CN112270833A - Trajectory fitting method and device, electronic equipment and storage medium - Google Patents
Trajectory fitting method and device, electronic equipment and storage medium Download PDFInfo
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- CN112270833A CN112270833A CN202011160585.5A CN202011160585A CN112270833A CN 112270833 A CN112270833 A CN 112270833A CN 202011160585 A CN202011160585 A CN 202011160585A CN 112270833 A CN112270833 A CN 112270833A
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- 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
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
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- 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
Abstract
The application provides a track fitting method and device, electronic equipment and a storage medium, and relates to the field of route data processing of traffic management. The track fitting method comprises the following steps: preprocessing the track data of a target vehicle to obtain a to-be-processed track point set; acquiring first road grasping information of a first track point set according to road network mapping data of a monitoring area; obtaining a target path combination passed by a target vehicle according to the first road grabbing information; the target path combination comprises at least one road; and fitting the to-be-processed track point set in the target path combination to obtain a fitted track of the target vehicle in the monitoring area. By using the track fitting method provided by the embodiment of the application, the dense track points of the vehicle can be removed, the road network mapping data of the monitoring area is called, the vehicle track fitting is realized, and the track fitting efficiency is improved.
Description
Technical Field
The present application relates to the field of route data processing for traffic management, and in particular, to a trajectory fitting method, apparatus, electronic device, and storage medium.
Background
With the development of society and the progress of economy, more and more public transport vehicles and domestic vehicles are provided, and traffic roads are crossed vertically and horizontally, so that how to manage vehicle tracks on the roads and how to establish track data of the vehicles become a current research problem.
In the current technical scheme, an operator identifies each track point data of a vehicle in map data, so that the generated vehicle tracks are scattered; for example, when a vehicle passes through a toll station, a large number of dense track points are generated due to slow speed and long waiting time, and the vehicle track is not clear.
Disclosure of Invention
The application aims to provide a track fitting method, a track fitting device, electronic equipment and a storage medium, which can remove dense track points of a vehicle, call road network mapping data of a monitoring area, realize fitting of vehicle tracks and improve the efficiency of track fitting.
The embodiment of the application can be realized as follows:
in a first aspect, an embodiment of the present application provides a trajectory fitting method, where the method includes:
preprocessing the track data of a target vehicle to obtain a to-be-processed track point set;
acquiring first road grasping information of a first track point set according to road network mapping data of a monitoring area;
the first road grabbing information is used for determining road information corresponding to at least one road identifier in the road network mapping data; the first track point set is a set of at least two track points which are continuously distributed in the track point set to be processed, the at least two track points are in the monitoring area, and the at least one road mark is a mark of a road which is determined according to the at least two track points and is passed by the target vehicle;
obtaining a target path combination passed by the target vehicle according to the first road grabbing information; the target path combination comprises at least one road;
and fitting the to-be-processed track point set in the target path combination to obtain a fitting track of the target vehicle in the monitoring area.
In an alternative embodiment, the road network mapping data is obtained by:
dividing the monitoring area to obtain a plurality of grids; each grid has a grid identification;
acquiring plaintext road network data of the monitoring area; the plaintext road network data represents road information of each road in the monitored area, and the road information comprises the length of the road, geographic information, road type, road grade, passing direction and road name;
matching the grid identification with the road identification of each road in the plaintext road network data to obtain a grid-road mapping relation; the grid-road mapping relation represents the corresponding relation between the road identifier and at least one grid identifier;
encrypting the plaintext road network data according to the grid-road mapping relation to obtain the road network mapping data; the road network mapping data is a binary data file.
In an optional embodiment, encrypting the plaintext road network data according to the mesh-road mapping relationship to obtain the road network mapping data includes:
numbering each road mark, and setting the description byte of each road as a preset length;
the description byte comprises a first byte, a second byte and a third byte, wherein the first byte represents the storage position of the road in the road network mapping data, the second byte represents the size of the storage space occupied by the road information in the road network mapping data, and the third byte represents the byte length occupied by the road name of the road in the road network mapping data;
binary encryption is carried out on each description byte to obtain binary road network data;
and solidifying the grid-road mapping relation to the binary road network data to obtain the road network mapping data.
