Method for quickly generating traffic distribution state of urban built-up area based on map interest points

Abstract

The application provides a city built-up area traffic distribution state rapid generation method based on map interest points, relates to the technical field of city traffic planning and management, and comprises the following steps: acquiring map interest points corresponding to a target region in a city map, which needs to generate a traffic distribution state, and obtaining a plurality of traffic partitions by processing the city map; dividing the target region into a plurality of fishnet units according to a block scale, and obtaining a functional diversity index corresponding to each fishnet unit according to map interest points in a statistical range corresponding to each fishnet unit; obtaining a traffic partition functional index and a traffic partition functional barycenter of each traffic partition by processing the functional diversity index of the fishnet units contained in the traffic partition; obtaining the attractive force values between all traffic partitions by using a gravity model according to all traffic partition functional indexes and all functional barycenters; and obtaining the traffic distribution state of the target region according to each attractive force value. The beneficial effects are convenient data acquisition, rapid processing, and accurate information mining.

Description

Technical Field

[0001] This invention relates to the field of urban traffic planning and management technology, and in particular to a method for rapidly generating traffic distribution status in urban built-up areas based on map points of interest. Background Technology

[0002] Traffic distribution reflects the flow characteristics of passengers or goods within a given area. It is closely related to the spatial structure, functional layout, population and industrial distribution, and economic ties of the target area, representing the spatial manifestation of the coupling relationship between economic and social development. The characteristics of traffic distribution form the basis for research in urban traffic planning, traffic infrastructure construction, and traffic operation management, and have significant guiding significance for urban operation management.

[0003] The basic idea behind modern traffic demand forecasting is to analyze and establish the fundamental relationship between travel and land use, and then quantify traffic demand characteristics through this comprehensive system model. The "four-stage method" is currently the most widely used traffic demand forecasting model. Traffic distribution is one of its four forecasting stages, which further expresses the predicted trip generation and attraction volumes from the previous stage as origin-destination (OD) traffic volumes between each pair of traffic zones in matrix form.

[0004] Traffic distribution based on the "four-stage method" relies on a large amount of basic data (including but not limited to resident population, job positions, car ownership, land use attributes, and resident travel rate). This basic data is difficult to obtain quickly and accurately, and the current travel survey is cumbersome and difficult. Moreover, due to the large amount of basic data, the cumulative error cannot be estimated, the accuracy of model prediction is difficult to control, and the prediction results are prone to deviate significantly from the actual situation. In addition, the cumbersome modeling process is not conducive to the transfer and reconstruction of the model.

[0005] However, when dealing with massive amounts of POI (Points of Interest) data, it is not feasible to use appropriate methods to mine the information contained within them. This would allow us to characterize the distribution of traffic demand in the target area by reflecting its socio-economic development status. Currently, there is no mature method for generating traffic distribution status based on POI big data. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a method for rapidly generating the traffic distribution status of urban built-up areas based on map points of interest, including:

[0007] Step S1: Obtain the map points of interest corresponding to the target area in the city map where traffic distribution status needs to be generated, and process the city map to obtain multiple traffic zones.

[0008] Step S2: Divide the target area into multiple fishing net units according to a preset street scale, and process the map interest points within the statistical range corresponding to each fishing net unit to obtain the functional diversity index corresponding to each fishing net unit.

[0009] Step S3: Process the functional diversity index of all fishing net units contained in each traffic zone to obtain the corresponding traffic zone functional index and traffic zone functional centroid.

[0010] Step S4: Based on all the traffic zone function indices and all the function centroids, the gravity model is used to process and obtain the gravity values ​​between each pair of the traffic zones, and the traffic distribution status of the target area is obtained based on each gravity value.

