CN111862586B - Method and device for determining abnormal road section of road area and storage medium - Google Patents
Method and device for determining abnormal road section of road area and storage medium Download PDFInfo
- Publication number
- CN111862586B CN111862586B CN201911266717.XA CN201911266717A CN111862586B CN 111862586 B CN111862586 B CN 111862586B CN 201911266717 A CN201911266717 A CN 201911266717A CN 111862586 B CN111862586 B CN 111862586B
- Authority
- CN
- China
- Prior art keywords
- track
- road
- weight value
- road section
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Traffic Control Systems (AREA)
- Navigation (AREA)
Abstract
The invention provides a method and a device for determining an abnormal road section of a road area and a storage medium. The method for determining the abnormal road section of the road area comprises the following steps: acquiring track data of a plurality of historical orders, and determining yaw information of the plurality of historical orders according to the track data; acquiring a plurality of target start and stop points and track mode information of each target start and stop point in a road area according to the track data and the yaw information; and determining an abnormal road section of the road area according to the track mode information. The technical scheme of the invention is different from a road network abnormity detection method for excavating road network abnormity by taking road section flow as an index through a statistical model and the like in the related technology, takes the problems of yaw information, road network space complexity, uneven road section heat distribution and the like into consideration, positions abnormity through track mode information of a plurality of target starting and stopping points, avoids the abnormity condition of the road section judged by a single road section flow, and improves the accuracy of road section abnormity positioning.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for determining an abnormal road section of a road area and a storage medium.
Background
In general, the research on the operation condition of the road network is based on the change of the traffic of the road network, and the data source includes the cross-sectional traffic collected by the coil detector, video and GPS (Global Positioning System) data of the vehicle.
The method in the related art generally detects traffic anomaly by using a statistical model based on the traffic of a single road segment. For example, normal data usually appears in a region with a higher probability in the random model, and when an event with a lower probability occurs, it is considered that an abnormality occurs, that is, the flow rate of the road section is statistically regular, and when an event with a low probability occurs, it is considered as an abnormal point. In the related technology, taxi GPS data is used for monitoring abnormal behaviors occurring in a certain area, LRT (Likelihood Ratio Test statistics) is used for describing traffic modes, grid drawing processing is carried out on road areas, the aim is to determine a set of adjacent grids and time periods, and the grids are deviated from expected behaviors in the time periods with high probability, so that the frequently occurring abnormality and the impending abnormality are found. Although the statistical method can be applied to anomaly detection of a single road section or a single area, a statistical model is required to be established for each road section, and the statistical model is easily over sensitive or over slow, so that the flow anomaly of a low-heat road section cannot be judged.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art or the related art.
To this end, an aspect of the present invention is to propose an abnormal section determination method for a road region.
Another aspect of the present invention is to provide an abnormal section determining apparatus for a road region.
Yet another aspect of the present invention is directed to a computer-readable storage medium.
In view of the above, according to an aspect of the present invention, there is provided a method for determining an abnormal link of a road area, including: acquiring track data of a plurality of historical orders, and determining yaw information of the plurality of historical orders according to the track data; acquiring a plurality of target start and stop points and track mode information of each target start and stop point in a road area according to the track data and the yaw information; and determining an abnormal road section of the road area according to the track mode information.
The method for determining the abnormal road section of the road area obtains the track data of a plurality of historical orders, wherein the track data can comprise navigation track data and actual track data, and the yaw information of the historical orders is determined by the two track data. Further, a plurality of target start and stop points of any road area are obtained according to the track data and the yaw information, the road area is a certain area through which the historical order track passes (the technical scheme of the present invention performs abnormal section judgment on the sections in the road area), the historical order track is divided into a plurality of sections in the road area, and the target start and stop points are the critical OD (Origin Destination, departure point and travel Destination) through which the historical order track passes in the road area. And then obtaining track mode information (pattern) of each target start and stop point, wherein each track mode information comprises information such as track data and proportion under the corresponding target start and stop point, for example, three target start and stop points are provided, the track data under the first target start and stop point is 18 (namely, 18 routes passing through the first target start and stop point are provided or 18 vehicles pass through the first target start and stop point in one day is indicated), the track data under the second target start and stop point is 20, the track data under the third target start and stop point is 12, and the proportion occupied by the first target start and stop point, the second target start and stop point and the third target start and stop point is respectively 36%, 40% and 24%. And finally, determining an abnormal road section in the road area according to the track mode information. The technical scheme of the invention is different from a road network abnormity detection method for excavating road network abnormity by taking road section flow as an index through a statistical model and the like in the related technology, the technical scheme of the invention considers the yaw information in the track information, considers the problems of complexity of a road network space, uneven road section heat distribution and the like, positions abnormity through the track mode information of a plurality of target starting and stopping points, avoids the abnormal condition of the road section judged by the flow of a single road section, and improves the accuracy of road section abnormity positioning.
According to the method for determining an abnormal link in a road area of the present invention, the following technical features may be further provided:
in the above technical solution, preferably, the yaw information includes information of a yaw rate and a yaw order, and the trajectory data includes navigation trajectory data and actual trajectory data; the method for acquiring the track pattern information of the multiple target start and stop points and each target start and stop point in the road area according to the track data and the yaw information specifically comprises the following steps: determining a plurality of high-heat start and stop points in the road area according to the track data; screening out high-heat start and stop points with the yaw rate larger than a preset threshold value and not repeated with other yaw orders of the high-heat start and stop points from the plurality of high-heat start and stop points, and taking the high-heat start and stop points as target start and stop points; and for any target starting and stopping point, acquiring the predicted track mode information of the target starting and stopping point in the navigation track data, and acquiring the actual track mode information of the target starting and stopping point in the actual track data.
