A method, device and storage medium for calculating matching degrees of urban rail transit multi-site associated area development status and potential

Abstract

The application provides a kind of urban rail transit multi-site associated area development status and potential matching degree calculation method, device and storage medium, and the calculation method includes: obtaining urban road network, building, POI and public transport data;Calculate the global network importance of rail transit station, the local clustering coefficient of rail transit station and the transfer level of rail and road public transport station, and construct rail transit station correlation index;The rail station correlation index is standardized and kernel density analysis, and the first data set of rail transit multi-site correlation degree distribution is constructed;Calculate the volume rate, functional mixing degree and road network density, and construct the second data set of built environment aggregation state;According to the first data set and the second data set, the matching degree of development status and potential is calculated.The application can realize the capture of urban rail transit key section dynamic boundary, the evaluation and prediction of development potential, and provide a basis for the delineation of urban renewal or development construction range and design direction.

Description

Technical Field

[0001] This invention relates to the field of urban land use and traffic impact assessment, specifically a method, apparatus, and storage medium for calculating the matching degree between the current development status and potential of multiple urban rail transit station-related areas. Background Technology

[0002] With the continuous construction of urban rail transit, cities are showing a trend of increasing density of rail transit stations and gradually more complex rail networks. The high accessibility brought by the dense and interconnected rail transit network provides passenger flow resources, which is generally reflected in the concentration of urban construction density, functions, and road networks within a certain area, so that urban construction utilization and public transportation resources are matched, thereby improving the overall operational efficiency of the city and its economic and environmental benefits.

[0003] However, due to the disconnect between rail transit and building and road planning and construction, a mismatch in development often occurs. For example, inadequate construction along rail transit lines and around stations in new cities often leads to resource waste and a lack of urban vitality; while insufficient rail transit resources in densely built-up areas of old cities cause people to rely too much on private cars for travel, resulting in traffic congestion and damaging the livability and sustainability of urban development.

[0004] The areas affected by the overlapping of multiple rail transit stations are regions where urban resources and economic and social activities are highly concentrated. Assessing the matching degree between the current development status and potential of these areas is a fundamental and crucial task for exploring intensive and efficient urban utilization and improving urban operational efficiency.

[0005] Currently, the main methods used by domestic and international academic circles to define the impact range of rail transit stations include empirical value method, field survey method and theoretical modeling method. The establishment of evaluation systems for station areas often adopts the "3D" (Density, Diversity, Design) model or its improved model, NP (Node-Place) model or its improved model.

[0006] However, these assessment studies mostly focus on evaluating the development of a station area by statistically analyzing various indicators within a certain radius of the station area. They cannot comprehensively reflect the urban construction situation beyond that radius but still under the influence of rail transit network nodes, and lack a holistic view of the city. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a method for calculating the matching degree between the current development status and potential of multi-station associated areas in urban rail transit, including:

[0008] S1. Obtain urban road network, building, POI and public transportation data, including road network centerline, building floor plan and number of floors, POI points, rail and road public transportation stations and lines.

[0009] S2. Calculate the global network importance of rail transit stations, the local clustering coefficient of rail transit stations, and the transfer level between rail and road public transportation stations. Use the analytic hierarchy process (AHP) to weight the data and construct a rail transit station correlation index.

[0010] S3. Standardize the correlation index of rail stations, perform kernel density analysis on the processing results, extract strongly correlated areas of attribute values ​​in the first threshold interval, and construct the first dataset of the correlation degree distribution of multiple rail transit stations.

[0011] S4. Calculate the plot ratio, functional mix, and road network density. Filter the plot ratio, functional mix, and road network density data to extract urban agglomeration area data and construct a second dataset.

[0012] S5. Based on the first and second data sets, calculate the matching degree between the current development status and potential of the urban rail transit multi-station associated areas.

[0013] Furthermore, in S2, stations in the rail transit system are represented as nodes, and road segments between two adjacent stations are represented as edges.