In an optional embodiment, the obtaining, according to road network mapping data of the monitoring area, first road capturing information of the first set of track points includes:
acquiring at least one grid identifier corresponding to the first track point set;
matching the at least one grid identifier with the road network mapping data to obtain the at least one road identifier;
and searching the road network mapping data according to the at least one road identifier to obtain the first road catching information.
In an optional embodiment, obtaining a target path combination that the target vehicle passes through according to the first road grasping information includes:
analyzing the first road grasping information to obtain an initial path combination; the initial path combination represents a passing path of the target vehicle determined according to the passing direction of the road and the communication relation of each road;
using a recursive algorithm to remove broken links in the initial path combination to obtain the target path combination; the broken road is a road without other entering and/or exiting roads except the road grabbing entrance and the road grabbing exit determined by the first road grabbing information.
In an optional embodiment, fitting the to-be-processed trajectory point set in the target path combination to obtain a fitted trajectory of the target vehicle in the monitored area includes:
acquiring the average distance between each track point to be processed in the track point set to be processed and each path in the target path combination;
and comparing all the average distances to obtain a path with the minimum average distance as the fitting track.
In an optional embodiment, the preprocessing the trajectory data of the target vehicle to obtain a set of trajectory points to be processed includes:
performing track suction and track noise reduction on the track data to obtain a second track point set; the track suction is used for eliminating track points with track point density larger than or equal to a density threshold value in a unit area in the track data, and the track denoising is used for eliminating track points with a concentrated drift distance larger than or equal to a first distance threshold value of the track points to be processed;
and if the distance between any two adjacent track points in the second track point set is greater than or equal to a second distance threshold value, adding point positions in any two adjacent track points to obtain the to-be-processed track point set.
In a second aspect, an embodiment of the present application provides a trajectory fitting apparatus, including:
the preprocessing module is used for preprocessing the track data of the target vehicle to obtain a track point set to be processed;
the acquisition module is used for acquiring first road grabbing information of the first track point set according to road network mapping data of the monitoring area;
the first road grabbing information is used for determining road information corresponding to at least one road identifier in the road network mapping data; the first track point set is a set of at least two track points which are continuously distributed in the track point set to be processed, the at least two track points are in the monitoring area, and the at least one road mark is a mark of a road which is determined according to the at least two track points and is passed by the target vehicle;
the path determining module is used for obtaining a target path combination which is passed by the target vehicle according to the first road grabbing information; the target path combination comprises at least one road;
and the fitting module is used for fitting the to-be-processed track point set in the target path combination to obtain a fitting track of the target vehicle in the monitoring area.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory, where the memory stores a computer program that can be executed by the processor, and the processor can execute the computer program to implement the method described in any one of the foregoing embodiments.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method of any one of the foregoing embodiments.
Compared with the prior art, the application provides a track fitting method, a track fitting device, electronic equipment and a storage medium, and relates to the field of route data processing of traffic management. The method comprises the following steps: preprocessing the track data of a target vehicle to obtain a to-be-processed track point set; acquiring first road grasping information of a first track point set according to road network mapping data of a monitoring area; the first road grabbing information is used for determining road information corresponding to at least one road identifier in the road network mapping data; the first track point set is a set of at least two track points which are continuously distributed in the track point set to be processed, the at least two track points are in the monitoring area, and the at least one road mark is a mark of a road which is determined according to the at least two track points and is passed by the target vehicle; obtaining a target path combination passed by the target vehicle according to the first road grabbing information; the target path combination comprises at least one road; and fitting the to-be-processed track point set in the target path combination to obtain a fitting track of the target vehicle in the monitoring area. By using the track fitting method provided by the embodiment of the application, the dense track points of the vehicle can be removed, the road network mapping data of the monitoring area is called, the vehicle track fitting is realized, and the track fitting efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic flowchart of a trajectory fitting method according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of another trajectory fitting method provided in the embodiment of the present application;
FIG. 3 is a schematic flow chart of another trajectory fitting method provided in the embodiments of the present application;
fig. 4 is a schematic diagram illustrating acquisition of road network mapping data according to an embodiment of the present disclosure;
FIG. 5 is a schematic flow chart of another trajectory fitting method provided in the embodiments of the present application;
FIG. 6 is a schematic flow chart of another trajectory fitting method provided in the embodiments of the present application;
FIG. 7 is a schematic diagram of a path combination according to an embodiment of the present application;
FIG. 8 is a schematic flow chart of another trajectory fitting method provided in the embodiments of the present application;
FIG. 9 is a schematic flow chart of another trajectory fitting method provided in the embodiments of the present application;
FIG. 10 is a schematic diagram illustrating an effect of track preprocessing according to an embodiment of the present application;
fig. 11 is a schematic effect diagram of a trajectory fitting method according to an embodiment of the present application;
FIG. 12 is a block diagram of a trajectory fitting device according to an embodiment of the present application;
fig. 13 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the features of the embodiments of the present application may be combined with each other without conflict.