According to the technical scheme, starting and stopping points with more passing tracks are used as high-heat starting and stopping points in a road area according to track data, high-heat starting and stopping points with a yaw rate larger than a preset threshold value and without repeating with yaw orders of other high-heat starting and stopping points are screened out from the high-heat starting and stopping points to be used as target starting and stopping points, predicted track mode information of the target starting and stopping points is obtained from navigation track data aiming at any target starting and stopping point, actual track mode information of the target starting and stopping points is obtained from actual track data, then mining of the target starting and stopping points and track mode information is completed, and judgment conditions are provided for positioning of abnormal road sections.
In any of the above technical solutions, preferably, the step of determining the abnormal section of the road region according to the track pattern information specifically includes: calculating a track selection probability, a track weight value and a road section weight value according to the predicted track mode information and the actual track mode information; and determining an abnormal road section of the road area according to the track selection probability, the track weight value and the road section weight value.
According to the technical scheme, the predicted track mode information and the actual track mode information are analyzed to obtain the track selection probability, the track weight value and the road section weight value, so that the track selection probability, the track weight value and the road section weight value are integrated to achieve accurate positioning of the abnormal road section of the road area.
In any of the above technical solutions, preferably, the step of calculating a trajectory selection probability, a trajectory weight value, and a link weight value according to the predicted trajectory mode information and the actual trajectory mode information specifically includes: acquiring the number of new tracks which exist in the actual track mode information but do not exist in the predicted track mode information, calculating the ratio of the number of the new tracks to the number of the predicted tracks in the predicted track mode information, and taking the ratio as track selection probability; acquiring the turning angle of the new track and the detour probability of the new track, and taking the number of the new track, the turning angle of the new track and the detour probability of the new track as track weight values; and determining the weight value of the road section according to the distance between the road section and the starting point.
In the technical scheme, the track selection probability is a driver path selection behavior decision: and calculating the posterior probability that the user intention is the predicted pattern under each newly-appeared pattern condition, namely the ratio of the newly-appeared track in the actual track mode information to the number of the predicted tracks in the predicted track mode information. The number of the new tracks, the turning angle of the new tracks and the detour probability of the new tracks are used as track weight values, and the track weight values can be the average value, the weight value, the addition value and the like of the three values. The link weight value may be determined according to a distance between the link and the starting point, and may be set to be larger as the link is closer to the starting point. When the abnormal positioning is carried out, the driver path selection behavior and the reason behind the driver path selection behavior are considered, semantic information is given to the track data, and the accurate positioning of the abnormal is realized through the difference between the actual behavior mode and the expected behavior mode of the driver.
In any of the above technical solutions, preferably, the step of determining the abnormal road segment in the road region according to the track selection probability, the track weight value, and the road segment weight value specifically includes: and acquiring the comprehensive score of each road section in the road area according to the track selection probability, the track weight value and the road section weight value, and taking the road section with the highest comprehensive score as an abnormal road section.
According to the technical scheme, the comprehensive score of each road section in the road area is obtained according to the track selection probability, the track weight value and the road section weight value, the comprehensive score can be an average value, a weight value, an addition value and the like of the three values, and the comprehensive score can be determined according to different requirements. And further, taking the road section with the highest comprehensive score as an abnormal road section.
According to another aspect of the present invention, an abnormal road segment determination apparatus for a road region is provided, including a memory, a processor, and a computer program stored on the memory and operable on the processor, the processor implementing when executing the computer program: acquiring track data of a plurality of historical orders, and determining yaw information of the plurality of historical orders according to the track data; acquiring a plurality of target start and stop points and track mode information of each target start and stop point in a road area according to the track data and the yaw information; and determining an abnormal road section of the road area according to the track mode information.
The device for determining the abnormal road section of the road area obtains the track data of a plurality of historical orders, wherein the track data can comprise navigation track data and actual track data, and the two types of track data determine the yaw information of the historical orders. Further, a plurality of target start and stop points of any road area are obtained according to the track data and the yaw information, the road area is a certain area through which the historical order track passes (the technical scheme of the present invention performs abnormal section judgment on the sections in the road area), the historical order track is divided into a plurality of sections in the road area, and the target start and stop points are the critical OD (Origin Destination, departure point and travel Destination) through which the historical order track passes in the road area. And then obtaining track mode information (pattern) of each target start and stop point, wherein each track mode information comprises information such as track data and proportion under the corresponding target start and stop point, for example, three target start and stop points are provided, the track data under the first target start and stop point is 18 (namely, 18 routes passing through the first target start and stop point are provided or 18 vehicles pass through the first target start and stop point in one day is indicated), the track data under the second target start and stop point is 20, the track data under the third target start and stop point is 12, and the proportion occupied by the first target start and stop point, the second target start and stop point and the third target start and stop point is respectively 36%, 40% and 24%. And finally, determining an abnormal road section in the road area according to the track mode information. The technical scheme of the invention is different from a road network abnormity detection method for excavating road network abnormity by taking road section flow as an index through a statistical model and the like in the related technology, the technical scheme of the invention considers the yaw information in the track information, considers the problems of complexity of a road network space, uneven road section heat distribution and the like, positions abnormity through the track mode information of a plurality of target starting and stopping points, avoids the abnormal condition of the road section judged by the flow of a single road section, and improves the accuracy of road section abnormity positioning.
The abnormal link determination device for a road area according to the present invention may further include the following technical features:
in the above technical solution, preferably, the yaw information includes information of a yaw rate and a yaw order, and the trajectory data includes navigation trajectory data and actual trajectory data; the processor executes the step of acquiring a plurality of target start and stop points and track mode information of each target start and stop point in the road area according to the track data and the yaw information, and specifically includes: determining a plurality of high-heat start and stop points in the road area according to the track data; screening out high-heat start and stop points with the yaw rate larger than a preset threshold value and not repeated with other yaw orders of the high-heat start and stop points from the plurality of high-heat start and stop points, and taking the high-heat start and stop points as target start and stop points; and for any target starting and stopping point, acquiring the predicted track mode information of the target starting and stopping point in the navigation track data, and acquiring the actual track mode information of the target starting and stopping point in the actual track data.
