JP7446872B2 - Road change detection system, road change detection method, road change detection program, and storage medium - Google Patents

Description

The present invention relates to a road change detection system, a road change detection method, a road change detection program, and a storage medium.

It is generally difficult to obtain information about changes in road markings such as lane markings and lane arrows on roads where vehicles travel. Therefore, there is a need for a system that detects changes in the local area (road) by detecting changes in traffic conditions using probe information that includes vehicle travel history information.
Patent Document 1 below discloses that an estimated value of the current traffic condition is calculated by checking changes in the traffic condition between the past and present of passing vehicles in the area surrounding the road link obtained from probe information, and calculating the estimated value. A system is disclosed that determines that the link is closed when the statistical error is less than the statistical error.

JP2019-053555A

However, with conventional systems, although it was possible to confirm whether a road was closed, it was difficult to detect changes in the road. In order to detect road changes, for example, even if probe information is aggregated on a daily basis and old and new traffic conditions are compared, characteristics that can detect local changes may not appear. Another problem is that the processing load increases at intersections with heavy traffic conditions.

In addition, if you can identify dates and times when traffic conditions are similar and compare the old and new ones, it will be easier to detect local changes. The impact on traffic conditions is significant, and if such conditions are classified in detail, it may not be possible to determine the date and time for comparison at intersections with low traffic conditions.

SUMMARY OF THE INVENTION Therefore, an object of the present invention is to provide a road change detection system that can comprehensively evaluate traffic conditions that change due to various external factors and detect road changes with high accuracy.

A road change detection system in one embodiment acquires probe information of a vehicle over time, including at least position information regarding the position of a vehicle traveling on the road and time information regarding the time when the vehicle acquired the position information. an acquisition unit that classifies the traveling speed characteristics of each of the plurality of vehicles in a predetermined area on the road into a plurality of classes using the plurality of probe information acquired by the acquisition unit; The apparatus includes a detection unit that detects the presence or absence of a road change in a predetermined area when the classification is based on date.

The road change detection system of one aspect may include an output unit that outputs detection result information regarding the presence or absence of a road change in a predetermined area detected by the detection unit to the outside.

In one aspect of the road change detection system, the probe information includes driving environment conditions specified from the probe information when the vehicle is running, and the classification unit uses the driving environment conditions included in the probe information to After selecting the probe information that is to be classified into multiple classes from among the probe information, the selected multiple probe information is used to determine multiple characteristics of the traveling speed of each of the multiple vehicles in a predetermined area on the road. may be classified into classes.

In one embodiment of the road change detection system, the probe information includes driving environment conditions specified from the probe information when the vehicle is running, and the classification unit includes speed features obtained from characteristics of the driving speed and the driving environment. The attributes of a condition may be classified into a plurality of classes as coordinate axes of a vector space.

In one aspect of the road change detection system, the driving environment condition may include at least one of the day of the week, date, time zone, and weather.

In one aspect of the road change detection system, the traveling speed characteristic may be an average value of traveling speeds in a predetermined region or a maximum speed.

In one embodiment of the road change detection system, the characteristic of the traveling speed may be a change in the traveling speed over time in a predetermined region.

In one aspect of the road change detection system, the probe information may include vehicle operation information regarding vehicle operation, and the change in travel speed may be identified using the vehicle operation information.

In one aspect of the road change detection method, a computer collects vehicle probe information over time, including at least position information regarding the position of a vehicle traveling on a road and time information regarding the time when the vehicle acquired the position information. a classification step of classifying the traveling speed characteristics of each of the plurality of vehicles in a predetermined area on the road into a plurality of classes using the plurality of probe information obtained in the acquisition step; When the classes are classified based on dates, a detection step of detecting the presence or absence of road changes in a predetermined area is executed.

In one embodiment, the road change detection program causes a computer to transmit probe information of a vehicle over time, including at least position information regarding the position of a vehicle traveling on a road, and time information regarding the time when the vehicle acquired the position information. a classification function that classifies the traveling speed characteristics of multiple vehicles in a predetermined area on the road into multiple classes using the multiple probe information acquired by the acquisition function; A detection function is realized for detecting the presence or absence of a road change in a predetermined area when classes are classified based on dates.

