Passenger flow corridor generation method, device and equipment and storage medium

Abstract

The invention discloses a passenger flow corridor generation method, apparatus and device, and a storage medium. The method comprises the steps of performing vectorization region division on a target region to obtain planar vector data; obtaining travel OD data of the target area to form an OD data set; constructing a triangulation network by adopting an incremental algorithm based on the planar vector data; constructing a directed traffic network based on the planar vector data and a triangulation network; identifying travel paths of OD pairs in the OD data set by matching starting and ending points in the OD data set with nodes in the directed traffic network, calculating traffic flows of edges in the directed traffic network according to the travel paths, and generating directed traffic flow vector data; and performing hierarchical visualization processing on the traffic flow vector data according to the traffic flow, and generating directed passenger flow corridors of different travel modes in the target area. According to the method, passenger flow corridor generation tasks of multiple cities or regions can be efficiently processed in batches, and multi-mode and direction-based passenger flow corridor identification is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of urban traffic planning, and in particular to a passenger flow corridor generation method, device, equipment and storage medium. Background Art

[0002] Passenger flow corridors refer to traffic paths with obvious directionality and flow characteristics formed in the urban transportation system due to the high concentration of residents' travel needs. They are usually distributed along major traffic arteries or transportation hubs and are the main carrying areas of urban traffic passenger flow. Therefore, accurate and rapid identification of passenger flow corridors is of great significance for optimizing the allocation of urban transportation resources and guiding the development of public transportation.

[0003] There are two main methods for identifying and generating passenger flow corridors in urban traffic: spider diagram display method based on professional software (such as TransCAD) and clustering-based identification method. However, the generation of passenger flow corridors by spider diagram display method relies on professional software, which is complicated to operate. When the research area changes, it is necessary to manually divide the traffic area, which is difficult to process in batches quickly, and it is impossible to distinguish the distribution of passenger flow corridors for different travel modes; the clustering-based identification method relies on clustering methods and parameter settings, which is complex and time-consuming to calculate, and it is difficult to distinguish the direction of passenger flow corridors. The granularity is large, and the applicability is poor in practical applications, which is difficult to fit the actual situation of the city. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a passenger flow corridor generation method, device, equipment and storage medium, which can efficiently batch process passenger flow corridor generation tasks for multiple cities or regions and realize multi-mode and directional passenger flow corridor identification.

[0005] An embodiment of the present invention provides a method for generating a passenger flow corridor, comprising:

[0006] Performing vectorized area division on the target area to obtain planar vector data of the target area;

[0007] Acquire the travel OD data of the target area to form an OD data set;

[0008] Based on the planar vector data, a triangulated network is constructed using an incremental algorithm;

[0009] Based on the planar vector data and the triangulated network, a directed transportation network is constructed, wherein the directed transportation network includes nodes, edges and edge weights;

[0010] By matching the start and end points in the OD data set with the nodes in the directed traffic network, the travel paths of the OD pairs in the OD data set are identified, and the traffic flow of the edges in the directed traffic network is calculated according to the travel paths to generate directed traffic flow vector data;

[0011] The traffic flow vector data is processed in a hierarchical and visualized manner according to the size of the traffic flow, so as to generate directional passenger flow corridors of different travel modes in the target area.

[0012] As an improvement of the above solution, the step of obtaining the travel OD data of the target area to form an OD data set includes:

[0013] Acquire the travel OD data of the target area; the travel OD data includes: the ID of the OD pair, the starting point coordinates, the end point coordinates and the travel volume of different travel modes;

[0014] Standardizing the travel OD data to integrate the travel OD data of different travel modes;

[0015] Based on the surface vector data and the travel OD data, the surface area information corresponding to the starting point coordinates and the end point coordinates of each OD pair is extracted through a spatial connection method to generate an OD data set; the specific fields of the OD data set include: the surface area ID corresponding to the starting point coordinates and the end point coordinates of the OD pair, and the traffic flow of different travel modes.

[0016] As an improvement of the above solution, the planar vector data includes: the ID of the planar area, the longitude and latitude coordinates of the center point;

[0017] The method of constructing a triangulated network based on the planar vector data by using an incremental algorithm includes:

[0018] The latitude and longitude coordinates of the center point of the planar area are used as the initial point set, and an incremental algorithm is used to generate an initial triangulated network; the initial triangulated network is line data with geographic space coordinates;

[0019] According to the topographic factors of the target area, the lines without direct traffic connection in the initial triangulated network are deleted to obtain a modified triangulated network;

[0020] Calculate the line length according to the starting point coordinates and the end point coordinates of each line data in the modified triangulated network;

[0021] The modified triangulated network and the planar vector data are spatially connected to generate planar region information corresponding to the triangulated network nodes to obtain a final triangulated network.

[0022] As an improvement of the above solution, the directed transportation network is constructed based on the planar vector data and the triangulated network, including:

[0023] Based on the ID of the planar area, construct a traffic network node;

[0024] According to the ID of the planar area corresponding to the starting and ending points in the triangulated network and the line length, a positive direction edge set from the starting point to the end point and a reverse direction edge set from the end point to the starting point are constructed to obtain a directed traffic network edge set;

[0025] Based on the edge set of the transportation network, the weight of the edge is calculated according to the length of the edge and the travel speed of the transportation mode corresponding to the edge;

[0026] A directed transportation network is obtained according to the transportation network nodes, the transportation network edge set and the weights of the edges.