According to the technical scheme, starting and stopping points with more passing tracks are used as high-heat starting and stopping points in a road area according to track data, high-heat starting and stopping points with a yaw rate larger than a preset threshold value and without repeating with yaw orders of other high-heat starting and stopping points are screened out from the high-heat starting and stopping points to be used as target starting and stopping points, predicted track mode information of the target starting and stopping points is obtained from navigation track data aiming at any target starting and stopping point, actual track mode information of the target starting and stopping points is obtained from actual track data, then mining of the target starting and stopping points and track mode information is completed, and judgment conditions are provided for positioning of abnormal road sections.
In any of the above technical solutions, preferably, the processor executes the step of determining the abnormal section of the road region according to the track pattern information, and specifically includes: calculating a track selection probability, a track weight value and a road section weight value according to the predicted track mode information and the actual track mode information; and determining an abnormal road section of the road area according to the track selection probability, the track weight value and the road section weight value.
According to the technical scheme, the predicted track mode information and the actual track mode information are analyzed to obtain the track selection probability, the track weight value and the road section weight value, so that the track selection probability, the track weight value and the road section weight value are integrated to achieve accurate positioning of the abnormal road section of the road area.
In any of the above technical solutions, preferably, the processor performs the step of calculating a trajectory selection probability, a trajectory weight value, and a link weight value according to the predicted trajectory mode information and the actual trajectory mode information, and specifically includes: acquiring the number of new tracks which exist in the actual track mode information but do not exist in the predicted track mode information, calculating the ratio of the number of the new tracks to the number of the predicted tracks in the predicted track mode information, and taking the ratio as track selection probability; acquiring the turning angle of the new track and the detour probability of the new track, and taking the number of the new track, the turning angle of the new track and the detour probability of the new track as track weight values; and determining the weight value of the road section according to the distance between the road section and the starting point.
In the technical scheme, the track selection probability is a driver path selection behavior decision: and calculating the posterior probability that the user intention is the predicted pattern under each newly-appeared pattern condition, namely the ratio of the newly-appeared track in the actual track mode information to the number of the predicted tracks in the predicted track mode information. The number of the new tracks, the turning angle of the new tracks and the detour probability of the new tracks are used as track weight values, and the track weight values can be the average value, the weight value, the addition value and the like of the three values. The link weight value may be determined according to a distance between the link and the starting point, and may be set to be larger as the link is closer to the starting point. When the abnormal positioning is carried out, the driver path selection behavior and the reason behind the driver path selection behavior are considered, semantic information is given to the track data, and the accurate positioning of the abnormal is realized through the difference between the actual behavior mode and the expected behavior mode of the driver.
In any of the above technical solutions, preferably, the processor executes the step of determining the abnormal road segment of the road region according to the track selection probability, the track weight value and the road segment weight value, and specifically includes: and acquiring the comprehensive score of each road section in the road area according to the track selection probability, the track weight value and the road section weight value, and taking the road section with the highest comprehensive score as an abnormal road section.
According to the technical scheme, the comprehensive score of each road section in the road area is obtained according to the track selection probability, the track weight value and the road section weight value, the comprehensive score can be an average value, a weight value, an addition value and the like of the three values, and the comprehensive score can be determined according to different requirements. And further, taking the road section with the highest comprehensive score as an abnormal road section.
According to a further aspect of the present invention, a computer-readable storage medium is proposed, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method for determining an abnormal section of a road area according to any one of the above-mentioned technical solutions.
The present invention provides a computer-readable storage medium, wherein a computer program is executed by a processor to implement the steps of the method for determining an abnormal link for a road area according to any one of the above-mentioned technical solutions, and therefore the computer-readable storage medium includes all the advantageous effects of the method for determining an abnormal link for a road area according to any one of the above-mentioned technical solutions.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 shows a flowchart of an abnormal section determination method of a road area according to an embodiment of the present invention;
FIG. 2 shows a schematic diagram of a trajectory at target start and stop points for one embodiment of the present invention;
fig. 3 shows a schematic block diagram of an abnormal section determination apparatus of a road region of one embodiment of the present invention;
fig. 4 is a schematic diagram illustrating a schematic principle of a road network anomaly discovery method based on OD-pattern according to an embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
Definitions of terms in the examples of the present invention are given:
a road section r: the road network structure is expressed by road segments and road-segment topological relations, wherein one directional edge is arranged in the road network, two end points of the road segment r are r.s and r.e respectively, vehicles in the road network range from r.s to r.e.
The trajectory traj: a track is represented by a succession of segments, being a sequence of successive segments, for example: traj ═ r1→r2→r3→...→rn，r(k+1).s＝rk.e，r(k+1).s＝rk.e，k∈[1,n-1]. The trajectory in this embodiment is divided into a navigation trajectory and an actual trajectory. Track set TRAJ ═ TRAJ1,traj2,...,trajm}。
OD: in the set of trajectories TRAJ studied, any one of the path subsequences of length 2 that appears in a trajectory < rO,rD＞。
pattern: for each < rO,rD>. Pattern is the number and proportion of traces corresponding to the route at OD, as shown in Table 1, < rO,rDThe route under includes < rO,r1,r2,rD＞、＜rO,r3,r4,r5,rD＞、＜rO,r6,rD>. The number of tracks is 18, 20, 12, respectively, i.e. at < rO,r1,r2,rDThe track of the vehicle passing above is 18, and r is lessO,r3,r4,r5,rD20 vehicle tracks passing above, at < rO,r6,rDThe number of the vehicle tracks passing through the vehicle is 12, and the vehicle tracks account for 36%, 40% and 24% respectively.