With the development of society and the progress of economy, more and more public transport vehicles and domestic vehicles are provided, and traffic roads are crossed vertically and horizontally, so that how to manage vehicle tracks on the roads and how to establish track data of the vehicles become a current research problem. In the current technical scheme, an operator identifies each track point data of a vehicle in map data, so that the generated vehicle tracks are scattered; for example, when a vehicle passes through a toll station, a large number of dense track points are generated due to slow speed and long waiting time, and the vehicle track is not clear.
In order to solve at least the above problems and the disadvantages of the background art, an embodiment of the present invention provides a trajectory fitting method, please refer to fig. 1, where fig. 1 is a schematic flow chart of the trajectory fitting method provided by the embodiment of the present invention, and the trajectory fitting method may include the following steps:
s210, preprocessing the track data of the target vehicle to obtain a track point set to be processed.
The pre-treatment process may include, but is not limited to: and one or more track processing modes of track pumping, track noise reduction and track interpolation.
S220, acquiring first road grabbing information of the first track point set according to the road network mapping data of the monitoring area.
The first road grabbing information is used for determining road information corresponding to at least one road identifier in the road network mapping data. The first track point set is a set of at least two track points which are continuously distributed in the track point set to be processed, the at least two track points are in a monitoring area, and at least one road mark is a mark of a road through which a target vehicle passes, which is determined according to the at least two track points. For example, the road identification may also be referred to as a road id (identity document). The monitoring area may be for a certain region, a certain province, etc.
And S230, obtaining a target path combination passed by the target vehicle according to the first road grabbing information.
The target path combination includes at least one road. For example, the target route combination may be a road combination determined as a road holding start point and a road holding end point, which are determined, but the paths thereof may be different.
And S240, fitting the track point set to be processed in the target path combination to obtain a fitting track of the target vehicle in the monitoring area.
It should be understood that when the track fitting method provided by the embodiment of the application is used for preprocessing the track data of the target vehicle, the dense track points of the vehicle can be removed; road network mapping data of the monitoring area are called, so that data circulation can be reduced, and the efficiency of track fitting is improved; the vehicle track fitting is realized in the target path combination of the monitoring area, and the track fitting accuracy of the target vehicle can be increased.
In an optional embodiment, the road network data is often plaintext data, and there is no corresponding relationship with the monitoring area, on the basis of fig. 1, in order to obtain the road network mapping data, a possible implementation manner is provided, please refer to fig. 2, and fig. 2 is a schematic flow diagram of another trajectory fitting method provided in the embodiment of the present application, where the road network mapping data may be obtained in the following manner:
s201, dividing the monitoring area to obtain a plurality of grids.
Each of the above-mentioned grids has a grid identification (grid ID); for example, national grids are established, the longitude ranges from 73.0 degrees east longitude to 135.1 degrees east longitude, the latitude ranges from 3.5 degrees north latitude to 53.5 degrees north latitude, the side length of each grid is 100 meters, the national grids can be numbered in the order from east to west and from north to south, and the grid ID of each grid is obtained.
S202, plaintext road network data of the monitoring area is obtained.
The plaintext road network data represents road information of each road in the monitored area, and the road information comprises the length of the road, geographic information, road type, road grade, traffic direction and road name.
S203, matching the grid mark with the road mark of each road in the plaintext road network data to obtain a grid-road mapping relation.
The grid-road mapping relationship represents a correspondence between road identifiers and at least one grid identifier. For example, through a spatial query algorithm, a road where each mesh intersects can be found, and a mesh ID and a road ID are set up to have a one-to-many relationship, i.e., a mesh-road mapping relationship.