In the road change detection system of the present invention, the classification unit uses a plurality of pieces of probe information to classify the running speed characteristics of each of a plurality of vehicles in a predetermined area on a road into a plurality of classes. Then, the detection unit checks the contents of the class classified by the classification unit, and detects the presence or absence of a road change in a predetermined area based on the date.

In this way, traffic conditions that vary due to various external factors can be comprehensively evaluated, and road changes can be detected with high accuracy.
Further, the road change detection method and the road change detection program according to one embodiment can achieve the same effects as the road change detection system according to one embodiment described above.

1 is a schematic diagram of a road change detection system according to an embodiment. It is a schematic diagram explaining an example of a road change. FIG. 2 is a block diagram illustrating the configuration of the road change detection system shown in FIG. 1. FIG. FIG. 3 is a diagram illustrating an example of a change in the traveling speed of a vehicle at an intersection. FIG. 2 is a conceptual diagram illustrating clustering data created by clustering performed by a classification unit. 4 is a flowchart illustrating processing in the processing unit shown in FIG. 3. FIG. It is a figure explaining the evaluation result of the running speed of the vehicle in an example.

An embodiment of the present invention will be described below.
FIG. 1 is a schematic diagram of a road change detection system 30 according to one embodiment.
Road change detection system 30 is used with vehicle 10 and server 20.
The vehicle 10 obtains its own probe information and transmits it to the server 20. Although only one vehicle 10 is shown in FIG. 1, there may be a plurality of vehicles 10.
The server 20 stores, for example, a plurality of pieces of probe information. Specifically, the server 20 is a traffic information collection device that stores a plurality of pieces of probe information transmitted from one or more vehicles 10.

The road change detection system 30 is, for example, a device that can process a large amount of information and is suitable for so-called big data processing, and mainly detects changes in the road and alerts the driver before driving on the relevant section. It has a function to output in advance. Here, a specific example of a road change will be explained using FIG. 2. FIG. 2 is a schematic diagram illustrating an example of a road change.

As shown in FIG. 2, a new left turn lane may be newly established in multiple approach lanes to an intersection. The construction of these new lanes will relieve congestion in the left lane and change the traffic situation. In addition to the addition of lanes, such changes to roads include changes in partition lines, changes in lane arrows marked on lanes (changes in the role of lanes), changes in speed regulations, changes in arrow signals, and changes in parking regulations. etc. Furthermore, changes in the road are not limited to these, but include, for example, changes in road shape such as changing the radius of a curve, and other artificial changes on the road that affect the speed of the vehicle 10 actually traveling. is included.

A navigation device (not shown) mounted on the vehicle 10 guides a route to the destination when the destination is set. The road change detection system 30 communicates wirelessly with the navigation device to display processing details on the navigation device.

Next, the road change detection system 30 will be explained in detail using FIG. 3.
FIG. 3 is a block diagram illustrating the configuration of the road change detection system 30.
As shown in FIG. 3, the road change detection system 30 includes a communication section 40, a storage section 50, and a processing section 100.

The communication unit 40 communicates with the server 20 or the vehicle 10 via a communication network, for example. The communication unit 40 acquires a plurality of pieces of probe information from the server 20, for example, based on the control of the road change detection system 30. Note that the communication unit 40 may receive probe information transmitted from the vehicle 10.

Here, the probe information is road traffic information generated using information such as the position where the car actually traveled and the vehicle speed.
The probe information includes at least traveling position information (position information) and identification information. The traveling position information is information regarding the traveling position of the vehicle 10. The identification information is information for identifying the vehicle 10 (vehicle ID). Further, the probe information includes measurement time information (time information) indicating the time when predetermined latitude and longitude information was measured.

