Dynamic risk assessment method for regional traffic network under sudden earthquake influence

Abstract

The invention discloses a regional traffic network dynamic risk assessment method under sudden earthquake influence, which belongs to the technical field of earthquake disaster assessment and comprises the steps of collecting data and preprocessing, constructing a traffic network model, constructing an earthquake vulnerability model and constructing a risk dynamic assessment system. According to the method, seismic parameters, social economy, a regional traffic network and real-time detection data after an earthquake are comprehensively considered when the earthquake occurs, the risk of the regional traffic network under the earthquake can be evaluated more comprehensively, a Monte Carlo simulation method is applied, an earthquake vulnerability model is combined, a damage scene of the earthquake to the traffic network is simulated, and the risk of the regional traffic network under the earthquake can be evaluated more comprehensively. Evaluating the damage probability and the function loss of the traffic facilities; calculating the connectivity of the traffic network model, and dynamically calculating the performance change of the traffic network after the earthquake based on the connectivity index and weight of the traffic network; and evaluating the toughness of the traffic network according to a network performance recovery curve under the recovery strategy, so that the performance change of the traffic network in the earthquake can be dynamically reflected in real time.

Description

Technical Field

[0001] The present invention belongs to the technical field of earthquake disaster assessment, and particularly relates to a method for dynamically assessing the risk of a regional traffic network under the influence of a sudden earthquake. Background Art

[0002] An earthquake is a sudden natural disaster with extremely high destructiveness and unpredictability, and its impact on the regional traffic network is particularly significant. When an earthquake occurs, infrastructure such as roads, bridges, and tunnels in the traffic network may be severely damaged, resulting in traffic interruption or a significant decline in traffic capacity. In such a situation, quickly and accurately assessing the dynamic risk of the traffic network is of great significance for emergency rescue, post-disaster recovery, and urban planning.

[0003] In the prior art, traditional traffic network risk assessment methods are mostly based on static data and cannot reflect the dynamic changes of the traffic network in real time after an earthquake. These methods usually rely on historical data and preset earthquake scenarios, lacking the ability to monitor and analyze the actual earthquake impact in real time. In addition, many existing assessment methods only focus on the physical damage degree of the traffic network and ignore other important factors such as traffic flow, population density, and network connectivity. This single-index assessment method cannot comprehensively reflect the comprehensive risk status of the traffic network under the influence of an earthquake. When traditional assessment methods evaluate the risk of the traffic network, they often ignore the real-time nature of rescue needs. After an earthquake occurs, rescue needs change over time, and traditional assessment methods cannot dynamically adjust the risk assessment results according to the changes in rescue needs.

[0004] In summary, traditional traffic network risk assessment methods have significant deficiencies in terms of real-time performance, multi-index comprehensive assessment, and dynamic change simulation, and are difficult to meet the actual needs of traffic network risk assessment under sudden disasters such as earthquakes. Therefore, it is of great significance to develop a method for dynamically assessing the risk of a regional traffic network under the influence of a sudden earthquake. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method for dynamically assessing the risk of a regional traffic network under the influence of a sudden earthquake, which can monitor and dynamically evaluate the impact of an earthquake on the traffic network in real time, providing a scientific basis for emergency rescue and post-earthquake recovery.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for dynamically assessing the risk of a regional traffic network under the influence of a sudden earthquake, comprising:

[0008] Collecting data and preprocessing, collecting earthquake parameter data, socio-economic data, regional traffic network data at the time of earthquake occurrence, and real-time detection data after the earthquake occurrence, and preprocessing the collected data;

[0009] Construct a traffic network model, use graph theory methods to construct a traffic network model, and use regional traffic network data and socioeconomic data to weight the traffic network model;

[0010] Construct an earthquake vulnerability model. Based on the types and structural characteristics of traffic network facilities, establish corresponding earthquake vulnerability models, and substitute earthquake parameter data into the earthquake vulnerability models to calculate the damage probability and damage degree of each node's traffic facilities during an earthquake;

[0011] Construct a dynamic risk assessment system. Use the Monte Carlo simulation method, combined with the earthquake vulnerability model, to simulate the damage scenarios of the earthquake to the traffic network, and evaluate the damage probability and functional loss of traffic facilities; calculate the connectivity of the traffic network model, and based on the connectivity index and weight of the traffic network, dynamically calculate the performance change of the traffic network after the earthquake; then, according to the network performance recovery curve under the recovery strategy, evaluate the resilience of the traffic network.