S204, the plaintext road network data are encrypted according to the grid-road mapping relation to obtain road network mapping data.
The road network mapping data is a binary data file. For example, the grid-road mapping relationship is solidified into a binary file to obtain road network mapping data, so that memory mapping can be realized, data flow is reduced, and the efficiency of track fitting is improved.
In an optional embodiment, in order to obtain the road network mapping data, a possible implementation is given on the basis of fig. 2, please refer to fig. 3, and fig. 3 is a flowchart illustrating another trajectory fitting method provided in the embodiment of the present application, which is directed to the above S204: encrypting the plaintext road network data according to the grid-road mapping relationship to obtain road network mapping data, which may include:
s204a, numbering each road mark, and setting the description byte of each road as a preset length.
The description bytes comprise a first byte, a second byte and a third byte, wherein the first byte represents the storage position of the road in the road network mapping data, the second byte represents the size of the storage space occupied by the road information in the road network mapping data, and the third byte represents the byte length occupied by the road name of the road in the road network mapping data.
S204b, binary encryption is carried out for each description byte to obtain binary road network data.
For example, in the encryption process, data is reasonably organized according to field types, for example, short type data occupies 2 bytes, int type data occupies 4 bytes, and double type data occupies 8 bytes; the encryption process may be that each Link is taken as a unit, Link data is structured data, and each Link contains fixed attributes, for example: latitude and longitude, length, road type, road grade, direction of traffic (one-way, two-way, etc.), road name, etc.
S204c, solidifying the grid-road mapping relation to the binary road network data to obtain the road network mapping data.
For example, the road ID is numbered as a positive integer, each road is allocated with a description byte with a fixed length, for example, 10 bytes are allocated, and bits 1-4 can be restored to int integer, which represents the start position of binary data of the road in the file; bits 5-8 can be reduced to int integer for representing the total byte number occupied by the actual road data content; the 9 th to 10 th bits can be restored to shot type short integer, and can represent the length of bytes occupied by the road name; by the algorithm, the index file of the road can be established, so that index support is provided for road grabbing.
To facilitate understanding of the road network mapping data, an embodiment of the present application provides a possible road network mapping data obtaining method, please refer to fig. 4, where fig. 4 is a schematic diagram of obtaining road network mapping data provided by the embodiment of the present application, and the schematic diagram includes three parts: road network data encryption (road data encryption), mesh index establishment and road ID index establishment.
(1) And road data encryption: in order to prevent road network data from leaking, plaintext road network data needs to be encrypted into a binary data file from the viewpoint of data security; in the encryption process, data needs to be reasonably organized according to field types, for example, short type data occupies 2 bytes, int type data occupies 4 bytes, and double type data occupies 8 bytes. The encryption process takes each Link (Link) as a unit, the Link data is structured data, and each Link contains fixed attributes, such as: latitude and longitude, length, road type, road grade, traffic direction (one-way or two-way), road name, etc.
(2) And establishing a grid index: establishing nationwide grids, wherein the longitude range from east longitude 73.0 degrees to east longitude 135.1 degrees, the latitude range from north latitude 3.5 degrees to north latitude 53.5 degrees, the grid side length is 100 meters, and the nationwide grids can be numbered according to the sequence from east to west and from north to south; through a space query algorithm, a road where each grid is intersected can be found, a one-to-many relationship is established between the grid id and the road id, and the corresponding relationship is solidified into a binary file.
(3) And establishing a road id index: numbering the roads id as positive integers, allocating description bytes with fixed length to each road, for example, allocating 10 bytes, wherein the 1 st-4 th bits can be restored to int integers, which represent the starting positions of the binary data of the roads in the file; bits 5-8 can be reduced to int integer for representing the total byte number occupied by the actual road data content; the 9 th to 10 th bits can be restored to shot type short integer, and can represent the length of bytes occupied by the road name; by the algorithm, an index file of the road ID can be established, so that index support is provided for road grabbing, and road network mapping data are obtained.
