CN110427360B - Processing method, processing device, processing system and computer program product of trajectory data - Google Patents
Processing method, processing device, processing system and computer program product of trajectory data Download PDFInfo
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- CN110427360B CN110427360B CN201910575595.6A CN201910575595A CN110427360B CN 110427360 B CN110427360 B CN 110427360B CN 201910575595 A CN201910575595 A CN 201910575595A CN 110427360 B CN110427360 B CN 110427360B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
Abstract
The invention discloses a method for processing track data, which comprises the following steps: performing multi-level grid division on the road network according to the road network density to obtain a plurality of grids, and encoding each grid according to the encoding length corresponding to the area of the grid; acquiring track data to be processed, and coding each positioning point contained in the track data; determining the attribution grid and the adjacent grid of each positioning point and extracting all candidate road sections of each positioning point from the attribution grid and the adjacent grid to form a respective candidate road section set of each positioning point; calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set, and determining the road section matched with each positioning point according to the matching degree; and performing position association on each positioning point and the matched road section to correct the positioning point with the misaligned position in the track data, and finishing the processing of the track data, thereby solving the problems of low accuracy of the track data and missing of the track data in the prior art.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a method, an apparatus, a system and a computer program product for processing trajectory data.
Background
With the popularization of locatable intelligent equipment and the development of wireless communication technology, large-scale user position data is collected and persistently stored, and massive track data is formed. The data contains rich knowledge, can reflect the movement rule of people and can reflect the traffic condition. The user trajectory not only includes the travel information of the user, but also includes the travel habits, life experiences and the like of the user.
In an ideal case, the accuracy of the GPS positioning data is 5-10 meters. However, due to various external disturbances, the actual positioning accuracy is lower than ideal. Factors influencing the positioning accuracy of the GPS mainly include: location hardware and environmental factors. Environmental factors include: satellite signal shielding, signal refraction, atmospheric or ionospheric interference. Under the conditions of high building density or bad weather conditions, the GPS signals are refracted or reflected for multiple times to cause signal errors, so that the positioning data are drifted. GPS position data drift can cause a number of problems. For example, when a GPS terminal is stationary, its positioning coordinates (latitude and longitude) often vary, and in a specific case, the variation is large. Sometimes, it even shows that the GPS terminal has a large speed or mileage statistic deviation. This case also includes: a vehicle with a GPS terminal stops at a particular location of a unit for one day, while GPS positioning data shows that it has traveled over ten kilometers, or even over a hundred kilometers. The case of signal occlusion is: when the vehicle passes through a tunnel or enters a ground depot, the vehicle cannot be positioned because the satellite cannot be searched.
In summary, in the prior art, when the data obtained by the positioning system has a large deviation and the positioning system cannot obtain data, the accuracy of the trajectory data is low and the trajectory data is missing. Because the accuracy of the data acquired by the positioning system directly affects the accuracy of the result when analyzing based on the data, processing the trajectory data to improve the accuracy of the data is a solution required in the prior art.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a processing method, a processing device, a processing system and a computer program product for trajectory data, which solve the problems of low accuracy of trajectory data and missing of trajectory data in the prior art.
According to one aspect, the present invention provides a method for processing trajectory data, comprising:
performing multi-level grid division on the road network according to the road network density to obtain a plurality of grids, and encoding each grid according to the encoding length corresponding to the area of the grid;
acquiring track data to be processed, and coding each positioning point contained in the track data;
matching the coded prefix of each positioning point and each grid to determine the home grid and the adjacent grid of each positioning point;
extracting all candidate road sections in the attribution grids and the adjacent grids of each positioning point to form respective candidate road section sets of each positioning point;
calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set, and determining the road section matched with each positioning point according to the matching degree; and
and carrying out position association on each positioning point and the matched road section to correct the positioning point with misaligned position in the track data, and finishing the processing of the track data.
The multi-stage meshing of the road network according to the road network density comprises the following steps:
performing grid division on the road network at the current level, and counting the number of road network nodes in each grid at the current level;
and if the quantity of the road network nodes in any grid at the current level exceeds a specified quantity threshold, performing next-level grid division on the grid at the current level until the quantity of the road network nodes in any grid at the last level does not exceed the specified quantity threshold, thereby completing the multi-level grid division of the road network.
The encoding each mesh according to the encoding length corresponding to the area of the mesh includes:
pre-establishing a corresponding relation between the area of a geographical area and the coding length;
calculating the geographical area of each grid;
acquiring the coding length of each grid according to the corresponding relation;
and encoding the coordinates of the central position of each grid by using the encoding length.
Wherein the geographic area of the grid of the current level is greater than the geographic area of the grid of the next level.
Before encoding the coordinates of the central position of each grid by using the encoding length, the two-dimensional longitude and latitude of the coordinates of the central position of each grid are respectively converted into character strings, wherein the longer the character string is, the more accurate the range represented by the character string is.
Encoding each anchor point contained in the trajectory data, including:
and determining the position coordinates of each positioning point contained in the track data, and encoding a character string formed by the longitude and the latitude of the position coordinates of each positioning point.
The extracting of all candidate road sections in the attribution grid and the adjacent grid of each positioning point comprises:
all road nodes contained in the home mesh and the neighboring mesh of each localization point are determined, and all candidate road segments associated with the road nodes are determined based on each road node.
The calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set, and the determining the road section matched with each positioning point according to the matching degree comprises the following steps:
sequentially selecting each positioning point in the positioning points as a current positioning point:
calculating the matching degree of the distance between the current positioning point and each candidate road section in the candidate road section set, and acquiring the maximum value of the probability value in the matching degree as P1，P1＝{p11，p12，......，p1mM is the number of candidate road sections;
calculating the contact ratio of the path contained in the historical track data and each candidate road section, and acquiring the maximum value of the probability value in the contact ratio as P2，P2＝{p21，p22，......，p2m}；
Calculating the matching degree of the driving distance, and acquiring the maximum value of the probability value in the matching degree as P3，P3＝{p31，p32，......，p3m}；
Comprehensively calculating three probability values of the current positioning point to obtain the matching degree P of the current positioning point and each candidate road section, wherein P is P1P2P3＝{p11p21p31，p12p22p32，......，p1mp2mp3mAnd the candidate road section with the maximum matching degree P is the road section matched with the current positioning point.
The calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set, and the determining the road section matched with each positioning point according to the matching degree comprises the following steps:
sequentially selecting each positioning point in the positioning points as a current positioning point:
at least one of the following three calculations is performed, and the matching degree of the current localization point and each candidate road segment in the candidate road segment set is determined based on the calculation result of at least one of the following three calculations:
calculating the matching degree of the distance between the current positioning point and each candidate road section in the candidate road section set, and acquiring the maximum value of the probability value in the matching degree as P1，P1＝{p11，p12，......，p1mM is the number of candidate road sections;
calculating the contact ratio of the path contained in the historical track data and each candidate road section, and acquiring the maximum value of the probability value in the contact ratio as P2，P2＝{p21，p22，......，p2m}；
Calculating the matching degree of the driving distance, and acquiring the maximum value of the probability value in the matching degree as P3，P3＝{p31，p32，......，p3m}。
The closer the distance between the positioning point and the candidate road section is, the greater the probability that the positioning point is matched with the candidate road section is;
the higher the coincidence degree of the path contained in the historical track data and the candidate road section is, the greater the probability of matching the positioning point with the candidate road section is; and
the higher the matching degree of the road network distance and the driving distance associated with the positioning point is, the greater the probability that the positioning point is matched with the candidate road section is.
According to another aspect, the invention provides a computer program product comprising a program executable by a processor, characterized in that the program realizes the following steps when executed by the processor:
performing multi-level grid division on the road network according to the road network density to obtain a plurality of grids, and encoding each grid according to the encoding length corresponding to the area of the grid;
acquiring track data to be processed, and coding each positioning point contained in the track data;
matching each positioning point with the prefix coded by each grid to determine the attribution of each positioning point
Belonging to a grid and an adjacent grid;
extracting all candidate road sections in the attribution grids and the adjacent grids of each positioning point to form respective candidate road section sets of each positioning point;
calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set, and determining the road section matched with each positioning point according to the matching degree; and
and correcting the positioning points with misaligned positions in the track data by performing position association on each positioning point and the matched road section, thereby finishing the processing of the track data.
According to another aspect, the present invention provides a processing system for trajectory data, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to: carrying out any of the methods as described above.
According to another aspect, the present invention provides a trajectory data processing apparatus, comprising:
the grid coding unit is used for performing multi-level grid division on the road network according to the road network density to obtain a plurality of grids and coding each grid according to the coding length corresponding to the area of the grid;
the positioning point coding unit is used for acquiring track data to be processed and coding each positioning point contained in the track data;
the positioning point grid determining unit matches each positioning point with the coded prefix of each grid so as to determine the attributive grid and the adjacent grid of each positioning point;
the candidate road section set forming unit is used for extracting the attribution grid of each positioning point and all candidate road sections in the adjacent grids so as to form a respective candidate road section set of each positioning point;
the matching road section determining unit is used for calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set, and determining the road section matched with each positioning point according to the matching degree; and
and the processing unit corrects the positioning points with misaligned positions in the track data by performing position association on each positioning point and the matched road section, so that the track data is processed.
The processing method of the track data provided by the invention carries out multi-stage meshing on the road network data according to the road network density and extracts the candidate road sections containing the road nodes. And calculating the matching degree of each positioning point and each candidate road section, determining the road section matched with each positioning point according to the matching degree, and correcting the positioning point with the position misalignment in the track data by performing position association on each positioning point and the matched road section. Through the mode, the method provided by the invention can be used for solving the problems of low accuracy of the track data and missing of the track data in the prior art by finishing the processing of the track data.
Drawings
FIG. 1 is a flow chart of a method for processing trajectory data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a city road network according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a multi-level indexing method for constructing a road network according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a correspondence relationship between a geographical area and a Geohash code length according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a binary tree structure of road network multi-level indexes according to an embodiment of the present invention;
fig. 6 is a schematic flowchart of matching a localization point with a candidate road segment according to an embodiment of the present invention; and
FIG. 7 is a flowchart of a method for processing trajectory data according to another embodiment of the present invention;
fig. 8 is a schematic structural diagram of a track data processing apparatus according to an embodiment of the present invention.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather construed as limited to the embodiments set forth herein.
Referring to fig. 1, fig. 1 is a flowchart of a track data preprocessing method according to an embodiment of the present invention. The method provided by the embodiment of the invention is explained in detail with reference to fig. 1.