[0012] As a further preference of the present invention, it further includes result visualization, and visualizes the evaluation results through a Geographic Information System (GIS) platform to intuitively present the damaged areas of the traffic network, the damage probability of key nodes, and the recovery progress.

[0013] As a further preference of the present invention, it further includes secondary disaster risk analysis. Combine the potential impact of secondary disasters caused by earthquakes on the traffic network to improve the dynamic risk assessment system; use historical earthquake data and disaster simulation results to quantify the additional risks of secondary disasters to the traffic network and incorporate them into the dynamic risk assessment system;

[0014] The total damage probability is the sum of the damage probability directly caused by the earthquake and the damage probability caused by secondary disasters; expressed as:

[0015] P 总 = P 地震 + P 次生

[0016] where, P 总 is the total damage probability, P 地震 is the damage probability directly caused by the earthquake, and P 次生 is the damage probability caused by secondary disasters.

[0017] As a further preference of the present invention, the earthquake parameter data includes earthquake intensity, epicenter location, focal depth, and geological conditions;

[0018] The socioeconomic data includes population density, economic activity distribution, and key facility locations in the region;

[0019] The regional traffic network data includes road network topology, road types, traffic flow, and the distribution of traffic facilities;

[0020] The real-time detection data after an earthquake includes road damage, changes in traffic flow, and the operating status of traffic facilities.

[0021] As a further preference of the present invention, in the traffic network model, nodes represent traffic facilities in the regional traffic network data, and edges represent connection facilities in the basic regional traffic network data; among them, traffic facilities include bridges, tunnels, and transportation hubs, and connection facilities include roads and railways; the weighting process includes traffic flow weighting, road topology weighting, and population density weighting;

[0022] The weight of traffic flow is obtained by the ratio of the traffic flow of the i-th road to the maximum traffic flow among the roads in the region;

[0023] The calculation of the traffic flow weight is expressed as:

[0024]

[0025] Among them, Flow i represents the traffic flow of the i-th road, and W 流量，i is the corresponding weight;

[0026] The weight of the road topology is calculated by the ratio of the road length, the number of lanes, and the speed limit of the i-th road to the maximum road length, the maximum number of lanes, and the maximum speed limit among the roads in the region, and corresponding weight coefficients are set for each of them;

[0027] The calculation of the road topology weighting is expressed as:

[0028]

[0029] Among them, a, b, and c are adjustable weight coefficients, and W 拓扑，i is the corresponding weight;

[0030] The weight of the population density is calculated by the ratio of the population density of the region where the i-th node is located to the maximum population density of the nodes in the region;

[0031] The calculation of the population density weight is expressed as:

[0032]

[0033] Among them, Population Density i represents the population density of the region where the i-th node is located, and W 人口，i is the corresponding weight;

[0034] Then, corresponding weight coefficients are set for the traffic flow weight, road topology weight, and population density weight respectively, and their sum is obtained as the comprehensive weight;

[0035] The comprehensive weighted processing is expressed as:

[0036] W 综合,i,j = α × W 流量，i，j + β × W 拓扑，i，j + γ × W 人口，i，j

[0037] where α, β, and γ are adjustable weight coefficients, and W 综合,i,j represents the comprehensive weight from node i to node j.

[0038] As a further preference of the present invention, the method of earthquake intensity and structural vulnerability curve is used to calculate the damage probability of each node's transportation facilities during an earthquake; the vulnerability curve represents the probability of transportation facilities being in different damage states under different earthquake intensities;

[0039] Let the vulnerability curve be P(D|I), which represents the probability of a transportation facility being in damage state D under earthquake intensity I. The damage probability is calculated by the following formula:

[0040]

[0041] where x is the logarithm of the earthquake intensity, and μ and σ are the parameters of the vulnerability curve;

[0042] According to the type and structural characteristics of each transportation facility, a damage probability function is established to calculate the damage degree of each node's transportation facilities during an earthquake. The damage probability function is expressed as:

[0043]

[0044] where P(D≥d|I) represents the probability that the damage degree of the facility is greater than or equal to d under the given earthquake intensity I, Φ represents the cumulative distribution function of the standard normal distribution, μ d and σ d respectively represent the mean and standard deviation of the damage degree d;

[0045] Then, the damage probability P(D|I) and the damage degree P(D≥d|I) are visually displayed through the Geographic Information System (GIS) platform.

