Method and system for evaluating urban rail transit network and land use in station area in coordination

Abstract

This invention provides a method and system for collaborative evaluation of urban agglomeration rail transit networks and station area land use. The method includes: constructing a two-layer network model (regional rail transit network layer and urban rail transit network layer) based on rail transit station data and train operation data, and calculating the topological characteristics of the multi-layer network; establishing a land use evaluation system encompassing three dimensions: land development benefits, land value-added benefits, and land vitality benefits; determining indicator weights through the global Moran index; and calculating the land use level of the station area. After normalizing the network topological characteristics and land use level, a synergy analysis is performed using an affinity propagation clustering algorithm to achieve five synergy level classifications: "high-high," "low-low," "high-low," "low-high," and "balanced development." This invention can provide a scientific basis for TOD planning and station area function optimization of urban agglomeration rail transit stations.

Description

Technical Field

[0001] This invention relates to urban transportation network assessment methods, specifically to a method for the collaborative assessment of urban agglomeration rail transit networks and station area land use. Background Technology

[0002] With the gradual development of metropolitan areas and urban agglomerations, rail transit has become the preferred mode of transportation for residents due to its advantages such as high capacity and punctuality. The gradual improvement of urban agglomeration rail transit can effectively promote regional integration and help guide the development of areas along the lines. In the era of networked rail transit development, scientifically assessing the synergy between the topological characteristics of urban agglomeration rail transit networks and the land use of station areas has significant practical value and strategic importance for optimizing the functional layout of station areas and for TOD (Transit-Oriented Development) planning and design.

[0003] To evaluate the synergy between the topological characteristics of urban agglomeration rail transit networks and the land use of station areas, key technologies are needed. These include: first, constructing urban agglomeration rail transit networks based on their operational status; second, measuring the land use level of station areas from multiple dimensions; and third, quantitatively assessing the degree of synergistic development between the topological characteristics of urban agglomeration rail transit networks and the land use of station areas.

[0004] The existing related technology, "A Method, Device, Medium, and Product for Determining the Integration Degree of Railway Passenger Stations with Urban Areas" (Patent No. CN202410491681.X), mainly uses a "node-place-perception" model to determine the integration degree of railway stations with urban areas. It reflects the node situation in four dimensions: railway service, transportation transfer, road traffic, and slow-moving network; and the place situation in four aspects: functional integration, human activity, land development, and place spirit. However, this method only analyzes railway stations, and the node dimension cannot accurately reflect the importance of stations within the comprehensive urban rail transit network system. Furthermore, existing related technologies for evaluating comprehensive urban rail transit networks focus on network planning evaluation (e.g., "Evaluation Method for Network Planning of Urban Rail Transit Based on Gray Fuzzy Design," Patent Application No. CN202310790677.9), with little research on the evaluation of the synergy between the topological characteristics of urban rail transit networks and land use in station areas. Summary of the Invention

[0005] Purpose of the invention: To address the problem in existing technologies that make it difficult to coordinate the evaluation of the topological characteristics of multi-level rail transit networks and the land use level of station areas within urban agglomerations, the purpose of this invention is to provide a method for the coordinated evaluation of urban agglomeration rail transit networks and station area land use.

[0006] This invention proposes a method for evaluating the synergy between the topological characteristics of urban agglomeration rail transit networks and the land use level of station areas, using a two-dimensional model of "topological characteristics of urban agglomeration rail transit networks - land use level of station areas" in three parts.

[0007] First, this invention constructs a model of urban agglomeration rail transit networks and establishes an evaluation dataset for the topological characteristics of these networks. Utilizing physical network data and train timetable data from multiple levels of rail transit within urban agglomerations, including high-speed rail, conventional rail, intercity rail, suburban rail, subway, and light rail, this invention extracts the physical connection networks of each station and the train operation network to construct a two-tiered regional-urban rail transit network model. It then calculates the topological characteristics of the urban agglomeration rail transit network. By using these topological characteristics to reflect the node value of urban agglomeration rail transit stations, this invention comprehensively considers the interactive effects of different types of rail transit and reflects the attribute differences of urban agglomeration rail transit stations across multiple dimensions, including region, station area, and station level, providing a more refined and comprehensive understanding.

