A multi-scale cross-region based comprehensive traffic accessibility measurement method

Abstract

The application discloses a kind of based on multi-scale cross-region comprehensive traffic accessibility measure method.S1.boundary of research area is drawn;S2.relevant data of research area are obtained;S3.cross-region boundary traffic transport channel information is obtained, relevant data are combined with investigation, and each node comprehensive level evaluation factor is designed, and each node comprehensive development level index is obtained;S4.road and railway route length are obtained, and the road network density of each region is calculated;S5.OD matrix is generated with each regional central city as starting point and end point, and the travel time between OD pairs of comprehensive traffic transport mode is calculated;S6.gravity model is constructed and corrected;S7.comprehensive accessibility index is calculated according to the corrected gravity model, combined with each item of trade data index.The practice verification finds that the application is more accurate in calculating the accessibility level between different regions by comprehensively considering various weight indexes under the condition of meeting the calculation accuracy, which provides a new method for realizing national regional strategy evaluation.

Description

Technical Field

[0001] This invention belongs to the fields of land spatial planning, urban planning, and transportation planning, and specifically relates to a multi-scale cross-regional comprehensive transportation accessibility measurement method. Background Technology

[0002] Urban agglomerations represent a mature stage of urban development, primarily undertaking two major tasks: new urbanization and coordinated regional economic development. In the context of integrated and coordinated development within urban agglomerations, studying the relationship between the strength of economic ties and accessibility between cities is of great significance.

[0003] Geographic Information System (GIS) is a computer system that, with the support of computer hardware and software resources, collects, manages, analyzes, and displays geographic attribute data of things or phenomena on the Earth's surface. GIS software provides a professional network analysis module to perform network analysis functions, including those for transportation networks and infrastructure networks. Network analysis requires comprehensive information or data such as spatial data, attribute data, and topological relationships of elements to analyze and calculate the overall spatial, quantitative, and qualitative characteristics of the network, meeting the needs of professional research and analysis such as resource allocation, facility site selection, and optimal path analysis.

[0004] Accessibility assessment of transportation networks is a specific application of the optimal route analysis principle. Measuring the "accessibility" of a regional transportation network involves calculating the path with the least resistance (travel time or actual travel distance) between two nodes (or origin and destination). Theoretically, the sum of the shortest path resistances (time or distance) between point A and all other nodes within the region represents the accessibility value of point A. Current technologies often employ gravity models, improved gravity models, weighted average travel times, and other methods, combined with the network analysis capabilities of GIS technology, to measure the relationship between accessibility and economic connectivity strength within urban agglomerations. However, in studies of urban agglomeration connectivity strength and accessibility, these methods often lack a comprehensive consideration of economic and travel connectivity strength, and the research on the relationship between connectivity strength and accessibility is not in-depth enough, resulting in generally less accurate accessibility assessment results. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-scale, cross-regional integrated transportation accessibility measurement method to address the problem that previous urban integrated transportation evaluations did not adequately consider regional economic integration.

[0006] The technical solution of this invention is: a comprehensive transportation accessibility measurement method based on multi-scale cross-regional travel, comprising the following steps:

[0007] S1. Draw the boundaries of the plots based on the digital topographic map and image map of the study area;

[0008] S2. Obtain data for the study area, including: road network vector data, traffic volume, regional urban node vector data, GDP, population, and commercial data;

[0009] S3. Based on the open map, obtain regional information and cross-regional transportation corridor information data, design comprehensive level evaluation factors for each node, and calculate the comprehensive development level indicators for each node;

[0010] S4. Calculate the road network density of each region based on the length of highways and railways;

[0011] S5. Generate OD pairs by taking the central cities of each region as the starting and ending points, construct the OD matrix, and use the time accessibility model to calculate the travel time between OD pairs of integrated transportation modes;

[0012] S6. Based on the travel time of S5, the road network density of S4, and the comprehensive development level of S3 nodes, a modified gravity model is constructed.

