the road section B10a is the reference value r1. Is less than "(step S404: NO), the process proceeds to step S407.

Next, based on the vehicle traffic condition index of the target road section and the risk correlation data D20 of the target road section, the risk prediction unit 420 has the “risk with respect to the average speed” of the target road section equal to or higher than the reference value r1. It is determined whether or not there is (step S405).
In the risk level correlation data D20 (average speed-risk level correlation data D20c) shown in FIG. 9, the reference value r1 of the “risk level with respect to average speed” of the road section B10a that is the target road section is set to 10%, for example. Yes. Further, the range of the average speed value at which the degree of risk is the reference value r1 (10%) or more is “v1 or more”.
For this reason, when the “average speed” of the vehicle traffic condition index of the road section B10a is included in the range of “v1 or more”, the risk prediction unit 420 indicates that “the risk level with respect to the average speed of the road section B10a is greater than or equal to the reference value r1. "Yes" (step S405: YES), the process proceeds to the next step S406.
On the other hand, when the “average speed” of the vehicle traffic condition index of the road section B10a is not included in the range of “v1 or more”, the risk prediction unit 420 indicates that “the degree of danger with respect to the average speed of the road section B10a is less than the reference value r1. Is determined "(step S405: NO), the process proceeds to step S407.

  The risk prediction unit 420 determines that the risk level for each of the “traffic volume”, “traffic density”, and “average speed” of the vehicle in the target road section is greater than or equal to the reference value r1 (step S403: YES, step S404: YES, And step S405: YES), it is determined that the target road section is “high risk” (step S406). Then, the risk prediction unit 420 outputs the road section determined to be “high risk” to the portable terminal data extraction unit 430 as a dangerous road section. Specifically, the danger prediction unit 420 outputs the “link ID” of the dangerous road section to the mobile terminal data extraction unit 430.

Further, the danger prediction unit 420 determines that the degree of danger with respect to any of “traffic volume”, “traffic density”, and “average speed” of the vehicle in the target road section is less than the reference value r1 (step S403: NO, step S404). : NO or step S405: NO), it is determined that the target road section is “not dangerous” (step S407).
In this way, the danger notification device 40 executes the process P40 for all road sections.

In addition, as shown in FIG. 13, the danger notification device 40 includes at least one of the mobile terminal T and the vehicle-mounted device A located in each dangerous road section that the risk prediction unit 420 determines as “high risk” in the process P40. In addition, processing P41 for transmitting alert information is performed.
In process P41, first, the mobile terminal data extraction unit 430 extracts data of the mobile terminal T located in the dangerous road section from the traffic situation table D10 of the traffic situation database 30 (step S410). Specifically, the mobile terminal data extraction unit 430 extracts data of the mobile terminal T having the “link ID” of the dangerous road section (data whose “device type” is “mobile terminal”). The mobile terminal data extraction unit 430 further extracts data of the mobile terminal T whose “alert setting” is “ON” from the extracted data of the mobile terminal T.
When the mobile terminal data extraction unit 430 extracts one or more data of the mobile terminal T having the “link ID” of the dangerous road section and the “alert setting” being “ON”, the extracted mobile terminal The data of T is output to the alert information transmitting unit 450.

Next, the onboard equipment data extraction part 440 extracts the data of the onboard equipment A located in a dangerous road area from the traffic condition table D10 of the traffic condition database 30 (step S411).
Specifically, the vehicle-mounted device data extraction unit 440 first identifies a dangerous road section where a pedestrian exists among the dangerous road sections output from the danger prediction unit 420. For example, the vehicle-mounted device data extraction unit 440 receives from the portable terminal data extraction unit 430 “hazardous road section where there is a pedestrian (data of the portable terminal T) whose“ alert setting ”of the portable terminal T is“ ON ”. By acquiring the “link ID”, such a dangerous road section is specified. And the onboard equipment data extraction part 440 extracts the data (data whose "equipment classification" is "onboard equipment") of the onboard equipment A which has the same "link ID" as the "link ID" of the specified dangerous road section. To do. Moreover, the onboard equipment data extraction part 440 further extracts the data of the onboard equipment A whose "alert setting" is "ON" among the extracted data of the onboard equipment A.
As described above, the on-vehicle device data extraction unit 440 has at least one data of the on-vehicle device A having “link ID” of the dangerous road section where the pedestrian exists and having “alert setting” “ON”. When extracted, the data of the onboard equipment A extracted is output to the alert information transmission unit 450.