[0011] Preferably, the map points of interest include multiple types; then step S2 includes:

[0012] Step S21: Divide the target area into multiple fishing net units according to the street block scale;

[0013] Step S22: Using the geometric center of the fishing net unit as the center, determine the corresponding statistical range with a preset statistical radius;

[0014] Step S23: Based on the total number of map points of interest within the statistical range corresponding to each fishing net unit and the number of map points of interest of each type, obtain the quantity ratio corresponding to each type, and process all the quantity ratios within each fishing net unit to obtain the functional diversity index of the corresponding fishing net unit.

[0015] Preferably, the formula for calculating the functional diversity index in step S23 is:

[0016]

[0017] Where h(i) is the functional diversity index of the i-th fishing net unit, P(j) is the proportion of the j-th type of map interest points in the i-th fishing net unit, and n is the total number of all map interest point types covered by the i-th fishing net unit.

[0018] Preferably, the formula for calculating the traffic zoning function index in step S3 is:

[0019]

[0020] Wherein, H(p) is the traffic zone function index of the p-th traffic zone, h(i) is the functional diversity index of the i-th fishing net unit in the traffic zone p, and t is the total number of fishing net units in the traffic zone p.

[0021] Preferably, the formula for calculating the functional centroid in step S3 is:

[0022]

[0023] Wherein, X p Y is the abscissa of the functional centroid of the p-th traffic zone. p Let x be the ordinate of the functional centroid of the p-th traffic zone, h(i) be the functional diversity index of the i-th fishing net unit in the traffic zone p, and x be the ordinate of the functional centroid of the p-th traffic zone p. pi y pi Let be the horizontal and vertical coordinates of the geometric center of the i-th fishing net unit in the traffic zone p, and t be the total number of fishing net units in the traffic zone p.

[0024] Preferably, step S4 includes:

[0025] Step S41: Obtain the spatial distance between each pair of all traffic zones based on the traffic zone functional centroid processing;

[0026] Step S42: Based on all the traffic zone function indices and all the spatial distances, the gravity values ​​between each pair of the traffic zones are obtained using a gravity model.

[0027] Step S43: Generate a gravity matrix based on all the gravity values. The gravity matrix represents the traffic distribution status between the traffic zones in the target area.

[0028] Preferably, the formula for calculating the spatial distance in step S41 is:

[0029]

[0030] Where, d mp X is the spatial distance between the corresponding functional centers of gravity of any two traffic zones. m Y m The x and y coordinates of the functional centroid of one of the traffic zones m are respectively. p Y p These are the horizontal and vertical coordinates of the functional centroid of another traffic zone p.

[0031] Preferably, the formula for calculating the gravitational value in step S42 is:

[0032]

[0033] Among them, O m D p Let H(m) be the gravitational force between the functional centers of gravity of any two traffic zones, H(p) be the traffic zone functional index of one of the traffic zones m, and H(p) be the traffic zone functional index of the other traffic zone p. mp The spatial distance between the two traffic zones is described.

[0034] The above technical solution has the following advantages or beneficial effects:

[0035] 1) Map Points of Interest (POI) data comes from open-source networks, with multiple acquisition channels and a simple acquisition process, making it easy to obtain data through the network;

[0036] 2) Map Points of Interest (POI) datasets contain information such as geographic coordinates, hierarchical categories, and locations. By mining the spatial information contained in POI big data, we can more accurately and objectively reflect the traffic distribution characteristics between traffic zones. Attached Figure Description

[0037] Figure 1 A flowchart illustrating a preferred embodiment of the present invention is shown below for a method for rapidly generating traffic distribution status in urban built-up areas based on map points of interest.

[0038] Figure 2 In a preferred embodiment of the present invention, a schematic diagram of the sub-process of step S2 is provided.

[0039] Figure 3 In a preferred embodiment of the present invention, a schematic diagram of the sub-process of step S4 is provided. Detailed Implementation

[0040] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The present invention is not limited to this embodiment; other embodiments that conform to the spirit of the present invention may also fall within the scope of the present invention.