Step S101, according to the road network density, the road network is divided into a plurality of grids in a multi-stage grid division mode to obtain a plurality of grids, and each grid is coded according to the coding length corresponding to the area of the grid.
The road network is a network structure formed by roads in a specific area, such as a city, a countryside, and the like. The road network is a network architecture which is composed of roads with different functions, grades and regions in a city range and is formed in a certain density and proper form. Road network density is used to indicate the density of roads in an entire area, a partial area, or a selected area of the road network. As can be seen from the city road network topology information graph in fig. 2, the road network density is high when the road networks in the center region of the city are dense, and the road network density is low when the road networks at the edge of the city are sparse. In general, a large number of roads and intersections are included in an area having a large road network density.
Aiming at the technical problem to be solved by the invention, when the positioning deviation of the positioning data obtained by the positioning system is large or the positioning cannot be carried out, the obtained track data has the problems of low accuracy and deficiency. The processing of the trajectory data is to correct the erroneous trajectory data or to supplement the missing data in time so that the trajectory data output by the positioning system is corrected when these problems occur. Then, when solving the problem, it is first necessary to find out the candidate road segments corresponding to the trajectory data from the current road network, and then perform further calculation through the following steps to complete the processing of the trajectory data. The candidate links refer to links similar to the trajectory data, which are most likely to be the actual travel trajectory to which the problematic trajectory data belongs. However, when the trajectory data is matched with the candidate road segments, if the matching is performed in the whole urban road network, the number of the candidate road segments is very large, which results in a very large amount of calculation and affects the calculation efficiency of the system.
Therefore, in order to improve the calculation efficiency of the system, the track data only needs to be matched with the candidate road sections in the road network within a certain range. If the whole road network is divided evenly by using the grid, in an area with high road network density, the number of candidate road segments matched by the trajectory data is definitely greater than that of candidate road segments matched by an area with low road network density, and the calculation efficiency of the system is also influenced. Therefore, the present embodiment proposes a mesh division method based on road network density, and performs multi-level mesh division on the road network. Such a method comprises: performing grid division on the road network at the current level, and counting the number of road network nodes in each grid at the current level; and if the quantity of the road network nodes in any grid at the current level exceeds a specified quantity threshold value, performing next-level grid division on the grid at the current level until the quantity of the road network nodes in any grid at the last level does not exceed the specified threshold value, thereby completing the multi-level grid division of the road network. For example, performing multi-level meshing on the road network data includes: carrying out grade 1 grid division on the road network data, and counting the number of road network nodes in each grade 1 grid; and if the number of the road network nodes in any grid of the 1 st level exceeds a specified number threshold, performing 2 nd level grid division on the grid, and repeating the steps until the number of the road network nodes in any grid of the last 1 level does not exceed the specified threshold, thereby completing the division of the road network data multi-level grid.
Since the present embodiment performs multi-level mesh division on the road network, the acquired meshes may not belong to the same mesh level. As shown in fig. 3, the level of the grid WY is one level higher than the level of the grid WX1, and the level of the grid WY is two levels higher than the level of the grid WX 12. It should be appreciated that different levels of the trellis correspond to different levels of the index, and that different levels of the index correspond to different code lengths. For example, grid WY corresponds to a primary index, grid WX1 corresponds to a secondary index, grid WX12 corresponds to a tertiary index, and grid WX111 corresponds to a quaternary index.
Therefore, the present embodiment encodes each mesh according to the encoding length corresponding to the area of the region of the mesh. First, the coding length of the trellis is determined according to the trellis level. The level of the grid where the grid is located is different, and then the coding length of the grid is also different. Each trellis may then be encoded according to its encoding length using a Geohash. Thus, each mesh has its own independent code. In addition, the present embodiment can determine the index level of each mesh by the encoding length, thereby constructing a multi-level index of the road network. Generally, the larger the area of the region of the grid, the higher the index level corresponding to the grid and the shorter the code length. In the Geohash coding scheme, the coding length of each level corresponds to a preset geographical area. In actual encoding, the present embodiment selects the encoding length according to the actual area of each divided mesh. And constructing the index level of each code according to different code lengths, thereby realizing a multi-level index structure. For example, codes with shorter code lengths are located at a higher level of the multi-level index structure, while codes with longer code lengths are located at a lower level of the multi-level index structure.
As shown in fig. 3, the grid WY corresponds to the primary index, the code length is 2 and the area of the region of the grid WY is 1/2 of the road network area (with the region in fig. 2 as the entire road network). Grid WX1 corresponds to a two-level index, the code length is 3 and the area of the region of grid WX1 is 1/4 of the road network area. Grid WX12 corresponds to three levels of indices, the code length is 4 and the area of the region of grid WX12 is 1/8 of the road network area. Grid WX111 corresponds to four levels of indices, the code length is 5 and the area of the region of grid WX111 is 1/16 of the road network area.
It should be understood that the present embodiment is illustrated with a Geohash encoding scheme, but that any similar or analogous encoding scheme may be used. Therefore, the encoding scheme of the present embodiment is not limited to the Geohash encoding scheme.