TABLE 1
| traj | Number of | Ratio of |
| ＜rO,r1,r2,rD＞ | 18 | 36％ |
| ＜rO,r3,r4,r5,rD＞ | 20 | 40％ |
| ＜rO,r6,rD＞ | 12 | 24％ |
Fig. 1 is a flowchart illustrating a method for determining an abnormal road segment in a road area according to an embodiment of the present invention. Wherein, the method comprises the following steps:
102, acquiring track data of a plurality of historical orders, and determining yaw information of the plurality of historical orders according to the track data;
104, acquiring a plurality of target start and stop points and track mode information of each target start and stop point in the road area according to the track data and the yaw information;
and step 106, determining an abnormal road section of the road area according to the track mode information.
The method for determining the abnormal road section of the road area obtains the track data of a plurality of historical orders, wherein the track data can comprise navigation track data and actual track data, and the yaw information of the historical orders is determined by the two track data. Further, a plurality of target start and stop points of any road area are obtained according to the track data and the yaw information, the road area is a certain area through which the historical order track passes (the technical scheme of the present invention performs abnormal section judgment on the sections in the road area), the historical order track is divided into a plurality of sections in the road area, and the target start and stop points are the critical OD (Origin Destination, departure point and travel Destination) through which the historical order track passes in the road area. And then obtaining track mode information (pattern) of each target start and stop point, wherein each track mode information comprises information such as track data and proportion under the corresponding target start and stop point, for example, three target start and stop points are provided, the track data under the first target start and stop point is 18 (namely, 18 routes passing through the first target start and stop point are provided or 18 vehicles pass through the first target start and stop point in one day is indicated), the track data under the second target start and stop point is 20, the track data under the third target start and stop point is 12, and the proportion occupied by the first target start and stop point, the second target start and stop point and the third target start and stop point is respectively 36%, 40% and 24%. And finally, determining an abnormal road section in the road area according to the track mode information. The technical scheme of the invention is different from a road network abnormity detection method for excavating road network abnormity by taking road section flow as an index through a statistical model and the like in the related technology, the technical scheme of the invention considers the yaw information in the track information, considers the problems of complexity of a road network space, uneven road section heat distribution and the like, positions abnormity through the track mode information of a plurality of target starting and stopping points, avoids the abnormal condition of the road section judged by the flow of a single road section, and improves the accuracy of road section abnormity positioning.
Optionally, the yaw information includes information of a yaw rate and a yaw order, and the trajectory data includes navigation trajectory data and actual trajectory data; step 104, obtaining a plurality of target start and stop points and track mode information of each target start and stop point in the road area according to the track data and the yaw information, specifically comprising: determining a plurality of high-heat start and stop points in the road area according to the track data; screening out high-heat start and stop points with the yaw rate larger than a preset threshold value and not repeated with other yaw orders of the high-heat start and stop points from the plurality of high-heat start and stop points, and taking the high-heat start and stop points as target start and stop points; and for any target starting and stopping point, acquiring the predicted track mode information of the target starting and stopping point in the navigation track data, and acquiring the actual track mode information of the target starting and stopping point in the actual track data.
In the embodiment, according to the track data, the start and stop points with more tracks are taken as high-heat start and stop points in the road area, high-heat start and stop points with a yaw rate larger than a preset threshold value and without repeating with the yaw orders of other high-heat start and stop points are screened out from the plurality of high-heat start and stop points as target start and stop points, for any target start and stop point, predicted track mode information of the target start and stop point is obtained from navigation track data, actual track mode information of the target start and stop point is obtained from actual track data, then mining of the target start and stop points and the track mode information is completed, and judgment conditions are provided for positioning of an abnormal road section.
The track mode information may include the number of tracks, as shown in fig. 2, a certain road area has target start and stop points O1D2, the target start and stop points O1D2 include tracks a, B, and C, that is, the number of tracks of the target start and stop points O1D2 is 3, the track a includes a road segment rA1, a road segment rA2, and a road segment rA3, the track B includes a road segment rB1, a road segment rB2, and a road segment rB3, and the track C includes a road segment rC1, a road segment rC2, a road segment rC3, and a road segment rC 4.
Optionally, in step 106, the step of determining an abnormal section of the road area according to the track mode information specifically includes: calculating a track selection probability, a track weight value and a road section weight value according to the predicted track mode information and the actual track mode information; and determining an abnormal road section of the road area according to the track selection probability, the track weight value and the road section weight value.
In this embodiment, the predicted track mode information and the actual track mode information are analyzed to obtain a track selection probability, a track weight value and a road section weight value, so that the track selection probability, the track weight value and the road section weight value are integrated to realize accurate positioning of an abnormal road section of the road area.
Optionally, the step of calculating a trajectory selection probability, a trajectory weight value, and a road section weight value according to the predicted trajectory mode information and the actual trajectory mode information specifically includes: acquiring the number of new tracks which exist in the actual track mode information but do not exist in the predicted track mode information, calculating the ratio of the number of the new tracks to the number of the predicted tracks in the predicted track mode information, and taking the ratio as track selection probability; acquiring the turning angle of the new track and the detour probability of the new track, and taking the number of the new track, the turning angle of the new track and the detour probability of the new track as track weight values; and determining the weight value of the road section according to the distance between the road section and the starting point.
In this embodiment, the trajectory selection probability is the driver path selection behavior decision: and calculating the posterior probability that the user intention is the predicted pattern under each newly-appeared pattern condition, namely the ratio of the newly-appeared track in the actual track mode information to the number of the predicted tracks in the predicted track mode information. The number of the new tracks, the turning angle of the new tracks and the detour probability of the new tracks are used as track weight values, and the track weight values can be the average value, the weight value, the addition value and the like of the three values. The link weight value may be determined according to a distance between the link and the starting point, and may be set to be larger as the link is closer to the starting point. When the abnormal positioning is carried out, the driver path selection behavior and the reason behind the driver path selection behavior are considered, semantic information is given to the track data, and the accurate positioning of the abnormal is realized through the difference between the actual behavior mode and the expected behavior mode of the driver.