Next, the alert information transmission unit 450 is sent to at least one of the mobile terminal T and the vehicle-mounted device A based on the data of the mobile terminal T and the vehicle-mounted device A extracted by the mobile terminal data extraction unit 430 and the vehicle-mounted device data extraction unit 440. The alert information is transmitted via the network NW (step S412).
When the alert information transmission unit 450 completes the transmission of the alert information to each mobile terminal T and each vehicle-mounted device A, the danger notification device 40 ends the process for the target road section (road section B10a).
As described above, the danger notification device 40 performs the process P41 on all dangerous road sections.

(Hardware configuration of danger notification device)
FIG. 14 is a diagram illustrating an example of a hardware configuration of the danger notification device according to the first embodiment of the present invention.
Hereinafter, an example of the hardware configuration of the danger notification device 40 of the present embodiment will be described with reference to FIG.

The computer 900 includes a CPU 901, a main storage device 902, an auxiliary storage device 903, an input / output interface 904, and a communication interface 905.
The above-described danger notification device 40 is mounted on the computer 900. The operation of each processing unit described above is stored in the auxiliary storage device 903 in the form of a program. The CPU 901 reads a program from the auxiliary storage device 903, develops it in the main storage device 902, and executes the above processing according to the program. In addition, the CPU 901 ensures a storage area corresponding to the recording medium 460 in the main storage device 902 according to the program. In addition, the CPU 901 ensures a storage area for storing data being processed in the auxiliary storage device 903 according to the program. Note that the computer 900 may be connected to the external storage device 910 via the input / output interface 904, and a storage area corresponding to the recording medium 460 may be secured in the external storage device 910. Further, the computer 900 may be connected to the external storage device 920 via the communication interface 905, and a storage area corresponding to the recording medium 460 may be secured in the external storage device 920.

  In at least one embodiment, the auxiliary storage device 903 is an example of a tangible medium that is not temporary. Other examples of the tangible medium that is not temporary include a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, and a semiconductor memory connected via the input / output interface 904. When this program is distributed to the computer 900 via a communication line, the computer 900 that has received the distribution may develop the program in the main storage device 902 and execute the above processing.

  The program may be for realizing a part of the functions described above. Further, the program may be a so-called difference file (difference program) that realizes the above-described function in combination with another program already stored in the auxiliary storage device 903.

  Further, the danger notification device 40 may be configured by a single computer 900 or may be configured by a plurality of computers connected so as to be communicable. For example, each function of the danger notification device 40 may be included in the traffic condition index generation unit 20.

(Function and effect)
As described above, the danger notification device 40 according to the present embodiment includes the traffic condition index acquisition unit 410 that acquires the vehicle traffic condition index for each road section from the traffic condition database 30, and each road based on the vehicle traffic condition index. A risk prediction unit 420 that predicts the risk level indicating the probability of occurrence of a traffic accident in the section, and if the risk prediction unit determines that the risk level of the road section is equal to or greater than a predetermined reference value r1, the vehicle located in the road section An alert information transmitting unit 450 that transmits alert information to at least one of the onboard device A and the portable terminal T carried by the pedestrian.
The risk level of a road section may vary depending on the actual vehicle traffic volume, traffic density, and average speed of the road section. For example, when many high-speed vehicles are traveling in the road section B10a (FIG. 2), that is, the risk for each of the traffic volume, traffic density, and average speed of the vehicle in the road section B10a is a predetermined reference value r1. If this is the case, there is a possibility that the occurrence probability of a traffic accident will increase in the road section B10a. On the other hand, when a number of slow vehicles are traveling in the road section B10a due to traffic congestion or the like, that is, the risk of traffic volume and traffic density of the vehicle is greater than or equal to a predetermined reference value r1, but the risk of average vehicle speed When the degree is less than the predetermined reference value r1, there is a possibility that the occurrence probability of a traffic accident is lowered.
However, even if the degree of risk differs according to the actual situation of the road section in this way, the risk prediction unit 420 of the present embodiment predicts the degree of risk according to the vehicle traffic condition index for each road section. Therefore, it is possible to improve the accuracy of predicting the risk level of each road section. Further, the alert information transmission unit 450 transmits alert information to at least one of the vehicle-mounted device A and the mobile terminal T located in the dangerous road section determined by the risk prediction unit 420 to be “high risk” to alert the user. In addition, it is possible to suppress unnecessary alert information from being transmitted to the vehicle-mounted device A and the mobile terminal T located in the road section that the risk prediction unit 420 has determined that “the degree of risk is low”.