[0046] As a further preference of the present invention, in calculating the connectivity of the transportation network model, the network average efficiency weighted by passenger flow is selected to represent the network connectivity performance,

[0047] that is, the reciprocal of the average shortest path length between all node pairs in the network;

[0048] The calculation formula for the average network efficiency before an earthquake is as follows:

[0049]

[0050] Among them, E avg(0) is the average network efficiency before the earthquake, n is the number of nodes in the initial network before the earthquake, and d ij is the shortest path length between node i and node j;

[0051] The calculation formula for the average network efficiency after the earthquake is as follows:

[0052]

[0053] Among them, E avg(t) is the average network efficiency at time t after the earthquake, N is the number of remaining effective nodes in the network after the earthquake damage, and d ij is the shortest path length between node i and node j;

[0054] N = n × (1 - P(D|I))

[0055] Then compare E avg(t) with E avg(0) to obtain the change in the connectivity rate index at time t after the earthquake, and draw a curve of the change in the connectivity rate.

[0056] As a further preference of the present invention, in the dynamic calculation of the post-earthquake network performance, the network efficiency formula is used to calculate the performance of the network at time t after the earthquake:

[0057]

[0058] Among them, W i and W j are the comprehensive weights of node i and node j respectively;

[0059] Then calculate the performance E weighted(t) of multiple post-earthquake networks according to different times t, and draw a curve of the change in network performance to obtain the change in the performance of the post-earthquake transportation network.

[0060] As a further preference of the present invention, the specific operation of evaluating the resilience of the transportation network includes quantitatively evaluating the seismic resilience index and resilience loss of the transportation network according to the network performance curve and resilience triangle at different times after the earthquake;

[0061] The calculation formula for the resilience index RI is as follows:

[0062]

[0063] Among them, RI is the resilience index, E weighted(t) is the network performance response function at time t, Eavg(0) is the initial system network connectivity performance during normal operation, \(t_0\) is the moment when the earthquake occurs, and \(t\) E is the moment when the post-earthquake repair is completed;

[0064] The calculation formula for resilience loss is:

[0065]

[0066] where \(RL\) is the resilience loss, and \(E\) weighted(t) is the network performance response function at time \(t\), and \(E\) avg(0) is the initial system network connectivity performance during normal operation, \(t_0\) is the moment when the earthquake occurs, and \(t\) E is the moment when the post-earthquake repair is completed;

[0067] Visualize and dynamically display the resilience index \(RI\) and resilience loss \(RL\) through the Geographic Information System (GIS) platform.

[0068] The beneficial effects of the present invention are as follows:

[0069] 1. The present invention comprehensively considers the earthquake parameters, social economy, regional transportation network during the earthquake, and real-time detection data after the earthquake, and can more comprehensively evaluate the risk of the regional transportation network under the earthquake. The present invention also uses the Monte Carlo simulation method, combines with the seismic vulnerability model, simulates the damage scenarios of the earthquake to the transportation network, evaluates the damage probability and functional loss of transportation facilities; calculates the connectivity of the transportation network model, and dynamically calculates the performance change of the transportation network after the earthquake based on the connectivity index and weight of the transportation network; then evaluates the resilience of the transportation network according to the network performance recovery curve under the recovery strategy, and can reflect the performance change of the transportation network in the earthquake in real time and dynamically.

[0070] 2. The present invention also uses historical earthquake data and disaster simulation results to quantify the additional risk of secondary disasters to the transportation network and incorporates it into the dynamic risk assessment system. Considering the potential impact of secondary disasters (such as fires, landslides) triggered by the earthquake on the transportation network, the risk assessment system is further improved.

[0071] 3. The present invention visually displays the evaluation results through the Geographic Information System (GIS) platform, intuitively presenting the damaged areas of the transportation network, the damage probability of key nodes, and the recovery progress. Provide a decision support module based on the risk assessment results, providing a scientific basis for the transportation management department to formulate emergency response strategies and optimize resource allocation.

