A passenger flow corridor generation method, device, equipment and storage medium

Abstract

The application discloses a passenger flow corridor generation method, device and equipment and a storage medium. The method comprises the following steps: performing vectorization region division on a target region to obtain planar vector data; obtaining OD data of the target region to form an OD data set; constructing a triangular mesh based on the planar vector data by using an incremental algorithm; constructing a directed traffic network based on the planar vector data and the triangular mesh; matching 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, calculating the traffic flow of the 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 size to generate directed passenger flow corridors of different travel modes in the target region. The application can efficiently process passenger flow corridor generation tasks of multiple cities or regions in batches, and realize multi-mode and direction-oriented passenger flow corridor identification.

Description

TECHNICAL FIELD

[0001] The present application 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

[0002] The passenger flow corridor refers to a traffic path with obvious directionality and flow characteristics formed due to the high concentration of resident travel demand in the urban traffic system, and is usually distributed along the main traffic trunk or traffic hub and is the main bearing area of urban traffic passenger flow. Therefore, accurately and quickly identifying the passenger flow corridor is of great significance for optimizing urban traffic resource allocation and guiding public transportation development.

[0003] There are mainly two existing methods for identifying and generating urban traffic passenger flow corridors, a cobweb diagram display method based on professional software (such as TransCAD) and a clustering-based identification method. However, the cobweb diagram display method for generating passenger flow corridors relies on professional software and is complex to operate, and when the research area is changed, the traffic zones need to be manually divided, which is difficult to process quickly in batches and cannot distinguish the distribution of passenger flow corridors of different travel modes. The clustering-based identification method relies on clustering methods and parameter settings, and is complex and time-consuming to calculate, difficult to distinguish the direction of passenger flow corridors, and has a large granularity, which is less applicable in actual application and difficult to fit the actual situation of the city. SUMMARY

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

[0005] The present application provides a passenger flow corridor generation method, which comprises:

[0006] Vectorizing region division is performed on a target region to obtain planar vector data of the target region;

[0007] Obtain the travel OD data of the target region to form an OD data set;

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

[0009] Based on the planar vector data and the triangulation network, a directed traffic network is constructed, which includes nodes, edges and weights of the edges;

[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 classified and visualized according to the size of the traffic flow, and a directed passenger flow corridor of different travel modes in the target area is generated.

[0012] As an improvement of the above scheme, the travel OD data of the target area is obtained to form an OD data set, including:

[0013] Obtain the travel OD data of the target area; the travel OD data includes: ID of OD pair, origin coordinate, destination coordinate and travel volume of different travel modes;

[0014] The travel OD data is standardized to integrate travel OD data of different travel modes;

[0015] Based on the planar vector data and the travel OD data, the planar region information corresponding to the origin coordinate and the destination coordinate of each OD pair is extracted by a spatial connection method to generate an OD data set; the specific fields of the OD data set include: the planar region ID corresponding to the origin coordinate and the destination coordinate of the OD pair, and the traffic flow of different travel modes.

[0016] As an improvement of the above scheme, the planar vector data includes: ID of planar region, central point longitude and latitude coordinates;

[0017] Based on the planar vector data, an incremental algorithm is used to construct a triangular mesh, including:

[0018] The central point longitude and latitude coordinates of the planar region are taken as the initial point set, and an incremental algorithm is used to generate an initial triangular mesh; the initial triangular mesh is a line data with geographic space coordinates;

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

[0020] According to the origin coordinate and the destination coordinate of each line data in the modified triangular mesh, the line length is calculated;

[0021] The modified triangular mesh and the planar vector data are spatially connected to generate the planar region information corresponding to the triangular mesh nodes, and the final triangular mesh is obtained.

[0022] As an improvement of the above scheme, based on the planar vector data and the triangular mesh, a directed traffic network is constructed, including:

[0023] Based on the ID of the planar region, a traffic network node is constructed;

[0024] According to the ID of the surface area corresponding to the terminal point in the triangulation network and the line length, a positive direction edge set with the starting point pointing to the terminal point and a reverse direction edge set with the terminal point pointing to the starting point are constructed, and a directed traffic network edge set is obtained;

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

[0026] According to the traffic network node, the traffic network edge set and the weight of the edge, a directed traffic network is obtained.