[0014] The formula for calculating the global network importance of rail transit stations is:

[0015]

[0016] In the formula, GNI(x) represents the global network importance of rail transit stations, and B x For the normalized betweenness centrality b x C x The normalized compact centrality c x A1 and A2 are weighting coefficients.

[0017] B x and C x The calculation formula is:

[0018]

[0019] In the formula b x For betweenness centrality, c x For tight centrality; b min b is the minimum value of the betweenness centrality of all nodes. max It is the maximum value of the betweenness centrality of all nodes; c min It is the minimum value of the tight centrality of all nodes, c max It is the maximum value of the tight centrality of all nodes.

[0020] b x The calculation formula is:

[0021]

[0022] In the formula n yk n is the number of shortest paths from node k to node y. yk (x) represents the number of paths that pass through node x; V is the set of all nodes.

[0023] c x The calculation formula is:

[0024]

[0025] In the formula d x It is the mean of the sum of distances between node x and other nodes m (m = 1 to n) in the network.

[0026] Furthermore, in S2, the formula for calculating the local clustering coefficient of rail transit stations is:

[0027]

[0028] In the formula, CC(x) represents the local clustering coefficient of the rail transit station, and n t N represents the number of triangles that have been formed between node x and its neighboring nodes. t Let x be the number of triangles that can be formed between node x and its adjacent nodes.

[0029] Furthermore, in S2, the formula for calculating the transfer level between rail and road public transport stations is as follows:

[0030] T(x) = A3TW x +A4TN x

[0031] In the formula, T(x) represents the transfer level between rail and road public transportation stations, and TW x To normalize the level of external transportation transfers in the city, TN x A3 represents the level of internal transportation transfers within the city after normalization; A4 and A5 are weighting coefficients.

[0032] TW x The calculation formula is:

[0033]

[0034] In the formula tw x To improve the level of external transportation transfer, d m p represents the number of connecting lines for the m-th mode of external transportation. m This represents the modal share of the m-th mode of external transportation in public transportation trips.

[0035] TN x The calculation formula is:

[0036]

[0037] In the formula tn x For internal transportation transfer level, d n p represents the number of connecting lines for the nth internal transportation mode. n This represents the modal share of the nth internal transportation mode in public transportation trips.

[0038] Furthermore, in S4, the formula for calculating the functional hybridity is:

[0039]

[0040] In the formula, ENT represents the functional mix degree, and P i This represents the percentage of function type i; k is the number of function types.

[0041] Furthermore, in S4, the formula for calculating road network density is:

[0042]

[0043] In the formula, RD is the road network density, and L i Let be the length of the i-th road, in km.

[0044] Furthermore, in S5, the formula for calculating the matching degree is:

[0045]

[0046] In the formula P m For the matching degree, S h The area for high-intensity, moderately developed development, i.e., the overlapping portion of the first and second data sets, S l The area of ​​low-intensity, moderately developed land is defined as the portion of the survey area that is not included in either the first or second dataset; S represents the area of ​​the survey area, in meters (m²). 2 .

[0047] Furthermore, S5 also includes calculating the mismatch between the current development status and potential of urban rail transit multi-station associated areas. The formula for calculating the mismatch is:

[0048]

[0049] In the formula P u S represents the degree of non-match. e The area of ​​overexploitation refers to the portion of the survey area that is included in the second dataset but not in the first dataset; Si The area underdeveloped refers to the portion of the survey area that is included in the first data set but not in the second data set.

[0050] The present invention also provides a computing device, including a processor and a memory, wherein the memory stores executable code, and when the executable code is executed by the processor, the processor performs the method described above.

[0051] The present invention also provides a non-transitory machine-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to perform the method described above.

[0052] This invention utilizes big data technology and network analysis methods to identify interconnected areas across multiple rail transit stations by measuring the service levels of each station and the spatial potential of inter-station connections. Simultaneously, it identifies urban agglomeration areas by measuring the intensity of urban development and construction. A comparison of these two methods allows for an assessment of the match between current development status and potential. This invention not only improves the efficiency and accuracy of identifying and assessing key urban areas but also captures urban development problems and predicts land development potential based on dynamic changes in urban construction, thus promoting sustainable urban development. It is widely adaptable and highly practical.

