Urban circle traffic network topology construction and flow matching method and device based on individual-level GPS trajectory data, medium and product

Abstract

The invention discloses an urban circle traffic network topology construction and flow matching method and device based on individual-level GPS trajectory data, a medium and a product, and relates to the field of urban traffic. The method comprises the following steps: connecting original GPS track points to form an original GPS track line; then uniformly breaking the original GPS track line to form a corrected GPS track point; identifying candidate intersections based on the corrected GPS track points; directed road sections are extracted based on the candidate intersections, then an optimized road topology network is generated, and candidate directed road sections are screened out; generating candidate trajectory lines based on the origin and destination points of the original GPS trajectory and the candidate directed road sections; determining a target trajectory line and a target directed road section which are most matched with the original GPS trajectory line from the candidate trajectory lines; and forming directional road flow based on all the target trajectory lines and the target directed road sections. By adopting the method provided by the invention, the calculation efficiency of urban circle traffic network topology construction and flow matching can be improved, and the computing power demand on hardware equipment is reduced.

Description

Technical Field

[0001] The present application relates to the field of urban transportation technology, and in particular to a method, device, medium and product for constructing a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data. Background Art

[0002] GPS individual vehicle trajectory data processing technology has become a relatively mature part of urban traffic research and related applications, and the extraction and spatial network matching methods of GPS trajectories are relatively mature. However, the technical integration of directly forming spatial network topology using GPS trajectories and matching trajectories to the topological network still faces challenges in operational efficiency and limitations in algorithm design at this stage. For example, using PC and desktop-level databases, the existing calculation methods are still difficult to achieve minute-level processing and backtracking of daily statistics of motor vehicle or motorcycle GPS trajectory data covering urban space with a radius of 50-60km in China (i.e., the scope of a typical metropolitan area or a one-hour traffic circle). From the design algorithm, the calculation efficiency of matching GPS trajectory paths from all sections of the city is not high. From the data requirements, the existing methods of matching GPS trajectories to electronic map road networks have high requirements for road accuracy. If the roads are drawn by themselves, there are problems with efficiency and accuracy, and the computing power of the hardware equipment is very high. Summary of the invention

[0003] The purpose of this application is to provide a method, device, medium and product for building a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data, so as to improve the computational efficiency of building a metropolitan area transportation network topology and matching traffic, and reduce the computing power requirements for hardware equipment.

[0004] To achieve the above objectives, this application provides the following solutions.

[0005] In a first aspect, the present application provides a method for constructing a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data, including:

[0006] Connect the original GPS track points to form a continuous original GPS track line;

[0007] The original GPS track line is evenly interrupted to form the corrected GPS track points;

[0008] Identify candidate intersections based on the corrected GPS trajectory points;

[0009] Extracting directed road segments based on candidate intersections;

[0010] Generate an optimized road topology network based on directed road segments;

[0011] Screen out candidate directed road segments based on the corrected GPS track points and the optimized road topology network;

[0012] Generate candidate trajectory routes based on the starting and ending points of the original GPS trajectory and the candidate directed sections;

[0013] Determine the trajectory route and its corresponding directional road segment that best matches the original GPS trajectory line from the candidate trajectory routes as the target trajectory route and the target directional road segment;

[0014] Directional road traffic is formed based on all target trajectory routes and target directional sections.

[0015] Optionally, the connecting the original GPS track points to form a continuous original GPS track line specifically includes:

[0016] Arrange all the original GPS track points contained in each unique travel code in ascending order of timestamp and connect them in sequence to form a continuous original GPS track line, which is recorded as point set T = {p 1 ,p 2 ,...,p n}, each point p i Including spatial coordinates (x i ,y i ) and timestamp t i ; n is the number of midpoints of the original GPS trajectory.

[0017] Optionally, the evenly interrupting the original GPS track line to form the corrected GPS track points specifically includes:

[0018] According to the segmentation conditions |p i+1 -p i |≥d th The original GPS trajectory line is evenly interrupted, and the interrupted trajectory segments are smoothed using Kalman filtering or sliding average method to form the corrected GPS trajectory points;

[0019] Each corrected GPS track point constitutes a corrected GPS track point set T'={P 1 ,P 2 ,...,P N}, where N is the number of corrected GPS track points; d th is the distance threshold.