[0027] As an improvement of the above scheme, the travel path of the OD pair in the OD data set is identified by matching the starting point and the terminal point in the OD data set with the node in the directed traffic network, and the traffic flow of the edge in the directed traffic network is calculated according to the travel path, and the directed traffic flow vector data is generated, including:

[0028] The starting point and the terminal point of each OD pair in the OD data set are matched with the traffic network node, and the shortest travel path of each OD pair is identified based on the weight of the edge in the directed traffic network, and a travel path set of the OD pair is obtained;

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

[0030] Based on the traffic flow data set, the positive direction and the reverse direction traffic flow vector data are obtained by matching the starting point and the terminal point in the triangulation network with the starting point and the terminal point of the edge in the directed traffic network; The specific fields of the traffic flow vector data include: the geographic coordinates of the triangulation network, the starting point ID, the terminal point ID, the starting point ID of the positive direction, the terminal point ID, the traffic flow of different modes, the starting point ID of the reverse direction, the terminal point ID, and the traffic flow of different modes.

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

[0032] The traffic flow attribute of different travel modes is added to the traffic network edge set, and the initial traffic flow of different travel modes corresponding to each edge is set to 0;

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

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

[0035]

[0036] Wherein, length i is the length of the i-th edge, v mode is the travel speed of the traffic mode.

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

[0038] A target area division module is configured to perform vectorization area division on a target area to obtain planar vector data of the target area.

[0039] An OD data acquisition module is configured to acquire trip OD data of the target area to form an OD data set.

[0040] A triangular net construction module is configured to construct a triangular net based on the planar vector data by using an incremental algorithm.

[0041] A traffic network construction module is configured to construct a directed traffic network based on the planar vector data and the triangular net, wherein the directed traffic network comprises nodes, edges and weights of the edges.

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

[0043] A passenger flow corridor generation module is configured to perform hierarchical visualization processing on the traffic flow vector data according to traffic flow size to generate directed passenger flow corridors of different travel modes in the target area.

[0044] The embodiment of the application further provides a computer device comprising a processor and a memory, wherein the memory stores a computer program, and the computer program is configured to be executed by the processor, and the processor implements the passenger flow corridor generation method according to any one of the above embodiments when executing the computer program.

[0045] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, wherein the computer program controls a device where the computer readable storage medium is located to execute the corridor generation method for passenger flow.

[0046] Compared with the prior art, the corridor generation method, device, equipment and storage medium for passenger flow provided by the embodiment of the present application have the beneficial effects that: by acquiring the surface vector data and the trip OD data of the target region, the incremental algorithm is used to construct a triangular mesh, and then a directed traffic network is constructed, the matching of the OD data set and the directed traffic network is performed to identify the trip path, the traffic flow is calculated, the directed traffic flow vector data is generated, and finally the hierarchical visualization processing is performed according to the traffic flow size to generate the directed passenger flow corridor of different trip modes in the target region, the efficient batch processing of the passenger flow corridor identification and generation task of multiple cities or regions can be realized, the accurate identification of the passenger flow corridor in different directions is realized, more refined data support is provided for urban traffic planning, the processing and aggregation of the original trip OD data are strengthened, the identification of the multi-mode passenger flow corridor is realized, and the difference in the distribution of the passenger flow corridor of different trip modes is helpful to comparison. The corridor generation method for passenger flow of the embodiment of the present application is simple in operation, high in applicability, can be quickly and batch processed, and can realize the identification and generation of the passenger flow corridor of different trip modes and different directions. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 is a flow schematic diagram of a corridor generation method for passenger flow provided by the embodiment of the present application;

[0048] Figure 2 is a surface vector data schematic diagram provided by the embodiment of the present application;

[0049] Figure 3 is an initial triangular mesh schematic diagram provided by the embodiment of the present application;

[0050] Figure 4 is a modified triangular mesh schematic diagram provided by the embodiment of the present application;

[0051] Figure 5 is a passenger flow corridor generation result schematic diagram of the car trip mode in different directions provided by the embodiment of the present application;

[0052] Figure 6 is a structure schematic diagram of a corridor generation device for passenger flow provided by the embodiment of the present application;

[0053] Figure 7 is a structure schematic diagram of a computer device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0054] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0055] Please refer to Figure 1 , Figure 1 is a flowchart of a passenger flow corridor generation method provided by an embodiment of the present application. The passenger flow corridor generation method comprises the following steps.