Specifically, encoding each mesh according to an encoding length corresponding to a region area of the mesh includes: pre-establishing a corresponding relation between the area of a geographical area and the coding length; calculating the geographical area of each grid; acquiring the coding length of each grid according to the corresponding relation; and encoding the coordinates of the central position of the grid by using the encoding length. Generally, a geocoding scheme such as Geohash assigns a code length or a code level according to an area of a mesh generated by dividing a road network. For this reason, the correspondence relationship between the geographical area and the coding length, which is usually established in advance in the geocoding scheme, is shown in fig. 4. In fig. 4, the first column indicates the code length and the fourth column indicates the area.
The geographical area of each grid is calculated by the following formula:
S＝dist(max(lng),min(lng))*dist(max(lat),min(lat))
where max (lng) is the maximum grid longitude, min (lng) is the minimum grid longitude, max (lat) is the maximum grid latitude, and min (lat) is the minimum grid latitude. In this embodiment, the geographical area of the grid is rectangular, so the above formula determines the geographical area of the grid by calculating the area of the rectangle. The geographic area of the grid of the current level is greater than the geographic area of the grid of the next level. It should be understood that the present embodiment is illustrated with the geographic area of the grid of the current level being 2 times the geographic area of the grid of the next level, but those skilled in the art will appreciate that the ratio of the geographic area of the grid of the current level to the geographic area of the grid of the next level may be any reasonable ratio.
The geographical area S of the grid calculated by the above formula is equal to the predetermined area S in FIG. 4
A complete match is not possible. Therefore, it is necessary to determine a region area most similar to a predetermined region area to determine the code length. For example, the geographical area S of the grid calculated by the above formula is 9700 square kilometers, and the code length should be determined to be 3. Therefore, the code length of each mesh is obtained from the correspondence, and the one closest to S is extracted by comparing with each item in the table of fig. 4
Then find out
The corresponding code length is taken as the code length of the trellis.
And then, according to the coding length of the grid, the coordinates of the central position of the grid, namely the longitude and latitude of the grid, are coded by using Geohash. The encoding mode is as shown in fig. 3, the encoding length is used for encoding the coordinate of the central position of the grid, and the two-dimensional longitude and latitude of the coordinate of the central position of the grid is converted into a character string. Wherein the longer the string, the more precise the range of representation. In this embodiment, when encoding is performed using Geohash, the encoding method of Geohash base32 is used. For example, an example of coding of a mesh in a road network region is: grid WY, grid WX1, grid WX12, grid WX111, and the like.
The multi-level index structure of the road network can also be represented by a binary tree structure, as shown in fig. 5. The structure of the bottommost layer can be understood as the whole road network of the city, then the whole road network is divided into two parts, a first-level index (for example, grid WY) of the road network is constructed, then the first-level index is divided into two parts, a second-level index (for example, grid WX1) of the road network is constructed, and the like, the n-level index of the road network is constructed. It can be seen that the multi-level index can be a binary tree structure. Before each level of index is divided, whether the next level of index structure is divided is judged according to the size relation between the number of nodes of the road network to be divided and the specified number threshold.
Step S102, acquiring track data to be processed, and encoding each positioning point contained in the track data. Wherein encoding each anchor point contained in the trajectory data comprises: and determining the position coordinates of each positioning point contained in the track data, and encoding a character string formed by the longitude and the latitude of the position coordinates of each positioning point. It should be understood that steps S101 and S102 do not need to be performed in chronological order or sequential order in the present embodiment, but may be performed in parallel or in other orders. For example, step S102 may be performed before step S101, or step S102 may be performed simultaneously with step S101.
Currently, the mainstream positioning systems include the BeiDou Navigation Satellite System (BDS), the Global Positioning System (GPS), and the like. The trajectory data is output by these positioning systems or similar positioning systems. Taking a vehicle-mounted navigation device as an example, the vehicle-mounted navigation device is used as an output device of a positioning system, and the positioning system positions the position of the current vehicle at regular time or at preset time intervals in the positioning process. Typically, there is a time difference between the two locations. For example, the position of the current vehicle is acquired every 3 seconds, 5 seconds, 8 seconds, 10 seconds, or the like, i.e., the time difference is 3 seconds, 5 seconds, 8 seconds, 10 seconds, or the like. Therefore, although the output position information seems continuous, the output position information is track data formed by connecting a plurality of different anchor points.
After the track data output by the positioning system is obtained, each positioning point contained in the track data is coded. In this embodiment, the encoding method for encoding the anchor point is the same as the encoding method for encoding the mesh, so that the encoding of the anchor point and the encoding of the mesh can perform prefix matching. For example, the encoding scheme for encoding anchor points and the encoding scheme for encoding meshes are both Geohash base32 encoding. Specifically, the longitude and latitude of each anchor point are determined, and a character string composed of the longitude and latitude of each anchor point is encoded according to a Geohash encoding scheme to generate a Geohash encoding of each anchor point.
Step S103, matching the coded prefix of each positioning point and each grid to determine the attributive grid and the adjacent grid of each positioning point. For example, the anchor point's code is WX168, then prefix matching the anchor point's code to all the mesh's codes (i.e., matching from the left side of the code) may determine that the anchor point matches the mesh WX 1. Then mesh WX1 is the home mesh for the anchor point. In addition, grid WX12 and grid WX111 are adjacent grids of anchor points. This is because, although the first three bits of grid WX12 and grid WX111 match the anchor point's code WX168, the latter characters do not match and are therefore the anchor point's neighbor grid.
