Matching degree calculation method and device for urban rail transit multi-station associated district development status and potential and storage medium

Abstract

The invention provides a matching degree calculation method and device for the development status and potential of urban rail transit multi-station associated districts and a storage medium. The calculation method comprises the following steps: acquiring urban road network, building, POI and public traffic data; calculating the global network importance of the rail transit stations, the local clustering coefficient of the rail transit stations and the transfer level of the rail and the road public transport stations, and constructing a rail transit station association index; performing standardization processing and kernel density analysis on the rail station association index, and constructing a first data set of rail transit multi-station association degree distribution; calculating a plot ratio, a function mixing degree and a road network density, and constructing a second data set of an environment aggregation state; and according to the first data set and the second data set, calculating the matching degree of the development current situation and the potential. According to the method, capturing of the dynamic boundary of the key section of the urban rail transit and evaluation and prediction of the development potential can be realized, and a basis is provided for delimiting and designing a city updating or development and construction range.

Description

Technical Field

[0001] The present invention relates to the field of urban land use and traffic impact assessment, and specifically to a matching degree calculation method, device and storage medium for the development status and potential of multi-station associated areas of urban rail transit. Background Art

[0002] With the continuous construction of urban rail transit, cities are showing a trend of increasing density of rail transit stations and increasingly complex lines. The high accessibility brought by the densely interwoven rail transit network provides human resources, which is generally reflected in the concentration of urban construction density, functions, and road networks in a certain area, so that urban construction utilization and public transportation resources are in a state of adaptation, thereby improving the overall operation efficiency and economic and environmental benefits of the city.

[0003] However, due to the disconnection between rail transit and building and road planning and construction, the two often have a mismatched development. For example, the lack of construction along the rail transit lines and around the stations in new cities often leads to a waste of resources and a loss of urban vitality; while the lack of rail transit resources in the high-density construction areas of old cities will make people overly dependent on cars, causing traffic congestion and undermining the livability and sustainable development of the city.

[0004] The associated areas affected by the superposition of multiple rail transit stations are regions with a high concentration of urban resources and economic and social activities. Assessing the matching degree between the development status and potential in these areas is a fundamental and key task in exploring intensive and efficient use of cities and improving urban operation efficiency.

[0005] At present, the main methods used by domestic and foreign academic circles to define the impact range of rail transit stations include empirical value method, field investigation method and theoretical modeling method. The establishment of the station area evaluation system mostly 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 the station area through statistical analysis of various indicator data in a certain radius circle of the station area. They cannot fully reflect the urban construction situation beyond this circle but still under the influence of the rail transit network node association, and lack urban integrity. Summary of the invention

[0007] In order to solve the problems existing in the above-mentioned prior art, the present invention provides a method for calculating the matching degree of development status and potential of multi-station associated areas of urban rail transit, including:

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

[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, and construct a rail transit station association index by weighting them through the hierarchical analysis method.

[0010] S3. Standardize the rail station correlation index, perform kernel density analysis on the processing results, extract the strongly correlated areas whose attribute values ​​are within the first threshold interval, and construct the first data set of the distribution of the correlation degree of rail transit multi-stations.

[0011] S4. Calculate the floor area ratio, functional mixture and road network density, screen the data on the floor area ratio, functional mixture and road network density, extract urban aggregation area data, and construct a second data set.

[0012] S5. Calculate the matching degree between the development status and potential of the urban rail transit multi-station associated area based on the first data set and the second data set.

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

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

[0015]

[0016] Where GNI(x) is the global network importance of rail transit stations, B x is the normalized betweenness centrality b x , C x is the normalized close centrality c x ; A 1 , A 2 is the weighting coefficient.

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

[0018]

[0019] Where b x is the betweenness centrality, c x is the close centrality; b min is the minimum value of the betweenness centrality of all nodes, b max is the maximum value of the betweenness centrality of all nodes; c min is the minimum value of the closeness centrality of all nodes, c maxIt is the maximum value of the closeness centrality of all nodes.

[0020] b x The calculation formula is:

[0021]

[0022] 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.