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

[0057] Specifically, according to the size of a research range (WGS-1984 coordinate system) to specify the accuracy requirement of the planar vector data, the target region is divided into a preset size of a grid (such as 1000*1000m, 2000*2000m, etc.) by using a fishnet tool in ArcGIS. Alternatively, municipal, county, town and street administrative division vector data can be selected, and the smallest unit of different levels of administrative division is directly taken as the planar vector data. For example, if the research is a provincial passenger flow corridor, the smallest unit can be municipal planar vector data; if the research is a municipal passenger flow corridor, the smallest unit can be county or town planar vector data.

[0058] Further, the geographic processing tool is used to extract the longitude and latitude coordinates of the center point of the planar vector of the target region to obtain the planar vector data of the target region, which is expressed as RA={(id i ,name i ,geom i ,lon i ,lat i )}, wherein id i , name i , geom i , lon i , and lat i represent the ID number, name, geographic coordinates, longitude coordinates of the center point, and latitude coordinates of the center point of the i-th planar region respectively.

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

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

[0061] Further, the obtaining of the travel OD data of the target region to form the OD data set comprises:

[0062] obtain trip OD data of the target region; the trip OD data comprises IDs of OD pairs, origin coordinates, destination coordinates, and trip volumes of different trip modes;

[0063] standardize the trip OD data to integrate trip OD data of different trip modes;

[0064] based on the areal vector data and the trip OD data, extract the areal region information corresponding to the origin coordinates and the destination coordinates of each OD pair by a spatial connection method, and generate an OD dataset; specific fields of the OD dataset comprise areal region IDs corresponding to the origin coordinates and the destination coordinates of the OD pairs, and traffic flows of different trip modes.

[0065] Specifically, the trip OD data of the target region is obtained, and different trip OD data is integrated in a table by standardizing the fields. Specific fields of the trip OD data comprise a trip ID (od_id), origin coordinates (source_x84, source_y84), destination coordinates (target_x84, target_y84), a departure time (go_time), a car trip volume (car_uv), a bus trip volume (bus_uv), a rail trip volume (subway_uv), a slow trip volume (bike_uv), and a total trip volume (uv). The origin and destination coordinates are in the WGS-1984 coordinate system.

[0066] According to the departure time field (go_time) in the OD data table, trip OD data at a preset time is extracted in combination with actual needs. The origin coordinates and the destination coordinates of each OD pair are extracted by a spatial connection method to obtain areal region IDs, and the trip OD dataset is counted into the areal vector data of the target region to generate an OD dataset for basic analysis. The OD dataset is expressed as OD={(o_id i ,d_id i ,flow ki )}, o_id i , d_id i , and flow ki represent the areal region ID corresponding to the origin coordinates, the areal region ID corresponding to the destination coordinates, and the traffic flow of the kth trip mode of the ith OD pair, respectively, wherein k takes values of 1, 2, 3, 4, and 5, k=1 represents the car trip volume (car_uv), k=2 represents the bus trip volume (bus_uv), k=3 represents the rail trip volume (subway_uv), k=4 represents the slow trip volume (bike_uv), and k=5 represents the total trip volume (uv).

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

[0068] Further, the planar vector data comprises: ID of a planar region, central point longitude and latitude coordinates;

[0069] The constructing of the triangulation network based on the planar vector data by using the incremental algorithm comprises:

[0070] Taking the central point longitude and latitude coordinates of the planar region as initial point set, an initial triangulation network is generated by using the incremental algorithm; the initial triangulation network is line data with geographic space coordinates;

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

[0072] According to the start point coordinates and end point coordinates of each line data in the modified triangulation network, the line length is calculated;

[0073] The modified triangulation network and the planar vector data are spatially connected to generate planar region information corresponding to the triangulation network nodes, and the final triangulation network is obtained.

[0074] Specifically, on the basis of the target region planar vector, an initial Delaunay triangulation network is generated by using the incremental algorithm, and a small amount of triangulation networks without direct traffic connection are removed by comprehensively considering factors such as mountains, rivers, etc.

