Urban circle three-dimensional traffic integrated toughness quantitative analysis method

Abstract

The invention discloses an urban circle three-dimensional traffic integrated toughness quantitative analysis method, solves the problem of low three-dimensional traffic integrated toughness analysis accuracy of an existing three-dimensional traffic network structure research method, and belongs to the field of traffic planning. The method comprises the following steps: dividing network layers according to transportation modes in an urban circle, wherein each transportation mode corresponds to one transportation layer; important regions in the urban circle are selected as three-dimensional traffic network nodes; connecting the selected nodes in the transportation layers to form a node incidence matrix in the transportation layers, and constructing traffic topological structures of different transportation layers; converting the urban circle three-dimensional super network model into a multi-layer urban circle three-dimensional traffic network topological structure according to different transportation layers, node incidence matrixes among the transportation layers and interlayer connecting edges; selecting a structure index, and performing toughness quantification on the multi-layer urban circle three-dimensional traffic network topology structure; and analyzing the toughness of the urban circle three-dimensional traffic network according to the toughness quantification result and the survivability attack strategy.

Description

Technical Field

[0001] The present invention relates to a method for quantitatively analyzing the resilience of urban agglomeration three-dimensional transportation integration, belonging to the field of transportation planning. Background Art

[0002] With the continuous acceleration of urban agglomeration, the economic level has been continuously improved. The non-agriculturalization of the agricultural population, the expansion of the urban population scale, and the outward expansion of urban land have become increasingly obvious. It has become an irresistible trend that multiple cities develop concentratedly to form an urban agglomeration. Although the current transportation industry is in the golden stage of booming development, in this brand-new field of urban agglomeration, it is impossible to know whether it can meet all the transportation needs of the urban spatial structure. Therefore, in order to further explore the development of urban agglomeration transportation, the overall planning of the three-dimensional transportation integration of urban agglomerations should be carried out. By investigating the current development status of the urban agglomeration transportation system, evaluating the development of urban agglomeration transportation integration from the network resilience level, it provides a theoretical basis for promoting the coordinated development of urban agglomeration transportation economy.

[0003] Currently, the research on the transportation network system mainly focuses on the calculation of traffic accessibility, ignoring the comprehensive and overall macroscopic analysis of the transportation system. Summary of the Invention

[0004] Aiming at the problems of unsystematic research methods, incomplete research indicators, and low accuracy of resilience analysis of three-dimensional transportation integration in the existing three-dimensional transportation network structure, the present invention provides a method for quantitatively analyzing the resilience of urban agglomeration three-dimensional transportation integration.

[0005] A method for quantitatively analyzing the resilience of urban agglomeration three-dimensional transportation integration of the present invention includes:

[0006] Step 1: Divide the network layer according to the transportation modes in the urban agglomeration, and each transportation mode corresponds to a transportation layer;

[0007] Step 2: Select important regions within the urban agglomeration as the nodes of the three-dimensional transportation network;

[0008] Step 3: In the transportation layer of Step 1, connect the nodes selected in Step 2 to form a node incidence matrix in the transportation layer, and construct the traffic topological structure of different transportation layers;

[0009] Step 4: According to different transportation layers, the node incidence matrix between each transportation layer, and the inter-layer connecting edges, convert the urban agglomeration three-dimensional super network model into a multi-layer urban agglomeration three-dimensional transportation network topological structure;

[0010] Step 5: Select structural indicators to quantitatively analyze the resilience of the multi-layer urban agglomeration three-dimensional transportation network topological structure;

[0011] Step 6: Analyze the resilience of the urban agglomeration three-dimensional transportation network according to the resilience quantitative analysis results and the anti-destruction attack strategy.

[0012] Preferably, step 3 includes:

[0013] Define X α as the set of nodes in transport layer α, and E α as the set of internal connecting edges in transport layer α. Then, the traffic topology structure matrix of transport layer α is G α =(X α , E α ). The set of transport layers is g = {G α ; α ∈ {1, …, M}}, and there are M transport layers in total;

[0014] where is the i-th node in transport layer α, and N α is the total number of nodes in transport layer α. The node incidence matrix in transport layer α is:

[0015]

[0016] where (1 ≤ i, j ≤ N α and 1 ≤ α ≤ M), is the connection status of nodes i and j in transport layer α. If nodes i and j in transport layer α are connected, then otherwise, it is is the j-th node in transport layer β, and E αβ represents the set of inter-layer connecting edges between transport layer α and transport layer β;

[0017] The traffic topology structures of different transport layers are composed of each transport layer, the node incidence matrix in each transport layer, and the connecting edges.