Further, the probe information includes vehicle operation information (CAN information). The vehicle operation information is various information regarding the operation of the vehicle 10, and is information for controlling the vehicle including, for example, steering angle information indicating a steering angle and a traveling speed estimated from the number of rotations of the wheels.
The vehicle operation information includes braking operation information that is information about a change in acceleration of the vehicle 10 due to a braking operation (brake).

Braking operation information is information indicating a braking operation performed on the vehicle 10 traveling on the road. That is, the braking operation information is information indicating that a braking operation was performed on the road, and the time at which the braking operation was performed is associated with information regarding the location where the braking operation was performed.
Further, the braking operation information may include information indicating the manner of the braking operation, that is, the amount of depression of the brake, the pedaling force (acceleration at the time of depression), etc., and vehicle operation information when the braking operation is performed. May be treated as part of

The storage unit 50 is a device that stores various information including map information, various control programs, and the like. The storage unit 50 stores probe information and clustering data, which will be described later.
The storage unit 50 stores in advance a point set as a destination, as an example of storing map information. A point set as a destination is a point set as an end point of a guide route (driving route), and is specified by position coordinate information on a map.

For example, the road change detection system 30 transmits the map information stored in the storage unit 50 as described above to the vehicle 10 via the communication unit 40, and causes the navigation device of the vehicle 10 to use the map information. It's okay.
Further, the road change detection system 30 may record the map information on a recording medium such as an external memory or an optical disc, for example. In this case, the vehicle 10 may read map information recorded on a recording medium and use the map information in the navigation device.

The processing unit 100 is a processor that has the function of performing various processes in the road change detection system 30. The processing unit 100 may be realized by a logic circuit (hardware) formed in an integrated circuit (IC (Integrated Circuit) chip, LSI (Large Scale Integration)) or a dedicated circuit, or may be realized by using a CPU (Central Processing Unit) and memory. It may also be realized by software.

The processing section 100 includes a reception section 110, an acquisition section 120, a classification section 130, a detection section 140, and an output section 150.
The reception unit 110 receives input of a front image photographed in front of the vehicle 10 from a photographing device. The photographing device is, for example, a drive recorder, and records the situation in front of the vehicle 10 over time while it is running or stopped. The photographing device may be a camera mounted on a smartphone of the driver or a fellow passenger. Further, the photographing device may be a camera installed outside the vehicle 10.

The acquisition unit 120 acquires vehicle operation information regarding the operation of the vehicle 10 over time. The acquisition unit 120 acquires vehicle operation information by acquiring probe information via a communication network.
The probe information includes position information regarding the position of the vehicle 10 traveling on the road, and time information regarding the time when the vehicle 10 acquired the position information. The acquisition unit 120 includes a position information acquisition unit 121 and a CAN information acquisition unit 122.

The position information acquisition unit 121 acquires latitude and longitude information measured by the GNSS positioning system while the vehicle 10 is traveling, along with measurement time information indicating the time at which the latitude and longitude information was measured, for one traveling route of the vehicle 10.
Note that the position information acquisition unit 121 may acquire a plurality of probe information of a plurality of vehicles heading from a departure point to a destination, or may acquire probe information of one vehicle multiple times. Further, the position information acquisition unit 121 may periodically acquire probe information at each preset sampling period. The sampling period can be arbitrarily set, for example, to several months, half a year, or one year.

The vehicle 10 can record traveling position information of the vehicle 10 by using, for example, GNSS (Global Navigation Satellite System) (hereinafter simply referred to as a positioning system). By continuously or intermittently acquiring the position information, the vehicle 10 can record the position information (driving position information) of the route traveled by the vehicle 10 in the probe information.

The vehicle 10 records a vehicle ID (identification information) for identifying the own vehicle and other vehicles in the probe information.
For example, the vehicle 10 records information (measurement time information) regarding the time when the traveling position information is acquired (or the traveling position information is recorded as probe information) in the probe information.

Here, when generating the probe information, the vehicle 10 may record the destination set by the navigation device in the probe information. For example, when a navigation device is used, the vehicle 10 may record the point (coordinates) at which the vehicle 10 leaves the link indicating the road in the probe information based on the travel position information.