[0020] Optionally, the identifying candidate intersections based on the corrected GPS trajectory points specifically includes:

[0021] Density estimation and cluster analysis are performed on the corrected GPS track points to identify several groups;

[0022] The high-density cluster center of each group is defined as the candidate intersection c k .

[0023] Optionally, the extracting of directed road segments based on candidate intersections specifically includes:

[0024] From the corrected GPS trajectory point set T' = {P 1 ,P 2 ,...,P N} extract the intersections that pass through each pair of candidate intersections (c i ,c j ) of the trajectory segment T ij ; where c i ,c j ∈c k ;

[0025] Calculate the trajectory segment T ij Direction vector Used to represent a directed road segment; where |T ij | indicates the number of trajectory segments.

[0026] Optionally, generating an optimized road topology network according to the directed road segments specifically includes:

[0027] The candidate intersection c k As the vertex V = {c k}, the directed segment As edge Construct a road topology network G = (V, E) and calculate the weight for each edge based on the road flow;

[0028] The graph optimization method is used to eliminate redundant edges in the road topology network and generate an optimized road topology network.

[0029] Optionally, the screening out candidate directed road segments based on the corrected GPS track points and the optimized road topology network specifically includes:

[0030] For each corrected GPS track point P i , based on the formula L ij = {l j ∣d(P i ,l j )≤50}Calculate P i All road sections within 50 meters L ij ; where d(P i ,l j ) represents the corrected GPS track point P i To section l j Distance; road section l j Selected from the optimized road topology network;

[0031] Using the formula θ i =arctan 2(y i -y i-1 ,x i -x i-1 )Calculate P i and the previous corrected GPS track point P i-1 The direction angle θ i ;

[0032] Using the formula Δθ ij =|θ i -φ j |Calculate the direction angle θ i And the matching road segment l j The direction angle φ j The absolute value difference Δθ between ij ;

[0033] When Δθ ij ≤90°, determine the corresponding road section l i is the selected candidate directional road segment.

[0034] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for constructing a metropolitan area transportation network topology and traffic matching based on individual-level GPS trajectory data.

[0035] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for constructing a metropolitan transportation network topology and matching traffic based on individual-level GPS trajectory data.

[0036] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for constructing a metropolitan transportation network topology and matching traffic based on individual-level GPS trajectory data.

[0037] According to the specific embodiments provided in this application, this application discloses the following technical effects.

[0038] The present application provides a method, device, medium and product for building a metropolitan traffic network topology and matching traffic based on individual-level GPS trajectory data. By using GPS trajectory data to identify road intersections and candidate directed sections, the difficulty of data input and cleaning of new metropolitan area road networks is avoided. In the process of generating a road topology network, the original GPS trajectory line is evenly interrupted, and the multi-lane road section can be better abstracted into a multi-segment line, which is conducive to simplifying the complexity of the road network, while improving the calculation efficiency, overcoming the problem of GPS trajectory drift, and providing redundancy for more accurate matching of GPS trajectories. The method of using the initial screening of candidate directed sections, the second screening of candidate trajectory lines, and the final screening of the most suitable trajectory lines and their corresponding directed sections avoids the computing pressure caused by the matching traffic of the entire road network topology, while meeting the higher quality of the traffic matching results. The present application method improves the computing efficiency of the construction of the metropolitan area road network topology and traffic matching, and does not require much computing power from the hardware equipment, and can be deployed on a conventional PC for use. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A flowchart of a method for constructing a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data is provided in this application;

[0041] Figure 2 A schematic diagram of a plurality of original GPS track lines formed by cleaning the original GPS track points in a certain exemplary embodiment;

[0042] Figure 3 A schematic diagram of a trajectory line formed by evenly interrupted corrected GPS trajectory points in a certain exemplary embodiment;

[0043] Figure 4 An optimized road topology network diagram generated in a certain exemplary embodiment;

[0044] Figure 5 FIG. 4 is a schematic diagram of road traffic in different directions formed in a certain exemplary embodiment. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] The purpose of this application is to propose a method, device, medium and product for urban circle transportation network topology construction and traffic matching based on individual-level GPS trajectory data, so as to improve the computational efficiency of urban circle transportation network topology construction and traffic matching and reduce the computing power requirements of hardware equipment.