As can be seen from fig. 3, the similarity of the Geohash codes of the grids with similar road network index levels is higher, and the distance between the grids with higher similarity of the Geohash codes is closer. The Geohash code is a character string which accords with a preset rule, so that the attributive grid and the adjacent grid of each positioning point can be obtained by a character string prefix matching method. In another example, the specific method of prefix matching is, for example, Geohash of anchor point is coded as wx4g0ec1, and its prefix wx4g0ec represents the mesh where the anchor point is currently located, and the meshes containing the prefix wx4g0e can be regarded as the meshes near the anchor point wx4g0ec1, so that the neighboring meshes of the anchor point can be obtained by the method of prefix matching.
Step S104, extracting all candidate road sections in the attribution grid and the adjacent grid of each positioning point to form a respective candidate road section set of each positioning point.
As described above, the home mesh and the neighboring mesh of each anchor point are determined by the prefix matching method. Since each mesh includes a candidate link formed by at least one road node, all road nodes included in the home mesh and the neighboring mesh of each anchor point are extracted, and all candidate links associated with the road nodes are determined based on each road node. A road node generally belongs to a certain road segment. The set of these segments associated with each anchor point constitutes a respective set of candidate segments for each anchor point.
And step S105, calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set, and determining the candidate road section matched with each positioning point according to the matching degree.
In order to solve the problems of low accuracy of the trajectory data and missing of the trajectory data in the prior art, the actual position of each positioning point in the acquired trajectory data needs to be determined. The actual trajectory of the user, i.e. the trajectory that the user carrying the positioning device actually passes through, can be determined from the actual position of each positioning point. For this reason, it is necessary to match each localization point with all candidate road segments to which it may be related, and determine the actual position of each localization point based on the result determined through the matching.
Firstly, a current positioning point is selected from a plurality of positioning points, and each candidate road section in the candidate road section set is obtained aiming at the current positioning point. In order to correct the trajectory data, the present embodiment performs matching calculation of the candidate link for each anchor point in the trajectory data. For illustration, the present embodiment describes a positioning point of a plurality of positioning points as a current positioning point. It should be appreciated that the present embodiment may select each anchor point in forward or reverse order according to the temporal order of the anchor points. Namely, after the current positioning point is subjected to matching calculation, each of the rest positioning points is subjected to matching calculation in sequence.
The flow of the matching calculation of the localization points and the candidate road segments is shown in fig. 6. For example, calculating the matching degree of the distance between the current localization point and each candidate road segment in the candidate road segment set comprises:
step S601, calculating a distance between the current localization point and each candidate road segment in the candidate road segment set. Specifically, the calculation is performed by the following formula,
wherein (x)0，y0) For the coordinates of the current positioning point, A is x1-x2，B＝y1-y2，C＝x1y2-x2y1Wherein (x)1，y1)，(x2，y2) Are any two coordinate points on the candidate road segment.
Step S602, sorting the distances between the current positioning point and each candidate road segment in an ascending order. In general, the probability that the candidate link closest to the current anchor point is the candidate link actually matching the current anchor point is high. In order to improve the calculation efficiency, the embodiment sorts the distance between the current localization point and each candidate link in an ascending order after step S601, so that the candidate links with closer distances are ranked more forward.
Step S603, calculating a matching degree between the current positioning point and each candidate road segment. When the matching degree of the current positioning point and each candidate road segment is calculated, the present embodiment determines three initial matching probabilities of the current positioning point and each candidate road segment through three calculation methods. And then, performing composite calculation on the three initial matching probabilities of the current positioning point and each candidate road section to determine the matching degree of the current positioning point and each candidate road section.
First, after calculating the distance between the current positioning point and the candidate road segment, it may be determined that the closer the distance between the current positioning point and the candidate road segment is, the greater the probability of matching is. Wherein the probability value of the first initial probability is P1＝{p11,p12,…,p1mWhere m is the number of candidate links. The first initial probability is used to indicate a distance-based matching probability of the current localization point with each candidate road segment.
Then, the coincidence degree of the path included in the historical track data and each candidate road segment is sequentially calculated according to the obtained sequence. The historical track data refers to track data formed by all positioning points before the current positioning point. And acquiring the coincidence degree of the path of the historical track data of the current positioning point and the candidate road section. Specifically, each positioning point (x) in the path of the historical track data is judged according to the sequencing resultn，yn) Whether on a candidate segment. I.e. whether each of all anchor points preceding the current anchor point is on a candidate segment.
Specifically, first, it is determined
(theta is a threshold value), where (x)1，y1)，(x2，y2) Any two coordinate points on a particular candidate road segment. The above formula is used to determine whether a path in the historical track data is on a candidate segment. When the distance is smaller than the threshold theta, judging that the path is on a specific candidate road section, and then continuing to judge the curve fitting; otherwise, judging that the path is not on the specific candidate road section, and not judging curve fitting.