[0023] c x The calculation formula is:

[0024]

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

[0026] Furthermore, in S2, the calculation formula of the local clustering coefficient of the rail transit station is:

[0027]

[0028] Where CC(x) is the local clustering coefficient of the rail transit station, n t N is the number of triangles formed between node x and its adjacent nodes; t is the number of triangles that can be formed between node x and its adjacent nodes.

[0029] Furthermore, in S2, the calculation formula for the transfer level between rail and road public transportation stations is:

[0030] T(x)=A 3 TW x +A 4 TN x

[0031] Where T(x) is the transfer level between rail and road public transportation stations, TW x is the level of external transportation transfer in the city after normalization, TN x A is the level of internal traffic transfer in the city after normalization; 3 , A 4 is the weighting coefficient.

[0032] TW x The calculation formula is:

[0033]

[0034] Where tw xis the external traffic transfer level, d m represents the number of connecting routes for the mth external transportation mode, p m It represents the share of the mth external transportation mode in public transportation trips.

[0035] TN x The calculation formula is:

[0036]

[0037] Where tn x is the internal traffic transfer level, d n represents the number of connecting routes for the nth internal transportation mode, p n It represents the share of the nth internal transportation mode in public transportation trips.

[0038] Furthermore, in S4, the calculation formula for functional mixing is:

[0039]

[0040] Where ENT is the functional mixing degree, P i represents the percentage of functional type i; k is the number of functional types.

[0041] Furthermore, in S4, the calculation formula of road network density is:

[0042]

[0043] Where RD is the road network density, L i is the length of the ith road, in km.

[0044] Furthermore, in S5, the calculation formula of the matching degree is:

[0045]

[0046] Where P m is the matching degree, S h The area with high intensity and moderate development is the overlapped part of the first and second data sets. l is the area of ​​low-intensity moderate development, that is, the part of the survey scope that is not included in the first data set and the second data set; S is the area of ​​the survey scope, unit m 2 .

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

[0048]

[0049] Where P u is the mismatch degree, S e S is the overdeveloped area, that is, the part of the survey area that is included in the second data set but not in the first data set; i It is the underdeveloped area, that is, the part of the survey area 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 codes, and when the executable codes are executed by the processor, the processor executes the method as described above.

[0051] The present invention also provides a non-transitory machine-readable storage medium having executable codes stored thereon. When the executable codes are executed by a processor of an electronic device, the processor is caused to execute the method as described above.

[0052] The present invention utilizes big data technology, network analysis methods, etc., on the one hand, by measuring the service level of each station in the urban rail transit network and the spatial potential of the association between stations, to form an identification of the rail transit multi-station association area, and on the other hand, by measuring the intensity of urban development and construction, to form an identification of urban aggregation areas, and the comparison of the two realizes the matching degree evaluation of development status and potential. The present invention not only improves the efficiency and accuracy of urban key area identification and evaluation, but also can capture urban development problems and predict land development potential based on the dynamic changes of urban construction, which is conducive to the sustainable development of cities, with wide adaptability and strong practicality.

[0053] The present invention constructs a rail transit station association index from three aspects: the importance of rail transit network nodes, the level of local clustering, and the level of transfer, identifies multi-station association areas with advantages in transportation resource endowment, and uses the four types of indicator combinations of "high floor area ratio, high / medium functional mixing, and high / medium road network density" in the multi-station association area as a definition to identify the aggregation areas of urban construction. Then it is superimposed with the strongly associated area, and the areas are divided into four categories of "high-intensity moderate development, low-intensity moderate development, overdevelopment, and underdevelopment" according to the degree of adaptation between the two, so as to achieve a matching evaluation of the development status and potential. The present invention considers the mutual superposition of the influence ranges of multiple stations adjacent to the rail transit with an urban holistic mindset, and provides a relatively effective and intuitive means for quickly and accurately identifying information such as multi-station association areas and aggregation areas of urban rail transit. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 is a flow chart of the present invention;

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

[0057] Figure 3 1 is a schematic diagram of a visualization grid result of the correlation degree distribution of multiple rail transit stations in an embodiment of the present invention;