The risk prediction unit 420 also calculates the probability of occurrence of a traffic accident for each of the road traffic condition index acquired by the traffic condition index acquisition unit 410 and the traffic volume, traffic density, and average speed in the past vehicle traffic condition index for each road section. A risk level is predicted based on the associated risk level correlation data D20.
By doing in this way, the risk prediction unit 420 can obtain the current vehicle traffic condition index acquired by the traffic condition index acquisition unit 410 and the risk correlation data D20 indicating the probability of occurrence of a traffic accident with respect to the past vehicle traffic condition index. By referring to both, the prediction accuracy of the risk can be further improved.

Further, the alert information transmission unit 450 travels along the road section when the risk prediction unit 420 determines that the risk level of the road section is equal to or higher than the predetermined reference value r1 and when a pedestrian exists in the road section. Alert information is transmitted to the onboard equipment A mounted in the inside vehicle.
Specifically, when the risk prediction unit 420 determines that the risk level of the road section is equal to or higher than a predetermined reference value r1, the road section is output to the on-board device data extraction unit 440 as a “high risk” risk road section. To do. The onboard equipment data extraction part 440 extracts the data of the onboard equipment A mounted in the vehicle currently drive | working the dangerous road area where a pedestrian exists. And the alert information transmission part 450 transmits alert information to the said onboard equipment A based on the data of the onboard equipment A which the onboard equipment data extraction part 440 extracted.
By doing in this way, the alert information transmission part 450 transmits alert information to the onboard equipment A and alerts it with respect to the vehicle which has high danger and is driving the road area where a pedestrian exists. In addition, it is possible to suppress transmission of unnecessary alert information to the vehicle-mounted device A that is located in a road section where a pedestrian is not present although the degree of danger is high.

In addition, the mobile terminal data extraction unit 430 and the vehicle-mounted device data extraction unit 440 extract data of the mobile terminal T and the vehicle-mounted device A whose “alert setting” is “ON”. Accordingly, the alert information transmission unit 450 transmits the alert information to the mobile terminal T and the vehicle-mounted device A whose “alert setting” is “ON”, so that pedestrians and vehicles who wish to receive the alert information receive the alert information. Alerts can be given only to the driver. Moreover, transmission of unnecessary alert information can be suppressed for the portable terminal T and the vehicle-mounted device A that do not wish to receive alert information.
For example, a pedestrian who may not notice the approach of a vehicle, such as a person with visual impairment, has a high risk level of a road section during travel by setting “alert setting” of the mobile terminal T to “ON”. Can be recognized via the alert information transmitted to the mobile terminal T.
Moreover, the onboard equipment data extraction part 440 extracts the data of the onboard equipment A located in the danger road area where the pedestrian who has set "alert setting" of the portable terminal T to "ON" exists. For this reason, the alert information transmission unit 450 is configured so that pedestrians whose “alert setting” of the mobile terminal T is “ON”, that is, pedestrians who may not notice the approach of the vehicle, such as a visually impaired person, are on the same road. It is possible to alert the vehicle on which the vehicle-mounted device A is mounted by transmitting alert information that the section is passing.