[0013] S7. Based on the gravity model constructed in step S6, and combined with various data factors, calculate the comprehensive accessibility index. In the aforementioned multi-scale cross-regional comprehensive transportation accessibility measurement method, step S3 is as follows:

[0014] S3.1. Select urban development indicators for each region through data acquisition channels such as statistical yearbooks;

[0015] S3.2. Organize the data obtained in step S3.1, and use the factor analysis module in SPSS to perform KMO and Bartlett's test of sphericity on the selected indicators.

[0016] S3.3. Based on the calculation results in step S3.2, analyze the results to determine whether the selected data indicators are suitable for factor analysis. Remove unsuitable indicators and recalculate the KMO and Bartlett sphericity tests on the proposed comprehensive indicators.

[0017] S3.4. After determining the final number of extracted factors and the correspondence between items in step S3.3, name the extracted factors based on the results of the rotated factor loading matrix;

[0018] S3.5. Calculate the comprehensive strength score G of each region using the variance contribution rate of the four common factors extracted in S3.4 as weights, and then perform negativeing ​​processing.

[0019] In the aforementioned multi-scale cross-regional integrated transportation accessibility measurement method, step S3.3 is as follows:

[0020] S3.3.1. First, use the results of the KMO test and Bartlett's test to make a judgment. If the KMO value is greater than 0.6, it means that the analysis is suitable, otherwise it means that the analysis is not suitable. The corresponding P value of the Bartlett test is less than 0.05, which means that the analysis is suitable, otherwise it means that the analysis is not suitable.

[0021] S3.3.2. If the results of factor KMO and Bartlett analysis in S3.3.1 fail, remove the indicators and re-analyze until all analysis items have a good correspondence with the factors;

[0022] In the aforementioned multi-scale cross-regional integrated transportation accessibility measurement method, step S3.5 is specifically as follows:

[0023] S3.5.1. Multiply the collected data from each region by the scores of each common factor in the component score coefficient table to obtain the scores of the four common factors G1, G2, G3, and G4. The expressions for G1, G2, G3, and G4 are as follows:

[0024]

[0025] Among them, G i X represents the score of the i-th common factor. k G represents the k-th data item from each region after collection and organization. i Indicates the score coefficient of component i;

[0026] S3.5.2. Based on the variance contribution rates of the four factors and G1, G2, G3, and G4 calculated in S3.5.1, the comprehensive strength score G of each region is calculated. The formula for calculating G is as follows:

[0027]

[0028] Among them, Y i (i = 1, 2, 3, 4) represents the variance percentage of the four components, and R represents the cumulative percentage of the last term in the sum of squares of the rotational loads;

[0029] S3.5.3 Since the final calculated overall strength score may contain negative values, to facilitate subsequent calculations, the score is denegated without changing the original ranking order. The formula is as follows:

[0030]

[0031] In the formula, G′ is the score after denegation, and Y... min Y is the minimum score among the original scores. max The maximum score among the original scores.

[0032] In the aforementioned multi-scale cross-regional integrated transportation accessibility measurement method, step S4 is as follows:

[0033] S4.1. The vector data obtained from the National Bureau of Surveying and Mapping's standard map service website and OMS website are placed into ArcGIS software. The road lengths of the preprocessed road data are measured and exported.

[0034] S4.2. Calculate the road network length data and area after measurement in S4.1 using the formula: Road network density (km / km2) = Road network length (km) / Area (km2).

[0035] In the aforementioned multi-scale cross-regional integrated transportation accessibility measurement method, step S5 is as follows:

[0036] S5.1. Set road attributes for the road network data processed in S4.1, and add a travel time attribute. Different travel times should be set for roads of different grades (the time can be set according to the "Technical Standard for Highway Engineering" (JTGB01-2014), "Design Specification for High-Speed ​​Railway" (TB10621-2014), and "Design Specification for Class III and IV Railways" (GB50012-2012).