[0053] This invention constructs a rail transit station correlation index by considering three aspects: the importance of rail transit network nodes, the level of local clustering, and the level of transfers. It identifies multi-station correlated areas with advantageous transportation resources, using a combination of four indicators—high plot ratio, high / medium functional mix, and high / medium road network density—as boundaries to identify agglomeration areas for urban development. These areas are then overlaid with strongly correlated areas, and based on their compatibility, they are categorized into four types: "high-intensity moderate development," "low-intensity moderate development," "overdeveloped," and "underdeveloped," to assess the matching degree between current development status and potential. This invention considers the overlapping effects of multiple adjacent rail transit stations from a holistic urban perspective, providing a relatively effective and intuitive means for quickly and accurately identifying multi-station correlated areas and agglomeration areas within urban rail transit systems. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart of the present invention;

[0056] Figure 2 This is a schematic diagram of the rail transit topology network in an embodiment of the present invention;

[0057] Figure 3 This is a schematic diagram of the visualization grid result of the degree of correlation of multiple rail transit stations in an embodiment of the present invention;

[0058] Figure 4 This is a multi-station associated area map in this embodiment of the invention;

[0059] Figure 5 This is a schematic diagram of a strongly correlated area of ​​multiple rail transit stations in an embodiment of the present invention;

[0060] Figure 6 This is a schematic diagram of the plot ratio calculation results in an embodiment of the present invention;

[0061] Figure 7 This is a schematic diagram of the functional hybridity calculation results in an embodiment of the present invention;

[0062] Figure 8 This is a schematic diagram of the road network density calculation results in an embodiment of the present invention;

[0063] Figure 9 This is a diagram illustrating the method for identifying aggregated built-up areas within a multi-site associated zone in this invention.

[0064] Figure 10 This is a schematic diagram of the aggregated location identification results in an embodiment of the present invention;

[0065] Figure 11 This is a schematic diagram of the overlay results of multi-site strongly correlated areas and aggregated areas in an embodiment of the present invention;

[0066] Figure 12 This is a schematic diagram illustrating the division of high-intensity moderate development in an embodiment of the present invention;

[0067] Figure 13 This is a schematic diagram illustrating the division of low-intensity, moderately developed areas in an embodiment of the present invention;

[0068] Figure 14 This is a schematic diagram illustrating overdevelopment in an embodiment of the present invention;

[0069] Figure 15 This is a schematic diagram illustrating the division of development deficiencies in this embodiment of the invention;

[0070] Figure 16 This is a summary diagram of the matching degree between the current development status and potential in the embodiments of the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] Example 1:

[0073] This embodiment uses a multi-station interconnected area within the 23 wards of Tokyo as an example for illustration, but it is not limited thereto. Those skilled in the art can apply the content of this embodiment to the evaluation of multi-station interconnected areas in other cities.

[0074] This embodiment uses a matching method to calculate the matching degree between the current development status and potential of multi-station associated areas in urban rail transit, such as... Figure 1 As shown, it includes the following steps:

[0075] S1. Obtain shapefile format data related to buildings, POIs, road networks, and rail and road public transportation within the 23 wards of Tokyo in 2023 through websites such as PLATEAU and OpenStreetMap. This includes building plans, building heights and number of floors, various transportation stops, route data, and their related attributes. Store and manage the acquired data in the ArcGIS platform. When data cleaning is required, verify and remove duplicate and unnecessary data.

[0076] S2. Construction and calculation of the correlation index of rail transit stations:

[0077] S2.1. Based on the Tokyo Metropolitan Area Rail Transit operating lines at the end of 2023, a complex rail transit network for the 23 wards of Tokyo was constructed. The segments between adjacent stations are represented as edges, and stations are represented as nodes. For example, when two stations are directly connected by rail lines, there is a connecting edge between these two nodes with a weight of 1, reflecting the connectivity between the stations. There are a total of 401 nodes and 857 edges. Figure 2 As shown.