[0072] Other advantages, objects and features of the present invention will be set forth in the following description, and to some extent will be obvious to those skilled in the art, or can be taught from the practice of the present invention. The objects and other advantages of the present invention can be achieved and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to make the objects, technical solutions and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:

[0074] Figure 1 It is a schematic flow chart of a method for dynamic risk assessment of a regional traffic network under the influence of a sudden earthquake according to the present invention;

[0075] Figure 2 It is a schematic structural diagram of the dynamic risk assessment system according to the present invention;

[0076] Figure 3 It is a schematic diagram of the seismic vulnerability curve according to the present invention;

[0077] Figure 4 It is a schematic diagram of the traffic network model according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0078] As Figures 1 to 4 shown, the present invention discloses a method for dynamic risk assessment of a regional traffic network under the influence of a sudden earthquake, including:

[0079] Collecting data and preprocessing, collecting seismic parameter data, socio-economic data, regional traffic network data during earthquake occurrence and real-time detection data after earthquake occurrence, and preprocessing the collected data.

[0080] The seismic parameter data includes data such as seismic intensity, epicenter location, focal depth and geological conditions; the socio-economic data includes data such as population density, economic activity distribution and key facility locations in the region; the regional traffic network data includes road network topology (such as the starting point, ending point, length, number of lanes, etc. of the road), road type, traffic flow (such as daily traffic flow, peak-hour flow, special-hour flow, etc.) and traffic facility distribution data; the real-time detection data after earthquake occurrence includes data such as road damage conditions, traffic flow changes and traffic facility operation status.

[0081] Data preprocessing: Cleaning and sorting the collected data, removing error data and duplicate data to ensure the accuracy and integrity of the data. For missing data, interpolation or statistical methods can be used for supplementation. For example, spatially matching the traffic network data with the ground motion intensity data to determine the ground motion intensity area where each traffic facility is located.

[0082] Build a traffic network model, using graph theory methods to build the traffic network model, and using regional traffic network data and socioeconomic data to perform weighted processing on the traffic network model.

[0083] In the traffic network model, nodes represent traffic facilities in the regional traffic network data, and edges represent connection facilities in the basic regional traffic network data; among them, traffic facilities include bridges, tunnels, and transportation hubs, and connection facilities include roads and railways. Take traffic facilities (such as bridges, tunnels, transportation hubs) as nodes in graph theory. Each node has its unique identifier and attributes, such as location, type (bridge, tunnel, transportation hub, etc.), and carrying capacity, etc. According to the actual situation of the traffic network, build an undirected graph or a directed graph. If the traffic flow can flow freely in both directions (such as a two-way road), then build an undirected graph; if the traffic flow has a clear directionality (such as a one-way road), then build a directed graph. Use an adjacency matrix or an adjacency list to store and represent the traffic network graph. The adjacency matrix is suitable for dense networks and can conveniently calculate the connectivity between nodes; the adjacency list is suitable for sparse networks, can save storage space, and is convenient for algorithms such as breadth-first search (BFS) and depth-first search (DFS).

[0084] The weighted processing includes traffic flow weighting, road topology weighting, and population density weighting.

[0085] Take the traffic flow as the weight of the edge. The road with a large traffic flow has a higher weight, indicating its higher importance in the traffic network. The traffic flow weight can be calculated by the following formula: The weight of the traffic flow is obtained by the ratio of the traffic flow of the i-th road to the maximum traffic flow in the regional roads.

[0086] The calculation of the traffic flow weight is expressed as:

[0087]

[0088] Among them, Flow i represents the traffic flow of the i-th road, and W 流量，i is the corresponding weight;

[0089] Road topology weighting: Consider the influence of factors such as the length of the road, the number of lanes, and the design speed on the traffic network. For example, a longer road may require more travel time, while a road with more lanes can carry a larger traffic flow. The weight of the road topology is calculated by the ratio of the road length, the number of lanes, and the speed limit of the i-th road to the maximum road length, the maximum number of lanes, and the maximum speed limit in the regional roads, and set corresponding weight coefficients for them respectively;

[0090] The calculation of the road topology weighting is expressed as:

[0091]

[0092] Among them, a, b, and c are adjustable weight coefficients, and W 拓扑，i is the corresponding weight;

[0093] Population density weighting: The population density is used as the weight of the node, indicating the importance of the area to the traffic network. Nodes with high population density have higher weights, indicating greater traffic demand in the area. The weight of the population density is calculated by the ratio of the population density in the area where the i-th node is located to the maximum population density of the nodes in the area;

[0094] The calculation of the population density weight is expressed as:

[0095]

[0096] Among them, the population density i represents the population density in the area where the i-th node is located, and W 人口，i is the corresponding weight;

[0097] Comprehensive weight calculation: The weights of social-physical factors such as traffic flow, road topology, and population density are comprehensively calculated to obtain a comprehensive weight matrix. Corresponding weight coefficients are set for the traffic flow weight, road topology weight, and population density weight respectively, and their sum is used to obtain the comprehensive weight;

[0098] The comprehensive weighting process is expressed as:

[0099] W 综合,i,j = α × W 流量，i，j + β × W 拓扑，i，j + γ × W 人口，i，j

[0100] Among them, α, β, and γ are adjustable weight coefficients, and W 综合,i,j represents the comprehensive weight from node i to node j.

[0101] Application examples:

[0102] Traffic congestion prediction: Using the traffic network model and the comprehensive weight, traffic congestion can be predicted. By monitoring and analyzing the traffic flow on high-weight roads, potential congestion areas can be detected in advance, and corresponding diversion measures can be formulated.

[0103] Emergency rescue route planning: In the event of disasters such as earthquakes, using the traffic network model and the comprehensive weight, the optimal emergency rescue route can be quickly planned. Roads and nodes with higher weights are preferentially selected to ensure that rescue personnel and supplies can reach the disaster area in a timely manner.

[0104] Through the above steps, a traffic network model based on graph theory can be constructed, and social-physical factors such as traffic flow, road topology, and population density are considered to weight the network model. This model can more accurately reflect the actual operating state of the traffic network and provide strong support for traffic planning, operation management, disaster response, etc.

[0105] Construct an earthquake vulnerability model. According to the type and structural characteristics of traffic network facilities, establish corresponding earthquake vulnerability models, substitute earthquake parameter data into the earthquake vulnerability models, and calculate the damage probability and damage degree of traffic facilities at each node during an earthquake. After an earthquake occurs, obtain real-time earthquake intensity distribution data, combine with the vulnerability model, and dynamically update the damage probability of traffic facilities. Consider the repair progress of traffic facilities and the impact of secondary disasters, and dynamically adjust the vulnerability parameters.

[0106] Use the method of earthquake intensity and structural vulnerability curves to calculate the damage probability of traffic facilities at each node during an earthquake; the vulnerability curve represents the probability of traffic facilities being in different damage states under different earthquake intensities.

[0107] Let the vulnerability curve be P(D|I), which represents the probability of traffic facilities being in damage state D under earthquake ground motion intensity I. Calculate the damage probability through the following formula:

[0108]

[0109] where x is the logarithm of earthquake intensity, and μ and σ are the parameters of the vulnerability curve.

[0110] According to the type and structural characteristics of each traffic facility, establish a damage probability function to calculate the damage degree of traffic facilities at each node during an earthquake. The damage probability function is expressed as:

[0111]

[0112] where P(D≥d|I) represents the probability that the damage degree of the facility is greater than or equal to d under the given earthquake ground motion intensity I, Φ represents the cumulative distribution function of the standard normal distribution, μ d and σ d represent the mean and standard deviation of the damage degree d respectively.

[0113] Then visualize the damage probability P(D|I) and the damage degree P(D≥d|I) through the Geographic Information System (GIS) platform.

[0114] Construct a risk dynamic assessment system, use the Monte Carlo simulation method, combine with the earthquake vulnerability model, simulate the damage scenarios of the earthquake on the traffic network, and evaluate the damage probability and functional loss of traffic facilities.

[0115] Monte Carlo Simulation:

[0116] Generate random samples: Generate a large number of random samples from the probability distribution of ground motion intensity, such as generating PGA samples from a normal distribution.

[0117] Simulate damage scenarios: Use the Monte Carlo simulation method, combined with the seismic vulnerability model, to simulate the damage scenarios of the earthquake on the transportation network. For each random sample, calculate the damage state and functional loss of transportation facilities.

[0118] Evaluate the probability of damage and functional loss:

[0119] Calculate the probability of damage: Statistically analyze the simulation results and calculate the probability of damage for each facility. For example, the probability of damage of a bridge at different damage levels can be calculated.

[0120] Evaluate functional loss: Evaluate the degree of functional loss according to the damage state of the facility. For example, the functional loss of a bridge at different damage levels can be calculated.