Moreover, the danger notification system 1 according to the present embodiment includes the above-described danger notification device 40, on-vehicle probe data including position information of the on-vehicle device A mounted on each of a plurality of vehicles, and a plurality of pedestrians. A traffic condition index for each road section is generated based on probe data collection unit 10 that collects portable terminal probe data including position information of portable terminal T to be carried, on-board probe data, and portable terminal probe data. And a traffic condition index generation unit 20 that records in the traffic condition database 30.
By doing in this way, the traffic condition parameter | index production | generation part 20 produces | generates the traffic condition parameter | index according to the condition of the actual road section based on the onboard equipment probe data and portable terminal probe data which the probe data collection part 10 collects sequentially. can do. For this reason, the traffic condition index generation unit 20 can increase the accuracy of the traffic condition index. In addition, the danger notification device 40 can improve the prediction accuracy of the degree of danger of each road section based on such a highly accurate traffic condition index.

<Modification of First Embodiment>
Next, a danger notification system 1 according to a modification of the first embodiment of the present invention will be described with reference to FIGS.
Constituent elements common to the first embodiment are denoted by the same reference numerals, and detailed description thereof is omitted.
In the first embodiment, the aspect in which the risk prediction unit 420 of the danger notification device 40 predicts the risk level based on the “vehicle traffic condition index” and the “risk level correlation data” has been described. In the present modification, the risk prediction unit 420 of the danger notification device 40 predicts the risk based on the “vehicle traffic condition index”, the “pedestrian traffic condition index”, and the “danger degree correlation data”. This is different from the first embodiment.

(Danger notification device processing flow)
FIG. 15 is a diagram illustrating a processing flow of the danger notification device according to the modification of the first embodiment of the present invention.
FIG. 16 is a diagram illustrating an example of the risk correlation data according to the modification of the first embodiment of the present invention.
The danger notification device 40 according to the present modification predicts a risk indicating the probability of occurrence of a traffic accident in each road section, as shown in FIG. 15, instead of the process P40 (FIG. 12) of the first embodiment. Process P42 is performed.

In process P42, first, the traffic condition index acquisition unit 410 selects a road section for which the degree of risk is predicted as the “target road section” (step S420).
For example, the traffic condition index acquisition unit 410 first selects the road section having the smallest link ID as the target road section. In the example of FIG. 2, the traffic condition index acquisition unit 410 selects, as a target road section, a road section B10a having “B10a” having the smallest value among a plurality of “link IDs”.

  Next, the traffic condition index acquisition unit 410 acquires the vehicle traffic condition index and the pedestrian traffic condition index of the target road section from the traffic condition database 30 via the network NW (step S421). Specifically, the traffic condition index acquisition unit 410 searches the traffic condition database 30 for a vehicle traffic condition index having the same “link ID” as the “link ID” of the target road section among the plurality of vehicle traffic condition indices. And get. Also, the traffic condition index acquisition unit 410 searches the traffic condition database 30 for a pedestrian traffic condition index having the same “link ID” as the “link ID” of the target road section among the plurality of pedestrian traffic condition indices. Get.

  Next, the risk prediction unit 420 acquires the risk correlation data D21 (FIG. 16) of the target road section from the recording medium 460 (step S422). Specifically, the risk prediction unit 420 obtains risk correlation data D21 having the same “link ID” as the “link ID” of the target road section from the plurality of risk correlation data D21 recorded in the recording medium 460. Search and get.