And performing curve fitting on the paths and the candidate links included in the historical track data to obtain curves y (f) (x) of the paths and the candidate links included in the historical track data. Calculating the curvature center of the curve by derivation of the curve, wherein the calculation formula is as follows:
where α is a curvature center of a path included in the historical track data, and β is a curvature center of a candidate link path. The distance between the curvature center of the route of the history track data and the curvature center of the candidate link is dist ((alpha)1,β1),(α2,β2)). Wherein (alpha)1,β1),(α2,β2) The curvature center coordinates of the two curves are respectively, the smaller the distance is, the higher the contact ratio of a path contained in the historical track data and the candidate road section is, the greater the matching probability of the current positioning point and the candidate road section is, and the probability value of the second initial probability is P2＝{p21,p22,…,p2m}. The second initial probability is used for indicating the matching probability of the current positioning point and each candidate road section based on the contact degree.
Finally, since the driving distance matching degree is also an important index for determining the similarity between the localization point and the candidate road section, for this reason, the present embodiment calculates the matching probability between the current localization point and the candidate road section based on the driving distance matching. The travel distance is the distance between two adjacent moments calculated from the current speed. The road network distance is the distance between the positioning points at two adjacent moments output by the positioning system. When the driving distance and the road network distance are calculated, the time values of two adjacent moments are the same. When the positioning system outputs the track data, any two adjacent positioning points are separated by time. Taking the vehicle-mounted navigation as an example, the running speed of the device or the vehicle can be determined according to the positions and the time intervals of two adjacent positioning points, and then the running distance of the device or the vehicle can be calculated according to the running speed and the running time of the device or the vehicle.
The method for determining the matching probability of the current positioning point and the candidate road section through the driving distance matching degree comprises the following steps: calculating a difference value between the road network distance associated with the current location point and the travel distance,
wherein c ist-1As the anchor point of the previous moment on the candidate road section, ctIs the current positioning point of the current time on the candidate road section, vt-1Velocity, v, of the current setpoint at the previous momenttThe current time speed of the current positioning point. The smaller the difference, the higher the matching degree of the travel distance. The higher the matching degree of the travel distance, the greater the probability of matching the candidate link. Summary of third initial probabilityValue of P3＝{p31,p32,…,p3m}. The third initial probability is used for indicating the matching probability of the current positioning point and each candidate road section based on the matching degree of the driving distance.
And comprehensively considering the probability values of the three probabilities to determine the candidate road section matched with the current positioning point. That is, the matching degree of the current localization point and each candidate road segment is P ═ P1P2P3＝{p11p21p31,p12p22p32,…,p1mp2mp3mIn which P is1Is a first initial probability, P2Is a second initial probability and P3Is the third initial probability.
And the candidate road section with the maximum value of the matching degree P is the candidate road section matched with the current positioning point. It should be understood that, when the matching degree P of the current localization point and each candidate road segment is calculated, the probability values of the three initial probabilities are only three of all the probability values when the matching degree P is calculated, and when the matching degree P is calculated, at least any one of all the matching degrees is taken. That is, the present embodiment may pass P1、P2And P3To determine a degree of matching P of the current localization point with each candidate road segment. In addition, the present embodiment is illustrated with three initial probabilities as an example, and those skilled in the art will appreciate that any reasonable number of initial probabilities may be used. In general, the closer the location point is to a candidate road segment, the greater the probability that the location point matches the candidate road segment. The higher the coincidence degree of the path contained in the historical track data and the candidate road section is, the higher the probability that the positioning point is matched with the candidate road section is. The higher the matching degree of the road network distance and the driving distance associated with the positioning point is, the greater the probability that the positioning point is matched with the candidate road section is.
And then, according to the method for determining the candidate road section for the current positioning point, the candidate road section matched with each positioning point is sequentially obtained.
And step S106, performing position association on each positioning point and the matched road section to correct the positioning points with misaligned positions in the track data, and finishing the processing of the track data. Each localization point is positionally related to a matching road segment, because in practice each localization point has only one position-related candidate road segment, i.e. the device or vehicle carrying the localization system belongs to only one road segment at the same time. In this implementation, each anchor point in the trajectory data to be processed is processed in time sequence. That is, the processing is started from the anchor point whose time is the earliest in the trajectory data to be processed until the processing of the anchor point whose time is the latest in the trajectory data to be processed is ended. Typically, when a certain anchor point in the trajectory data to be processed is not in a road segment (i.e. not in a position where it can travel, walk or form), this anchor point is determined to be an anchor point that is out of position. In addition, when the former locating point and the latter locating point in the track data to be processed are both in the road segment a and the current locating point is in the road segment B, it may also be determined that the current locating point is a locating point with a misaligned position. That is, the present embodiment determines the anchor point where the position coordinates are obviously problematic as the anchor point where the position is misaligned.
When the current positioning point is processed, if the current positioning point is determined to be a positioning point with a misaligned position, the road section to which the current positioning point belongs is determined. And determining the correct position of the current positioning point on the attributive road section according to the position of the previous positioning point before the current positioning point on the attributive road section and the speed of the user (or the mobile terminal or the vehicle carrying the mobile terminal). And taking the correct position of the current positioning point on the road section to which the current positioning point belongs as the actual position of the current positioning point, so as to correct the current positioning point with the misaligned position in the track data.