[0027] As an improvement of the above scheme, the travel paths of the OD pairs in the OD data set are identified by matching the start and end points in the OD data set with the nodes in the directed traffic network, and the traffic flow of the edges in the directed traffic network is calculated according to the travel paths to generate directed traffic flow vector data, including:

[0028] Matching the starting and ending points of each OD pair in the OD data set with the nodes of the traffic network, identifying the shortest travel path of each OD pair based on the weights of the edges in the directed traffic network, and obtaining a travel path set of the OD pair;

[0029] According to the travel path set and the traffic flows of different travel modes in the OD data set, the traffic flows of different travel modes of each edge in the directed traffic network are calculated to obtain a traffic flow data set;

[0030] Based on the traffic flow data set, traffic flow vector data in the positive and reverse directions are obtained by matching the starting and ending points in the triangulated network with the starting and ending points of the edges in the directed traffic network; the specific fields of the traffic flow vector data include: the geographical coordinates, starting point ID, and end point ID of the triangulated network, the starting point ID, end point ID, and traffic flow in different modes in the positive direction, and the starting point ID, end point ID, and traffic flow in different modes in the reverse direction.

[0031] As an improvement of the above solution, the traffic flow of different travel modes of each edge in the directed traffic network is calculated according to the travel path set and the traffic flow of different travel modes in the OD data set, including:

[0032] Adding traffic flow attributes of different travel modes to the traffic network edge set, and setting the initial traffic flow of different travel modes corresponding to each edge to 0;

[0033] According to the travel path set and the traffic flow of different travel modes in the OD data set, the travel paths of each OD pair are traversed in turn, the traffic flow of different travel modes corresponding to each edge is iteratively superimposed, and the traffic flow of different travel modes corresponding to each edge is calculated.

[0034] As an improvement of the above solution, the calculation formula of the edge weight w is:

[0035]

[0036] Among them, length i is the length of the ith edge, v mode is the travel speed of the transport mode.

[0037] The embodiment of the present invention further provides a passenger flow corridor generation device, comprising:

[0038] A target area division module is used to perform vectorized area division on the target area to obtain planar vector data of the target area;

[0039] An OD data acquisition module, used to acquire the travel OD data of the target area to form an OD data set;

[0040] A triangulated network construction module, used for constructing a triangulated network using an incremental algorithm based on the planar vector data;

[0041] A traffic network construction module, used to construct a directed traffic network based on the planar vector data and the triangulated network, wherein the directed traffic network includes nodes, edges and edge weights;

[0042] A traffic flow calculation module, used to identify the travel paths of the OD pairs in the OD data set by matching the start and end points in the OD data set with the nodes in the directed traffic network, and calculate the traffic flow of the edges in the directed traffic network according to the travel paths to generate directed traffic flow vector data;

[0043] The passenger flow corridor generation module is used to perform hierarchical visualization processing on the traffic flow vector data according to the traffic flow size, and generate directional passenger flow corridors for different travel modes in the target area.

[0044] An embodiment of the present invention further provides a computer device, including a processor and a memory, wherein a computer program is stored in the memory, and the computer program is configured to be executed by the processor, and when the processor executes the computer program, the passenger flow corridor generation method described in any one of the above items is implemented.

[0045] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any of the above-mentioned passenger flow corridor generation methods.

[0046] Compared with the prior art, the beneficial effects of a passenger flow corridor generation method, device, equipment and storage medium provided by the embodiment of the present invention are: by acquiring the surface vector data and travel OD data of the target area, using an incremental algorithm to construct a triangulated network, and then constructing a directed traffic network, and then matching the OD data set with the directed traffic network to identify the travel path, calculate the traffic flow, generate directed traffic flow vector data, and finally perform hierarchical visualization processing according to the size of the traffic flow, and generate directed passenger flow corridors of different travel modes in the target area, which can realize efficient batch processing of passenger flow corridor identification and generation tasks for multiple cities or regions, realize accurate identification of passenger flow corridors in different directions, provide more refined data support for urban traffic planning, and also strengthen the processing and aggregation of original travel OD data, and can realize the identification of multi-mode passenger flow corridors, which is helpful to compare the differences in the distribution of passenger flow corridors of different travel modes. The passenger flow corridor generation method of the embodiment of the present invention is simple to operate, highly applicable, can be quickly processed in batches, and can realize the identification and generation of passenger flow corridors of different travel modes and directions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of a method for generating a passenger flow corridor provided by an embodiment of the present invention;

[0048] Figure 2 is a schematic diagram of planar vector data provided by an embodiment of the present invention;

[0049] Figure 3 is a schematic diagram of an initial triangulated network provided by an embodiment of the present invention;

[0050] Figure 4 is a modified triangulated network schematic diagram provided by an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of the generation result of passenger flow corridors in different directions for a car travel mode provided by an embodiment of the present invention;

[0052] Figure 6 It is a structural schematic diagram of a passenger flow corridor generating device provided by an embodiment of the present invention;

[0053] Figure 7 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] See also Figure 1 , Figure 1 1 is a flow chart of a method for generating a passenger flow corridor provided by an embodiment of the present invention. The method for generating a passenger flow corridor comprises:

[0056] S1: performing vectorized region division on the target region to obtain planar vector data of the target region;

[0057] Specifically, the accuracy requirements of the surface vector data are specified according to the size of the research scope (WGS-1984 coordinate system), and the fishnet tool in ArcGIS is used to divide the target area into grids of preset sizes (such as 1000*1000m, 2000*2000m, etc.). You can also select municipal, district, county, town and street administrative division vector data, and directly use the smallest unit of administrative divisions at different levels as surface vector data. For example, when studying provincial passenger flow corridors, the municipal surface vector data can be used as the smallest unit, and when studying municipal passenger flow corridors, the district, county or town and street level surface vector data can be used as the smallest unit.