[0075] Firstly, the target region planar central point longitude and latitude coordinates (lon i ,lat i ) are taken as initial point set, and a Delaunay triangulation network is generated by using the incremental construction algorithm to ensure that the initial triangulation network has the characteristics of no overlapping edges and maximized minimum angle. The generated initial triangulation network is line data with geographic space coordinates, expressed as {geom1, geom2, …, geom i}, and geom i is the geographic space coordinates of the ith line data;

[0076] Further, the initial triangulation network with geographic space vector is imported into the GIS software, a small amount of lines without direct traffic connection are deleted by considering factors such as mountains, rivers, etc., and the modified triangulation network data is formed;

[0077] Finally, the start point and end point coordinates of each line data of the triangulation network are extracted by using the geographic space processing algorithm, the triangulation network line length is calculated, and the start point node corresponding planar region ID and the end point node corresponding planar region ID of the triangulation network are generated by spatially connecting with the planar vector data, and the triangulation 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 are the longitude and latitude of the center point of the planar region corresponding to the origin of the i-th OD pair, d_lon i and d_lat i are the longitude and latitude of the center point of the planar region corresponding to the destination of the i-th OD pair, o_id i and d_id i are the planar region IDs corresponding to the origin and destination of the i-th OD pair respectively, and length i is the length of the origin-destination line of the i-th OD pair.

[0078] Further, since the above-constructed triangular mesh only generates geographic information data without direction, daily traffic travel is in both positive and negative directions. Therefore, the following step constructs a directed traffic network based on planar vector and triangular mesh data.

[0079] S4: Construct a directed traffic network based on the planar vector data and the triangular mesh, the directed traffic network including nodes, edges and weights of the edges;

[0080] Further, the step of constructing a directed traffic network based on the planar vector data and the triangular mesh includes:

[0081] constructing traffic network nodes based on the IDs of the planar regions;

[0082] constructing a set of positive direction edges with origins pointing to destinations and a set of negative direction edges with destinations pointing to origins based on the IDs of the planar regions corresponding to origins and destinations in the triangular mesh and the lengths of the lines, to obtain a set of edges of the directed traffic network;

[0083] calculating weights of the edges based on the lengths of the edges and travel speeds of the traffic modes corresponding to the edges based on the set of edges of the traffic network;

[0084] obtaining the directed traffic network based on the traffic network nodes, the set of edges of the traffic network and the weights of the edges.

[0085] Specifically, traffic network nodes are constructed based on planar region IDs (id i ), and the node set expression is V = {id1, id2, …, id i};

[0086] Secondly, according to the planar region ID and the line length (o_id i ,d_id i ,length i ) corresponding to the start and end points of the triangular net, a positive direction edge set is constructed, in which the start point points to the end point At the same time, the start and end node IDs are interchanged (d_id i ,o_id i ,length i ), and a reverse direction edge set is constructed, in which the end point points to the start point Thus, a directional traffic network edge set E = E o→d ∪E d→o is formed.

[0087] Finally, a weight attribute w is added to the edges in the traffic network, so that a directed traffic network G = (V, E, w) is formed based on the traffic network nodes, edge set and weight.

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

[0089]

[0090] where length i is the length of the ith edge, and v mode is the travel speed of the traffic mode.

[0091] S5: 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;

[0092] Further, the matching of the start and end points in the OD data set with the nodes in the directed traffic network, the identification of the travel paths of the OD pairs in the OD data set, and the calculation of the traffic flow of the edges in the directed traffic network according to the travel paths to generate directed traffic flow vector data, include:

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

[0094] According to the travel path set and the traffic flow of different travel modes in the OD data set, the traffic flow of different travel modes of each edge in the directed traffic network is calculated, and a traffic flow data set is obtained.

[0095] Based on the traffic flow dataset, through the matching of the starting and ending points of the triangular network with the starting and ending points of the edges of the directed traffic network, the traffic flow vector data of the positive direction and the reverse direction are obtained; The specific fields of the traffic flow vector data include: the geographic coordinates of the triangular network, the starting point ID, the ending point ID, the starting point ID of the positive direction, the ending point ID, the traffic flow of different modes, the starting point ID of the reverse direction, the ending point ID, and the traffic flow of different modes.

[0096] Further, the calculation of the traffic flow of different travel modes of each edge in the directed traffic network according to the set of travel paths and the traffic flow of different travel modes in the OD dataset includes:

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

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

[0099] Specifically, the starting and ending point 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 = {(e i1 ,e i2 ,…,e in )} of each OD pair is obtained in turn;

[0100] Adding the traffic flow attribute of different travel modes to the edge set of the traffic network and setting the initial traffic flow of different travel modes of each edge to 0, traversing the travel paths of each OD pair in turn, and iterating according to the traffic flow data of different modes in the OD dataset to calculate the traffic flow of different travel modes of each edge in the traffic network;

[0101] Extracting the starting point ID (o_node_id i ), the ending point ID (d_node_id i ) and the traffic flow attribute of each edge in the directed traffic network Constructing the traffic flow dataset of different travel modes, the expression is