Optionally, the step of determining an abnormal road segment of the road region according to the track selection probability, the track weight value, and the road segment weight value specifically includes: and acquiring the comprehensive score of each road section in the road area according to the track selection probability, the track weight value and the road section weight value, and taking the road section with the highest comprehensive score as an abnormal road section.
In this embodiment, a comprehensive score of each road segment in the road area is obtained according to the trajectory selection probability, the trajectory weight value, and the road segment weight value, the comprehensive score may be an average value, a weight value, a sum value, and the like of the above three values, and the comprehensive score may be determined according to different needs. And further, taking the road section with the highest comprehensive score as an abnormal road section.
In the embodiment of the second aspect of the present invention, an abnormal link determining device for a road area is proposed, and fig. 3 shows a schematic block diagram of an abnormal link determining device 30 for a road area according to an embodiment of the present invention. The abnormal road segment determination device 30 for the road region includes a memory 302, a processor 304, and a computer program stored on the memory 302 and operable on the processor 304, and the processor 304 implements, when executing the computer program:
acquiring track data of a plurality of historical orders, and determining yaw information of the plurality of historical orders according to the track data; acquiring a plurality of target start and stop points and track mode information of each target start and stop point in a road area according to the track data and the yaw information; and determining an abnormal road section of the road area according to the track mode information.
The abnormal road section determining device 30 of the road area provided by the invention obtains track data of a plurality of historical orders, wherein the track data can comprise navigation track data and actual track data, and the two track data determine yaw information of the historical orders. Further, a plurality of target start and stop points of any road area are obtained according to the track data and the yaw information, the road area is a certain area through which the historical order track passes (the technical scheme of the present invention performs abnormal section judgment on the sections in the road area), the historical order track is divided into a plurality of sections in the road area, and the target start and stop points are the critical OD (Origin Destination, departure point and travel Destination) through which the historical order track passes in the road area. And then obtaining track mode information (pattern) of each target start and stop point, wherein each track mode information comprises information such as track data and proportion under the corresponding target start and stop point, for example, three target start and stop points are provided, the track data under the first target start and stop point is 18 (namely, 18 routes passing through the first target start and stop point are provided or 18 vehicles pass through the first target start and stop point in one day is indicated), the track data under the second target start and stop point is 20, the track data under the third target start and stop point is 12, and the proportion occupied by the first target start and stop point, the second target start and stop point and the third target start and stop point is respectively 36%, 40% and 24%. And finally, determining an abnormal road section in the road area according to the track mode information. The technical scheme of the invention is different from a road network abnormity detection method for excavating road network abnormity by taking road section flow as an index through a statistical model and the like in the related technology, the technical scheme of the invention considers the yaw information in the track information, considers the problems of complexity of a road network space, uneven road section heat distribution and the like, positions abnormity through the track mode information of a plurality of target starting and stopping points, avoids the abnormal condition of the road section judged by the flow of a single road section, and improves the accuracy of road section abnormity positioning.
Optionally, the yaw information includes information of a yaw rate and a yaw order, and the trajectory data includes navigation trajectory data and actual trajectory data; the processor 304 executes a step of acquiring a plurality of target start and stop points and track pattern information of each target start and stop point in the road area according to the track data and the yaw information, which specifically includes: determining a plurality of high-heat start and stop points in the road area according to the track data; screening out high-heat start and stop points with the yaw rate larger than a preset threshold value and not repeated with other yaw orders of the high-heat start and stop points from the plurality of high-heat start and stop points, and taking the high-heat start and stop points as target start and stop points; and for any target starting and stopping point, acquiring the predicted track mode information of the target starting and stopping point in the navigation track data, and acquiring the actual track mode information of the target starting and stopping point in the actual track data.
In the embodiment, according to the track data, the start and stop points with more tracks are taken as high-heat start and stop points in the road area, high-heat start and stop points with a yaw rate larger than a preset threshold value and without repeating with the yaw orders of other high-heat start and stop points are screened out from the plurality of high-heat start and stop points as target start and stop points, for any target start and stop point, predicted track mode information of the target start and stop point is obtained from navigation track data, actual track mode information of the target start and stop point is obtained from actual track data, then mining of the target start and stop points and the track mode information is completed, and judgment conditions are provided for positioning of an abnormal road section.
Optionally, the processor 304 executes a step of determining an abnormal road segment of the road area according to the track mode information, which specifically includes: calculating a track selection probability, a track weight value and a road section weight value according to the predicted track mode information and the actual track mode information; and determining an abnormal road section of the road area according to the track selection probability, the track weight value and the road section weight value.
In this embodiment, the predicted track mode information and the actual track mode information are analyzed to obtain a track selection probability, a track weight value and a road section weight value, so that the track selection probability, the track weight value and the road section weight value are integrated to realize accurate positioning of an abnormal road section of the road area.
Optionally, the processor 304 executes a step of calculating a trajectory selection probability, a trajectory weight value and a road segment weight value according to the predicted trajectory mode information and the actual trajectory mode information, and specifically includes: acquiring the number of new tracks which exist in the actual track mode information but do not exist in the predicted track mode information, calculating the ratio of the number of the new tracks to the number of the predicted tracks in the predicted track mode information, and taking the ratio as track selection probability; acquiring the turning angle of the new track and the detour probability of the new track, and taking the number of the new track, the turning angle of the new track and the detour probability of the new track as track weight values; and determining the weight value of the road section according to the distance between the road section and the starting point.