[0047] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0048] In an exemplary embodiment, Figure 1 As shown, a method for constructing a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data is provided, including the following steps 1 to 9.

[0049] Step 1: Connect the original GPS track points to form a continuous original GPS track line.

[0050] For each individual vehicle such as a motor vehicle or motorcycle, all GPS track data obtained for each trip has a unique trip code. This application arranges all the original GPS track points contained in each unique trip code in increasing order of timestamps and connects them in sequence to form a continuous original GPS track line, which is recorded as point set T = {p 1 ,p 2 ,...,p n}, where each point (i.e. GPS track point) p i Including spatial coordinates (x i ,y i ) and timestamp t i ; n is the number of midpoints of the original GPS trajectory.

[0051] Step 2: Evenly interrupt the original GPS track line to form the corrected GPS track points.

[0052] According to the segmentation condition (1), the original GPS trajectory line T is evenly interrupted to form several interrupted trajectory segments. Each trajectory segment can be recorded as (p' i ,p' i+1 ) or (p' i-1 ,p' i ). where p' i-1 For point p'i The previous point, p' i+1 For point p' i The latter point.

[0053] |p i+1 -p i |≥d th (1)

[0054] d th Considering that the general distance between major intersections in China is more than 250 meters (based on the 8km / km specified in the national standard GB / T51328-2018), 2 The road network density is used as a reference standard), the red line width of a single road generally does not exceed 80 meters (calculated based on a maximum of eight lanes in both directions plus pedestrian and non-motor vehicle space), and the calculation accuracy and other requirements. It is preferred to set the interruption distance at 100 meters, that is, set |p i+1 -p i |=100. The interruption method based on this distance can accurately abstract the multi-lane road into a polyline, that is, the length and width meet the general scale requirements of Chinese road length and width respectively.

[0055] Furthermore, the Kalman filter or sliding average method is used to analyze the interrupted trajectory segments (p' i-1 ,p' i ) is smoothed to form a corrected GPS track point. In an exemplary embodiment, the smoothing formula is as follows:

[0056]

[0057] Where α is a random number between 0 and 1. As the corrected GPS track point. For the convenience of subsequent description, the corresponding Denoted as P i , similarly, each point P i Including spatial coordinates (x i ,y i ) and timestamp t i Each corrected GPS track point P i The corrected GPS trajectory point set T' = {P 1 ,P 2 ,...,P N}, where N is the number of corrected GPS track points.

[0058] Step 3: Identify candidate intersections based on the corrected GPS trajectory points.

[0059] First, the corrected GPS trajectory point P iDensity estimation and cluster analysis are performed to identify several clusters. Then, intersections are evaluated and detected.

[0060] Specifically, the corrected GPS trajectory point P is calculated using formula (3): i The local density ρ(P i ), using Gaussian kernel density estimation, where σ is the kernel bandwidth parameter:

[0061]

[0062] Use a density-based clustering algorithm (such as DBSCAN) to identify high-density areas. The formula is as follows:

[0063] C k = {P i ∣ρ(P i )>ρ th , and satisfy the neighborhood connection condition} (4)

[0064] where ρ th is the density threshold. C k represents the kth group.

[0065] The high-density cluster center c of each group k Defined as candidate intersection locations:

[0066]

[0067] Where |C k | represents the number of clusters. The high-density cluster center of each cluster is defined as the candidate intersection c k .

[0068] Step 4: Extract directed road segments (or road segments) based on candidate intersections.

[0069] In the road segment extraction phase, trajectory segmentation is first performed to extract the trajectory segments that pass through each pair of candidate intersections from the trajectory T'. Then, direction vector calculation is performed.