The position information acquisition unit 121 acquires probe information from the server 20 (traffic information collection device) that accumulates probe information of the vehicle 10 as latitude and longitude information and measurement time information on one driving route of one specific vehicle 10. do.
The CAN information acquisition unit 122 acquires CAN information that is vehicle control information of the vehicle 10 from among the probe information.

The classification unit 130 uses the plurality of pieces of probe information acquired by the acquisition unit 120 to classify (clustering) the traveling speed characteristics of each of the plurality of vehicles in a predetermined area on the road into a plurality of classes.
Here, the predetermined area refers to a certain continuous area on a road section, such as between traffic lights installed at the same intersection or on the same travel route, and includes a certain area within a certain range of several meters to several tens of meters. It is a concept that refers to In other words, it refers to a region where a group of vehicles 10 traveling on the road section influence each other's traveling speeds.

The traveling speed characteristic is, for example, the average value of the traveling speed in a predetermined region or the maximum speed. That is, a value obtained by averaging the traveling speeds of a plurality of vehicles 10 within a predetermined region can be used as the characteristic of the traveling speed. At this time, if it is confirmed from the probe information that the vehicle 10 was stopped near a traffic light, the average value of the traveling speed may be calculated excluding the time when the vehicle 10 was stopped. Alternatively, the maximum speed may be taken as a characteristic of the running speed. This content will be explained in detail using FIGS. 4 and 5.
FIG. 4 is a diagram illustrating an example of a change in the traveling speed of a vehicle at an intersection. FIG. 5 is a conceptual diagram illustrating clustering data created by clustering performed by the classification unit 130.

First, the relationship between changes in travel speed and changes in road will be explained using FIG. 4.
As shown in FIG. 4, for example, if you check the monthly intersection speed ratio, you can see that there is a large change between August and September. In this case, it can be confirmed that the number of vehicles 10 with an approach speed of 40 km/h to 60 km/h has increased, and the number of vehicles 10 with an approach speed of 20 km/h to 40 km/h has decreased. In such a case, for example, on a road that does not have separate left-turn lanes, the construction of a new left-turn lane may have reduced the number of cars waiting for left-turn vehicles and increased the speed at which they entered the intersection. It turns out.

Next, classification of changes in traveling speed by the classification unit 130 will be explained.
In FIG. 5, clustering is performed using a plurality of travel data on March 1st, March 2nd, and March 3rd. In this process, clustering is performed using two parameters: the driver's driving history and the average traveling speed. Furthermore, an intersection is set as the predetermined area.

As shown in FIG. 5, generally speaking, the longer a driver has a driving history, the higher the driving speed in a predetermined area. On the other hand, in this clustering result, the average value of the traveling speed on March 3rd is higher than the average value of the traveling speeds on March 1st and March 2nd. Therefore, it can be assumed that congestion near intersections has been alleviated due to the introduction of left turn lanes on March 3rd, for example.

Note that this clustering process is just an example. The manner of clustering is not limited to the classification shown in FIG. 5, as long as the information can capture speed changes. For example, data on the driving speed of one driver during commuting may be collected for a certain period of time and clustered to estimate when a speed change occurred.

Further, the characteristic of the traveling speed may be a change in the traveling speed over time in a predetermined region. The time course of the traveling speed is the magnitude of the acceleration of the vehicle 10 obtained by differentiating the traveling speed with respect to time. That is, information such as whether the plurality of vehicles 10 are accelerating or decelerating within a predetermined region can be used as the characteristic of the traveling speed.
Further, the traveling speed characteristics are not limited to these examples, and include various types of information obtained by performing mathematical processing on speed information.

Furthermore, the classification unit 130 classifies the characteristics of the traveling speed into a plurality of classes using the traveling environmental conditions as well as the change in the traveling speed. Specifically, the detection unit 140 selects the probe information to be clustered from among the plurality of pieces of probe information, and then uses the selected pieces of probe information to detect a predetermined location on the road for each of the plurality of vehicles. The characteristics of traveling speed in a region are classified into multiple classes.