[0102] Matching the starting point ID and the ending point ID of the triangular network with the records of the starting point ID and the ending point ID of the edges in the directed traffic network, forming the traffic flow vector data AB = {(geomi , o_id i , d_id i , ab_o_id i , ab_d_id i , ab_flow ki}, wherein ab_o_id i , ab_d_id i , ab_flow ki are the positive direction start ID, end ID and different mode traffic flow; the triangle network end ID, start ID and the same record of the traffic network edge start ID, end ID are matched to form the reverse direction traffic flow vector data BA = {(gerom i , o_id i , d_id i , ba_o_id i , ba_d_id i , ba_flow ki )}, wherein ba_o_id i , ba_d_id i , ba_flow ki are the reverse direction start ID, end ID and different mode traffic flow;

[0103] Based on the triangle network geographic information (geom i ), start end Connect the positive direction and reverse direction traffic flow vector data to form the final different mode traffic flow vector data, which is expressed as:

[0104] S6: The traffic flow vector data is processed by hierarchical visualization according to the traffic flow size to generate the directed passenger flow corridor of different travel modes in the target area.

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

[0106] Further, the following takes the travel OD data and town street surface vector data of a city as an example to describe the embodiment of the application.

[0107] S1: Obtain the town street surface vector data of the city, as shown in the following table. Figure 2 The coordinates adopt the WGS-1984 coordinate system, and the geographic processing tool is used to extract the longitude and latitude coordinates of the center points of the towns and streets. 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, longitude coordinate of the center point, and latitude coordinate of the center point of the ith town and street.

[0108] S2: Obtain the travel OD data of the city, as shown in the following table. Further, the travel OD of different travel modes is integrated in a table through the standardization field processing. In this case, the early morning peak travel OD data is used. According to the departure time field of the OD table, the 8:00-9:00 travel OD data is extracted.

[0109] Table 1: Example of original travel 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] Further, the spatial connection method is used to extract the town and street IDs corresponding to the start point coordinates and end point coordinates of each OD, and the travel OD data set is aggregated to the towns and streets to generate the OD data set for the basic analysis, as shown in the following table 2. The expression of the OD data set is OD = {(o_id i , d_id i , flow ki )}, which respectively represent the town and street ID corresponding to the start point coordinates of the ith OD, the town and street ID corresponding to the end point coordinates, and the traffic volume of the kth travel mode in the early morning peak period from 8:00 to 9:00, wherein 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: Example of aggregated OD data set of different travel modes

[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] In order to show the passenger flow corridor, the car travel volume, i.e., flow1, is taken as an example in the following.

[0115] S3: On the basis of the town and street planar vector, an incremental construction algorithm is used to generate an initial Delaunay triangulation, as shown in FIG. 3. Then, the initial triangulation with geographic spatial vectors is imported into a GIS software, and a small amount of lines without direct traffic connection are deleted considering factors such as mountains and rivers, to form a modified triangulation data, as shown in FIG. 4. Finally, the geographic spatial processing algorithm is used to extract the start and end point coordinates of each line data of the triangulation, and the triangulation line length is calculated, and through the spatial connection with the town and street vector data, the ID corresponding to the start and end point nodes of the triangulation is generated, to obtain the final triangulation. Figure 3 Figure 4

[0116] S4: The traffic network nodes are constructed through the town and street center point ID; the directed traffic network edge set is constructed through the triangulation start and end point ID and the line length; the edge weight is calculated according to the length of the edge and the travel speed of the traffic mode, to form a directed traffic network.

[0117]

[0118] S5: The OD data set start and end point ID is matched with the traffic network node ID, each pair of OD travel path is identified based on the weight, all travel paths are fused, the car traffic volume of each edge in the traffic network is calculated, and finally the car traffic flow vector data is formed. Figure 5

[0119] Compared with the prior art, the embodiment of the present application has the following beneficial effects:

[0120] Efficiently realize the rapid identification and generation of multi-mode directed passenger flow corridors: the present application strengthens the processing and aggregation of the original travel OD data, generates different time period, different travel mode or full mode OD data sets based on planar ID aggregation, can realize the identification of different travel mode or full mode passenger flow corridors, and is convenient for comparing and analyzing the similarities and differences of different travel mode passenger flow corridors. Through the construction of multiple directed traffic networks, the problem that the existing identification method cannot realize the identification and generation of the directional passenger flow corridor is effectively solved, the directional and refined identification of the passenger flow corridor is realized, and more refined data support can be provided for urban traffic planning.