The risk correlation data D21 is data indicating the correlation between the traffic volume, traffic density, and average speed of vehicles and pedestrians and the risk indicating the occurrence probability of a traffic accident for each road section. As shown in FIG. 16, each risk level correlation data D21 has “traffic volume-risk level correlation data D21a”, “traffic density-risk level correlation data D21b”, and “average speed-risk level correlation data D21c”. ing.
In the “traffic volume-risk degree correlation data D21a”, the horizontal axis represents the vehicle traffic volume (vehicles / second) and the pedestrian traffic volume (person / second), and the vertical axis represents the risk level (%) relative to the vehicle traffic volume. Indicates the degree of danger (%) with respect to the traffic volume of pedestrians. The solid line indicates the degree of danger with respect to the traffic volume of the vehicle, and the broken line indicates the degree of danger with respect to the traffic volume of the pedestrian. The degree of danger with respect to the traffic volume of the vehicle is the same as in the first embodiment. In addition, the degree of risk for pedestrian traffic is calculated as the probability (%) of traffic accidents with respect to pedestrian traffic by statistically counting the number of traffic accidents with respect to pedestrian traffic in each past road section. It is the data represented.
In the “traffic density-risk degree correlation data D21b”, the horizontal axis represents the traffic density (vehicle / m) of the vehicle and the traffic density of the pedestrian (person / m), and the vertical axis represents the degree of risk (%) relative to the traffic density of the vehicle. Indicates the degree of danger (%) for the traffic density of pedestrians. The solid line indicates the degree of danger with respect to the traffic density of the vehicle, and the broken line indicates the degree of danger with respect to the traffic density of the pedestrian. The degree of danger with respect to the traffic density of the vehicle is the same as in the first embodiment. In addition, the degree of danger for traffic density of pedestrians is calculated as the probability of occurrence of traffic accidents (%) against the traffic density of pedestrians by statistically counting the number of traffic accidents with respect to the traffic density of pedestrians in each past road section. It is the data represented.
In the “average speed-risk degree correlation data D21c”, the horizontal axis represents the average speed (m / s) of the vehicle and the average speed of the pedestrian (m / s), and the vertical axis represents the degree of danger (%) with respect to the average speed of the vehicle. Indicates the degree of danger (%) with respect to the average speed of pedestrians. The solid line indicates the degree of danger with respect to the average speed of the vehicle, and the broken line indicates the degree of danger with respect to the average speed of the pedestrian. The degree of danger with respect to the average speed of the vehicle is the same as in the first embodiment. In addition, the degree of danger to the average speed of pedestrians is calculated as the probability of occurrence of traffic accidents (%) relative to the average speed of pedestrians by statistically counting the number of traffic accidents with respect to the average speed of pedestrians in each past road It is the data represented.

Next, the risk prediction unit 420 determines the “risk for vehicle traffic volume” of the target road section based on the vehicle traffic condition index and the pedestrian traffic condition index of the target road section and the risk correlation data D21 of the target road section. It is determined whether or not “degree” is greater than or equal to the reference value r1 and “risk degree of pedestrian traffic” is greater than or equal to the reference value r1 (step S423).
In the risk level correlation data D21 (traffic volume-risk level correlation data D21a) shown in FIG. 16, the reference value r1 of the “risk level with respect to traffic volume” of the road section B10a that is the target road section is set to 10%, for example. Yes. Further, the range of the traffic volume value of the vehicle having a risk level equal to or higher than the reference value r1 (10%) is “q1 or more and less than q2”, and the pedestrian traffic volume value range is “q3 or more and less than q4”. is there.
Therefore, when the “traffic volume” of the vehicle traffic condition index of the road section B10a is included in the range of “q1 or more and less than q2,” and the “traffic volume” of the pedestrian traffic condition index is “q3 or more and less than q4”. When included in the range, the risk prediction unit 420 determines that “the degree of risk with respect to the traffic volume of the road section B10a is greater than or equal to the reference value r1” (step S423: YES), and proceeds to the next step S424.
On the other hand, when the “traffic volume” of the vehicle traffic condition index of the road section B10a is not included in the range of “q1 or more and less than q2”, or the “traffic volume” of the pedestrian traffic condition index is “q3 or more and less than q4” When not included in the range, the risk prediction unit 420 determines that “the degree of risk with respect to the traffic volume of the road section B10a is less than the reference value r1” (step S423: NO), and proceeds to step S427.