[0018] Preferably, in step 4, the node incidence matrix b αi between each transport layer is:

[0019]

[0020] where represents node i in transport layer α;

[0021] The set of connecting edges of each transport layer node is:

[0022] C = {E αβ ∈ X α × X β ; α, β ∈ {1, …, M}, α ≠ β};.

[0023] where E αβ is the set of inter-layer connecting edges between transport layer α and transport layer β.

[0024] Preferably, the structural indicators selected in step 5 include the average degree, network diameter, network efficiency, and clustering coefficient of the multi-layer urban agglomeration three-dimensional transportation network topology structure.

[0025] Preferably, the degree of node i in different transportation layers is:

[0026]

[0027] Among them, represents the degree of node i in the α-th layer, n is the total number of nodes in the α-th layer;

[0028] Average degree is:

[0029]

[0030] Among them, N is the total number of nodes in the multi-layer urban agglomeration three-dimensional transportation network topology structure;

[0031] The network diameter L(M) is:

[0032]

[0033] The network efficiency is:

[0034]

[0035] Set the nodes in the transportation layer as the starting vertex u, and the intra-layer node adjacency matrix as the initial distance matrix from the starting vertex u to other nodes v; compare the distance value from the starting vertex u directly to other nodes v and the distance value from the starting vertex through the third node to other nodes v, retain the smaller distance value, and iteratively update the distance matrix to generate the shortest path d between nodes u and v by the Dijkstra algorithm uv ; X M represents the nodes in the M transportation layers;

[0036] Clustering coefficient C M (i) is:

[0037]

[0038] Among them, N α (i) is the intersection of all adjacent nodes of node i and the adjacent nodes existing in the α-th layer, that is, the ideal number of edges;

[0039] is the actual number of edges of node i in the α-th layer.

[0040] Preferably, step 6 includes:

[0041] Quantify the importance of nodes and sort them in ascending order according to the numerical values of the importance indicators, indicating that the degree of importance increases from low to high;

[0042] The importance indicator is degree centrality, and the degree centrality of node i is:

[0043]

[0044] The process of analyzing the resilience of the urban agglomeration's three-dimensional transportation network includes:

[0045] According to the network invulnerability attack strategy, sequentially delete the nodes with the highest degree of importance in the multi-layer urban agglomeration's three-dimensional transportation network topology structure, calculate the structural indicators of the multi-layer urban agglomeration's three-dimensional transportation network topology structure after deleting the nodes, and quantify the resilience of the multi-layer urban agglomeration's three-dimensional transportation network topology structure;

[0046] Continuously repeat the above process until all nodes in the multi-layer urban agglomeration's three-dimensional transportation network topology are deleted.

[0047] Preferably, calculate the clustering coefficient and network efficiency of the multi-layer urban agglomeration's three-dimensional transportation network topology structure after deleting the nodes.

[0048] The beneficial effects of the present invention are as follows: Based on the natural attributes of comprehensive transportation, the present invention determines the transportation layer structure from five aspects: the road layer, the railway layer, the aviation layer, the water transportation layer, and the pipeline layer; at the same time, in combination with administrative divisions, traffic network nodes are selected to construct a super network model of the urban agglomeration's three-dimensional transportation, and it is transformed into a multi-layer urban agglomeration's three-dimensional transportation network topology structure to visualize the integrated network of the urban agglomeration's three-dimensional transportation; then, macroscopically quantify the topological structure indicators, such as data on the average degree, network diameter, clustering coefficient, and network efficiency, etc., to analyze the current situation of the integrated three-dimensional transportation; finally, remove the nodes of the urban agglomeration's three-dimensional transportation network in sequence according to the node degree strategy, and combine with the Networkx algorithm to iteratively calculate the network clustering coefficient and network efficiency, conduct an invulnerability test on the load capacity of the transportation system, and then conduct a resilience analysis on the integrated urban agglomeration's three-dimensional transportation. Brief Description of the Drawings

[0049] Figure 1 It is the flowchart of the method of the present invention;