[0008] Secondly, this invention establishes a land use evaluation system for the station area and constructs a land use level dataset. The invention establishes an evaluation index system from three dimensions: land development benefits, land value-added benefits, and land vitality benefits. This system includes nine sub-indicators: land mix, land development intensity, average land transfer price per unit area, average housing price, total land transfer revenue, average nighttime light index, public facility density, enterprise density, and the proportion of park green space. Simultaneously, the global Moran index is used to determine the weights of each indicator. This evaluation scheme possesses strong objectivity and can comprehensively measure the land use development level of the station area.

[0009] Third, this invention constructs a "topological characteristics of urban agglomeration rail transit network - land use level of station areas" model to quantitatively assess the degree of synergy between urban agglomeration rail transit network indicators and station area land use levels. This invention establishes an evaluation model of "topological characteristics of urban agglomeration rail transit network - land use level of station areas," and combines it with an affinity propagation clustering algorithm to analyze the degree of synergy between network indicators and station area land use levels of urban agglomeration rail transit stations. This effectively assesses the current land use status of station areas and provides a basis for subsequent planning layout adjustments and urban design optimization.

[0010] Technical solution: The method for collaborative assessment of urban agglomeration rail transit network and station area land use is characterized by the following steps: (1) Construct a two-layer network model of urban rail transit and calculate the network topology characteristics; (2) Construct a land use level evaluation system for rail transit station areas and calculate the land use level of station areas; (3) Normalize the network topology features and land use level, and evaluate the synergy based on the affinity propagation clustering algorithm to obtain the synergy classification results.

[0011] Further, the method of step S1: (1.1) Obtain data on urban rail transit stations, lines, and train timetables; (1.2) Construct regional rail transit network layer and urban rail transit network layer based on SPACE-L method, and establish cross-layer connections; (1.3) Calculate the multi-level node degree centrality, multi-level strength centrality, multi-level intermediary centrality and multi-level PageRank centrality to obtain the network topology feature dataset.

[0012] Furthermore, in step (1.2): Within the regional rail transit network layer, a virtual variable indicating whether there are connections between nodes, based on operational information, can be represented as: The edge weights within the regional rail transit layer are represented as follows: , Regional rail transit layer node matrix With edge matrix The set is , Within the urban rail transit network layer, based on operational information, the virtual variable indicating whether there are connections between nodes can be represented as: , The edge weights within the rail transit layer are represented as follows: , Urban rail transit layer node matrix With edge matrix The set is , The weight of cross-layer edges is represented as follows: , in, The average walking speed is taken as 6 km / h. For passing stations The cumulative daily average number of rail transit trips across all regions; For passing stations The cumulative daily average number of train trips across all urban rail transit services, and the set of cross-layer connections between the regional rail transit and urban rail transit networks are as follows: .

[0013] Further, step (2) includes the following methods: (2.1) Constructing a land use evaluation index system with three dimensions: land development benefits, land value-added benefits, and land vitality benefits, comprising nine sub-indicators; (2.2) Calculating the sub-indicator values ​​corresponding to each station based on POI data, land transfer data, housing price data, nighttime light data, and green space AOI data; (2.3) Determining the weight of each sub-indicator using the global Moran index and calculating the land use level of the station area. .

[0014] Furthermore, the nine sub-indicators include: land mixing degree Land development intensity Average unit price of land transfer Average house price and total land transfer revenue Average nighttime light index Public facility density Enterprise density and the proportion of park green space .

[0015] Furthermore, step (3) specifically includes: (3.1) network topology characteristics , , , and land use level

[0016] Normalization was performed; (3.2) The normalized data was clustered using the affinity propagation clustering algorithm; (3.3) Based on the clustering results and the scatter plot distribution of station network values ​​and land use level values ​​in the "Urban Agglomeration Rail Transit Network Topology Characteristics - Station Area Land Use Level" model, the synergy was divided into five categories: high network level and high land use level, balanced development, low network level and low land use level, high network level and low land use level, and low network level and high land use level.

[0017] Furthermore, the normalization processing method: .

[0018] Furthermore, the method also includes: visualizing the collaborative classification results in a two-dimensional coordinate system of land use level of the urban agglomeration rail transit network topology feature station area.