[0070] Specifically, from the corrected GPS trajectory point set T'={P 1 ,P 2 ,...,P N} extract the intersections that pass through each pair of candidate intersections (c i ,c j ) of the trajectory segment T ij =(P i ,P j ). Where c i ,c j ∈c k , that is, each pair of candidate intersections (c i ,c j) are selected from the candidate intersections c identified in step 3 k .

[0071] Furthermore, the trajectory segment T is calculated using formula (6) ij Direction vector Used to represent directed road segments:

[0072]

[0073] Where |T ij | indicates the number of trajectory segments.

[0074] For each road segment with multiple lanes, it is better to use the GPS 100-meter interruption method in step 2 to abstract it into a single line. Then, after steps 3 and 4, multiple lanes can form a bidirectional topological road topology line segment, which is called a directed road segment in this application. The direction vector of the road segment is denoted by express.

[0075] Step 5: Generate an optimized road topology network based on the directed road segments.

[0076] First, generate a topological network. Specifically, the candidate intersection c k As the vertex V = {c k}, the directed segment As edge Construct a road topology network G = (V, E) and calculate the weight w for each edge based on the road flow ij :

[0077]

[0078] Road traffic refers to the number of trips on a road.

[0079] Then, topology optimization is performed. Specifically, a minimum spanning tree (MST) or other graph optimization methods are used to eliminate redundant edges in the road topology network G = (V, E) to generate an optimized road topology network.

[0080] Step 6: Screen out candidate directed road segments based on the corrected GPS trajectory points and the optimized road topology network.

[0081] Step 6 uses the GPS track points corrected in step 2 and the basic distribution road topology network formed in steps 3 to 5 as input to screen candidate directional road segments. First, select the candidate road buffer to reduce the amount of calculation and avoid the meaningless increase in the calculation of the direction angle of the entire road network topology. Specifically, the corrected GPS track point P i , calculate all the road sections L within 50 meters of each trajectory point ijAs a buffer zone. Secondly, the direction angle difference is calculated for the road section L selected through the 50-meter range. ij Perform secondary screening. Each evenly interrupted trajectory point P i , calculate the difference with the previous trajectory point P i-1 The direction angle θ i , and the trajectory point direction angle θ i The direction angle φ that matches the road topology j For comparison, if the absolute value difference Δθ ij If the angle is greater than 90° (i.e., it deviates to the left or right by more than 90°), it is considered that the trajectory point P should not be i With road section l i The specific method for selecting candidate directed road segments is as follows.

[0082] For each corrected GPS track point P i , based on formula (8) calculate P i All road sections within 50 meters L ij :

[0083] L ij = {l j ∣d(P i ,l j )≤50} (8)

[0084] Where d(P i ,l j ) represents the corrected GPS track point P i To section l j Distance; road section l j The road topology network selected from the optimized one in step 5.

[0085] Next, use formula (9) to calculate P i and the previous corrected GPS track point P i-1 The direction angle θ i :

[0086] θ i =arctan2(y i -y i-1 ,x i -x i-1 ) (9)

[0087] Where (x i ,y i ) and (x i-1 ,y i-1 ) are points P i and point P i-1 The spatial coordinates of .

[0088] Next, use formula (10) to calculate the direction angle θ i And the matching road segment l j The direction angle φ j The absolute value difference Δθ between ij :

[0089] Δθ ij =|θ i -φ j | (10)

[0090] Among them, the matched road segment l j The direction angle φ j It refers to the angle between the corrected GPS track point line and the road section at the same location.

[0091] When Δθ ij ≤90°, determine the corresponding road section l i is the selected candidate directional road segment.

[0092] Step 7: Generate candidate trajectory routes based on the starting and ending points of the original GPS trajectory and the candidate directed segments.

[0093] First, the starting and ending points of the original GPS trajectory corresponding to each travel code are screened out, that is, the starting and ending points of the original GPS trajectory. The possible roads between the starting and ending points are the candidate roads screened out in step 6. Secondly, the starting and ending point Dijkstra shortest path algorithm is executed on the basis of the candidate roads to form several candidate trajectory routes whose directions and starting and ending points are consistent with the original GPS trajectory.