[0121] ​​​​Multiple urban or regional passenger flow corridors can be batch generated: the application breaks away from the strong dependence on professional traffic planning software such as TransCAD, generates a Delaunay triangulation network based on the planar vector of the research area, and constructs a multiple directed traffic network containing nodes, edge sets and weights, realizes the rapid identification and generation of multiple mode directed passenger flow corridors through network analysis, has high calculation efficiency and is easy to operate, and only needs to prepare the planar vector data of the target area in advance, so that the batch and rapid identification of passenger flow corridors of multiple cities or research areas can be realized.

[0122] Correspondingly, the application also provides a passenger flow corridor generation device capable of realizing all processes of the passenger flow corridor generation method in the above embodiments.

[0123] Please refer to Figure 6 , Figure 6 is a structural schematic diagram of a passenger flow corridor generation device provided by an embodiment of the application. The passenger flow corridor generation device comprises:

[0124] A target area division module 601 is configured to perform vectorized area division on a target area to obtain planar vector data of the target area.

[0125] An OD data acquisition module 602 is configured to acquire travel OD data of the target area to form an OD data set.

[0126] A triangulation network construction module 603 is configured to construct a triangulation network based on the planar vector data by using an incremental algorithm.

[0127] A traffic network construction module 604 is configured to construct a directed traffic network based on the planar vector data and the triangulation network, wherein the directed traffic network comprises nodes, edges and weights of the edges.

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

[0129] A passenger flow corridor generation module 606 is configured to perform hierarchical visual processing on the traffic flow vector data according to traffic flow sizes to generate directed passenger flow corridors of different travel modes in the target area.

[0130] Preferably, the OD data acquisition module 602 is specifically configured to:

[0131] acquire travel OD data of the target area, wherein the travel OD data comprises IDs of OD pairs, coordinates of start points, coordinates of end points and travel volumes of different travel modes.

[0132] The trip OD data is standardized to integrate trip OD data of different trip modes.

[0133] Based on the planar vector data and the trip OD data, the start point coordinate and the end point coordinate corresponding to the planar region information of each OD pair are extracted by a spatial connection method to generate an OD data set; specific fields of the OD data set include: planar region IDs corresponding to the start point coordinate and the end point coordinate of the OD pair, and traffic flows of different trip modes.

[0134] Preferably, the planar vector data includes: an ID of a planar region, and central point longitude and latitude coordinates.

[0135] The triangular mesh construction module 603 is specifically configured to:

[0136] The central point longitude and latitude coordinates of the planar region are taken as an initial point set, and an incremental algorithm is used to generate an initial triangular mesh; the initial triangular mesh is line data with geographic space coordinates.

[0137] According to a topographic and geomorphic factor of the target region, lines without direct traffic connection in the initial triangular mesh are deleted to obtain a modified triangular mesh.

[0138] According to the start point coordinate and the end point coordinate of each line data in the modified triangular mesh, a line length is calculated.

[0139] The modified triangular mesh and the planar vector data are spatially connected to generate planar region information corresponding to a triangular mesh node to obtain a final triangular mesh.

[0140] Preferably, the traffic network construction module 604 is specifically configured to:

[0141] Based on the ID of the planar region, a traffic network node is constructed.

[0142] According to the ID of the planar region corresponding to the start and end points in the triangular mesh and the line length, a positive direction edge set with the start point pointing to the end point and a reverse direction edge set with the end point pointing to the start point are constructed to obtain a directed traffic network edge set;

[0143] Based on the traffic network edge set, a weight of an edge is calculated according to a length of the edge and a trip speed of a traffic mode corresponding to the edge;

[0144] According to the traffic network node, the traffic network edge set, and the weight of the edge, a directed traffic network is obtained.

[0145] Preferably, the traffic flow calculation module 605 is specifically configured to:

[0146] Matching the start and end 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, obtaining a travel path set of the OD pair;

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

[0148] Based on the traffic flow data set, obtaining the traffic flow vector data of the positive direction and the reverse direction through the matching of the start and end points in the triangular net with the start and end points of the edges in the directed traffic network; the specific fields of the traffic flow vector data include: the geographic coordinates of the triangular net, the start point ID, the end point ID, the start point ID of the positive direction, the end point ID, the traffic flow of different modes, the start point ID of the reverse direction, the end point ID, and the traffic flow of different modes.