Next, the risk prediction unit 420 determines the “risk for vehicle traffic density of the target road section based on the vehicle traffic condition index and the pedestrian traffic condition index of the target road section and the risk correlation data D21 of the target road section. It is determined whether or not “degree” is greater than or equal to the reference value r1 and “risk degree of pedestrian traffic density” is greater than or equal to the reference value r1 (step S424).
In the risk level correlation data D21 (traffic density-risk level correlation data D21b) shown in FIG. 16, the reference value r1 of the “risk level with respect to traffic density” of the road section B10a that is the target road section is set to 10%, for example. Yes. In addition, the range of the traffic density value of the vehicle whose danger level is the reference value r1 (10%) or more is “k1 or more and less than k2”, and the range of the pedestrian traffic density value is “k3 or more and less than k4”. is there.
Therefore, when the “traffic density” of the vehicle traffic condition index of the road section B10a is included in the range of “k1 or more and less than k2”, and the “traffic density” of the pedestrian traffic condition index is “k3 or more and less than k4” If it is included in the range, the risk prediction unit 420 determines that “the risk level with respect to the traffic density of the road section B10a is greater than or equal to the reference value r1” (step S424: YES), and proceeds to the next step S425.
On the other hand, when the “traffic density” of the vehicle traffic condition index of the road section B10a is not included in the range of “k1 or more and less than k2”, or the “traffic density” of the pedestrian traffic condition index is “k3 or more and less than k4” When not included in the range, the risk prediction unit 420 determines that “the degree of risk with respect to the traffic density of the road section B10a is less than the reference value r1” (step S424: NO), and proceeds to step S427.

Next, the risk prediction unit 420 determines that the “danger with respect to the average speed of the vehicle of the target road section” based on the vehicle traffic condition index and the pedestrian traffic condition index of the target road section and the risk correlation data D21 of the target road section. It is determined whether or not “degree” is greater than or equal to reference value r1 and “risk with respect to the average speed of pedestrians” is greater than or equal to reference value r1 (step S425).
In the risk level correlation data D21 (average speed-risk level correlation data D21c) shown in FIG. 16, the reference value r1 of the “risk level with respect to average speed” of the road section B10a that is the target road section is set to 10%, for example. Yes. Further, the range of the average speed value of the vehicle having the risk level equal to or higher than the reference value r1 (10%) is “v1 or higher”, and the range of the average speed value of the pedestrian is “v2 or higher”.
For this reason, when the “average speed” of the vehicle traffic condition index of the road section B10a is included in the range of “v1 or more”, and the “average speed” of the pedestrian traffic condition index is included in the range of “v2 or more”. In this case, the risk prediction unit 420 determines that “the degree of risk with respect to the average speed of the road section B10a is equal to or greater than the reference value r1” (step S425: YES), and proceeds to the next step S426.
On the other hand, when the “average speed” of the vehicle traffic condition index of the road section B10a is not included in the range of “v1 or higher”, or the “average speed” of the pedestrian traffic condition index is included in the range of “v2 or higher”. If not, the risk prediction unit 420 determines that “the degree of risk with respect to the average speed of the road section B10a is less than the reference value r1” (step S425: NO), and proceeds to step S427.

  The risk prediction unit 420 determines that the risk level for each of the “traffic volume”, “traffic density”, and “average speed” of the vehicle and pedestrian in the target road section is greater than or equal to the reference value r1 (step S423: YES, step S424). : YES, and step S425: YES), it is determined that the target road section is “high risk” (step S426). Then, the risk prediction unit 420 outputs the road section determined to be “high risk” to the portable terminal data extraction unit 430 as a dangerous road section. Specifically, the danger prediction unit 420 outputs the “link ID” of the dangerous road section to the mobile terminal data extraction unit 430.

Further, the risk prediction unit 420 determines that the risk level for any of the “traffic volume”, “traffic density”, and “average speed” of the vehicle and pedestrian in the target road section is less than the reference value r1 (step S423: NO). Step S424: NO or Step S425: NO), it is determined that the target road section is “low risk” (Step S427).
In this way, the danger notification device 40 executes the process P42 for all road sections.

(Function and effect)
As described above, in the danger notification device 40 according to the present modification, the traffic condition index acquisition unit 410 acquires the vehicle traffic condition index and the pedestrian traffic condition index for each road section from the traffic condition database 30. The risk prediction unit 420 predicts a risk level indicating the probability of occurrence of a traffic accident in each road section based on the vehicle traffic condition index for each road section and the pedestrian traffic condition index.
The risk level of a road section may vary depending on the actual vehicle traffic volume, traffic density, and average speed of the road section, and the actual pedestrian traffic volume, traffic density, and average speed. For example, when many high-speed vehicles are traveling in the road section B10a (FIG. 2), that is, the risk for each of the traffic volume, traffic density, and average speed of the vehicle in the road section B10a is a predetermined reference value r1. Even in the case described above, when the traffic volume or traffic density of the pedestrian is low, the risk may be low.
However, even if the degree of risk differs according to the situation of the vehicle and the pedestrian in the road section, the risk prediction unit 420 of the present embodiment does not include the vehicle traffic condition index for each road section and the pedestrian traffic. Since the risk level is predicted according to both the situation and the risk level, the accuracy of the risk level prediction of each road section can be improved.