[0058] Then, the latitude and longitude coordinates of the center point of the surface vector of the target area are extracted by using the geoprocessing tool to obtain the surface vector data of the target area, and the expression is RA = {(id i ,name i ,geom i ,lon i ,lat i )}, where id i 、name i 、geom i ,lon i ,lat i They respectively represent the ID number, name, geographic coordinates, center point longitude coordinates, and center point latitude coordinates of the i-th surface area.

[0059] S2: Acquire the travel OD data of the target area to form an OD data set;

[0060] Specifically, travel OD (Original, Destination) data represents the traffic volume data between the starting point and the end point.

[0061] Furthermore, the obtaining of the travel OD data of the target area to form an OD data set includes:

[0062] Acquire the travel OD data of the target area; the travel OD data includes: the ID of the OD pair, the starting point coordinates, the end point coordinates and the travel volume of different travel modes;

[0063] Standardizing the travel OD data to integrate the travel OD data of different travel modes;

[0064] Based on the surface vector data and the travel OD data, the surface area information corresponding to the starting point coordinates and the end point coordinates of each OD pair is extracted through a spatial connection method to generate an OD data set; the specific fields of the OD data set include: the surface area ID corresponding to the starting point coordinates and the end point coordinates of the OD pair, and the traffic flow of different travel modes.

[0065] Specifically, the travel OD data of the target area is obtained, and the travel OD data of different travel modes are integrated into a table through standardized field processing. The specific fields of the travel OD data include: travel ID (od_id), starting point coordinates (source_x84, source_y84), end point coordinates (target_x84, target_y84), departure time (go_time), car travel volume (car_uv), bus travel volume (bus_uv), rail travel volume (subway_uv), slow travel volume (bike_uv) and total travel volume (uv), where the start and end point coordinates are in the WGS-1984 coordinate system.

[0066] In combination with actual needs, according to the departure time field (go_time) in the OD data table, the travel OD data under the preset time is extracted. The spatial connection method is used to extract the surface area ID corresponding to the starting point coordinates and the end point coordinates of each OD pair, and the travel OD data set is counted into the surface vector data of the target area to generate the OD data set for basic analysis. The OD data set is expressed as OD = {(o_id i ,d_id i ,flow ki )}, o_id i 、d_id i 、flow ki They respectively represent the surface area ID corresponding to the starting point coordinates of the i-th OD pair, the surface area ID corresponding to the end point coordinates, and the traffic flow of the k-th travel mode, where k is 1, 2, 3, 4, and 5. When k=1, it represents the car travel volume (car_uv), when k=2, it represents the bus travel volume (bus_uv), when k=3, it represents the rail travel volume (subway_uv), when k=4, it represents the slow travel volume (bike_uv), and when k=5, it represents the total travel volume (uv).

[0067] S3: constructing a triangulated network using an incremental algorithm based on the planar vector data;

[0068] Furthermore, the planar vector data includes: the ID of the planar area, the longitude and latitude coordinates of the center point;

[0069] The method of constructing a triangulated network based on the planar vector data by using an incremental algorithm includes:

[0070] The latitude and longitude coordinates of the center point of the planar area are used as the initial point set, and an incremental algorithm is used to generate an initial triangulated network; the initial triangulated network is line data with geographic space coordinates;

[0071] According to the topographic factors of the target area, the lines without direct traffic connection in the initial triangulated network are deleted to obtain a modified triangulated network;

[0072] Calculate the line length according to the starting point coordinates and the end point coordinates of each line data in the modified triangulated network;

[0073] The modified triangulated network and the planar vector data are spatially connected to generate planar region information corresponding to the triangulated network nodes to obtain a final triangulated network.

[0074] Specifically, based on the surface vectors of the target area, an incremental algorithm is used to generate the initial Delaunay triangulation, and a small number of triangulations without direct traffic connections are eliminated by comprehensively considering factors such as mountains and rivers.

[0075] First, the longitude and latitude coordinates of the center point of the target area (lon i ,lat i ) as the initial point set, and use the incremental construction algorithm to generate the Delaunay triangulation to ensure that the initial triangulation has no overlapping edges and maximizes the minimum angle characteristics. The generated initial triangulation is line data with geospatial coordinates, expressed as {geom1, geom2,…, geom i}, geom i is the geographic space coordinates of the i-th line data;

[0076] Then, the initial triangulated network with geospatial vectors is imported into GIS software, and factors such as mountains and rivers are considered, and a small number of lines without direct traffic connections are deleted to form modified triangulated network data;

[0077] Finally, the geospatial processing algorithm is used to extract the starting and ending coordinates of each line data of the triangulated network, and the length of the triangulated network line is calculated. Then, the surface area ID corresponding to the starting node of the triangulated network and the surface area ID corresponding to the end node are generated by spatial connection with the surface vector data. The triangulated network expression is {(geomi ,o_lon i ,o_lat i ,d_lon i ,d_lat i ,o_id i ,d_id i ,length i )}, where o_lon i and o_lat i is the latitude and longitude coordinates of the center point of the surface area corresponding to the starting point of the i-th OD pair, d_lon i and d_lat i o_id is the latitude and longitude coordinates of the center point of the surface area corresponding to the end point of the i-th OD pair, i and d_id i are the surface area IDs corresponding to the starting point and end point of the i-th OD pair, and length i is the length of the starting and ending lines of the i-th OD pair.