Here, the driving environment condition is information indicating the environment in which the vehicle 10 travels. The driving environment conditions are included in the probe information.
The driving environment conditions include at least one of the day of the week, the date, the pattern of the day, the driving time (time zone), the weather, and the driver's driving history. The daily pattern is a factor that affects traffic conditions, such as the day of the week, the 50th day, weekdays/holidays, long holidays, school holidays, etc., so by performing clustering using the daily pattern information as an attribute, Accurate detection can be performed.

Weather, like the weather, is also a factor that affects traffic conditions. Regarding a driver's driving history, for example, when making a right or left turn at an intersection, those with less driving history tend to drive more carefully, so by clustering this information as an attribute, accurate classification can be performed. .
In this manner, by selecting the probe information to be clustered based on the driving environment conditions, it is possible to perform clustering using a data group that allows speed changes to be easily compared.

Further, the classification unit 130 may set the speed feature amount obtained from the characteristics of the traveling speed and the attributes of the traveling environment conditions as coordinate axes of a vector space, and perform the classification process. In other words, the characteristics of the traveling speed obtained from the probe information and the attributes of the traveling environment conditions are expressed as vectors expressed in multiple dimensions, and by comparing the degree of similarity between these, it is possible to estimate when the speed changed. You can also.

By statistically understanding the traveling speed of the vehicle 10, it is possible to evaluate whether or not there has been a road change in a predetermined area. Here, the change in traveling speed may be acquired from probe information or from vehicle operation information.

The detection unit 140 detects the presence or absence of a road change in a predetermined area when the plurality of classes classified by the classification unit 130 are classified based on dates. That is, if the manner in which the traveling speed changes significantly based on the date, it is detected that there has been a road change based on the date of the change.

The output unit 150 outputs detection result information regarding the presence or absence of a road change in a predetermined area detected by the detection unit 140 to the outside. There are various methods for outputting information regarding the presence or absence of road changes to the outside.

For example, by outputting information indicating the presence or absence of a road change to the navigation device of the vehicle 10, it is possible to alert the vehicle 10 before driving in the section. Specifically, a message such as "The road appears to be changing at the intersection ahead" can be output to the display monitor of the navigation device.

Next, the processing contents of the processing unit 100 in the road change detection system 30 will be explained using FIG. 6. FIG. 6 is a flowchart illustrating processing in the processing unit 100.
As shown in FIG. 6, the acquisition unit 120 in the processing unit 100 acquires probe information (ST10: acquisition step). At this time, the position information acquisition unit 121 acquires position information from among the probe information, and the CAN information acquisition unit 122 acquires driving operation information from among the probe information.

Next, the classification unit 130 performs clustering to classify the traveling speed characteristics (ST20: classification step). As a result, clustering data is created and stored in the storage unit 50.
Next, the detection unit 140 detects the presence or absence of a road change (ST30: detection step). At this time, the detection unit 140 refers to the clustering data and determines whether there is a significant change in the traveling speed characteristics. In this case, the presence or absence of a significant change may be determined based on, for example, whether the amount of change exceeds a preset threshold.
Next, the output unit 150 outputs detection result information, which is information about the presence or absence of a road change and what kind of road change there has been (ST40: output step).

<Example>
Here, an example verified on an actual road will be described using FIG. 7. FIG. 7 is a diagram illustrating the evaluation results of vehicle running speeds at intersections.
As shown in FIG. 7, changes in the approach speeds of left-turning vehicles and straight-going vehicles were evaluated when the left-turn lane and the straight-forward lane were the same lane and when they were each set up as separate lanes. In this case, no major changes were observed in left-turning vehicles, but the vehicle speed range changed significantly in straight-going vehicles. Specifically, it was confirmed that the proportion of vehicles 10 traveling at 20 km/h to 40 km/h decreased, and the proportion of vehicles 10 traveling at 40 km/h to 60 km/h increased. In this example, it was confirmed that detection is easier by using the probe information of a vehicle going straight instead of a vehicle turning left.