In an alternative embodiment, for the above-mentioned road-grabbing process, a possible implementation is given on the basis of fig. 2, please refer to fig. 5, where fig. 5 is a schematic flow chart of another trajectory fitting method provided in the embodiment of the present application, and is directed to the above-mentioned S220: obtaining first road grasping information of a first track point set according to road network mapping data of a monitoring area, which may include:
s220a, at least one grid mark corresponding to the first track point set is obtained.
For example, the grid ID of the grid where the track point is located can be calculated from the range and point coordinates of the grid.
S220b, matching at least one grid mark with the road network mapping data to obtain at least one road mark.
For example, the mesh id of the adjacent mesh can be calculated according to the distance of the road-grabbing, and the road id corresponding to the mesh id can be found in the mesh index file through the mesh id.
S220c, according to at least one road mark, finding road network mapping data to obtain first road catching information.
For example, the position and the total occupied space of the road data in the road network data file and the number of bytes occupied by each attribute field in the road can be determined in the road id index through the road id, and then the byte code is restored into the plain text road network data, so that the road grabbing function is realized.
For the above process of obtaining the first road grabbing information, for example, a road grabbing scene is a road searched within a certain distance around a certain point, a possible implementation manner is given: firstly, the id of the grid where the point is located can be calculated through the range of the grid and the point coordinates, then the id of the adjacent grid can be calculated according to the distance of road grabbing, the id of the road intersecting the grid can be found in the grid index file through the grid id, the position of the road data in the road network data file and the total occupied space and the number of bytes occupied by each attribute field in the road can be determined in the road id index through the road id, and then the byte codes are reduced into the plaintext road network data, so that the function of road grabbing is realized.
In an optional embodiment, in order to obtain a target path combination, a path matching is required to be performed on track points and road network mapping data, and a possible implementation is provided on the basis of fig. 1, please refer to fig. 6, where fig. 6 is a schematic flow diagram of another trajectory fitting method provided in the embodiment of the present application, and is directed to the above-mentioned S230: obtaining a target path combination passed by the target vehicle according to the first road-holding information, wherein the target path combination may include:
s230a, analyzing the first grab path information to obtain an initial path combination.
The initial path combination represents a passing path of the target vehicle determined according to the passing direction of the road and the communication relation of each road. For example, a path-grabbing link can be entered after the track preprocessing, the path-grabbing link needs to rely on a path-grabbing service and call a path-grabbing interface, and the required parameters are the longitude and latitude of the track point and the path-grabbing distance (such as 150 m). And (3) possibly causing intersection between two adjacent track point road grabs, filtering repeated roads at this time, and then establishing a road connectivity relation to obtain an initial path combination. For example, after the first road grabbing information is obtained through the road grabbing process, a connection relation of roads is established according to the start and end point coordinates of each road and the passing direction of the road, the connection relation is a data structure similar to a network and can be stored by map, key is road id, value is entry roads (entry roads: road id of entering the road) and exit roads (leave roads: road id of exiting the road).
And S230b, using a recursive algorithm to remove the broken links in the initial path combination to obtain the target path combination.
The broken road is a road without other entering and/or exiting roads except the road grabbing entrance and the road grabbing exit determined by the first road grabbing information.
As shown in fig. 7, fig. 7 is a schematic diagram of a path combination provided by the embodiment of the present application, where load 1 and load 3 in (a) shown in fig. 7 are head-off roads, and a recursive algorithm is used, where load 1 and load 3 are removed first, then load 2 and load 4 become head-off roads, and so on, a Road network similar to that shown in fig. 7(b) is obtained, through the above steps, the Road network may be refined, and the number of subsequent Road combinations is reduced, as shown in (c) in fig. 7, a target path combination includes: road5 → Road6 → Road7 → Road8, and Road5 → Road6 → Road9 → Road10 → Road11 → Road 8.
In an alternative embodiment, since the target route combination may have a plurality of possible routes to pass through, for the purpose of fitting the trajectory of the target vehicle, a possible implementation manner is provided on the basis of fig. 1, please refer to fig. 8, where fig. 8 is a schematic flow diagram of another trajectory fitting method provided in the embodiment of the present application, and is directed to the above-mentioned S240: fitting a to-be-processed track point set in the target path combination to obtain a fitted track of the target vehicle in the monitoring area, wherein the fitting track may include:
s240a, obtaining the average distance between each track point to be processed in the track point set to be processed and each path in the target path combination.