[0041] In a preferred embodiment of the present invention, based on the aforementioned problems existing in the prior art, a method for rapidly generating the traffic distribution status of urban built-up areas based on map points of interest is provided, such as... Figure 1 As shown, it includes:

[0042] Step S1: Obtain the map points of interest corresponding to the target area in the city map where the traffic distribution status needs to be generated, and process the city map to obtain multiple traffic zones.

[0043] Step S2: Divide the target area into multiple fishing net units according to the preset street scale, and process the map interest points within the statistical range corresponding to each fishing net unit to obtain the functional diversity index corresponding to each fishing net unit.

[0044] Step S3: Based on the functional diversity index of all fishing net units contained in each traffic zone, obtain the corresponding traffic zone functional index and traffic zone functional centroid.

[0045] Step S4: Based on the functional indices of all traffic zones and the center of gravity of all functions, the gravity model is used to process the gravity values ​​between each pair of traffic zones, and the traffic distribution status of the target area is obtained based on each gravity value.

[0046] Specifically, in this embodiment, the jurisdiction of a certain city is taken as the target area for analyzing the traffic distribution status. After obtaining the target area, it is necessary to divide the target area into traffic zones. The division of traffic zones is based on the actual situation of the target area, such as using obstacles such as rivers and railways as boundaries or using the boundaries of administrative divisions as boundaries. In this embodiment, it is preferred to use the town (township) administrative areas within the jurisdiction of the city as the basis for traffic zone division.

[0047] Approximately 372,000 map points of interest (POIs) obtained from the internet covering all administrative districts of the city were cleaned. Each map POI corresponds to a single map point of interest and includes fields such as ID, map point of interest name, map point of interest category name, map point of interest subcategory name, map point of interest subcategory name, longitude, and latitude. Map points of interest are classified into three levels: major, medium, and minor, with each major category containing several medium categories, and each medium category containing several minor categories.

[0048] The city is divided into multiple fishing net units with street block spacing as the interval. Each fishing net unit contains multiple map points of interest. The functional diversity index of each fishing net unit can be obtained by processing the map points of interest contained within the statistical range of each fishing net unit. In this embodiment, the functional diversity indices of fishing net units within the same traffic zone are preferably accumulated and summarized to obtain the traffic zone functional index of that traffic zone. The spatial distribution of the functional diversity indices of fishing net units in different traffic zones is different. Therefore, the functional centroid of the corresponding traffic zone is calculated based on the distribution of the functional diversity indices of fishing net units in each traffic zone.

[0049] The spatial distance between any two traffic zones can be obtained based on the functional centroid coordinates of each traffic zone. Using a gravity model, the gravitational force between any two traffic zones can be calculated. The gravitational matrix formed by the pairwise gravitational forces between all traffic zones describes the traffic distribution characteristics within the target area. In this embodiment, the method of the present invention can quickly acquire map point-of-interest data and concisely and accurately represent the traffic distribution between each traffic zone.

[0050] In a preferred embodiment of the present invention, map points of interest include multiple types; such as Figure 2 As shown, step S2 includes:

[0051] Step S21: Divide the target area into multiple fishing net units according to the block scale;

[0052] Step S22: Using the geometric center of the fishing net unit as the center, determine the corresponding statistical range with a preset statistical radius;

[0053] Step S23: Based on the total number of map interest points within the statistical range corresponding to each fishing net unit and the number of map interest points of each type, obtain the corresponding quantity ratio for each type, and process the quantity ratios within each fishing net unit to obtain the corresponding functional diversity index.

[0054] In a preferred embodiment of the present invention, the formula for calculating the functional diversity index in step S23 is as follows:

[0055]

[0056] Where h(i) is the functional diversity index of the i-th fishing net unit, P(j) is the proportion of the j-th type of map interest points in the i-th fishing net unit, and n is the total number of all map interest point types covered by the i-th fishing net unit.