[0019] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the method described thereon.

[0020] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method as claimed in claim 1.

[0021] Compared with the prior art, the present invention has the following beneficial effects: 1) A two-layer network model of urban agglomeration rail transit based on actual operational conditions was constructed, and the topological characteristics of the urban agglomeration rail transit network were obtained, including multi-layer node degree centrality, multi-layer strength centrality, multi-layer betweenness centrality, and multi-layer PageRank centrality. Traditional rail transit station evaluation systems require information at the regional, station area, and station levels, such as station location and station grade, and cannot comprehensively consider the interactive effects of different types of rail transit. The urban agglomeration rail transit network topological characteristic indicators adopted in this invention, based on considering the interactive effects of different types of rail transit, reflect the attribute differences of urban agglomeration rail transit stations in multiple dimensions such as region, station area, and station, making them more refined and comprehensive.

[0022] 2) A comprehensive land use evaluation system for rail transit station areas was proposed. Nine sub-indicators from three dimensions—land development benefits, land value-added benefits, and land vitality benefits—objectively and comprehensively reflect the level of land use development in the station area.

[0023] 3) A model of "topological characteristics of urban agglomeration rail transit network - land use level of station area" was constructed. Combined with the affinity propagation clustering algorithm, urban agglomeration rail transit stations were clustered to effectively optimize the accuracy of the synergy evaluation results. Attached Figure Description

[0024] Figure 1 This is a flowchart of the present invention; Figure 2 This is a model for "topological characteristics of urban agglomeration rail transit network - land use level of station area". Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be further described below.

[0026] Step 1: Construct a model of the urban agglomeration rail transit network and establish an evaluation dataset for the topological features of the urban agglomeration rail transit network.

[0027] 1) Obtain basic data on urban rail transit stations and lines, and convert it into geographic information using the Baidu Maps API. Represent the station number, station longitude, station latitude, station type, line to which the station belongs, and city where the station is located as follows: , in Indicates the station number; and Indicates the latitude and longitude of the station; This indicates the station type, including regional rail transit station R, urban rail transit station M, and multimodal transport station RM. Indicates the line where the station is located; This indicates the city where the station is located. Here, n is the total number of stations, and m is the total number of bus routes.

[0028] 2) Train timetable data is obtained from the official 12306 website, which includes train numbers and station sequence information, and can be represented as... ,in This indicates the average number of trains passing through this station per day. This indicates the station's stop order for this train service. This indicates the train's operating speed. According to... The data allows us to obtain the train speeds and average daily train frequency between different stations, which can be expressed as: .

[0029] 3) The urban agglomeration rail transit network is divided into two layers: regional rail transit network and urban rail transit network. The SPACE-L method is selected for modeling each layer, and each rail transit station is regarded as a node. There are edges between two adjacent stations on the same line.

[0030] Within the regional rail transit network layer, a virtual variable indicating whether there are connections between nodes, based on operational information, can be represented as: , Meanwhile, the edge weights within the regional rail transit layer can be expressed as: , Regional rail transit layer node matrix With edge matrix The set is .

[0031] Within the urban rail transit network layer, based on operational information, the virtual variable indicating whether there are connections between nodes can be represented as: , Meanwhile, the inner edge weights of the urban rail transit layer can be expressed as: , Urban rail transit layer node matrix With edge matrix The set is .

[0032] Based on operational information, the weight of the cross-layer connection between the regional rail transit layer and the urban rail transit layer can be expressed as: , in, The average walking speed is taken as 6 km / h. For passing stations The cumulative daily average number of rail transit trips across all regions; For passing stations The cumulative daily average number of train trips for all urban rail transit services. The set of cross-layer connections in the regional rail transit-urban rail transit network is... .

[0033] Define the spatiotemporal topology model of urban agglomeration rail transit network based on operational conditions as follows: ,in These represent the adjacency matrices of the regional rail transit layer and the urban rail transit layer, respectively, as well as the set of cross-layer connections between the regional and urban rail transit layers.

[0034] 4) The collected dataset is used to model the urban agglomeration rail transit network using the R package MuxViz, and the topological characteristics of the urban agglomeration rail transit network based on operational information are calculated, including the following four indicators: a) Multi-level node degree centrality: The number of sites directly connected to a site, reflecting the importance of the node.