In this embodiment, the trajectory selection probability is the driver path selection behavior decision: and calculating the posterior probability that the user intention is the predicted pattern under each newly-appeared pattern condition, namely the ratio of the newly-appeared track in the actual track mode information to the number of the predicted tracks in the predicted track mode information. The number of the new tracks, the turning angle of the new tracks and the detour probability of the new tracks are used as track weight values, and the track weight values can be the average value, the weight value, the addition value and the like of the three values. The link weight value may be determined according to a distance between the link and the starting point, and may be set to be larger as the link is closer to the starting point. When the abnormal positioning is carried out, the driver path selection behavior and the reason behind the driver path selection behavior are considered, semantic information is given to the track data, and the accurate positioning of the abnormal is realized through the difference between the actual behavior mode and the expected behavior mode of the driver.
Optionally, the processor 304 executes a step of determining an abnormal road segment of the road region according to the track selection probability, the track weight value and the road segment weight value, which specifically includes: and acquiring the comprehensive score of each road section in the road area according to the track selection probability, the track weight value and the road section weight value, and taking the road section with the highest comprehensive score as an abnormal road section.
In this embodiment, a comprehensive score of each road segment in the road area is obtained according to the trajectory selection probability, the trajectory weight value, and the road segment weight value, the comprehensive score may be an average value, a weight value, a sum value, and the like of the above three values, and the comprehensive score may be determined according to different needs. And further, taking the road section with the highest comprehensive score as an abnormal road section.
The invention provides a road network anomaly discovery method based on OD-pattern, which is characterized in that the driving habits of drivers are mined through driver behavior patterns to realize accurate positioning of anomalies in a road network, road sections are used as basic nodes to express a road network structure, and anomalies in the road network are classified into node anomalies and anomalies among the nodes, namely road closure, intersection constraint and the like. The principle of the road network anomaly discovery method based on OD-pattern is shown in FIG. 4:
1. obtaining order yaw information through a navigation track and an actual track, counting a high yaw section, taking the high yaw section as a research object, screening out a key OD influencing yaw according to a key OD combination selection method based on a decision tree idea, specifically, obtaining a high-heat OD in a region as a candidate OD set by adopting a Prefix span algorithm (sequence mode algorithm), and screening OD pairs in the candidate OD set by combining the yaw information candidate OD set to obtain the key OD capable of really reflecting yaw reasons.
2. And mining the corresponding actual pattern from the actual track and the corresponding expected pattern from the navigation track through the key OD, and finishing the mining of the OD pattern.
3. Analyzing the expected pattern and the actual pattern, and realizing the accurate positioning of the abnormal road section through the following factors:
a) driver routing behavior decision (driver behavior mining): calculating the posterior probability that the user intention is the predicted pattern under each newly appeared pattern condition, namely obtaining the number of newly appeared patterns which exist in the actual pattern but do not exist in the predicted pattern, and calculating the ratio of the number of newly appeared patterns to the number of predicted patterns, for example, the predicted paths comprise M paths and N paths, the newly appeared paths are P paths, and the probability that the number of vehicles walking on the P paths respectively corresponds to the number of vehicles walking on the M paths and the number of vehicles walking on the N paths is calculated.
b) The weight of the newly appeared pattern includes the number, the shape of the route (i.e., whether the vehicle is turning around), and the distance compared to the predicted pattern (i.e., whether the newly appeared pattern is detouring).
c) The contribution of the link in the route change (because of the probability of the change due to the problem), the more ahead, the higher the probability is considered, that is, the link weight value is determined according to the distance between the link and the starting point, and the closer the link is to the starting point, the higher the link weight value is.
The embodiment of the invention is different from a road network abnormity detection method which generally takes road section flow as an index and excavates road network abnormity through a statistical model and the like, the embodiment of the invention considers the problems of road network space complexity, uneven road section heat distribution and the like, and considers the context information of the occurrence of deviation in track information; when the abnormal positioning is carried out, the driver path selection behavior and the reason behind the driver path selection behavior are considered, semantic information is given to the track data, and the accurate positioning of the abnormal is realized through the difference between the actual behavior mode and the expected behavior mode of the driver.
In an embodiment of the third aspect of the present invention, a computer-readable storage medium is proposed, on which a computer program is stored, which when being executed by a processor, implements the steps of the method for determining an abnormal segment of a road area according to any one of the above-mentioned embodiments.
The present invention provides a computer-readable storage medium, which when being executed by a processor implements the steps of the method for determining an abnormal link for a road area according to any one of the above embodiments, and therefore, includes all the advantageous effects of the method for determining an abnormal link for a road area according to any one of the above embodiments.
In the description herein, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance unless explicitly stated or limited otherwise; the terms "connected," "mounted," "secured," and the like are to be construed broadly and include, for example, fixed connections, removable connections, or integral connections; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the description herein, the description of the terms "one embodiment," "some embodiments," "specific embodiments," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A method for determining an abnormal link in a road area, comprising:
obtaining track data of a plurality of historical orders, and determining yaw information of the plurality of historical orders according to the track data, wherein the yaw information comprises yaw rate and information of yaw orders, and the track data comprises navigation track data and actual track data;
determining a plurality of high heat start points and stop points in the road area according to the track data;
screening out high heat start and stop points of which the yaw rate is greater than a preset threshold value and is not repeated with other yaw orders of the high heat start and stop points from the plurality of high heat start and stop points, and taking the high heat start and stop points as target start and stop points; for any target starting and stopping point, acquiring predicted track mode information of the target starting and stopping point in the navigation track data, and acquiring actual track mode information of the target starting and stopping point in the actual track data;
and determining an abnormal road section of the road area according to the track mode information.
2. The method for determining the abnormal link of the road area according to claim 1, wherein the step of determining the abnormal link of the road area according to the track pattern information specifically includes:
calculating a track selection probability, a track weight value and a road section weight value according to the predicted track mode information and the actual track mode information;
and determining an abnormal road section of the road area according to the track selection probability, the track weight value and the road section weight value.