[0058] Figure 4 It is a circle map of rail transit multi-station associated areas in an embodiment of the present invention;

[0059] Figure 5 This is a schematic diagram of a rail transit multi-station strongly associated area in an embodiment of the present invention;

[0060] Figure 6 is a schematic diagram of the calculation result of the volume ratio in an embodiment of the present invention;

[0061] Figure 7 is a schematic diagram of the calculation result of the functional mixing degree in an embodiment of the present invention;

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

[0063] Fig. 9 It is a diagram of a method for identifying built environment aggregation areas in a multi-site associated area in an embodiment of the present invention;

[0064] Fig.10 is a schematic diagram of the result of identifying aggregated sections in an embodiment of the present invention;

[0065] Fig.11 This is a schematic diagram of the superposition results of multi-site strongly associated areas and aggregated sections in an embodiment of the present invention;

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

[0067] Fig.13 Schematic diagram of the division of low-intensity moderate development in an embodiment of the present invention;

[0068] Fig.14 is a schematic diagram of overdevelopment division in an embodiment of the present invention;

[0069] Fig.15 It is a schematic diagram of the division of underdeveloped embodiments of the present invention;

[0070] Fig.16 It is a summary diagram of the matching type classification of the development status and potential in the embodiment of the present invention. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] Embodiment 1:

[0073] This embodiment is described by taking the rail transit multi-station associated area within the 23 wards of Tokyo as an example, but is not limited to this. Those skilled in the art can apply the content of this embodiment to the evaluation of rail transit multi-station associated areas in other cities.

[0074] The matching degree calculation method for the development status and potential of the multi-station associated area of ​​urban rail transit in this embodiment is as follows: Figure 1 As shown, the following steps are included:

[0075] S1. Obtain the shapefile data related to buildings, POI points, road networks, rail and road public transportation in the 23 wards of Tokyo in 2023 through the PLATEAU website and OpenStreetMap website, including building plan, building height and number of floors, various transportation stations, line data and related attributes, etc. The acquired data will be stored and managed in the ArcGIS platform. When data cleaning is required, check and clean duplicate and unnecessary data information.

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

[0077] S2.1. Based on the Tokyo Metro operating lines by the end of 2023, a complex network of Tokyo 23 wards rail transit was constructed. The road sections between two adjacent stations are represented by edges, and the stations are represented by nodes. For example, when there is a directly connected rail line between two stations, there is an edge between the two nodes and the edge weight is assigned to 1, which reflects the connection relationship between the stations. A total of 401 nodes and 857 edges, such as Figure 2 shown.

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

[0080]

[0081] Where n yk is the number of shortest paths from node k to node y; n yk (x) represents the number of paths passing through node x.

[0082] Closeness centrality (c x ) is calculated as:

[0083]

[0084] Where, d x It represents the mean of the sum of the distances between node x and other nodes m (m=1~n) in the network.

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

[0086]

[0087] Where b min is the minimum value of the betweenness centrality of all nodes, b max is the maximum value of the betweenness centrality of all nodes; c min is the minimum value of the closeness centrality of all nodes, c max is the maximum value of the close centrality of all nodes. In other embodiments, other weight coefficients may also be selected according to actual needs.

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

[0089]

[0090] Where n tRepresents the number of triangles formed between node x and its adjacent nodes; N t Represents the number of possible triangles between node x and its adjacent nodes.

[0091] S2.4. Calculate the level of rail transit transfer. Considering the differences between the operating environment of Tokyo's rail transit network and that of China, various types of rail transit lines are classified, counted and sorted out, mainly including five types of rail transit: subway, tram, rail, lightrail and monorail. Among them, "rail" is an external transportation line. Since there is only one data, the number of rails connected to the station is directly normalized to obtain the external transportation transfer level TW x In other embodiments, TN in this embodiment may be used. x Similar calculation method. "Subway", "tram", "light rail" and "monorail" mainly serve urban transportation. The buffer method is used to count the bus stops within 300m of each rail transit station, and the number of bus lines passing through is counted. The internal transportation transfer level tn can be calculated based on their respective share rates. x , the calculation formula is:

[0092]

[0093] Where, d n represents the number of connecting routes for the nth internal transportation mode, p n It represents the share of the nth internal transportation mode in public transportation trips.