[0149] Preferably, the calculation of 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 comprises:

[0150] Adding the traffic flow attribute 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, sequentially traversing the travel path of each OD pair, iteratively superimposing the traffic flow of different travel modes corresponding to each edge, and calculating the traffic flow of different travel modes corresponding to each edge.

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

[0153]

[0154] Wherein, length i is the length of the i-th edge, v mode is the travel speed of the traffic mode.

[0155] In specific implementation, the working principle, control process and technical effects of the passenger flow corridor generation device provided by the embodiment of the application correspond to the same as the passenger flow corridor generation method in the above embodiment, and will not be repeated here.

[0156] Referring to Figure 7 , Figure 7is a structural block diagram of a computer device provided by an embodiment of the present application, and the computer device comprises a processor 701, a memory 702, and a computer program stored in the memory 702 and capable of running on the processor 701. The processor 701 implements the steps in the above-mentioned passenger flow corridor generation method embodiment when executing the computer program. Alternatively, the processor 701 implements the functions of each module / unit in the above-mentioned each device embodiment when executing the computer program.

[0157] For example, the computer program can be divided into one or more modules / units, which are stored in the memory 702 and executed by the processor 701 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the computer device.

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

[0159] The processor 701 can be a central processing unit (CPU), and can also be 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. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The processor 701 is the control center of the computer device, which connects each part of the computer device through various interfaces and lines.

[0160] The memory 702 can be used to store the computer programs and / or modules, and the processor 701 realizes various functions of the computer device by running or executing the computer programs and / or modules 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, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), and the like; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), and the like. In addition, the memory 702 can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0161] The modules / units integrated in the computer device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can realize the steps of the above-mentioned various method embodiments when executed by the processor 701. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0162] The embodiment of the present application also provides a computer readable storage medium, which includes a stored computer program, wherein when the computer program runs, the device where the computer readable storage medium is located executes the passenger flow corridor generation method in any of the above-mentioned embodiments.

[0163] The embodiment of the present application provides a passenger flow corridor generation method, device and equipment and a storage medium, which has the beneficial effect that by acquiring the planar vector data and the trip OD data of a target area, a triangular net is constructed by using an incremental algorithm, then a directed traffic network is constructed, the matching of the OD data set and the directed traffic network is performed to identify a trip path, the traffic flow is calculated, the directed traffic flow vector data is generated, and finally the hierarchical visualization processing is performed according to the traffic flow size to generate the directed passenger flow corridor of different trip modes in the target area, so that the efficient batch processing of the passenger flow corridor identification and generation task of multiple cities or regions can be realized, the accurate identification of the passenger flow corridor in different directions is realized, more refined data support is provided for the urban traffic planning, the processing and aggregation of the original trip OD data are strengthened, the identification of the multi-mode passenger flow corridor can be realized, and the difference in the distribution of the passenger flow corridor of different trip modes is helpful to comparison. The passenger flow corridor generation method of the embodiment of the present application is simple in operation, high in applicability, can be quickly and batch processed, and can realize the identification and generation of the passenger flow corridor of different trip modes and different directions.

[0164] The above is the preferred embodiment of the present application. It should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present application. These improvements and refinements are also considered within the scope of protection of the present application.

Claims

1. A passenger flow corridor generation method characterized by comprising: The method comprises the following steps: vectorizing the target area to obtain planar vector data of the target area; obtaining OD data of the target area to form an OD data set; constructing a triangulation network based on the planar vector data by using an incremental algorithm; constructing a directed traffic network based on the planar vector data and the triangulation network, wherein the directed traffic network comprises nodes, edges and weights of the edges; identifying travel paths of OD pairs in the OD data set by matching start and end points in the OD data set with nodes in the directed traffic network, and calculating traffic flow of the edges in the directed traffic network according to the travel paths to generate directed traffic flow vector data; performing hierarchical visualization processing on the traffic flow vector data according to the size of the traffic flow to generate directed passenger flow corridors of different travel modes in the target area.

2. The passenger flow corridor generation method according to claim 1, wherein The method for obtaining OD data of the target area to form an OD data set comprises the following steps: obtaining OD data of the target area, wherein the OD data comprises IDs of OD pairs, coordinates of start points, coordinates of end points and travel volumes of different travel modes; performing standardization processing on the OD data to integrate travel OD data of different travel modes; extracting planar region information corresponding to the coordinates of start points and the coordinates of end points of each OD pair by using a spatial connection method based on the planar vector data and the OD data to generate an OD data set, wherein specific fields of the OD data set comprise IDs of planar regions corresponding to the coordinates of start points and the coordinates of end points of OD pairs, and traffic flow of different travel modes.