[0037] S5.2. After completing S5.1, construct the network dataset and set attributes such as intersection turning, connectivity, elevation modeling, and toll cost in sequence. Note that when setting the toll cost for the network, select Drivetime as the default field. The constructed traffic network should include both nodes and edges.

[0038] S5.3. Use ArcGIS software to construct the OD cost matrix, set all nodes as the starting point and destination point, and solve for the cost.

[0039] S5.4. Input the solved cost matrix results and time distance data attributes into the accessibility model to calculate the accessibility value of each region.

[0040] In the aforementioned multi-scale cross-regional integrated transportation accessibility measurement method, step S5.4 is specifically as follows:

[0041] S5.4.1. In step S5.4, the reachability value of the target node is defined as the average shortest travel time from study area A to study area B.

[0042] S5.4.2. The method for calculating the reachability value selected in this invention is as follows:

[0043]

[0044] Where: tij T represents the shortest time required to travel from node i to node j, where n represents the number of nodes in the region. i This represents the reachability value of node i.

[0045] In the aforementioned multi-scale cross-regional integrated transportation accessibility measurement method, step S6 is as follows:

[0046] The formula for calculating the gravitational model mentioned in step S6 is as follows:

[0047]

[0048] In the formula: A i M is the comprehensive reachability index of node i; i T is the influencing factor of road network density. ij G represents the reachability value between node i and node j. i β is the comprehensive development level index of node j; β is the impedance coefficient; n is the total number of nodes.

[0049] In summary, the present invention employs the aforementioned comprehensive transportation evaluation method for urban agglomerations, which has the following advantages:

[0050] 1. The concept of comprehensive connectivity intensity of urban agglomerations is proposed. It studies the connectivity intensity between urban agglomerations from two aspects: travel connectivity intensity and economic connectivity intensity. Compared with the previous study that only focused on travel connectivity intensity or economic connectivity intensity, comprehensive connectivity intensity can more comprehensively characterize the mutual connections between cities within an urban agglomeration.

[0051] 2. When evaluating the comprehensive transportation system of an urban agglomeration, in addition to considering the accessibility of comprehensive transportation, the coupling relationship between accessibility and the intensity of comprehensive connectivity should also be considered. This is conducive to promoting the coordinated development of comprehensive transportation and regional economy, and realizing the high-quality development of the urban agglomeration. Attached Figure Description

[0052] Figure 1 A flowchart illustrating the multi-scale cross-regional integrated transportation accessibility calculation method provided by this invention;

[0053] Figure 2 A schematic diagram illustrating the process of calculating the comprehensive development level of each node;

[0054] Figure 3 Build a flowchart for the road network between nodes.

Claims

1. A comprehensive transportation accessibility measurement method based on multi-scale cross-regional transport, characterized in that, Includes the following steps: S1. Determine the boundaries of the regional land parcels based on the digital topographic map and image map of the study area; S2. Obtain data for the study area, including: road network vector data, traffic volume data, regional urban node vector data, GDP data, population data, and commercial data; S3. Obtain regional information and cross-regional transportation corridor information, organize the data of each regional node, construct the comprehensive level evaluation factor of each node, and calculate the comprehensive development level index of each node; Step S3 is as follows: S3.

1. Select urban development indicators for each region and organize the acquired data; S3.

2. Use the factor analysis module in SPSS to perform KMO and Bartlett's test of sphericity on the indicators selected in S3.1; S3.

3. Based on the calculation results in step S3.2, determine whether the selected data indicators are suitable for factor analysis, remove unsuitable indicators, and recalculate the KMO and Bartlett sphericity tests on the comprehensive indicators after removal. S3.

4. Name the extracted factors based on the final number of factors and the correspondence between items determined in step S3.3, combined with the result of the rotated factor loading matrix. S3.

5. Using the variance contribution rate of the common factors extracted in S3.4 as weights, calculate the comprehensive strength score G of each region and perform denegation processing. The specific process of step S3.3 is as follows: S3.3.