3. The method according to claim 2, wherein the step of calculating a trajectory selection probability, a trajectory weight value, and a link weight value according to the predicted trajectory pattern information and the actual trajectory pattern information specifically includes:
acquiring the number of new tracks which exist in the actual track mode information but do not exist in the predicted track mode information, calculating the ratio of the number of the new tracks to the number of the predicted tracks in the predicted track mode information, and taking the ratio as track selection probability;
acquiring the turning angle of the new track and the detour probability of the new track, and taking the average value, the weight value and the sum value of the three values of the number of the new track, the turning angle of the new track and the detour probability of the new track as the track weight value;
and determining the weight value of the road section according to the distance between the road section and the starting point.
4. The method for determining the abnormal link of the road area according to claim 2 or 3, wherein the step of determining the abnormal link of the road area according to the track selection probability, the track weight value and the link weight value specifically comprises:
and acquiring a comprehensive score of each road section in the road area according to the track selection probability, the track weight value and the road section weight value, and taking the road section with the highest comprehensive score as the abnormal road section.
5. An abnormal segment determination device for a road region, comprising a memory, a processor and a computer program stored on the memory and operable on the processor, the processor when executing the computer program implementing:
obtaining track data of a plurality of historical orders, and determining yaw information of the plurality of historical orders according to the track data, wherein the yaw information comprises yaw rate and information of yaw orders, and the track data comprises navigation track data and actual track data;
determining a plurality of high heat start points and stop points in the road area according to the track data;
screening out high heat start and stop points of which the yaw rate is greater than a preset threshold value and is not repeated with other yaw orders of the high heat start and stop points from the plurality of high heat start and stop points, and taking the high heat start and stop points as target start and stop points; for any target starting and stopping point, acquiring predicted track mode information of the target starting and stopping point in the navigation track data, and acquiring actual track mode information of the target starting and stopping point in the actual track data;
and determining an abnormal road section of the road area according to the track mode information.
6. The apparatus according to claim 5, wherein the processor performs the step of determining the abnormal link for the road region according to the track pattern information, and specifically comprises:
calculating a track selection probability, a track weight value and a road section weight value according to the predicted track mode information and the actual track mode information;
and determining an abnormal road section of the road area according to the track selection probability, the track weight value and the road section weight value.
7. The apparatus according to claim 6, wherein the processor performs the step of calculating a trajectory selection probability, a trajectory weight value, and a link weight value according to the predicted trajectory pattern information and the actual trajectory pattern information, and specifically includes:
acquiring the number of new tracks which exist in the actual track mode information but do not exist in the predicted track mode information, calculating the ratio of the number of the new tracks to the number of the predicted tracks in the predicted track mode information, and taking the ratio as track selection probability;
acquiring the turning angle of the new track and the detour probability of the new track, and taking the average value, the weight value and the sum value of the three values of the number of the new track, the turning angle of the new track and the detour probability of the new track as the track weight value; and determining the weight value of the road section according to the distance between the road section and the starting point.
8. The apparatus according to claim 6 or 7, wherein the processor performs the step of determining the abnormal link of the road region according to the trajectory selection probability, the trajectory weight value and the link weight value, and specifically comprises:
and acquiring a comprehensive score of each road section in the road area according to the track selection probability, the track weight value and the road section weight value, and taking the road section with the highest comprehensive score as the abnormal road section.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for determining an abnormal section of a road area according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911266717.XA CN111862586B (en) | 2019-12-11 | 2019-12-11 | Method and device for determining abnormal road section of road area and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911266717.XA CN111862586B (en) | 2019-12-11 | 2019-12-11 | Method and device for determining abnormal road section of road area and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111862586A CN111862586A (en) | 2020-10-30 |
| CN111862586B true CN111862586B (en) | 2021-10-15 |
Family
ID=72970769
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911266717.XA Active CN111862586B (en) | 2019-12-11 | 2019-12-11 | Method and device for determining abnormal road section of road area and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111862586B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112885128B (en) * | 2021-01-14 | 2022-07-05 | 北京中交兴路信息科技有限公司 | Method, device and equipment for identifying blocked road section and storage medium |
| CN113593218B (en) * | 2021-06-28 | 2022-10-18 | 北京百度网讯科技有限公司 | Method and device for detecting traffic abnormal event, electronic equipment and storage medium |
| CN113865610A (en) * | 2021-09-30 | 2021-12-31 | 北京百度网讯科技有限公司 | Method, apparatus, device, medium and product for generating navigation information |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1996039A (en) * | 2006-12-22 | 2007-07-11 | 凯立德欣技术（深圳）有限公司 | Method, device, and apparatus for recording navigation path, and navigation method |