As explained above, according to the road change detection system 30 according to the present embodiment, the classification unit 130 uses a plurality of pieces of probe information to determine the characteristics of the traveling speed of each of the plurality of vehicles 10 in a predetermined area on the road. Classify into multiple classes.
Then, the detection unit 140 checks the contents of the classes classified by the classification unit 130, and detects the presence or absence of a road change in a predetermined area based on the date.

In this way, traffic conditions that vary due to various external factors can be comprehensively evaluated, and road changes can be detected with high accuracy.
Further, by using such a road change detection method and a road change detection program, it is possible to achieve the same effects as the road change detection system 30 of one embodiment described above.

In addition, the output unit 150 outputs detection result information regarding the presence or absence of road changes in the predetermined area detected by the detection unit 140 to the outside, so by outputting it to the driver, the driver is notified of the road change in advance. It can encourage attention.

Further, the classification unit 130 selects the probe information to be classified into the plurality of classes from among the plurality of probe information using the driving environment conditions of the vehicle 10 specified from the probe information, and then Using the probe information, the traveling speed characteristics of each of the plurality of vehicles in a predetermined area on the road are classified into a plurality of classes.
Therefore, among the large amount of probe information obtained by the acquisition unit 120, it is possible to select probe information with similar attributes of driving environment conditions and classify it into multiple classes, making it possible to perform highly accurate detection. can.

Further, since the driving environment conditions include at least one of the day of the week, date, time zone, and weather, accurate detection can be performed using various driving environment conditions.
In addition, if the traveling speed characteristic is the average value of the traveling speed in a predetermined area, the presence or absence of a road change can be detected by simple processing using the traveling speed information obtained from the probe information. .
Furthermore, if the characteristic of the traveling speed is a change in the traveling speed over time in a predetermined region, the process takes into account the timing of the braking operation by the driver of the vehicle 10, etc., so that the presence or absence of road changes can be determined with even higher accuracy. can be detected.

Further, when the probe information includes vehicle operation information regarding the operation of the vehicle 10 and a change in traveling speed is specified using the vehicle operation information, generally, vehicle operation information that is measured more frequently than the probe information is used. By using this method, highly accurate detection can be performed even in environments where it is difficult to obtain probe information, for example.

Note that the configuration described above is just one embodiment, and various changes and functions can be integrated or separated without departing from the spirit of the present invention.
For example, in this embodiment, a configuration is shown in which the road change detection system 30 is a device separate from the navigation device mounted on the vehicle 10, but the present invention is not limited to such an aspect. A part of the functions of the road change detection system 30 may be realized by a control unit installed in a navigation device.

Moreover, each part of the road change detection system 30 described above may be realized as a function of a computer processing unit or the like. That is, the reception unit 110, acquisition unit 120, classification unit 130, detection unit 140, and output unit 150 in the processing unit 100 of the road change detection system 30 have reception functions, acquisition functions, classification functions, They may be implemented as a detection function and an output function, respectively. The acquisition function includes a position information acquisition function and a CAN information acquisition function.

Moreover, the road change detection program can cause a computer to implement each of the above-mentioned functions. The road change detection program may be recorded on a computer-readable non-transitory recording medium such as an external memory or an optical disk.

Further, as described above, each part of the road change detection system 30 may be realized by a computer processing unit or the like. The arithmetic processing device and the like are constituted by, for example, an integrated circuit or the like. Therefore, each part of the road change detection system 30 may be realized as a circuit that constitutes an arithmetic processing unit or the like.