[0057] Specifically, in this embodiment, the city's administrative district is taken as the target area, and fishing net units are divided at equal intervals in the target area according to the street block scale. For example, if the street block scale is 300 meters, then several fishing net units of 300 meters * 300 meters are divided at 300-meter intervals.

[0058] The geometric center of the fishing net unit can be obtained by using the latitude and longitude of its boundary. Then, the statistical range of the fishing net unit is determined by using the geometric center as the center and a preset statistical radius. The total number of map points of interest (POIs) and the number of each type of POIs within the statistical range are counted. The proportion of each type is calculated, and the functional diversity index of the fishing net unit can be obtained based on the proportion of each type.

[0059] In a preferred embodiment of the present invention, the formula for calculating the traffic zoning function index in step S3 is as follows:

[0060]

[0061] Where H(p) is the traffic zone function index of the p-th traffic zone, h(i) is the functional diversity index of the i-th fishing net unit in traffic zone p, and t is the total number of fishing net units in traffic zone p.

[0062] In a preferred embodiment of the present invention, the formula for calculating the functional centroid in step S3 is:

[0063]

[0064] Among them, X p Y is the x-coordinate of the functional centroid of the p-th traffic zone. p Let x be the ordinate of the functional centroid of the p-th traffic zone, h(i) be the functional diversity index of the i-th fishing net unit in traffic zone p, and x be the ordinate of the functional centroid of the p-th traffic zone. pi y pi Let be the x and y coordinates of the geometric center of the i-th fishing net unit in traffic zone p, and t be the total number of fishing net units in traffic zone p.

[0065] Specifically, in this embodiment, a traffic zone contains multiple fishing net units, and the functional diversity index of the fishing net units contained in the traffic zone is accumulated as the functional index of the traffic zone.

[0066] The number and distribution of each fishing net unit within a traffic zone vary. The functional centroid of the traffic zone is calculated based on the horizontal and vertical coordinates of the geometric center of each fishing net unit and its functional diversity index.

[0067] In a preferred embodiment of the present invention, such as Figure 3 As shown, step S4 includes:

[0068] Step S41: Obtain the spatial distance between all traffic zones based on the traffic zone functional centroid processing;

[0069] Step S42: Based on the functional indices of all traffic zones and all spatial distances, the gravity values ​​between each pair of traffic zones are obtained using the gravity model.

[0070] Step S43: Generate a gravity matrix based on all gravity values. The gravity matrix represents the traffic distribution status between traffic zones in the target area.

[0071] In a preferred embodiment of the present invention, the formula for calculating the spatial distance in step S41 is:

[0072]

[0073] Where, d mp Let X be the spatial distance between the corresponding functional centers of any two traffic zones. m Y m The x and y coordinates of the functional centroid of one of the traffic zones m are respectively. p Y p These are the horizontal and vertical coordinates of the functional centroid of another traffic zone p.

[0074] In a preferred embodiment of the present invention, the formula for calculating the gravitational value in step S42 is as follows:

[0075]

[0076] Among them, O m D p Let H(m) be the gravitational force between the functional centers of gravity of any two traffic zones, H(p) be the traffic zone functional index of one traffic zone m, and H(p) be the traffic zone functional index of the other traffic zone p. mp This represents the spatial distance between the two traffic zones.

[0077] Specifically, in this embodiment, 61 administrative districts within a certain city are used as traffic zones. The gravitational values ​​between each pair of traffic zones are calculated, and the resulting gravitational matrix is ​​a full-rank matrix of 61*61, which is the traffic distribution state matrix between the 61 traffic zones (i.e., administrative districts). The magnitude of the value between a certain traffic zone and other different zones reflects the relative strength of the traffic connection between each pair.

[0078] The above are merely preferred embodiments of the present invention and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should recognize that any equivalent substitutions and obvious changes made using the content of this specification and illustrations should be included within the protection scope of the present invention.