[0078] Furthermore, since the triangulated network constructed above only generates geographic information data without direction, and daily traffic has positive and negative directions, the following steps construct a directed traffic network based on the surface vector and triangulated network data.

[0079] S4: constructing a directed transportation network based on the planar vector data and the triangulated network, wherein the directed transportation network includes nodes, edges, and edge weights;

[0080] Furthermore, the construction of a directed transportation network based on the planar vector data and the triangulated network includes:

[0081] Based on the ID of the planar area, construct a traffic network node;

[0082] According to the ID of the planar area corresponding to the starting and ending points in the triangulated network and the line length, a positive direction edge set from the starting point to the end point and a reverse direction edge set from the end point to the starting point are constructed to obtain a directed traffic network edge set;

[0083] Based on the edge set of the transportation network, the weight of the edge is calculated according to the length of the edge and the travel speed of the transportation mode corresponding to the edge;

[0084] A directed transportation network is obtained according to the transportation network nodes, the transportation network edge set and the weights of the edges.

[0085] Specifically, the area ID (id i ) constructs the traffic network nodes, and the node set expression is V = {id1, id2, ..., id i};

[0086] Secondly, according to the surface area ID and line length (o_id i ,d_id i ,length i ), construct a positive direction edge set from the starting point to the end point At the same time, the starting and ending node IDs are swapped (d_id i ,o_id i ,length i ), construct a set of reverse edges from the end point to the starting point Thus forming a traffic network edge set E=E with direction o→d ∪E d→o ;

[0087] Finally, a weight attribute w is added to the edges in the transportation network, thereby forming a directed transportation network G = (V, E, w) based on the transportation network nodes, edge sets and weights.

[0088] Furthermore, the calculation formula of the edge weight w is:

[0089]

[0090] Among them, length i is the length of the ith edge, v mode is the travel speed of the transport mode.

[0091] S5: identifying the travel paths of the OD pairs in the OD data set by matching the start and end points in the OD data set with the nodes in the directed traffic network, and calculating the traffic flow of the edges in the directed traffic network according to the travel paths to generate directed traffic flow vector data;

[0092] Furthermore, the travel paths of the OD pairs in the OD data set are identified by matching the start and end points in the OD data set with the nodes in the directed traffic network, and the traffic flow of the edges in the directed traffic network is calculated according to the travel paths to generate directed traffic flow vector data, including:

[0093] Matching the starting and ending points of each OD pair in the OD data set with the nodes of the traffic network, identifying the shortest travel path of each OD pair based on the weights of the edges in the directed traffic network, and obtaining a travel path set of the OD pair;

[0094] According to the travel path set and the traffic flows of different travel modes in the OD data set, the traffic flows of different travel modes of each edge in the directed traffic network are calculated to obtain a traffic flow data set;

[0095] Based on the traffic flow data set, traffic flow vector data in the positive and reverse directions are obtained by matching the starting and ending points in the triangulated network with the starting and ending points of the edges in the directed traffic network; the specific fields of the traffic flow vector data include: the geographical coordinates, starting point ID, and end point ID of the triangulated network, the starting point ID, end point ID, and traffic flow in different modes in the positive direction, and the starting point ID, end point ID, and traffic flow in different modes in the reverse direction.

[0096] Furthermore, the traffic flow of different travel modes of each edge in the directed traffic network is calculated according to the travel path set and the traffic flow of different travel modes in the OD data set, including:

[0097] Adding traffic flow attributes of different travel modes to the traffic network edge set, and setting the initial traffic flow of different travel modes corresponding to each edge to 0;

[0098] According to the travel path set and the traffic flow of different travel modes in the OD data set, the travel paths of each OD pair are traversed in turn, the traffic flow of different travel modes corresponding to each edge is iteratively superimposed, and the traffic flow of different travel modes corresponding to each edge is calculated.

[0099] Specifically, the starting and ending IDs of each OD pair are matched with the node IDs of the directed traffic network, and the shortest travel path of each OD pair is identified based on the weight attribute of the directed traffic network, and the travel path R of each OD pair is obtained in turn. i1 ,e i2 ,…,e in )};

[0100] Add traffic flow attributes of different travel modes to the traffic network edge set The initial traffic flow of each edge with different travel modes is set to 0, and the travel paths of each OD pair are traversed in turn, and the traffic flow data of different modes in the OD dataset are used to calculate the travel paths of each OD pair. Iterate and calculate the traffic flow of different travel modes for each edge in the transportation network;

[0101] Extract the starting point ID (o_node_id) of each edge in the directed traffic network i )、Destination ID(d_node_id i ) and traffic flow attributes Construct a traffic flow dataset of different travel modes, the expression is