And S240b, performing mean comparison on all the average distances to obtain a path with the minimum average distance as a fitting track.
For example, path screening refers to filtering the combined paths according to the average distance to find the best path, i.e. the fitting track. The logic of path screening is to calculate the average distance between the track point and each path, and find out the path with the minimum tie distance by mean value comparison, thereby obtaining the path matching result, i.e. the fitting path. In the actual path matching process, the track data is often very long, and path matching needs to be performed in a segmented manner under the condition, for example, path matching is performed every 1000 meters, so that the path matching speed can be increased, and the consumption of a memory can be reduced.
In an optional embodiment, in order to reduce dense track points and improve smoothness of a fitting track, a possible implementation is given on the basis of fig. 1, please refer to fig. 9, where fig. 9 is a schematic flow diagram of another track fitting method provided in the embodiment of the present application, and is directed to the above-mentioned S210: preprocessing the trajectory data of the target vehicle to obtain a set of trajectory points to be processed, which may include:
s210a, carrying out track suction and track noise reduction on the track data to obtain a second track point set.
The track suction is used for removing track points with track point density larger than or equal to a density threshold value in a unit area in track data, and the track noise reduction is used for removing track points with a concentrated drift distance larger than or equal to a first distance threshold value of the track points to be processed.
And S210b, if the distance between any two adjacent track points in the second track point set is greater than or equal to the second distance threshold, adding point positions in any two adjacent track points to obtain a to-be-processed track point set.
And if the distance between any two adjacent track points in the second track point set is smaller than the second distance threshold, taking the second track point set as a track point set to be processed. For example, the role of trajectory preprocessing may include: the distribution of the locus points is more uniform, and preparation is made for the next road grabbing; trajectory preprocessing may include trajectory pumping, trajectory noise reduction, trajectory interpolation.
The track suction means that positions with too high point density are selectively removed, for example, in the process of waiting for traffic lights, a large number of dense track points are generated, track point redundancy is caused, redundant points can cause repeated road grabbing, the road matching performance is influenced, and therefore the points are removed. The trajectory pumping can adopt a Douglas pumping algorithm, and the process can be as follows: firstly, adding the first point and the last point into a queue and traversing the queue; secondly, calculating the maximum distance from other points to a straight line connecting the first point and the last point and comparing the maximum distance with a limit difference; and thirdly, if the difference is larger than or equal to the tolerance, adding the point between two points in the queue, and traversing the sequence again by taking the adjacent points as the starting point and the ending point. If the difference is smaller than the limit difference, all the intermediate points of the first and the last points are deleted.
Trajectory denoising refers to selective culling of points that drift too much, for example, points that suddenly deviate more than 1000 meters from the trajectory. The rule of filtering can be to filter according to the threshold values of the speed (threshold value 100m/s), the acceleration (threshold value 15m/s2) and the angular speed (threshold value pi/s) of the track point, and a more reasonable track can be obtained after filtering.
The track interpolation is to add point positions according to the interpolation distance for a segment with an overlarge distance between two points (for example, the distance between any two adjacent track points in a second track point set is larger than or equal to a second distance threshold), so as to prevent the missed grabbing when grabbing the road according to a certain distance.
As shown in fig. 10, fig. 10 is a schematic diagram illustrating the effect of track preprocessing provided by the embodiment of the present application, where (a) in fig. 10 is an original track, it can be seen that when a vehicle passes through a toll gate, a large number of dense gps track points are generated due to slow speed and long waiting time; fig. 10 (b) is vehicle trajectory data obtained after trajectory preprocessing, and smooth and uniform trajectory data is obtained after preprocessing in the trajectory fitting scheme provided by the embodiment of the present application.
As shown in fig. 11, fig. 11 is a schematic view illustrating an effect of the trajectory fitting method provided in the embodiment of the present application, where (a) in fig. 11 is an original trajectory of a target vehicle, and (b) in fig. 11 is a fitted trajectory of the target vehicle obtained by using the trajectory fitting method provided in the embodiment of the present application, and as can be seen from fig. 11, the fitted trajectory is smoother and more fluent than the original trajectory.