Claims

1. A method for rapidly generating traffic distribution status in urban built-up areas based on map points of interest, characterized in that, include: Step S1: Obtain the map points of interest corresponding to the target area in the city map where traffic distribution status needs to be generated, and process the city map to obtain multiple traffic zones. Step S2: Divide the target area into multiple fishing net units according to a preset street scale, and process the map interest points within the statistical range corresponding to each fishing net unit to obtain the functional diversity index corresponding to each fishing net unit. Step S3: Process the functional diversity index of all fishing net units contained in each traffic zone to obtain the corresponding traffic zone functional index and traffic zone functional centroid. Step S4: Based on all the traffic zone function indices and all the function centroids, the gravity model is used to process and obtain the gravity values ​​between each pair of the traffic zones, and the traffic distribution status of the target area is obtained based on each gravity value. The map points of interest include various types; therefore, step S2 includes: Step S21: Divide the target area into multiple fishing net units according to the street block scale; Step S22: Using the geometric center of the fishing net unit as the center, determine the corresponding statistical range with a preset statistical radius; Step S23: Based on the total number of map points of interest within the statistical range corresponding to each fishing net unit and the number of map points of interest of each type, obtain the quantity ratio corresponding to each type, and process all the quantity ratios within each fishing net unit to obtain the functional diversity index of the corresponding fishing net unit. Step S4 includes: Step S41: Obtain the spatial distance between each pair of all traffic zones based on the traffic zone functional centroid processing; Step S42: Based on all the traffic zone function indices and all the spatial distances, the gravity values ​​between each pair of the traffic zones are obtained using a gravity model. Step S43: Generate a gravity matrix based on all the gravity values. The gravity matrix represents the traffic distribution status between the traffic zones in the target area.

2. The method for rapidly generating traffic distribution status in urban built-up areas according to claim 1, characterized in that, The formula for calculating the functional diversity index in step S23 is as follows: ； Where h(i) is the functional diversity index of the i-th fishing net unit, P(j) is the proportion of the j-th type of map interest points in the i-th fishing net unit, and n is the total number of all map interest point types covered by the i-th fishing net unit.

3. The method for rapidly generating traffic distribution status in urban built-up areas according to claim 1, characterized in that, The formula for calculating the traffic zoning function index in step S3 is as follows: ； Wherein, H(p) is the traffic zone function index of the p-th traffic zone, h(i) is the functional diversity index of the i-th fishing net unit in traffic zone p, and t is the total number of fishing net units in traffic zone p.

4. The method for rapidly generating traffic distribution status in urban built-up areas according to claim 1, characterized in that, The formula for calculating the functional centroid in step S3 is as follows: ； Wherein, X p Y is the abscissa of the functional centroid of the p-th traffic zone. p Let x be the ordinate of the functional centroid of the p-th traffic zone, h(i) be the functional diversity index of the i-th fishing net unit in traffic zone p, and x be the ordinate of the functional centroid of the p-th traffic zone. pi y pi Let be the horizontal and vertical coordinates of the geometric center of the i-th fishing net unit in the traffic zone p, and t be the total number of fishing net units in the traffic zone p.

5. The method for rapidly generating traffic distribution status in urban built-up areas according to claim 1, characterized in that, The formula for calculating the spatial distance in step S41 is as follows: ； in, Let be the spatial distance between the corresponding functional centers of gravity of any two traffic zones. Let x and y be the x and y coordinates of the functional centroid of one of the traffic zones m, respectively. These are the horizontal and vertical coordinates of the functional centroid of another traffic zone p.

6. The method for rapidly generating traffic distribution status in urban built-up areas according to claim 5, characterized in that, The formula for calculating the gravitational value in step S42 is as follows: ； in, The gravitational force between the functional centers of gravity of any two traffic zones is given. Let m be the traffic zone function index of one of the traffic zones. For another traffic zone p, the traffic zone function index, The spatial distance between the two traffic zones is described.

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