3. The passenger flow corridor generation method according to claim 2, wherein The planar vector data comprises IDs of planar regions and central point longitude and latitude coordinates. The method for constructing a triangulation network based on the planar vector data by using an incremental algorithm comprises the following steps: taking the central point longitude and latitude coordinates of the planar regions as an initial point set, and generating an initial triangulation network by using an incremental algorithm, wherein the initial triangulation network is line data with geographic spatial coordinates; deleting lines in the initial triangulation network that have no direct traffic connection according to topographic and geomorphic factors of the target area to obtain a modified triangulation network; calculating line lengths according to the coordinates of start points and the coordinates of end points of each line data in the modified triangulation network; performing spatial connection on the modified triangulation network and the planar vector data to generate planar region information corresponding to nodes of the triangulation network to obtain a final triangulation network.

4. The passenger flow corridor generation method according to claim 3, wherein The method for constructing a directed traffic network based on the planar vector data and the triangulation network comprises the following steps: constructing traffic network nodes based on IDs of the planar regions; constructing a positive direction edge set in which start points point to end points and a reverse direction edge set in which end points point to start points according to IDs of the planar regions corresponding to start and end points in the triangulation network and the line lengths to obtain a directed traffic network edge set; calculating weights of the edges based on the lengths of the edges and travel speeds of traffic modes corresponding to the edges; obtaining a directed traffic network according to the traffic network nodes, the traffic network edge set and the weights of the edges.

5. The passenger flow corridor generation method according to claim 4, wherein The method comprises the following steps: Matching the origin and destination of each OD pair in the OD data set with the nodes in the directed traffic network, identifying the shortest travel path of each OD pair based on the weight of the edges in the directed traffic network, and obtaining a set of travel paths of OD pairs; According to the set of travel paths and the traffic flow of different travel modes in the OD data set, the traffic flow of different travel modes of each edge in the directed traffic network is calculated to obtain a traffic flow data set; Based on the traffic flow data set, the traffic flow vector data of the positive direction and the reverse direction is obtained by matching the origin and destination in the triangulation network with the origin and destination of the edges in the directed traffic network; The specific fields of the traffic flow vector data include: the geographic coordinates of the triangulation network, the origin ID, the destination ID, the origin ID of the positive direction, the destination ID, the traffic flow of different modes, the origin ID of the reverse direction, the destination ID, and the traffic flow of different modes.

6. The passenger flow corridor generation method according to claim 5, wherein The method comprises the following steps: Adding traffic flow attributes of different travel modes to the edge set of the traffic network, and setting the initial traffic flow of different travel modes corresponding to each edge to 0; According to the set of travel paths and the traffic flow of different travel modes in the OD data set, the traffic flow of different travel modes corresponding to each edge is calculated by iteratively superimposing the traffic flow of different travel modes corresponding to each edge in turn.

7. The passenger flow corridor generation method according to claim 1, wherein The calculation formula of the weight w of the edge is: where length i is the length of the ith edge, v mode is the travel speed of the mode of transportation.

8. A passenger flow corridor generating device characterized by comprising: The method comprises the following steps: The target area division module is used for vectorizing the target area to obtain the planar vector data of the target area; The OD data acquisition module is used for acquiring the travel OD data of the target area to form an OD data set; The triangulation network construction module is used for constructing a triangulation network based on the planar vector data by using an incremental algorithm; The traffic network construction module is used for constructing a directed traffic network based on the planar vector data and the triangulation network, wherein the directed traffic network comprises nodes, edges, and the weight of the edges; The traffic flow calculation module is used for identifying the travel path of each OD pair in the OD data set by matching the origin and destination of 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 path to generate directed traffic flow vector data; The passenger flow corridor generation module is used for performing hierarchical visualization processing on the traffic flow vector data according to the traffic flow size to generate the directed passenger flow corridors of different travel modes in the target area.

9. A computer device, comprising: The computer readable storage medium stores a computer program, and when a device in which the computer readable storage medium is located executes the computer program, the passenger flow corridor generation method in 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, and when a device in which the computer readable storage medium is located executes the computer program, the passenger flow corridor generation method in any one of claims 1 to 7 is implemented.

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