In order to implement the trajectory fitting method provided in any one of the above embodiments, an embodiment of the present application provides a trajectory fitting device, please refer to fig. 12, where fig. 12 is a schematic block diagram of a trajectory fitting device provided in an embodiment of the present application, and the trajectory fitting device 40 includes: a preprocessing module 41, an acquisition module 42, a path determination module 43 and a fitting module 44.
The preprocessing module 41 is configured to preprocess trajectory data of the target vehicle to obtain a set of trajectory points to be processed.
The obtaining module 42 is configured to obtain first road-catching information of the first track point set according to the road network mapping data of the monitoring area.
The first road grabbing information is used for determining road information corresponding to at least one road identifier in the road network mapping data. The first track point set is a set of at least two track points which are distributed in a track point set to be processed continuously, the at least two track points are in a monitoring area, and at least one road mark is a mark of a road through which a target vehicle passes, wherein the mark is determined according to the at least two track points.
The path determining module 43 is configured to obtain a target path combination that the target vehicle passes through according to the first road grasping information. The target path combination includes at least one road.
The fitting module 44 is configured to fit the to-be-processed track point set in the target path combination to obtain a fitting track of the target vehicle in the monitoring area.
It should be understood that the preprocessing module 41, the obtaining module 42, the path determining module 43 and the fitting module 44 may cooperate to implement the steps and possible sub-steps corresponding to the trajectory fitting method of the shutdown in any of the above embodiments.
An electronic device is provided in an embodiment of the present application, and as shown in fig. 13, fig. 13 is a block schematic diagram of an electronic device provided in an embodiment of the present application. The electronic device 60 comprises a memory 61, a processor 62 and a communication interface 63. The memory 61, processor 62 and communication interface 63 are electrically connected to each other, directly or indirectly, to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 61 may be used to store software programs and modules, such as program instructions/modules corresponding to the trajectory fitting method provided in the embodiment of the present application, and the processor 62 executes the software programs and modules stored in the memory 61, so as to execute various functional applications and data processing. The communication interface 63 may be used for communicating signaling or data with other node devices. The electronic device 60 may have a plurality of communication interfaces 63 in this application.
The Memory 61 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 62 may be an integrated circuit chip having signal processing capabilities. The Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), etc.; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc.
The electronic device 60 may implement any of the trajectory fitting methods provided herein. The electronic device 60 may be, but is not limited to, a cell phone, a tablet computer, a notebook computer, a server, or other electronic device with processing capabilities.
The present application provides a computer readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the trajectory fitting method according to any one of the foregoing embodiments. The computer readable storage medium may be, but is not limited to, various media that can store program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a PROM, an EPROM, an EEPROM, a magnetic or optical disk, etc.
In summary, the present application provides a trajectory fitting method, an apparatus, an electronic device and a storage medium, and relates to the field of route data processing for traffic management. The track fitting method comprises the following steps: preprocessing the track data of a target vehicle to obtain a to-be-processed track point set; acquiring first road grasping information of a first track point set according to road network mapping data of a monitoring area; the first road grabbing information is used for determining road information corresponding to at least one road identifier in the road network mapping data; the first track point set is a set of at least two track points which are continuously distributed in the track point set to be processed, the at least two track points are in a monitoring area, and at least one road mark is a mark of a road which a target vehicle passes through and is determined according to the at least two track points; obtaining a target path combination passed by a target vehicle according to the first road grabbing information; the target path combination comprises at least one road; and fitting the to-be-processed track point set in the target path combination to obtain a fitted track of the target vehicle in the monitoring area. By using the track fitting method provided by the embodiment of the application, the dense track points of the vehicle can be removed, the road network mapping data of the monitoring area is called, the vehicle track fitting is realized, and the track fitting efficiency is improved.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A trajectory fitting method, characterized in that the method comprises:
preprocessing the track data of a target vehicle to obtain a to-be-processed track point set;
acquiring first road grasping information of a first track point set according to road network mapping data of a monitoring area;
the first road grabbing information is used for determining road information corresponding to at least one road identifier in the road network mapping data; the first track point set is a set of at least two track points which are continuously distributed in the track point set to be processed, the at least two track points are in the monitoring area, and the at least one road mark is a mark of a road which is determined according to the at least two track points and is passed by the target vehicle;
obtaining a target path combination passed by the target vehicle according to the first road grabbing information; the target path combination comprises at least one road;
and fitting the to-be-processed track point set in the target path combination to obtain a fitting track of the target vehicle in the monitoring area.