[0094] Further improve the internal transfer level of rail transit x After normalization, we get TN x .

[0095] The overall transfer level T(x) of rail transit stations is calculated by the proportion of internal and external passenger flows in Tokyo. The calculation formula is:

[0096] T(x)=TW x ×A 3 +TN x ×A 4

[0097] Where TW x TN x They represent the external and internal traffic transfer levels in the city after normalization. In this embodiment, A 3 , A 4 They are set to 20% and 80% respectively. In other embodiments, other weighting coefficients may also 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 respectively to obtain the standardized matrix of each indicator, and the rail transit station association index CI is obtained by weighting through the hierarchical analysis method. The weight distribution of each indicator is shown in the following table:

[0099]

[0100] The specific calculation formula is:

[0101]

[0102] In the formula, S i represents the i-th traffic index value in the above three groups of indicators; Q i represents the calculation weight of the ith traffic index in the station association index. The size of this index can reflect the energy level of the rail transit stations in the Tokyo rail transit network and their degree of aggregation association in the network. Of course, in other embodiments, a suitable weight coefficient can also be selected.

[0103] S3. Standardize the calculation results of the rail station correlation index to the range of 0.5 to 1, and use 800m as the kernel density calculation bandwidth. Perform kernel density analysis on Tokyo rail transit stations in ArcGIS, with a grid unit of 100m×100m, and obtain a visual grid result of the correlation degree distribution of Tokyo rail transit multi-stations, as shown in the figure below: Figure 3 As shown in Figure 2, the grid data with attribute values ​​greater than 0.9 and greater than 0.5 and less than or equal to 0.9 are further extracted as the rail transit multi-station strong correlation area and general correlation area, and the attribute value is less than or equal to 0.5 as the non-correlated area, such as Figure 4 As shown. In this embodiment, the first data set is established in the form of a visual grid map. In other embodiments, the first data set can also be established in other forms. In this embodiment, the first threshold interval is set to a range where the attribute value is greater than 0.9, and the second threshold interval is set to a range where the attribute value is greater than 0.5 and less than or equal to 0.9. In other embodiments, the corresponding threshold interval can also be set according to actual needs.

[0104] S4. Identify clusters of locations in the built environment:

[0105] S4.1. In the identified multi-station associated areas, the strongly associated area where Shinjuku Station is located is selected as the survey object, see Figure 5 The research area was extended by 1500m (approximately 34.55km 2) Identify the aggregation areas of urban construction. In order to avoid the influence of block size on its own data value, that is, to ensure the comparability of data in different regions, and to better evaluate the matching degree with the multi-station associated areas of rail transit, the "Create Fishnet" tool was used in ArcGIS to establish a grid of the same size of 100m×100m.

[0106] S4.2. Calculate the block plot ratio. Use the "Table Display Zoning Statistics" to assign the calculated block plot ratio value to the grid in the form of an average. The specific method for calculating the plot ratio can be the usual method.

[0107] The POI function categories provided by OpenStreetMap are reclassified, and finally the city functions are divided into six categories: residential, commercial, office, education, cultural and sports leisure, and hospital. The Shannon entropy is used to calculate the function mixing degree ENT. The calculation formula is:

[0108]

[0109] Where P i It represents the percentage of functional type i in the grid; k is the number of functional types (k≥2).

[0110] Using the "buffer" and "merge separate roads" tools, the double-line road data obtained from OpenStreetMap is converted into single-line centerline data, and the road level and attributes are retained. At the same time, the three-dimensional base surface is modeled and the three-dimensional road network data is supplemented to calculate the road network density RD. The calculation formula is:

[0111]

[0112] Where, L i is the length of the i-th road in the grid, in km.