[0050] Figure 2 It is the super network model of the urban agglomeration's three-dimensional transportation;

[0051] Figure 3 It is the multi-layer urban agglomeration's three-dimensional transportation network topology structure;

[0052] Figure 4 It is the symbiotic axis of the Harbin urban agglomeration;

[0053] Figure 5 It is the transportation road network of the Harbin urban agglomeration;

[0054] Figure 6 is the traffic topological structure of the urban expressway layer;

[0055] Figure 7 is the traffic topological structure of the highway layer;

[0056] Figure 8 is the traffic topological structure of the railway layer;

[0057] Figure 9 is the traffic topological structure of the water transportation layer;

[0058] Figure 10 is the three-dimensional traffic super network model of the Harbin Metropolitan Area;

[0059] Figure 11 is the three-dimensional traffic multi-layer topological structure of the Harbin Metropolitan Area;

[0060] Figure 12 is the anti-destruction analysis of the three-dimensional traffic network of the Harbin Metropolitan Area. Detailed implementation manners

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0063] Next, the present invention will be further described in conjunction with the accompanying drawings and specific embodiments, but it is not a limitation of the present invention.

[0064] The method for quantitatively analyzing the integration resilience of the three-dimensional traffic in the metropolitan area of this embodiment includes the following steps:

[0065] Step 1: Divide the network layer according to the transportation modes in the metropolitan area, and each transportation mode corresponds to a transportation layer;

[0066] Refers to the current existing five comprehensive transportation modes, including air transportation, road transportation, waterway transportation, pipeline transportation, and railway transportation. The network layer can be divided into 5 dimensions such as the road layer, railway layer, air layer, water transportation layer, and pipeline network according to the comprehensive transportation modes.

[0067] Develop the integration of the three-dimensional transportation network, among which the main framework of the three-dimensional transportation is particularly important. According to the development requirements and the characteristics of the spatial organizational structure of the Harbin Metropolitan Area, six symbiotic axes in it ("Harbin-Daqing-Qiqihar-Mudanjiang", "Beijing-Harbin-Harbin-Suihua", "Harbin-Wuchang", "Tongjiang-Sanya", "Harbin-Luobei" and the symbiotic axis along the Songhua River) are taken as the research directions to analyze the traffic network structure of the Harbin Metropolitan Area, as shown in Figure 4 .

[0068] First of all, before constructing the multi-layer complex network structure, it is necessary to clarify the existing traffic operation conditions in the Harbin Metropolitan Area. The research finds that the current passenger and freight transportation modes in the Harbin Metropolitan Area are road transportation, railway transportation, water transportation and air transportation respectively.

[0069] Then, since the traffic development in the metropolitan area needs to analyze the closeness of the connection between the central city and the surrounding cities, as well as the rationality of the traffic infrastructure within a single city, road transportation is mainly split into two parts: intercity transportation and urban road transportation for analysis. At the same time, the urban roads mainly consist of urban expressways to construct the transportation layer, and the intercity transportation mainly consists of expressways to construct the transportation layer.

[0070] Then, the railway operation tracks in the Harbin Metropolitan Area usually accommodate both bullet trains and high-speed trains. Therefore, the railway transportation is not subdivided and is taken as the railway layer alone.

[0071] Then, the Harbin water transportation port is a seasonal production port and is the largest waterway transshipment hub port in the inland rivers of Northeast China. Therefore, the secondary water systems within the Harbin Metropolitan Area are selected to form the water transportation layer.

[0072] Finally, since there is only one airport in the Harbin Metropolitan Area, namely Taiping International Airport, the air routes cannot meet the traffic exchanges within the metropolitan area, and there is no information on pipeline transportation recorded in the "Harbin Statistical Yearbook". Therefore, no air network layer and pipeline network layer structures are set up separately. Thus, the division of the three-dimensional transportation layer of the entire Harbin Metropolitan Area is completed, and the traffic road network of the Harbin Metropolitan Area is shown in Figure 5 .

[0073] Step 2: Select important regions within the metropolitan area as the nodes of the three-dimensional transportation network;

[0074] Select the traffic map of the entire Heilongjiang Province to construct the three-dimensional transportation network of the metropolitan area. Use the QGIS software as the data collection platform to extract the location information and traffic lines of the jurisdiction of the Harbin Metropolitan Area. Take the centroids of 13 counties and districts, namely the center of Harbin, Shuangcheng District, Acheng District, Shangzhi City, Wuchang City, Bin County, and Zhaodong City, as the nodes of the network structure. The node numbers and corresponding regions are shown in Table 1. The expressways or highways connecting two nodes are the edges, and different transportation modes are the edge sets of different inter-layer connections.