10 Vehicle 20 Server 30 Road change detection system 40 Communication unit 50 Storage unit 100 Processing unit 110 Reception unit 120 Acquisition unit 121 Position information acquisition unit 122 CAN information acquisition unit 130 Classification unit 140 Detection unit 150 Output unit

Claims (10)

an acquisition unit that acquires vehicle probe information over time, including at least position information regarding the position of a vehicle traveling on a road and time information regarding the time when the vehicle acquired the position information;
Using the plurality of pieces of probe information acquired by the acquisition unit, the temporal change in running speed is classified into a plurality of classes as a characteristic of the running speed in a predetermined area on the road for each of the plurality of vehicles , and clustering data is generated. A classification section to be created ,
When the plurality of classes are classified based on dates, the clustering data is referred to, and the road in the predetermined area is determined based on whether the amount of change in the traveling speed characteristic exceeds a predetermined threshold. A road change detection system comprising: a detection unit that detects the presence or absence of a change. The road change detection system according to claim 1, further comprising an output unit that outputs detection result information regarding the presence or absence of a road change in the predetermined area detected by the detection unit to the outside. The probe information includes driving environmental conditions specified from the probe information when the vehicle is traveling,
The classification unit selects probe information to be classified into a plurality of classes from among the plurality of probe information using the driving environment condition included in the probe information, and then selects the plurality of selected probe information. The road change detection system according to claim 1 or 2, wherein the characteristics of the traveling speed of each of the plurality of vehicles in a predetermined area on the road are classified into a plurality of classes using the method. The probe information includes driving environmental conditions specified from the probe information when the vehicle is traveling,
The road change detection system according to claim 1, wherein the classification unit classifies the speed feature amount obtained from the characteristics of the traveling speed and the attribute of the driving environment condition into a plurality of classes as coordinate axes of a vector space. The road change detection system according to claim 3 or 4, wherein the driving environment conditions include at least one of day of the week, date, time zone, and weather. The road change detection system according to any one of claims 1 to 5, wherein the characteristic of the traveling speed is an average value or a maximum speed of the traveling speed in the predetermined region. The probe information includes vehicle operation information regarding the operation of the vehicle,
The road change detection system according to claim 1 , wherein the temporal change in the traveling speed is specified using the vehicle operation information. The computer is
an acquisition step of acquiring probe information of a vehicle over time, including at least position information regarding the position of the vehicle traveling on the road and time information regarding the time when the vehicle acquired the position information;
Using the plurality of pieces of probe information acquired in the acquisition step, clustering data is generated by classifying temporal changes in running speed into a plurality of classes as a characteristic of running speed in a predetermined area on the road for each of the plurality of vehicles. a classification step to create ;
When the plurality of classes are classified based on dates, the clustering data is referred to, and the road in the predetermined area is determined based on whether the amount of change in the traveling speed characteristic exceeds a predetermined threshold. a detection step of detecting the presence or absence of a change; and a road change detection method. to the computer,
an acquisition function that acquires vehicle probe information over time, including at least position information regarding the position of a vehicle traveling on a road and time information regarding the time when the vehicle acquired the position information;
Using the plurality of pieces of probe information acquired by the acquisition function, the temporal change in running speed is classified into a plurality of classes as a characteristic of the running speed in a predetermined area on the road for each of the plurality of vehicles , and clustering data is generated. The classification function to be created ,
When the plurality of classes are classified based on dates, the clustering data is referred to, and the road in the predetermined area is determined based on whether the amount of change in the traveling speed characteristic exceeds a predetermined threshold. A road change detection program that realizes a detection function that detects the presence or absence of changes. to the computer,
an acquisition function that acquires vehicle probe information over time, including at least position information regarding the position of a vehicle traveling on a road and time information regarding the time when the vehicle acquired the position information;
Using the plurality of pieces of probe information acquired by the acquisition function, the temporal change in running speed is classified into a plurality of classes as a characteristic of the running speed in a predetermined area on the road for each of the plurality of vehicles , and clustering data is generated. The classification function to be created ,
When the plurality of classes are classified based on dates, the clustering data is referred to, and the road in the predetermined area is determined based on whether the amount of change in the traveling speed characteristic exceeds a predetermined threshold. A storage medium that stores a road change detection program that realizes a detection function that detects the presence or absence of a change.

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