[0113] S4.3, the calculated three indicators of volume ratio, mixed function and road network density are divided into three levels: high, medium and low by quantile method. The results are as follows Figure 6-8 This example uses the combination of four indicators of "high volume ratio, high / medium functional mixing, and high / medium road network density" as the definition and identification of the aggregation area. Fig. 9 , the four types indicated in gray are clustered areas. In other words, "medium / low" plot ratio, "low" functional mixing, and "low" road network density all have a "veto power" to be considered as clustered areas. Based on this definition, the three indicators of the built environment of the Shinjuku area are superimposed to extract the grid of "high plot ratio, high / medium functional mixing, and high / medium road network density". The results are as follows Fig.10As shown. It can be seen that the eight rail transit stations, namely, Seibu Shinjuku Station, Nishi Shinjuku Station, Shinjuku West Exit Station, Shinjuku Station, Shinjuku Sanchome Station, Shinjuku Gyoenmae Station, Tochomae Station, and Shinsen Shinjuku Station, form a relatively obvious sheet aggregation area. In this embodiment, the second data set is established in the form of a grid map, and other forms of the second data set may also be selected in other embodiments.

[0114] S5. By comparing the identified strongly associated areas with the clustered areas, the matching relationship between the development status and potential of the rail transit multi-station associated areas is evaluated, see Fig.11 According to the degree of fit between the two, the grids are divided into four categories: high intensity moderate development, low intensity moderate development, overdevelopment and underdevelopment, see Figure 12-16 Among the four types, "high-intensity moderate development" and "low-intensity moderate development" represent the matching equilibrium state of "current situation-potential", while "overdevelopment" and "underdevelopment" are both non-matching states. The matching degree of "current situation-potential" is evaluated and the calculation formula is as follows:

[0115]

[0116] Where P m is the matching degree, S h The area with high intensity and moderate development is the overlapped part of the first and second data sets. l The area is a low-intensity moderately developed area, that is, the part within the survey scope that is not included in the first data set and the second data set; u is the mismatch degree, S e S is the overdeveloped area, that is, the part of the survey area that is included in the second data set but not in the first data set; i is the underdeveloped area, that is, the part of the survey scope included in the first data set but not included in the second data set; S is the survey scope area, unit: m 2 .

[0117] The total area of ​​high-intensity and low-intensity moderate development grids in Tokyo Shinjuku area is 24,210,000 square meters, the area of ​​overdevelopment grid is 1,800,000 square meters, and the area of ​​underdevelopment grid is 8,540,000 square meters. It can be calculated that the "current situation-potential" matching degree is 0.70 and the mismatch degree is 0.30. Overall, the area has achieved a good balance between the development status and potential, but it still needs to update and optimize the mismatched areas by improving space utilization efficiency or increasing traffic accessibility to achieve intensive and sustainable development of the city.

[0118] Embodiment 2:

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

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

[0121] The memory may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage. Among them, ROM can store static data or instructions required by the processor or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory may 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 may also be used. In some embodiments, the memory may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (e.g., DVD-ROM, double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. The computer-readable storage medium does not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0122] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is enabled to execute the above method.

[0123] Embodiment 3:

[0124] This embodiment provides a non-transitory machine-readable memory having executable codes stored thereon. When the executable codes are executed by a processor of an electronic device, the processor is caused to execute the method in the above embodiment.

[0125] A non-temporary machine-readable memory (or computer-readable memory, or machine-readable memory) having executable code (or computer program, or computer instruction code) stored thereon, which, when executed by a processor of an electronic device (or computing device, server, etc.), enables the processor to perform the various steps of the above-mentioned method according to the present invention.

[0126] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or a combination of both.

[0127] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A matching degree calculation method for the development status and potential of multi-station associated areas of urban rail transit, characterized in that: include: S1. Obtain urban road network, building, POI and public transportation data, including road network centerline, building plane and number of floors, POI points, rail and road public transportation stations and routes; 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, and construct a rail transit station association index by weighting them through the hierarchical analysis method; S3, standardizing the rail station correlation index, performing kernel density analysis on the processing result, extracting strongly correlated areas with attribute values ​​within the first threshold interval, and constructing a first data set of the distribution of correlation degrees of rail transit multi-stations; S4, calculating the volume ratio, functional mixing degree and road network density, extracting urban aggregation area data, and constructing a second data set; S5. Calculate the matching degree between the development status and potential of the urban rail transit multi-station associated area based on the first data set and the second data set.