[0035] b) Multi-level strength centrality: Considers the number of sites directly connected to a site and the weight of connections between sites, reflecting the importance of nodes.

[0036] c) Multilevel betweenness centrality: measures the frequency with which a site appears in all shortest paths, reflecting the site’s importance in the network.

[0037] d) Multi-level PageRank centrality: Considers the connections between sites in the network and reflects the influence of a site in the entire network.

[0038] 5) Represent the site number, site longitude, site latitude, site multi-level node degree centrality, site multi-level strength centrality, site multi-level intermediary centrality, and site multi-level PageRank centrality as follows: , in This represents the node value dataset of the site. This indicates the multi-level node degree centrality of a site; This indicates the multi-layered strength centrality of the site; This indicates the multi-layered centrality of a site; This indicates the multi-level PageRank centrality of the site.

[0039] Step 2: Define the land use level evaluation system for urban agglomeration rail transit station areas and establish a land use level dataset.

[0040] 1) Establish evaluation indicators for land use level in urban agglomeration rail transit station areas, wherein the land development benefits include land mixing degree. Land development intensity Two sub-indicators; the land appreciation benefit includes the average unit price of land transfer. Average house price and total land transfer revenue Three sub-indicators; Land vitality benefits include the average nighttime light index. Public facility density Enterprise density and the proportion of park green space Four sub-indicators.

[0041] 2) Obtain data for each sub-indicator at each site and calculate the sub-indicator scores for each site.

[0042] Extract Points of Interest (POIs) within a 3km radius of rail transit stations, according to the formula.

[0043] , , Calculate the land use mix ratio, public facility density, and enterprise density in the station area separately. Indicates the number of POI categories; Indicates the area within the station. The number of POIs; Indicates the number of Public Facilities Points (POIs); This indicates the number of company / enterprise POIs; The number of all POIs within the station area; A This refers to the area of ​​the station area.

[0044] Based on the land market network, acquire land parcels available for sale within a 3km radius of rail transit stations, according to the formula... , , , Calculate the land development intensity, average land transfer price per unit area, and total land transfer revenue within the station area. Among these... Indicates land parcel The plot ratio; Indicates the area of ​​land supplied for transfer; The transaction price for land transfer.

[0045] Based on Lianjia.com, we obtained secondhand housing transaction data within a 3km radius of rail transit stations and calculated the average housing price within the station area. .

[0046] Based on nighttime light data from the National Oceanic and Atmospheric Administration (NOAA), the average nighttime light index was calculated within a 3km radius of rail transit stations. .

[0047] Based on Baidu Maps, AOI data of parks and green spaces within a 3km radius of rail transit stations were obtained, and the area of ​​parks and green spaces was calculated to obtain the proportion of parks and green spaces. .

[0048] The dataset for land use level assessment of urban agglomeration rail transit stations can be represented as: , 3) Transfer the dataset Import into ArcGIS and calculate the global Moran index values ​​for each indicator. The spatial weight matrix is ​​chosen as the inverse distance spatial matrix. The results are shown in the table. The weights of each indicator can be expressed as follows: ,in b .

[0049]

[0050] 4) According to the formula Calculate the land use level indicators of urban rail transit stations. Among them, For the land use level of rail transit stations, These are the various indicator values ​​for the station. The station number and land use level of the station area are expressed as: .in This represents the location value dataset for a site.

[0051] Step 3: Import the dataset into the "Urban Agglomeration Rail Transit Network Topology Characteristics - Station Area Land Use Level" model to evaluate the degree of synergy between urban agglomeration rail transit network indicators and land use.

[0052] 1) According to the formula Topological characteristics of urban agglomeration rail transit networks , , , With the level of land use in the station area After normalization, we get , , , , .

[0053] 2) Based on the Sklearn package in Python, four standardized urban agglomeration rail transit network indicators were developed for different types of urban agglomeration rail transit stations (multimodal transport stations RM, regional rail transit stations R, and urban rail transit stations M). , , , Land use level indicators Perform affinity propagation clustering analysis. The affinity propagation algorithm can automatically generate the optimal number of clusters without manual setting.