[0102] Match the starting point ID and end point ID of the triangulated network with the records with the same starting point ID and end point ID of the edge in the directed traffic network to form the traffic flow vector data AB in the positive direction = {(geomi ,o_id i ,d_id i ,ab_o_id i ,ab_d_id i ,ab_flow ki )}, where ab_o_id i 、ab_d_id i 、ab_flow ki = is the starting point ID, end point ID and traffic flow in different modes in the positive direction; the records with the same triangulated end point ID and starting point ID and the same traffic network edge starting point ID and end point ID are matched to form the traffic flow vector data BA in the reverse direction = {(gerom i ,o_id i ,d_id i ,ba_o_id i ,ba_d_id i ,ba_flow ki )}, where ba_o_id i ,ba_d_id i , ba_flow ki The reverse direction starting point ID, end point ID and traffic flow in different modes;

[0103] Based on triangulated geographic information (geom i ),starting point end Connect the traffic flow vector data in the positive direction and the reverse direction to form the final traffic flow vector data in different modes, which is expressed as:

[0104] S6: Performing hierarchical visualization processing on the traffic flow vector data according to the traffic flow size, and generating directional passenger flow corridors of different travel modes in the target area.

[0105] Specifically, according to ab_flow ki , ba_flow ki field, visualize the directional traffic flow vector data generated above. In order to intuitively display the distribution of traffic flow in each section, it is divided into five levels according to the size of traffic flow. The first level is the top 5% sections with the highest traffic flow, the second level is the sections with the top 15% but less than 5% of traffic flow, the third level is the sections with the top 30% but less than 15% of traffic flow, the fourth level is the sections with the top 60% but less than 30% of traffic flow, and the fifth level is the sections with the bottom 40% of traffic flow. In particular, the present invention defines the sections with the top 30% of traffic flow (i.e., the first, second, and third level sections) as passenger flow corridors, and the specific distribution of passenger flow corridors in different directions for different travel modes in the target area can be clearly identified through graded visualization.

[0106] Furthermore, the following describes an embodiment of the present invention by taking a city's travel OD data and town and street surface vector data as an example.

[0107] S1: Get the street vector data of the town, such as Figure 2 As shown, the coordinates use the WGS-1984 coordinate system, and the longitude and latitude coordinates of the center points of each town and street are extracted using geoprocessing tools. The expression of the surface vector data is RA = {(id i ,name i ,geom i ,lon i ,lat i )}, which respectively represent the ID number, name, surface vector, center point longitude coordinates, and center point latitude coordinates of the ith town or street.

[0108] S2: Get the travel OD data of the city, as shown in Table 1. Then, through standardized field processing, the travel OD of different travel modes is integrated into one table. This time, the morning peak travel OD data is used, and the 8:00-9:00 travel OD data is extracted according to the departure time field of the OD table.

[0109] Table 1. Example of original trip OD data

[0110] od_id go_time car_uv bus_uv subway_uv bike_uv uv source_x84 source_y84 target_x84 target_y84 1 11:15 1 1 2 113.1014 23.34941 113.3024 23.14078 2 13:15 1 1 113.1832 23.34053 113.3014 23.13809 3 6:45 1 1 113.1852 23.41613 113.3014 23.14169 4 8:00 1 1 113.1961 23.1498 113.3024 23.14078 5 10:15 1 1 2 113.197 23.1471 113.2975 23.14529 6 10:15 1 1 113.198 23.39185 113.3024 23.13988 7 9:15 1 1 2 113.199 23.39185 113.3024 23.13988 8 7:30 1 1 113.2 23.1516 113.2975 23.14529

[0111] Furthermore, the spatial connection method is used to extract the town and street IDs corresponding to the starting and ending coordinates of each OD, and the travel OD data set is included in the town and street to generate the OD data set for basic analysis, as shown in Table 2 below. The OD data set is expressed as OD = {(o_id i ,d_id i ,flow ki )}, which respectively represent the town and street ID corresponding to the i-th OD starting point coordinate, the town and street ID corresponding to the end point coordinate, and the traffic volume of the k-th travel mode during the morning peak period of 8:00-9:00, where k=1 represents the car travel volume (car_uv), k=2 represents the bus travel volume (bus_uv), k=3 represents the rail travel volume (subway_uv), k=4 represents the slow travel volume (bike_uv), and k=5 represents the total travel volume (uv).

[0112] Table 2 Examples of OD datasets of different travel modes after calculation

[0113] o_id d_id flow1 flow2 flow3 flow4 flow5 17 16 53 20 83 132 288 17 28 96 45 112 31 284 17 29 78 30 126 52 286 19 30 353 102 378 47 880 19 31 61 10 82 22 175 22 34 59 5 78 56 198 22 35 220 51 183 39 493 24 72 51 8 35 5 99 25 38 155 20 126 13 314 27 28 88 32 96 120 336

[0114] To facilitate the display of passenger flow corridors, the following uses car travel volume, i.e. flow1, as an example.

[0115] S3: Based on the town and street surface vectors, an incremental construction algorithm is used to generate the initial Delaunay triangulation, such as Figure 3 Then, the initial triangulated network with geographic spatial vectors is imported into the GIS software, and a small number of lines without direct traffic connections are deleted to form the modified triangulated network data, as shown in Figure 1. Figure 4 Finally, the geospatial processing algorithm is used to extract the starting and ending coordinates of each line data of the triangulated network, and the length of the triangulated network line is calculated. By spatially connecting with the town and street vector data, the IDs corresponding to the starting and ending nodes of the triangulated network are generated to obtain the final triangulated network.