2. The matching degree calculation method for the development status and potential of multi-station associated areas of urban rail transit according to claim 1 is characterized in that: In S2, rail transit stations are represented as nodes, and the sections between two adjacent stations are represented as edges; The calculation formula for the global network importance of rail transit stations is: Where GNI(x) is the global network importance of rail transit stations, B x is the normalized betweenness centrality b x , C x is the normalized close centrality c x ; A1 and A2 are weighting coefficients; Where b x is the betweenness centrality, c x is the close centrality; b min is the minimum value of the betweenness centrality of all nodes, b max is the maximum value of the betweenness centrality of all nodes; c min is the minimum value of the closeness centrality of all nodes, c max is the maximum value of the closeness centrality of all nodes; Where n yk is the number of shortest paths from node k to node y; n yk (x) is the number of paths passing through node x; V is the set of all nodes; Where d x It is the mean of the sum of the distances between node x and other nodes m (m=1~n) in the network.

3. The matching degree calculation method for the development status and potential of multi-station associated areas of urban rail transit according to claim 1 is characterized in that: In S2, the calculation formula of the local clustering coefficient of rail transit stations is: Where CC(x) is the local clustering coefficient of the rail transit station, n t N is the number of triangles formed between node x and its adjacent nodes; t is the number of triangles that can be formed between node x and its adjacent nodes.

4. The matching degree calculation method for the development status and potential of multi-station associated areas of urban rail transit according to claim 1 is characterized in that: In S2, the calculation formula for the transfer level between rail and road public transportation stations is: T(x)=A3TW x +A4TN x Where T(x) is the transfer level between rail and road public transportation stations, TW x is the level of external transportation transfer in the city after normalization, TN x is the level of internal traffic transfer in the city after normalization; A3 and A4 are weighting coefficients; Where tw x is the external traffic transfer level, d m represents the number of connecting routes for the mth external transportation mode, p m represents the share of the mth external transportation mode in public transportation trips; Where tn x is the internal traffic transfer level, d n represents the number of connecting routes for the nth internal transportation mode, p n It represents the share of the nth internal transportation mode in public transportation trips.

5. The matching degree calculation method for the development status and potential of multi-station associated areas of urban rail transit according to claim 1 is characterized in that: In S4, the calculation formula for functional mixing is: Where ENT is the functional mixing degree, P i represents the percentage of functional type i; k is the number of functional types.

6. The matching degree calculation method for the development status and potential of multi-station associated areas of urban rail transit according to claim 1 is characterized in that: In S4, the calculation formula for road network density is: Where RD is the road network density, L i is the length of the ith road, in km.

7. The matching degree calculation method for the development status and potential of multi-station associated areas of urban rail transit according to claim 1 is characterized in that: In S5, the calculation formula for the matching degree is: Where P m is the matching degree, S h The area with high intensity and moderate development is the overlapped part of the first and second data sets. l is the area of ​​low-intensity moderate development, that is, the part of the survey scope that is not included in the first data set and the second data set; S is the area of ​​the survey scope, unit m 2 .

8. The matching degree calculation method for the development status and potential of multi-station associated areas of urban rail transit according to claim 1 is characterized in that: S5 also includes calculating the mismatch between the current status and potential of the development of the urban rail transit multi-station associated area. The mismatch calculation formula is: Where P u is the mismatch degree, S e S is the overdeveloped area, that is, the part of the survey area that is included in the second data set but not in the first data set; i It is the underdeveloped area, that is, the part of the survey area included in the first data set but not in the second data set.

9. A computing device, characterized in that: The method comprises a processor and a memory, wherein the memory stores executable codes, and when the executable codes are executed by the processor, the processor executes the method according to any one of claims 1 to 8.

10. A non-transitory machine-readable storage medium, characterized in that: An executable code is stored thereon, and when the executable code is executed by a processor of an electronic device, the processor is caused to execute any one of the methods described in claims 1-8.

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