As mentioned above, although embodiment of this invention was described in detail, unless it deviates from the technical idea of this invention, it is not limited to these, A some design change etc. are possible.
For example, in the embodiment, the mode in which the pedestrian is carrying the mobile terminal T has been described, but the present invention is not limited to this. For example, the mobile terminal T may be attached to a light vehicle such as a bicycle. In this case, the alert information transmission unit 450 is provided on either or both of the portable terminal T carried by a pedestrian traveling in the dangerous road section and the vehicle-mounted device A mounted on the vehicle traveling on the dangerous road section. Alert information indicating that a light vehicle is present in the dangerous road section may be transmitted. Moreover, the alert information transmission part 450 may transmit the alert information which shows that a pedestrian and a vehicle exist in the said dangerous road area to the portable terminal T attached to the light vehicle which is passing the dangerous road area. .

DESCRIPTION OF SYMBOLS 1 Risk notification system 10 Probe data collection part 20 Traffic condition parameter | index production | generation part 30 Traffic condition database 40 Risk notification apparatus 410 Traffic condition parameter | index acquisition part 420 Risk prediction part 430 Portable terminal data extraction part 440 Onboard equipment data extraction part 450 Alert information transmission part 460 recording medium 900 computer 901 CPU
902 Main storage device 903 Auxiliary storage device 904 Input / output interface 905 Communication interface 910 External storage device 920 External storage device A On-board unit NW Network T Mobile terminal

Claims (6)

A traffic condition index acquisition unit for acquiring a traffic condition index of a road section from a traffic condition database;
Based on the traffic condition index, a risk prediction unit that predicts a risk indicating the probability of occurrence of a traffic accident in the road section,
When the risk prediction unit determines that the risk of the road section is equal to or higher than a predetermined reference value, alerts at least one of an on-board device mounted on a vehicle located in the road section and a portable terminal carried by a pedestrian An alert information transmitting unit for transmitting information;
A danger notification device comprising: The risk prediction unit
Predicting the risk based on the traffic condition index acquired by the traffic condition index acquisition unit and the risk correlation data that associates the occurrence probability of a traffic accident with respect to a past traffic condition index for each road section;
The danger notification device according to claim 1. The alert information transmitting unit
When the risk prediction unit determines that the risk of the road section is equal to or higher than a predetermined reference value, and when a pedestrian exists in the road section, the alert information is transmitted to the vehicle-mounted device.
The danger notification device according to claim 1 or 2. The danger notification device according to any one of claims 1 to 3,
A probe data collection unit that collects OBE probe data including position information of the OBE mounted in each of a plurality of vehicles, and portable terminal probe data including position information of portable terminals carried by a plurality of pedestrians. When,
Based on the on-vehicle probe data and the mobile terminal probe data, a traffic condition index generating unit that generates a traffic condition index for each road section and records it in a traffic condition database;
A danger notification system comprising: A traffic condition index acquisition step of acquiring a traffic condition index of a road section from a traffic condition database;
A risk prediction step of predicting a risk level in the road section based on the traffic condition index;
When it is determined in the risk prediction step that the degree of risk of the road section is equal to or higher than a predetermined reference value, an alert is issued to at least one of an on-board device mounted on a vehicle located in the road section and a portable terminal carried by a pedestrian An alert information sending step for sending information;
Hazard notice method. Danger notification device computer
A traffic condition index acquisition unit for acquiring a traffic condition index of a road section from a traffic condition database;
A risk prediction unit that predicts a risk level in the road section based on the traffic condition index;
When the risk prediction unit determines that the risk level of the road section is equal to or higher than a predetermined reference value, alert information is sent to at least one of an on-board device mounted on a vehicle located in the road section and a mobile terminal carried by a pedestrian Alert information transmission unit,
Program to function as.

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