[0116] S4: Construct traffic network nodes through town and street center point IDs; construct directed traffic network edge sets through triangulated network start and end point IDs and line lengths; calculate edge weights based on edge lengths and travel speeds of traffic modes to form directed traffic networks.

[0117] S5: Match the starting and ending point IDs of the OD dataset with the node IDs of the traffic network, identify the travel paths of each pair of ODs based on the weights, merge all travel paths, calculate the car traffic volume of each edge in the traffic network, and finally form the car traffic flow vector data.

[0118] S6: Visualize the car traffic flow vector data, divide it into five levels according to the traffic flow size, define the top 30% of the traffic flow sections (i.e., first-level, second-level, and third-level sections) as passenger flow corridors, and obtain the generation results of the passenger flow corridors of the target area by car travel mode, such as Figure 5 shown.

[0119] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0120] Efficiently realize the rapid identification and generation of multi-mode directional passenger flow corridors: The present invention strengthens the processing and aggregation of original travel OD data, generates OD data sets for different time periods, different travel modes or all modes based on the aggregation of surface IDs, and can realize the identification of passenger flow corridors for different travel modes or all modes, and facilitate the comparative analysis of the similarities and differences of passenger flow corridors for different travel modes. By constructing multiple directed traffic networks, it effectively solves the problem that existing identification methods are difficult to realize the identification and generation of directional passenger flow corridors, and realizes the directional and refined identification of passenger flow corridors, which can provide more refined data support for urban traffic planning;

[0121] Passenger flow corridors for multiple cities or regions can be generated in batches: The present invention gets rid of the strong dependence on professional traffic planning software such as TransCAD, generates a Delaunay triangulation based on the surface vector of the study area, and constructs multiple directed traffic networks containing nodes, edge sets, and weights. Through network analysis, it can realize the rapid identification and generation of multi-mode directed passenger flow corridors. It has high computational efficiency and is easy to operate. It only needs to prepare the surface vector data of the target area in advance to realize batch and rapid identification of passenger flow corridors in multiple cities or study areas.

[0122] Correspondingly, the present invention also provides a passenger flow corridor generating device, which can implement all the processes of the passenger flow corridor generating method in the above embodiment.

[0123] See also Figure 6 , Figure 6 1 is a schematic diagram of a passenger flow corridor generation device provided by an embodiment of the present invention. The passenger flow corridor generation device comprises:

[0124] The target area division module 601 is used to perform vectorized area division on the target area to obtain planar vector data of the target area;

[0125] OD data acquisition module 602, used to acquire the travel OD data of the target area to form an OD data set;

[0126] A triangulated network construction module 603 is used to construct a triangulated network using an incremental algorithm based on the planar vector data;

[0127] A traffic network construction module 604, configured to construct a directed traffic network based on the planar vector data and the triangulated network, wherein the directed traffic network includes nodes, edges and edge weights;

[0128] The traffic flow calculation module 605 is used to identify the travel paths of the OD pairs in the OD data set by matching the start and end points in the OD data set with the nodes in the directed traffic network, and calculate the traffic flow of the edges in the directed traffic network according to the travel paths to generate directed traffic flow vector data;

[0129] The passenger flow corridor generation module 606 is used to perform hierarchical visualization processing on the traffic flow vector data according to the traffic flow size, and generate directional passenger flow corridors for different travel modes in the target area.

[0130] Preferably, the OD data acquisition module 602 is specifically used for:

[0131] Acquire the travel OD data of the target area; the travel OD data includes: the ID of the OD pair, the starting point coordinates, the end point coordinates and the travel volume of different travel modes;

[0132] Standardizing the travel OD data to integrate the travel OD data of different travel modes;

[0133] Based on the surface vector data and the travel OD data, the surface area information corresponding to the starting point coordinates and the end point coordinates of each OD pair is extracted through a spatial connection method to generate an OD data set; the specific fields of the OD data set include: the surface area ID corresponding to the starting point coordinates and the end point coordinates of the OD pair, and the traffic flow of different travel modes.

[0134] Preferably, the planar vector data includes: the ID of the planar area, and the longitude and latitude coordinates of the center point;

[0135] The triangulated network construction module 603 is specifically used for:

[0136] The latitude and longitude coordinates of the center point of the planar area are used as the initial point set, and an incremental algorithm is used to generate an initial triangulated network; the initial triangulated network is line data with geographic space coordinates;

[0137] According to the topographic factors of the target area, the lines without direct traffic connection in the initial triangulated network are deleted to obtain a modified triangulated network;

[0138] Calculate the line length according to the starting point coordinates and the end point coordinates of each line data in the modified triangulated network;

[0139] The modified triangulated network and the planar vector data are spatially connected to generate planar region information corresponding to the triangulated network nodes to obtain a final triangulated network.