2. The method of claim 1, wherein said road network mapping data is obtained by:
dividing the monitoring area to obtain a plurality of grids; each grid has a grid identification;
acquiring plaintext road network data of the monitoring area; the plaintext road network data represents road information of each road in the monitored area, and the road information comprises the length of the road, geographic information, road type, road grade, passing direction and road name;
matching the grid identification with the road identification of each road in the plaintext road network data to obtain a grid-road mapping relation; the grid-road mapping relation represents the corresponding relation between the road identifier and at least one grid identifier;
encrypting the plaintext road network data according to the grid-road mapping relation to obtain the road network mapping data; the road network mapping data is a binary data file.
3. The method according to claim 2, wherein encrypting the plaintext road network data according to the mesh-road mapping relationship to obtain the road network mapping data comprises:
numbering each road mark, and setting the description byte of each road as a preset length;
the description byte comprises a first byte, a second byte and a third byte, wherein the first byte represents the storage position of the road in the road network mapping data, the second byte represents the size of the storage space occupied by the road information in the road network mapping data, and the third byte represents the byte length occupied by the road name of the road in the road network mapping data;
binary encryption is carried out on each description byte to obtain binary road network data;
and solidifying the grid-road mapping relation to the binary road network data to obtain the road network mapping data.
4. The method according to claim 2 or 3, wherein obtaining the first road-catching information of the first set of trajectory points according to the road network mapping data of the monitored area comprises:
acquiring at least one grid identifier corresponding to the first track point set;
matching the at least one grid identifier with the road network mapping data to obtain the at least one road identifier;
and searching the road network mapping data according to the at least one road identifier to obtain the first road catching information.
5. The method according to any one of claims 1 to 3, wherein obtaining the target path combination passed by the target vehicle according to the first road-holding information comprises:
analyzing the first road grasping information to obtain an initial path combination; the initial path combination represents a passing path of the target vehicle determined according to the passing direction of the road and the communication relation of each road;
using a recursive algorithm to remove broken links in the initial path combination to obtain the target path combination; the broken road is a road without other entering and/or exiting roads except the road grabbing entrance and the road grabbing exit determined by the first road grabbing information.
6. The method according to any one of claims 1 to 3, wherein the fitting of the set of trajectory points to be processed in the target path combination to obtain a fitted trajectory of the target vehicle in the monitored area comprises:
acquiring the average distance between each track point to be processed in the track point set to be processed and each path in the target path combination;
and comparing all the average distances to obtain a path with the minimum average distance as the fitting track.
7. The method of claim 1, wherein preprocessing the trajectory data of the target vehicle to obtain a set of trajectory points to be processed comprises:
performing track suction and track noise reduction on the track data to obtain a second track point set; the track suction is used for eliminating track points with track point density larger than or equal to a density threshold value in a unit area in the track data, and the track denoising is used for eliminating track points with a concentrated drift distance larger than or equal to a first distance threshold value of the track points to be processed;
and if the distance between any two adjacent track points in the second track point set is greater than or equal to a second distance threshold value, adding point positions in any two adjacent track points to obtain the to-be-processed track point set.
8. A trajectory fitting apparatus, characterized in that the apparatus comprises:
the preprocessing module is used for preprocessing the track data of the target vehicle to obtain a track point set to be processed;
the acquisition module is used for acquiring first road grabbing information of the first track point set according to road network mapping data of the monitoring area;
the first road grabbing information is used for determining road information corresponding to at least one road identifier in the road network mapping data; the first track point set is a set of at least two track points which are continuously distributed in the track point set to be processed, the at least two track points are in the monitoring area, and the at least one road mark is a mark of a road which is determined according to the at least two track points and is passed by the target vehicle;
the path determining module is used for obtaining a target path combination which is passed by the target vehicle according to the first road grabbing information; the target path combination comprises at least one road;
and the fitting module is used for fitting the to-be-processed track point set in the target path combination to obtain a fitting track of the target vehicle in the monitoring area.
9. An electronic device comprising a processor and a memory, the memory storing a computer program executable by the processor, the processor being configured to execute the computer program to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
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