1. First, use the results of the KMO test and Bartlett's test to determine whether the collected variables are suitable for factor analysis. If the KMO value is greater than 0.6, it indicates that the analysis is suitable; otherwise, it indicates that the analysis is not suitable. If the corresponding P value of the Bartlett test is less than 0.05, it indicates that the analysis is suitable; otherwise, it indicates that the analysis is not suitable. S3.3.

2. If the results of factor KMO and Bartlett analysis in S3.3.1 fail, remove the indicators and re-analyze until all analysis items have a good correspondence with the factors; Step S3.5 is as follows: S3.5.

1. Multiply the collected and organized study area data by the scores of each common factor in the component score coefficient table to obtain the score of each common factor. The expression for the common factor score is as follows: in, Indicates the first Common factor score, This indicates the number of regions after collection and organization. Item data, Indicates ingredients Score coefficient; S3.5.

2. Based on the variance contribution rate of each common factor and the common factor scores calculated in S3.5.1, the comprehensive strength scores of each region are calculated. Perform calculations, The calculation formula is as follows: in, , This represents the percentage of variance for the four components. This represents the cumulative percentage of the last term in the sum of squares of the rotational loads; S3.5.

3. Since the final calculated overall strength score may contain negative values, to facilitate subsequent calculations, the score is denegated without changing the original ranking order. The formula is as follows: in the formula The score after denegation. The minimum score among the original scores. The maximum score among the original scores; S4. Determine the area of ​​the region based on the length of highways and railways, and calculate the road network density of each region; S5. Generate OD pairs by taking the central cities of each region as the starting and ending points, construct the OD matrix, and use the time accessibility model to calculate the time accessibility value between OD pairs of integrated transportation modes; S6. Based on the travel time of S5, the road network density of S4, and the comprehensive development level index of each node of S3, a modified gravity model is constructed; The revised gravity model is constructed as follows: In the formula: For nodes The comprehensive accessibility index; For nodes Factors affecting road network density; For nodes and nodes The reachability value between them; For nodes The comprehensive development level indicators; The impedance coefficient can be calibrated through regression analysis based on the actual traffic characteristics of the study area. The total number of nodes; S7. Based on the gravity model constructed in step S6, and combined with various data factors, calculate the comprehensive accessibility index.

2. The method for measuring comprehensive transportation accessibility across regions based on multi-scale scales as described in claim 1, characterized in that, Step S4 is as follows: S4.

1. Import the vector data obtained from the National Bureau of Surveying and Mapping's standard map service website and OSM website into ArcGIS software, measure the road length of the preprocessed road data, and export it. S4.

2. Calculate the road network length data and area after measurement in S4.1 using the formula: Road network density = Road network length / Area.

3. The method for measuring comprehensive transportation accessibility across regions based on multi-scale scales as described in claim 1, characterized in that, Step S5 is as follows: S5.

1. Set road attributes for the road network data processed in S4.1, and add a travel time attribute. Different travel times should be set for different road levels, based on the statutory design speeds corresponding to different road levels in the current national and industry road design specifications. S5.

2. After completing S5.1, construct the network dataset and set the intersection turning, connectivity, elevation modeling, and toll cost attributes in sequence. When setting the toll cost for the network, select Drivetime as the default field. The constructed traffic network should include two types of elements: nodes and edges. S5.

3. Use ArcGIS software to construct the OD cost matrix, set all nodes as the starting point and destination point, and solve for the cost. S5.

4. Input the solved cost matrix results and time distance data attributes into the accessibility model to calculate the accessibility value of each region.

4. The method for measuring comprehensive transportation accessibility across regions based on multi-scale scales as described in claim 3, characterized in that, Step S5.4 is as follows: S5.4.

1. The time reachability value of a target node is defined as the average shortest travel time from that node to all other nodes within the study area; S5.4.

2. The method for calculating the time reachability value selected in this invention is as follows: in: Represents a node To the node The shortest time required Represents the total number of nodes. Represents a node Time availability value.

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