[0078] S2.2 Calculate the global network importance of each node using the Gephi platform to measure the importance of a certain rail transit station in the urban rail network. It consists of two parts: one is betweenness centrality, which represents the hub role of the rail transit station in the rail network; the other is compact centrality, which represents the accessibility of the rail transit station in the rail network.

[0079] Among them, betweenness centrality (b x The specific calculation formula is as follows:

[0080]

[0081] In the formula, n yk n is the number of shortest paths from node k to node y. yk (x) represents the number of paths that pass through node x.

[0082] Close centrality (c x The calculation formula is:

[0083]

[0084] In the formula, d x This represents the average of the sum of distances between node x and other nodes m (m = 1 to n) in the network.

[0085] Further, betweenness centrality b x and tight centrality c x After normalization, we get B. x C x The weighted sums are calculated in a 1:1 ratio to obtain the global network importance GNI(x) of the rail transit station at node x. The specific calculation formula is as follows:

[0086]

[0087] In the formula, b min b is the minimum value of the betweenness centrality of all nodes. max It is the maximum value of the betweenness centrality of all nodes; c min It is the minimum value of the tight centrality of all nodes, c max It is the maximum value of the tight centrality of all nodes. In other embodiments, other weighting coefficients can be selected according to actual needs.

[0088] S2.3 Calculate the local clustering coefficient of rail transit stations. This coefficient represents the degree of clustering between a given rail transit station and its surrounding stations. The calculation formula is as follows:

[0089]

[0090] In the formula, n t N represents the number of triangles that have been formed between node x and its neighboring nodes; t This represents the number of triangles that node x can form with its neighboring nodes.

[0091] S2.4 Calculating Rail Transit Transfer Level. Considering the differences between Tokyo's rail transit network operating environment and that of mainland China, various types of rail transit lines were categorized and statistically analyzed, mainly including five types: subway, tram, rail, light rail, and monorail. For "rail" as an external transportation line, since only one data point is available, the number of rail connections to each station was normalized to obtain the external transportation transfer level TW. x In other embodiments, the TN in this embodiment can be used. x A similar calculation method is used. "Subway," "tram," "light rail," and "monorail" primarily serve urban transportation. A buffer zone method is employed to count bus stops within a 300m radius of each rail transit station, and the number of bus routes passing through them is counted. Based on their respective modal shares, the internal transport transfer level (tn) can be calculated. x The calculation formula is:

[0092]

[0093] In the formula, d n p represents the number of connecting lines for the nth internal transportation mode. n This represents the modal share of the nth internal transportation mode in public transportation trips.

[0094] Further improve the level of internal transfers within the rail transit system. x After normalization, TN is obtained. x .

[0095] The overall transfer level T(x) of rail transit stations is calculated based on the proportion of intra-city and inter-city passenger traffic in Tokyo. The calculation formula is as follows:

[0096] T(x) = TW x ×A3+TN x ×A4

[0097] In the formula, TW x TN x These represent the level of external and internal transportation transfer in the city after normalization. In this embodiment, A3 and A4 are set to 20% and 80% respectively. In other embodiments, other weighting coefficients can be selected as needed.

[0098] S2.5. The global network importance GNI(x), local clustering coefficient CC(x), and transfer level T(x) obtained above are dimensionless to obtain the standardized matrix of each index. The rail transit station association index CI is then obtained by weighting the matrices using the analytic hierarchy process (AHP). The weight distribution of each index is shown in the table below:

[0099]

[0100] The specific calculation formula is as follows:

[0101]

[0102] In the formula, S i Q represents the value of the i-th traffic indicator in the above three sets of indicators; i This represents the weight of the i-th traffic indicator in the station association index. The magnitude of this index reflects the functional level of each rail transit station in the Tokyo rail transit network and its degree of clustering and association within the network. Of course, appropriate weighting coefficients can be selected in other embodiments.