[0075] Table 1 Location Corresponding Numbers

[0076]

[0077] Step 3: In the transport layer of Step 1, connect the nodes selected in Step 2 to form a node association matrix in the transport layer and construct the traffic topology structure of different transport layers;

[0078] Define X α as the set of nodes in layer α, and E α as the set of intra-layer connected edges in layer α. Then, the traffic topology structure matrix of transport layer α is G α =(X α , E α ). The transport layer set is g = {G α ; α ∈ {1, …, M}}, with a total of M layers.

[0079] Construct a node association matrix according to the connection conditions of each node in different transport layers:

[0080] The set of nodes in layer G α is where is the i-th node in transport layer α, and N α is the total number of nodes in transport layer α. is the connection condition between the i-th and j-th nodes in transport layer α:

[0081]

[0082] If the i-th and j-th nodes in layer α are connected, Conversely, it is

[0083] Then, the node association matrix of transport layer α is:

[0084]

[0085] The traffic topology structures of different transport layers are composed of each transport layer, the node association matrix in each transport layer, and the connected edges.

[0086] According to the description in Step 1, the set of the three-dimensional traffic integration transport layer of the Harbin Metropolitan Area is g = {urban expressway layer, highway layer, railway layer, waterway layer}, and the node adjacency matrices of each transport layer are as follows:

[0087] Node adjacency matrix of the urban expressway layer:

[0088]

[0089] Node adjacency matrix of the highway layer:

[0090]

[0091] Adjacency matrix of railway layer nodes:

[0092]

[0093] Adjacency matrix of water transportation layer nodes:

[0094]

[0095] Construct the traffic topological structures of different transportation layers based on the node correlation matrices and inter-layer connecting edges between different transportation layers and between each transportation layer, as shown in Figures 6 to 9 .

[0096] Step 4: Transform the urban agglomeration three-dimensional super network model into a multi-layer urban agglomeration three-dimensional traffic network topological structure according to different transportation layers, the node correlation matrices between each transportation layer, and the inter-layer connecting edges;

[0097] When defining a single-layer network as G=(X,E), the superstructure between its networks is S=(X,E,H), where H is the set of inter-layer connecting edges of a single layer, that is, the connecting edges of the same nodes in different transportation layers. The set of node connecting edges of each transportation layer is C={E αβ ∈X α ×X β ; α,β∈{1,…,M}, α≠β}, where E αβ is the set of inter-layer connecting edges between transportation layer α and transportation layer β, and the multi-transportation layer adjacency matrix is where

[0098]

[0099] is the connection situation between node i in transportation layer α and node j in transportation layer β: if nodes i and j are connected, Conversely, it is

[0100] Due to the basic attributes of the traffic system, the inter-layer connecting edges of different transportation layers are the connection situations of the same nodes in different transportation layers: assuming that any transportation layer has the same node, it is considered that there are inter-layer connecting edges in the above transportation layer.

[0101] To represent the connection situations of the same nodes in each transportation layer, construct the node correlation matrix b αi , that is

[0102]

[0103] where b αi is the existence situation of node i in transportation layer α: if node i in layer α exists, then b αi =1, conversely, b αi= 0. Based on the node correlation matrix between transportation layers, the urban agglomeration three-dimensional super network model can be obtained, as shown in Figure 2 .

[0104] According to the three-dimensional transportation layers of different urban agglomerations, the node correlation matrix between each transportation layer, and the inter-layer connecting edges, the urban agglomeration three-dimensional super network model is transformed into a multi-layer urban agglomeration three-dimensional traffic network topological structure.

[0105] Due to the basic attributes of the traffic system, the inter-layer connecting edges of different transportation layers are the connection conditions of the same node in different transportation layers: assuming that any transportation layer has the same node, it is considered that there are inter-layer connecting edges in the above-mentioned transportation layers. According to the traffic topological structures of different transportation layers, a three-dimensional traffic super network model of the Harbin urban agglomeration is constructed, as shown in Figure 10 .