[0054] 3) Analyze the node value data of the site. , , , Land use level data They are respectively placed into the rectangular coordinate system of the "Urban Agglomeration Rail Transit Network Topology Characteristics - Station Area Land Use Level" model.

[0055] 4) Combining the clustering results with the scatter plot distribution of station network values ​​and land use level values ​​in the "Urban Agglomeration Rail Transit Network Topology Characteristics - Station Area Land Use Level" model, the synergy between urban agglomeration rail transit network indicators and land use can be divided into five levels, including high network level - high land use level, balanced development, low network level - low land use level, high network level - low land use level, and low network level - high land use level.

[0056] The above are merely preferred embodiments of the present invention and do not constitute any limitation on the present invention. Any equivalent substitutions or modifications made by those skilled in the art to the technical solutions and content disclosed in the present invention without departing from the scope of the present invention shall be deemed to have remained within the protection scope of the present invention.

Claims

1. A method for collaborative assessment of urban agglomeration rail transit networks and station area land use, characterized in that, Includes the following steps: (1) Construct a two-layer network model of urban rail transit and calculate the network topology characteristics; (2) Construct a land use level evaluation system for rail transit station areas and calculate the land use level of station areas; (3) Normalize the network topology features and land use level, and evaluate the synergy based on the affinity propagation clustering algorithm to obtain the synergy classification results.

2. The method according to claim 1, characterized in that, The method of step S1: (1.1) Obtain data on urban rail transit stations, lines, and train timetables; (1.2) Construct regional rail transit network layer and urban rail transit network layer based on SPACE-L method, and establish cross-layer connections; (1.3) Calculate the multi-level node degree centrality, multi-level strength centrality, multi-level intermediary centrality and multi-level PageRank centrality to obtain the network topology feature dataset.

3. The method according to claim 2, characterized in that, In step (1.2): Within the regional rail transit network layer, a virtual variable indicating whether there are connections between nodes, based on operational information, can be represented as: The edge weights within the regional rail transit layer are represented as follows: ， Regional rail transit layer node matrix With edge matrix The set is , Within the urban rail transit network layer, based on operational information, the virtual variable indicating whether there are connections between nodes can be represented as: ， The edge weights within the urban rail transit layer are represented as follows: ， Urban rail transit layer node matrix With edge matrix The set is , The weight of cross-layer edges is represented as follows: ， in, The average walking speed is taken as 6 km / h. For passing stations The cumulative daily average number of rail transit trips across all regions; For passing stations The cumulative daily average number of train trips across all urban rail transit services, and the set of cross-layer connections between the regional rail transit and urban rail transit networks are as follows: .

4. The method according to claim 3, characterized in that, Step (2) includes the following methods: (2.1) Construct a land use evaluation index system that includes three dimensions: land development benefits, land value-added benefits, and land vitality benefits, comprising a total of nine sub-indicators; (2.2) Based on POI data, land transfer data, housing price data, nighttime light data and green space AOI data, calculate the sub-indicator values ​​corresponding to each station; (2.3) Use the global Moran index to determine the weight of each sub-indicator and calculate the land use level of the station area. .

5. As described in claim 3, characterized in that, The nine sub-indicators include: land mixing degree Land development intensity Average unit price of land transfer Average house price and total land transfer revenue Average nighttime light index Public facility density Enterprise density and the proportion of park green space .

6. As described in claim 1, characterized in that, Step (3) specifically includes: (3.1) Network topology characteristics , , , and land use level Perform normalization processing; (3.2) Cluster the normalized data using the affinity propagation clustering algorithm; (3.3) Based on the clustering results and the scatter plot distribution of station network values ​​and land use level values ​​in the urban agglomeration rail transit network topology characteristics-station area land use level model, the station synergy is divided into five categories: high network level and high land use level, balanced development, low network level and low land use level, high network level and low land use level, and low network level and high land use level.

7. The method according to claim 6, characterized in that, The normalization method: 。 8. The method according to claim 1, characterized in that, The method further includes: visualizing the collaborative classification results in a two-dimensional coordinate system of land use level of urban agglomeration rail transit network topology feature station areas.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-8.

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