Generally, the technical solution provided by this embodiment is started when trajectory data is to be analyzed and processed after the trajectory data has been collected, and the trajectory data is preprocessed. However, in some cases, when a significant misalignment occurs in the position of the positioning point in the trajectory data, for example, when one or more positioning points in the trajectory data occur at a clearly wrong position, in response to a data processing request from a user, the matching degree of the positioning point and the candidate road segment may be calculated through steps S101 to S106, the candidate road segment for which the positioning point matches is obtained as a, and then the position association of the positioning point and the road segment a is established. Then, the positioning point is moved to the road section a again through position association, so that the positioning point with the misaligned position in the track data is corrected, and the processing of the track data is completed. For another example, in the road segment a where the track data is located, there is a section of track data missing due to information occlusion and the like, and through the steps S101 to S106, the overlap ratio between the path included in the historical track data and the candidate road segment is calculated, the candidate road segment matched with the existing positioning point in the track data is obtained as a, and then the position association between the existing positioning point and the road segment a is established. And then, determining that the existing positioning points are all on the road section a through position association, and then determining that the missing track data is also on the road section a, so as to fill the missing positioning points in the track data, and complete the processing of the track data.
The method for processing the trajectory data provided by this embodiment mainly includes two links, as shown in fig. 7, when the problems of low accuracy of the trajectory data and missing of the trajectory data occur, the candidate road segments are selected on the road network S701, then the matching degree between the positioning points and the candidate road segments is calculated S702, the road segment with the highest matching probability is selected from the candidate road segments, and the data of the road segment with the highest matching probability is used to correct the trajectory data with the problems, so that the problems of low accuracy of the trajectory data and missing of the trajectory data are solved, and the trajectory data of the positioning system is more accurate.
Corresponding to the above-provided method for processing trajectory data, the present embodiment also provides a device 800 for processing trajectory data. As shown in fig. 8, includes:
a mesh encoding unit 810 for performing multi-level mesh division on the road network according to the road network density to obtain a plurality of meshes, and encoding each mesh according to an index level to construct a multi-level index of the road network;
a positioning point encoding unit 820, which acquires trajectory data to be processed and encodes each positioning point included in the trajectory data;
a positioning point grid determining unit 830, configured to match each positioning point with the prefix encoded in each grid, so as to determine a home grid and an adjacent grid of each positioning point;
a candidate road section set forming unit 840, which extracts all candidate road sections in the attribution grid and the adjacent grid of each positioning point to form a respective candidate road section set of each positioning point;
a matching road section determining unit 850 for calculating the matching degree of each positioning point with each candidate road section in the respective candidate road section set, and determining the road section matched with each positioning point according to the matching degree; and
and a processing unit 860 for correcting the position-misaligned positioning points in the trajectory data by performing position association between each positioning point and the matched road segment, thereby completing the processing of the trajectory data.
Furthermore, the present embodiment also provides a computer program product, which includes a program executable by a processor, and is characterized in that the program implements the following steps when being executed by the processor:
performing multi-level grid division on the road network according to the road network density to obtain a plurality of grids, and encoding each grid according to the encoding length corresponding to the area of the grid;
acquiring track data to be processed, and coding each positioning point contained in the track data;
matching the coded prefix of each positioning point and each grid to determine the home grid and the adjacent grid of each positioning point;
extracting all candidate road sections in the attribution grids and the adjacent grids of each positioning point to form respective candidate road section sets of each positioning point;
calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set, and determining the road section matched with each positioning point according to the matching degree; and
and correcting the positioning points with misaligned positions in the track data by performing position association on each positioning point and the matched road section, thereby finishing the processing of the track data.
Further, this embodiment also provides a processing system of trajectory data, where the processing system includes:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to: performing any one or a combination of the steps as described above.
Claims (13)
1. A method for processing trajectory data, comprising:
performing multi-level grid division on the road network according to the road network density to obtain a plurality of grids, and encoding each grid according to the encoding length corresponding to the area of the grid;
acquiring track data to be processed, and coding each positioning point contained in the track data;
matching the coded prefix of each positioning point and each grid to determine the home grid and the adjacent grid of each positioning point;
extracting all candidate road sections in the attribution grids and the adjacent grids of each positioning point to form respective candidate road section sets of each positioning point;
calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set, and determining the road section matched with each positioning point according to the matching degree; and
carrying out position association on each positioning point and the matched road section to correct the positioning points with misaligned positions in the track data, and finishing the processing of the track data;
wherein calculating a degree of match of each localization point to each candidate road segment in the respective set of candidate road segments comprises:
and calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set according to the matching probability of each positioning point and each candidate road section in the respective candidate road section set based on the contact degree.
2. The processing method according to claim 1, wherein said multi-stage meshing of the road network according to the road network density comprises:
performing grid division on the road network at the current level, and counting the number of road network nodes in each grid at the current level;
and if the quantity of the road network nodes in any grid at the current level exceeds a specified quantity threshold, performing next-level grid division on the grid at the current level until the quantity of the road network nodes in any grid at the last level does not exceed the specified quantity threshold, thereby completing the multi-level grid division of the road network.
3. The processing method according to claim 1, wherein said encoding each mesh according to an encoding length corresponding to a region area of the mesh comprises:
pre-establishing a corresponding relation between the area of a geographical area and the coding length;
calculating the geographical area of each grid;
acquiring the coding length of each grid according to the corresponding relation;
and encoding the coordinates of the central position of each grid by using the encoding length.
4. A processing method according to claim 1 or 3, wherein the geographical area of the grid of the current level is larger than the geographical area of the grid of the next level.
5. The processing method according to claim 3, wherein before encoding the coordinates of the center position of each grid using the encoding length, the method further comprises converting the two-dimensional longitude and latitude of the coordinates of the center position of each grid into character strings respectively, wherein the longer the character string is, the more precise the range represented by the character string is.