[0094] Step 8: Determine the trajectory route that best matches the original GPS trajectory line and its corresponding directed road segment from the candidate trajectory routes as the target trajectory route and target directed road segment.

[0095] According to the shortest sum of distances from each original GPS track point in the original GPS track line to the nearest road section in each candidate track line, the most suitable path in the D* algorithm is determined by this standard, and the track line that best matches the original GPS track line can be determined from the candidate track lines. The track line that best matches the original GPS track line and its corresponding road section are used as the target track line and target directed road section.

[0096] Step 9: Generate directional road traffic based on all target trajectory routes and target directional road segments.

[0097] The present application is aimed at the traffic network topology within the scope of a metropolitan area or a one-hour traffic circle, in which different trips of different vehicles correspond to different trip codes and different GPS trajectory data. The above steps 1 to 8 describe a method for matching the target trajectory route and the target directional section for each original GPS trajectory line. For all candidate trajectory routes and trajectory points within the metropolitan area, all the most suitable target trajectory routes and target directional sections are determined according to the principle of the shortest distance from the original GPS trajectory line to the candidate trajectory route. By counting all target trajectory routes and target directional sections on the optimized road topology network, the road flow of each section can be obtained. And because the sections of this application all have direction vectors, directional road flow can be formed.

[0098] The present invention proposes a highly integrated and efficient method for constructing a metropolitan area traffic network topology and matching traffic flow, which mainly solves the limitations of efficiency and design when matching and distributing traditional GPS trajectory data with road networks. By using GPS trajectory data to identify road intersections and candidate directed road sections, the difficulty of data input and cleaning of new metropolitan area road networks is avoided. In the method of generating a road topology network, taking 100 meters as a benchmark, multi-lane road sections can be relatively well abstracted into a multi-segment line, which is conducive to simplifying the complexity of the road network, while improving the calculation efficiency and overcoming the problem of GPS trajectory drift, providing redundancy for more accurate matching of GPS trajectories. By using the method of initial screening of the road network using the trajectory point buffer zone, secondary screening of the Dijkstra start and end points, and final screening of the shortest distance between trajectory points, the computing pressure caused by the topological matching traffic of the entire road network is avoided, while meeting a higher result quality. This method can efficiently extract the transportation networks such as public transportation, motor vehicle lanes, and non-motor vehicle lanes within the scope of China's metropolitan areas, and quickly form the road segment flow containing the simplified topological road network direction. It can also support the re-making, matching, and backtracking of travel trajectories of single, multiple, or metropolitan area road segments, thus improving the computational efficiency of metropolitan area transportation network topology construction and flow matching. In addition, the application method does not require much computing power from hardware devices and can be deployed on a regular PC for use.

[0099] Figures 2 to 5 FIG. 1 shows an exemplary embodiment of using the method of the present application to construct a metropolitan area transportation network topology and flow matching, wherein the blue line segment represents the generation process of the road network topology. Specifically, the multiple original GPS track lines formed by cleaning the original GPS track points in step 1 are as follows: Figure 2 As shown in the figure. The trajectory line formed by the corrected GPS trajectory points formed by uniform interruption in step 2 is as follows Figure 3 As shown. The optimized road topology network generated by steps 3 to 5 is as follows Figure 4 As shown. The directional road traffic formed through steps 6 to 9 is reflected in the road topology network as follows Figure 5As shown, the red section has the heaviest traffic, followed by yellow and then green.

[0100] In an exemplary embodiment, the present application also provides a computer device, which can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the metropolitan area traffic network topology construction and flow matching method based on individual-level GPS trajectory data is implemented.

[0101] In an exemplary embodiment, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for constructing a metropolitan transportation network topology and matching traffic based on individual-level GPS trajectory data.

[0102] In an exemplary embodiment, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the method for constructing a metropolitan transportation network topology and matching traffic based on individual-level GPS trajectory data.