[0106] According to the existence of inter-layer nodes in each transportation layer of the super network model, the super network model is transformed to obtain a multi-layer urban agglomeration three-dimensional traffic network topological structure, as shown in Figure 11 .

[0107] Step 5: Select structural indicators to quantify the resilience of the multi-layer urban agglomeration three-dimensional traffic network topological structure;

[0108] Based on the inter-layer node correlation matrix b αi and the intra-layer node correlation matrix A [α] information such as the degree of a single node and the total number of nodes in each transportation layer can be calculated, and further quantitative analysis of the traffic network resilience can be carried out.

[0109] By constructing a multi-layer urban agglomeration three-dimensional traffic network based on the comprehensive transportation situation, scientific and feasible indicators are selected to analyze the three-dimensional traffic topological structure of the Harbin urban agglomeration. Currently, the indicators commonly used to analyze super networks mainly include indicators such as clustering coefficient, average degree, network diameter, and network efficiency.

[0110] Degree refers to the total number of edges connected to a node in the network structure. The degree distribution describes the distribution of the degrees of nodes, and the average degree is the average of the degrees of all nodes, which can reflect the connection situation between nodes. The expression of the degree of node i in different transportation layers in a multi-layer traffic network is:

[0111]

[0112] Among them, represents the degree of node i in the α-th layer, that is, n is the total number of nodes in the α-th layer.

[0113] The average degree is:

[0114]

[0115] Among them, N is the total number of nodes.

[0116] The network diameter refers to the average link between any two nodes in the network, that is, the average value of the edges in the line connecting two arbitrary nodes. The network diameter is:

[0117]

[0118] X M represents the nodes in M transport layers;

[0119] Set the nodes in the transport layer as the starting vertex u, and the intra-layer node adjacency matrix as the initial distance matrix from the starting vertex u to other nodes v; compare the distance value from the starting vertex u directly to other nodes v and the distance value from the starting vertex through the third node to other nodes v, retain the smaller distance value, and iteratively update the distance matrix to generate the shortest path d between nodes u and v using the Dijkstra algorithm uv ; X M represents the nodes in M transport layers.

[0120] The clustering coefficient is used to capture the degree of connection between the adjacent nodes of a given node. For a node, the calculation formula for the clustering coefficient of its multi-layer network is:

[0121]

[0122] where N α (i) is the intersection of all adjacent points of node i and the adjacent points existing in layer α, that is, the ideal number of edges;

[0123] is the actual number of edges of node i in layer α.

[0124] The network efficiency is the reciprocal of the distance and is an important indicator to measure the overall connectivity of the network. When the network is subject to external shocks, the network efficiency will decrease. The calculation formula for the network efficiency is:

[0125]

[0126] Use the corresponding algorithms in the networkx database of Python software, combined with the topological structure of the three-dimensional transportation network in the Harbin Metropolitan Area, to calculate the above-mentioned indicators of the multi-layer complex network.

[0127] Among them, the calculation of the clustering coefficient:

[0128]

[0129] The calculation of the average degree:

[0130]

[0131] Network efficiency calculation

[0132]

[0133] Network diameter calculation:

[0134]

[0135] The numerical values of each index as shown in Table 2 are obtained.

[0136] Table 2 Numerical values of multi - layer network topology structure indexes

[0137]

[0138] Through data discovery: The urban agglomeration's three - dimensional transportation network structure shows a radial shape, and the data of the clustering coefficient and network efficiency perform poorly. The main reason may be that the surrounding urban areas such as Wuchang City, Shangzhi City, and Bin County are far from the central urban area of Harbin and have less connection. There are more inter - connected routes in the central urban area, and the agglomeration is better, but the surrounding urban areas rely only on one expressway or arterial road for transportation, making it difficult to bear a large traffic pressure, and there is still room for optimization. The average degree and network diameter perform well, and it is analyzed that the inter - layer connection of the multi - layer network is relatively tight.

[0139] Step 6: Analyze the resilience of the urban agglomeration's three - dimensional transportation network based on the resilience quantification results and anti - destruction attack strategies.