[0103] S3. Standardize the calculated correlation index of rail stations to a range of 0.5 to 1, and use 800m as the kernel density calculation bandwidth. Perform kernel density analysis on each rail transit station in Tokyo in ArcGIS, with a raster cell size of 100m × 100m, to obtain a visualized raster result of the correlation distribution of multiple rail transit stations in Tokyo, such as... Figure 3 As shown. Further extraction of raster data with attribute values ​​greater than 0.9 and greater than 0.5 but less than or equal to 0.9 is used as strongly correlated and generally correlated areas for multiple rail transit stations. Attribute values ​​less than or equal to 0.5 are considered uncorrelated areas. For example... Figure 4 As shown. This embodiment establishes the first data set in the form of a visual raster chart. In other embodiments, other forms can also be used to establish the first data set. In this embodiment, the first threshold interval is set to the range of attribute values ​​greater than 0.9, and the second threshold interval is set to the range of values ​​greater than 0.5 and less than or equal to 0.9. In other embodiments, corresponding threshold intervals can also be set according to actual needs.

[0104] S4. Identify clustered areas in the built environment:

[0105] S4.1 Within the identified multi-site associated areas, the strongly associated area where Shinjuku Station is located was selected as the survey object. See [link to relevant documentation]. Figure 5 The study area was extended outward by 1500m (approximately 34.55km²). 2 Identify clustered areas of urban development. To avoid the influence of block size on its own data values, that is, to ensure the comparability of data from different areas, and to better assess the matching degree with the multi-station associated areas of rail transit, the "Create Fishing Net" tool in ArcGIS is used to create a grid of the same size, 100m×100m.

[0106] S4.2 Calculate the block floor area ratio. Then, use the "Table Display of Zone Statistics" to assign the calculated block floor area ratio values ​​to the grid using the average method. The specific method for calculating the floor area ratio can be the usual method.

[0107] The POI functional categories provided by OpenStreetMap are reclassified, ultimately dividing city functions into six major categories: residential, commercial, office, educational, cultural and recreational, and hospital. The Shannon entropy is then used to calculate the functional mix degree (ENT). The calculation formula is as follows:

[0108]

[0109] In the formula, P i This represents the percentage of function type i in the grid; k is the number of function types (k≥2).

[0110] Using the "buffer" and "merge separated roads" tools, bi-lane road data obtained from OpenStreetMap is converted into single-lane centerline data, while preserving road class and attributes. Simultaneously, a 3D baseline model is created to supplement the 3D road network data, thereby calculating the road network density RD. The calculation formula is as follows:

[0111]

[0112] In the formula, L i The length of the i-th road within the grid is expressed in km.

[0113] S4.3. The calculated plot ratio, functional mixing ratio, and road network density are divided into three levels (high, medium, and low) using the quantile method. The results are as follows: Figure 6-8 As shown. This embodiment uses a combination of four indicators—high plot ratio, high / medium functional mix, and high / medium road network density—as the basis for defining and identifying aggregated land parcels. (See [link]). Figure 9 The four gray-scale types represent aggregated areas. In other words, "medium / low" plot ratio, "low" functional mix, and "low" road network density all have a "veto power" against being classified as an aggregated area. Based on this definition, the three indicators of the built environment in the Shinjuku area are overlaid to extract a grid of "high plot ratio, high / medium functional mix, and high / medium road network density," as shown in the figure. Figure 10 As shown, the eight rail transit stations—Seibu Shinjuku Station, Nishi Shinjuku Station, Shinjuku West Exit Station, Shinjuku Station, Shinjuku Sanchome Station, Shinjuku Gyoenmae Station, Tochomae Station, and Shinjuku Shinsen Line Station—form a relatively obvious clustered area. This embodiment establishes the second dataset in the form of a grid diagram; other forms of the second dataset can be selected in other embodiments.