[0140] Preferably, the traffic network construction module 604 is specifically used for:

[0141] Based on the ID of the planar area, construct a traffic network node;

[0142] According to the ID of the planar area corresponding to the starting and ending points in the triangulated network and the line length, a positive direction edge set from the starting point to the end point and a reverse direction edge set from the end point to the starting point are constructed to obtain a directed traffic network edge set;

[0143] Based on the edge set of the transportation network, the weight of the edge is calculated according to the length of the edge and the travel speed of the transportation mode corresponding to the edge;

[0144] A directed transportation network is obtained according to the transportation network nodes, the transportation network edge set and the weights of the edges.

[0145] Preferably, the traffic flow calculation module 605 is specifically used for:

[0146] Matching the starting and ending points of each OD pair in the OD data set with the nodes of the traffic network, identifying the shortest travel path of each OD pair based on the weights of the edges in the directed traffic network, and obtaining a travel path set of the OD pair;

[0147] According to the travel path set and the traffic flows of different travel modes in the OD data set, the traffic flows of different travel modes of each edge in the directed traffic network are calculated to obtain a traffic flow data set;

[0148] Based on the traffic flow data set, traffic flow vector data in the positive and reverse directions are obtained by matching the starting and ending points in the triangulated network with the starting and ending points of the edges in the directed traffic network; the specific fields of the traffic flow vector data include: the geographical coordinates, starting point ID, and end point ID of the triangulated network, the starting point ID, end point ID, and traffic flow in different modes in the positive direction, and the starting point ID, end point ID, and traffic flow in different modes in the reverse direction.

[0149] Preferably, the calculating the traffic flow of different travel modes of each edge in the directed traffic network according to the travel path set and the traffic flow of different travel modes in the OD data set includes:

[0150] Adding traffic flow attributes of different travel modes to the traffic network edge set, and setting the initial traffic flow of different travel modes corresponding to each edge to 0;

[0151] According to the travel path set and the traffic flow of different travel modes in the OD data set, the travel paths of each OD pair are traversed in turn, the traffic flow of different travel modes corresponding to each edge is iteratively superimposed, and the traffic flow of different travel modes corresponding to each edge is calculated.

[0152] Preferably, the calculation formula of the edge weight w is:

[0153]

[0154] Among them, length i is the length of the ith edge, v mode is the travel speed of the transport mode.

[0155] In the specific implementation, the working principle, control process and technical effect achieved by the passenger flow corridor generation device provided in the embodiment of the present invention are the same as those of the passenger flow corridor generation method in the above embodiment, and will not be repeated here.

[0156] See also Figure 7 , Figure 77 is a structural block diagram of a computer device provided in an embodiment of the present invention, wherein the computer device includes: a processor 701, a memory 702, and a computer program stored in the memory 702 and executable on the processor 701. When the processor 701 executes the computer program, the steps in the above-mentioned passenger flow corridor generation method embodiment are implemented. Alternatively, when the processor 701 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0157] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 702 and executed by the processor 701 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the computer device.

[0158] The computer device may include, but is not limited to, a processor 701 and a memory 702. Those skilled in the art will appreciate that the schematic diagram is merely an example of a computer device and does not limit the computer device. The computer device may include more or fewer components than shown in the diagram, or may combine certain components, or may include different components. For example, the computer device may also include an input / output device, a network access device, a bus, etc.

[0159] The processor 701 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 701 is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0160] The memory 702 can be used to store the computer program and / or module, and the processor 701 realizes various functions of the computer device by running or executing the computer program and / or module stored in the memory 702, and calling the data stored in the memory 702. The memory 702 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 702 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0161] Wherein, if the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor 701. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0162] An embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the passenger flow corridor generation method described in any of the above embodiments.

[0163] The embodiment of the present invention provides a passenger flow corridor generation method, device, equipment and storage medium, which has the beneficial effect of: by acquiring the surface vector data and travel OD data of the target area, using an incremental algorithm to construct a triangulated network, and then constructing a directed traffic network, and then matching the OD data set with the directed traffic network to identify the travel path, calculate the traffic flow, generate directed traffic flow vector data, and finally perform hierarchical visualization processing according to the size of the traffic flow, and generate directed passenger flow corridors of different travel modes in the target area, which can realize efficient batch processing of passenger flow corridor identification and generation tasks for multiple cities or regions, realize accurate identification of passenger flow corridors in different directions, provide more refined data support for urban traffic planning, and also strengthen the processing and aggregation of original travel OD data, and can realize the identification of multi-mode passenger flow corridors, which is helpful to compare the differences in the distribution of passenger flow corridors of different travel modes. The passenger flow corridor generation method of the embodiment of the present invention is simple to operate, highly applicable, can be quickly processed in batches, and can realize the identification and generation of passenger flow corridors of different travel modes and directions.

[0164] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for generating a passenger flow corridor, characterized in that: include: Performing vectorized area division on the target area to obtain planar vector data of the target area; Acquire the travel OD data of the target area to form an OD data set; Based on the planar vector data, a triangulated network is constructed using an incremental algorithm; Based on the planar vector data and the triangulated network, a directed transportation network is constructed, wherein the directed transportation network includes nodes, edges and edge weights; By matching the start and end points in the OD data set with the nodes in the directed traffic network, the travel paths of the OD pairs in the OD data set are identified, and the traffic flow of the edges in the directed traffic network is calculated according to the travel paths to generate directed traffic flow vector data; The traffic flow vector data is processed in a hierarchical and visualized manner according to the size of the traffic flow, so as to generate directional passenger flow corridors of different travel modes in the target area.