[0140] In the overall network, due to different parameter characteristics, the importance of each node is different. According to certain parameter properties, the importance of nodes is quantified, and they are sorted in ascending order according to the numerical values of the importance indexes, indicating that the degree of importance increases from low to high;

[0141] The importance index is degree centrality, and the degree centrality of node i is:

[0142]

[0143] The process of analyzing the resilience of the urban agglomeration's three - dimensional transportation network includes:

[0144] According to the network anti - destruction attack strategy, the nodes with the highest importance in the multi - layer urban agglomeration's three - dimensional transportation network topology structure are deleted in turn, and the clustering coefficient and network efficiency of the multi - layer urban agglomeration's three - dimensional transportation network topology structure after deleting the nodes are calculated to quantify the resilience of the multi - layer urban agglomeration's three - dimensional transportation network topology structure;

[0145] The above process is continuously repeated until all nodes in the multi - layer urban agglomeration's three - dimensional transportation network topology structure are deleted.

[0146] Based on the characteristics of the network topology, to further verify its reliability, the resilience of the three-dimensional transportation network in the Harbin Metropolitan Area is tested. Through two indicators, the clustering coefficient and the network efficiency, the strain capacity of the three-dimensional transportation network structure when attacked is analyzed, and then the rationality of the road network layout is analyzed.

[0147] A node attack strategy based on node degree is adopted to damage the road network structure in the Harbin Metropolitan Area. By writing an index calculation program in Python, the numerical values of the degree centrality index are calculated, and the importance ranking of each node degree is obtained, as shown in Table 3.

[0148] Table 3 Ranking of the importance of regional nodes

[0149]

[0150]

[0151] Delete the nodes in the three-dimensional transportation network of the Harbin Metropolitan Area in turn, substitute them into the calculation formulas of the network clustering coefficient and the global network efficiency, and obtain the change curves of the complex network clustering coefficient and the global network efficiency with the node failure, as Figure 12 shown.

[0152] It is found from the curve that when attacking the three-dimensional transportation network according to the node degree strategy, when the node failure rate reaches about 10%, the clustering coefficient of the road network shows a cliff-like drop; when the node failure rate reaches about 90%, the clustering coefficient and the global network efficiency of the road network are 0, and the entire network structure is basically paralyzed.

[0153] According to the trend of the network efficiency in the curve graph, the connectivity of the four network layers in the Harbin Metropolitan Area decreases rapidly with the decrease of the node failure rate, and the overall system is relatively stable. The curve is relatively flat as a whole, and there is no large cliff-like drop in the network efficiency during the process of deleting nodes one by one, indicating that the intra-layer connection of the network is relatively tight. After a node in the same network layer is damaged, there are still other paths for traffic.

[0154] According to the data, the overall clustering effect of the transportation network integration in the Harbin Metropolitan Area is relatively good, that is, the connectivity of the three-dimensional transportation integration is relatively good. In real life, when a node in a certain network layer of the three-dimensional transportation network structure is attacked, transportation activities can be carried out more conveniently through other network layers. The clustering coefficient has a certain recovery when node 4 is damaged. According to the formula, the trend of the decrease in the ideal number of connected edges of multiple network layers after node 4 is damaged is greater than the trend of the decrease in the actual number of connected edges, that is, the node degree of node 4 accounts for a relatively large proportion among all nodes. Therefore, after deleting node 4, the network overlap decreases and the structure is more optimal.

[0155] Although the present invention has been described herein with reference to particular embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. Accordingly, it should be understood that numerous modifications may be made to the exemplary embodiments, and other arrangements may be devised, without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the different dependent claims and the features described herein may be combined in a manner different from that described in the original claims. It should also be understood that the features described in connection with a separate embodiment may be used in other described embodiments.

Claims

1. A quantitative analysis method for the resilience of integrated urban transportation, characterized by: include: Step 1: Divide the network layers according to the transportation modes in the metropolitan area, with each transportation mode corresponding to a transportation layer; Step 2: Select important areas in the metropolitan area as nodes of the three-dimensional transportation network; Step 3: In the transport layer of step 1, the nodes selected in step 2 are connected to form a node association matrix in the transport layer, and the traffic topology structure of different transport layers is constructed; Step 4: According to different transport layers, node association matrices between transport layers and inter-layer links, the metropolitan area three-dimensional super network model is converted into a multi-layer metropolitan area three-dimensional transportation network topology structure; Step 5: Select structural indicators to quantify the resilience of the topological structure of the multi-layer urban agglomeration transportation network; Step 6: Analyze the resilience of the urban agglomeration's three-dimensional transportation network based on the resilience quantification results and anti-destruction attack strategies.

2. The quantitative analysis method of urban agglomeration three-dimensional transportation integration resilience according to claim 1 is characterized in that: Step 3 includes: Define X α is the node set of transport layer α, E α is the edge set in the transport layer α, then the traffic topology matrix of the transport layer α is G α =(X α ,E α ), the transport layer set is g = {G α ; α∈{1,…,M}}, there are M transport layers in total; in is the i-th node of transport layer α, N α is the total number of nodes in transport layer α, and the node association matrix in transport layer α is: in, (1≤i,j≤N α and 1≤α≤M), is the connectivity of nodes i and j in transport layer α. If nodes i and j are connected in transport layer α, then On the contrary, is the jth node of transport layer β, E αβ represents the set of edges between transport layer α and transport layer β; Each transport layer, the node association matrix in each transport layer and the edges constitute the traffic topology structure of different transport layers.

3. The quantitative analysis method of urban agglomeration three-dimensional transportation integration resilience according to claim 2 is characterized in that: In step 4, the node association matrix b between each transport layer αi : in, represents node i at transport layer α; The set of edges connecting each transport layer node is: C={E αβ ∈X α ×X β ;α,β∈{1,…,M},α≠β};。 Among them, E αβ is the set of edges between transport layer α and transport layer β.

4. The quantitative analysis method of urban agglomeration three-dimensional transportation integration resilience according to claim 3 is characterized in that: The structural indicators selected in step 5 include the average degree, network diameter, network efficiency and clustering coefficient of the topological structure of the multi-layer urban agglomeration three-dimensional transportation network.

5. The quantitative analysis method of urban agglomeration three-dimensional transportation integration resilience according to claim 4 is characterized in that: The degree of node i in different transport layers is: in, represents the degree of node i in the αth layer, n is the total number of nodes in the α layer; Average for: Where N is the total number of nodes in the multi-layer urban agglomeration three-dimensional transportation network topology; The network diameter L(M) is: The network efficiency is: Set the node in the transport layer as the departure vertex u, and the node association matrix in the layer as the initial distance matrix from the departure vertex u to other nodes v; compare the distance value from the departure vertex u directly to other nodes v and the distance value from the departure vertex to other nodes v through the third node, retain the smaller distance value, iteratively update the distance matrix, and generate the Dijkstra algorithm shortest path d between nodes u and v. uv ;X M Represents nodes in M ​​transport layers; Clustering coefficient C M (i) is: Among them, N α (i) is the intersection of all neighboring points of node i and the neighboring points existing in layer α, that is, the ideal number of edges; is the actual number of edges of node i in layer α.

6. The quantitative analysis method of urban agglomeration three-dimensional transportation integration resilience according to claim 5 is characterized in that: Step 6 includes: The importance of nodes is quantified and sorted in positive order according to the importance index value, indicating that the importance is from low to high; The importance index is degree centrality, and the degree centrality of node i is: The process of analyzing the resilience of the metropolitan area's three-dimensional transportation network includes: According to the network anti-destruction attack strategy, the most important nodes in the multi-layer urban circle three-dimensional transportation network topology structure are deleted one by one, the structural indicators of the multi-layer urban circle three-dimensional transportation network topology structure after the nodes are deleted are calculated, and the resilience of the multi-layer urban circle three-dimensional transportation network topology structure is quantified; The above process is repeated continuously until all nodes in the multi-layer urban circle three-dimensional transportation network topology are deleted.

7. The quantitative analysis method of urban agglomeration three-dimensional transportation integration resilience according to claim 6 is characterized in that: Calculate the clustering coefficient and network efficiency of the multi-layer urban agglomeration three-dimensional transportation network topology structure after deleting nodes.

8. A computer-readable storage device storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for quantitatively analyzing the resilience of integrated three-dimensional transportation in an urban area as described in any one of claims 1 to 7 are implemented.

9. A device for quantitatively analyzing the resilience of integrated urban transportation, comprising a storage device, a processor, and a computer program stored in the storage device and executable on the processor, characterized in that: The processor executes the computer program to implement the steps of the method for quantitatively analyzing the resilience of integrated three-dimensional transportation in an urban area as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for quantitatively analyzing the resilience of integrated urban transportation in any one of claims 1 to 7 are implemented.

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