[0103] It can be understood by a person of ordinary skill in the art that all or part of the processes in the above-mentioned embodiment method can be completed by hardware related to computer program instructions, and the computer program can be stored in a non-volatile computer-readable storage medium, and the computer program can include the process of the embodiment of the above-mentioned method when executed. Among them, any reference to memory or other media in each embodiment provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0104] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0105] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for constructing a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data, characterized in that: include: Connect the original GPS track points to form a continuous original GPS track line; The original GPS track line is evenly interrupted to form the corrected GPS track points; Identify candidate intersections based on the corrected GPS trajectory points; Extracting directed road segments based on candidate intersections; Generate an optimized road topology network based on directed road segments; Screen out candidate directed road segments based on the corrected GPS track points and the optimized road topology network; Generate candidate trajectory routes based on the starting and ending points of the original GPS trajectory and the candidate directed sections; Determine the trajectory route and its corresponding directional road segment that best matches the original GPS trajectory line from the candidate trajectory routes as the target trajectory route and the target directional road segment; Directional road traffic is formed based on all target trajectory routes and target directional sections.

2. The method for constructing a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data according to claim 1, characterized in that: The connecting of the original GPS track points to form a continuous original GPS track line specifically includes: All original GPS track points contained in each unique travel code are arranged in ascending order of timestamps and connected in sequence to form a continuous original GPS track line, which is recorded as point set T = {p1, p2, ..., p n }, each point p i Including spatial coordinates (x i ,y i ) and timestamp t i ; n is the number of midpoints of the original GPS trajectory.

3. The method for constructing a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data according to claim 2, characterized in that: The method of evenly interrupting the original GPS track line to form the corrected GPS track points specifically includes: According to the segmentation conditions |p i+1 -p i |≥d th The original GPS trajectory line is evenly interrupted, and the interrupted trajectory segments are smoothed using Kalman filtering or sliding average method to form the corrected GPS trajectory points; Each corrected GPS track point constitutes a corrected GPS track point set T' = {P1, P2, ..., P N }, where N is the number of corrected GPS track points; d th is the distance threshold.

4. The method for constructing a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data according to claim 3 is characterized in that: The identifying of candidate intersections based on the corrected GPS trajectory points specifically includes: Density estimation and cluster analysis are performed on the corrected GPS track points to identify several groups; The high-density cluster center of each group is defined as the candidate intersection c k .

5. The method for constructing a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data according to claim 4, characterized in that: The step of extracting a directed road segment based on a candidate intersection specifically includes: From the corrected GPS trajectory point set T' = {P1, P2, ..., P N } extract the intersections that pass through each pair of candidate intersections (c i ,c j ) of the trajectory segment T ij ; where c i ,c j ∈c k ; Calculate the trajectory segment T ij Direction vector Used to represent a directed road segment; where |T ij | indicates the number of trajectory segments.

6. The method for constructing a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data according to claim 5, characterized in that: The step of generating an optimized road topology network according to the directed road segments specifically includes: The candidate intersection c k As the vertex V = {c k }, the directed segment As edge Construct a road topology network G = (V, E) and calculate the weight for each edge based on the road flow; The graph optimization method is used to eliminate redundant edges in the road topology network and generate an optimized road topology network.

7. The method for constructing a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data according to claim 6, characterized in that: The method of selecting candidate directed road segments based on the corrected GPS track points and the optimized road topology network specifically includes: For each corrected GPS track point P i , based on the formula L ij = {l j |d(P i , l j )≤50}Calculate P i All road sections within 50 meters L ij ; where d(P i , l j ) represents the corrected GPS track point P i To section l j Distance; road section l j Selected from the optimized road topology network; Using the formula θ i =arctan 2(y i -y i-1 , x i -x i-1 )Calculate P i and the previous corrected GPS track point P i-1 The direction angle θ i ; Using the formula Δθ ij =|θ i -φ j |Calculate the direction angle θ i And the matching road segment l j The direction angle φ j The absolute value difference Δθ between ij ; When Δθ ij ≤90°, determine the corresponding road section l j is the selected candidate directional road segment.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for constructing a metropolitan area transportation network topology and flow matching based on individual-level GPS trajectory data as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for constructing a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for constructing a metropolitan area transportation network topology and matching traffic based on individual-level GPS trajectory data described in any one of claims 1 to 7 is implemented.

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