[0114] S5. By overlaying and comparing the identified strongly correlated areas and aggregated areas, the matching relationship between the current development status and potential of rail transit multi-station correlated areas is evaluated. (See below) Figure 11 Based on the degree of compatibility between the two, the grid is divided into four categories: high-intensity moderate development, low-intensity moderate development, over-development, and under-development. See [link to relevant documentation]. Figure 12-16 Of the four types, "high-intensity moderate development" and "low-intensity moderate development" represent a balanced state of "current situation-potential," while "overdevelopment" and "underdevelopment" are both mismatched states. The formula for evaluating the "current situation-potential" matching degree is as follows:

[0115]

[0116] In the formula, P m For the matching degree, S h The area for high-intensity, moderately developed development, i.e., the overlapping portion of the first and second data sets, S l The area defined as low-intensity, moderately developed, i.e., the portion within the survey area that is not included in the first dataset and not included in the second dataset; P u S represents the degree of non-match. e The area of ​​overexploitation refers to the portion of the survey area that is included in the second dataset but not in the first dataset; S i The underdeveloped area refers to the portion of the survey area that is included in the first data set but not in the second data set; S represents the area of ​​the survey area, in meters (m). 2 .

[0117] The total area of ​​high-intensity and low-intensity moderately developed grids in the Shinjuku area of ​​Tokyo is 24,210,000 square meters, the area of ​​overdeveloped grids is 1,800,000 square meters, and the area of ​​underdeveloped grids is 8,540,000 square meters. The calculated "current state-potential" matching degree is 0.70, and the mismatch degree is 0.30. Overall, the area has achieved a relatively good balance between its current development status and potential. However, it still needs to renovate and optimize mismatched areas by improving space utilization efficiency or increasing transportation accessibility to achieve intensive and sustainable urban development.

[0118] Example 2:

[0119] This embodiment is a computing device, including a processor and a memory, wherein the memory stores code for executing the methods in the above embodiment.

[0120] The processor can be a multi-core processor or may contain multiple processors. In some embodiments, the processor may include a general-purpose main processor and one or more specialized coprocessors, such as a graphics processing unit (GPU), a digital signal processor (DSP), etc. In some embodiments, the processor may be implemented using custom circuitry, such as an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA).

[0121] Memory can include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM can store static data or instructions required by the processor or other modules of the computer. Permanent storage devices can be read-write storage devices. Permanent storage devices can be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices use high-capacity storage devices (e.g., magnetic or optical disks, flash memory) as permanent storage devices. In other embodiments, permanent storage devices can be removable storage devices (e.g., floppy disks, optical drives). System memory can be a read-write storage device or a volatile read-write storage device, such as dynamic random access memory. System memory can store some or all of the instructions and data required by the processor during operation. Furthermore, memory can include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks can also be used. In some implementations, the memory may include removable storage devices that are readable and / or writable, such as laser discs (CDs), read-only digital versatile optical discs (e.g., DVD-ROMs, dual-layer DVD-ROMs), read-only Blu-ray discs, ultra-high density optical discs, flash memory cards (e.g., SD cards, mini SD cards, Micro-SD cards, etc.), magnetic floppy disks, etc. Computer-readable storage media do not contain carrier waves or transient electronic signals transmitted wirelessly or via wired connections.

[0122] The memory stores executable code, which, when executed by the processor, causes the processor to perform the method described above.

[0123] Example 3:

[0124] This embodiment provides a non-transitory machine-readable storage device that stores executable code. When the executable code is executed by a processor of an electronic device, the processor performs the method described in the above embodiment.

[0125] A non-transitory machine-readable storage device (or computer-readable storage device, or machine-readable memory) storing executable code (or computer program, or computer instruction code) that, when executed by a processor of an electronic device (or computing device, server, etc.), causes the processor to perform the steps of the method described above according to the present invention.

[0126] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein can be implemented as electronic hardware, computer software, or a combination of both.