2. The passenger flow corridor generation method according to claim 1, characterized in that: The step of acquiring the travel OD data of the target area to form an OD data set includes: Acquire the travel OD data of the target area; the travel OD data includes: the ID of the OD pair, the starting point coordinates, the end point coordinates and the travel volume of different travel modes; Standardizing the travel OD data to integrate the travel OD data of different travel modes; Based on the surface vector data and the travel OD data, the surface area information corresponding to the starting point coordinates and the end point coordinates of each OD pair is extracted through a spatial connection method to generate an OD data set; the specific fields of the OD data set include: the surface area ID corresponding to the starting point coordinates and the end point coordinates of the OD pair, and the traffic flow of different travel modes.

3. The passenger flow corridor generation method according to claim 2, characterized in that: The planar vector data includes: the ID of the planar area and the longitude and latitude coordinates of the center point; The method of constructing a triangulated network based on the planar vector data by using an incremental algorithm includes: The latitude and longitude coordinates of the center point of the planar area are used as the initial point set, and an incremental algorithm is used to generate an initial triangulated network; the initial triangulated network is line data with geographic space coordinates; According to the topographic factors of the target area, the lines without direct traffic connection in the initial triangulated network are deleted to obtain a modified triangulated network; Calculate the line length according to the starting point coordinates and the end point coordinates of each line data in the modified triangulated network; The modified triangulated network and the planar vector data are spatially connected to generate planar region information corresponding to the triangulated network nodes to obtain a final triangulated network.

4. The passenger flow corridor generation method according to claim 3, characterized in that: The method of constructing a directed transportation network based on the planar vector data and the triangulated network includes: Based on the ID of the planar area, construct a traffic network node; According to the ID of the planar area corresponding to the starting and ending points in the triangulated network and the line length, a positive direction edge set from the starting point to the end point and a reverse direction edge set from the end point to the starting point are constructed to obtain a directed traffic network edge set; Based on the edge set of the transportation network, the weight of the edge is calculated according to the length of the edge and the travel speed of the transportation mode corresponding to the edge; A directed transportation network is obtained according to the transportation network nodes, the transportation network edge set and the weights of the edges.

5. The passenger flow corridor generation method according to claim 4, characterized in that: The matching of the start and end points in the OD data set with the nodes in the directed traffic network, identifying the travel paths of the OD pairs in the OD data set, and calculating the traffic flow of the edges in the directed traffic network according to the travel paths, to generate directed traffic flow vector data, includes: Matching the starting and ending points of each OD pair in the OD data set with the nodes of the traffic network, identifying the shortest travel path of each OD pair based on the weights of the edges in the directed traffic network, and obtaining a travel path set of the OD pair; According to the travel path set and the traffic flows of different travel modes in the OD data set, the traffic flows of different travel modes of each edge in the directed traffic network are calculated to obtain a traffic flow data set; Based on the traffic flow data set, traffic flow vector data in the positive and reverse directions are obtained by matching the starting and ending points in the triangulated network with the starting and ending points of the edges in the directed traffic network; the specific fields of the traffic flow vector data include: the geographical coordinates, starting point ID, and end point ID of the triangulated network, the starting point ID, end point ID, and traffic flow in different modes in the positive direction, and the starting point ID, end point ID, and traffic flow in different modes in the reverse direction.

6. The passenger flow corridor generation method according to claim 5, characterized in that: The calculating the traffic flow of different travel modes of each edge in the directed traffic network according to the travel path set and the traffic flow of different travel modes in the OD data set includes: Adding traffic flow attributes of different travel modes to the traffic network edge set, and setting the initial traffic flow of different travel modes corresponding to each edge to 0; According to the travel path set and the traffic flow of different travel modes in the OD data set, the travel paths of each OD pair are traversed in turn, the traffic flow of different travel modes corresponding to each edge is iteratively superimposed, and the traffic flow of different travel modes corresponding to each edge is calculated.

7. The passenger flow corridor generation method according to claim 1, characterized in that: The calculation formula of the edge weight w is: Among them, length i is the length of the ith edge, v mode is the travel speed of the transport mode.

8. A passenger flow corridor generating device, characterized in that: include: A target area division module is used to perform vectorized area division on the target area to obtain planar vector data of the target area; An OD data acquisition module, used to acquire the travel OD data of the target area to form an OD data set; A triangulated network construction module, used for constructing a triangulated network using an incremental algorithm based on the planar vector data; A traffic network construction module, used to construct a directed traffic network based on the planar vector data and the triangulated network, wherein the directed traffic network includes nodes, edges and edge weights; A traffic flow calculation module, used to identify the travel paths of the OD pairs in the OD data set by matching the start and end points in the OD data set with the nodes in the directed traffic network, and calculate the traffic flow of the edges in the directed traffic network according to the travel paths to generate directed traffic flow vector data; The passenger flow corridor generation module is used to perform hierarchical visualization processing on the traffic flow vector data according to the traffic flow size, and generate directional passenger flow corridors for different travel modes in the target area.

9. A computer device, characterized in that: It comprises a processor and a memory, wherein a computer program is stored in the memory, and the computer program is configured to be executed by the processor, and when the processor executes the computer program, the passenger flow corridor generation method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the passenger flow corridor generation method according to any one of claims 1 to 7 is implemented.

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