6. The processing method according to claim 1, wherein encoding each anchor point included in the trajectory data comprises:
and determining the position coordinates of each positioning point contained in the track data, and encoding a character string formed by the longitude and the latitude of the position coordinates of each positioning point.
7. The processing method according to claim 1, wherein said extracting all candidate segments in the home mesh and the neighboring mesh of each localization point comprises:
all road nodes contained in the home mesh and the neighboring mesh of each localization point are determined, and all candidate road segments associated with the road nodes are determined based on each road node.
8. The processing method according to claim 1, wherein the calculating of a degree of matching of each localization point with each candidate road segment in the respective set of candidate road segments, the determining of the road segment matching each localization point according to the degree of matching comprises:
sequentially selecting each positioning point in the positioning points as a current positioning point:
calculating the matching degree of the distance between the current positioning point and each candidate road section in the candidate road section set, and acquiring the maximum value of the probability value in the matching degree as P1，P1＝{p11，p12，......，p1mM is the number of candidate road sections;
calculating the contact ratio of the path contained in the historical track data and each candidate road section, and acquiring the maximum value of the probability value in the contact ratio as P2，P2＝{p21，p22，......，p2m}；
Calculating the matching degree of the driving distance, and acquiring the maximum value of the probability value in the matching degree as P3，P3＝{p31，p32，......，p3m}；
Comprehensively calculating three probability values of the current positioning point to obtain the matching degree P, P of the current positioning point and each candidate road section＝P1P2P3＝{p11p21p31，p12p22p32，......，p1mp2mp3mAnd the candidate road section with the maximum matching degree P is the road section matched with the current positioning point.
9. The processing method according to claim 1, wherein the calculating of a degree of matching of each localization point with each candidate road segment in the respective set of candidate road segments, the determining of the road segment matching each localization point according to the degree of matching comprises:
sequentially selecting each positioning point in the positioning points as a current positioning point:
at least one of the following three calculations is performed, and the matching degree of the current localization point and each candidate road segment in the candidate road segment set is determined based on the calculation result of at least one of the following three calculations:
calculating the matching degree of the distance between the current positioning point and each candidate road section in the candidate road section set, and acquiring the maximum value of the probability value in the matching degree as P1，P1＝{p11，p12，......，p1mM is the number of candidate road sections;
calculating the contact ratio of the path contained in the historical track data and each candidate road section, and acquiring the maximum value of the probability value in the contact ratio as P2，P2＝{p21，p22，......，p2m}；
Calculating the matching degree of the driving distance, and acquiring the maximum value of the probability value in the matching degree as P3，P3＝{p31，p32，......，p3m}。
10. The processing method according to claim 8 or 9, wherein the closer the location point is to the candidate segment, the greater the probability that the location point matches the candidate segment;
the higher the coincidence degree of the path contained in the historical track data and the candidate road section is, the greater the probability of matching the positioning point with the candidate road section is; and
the higher the matching degree of the road network distance and the driving distance associated with the positioning point is, the greater the probability that the positioning point is matched with the candidate road section is.
11. A computer program product comprising a program executable by a processor, the program realizing the following steps when executed by the processor:
performing multi-level grid division on the road network according to the road network density to obtain a plurality of grids, and encoding each grid according to the encoding length corresponding to the area of the grid;
acquiring track data to be processed, and coding each positioning point contained in the track data;
matching the coded prefix of each positioning point and each grid to determine the home grid and the adjacent grid of each positioning point;
extracting all candidate road sections in the attribution grids and the adjacent grids of each positioning point to form respective candidate road section sets of each positioning point;
calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set, and determining the road section matched with each positioning point according to the matching degree; and
correcting the positioning points with misaligned positions in the track data by performing position association on each positioning point and the matched road section, thereby completing the processing of the track data;
wherein calculating a degree of match of each localization point to each candidate road segment in the respective set of candidate road segments comprises:
and calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set according to the matching probability of each positioning point and each candidate road section in the respective candidate road section set based on the contact degree.
12. A system for processing trajectory data, the system comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to: performing the method of any one of claims 1 to 10.
13. An apparatus for processing trajectory data, comprising:
the grid coding unit is used for performing multi-level grid division on the road network according to the road network density to obtain a plurality of grids and coding each grid according to the coding length corresponding to the area of the grid;
the positioning point coding unit is used for acquiring track data to be processed and coding each positioning point contained in the track data;
the positioning point grid determining unit matches each positioning point with the coded prefix of each grid so as to determine the attributive grid and the adjacent grid of each positioning point;
the candidate road section set forming unit is used for extracting the attribution grid of each positioning point and all candidate road sections in the adjacent grids so as to form a respective candidate road section set of each positioning point;
the matching road section determining unit is used for calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set, and determining the road section matched with each positioning point according to the matching degree; and
the processing unit corrects the positioning points with misaligned positions in the track data by performing position association on each positioning point and the matched road section, so that the track data is processed;
wherein calculating a degree of match of each localization point to each candidate road segment in the respective set of candidate road segments comprises:
and calculating the matching degree of each positioning point and each candidate road section in the respective candidate road section set according to the matching probability of each positioning point and each candidate road section in the respective candidate road section set based on the contact degree.
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