[0127] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for calculating the matching degree of the development status and potential of a multi-station associated area of urban rail transit, characterized in that, Comprise: S1, acquire urban road network, building, POI and public transportation data, including road network center line, building plane and number of layers, POI point, rail and road public transportation station and line; S2, the road section between adjacent two stations is represented as an edge, and the station is represented as a node, the global network importance of rail transit station, the local clustering coefficient of rail transit station and the transfer level of rail and road public transportation station are calculated, the correlation index of rail transit station is constructed by weighting through analytic hierarchy process; The calculation formula of the transfer level of rail and road public transportation station is: ； In the formula is the level of interchange between rail and road public transport stations, TWx is the normalized level of external traffic interchange in the city, TNx is the normalized level of internal traffic interchange in the city; A3, A4 are weighting coefficients; ； In the formula For the level of external transport interchange, d m The number of connecting lines of the mth external transport mode, p m The share of the mth external transport mode in public transport travel; ； In the formula is the level of inter-transportation transfer, d n represents the number of connecting lines of the nth inter-transportation mode, p n represents the share of the nth inter-transportation mode in public transportation travel; S3, the rail transit station correlation index is standardized, the processing result is kernel density analysis, the strong correlation area with attribute value in the first threshold interval is extracted, and the first data set of rail transit multi-station correlation degree distribution is constructed; S4, calculate the volume rate, functional mixing degree and road network density, extract the urban aggregation section data, and construct the second data set; Specifically, the calculated volume rate, functional mixing degree and road network density are divided into high, medium and low three grades by quantile method, and the combination of "high volume rate, high / medium functional mixing degree and high / medium road network density" is used as the definition and identification of aggregation section, and the grid with "high volume rate, high / medium functional mixing degree and high / medium road network density" is extracted to establish the second data set in the form of grid map; S5, according to the first data set and the second data set, the matching degree of urban rail transit multi-station correlation area development status and potential is calculated; The calculation formula of the matching degree is: ； wherein S is the area of the match, S h S is the area of the high intensity moderate development, i.e. the portion of the survey area that is contained in both the first and second data sets; S is the survey area, in m l S is the area of the low intensity moderate development, i.e. the portion of the survey area that is contained in neither the first nor second data sets; S is the survey area, in m 2 .

2. The method according to claim 1, wherein, In S2, the station in rail transit is represented as a node, and the road section between adjacent two stations is represented as an edge; The calculation formula of the global network importance of rail transit station is: ； In the formula is the global network importance of the rail transit station, is the normalized betweenness centrality b x , is the normalized closeness centrality c x ; A1 and A2 are weighting coefficients; ； ； where b x is betweenness centrality, c is closeness centrality; b min is the minimum of all node betweenness centralities, b max is the maximum of all node betweenness centralities; c min is the minimum of all node closeness centralities, c max is the maximum of all node closeness centralities; ； where n yk is the number of shortest paths from node k to node y; n yk (x) is the number of paths that pass through node x; V is the set of all nodes; ； ； where d x is the average of the sum of distances between node x and other nodes m (m = 1 ~ n) in the network.

3. The method of claim 1, wherein the method is characterized by, In S2, the calculation formula of the local clustering coefficient of rail transit station is: ； In the formula is the local clustering coefficient of the rail transit station, n t is the number of triangles that have been formed between node x and adjacent nodes; N t is the number of triangles that can be formed between node x and adjacent nodes.

4. The method of claim 1, wherein the method is characterized by, In S4, the calculation formula of the functional mixing degree is: ； wherein P is the functional mixing degree, P i denotes the percentage of the functional type i; k is the number of functional types.

5. The method of claim 1, wherein the method is characterized by: In S4, the calculation formula of the road network density is: ； wherein L is the road network density, L i Li is the length of the ith road in km.

6. The method of claim 1, wherein the method is characterized by, In S5, the non-matching degree of urban rail transit multi-station correlation area development status and potential is also calculated, and the calculation formula of the non-matching degree is: ； wherein is the non-match degree, S e is the overdeveloped area, i.e. the part of the survey area which is contained in the second data set and not in the first data set; S i is the underdeveloped area, i.e. the part of the survey area which is contained in the first data set and not in the second data set.

7. A computing device, comprising: Comprise a processor and a memory, the memory has executable code stored thereon, when the executable code is executed by the processor, the processor executes the method as claimed in any one of claims 1-6.

8. A non-transitory machine-readable storage medium, characterized in that, The memory has executable code stored thereon, when the executable code is executed by the processor of the electronic device, the processor executes the method as claimed in any one of claims 1-6.

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