| EP1202029B1 (en) * | 2000-08-25 | 2012-04-11 | Navteq North America, LLC | Method and system for compact representation of routes |
| KR101518894B1 (en) * | 2013-07-11 | 2015-05-11 | 현대자동차 주식회사 | Method for setting warning reference of advanced driver assistance system |
| CN105654761A (en) * | 2016-01-15 | 2016-06-08 | 刘江华 | Driving route monitoring method, terminal, server and system |
| CN105785411A (en) * | 2016-02-24 | 2016-07-20 | 重庆邮电大学 | Abnormal locus detection method based on area division |
| CN107228677A (en) * | 2016-03-23 | 2017-10-03 | 腾讯科技（深圳）有限公司 | Driftage recognition methods and device |
| CN108550264A (en) * | 2018-06-22 | 2018-09-18 | 泉州创先力智能科技有限公司 | A kind of road monitoring method, device, equipment and storage medium |
| CN108731691A (en) * | 2017-04-19 | 2018-11-02 | 腾讯科技（深圳）有限公司 | The determination method and apparatus of the yaw point of navigation equipment |
| CN109544966A (en) * | 2018-11-27 | 2019-03-29 | 江苏本能科技有限公司 | Special vehicle path deviations analysis method and system |
| CN109671182A (en) * | 2018-12-19 | 2019-04-23 | 义乌市腾飞汽车代驾服务有限公司 | A kind of appraisal procedure and device of route or travel by vehicle |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7680596B2 (en) * | 2004-04-06 | 2010-03-16 | Honda Motor Co., Ltd. | Route calculation method for a vehicle navigation system |
| KR100714916B1 (en) * | 2005-07-27 | 2007-05-04 | 에스케이 텔레콤주식회사 | Path searching method |
| CN103996053B (en) * | 2014-06-05 | 2017-03-22 | 中交第一公路勘察设计研究院有限公司 | Lane departure alarm method based on machine vision |
| KR101735080B1 (en) * | 2014-12-29 | 2017-05-12 | 재단법인대구경북과학기술원 | System and method for warning danger in driving section |
| US9869561B2 (en) * | 2015-11-23 | 2018-01-16 | Here Global B.V. | Method and apparatus for providing traffic event notifications |
| CN107144286B (en) * | 2016-03-01 | 2021-08-24 | 阿里巴巴集团控股有限公司 | Navigation method and device |
| US20180018880A1 (en) * | 2016-07-12 | 2018-01-18 | Caterpillar Inc. | System and method for worksite route management |
| CN108765930B (en) * | 2018-06-26 | 2021-02-09 | 上海掌门科技有限公司 | Driving monitoring method and device |
| CN109766777B (en) * | 2018-12-18 | 2021-08-13 | 东软集团股份有限公司 | Abnormal track detection method and device, storage medium and electronic equipment |
| CN109747638B (en) * | 2018-12-25 | 2020-06-16 | 东软睿驰汽车技术（沈阳）有限公司 | Vehicle driving intention identification method and device |
-
2019
- 2019-12-11 CN CN201911266717.XA patent/CN111862586B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1202029B1 (en) * | 2000-08-25 | 2012-04-11 | Navteq North America, LLC | Method and system for compact representation of routes |
| CN1996039A (en) * | 2006-12-22 | 2007-07-11 | 凯立德欣技术（深圳）有限公司 | Method, device, and apparatus for recording navigation path, and navigation method |
| KR101518894B1 (en) * | 2013-07-11 | 2015-05-11 | 현대자동차 주식회사 | Method for setting warning reference of advanced driver assistance system |
| CN105654761A (en) * | 2016-01-15 | 2016-06-08 | 刘江华 | Driving route monitoring method, terminal, server and system |
| CN105785411A (en) * | 2016-02-24 | 2016-07-20 | 重庆邮电大学 | Abnormal locus detection method based on area division |
| CN107228677A (en) * | 2016-03-23 | 2017-10-03 | 腾讯科技（深圳）有限公司 | Driftage recognition methods and device |
| CN108731691A (en) * | 2017-04-19 | 2018-11-02 | 腾讯科技（深圳）有限公司 | The determination method and apparatus of the yaw point of navigation equipment |
| CN108550264A (en) * | 2018-06-22 | 2018-09-18 | 泉州创先力智能科技有限公司 | A kind of road monitoring method, device, equipment and storage medium |
| CN109544966A (en) * | 2018-11-27 | 2019-03-29 | 江苏本能科技有限公司 | Special vehicle path deviations analysis method and system |
| CN109671182A (en) * | 2018-12-19 | 2019-04-23 | 义乌市腾飞汽车代驾服务有限公司 | A kind of appraisal procedure and device of route or travel by vehicle |
Non-Patent Citations (2)
| Title |
|---|
| 《基于经纬度判断车辆是否偏离预定路线的方法》;黄运峰;《电子技术与软件工程》;20151215(第23期);全文 * |
| 《面向浮动车GPS数据的质量评价系统设计与实现》;黄金特;《中国优秀硕士学位论文全文数据库 信息科技辑》;20120515(第05期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111862586A (en) | 2020-10-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111862586B (en) | Method and device for determining abnormal road section of road area and storage medium | |
| JP4978692B2 (en) | Map matching system, map matching method and program | |
| JP6671248B2 (en) | Abnormality candidate information analyzer | |
| EP2630443B1 (en) | Method of determining and validating navigational priority settings utilizing probe data | |
| CN112509356B (en) | Vehicle driving route generation method and system | |
| Bartin et al. | Clustering-based methodology for determining optimal roadway configuration of detectors for travel time estimation | |
| JP4506663B2 (en) | Traffic situation prediction apparatus, method and program, route search system, and traffic situation provision system | |
| JP2015166894A5 (en) | ||
| JP2019079199A (en) | Signal switching control device, signal switching control method and signal switching control program | |
| KR101878617B1 (en) | Method and system for processing traictory data | |
| CN112862204A (en) | Path planning method, system, computer equipment and readable storage medium | |
| US20190088116A1 (en) | Predicting vehicle travel times by modeling heterogeneous influences between arterial roads | |
| JP6201040B2 (en) | Transportation system optimization device | |
| CN111951544A (en) | Method and device for determining monitoring point | |
| CN104169986A (en) | Method for model construction for a travel-time database | |
| JP2010205089A (en) | Apparatus and method for creating road traffic information | |
| CN113724490B (en) | Road congestion detection method | |
| JP4957612B2 (en) | Travel pattern information acquisition device, travel pattern information acquisition method, and travel pattern information acquisition program | |
| Zhang et al. | Effective recycling planning for dockless sharing bikes | |
| Kim et al. | A study on travel time estimation of diverging traffic stream on highways based on timestamp data | |
| JP3975004B2 (en) | Traffic flow data prediction apparatus and traffic flow data prediction method | |
| Hu et al. | Spatial econometrics models for congestion prediction with in-vehicle route guidance | |
| JP6835404B2 (en) | Probe information creation program, probe information creation method and probe information creation system | |
| JP2005316739A (en) | Method and device for associating vehicle detection position with link, jam spot determination method, jam spot display method, and program | |
| CN110942178A (en) | Charging pile recommendation method based on link prediction method of resource allocation index |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |