A method and system for assessing the resilience of a regional transport network

Abstract

The application discloses a kind of regional traffic network resilience evaluation method and system, comprising: according to the traffic network planning of setting region, construct undirected network topology diagram, station and traffic hub are as the node of undirected network topology diagram;Respectively calculate the degree centrality, closeness centrality and intermediate centrality of each node, select key node;Random attack or specified attack is carried out to key node, and the vulnerability and recovery index of regional traffic network are calculated;Determine the influence factor of rapidity and adaptability, respectively construct the evaluation matrix of rapidity, the evaluation matrix of adaptability, the evaluation matrix of vulnerability and the evaluation matrix of recovery;The resilience evaluation result of regional traffic network is obtained based on the above evaluation matrix.The application can not depend on the historical data when regional traffic is influenced, not limited to the size of the region disturbed, realize the accurate evaluation of the resilience of regional traffic network.

Description

Technical Field

[0001] This invention relates to the field of regional transportation network management and characteristic analysis technology, and in particular to a method and system for assessing the resilience of regional transportation networks. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In recent years, research on the resilience of transportation networks has proliferated. The term "resilience" originates from Latin, meaning the ability to bounce back or resist. The resilience of an object is defined as its ability to recoil or rebound after being bent, stretched, or compressed. Initially, there were two different ways to define resilience. One definition, first used in ecology, refers to the disturbance a system can absorb before changing from one state to another. This definition does not require the system to always be in or near a certain equilibrium; it refers to the system's ability to absorb and maintain a certain state after experiencing changes in state or parameters. The other definition, later applied to engineering, refers to the system's ability to maintain itself near a stable equilibrium. This definition uses the time required for the system to recover to an equilibrium or stable state after being disturbed as a measure of its recovery. In recent years, this concept has also been applied to economics and space science.

[0004] A good transportation network is essential for the operation of a region, as it not only supports the daily movement of people and the transport of goods, but is also a prerequisite for the timely rescue and repair of other damaged infrastructure.

[0005] Assessing the resilience of transportation networks helps decision-makers allocate resources rationally and make informed decisions, enabling the networks to quickly recover their functions in the face of unforeseen events. Unforeseen events can be broadly categorized into internal and external factors. External factors include natural disasters such as heavy rain, snowfall, landslides, mudslides, earthquakes, floods, tsunamis, and tornadoes; internal factors include technical failures or component damage. These factors are key disruptors of transportation networks, and the resilience of a transportation network can be viewed as a comprehensive consideration of its absorption, adaptation, and recovery capabilities. Currently, assessing regional transportation resilience relies heavily on extensive historical data. For small or very large areas, a lack of data on traffic disruptions makes it impossible to effectively assess the region's transportation resilience, and consequently, to intervene or mitigate unknown disruptions in advance. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a method and system for assessing the resilience of regional transportation networks. Based on a regional transportation network topology map, a dynamic resilience function map, and expert evaluation, an assessment matrix is ​​constructed. The assessment indicators for regional transportation resilience focus on vulnerability, resilience, speed, and adaptability. This method can predict the resilience level of regional transportation networks of all sizes during periods of disruption and identify weaknesses in regional transportation resilience in advance.

[0007] In some implementations, the following technical solutions are adopted:

[0008] A method for assessing the resilience of a regional transportation network, comprising:

[0009] Construct an undirected network topology graph based on the transportation network plan of the designated area, and use stations and transportation hubs as nodes in the undirected network topology graph;

[0010] Calculate the degree centrality, proximity centrality, and center centrality of each node, and select key nodes;

[0011] Perform random or targeted attacks on key nodes to calculate the vulnerability and resilience indicators of the regional transportation network.

[0012] Identify the factors influencing speed and adaptability, and construct evaluation matrices for speed, adaptability, vulnerability, and resilience, respectively.

[0013] The resilience assessment results of the regional transportation network are derived based on the above evaluation matrix.

[0014] The resilience of a regional transportation network is derived by comprehensively evaluating its vulnerability, resilience, adaptability, and speed; the evaluation matrix B corresponds to these four indicators. gx1 B gx2 B gx3 B gx4 Composition of comprehensive matrix In this matrix, the first, second, third, and fourth rows represent the evaluation matrix elements for vulnerability, resilience, adaptability, and speed, respectively. Multiplying the weights of each indicator by the comprehensive matrix yields the overall regional transportation network resilience evaluation matrix R. u =γS=[s1s2s3s4s5], and perform Processing, where ∑s i It is the sum of the elements of the overall resilience assessment matrix;

[0015] The final percentage of the regional transportation network resilience assessment matrix is ​​obtained. Experts believe that the region's transportation network has an extremely high level of resilience, with a percentage of [missing information]. Experts believe the resilience is at a high level, 100%. Experts consider the resilience to be at a moderate level, 100%. Experts believe the resilience is low, at 100%. Experts consider the toughness to be at an extremely low level.

[0016] In other embodiments, the following technical solutions are adopted:

[0017] A regional transportation network resilience assessment system includes:

[0018] The network topology construction module is used to construct an undirected network topology graph based on the traffic network planning of a defined area, with stations and transportation hubs as nodes in the undirected network topology graph;

[0019] The key node selection module is used to calculate the degree centrality, proximity centrality, and center centrality of each node and select key nodes.

[0020] The vulnerability and resilience index calculation module is used to perform random or targeted attacks on key nodes to calculate the vulnerability and resilience index of the regional transportation network.

[0021] The evaluation matrix construction module is used to determine the factors affecting speed and adaptability, and to construct evaluation matrices for speed, adaptability, vulnerability, and resilience, respectively.

[0022] The resilience assessment module is used to derive the resilience assessment results of the regional transportation network based on the aforementioned evaluation matrix. Compared with the prior art, the beneficial effects of this invention are:

[0023] (1) The regional transportation network resilience assessment method of the present invention can accurately assess the resilience of the regional transportation network without relying on historical data when regional transportation is affected, and is not limited to the size of the affected area. By constructing a rapid assessment matrix, an adaptive assessment matrix, a vulnerability assessment matrix, and a resilient assessment matrix, it can identify the weak points of regional transportation resilience in advance and provide constructive and targeted opinions for improving regional transportation resilience.

[0024] (2) Based on the regional transportation network planning, this invention constructs an undirected network topology by taking each station or transportation hub as a node and routes or roads as links. It then calculates the degree centrality, intermediate centrality, and proximity centrality of each node and sorts them accordingly. The importance of each node is calculated by summing the degree centrality and intermediate centrality rankings of the nodes, and supplemented by the proximity centrality rankings of the nodes. This allows for the ranking of the importance of each node and the selection of key nodes. Based on the processing of key nodes, the evaluation results of various indicators of the regional transportation network become more accurate.

[0025] Other features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0026] Figure 1 This is a flowchart of the regional transportation network resilience assessment method in an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of the regional traffic resilience assessment index system in an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of the regional transportation network resilience performance curve in an embodiment of the present invention. Detailed Implementation

[0029] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0030] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0031] Example 1

[0032] In one or more embodiments, a method for assessing the resilience of a regional transportation network is disclosed, combining... Figure 1 Specifically, it includes the following processes:

[0033] (1) Construct an undirected network topology based on the traffic network planning of the designated area, and take the stations and traffic hubs as nodes of the undirected network topology;

[0034] In this embodiment, an undirected network topology graph is first constructed based on the transportation network planning of each region. Each station or transportation hub (airport, train station, bus station, port, intersection, etc.) is designated as node N1, N2, N3, etc., and the flight route or road is designated as L connected by the nodes. i,j Link, L i,j This represents a link connecting node i and node j. Nodes and links constitute an undirected network topology graph G(N,L).

[0035] (2) Combination Figure 2 Calculate the degree centrality, proximity centrality, and center centrality of each node, and select key nodes;

[0036] In this embodiment, the formula for calculating the degree centrality of a node is:

[0037]

[0038] Where J(i) represents a direct connection to node N. i The number of nodes, where n represents the number of nodes in the network topology graph.

[0039] The formula for calculating the center centrality of a node is:

[0040]

[0041]

[0042] Where, d jk d represents the number of shortest paths from node j to node k. jk (i) represents the number of shortest paths from node j to node k that pass through node i; Z i For the normalized center centrality of node i, Z Si For the absolute center centrality of node i, z jk (i) is the ratio of the number of shortest paths between any two points in the network that pass through node i to the total number of shortest paths between these two points.

[0043] The formula for calculating the proximity centrality of a node is:

[0044]

[0045]

[0046] Among them, c ij is the geodesic distance from node i to node j, n is the number of nodes in the network topology graph, and the proximity centrality of node i is the ratio of the sum of the geodesic distances from node i to all other nodes to the number of nodes in the network topology graph minus 1.

[0047] In this embodiment, the nodes are sorted from high to low according to degree centrality, center centrality, and proximity centrality respectively;

[0048] Then sort them from smallest to largest according to the sum of the rankings of degree centrality and center centrality;

[0049] The smaller the sum of the rankings, the higher the importance of the node. When two nodes have the same sum of their rankings, the node with the higher proximity centrality ranking is more important. For example, node A has a degree centrality ranking of 1 and a center centrality ranking of 3, while node B has a degree centrality ranking of 3 and a center centrality ranking of 1, both totaling 4. However, node A has a proximity centrality ranking of 2, and node B has a proximity centrality ranking of 3. Therefore, node A is more important than node B.

[0050] Finally, the nodes are ranked by importance, and a set number of nodes with the highest importance are selected as key nodes.

[0051] (3) Perform random or targeted attacks on key nodes and calculate the vulnerability and resilience indicators of the regional transportation network.

[0052] In this embodiment, attacks are divided into targeted attacks and random attacks, namely, causing a targeted node to fail or causing a random node to fail.

[0053] Designated attacks are based on the order of importance of critical nodes, causing 5%, 10%, 15%, 20%, 25%, 30%, 40%, 60%, and 80% of nodes to fail in descending order. Random attacks, on the other hand, randomly cause nodes of the same importance to fail. The vulnerability of the local area network is calculated, and the sensitivity of the local area network to designated and random attacks is analyzed.

[0054] Vulnerability is calculated using the following formula:

[0055]

[0056]

[0057] Where n is the number of nodes in the current network; C(G0) is the vulnerability calculation result of the initial network, that is, the network in which all nodes have not failed, where n represents the total number of nodes in the initial topology network graph; C(G) is the network vulnerability calculation result after a set number of nodes are attacked, the recovered nodes plus the remaining unattacked nodes, where n represents the number of recovered nodes plus the remaining unattacked nodes.

[0058] Let c be the Euclidean distance between nodes i and j, which is the intuitive shortest distance between two points. ij This represents the actual distance between node i and node j.

[0059] The vulnerability of a regional transportation network is quantified by calculating the difference between C(G0) and C(G). For example, the difference between the two calculated when 80% of the nodes fail is compared with the difference calculated when 60% of the nodes fail. The greater the difference, the worse the vulnerability of the regional transportation network.

[0060] Recovery is divided into specified recovery and random recovery. For a transportation network with 80% node damage, specified recovery restores nodes in descending order of importance as follows: 5%, 10%, 15%, 20%, 25%, 30%, 40%, 60%, and 80%. Random recovery restores nodes with the same gradient. The relative function of the network is calculated to obtain the following results: Figure 3 The regional transportation network resilience curve shown is used to derive the resilience of the regional transportation network.

[0061] The relative function is calculated using this formula:

[0062]

[0063] Where X(G) represents the relative function of the network before and after recovery, which can reflect the network recovery capability when different numbers of nodes are recovered. C(G0) is the vulnerability calculation result of the initial network, that is, the network where all nodes are not failed. C(G) is the network vulnerability calculation result after a set number of nodes are attacked, including the recovered nodes and the remaining unattacked nodes.

[0064] (4) Identify the factors affecting speed and adaptability, and construct evaluation matrices for speed, adaptability, vulnerability, and resilience respectively;

[0065] In this embodiment, the impact of several common influencing factors on speed and adaptability is analyzed, including daily passenger flow, average travel distance, average commute time, per capita road area, repair technology level, regional economy, number of transportation employees in the region, and investment in regional transportation infrastructure. Factors with minor impact are eliminated, while those with major impact are retained.

[0066] Each factor is represented by a value of extremely high, high, medium, low, and very low. The expert evaluation results are then compiled into a matrix. Where m represents the number of influencing factors, a i1 a i2 a i3 a i4 a i5 These represent the percentages of experts who believe a certain factor related to speed or adaptability reaches an extremely high, high, medium, low, or very low level. For example, if 10 experts participated in evaluating a factor related to speed, such as "restoration technique level," and 2 experts considered it to be at an extremely high level, 4 at a high level, 3 at a medium level, 1 at a low level, and 0 at a very low level, then a... 11 a 12 a 13 a 14 a 15 The values ​​are 0.2, 0.4, 0.3, 0.1, and 0, respectively.

[0067] Then, the weights of each factor are set to form matrix A. x =[r1r2r3…r m ], where m and D x The 'm' in the equation is consistent.

[0068] Finally, the evaluation matrix B for speed or adaptability is derived.xi =A x D x and perform matrix Processing, where ∑b i For B xi The sum of all elements in the expression.

[0069] Finally, the speed evaluation matrix B is obtained. gx1 =[c 11 c 12 c 13 c 14 c 15 ]

[0070] Similarly, we obtain the fitness evaluation matrix B. gx2 =[c 21 c 22 c 23 c 24 c 25 ].

[0071] Based on the same approach, the vulnerability changes and trends observed by experts in step three under network node failure scenarios of 5%, 10%, and 15% are taken as an influencing factor, resulting in D. x It is a single-row matrix, and the corresponding weight matrix A x It is 1, and then according to Obtain the evaluation matrix B of vulnerability C(G). gx3 =[c 31 c 32 c 33 c 34 c 35 ];

[0072] Similarly, by taking the changes and trends in the relative function X(G) of nodes under recovery conditions of 5%, 10%, and 15% as an influencing factor, the resulting D... x It is a single-row matrix, and the corresponding weight matrix A x It is 1, and then according to Obtain the evaluation matrix of vulnerability C(G)

[0073] B gx4 =[c 41 c 42 c 43 c 44 c 45 ].

[0074] (5) Based on the above evaluation matrix, the resilience assessment results of the regional transportation network are obtained.

[0075] In this embodiment, the resilience of a regional transportation network is derived by comprehensively considering its vulnerability, recoverability, adaptability, and speed, corresponding to the evaluation matrix B for these four indicators. gx1 B gx2 B gx3 B gx4 Composition of comprehensive matrix The first, second, third, and fourth rows are the evaluation matrix elements for vulnerability, resilience, adaptability, and speed, respectively. Multiplying the weights of each indicator by the comprehensive matrix yields the overall evaluation matrix R of the regional transportation network resilience. u =γS=[s1s2s3s4s5], and perform Processing, ∑s i This represents the sum of elements in the overall resilience assessment matrix. γ is generally γ = [0.25 0.25 0.25 0.25], but in special cases, such as when the region is too large or too small, the value depends on the specific circumstances. The percentage of elements in the overall resilience assessment matrix of the regional transportation network is... Experts believe that the region's transportation network has an extremely high level of resilience, with a percentage of [missing information]. Experts believe the resilience is at a high level, 100%. Experts consider the resilience to be at a moderate level, 100%. Experts believe the resilience is low, at 100%. Experts consider the resilience level to be extremely low. Furthermore, targeted suggestions for improvement can be made for indicators at medium, low, and extremely low levels.

[0076] Example 2

[0077] In one or more embodiments, a regional transportation network resilience assessment system is disclosed, comprising:

[0078] The network topology construction module is used to construct an undirected network topology graph based on the traffic network planning of a defined area, with stations and transportation hubs as nodes in the undirected network topology graph;

[0079] The key node selection module is used to calculate the degree centrality, proximity centrality, and center centrality of each node and select key nodes.

[0080] The vulnerability and resilience index calculation module is used to perform random or targeted attacks on key nodes to calculate the vulnerability and resilience index of the regional transportation network.

[0081] The evaluation matrix construction module is used to determine the factors affecting speed and adaptability, and to construct evaluation matrices for speed, adaptability, vulnerability, and resilience, respectively.

[0082] The resilience assessment module is used to derive the resilience assessment results of the regional transportation network based on the above-mentioned evaluation matrix.

[0083] The specific implementation methods of the above modules have been described in Example 1, and will not be detailed here.

[0084] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for assessing the resilience of a regional transportation network, characterized in that, include: Construct an undirected network topology graph based on the transportation network plan of the designated area, and use stations and transportation hubs as nodes in the undirected network topology graph; Calculate the degree centrality, proximity centrality, and center centrality of each node, and select key nodes; Perform random or targeted attacks on key nodes to calculate the vulnerability and resilience indicators of the regional transportation network. The factors influencing speed and adaptability were identified, and evaluation matrices for speed, adaptability, vulnerability, and resilience were constructed respectively. Specifically, the evaluation matrix for speed is constructed as follows: Each factor is represented by a value of extremely high, high, medium, low, and very low. The expert evaluation results are then compiled into a matrix. ,in , , , , These are the proportions of experts who believe a certain factor related to speed reaches extremely high, high, medium, low, and extremely low levels, respectively. A matrix is ​​then constructed by assigning weights to each factor. Finally, a speed evaluation matrix was derived. And perform the evaluation matrix Processing, among which, for The sum of all elements in the formula, where m represents the number of factors; Ultimately, a speed evaluation matrix is ​​obtained. ; Similarly, the evaluation matrix for adaptability is obtained. ; Based on the same approach, the vulnerability changes and trends observed by experts in step three at network node failure rates of 5%, 10%, 15%, 20%, 25%, 30%, 40%, 60%, and 80% are considered as an influencing factor. It is a single-row matrix, and the corresponding weight matrix is... It is 1, and then according to Obtain the evaluation matrix of vulnerability C(G) ; Similarly, by taking the changes and trends of the relative function X(G) of the node under recovery rates of 5%, 10%, 15%, 20%, 25%, 30%, 40%, 60%, and 80% as an influencing factor, the following results were obtained. It is a single-row matrix, and the corresponding weight matrix is... It is 1, and then according to Obtain the evaluation matrix of restorability C(G) ; The vulnerability index for calculating the regional transportation network is specifically as follows: Where n is the number of nodes in the current network; C(G) represents the vulnerability calculation result of the initial network, i.e., the network where all nodes are intact. Here, n represents the total number of nodes in the initial topology network graph. C(G) represents the network vulnerability calculation result after a set number of nodes are attacked, including the recovered nodes and the remaining unattacked nodes. Here, n represents the number of recovered nodes and the remaining unattacked nodes. Let be the Euclidean distance between nodes i and j, which is intuitively the shortest distance between two points. This represents the actual distance between node i and node j; Through calculation The difference between C(G) and C(G) is used to quantify the vulnerability of the regional transportation network; Specifically, the restorative indicators for the regional transportation network are calculated as follows: Restore nodes that have failed due to attacks and calculate the relative functionality of the regional transportation network: ； The resilience assessment results of the regional transportation network are derived based on the above evaluation matrix.

2. The method for assessing the resilience of a regional transportation network as described in claim 1, characterized in that, Calculate the degree centrality of each node, specifically: directly connected to the node The ratio of the number of nodes to the number of nodes in the network topology graph n minus 1; The center centrality of each node is calculated as follows: in, This represents the number of shortest paths from node j to node k. This represents the number of shortest paths from node j to node k that pass through node i. For the normalized centrality of node i, Let i be the absolute center centrality of node i; It is the ratio of the number of shortest paths between any two points in the network passing through node i to the total number of shortest paths between those two points; The proximity centrality of each node is calculated as the ratio of the sum of the geodesics from node i to all other nodes to the number of nodes in the network topology graph minus 1.

3. The method for assessing the resilience of a regional transportation network as described in claim 1, characterized in that, The method for selecting key nodes is as follows: Sort all nodes by degree centrality, center centrality, and proximity centrality respectively; Sort all nodes by the sum of their degree centrality and center centrality rankings; the smaller the sum of the rankings, the higher the importance of the node. If the sum of the rankings of two nodes is equal, then the node with the higher centrality ranking is more important than the node with the lower centrality ranking. Finally, the ranking of all nodes is obtained, and a set number of nodes with the highest ranking are selected as key nodes.

4. The method for assessing the resilience of a regional transportation network as described in claim 1, characterized in that, A random attack on a critical node refers to causing a random node to fail; a targeted attack on a critical node refers to causing a specific node to fail.

5. The method for assessing the resilience of a regional transportation network as described in claim 1, characterized in that, Identify factors influencing speed and adaptability, including: daily passenger volume, average travel distance, average commute time, road area per capita, repair technology level, regional economy, regional transportation employment, and regional transportation infrastructure investment.

6. The method for assessing the resilience of a regional transportation network as described in claim 1, characterized in that, Based on the above evaluation matrix, the resilience assessment results of the regional transportation network are as follows: The resilience of a regional transportation network is comprehensively assessed using indicators of vulnerability, resilience, adaptability, and speed of the network; a corresponding evaluation matrix for these four indicators is also provided. , , , Composition of comprehensive matrix The first, second, third, and fourth rows represent the evaluation matrix elements for vulnerability, resilience, adaptability, and speed, respectively. Multiplying the weights of each indicator by the comprehensive matrix yields the overall evaluation matrix for the regional transportation network resilience. and conduct Processing, among which, It is the sum of the elements of the overall resilience assessment matrix; The final percentage of the regional transportation network resilience assessment matrix is ​​obtained. Experts believe that the region's transportation network has an extremely high level of resilience, with a percentage of [missing information]. Experts believe the resilience is at a high level, 100%. Experts consider the resilience to be at a moderate level, 100%. Experts believe the resilience is low, at 100%. Experts consider the toughness to be at an extremely low level.

7. A regional transportation network resilience assessment system, characterized in that, include: The network topology construction module is used to construct an undirected network topology graph based on the traffic network planning of a defined area, with stations and transportation hubs as nodes in the undirected network topology graph; The key node selection module is used to calculate the degree centrality, proximity centrality, and center centrality of each node and select key nodes. The vulnerability and resilience index calculation module is used to perform random or targeted attacks on key nodes to calculate the vulnerability and resilience index of the regional transportation network. The evaluation matrix construction module is used to determine the influencing factors on speed and adaptability, and to construct evaluation matrices for speed, adaptability, vulnerability, and resilience, as described above. Specifically, the construction of the speed evaluation matrix is ​​as follows: Each factor is represented by a value of extremely high, high, medium, low, and very low. The expert evaluation results are then compiled into a matrix. ,in , , , , These are the proportions of experts who believe a certain factor related to speed reaches extremely high, high, medium, low, and extremely low levels, respectively. A matrix is ​​then constructed by assigning weights to each factor. Finally, a speed evaluation matrix was derived. And perform the evaluation matrix Processing, among which, for The sum of all elements in the formula, where m represents the number of factors; Ultimately, a speed evaluation matrix is ​​obtained. ; Similarly, the evaluation matrix for adaptability is obtained. ; Based on the same approach, the vulnerability changes and trends observed by experts in step three at network node failure rates of 5%, 10%, 15%, 20%, 25%, 30%, 40%, 60%, and 80% are considered as an influencing factor. It is a single-row matrix, and the corresponding weight matrix is... It is 1, and then according to Obtain the evaluation matrix of vulnerability C(G) ; Similarly, by taking the changes and trends of the relative function X(G) of the node under recovery rates of 5%, 10%, 15%, 20%, 25%, 30%, 40%, 60%, and 80% as an influencing factor, the following results were obtained. It is a single-row matrix, and the corresponding weight matrix is... It is 1, and then according to Obtain the evaluation matrix of restorability C(G) ; The vulnerability index for calculating the regional transportation network is specifically as follows: Where n is the number of nodes in the current network; C(G) represents the vulnerability calculation result of the initial network, i.e., the network where all nodes are intact. Here, n represents the total number of nodes in the initial topology network graph. C(G) represents the network vulnerability calculation result after a set number of nodes are attacked, including the recovered nodes and the remaining unattacked nodes. Here, n represents the number of recovered nodes and the remaining unattacked nodes. Let be the Euclidean distance between nodes i and j, which is intuitively the shortest distance between two points. This represents the actual distance between node i and node j; Through calculation The difference between C(G) and C(G) is used to quantify the vulnerability of the regional transportation network; Specifically, the restorative indicators for the regional transportation network are calculated as follows: Restore nodes that have failed due to attacks and calculate the relative functionality of the regional transportation network: ； The resilience assessment module is used to derive the resilience assessment results of the regional transportation network based on the above-mentioned evaluation matrix.

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