[0121] Case study

[0122] Taking the Beijing rail transit network as an example, calculate the network efficiency and resilience index under different seismic intensities. Calculate the seismic failure probability of stations, tunnels and bridge structures in the rail transit network according to the ground motion intensity and structural vulnerability model.

[0123] Analysis under different seismic intensities: Calculate the network performance indicators under different seismic intensities (such as PGA of 0.20g, 0.30g, 0.40g, 0.50g, 0.60g), and compare the changes in network performance under different seismic intensities.

[0124] Result analysis: Analyze the simulation results and evaluate the risk and resilience of the transportation network during an earthquake. For example, the resilience index and resilience loss of the network can be calculated to evaluate the seismic performance of the network.

[0125] Decision support: Provide decision support for the seismic design, maintenance and emergency response of the transportation network. For example, corresponding seismic measures and recovery strategies can be formulated according to the evaluation results.

[0126] Through the above steps, the Monte Carlo simulation method can be used, combined with the seismic vulnerability model, to simulate the damage scenarios of the earthquake on the transportation network, evaluate the probability of damage and functional loss of transportation facilities, and provide a scientific basis for the seismic performance evaluation and resilience improvement of the transportation network. Verify the accuracy and reliability of the model through actual data and simulation data, and optimize and adjust the model according to the verification results.

[0127] Calculate the connectivity of the traffic network model, and dynamically calculate the performance change of the traffic network after an earthquake based on the connectivity index and weight of the traffic network; then evaluate the resilience of the traffic network according to the network performance recovery curve under the recovery strategy.

[0128] In the calculation of the connectivity of the traffic network model, the network average efficiency weighted by passenger flow is selected to represent the network connectivity performance,

[0129] that is, the reciprocal of the average shortest path length between all node pairs in the network;

[0130] The calculation formula for the network average efficiency before the earthquake is:

[0131]

[0132] where, E avg(0) is the network average efficiency before the earthquake, n is the number of nodes in the initial network before the earthquake, and d ij is the shortest path length between node i and node j;

[0133] The calculation formula for the network average efficiency after the earthquake is:

[0134]

[0135] where, E avg(t) is the network average efficiency at time t after the earthquake, N is the number of remaining effective nodes in the network after the earthquake damage, and d ij is the shortest path length between node i and node j;

[0136] N = n × (1 - P(D|I))

[0137] Then compare E avg(t) with E avg(0) to obtain the change in the connectivity rate index at time t after the earthquake, and draw the connectivity rate change curve.

[0138] In the dynamic calculation of the network performance after the earthquake, the network efficiency formula is used to calculate the network performance at time t after the earthquake:

[0139]

[0140] where, W i and W j are the comprehensive weights of node i and node j respectively;

[0141] Then calculate the performance E weighted(t) of multiple post-earthquake networks at different times t, and draw the network performance change curve to obtain the performance change of the traffic network after the earthquake.

[0142] The specific operations for evaluating the resilience of the transportation network include quantitatively evaluating the seismic resilience index and resilience loss of the transportation network based on the network performance curve and resilience triangle at different times after the earthquake;

[0143] The calculation formula for the resilience index RI is:

[0144]

[0145] where RI is the resilience index, E weighted(t) is the network performance response function at time t, E avg(0) is the initial system network connectivity performance during normal operation, t0 is the earthquake occurrence time, t E is the time when the post-earthquake repair is completed;

[0146] The calculation formula for the resilience loss is:

[0147]

[0148] where RL is the resilience loss, E weighted(t) is the network performance response function at time t, E avg(0) is the initial system network connectivity performance during normal operation, t0 is the earthquake occurrence time, t E is the time when the post-earthquake repair is completed;

[0149] Visualize and dynamically display the resilience index RI and resilience loss RL through the Geographic Information System (GIS) platform.

[0150] Use the Monte Carlo simulation method, combined with the seismic vulnerability model, to simulate the damage scenarios of the transportation network by earthquakes, and evaluate the damage probability and functional loss of transportation facilities.

[0151] Based on the connectivity indicators of the transportation network (such as network efficiency, average path length) and traffic flow weighting, dynamically calculate the performance changes of the post-earthquake transportation network.

[0152] Consider the impact of emergency response capabilities (such as the number of rescue teams, availability of repair equipment) on the recovery of the transportation network, and evaluate the resilience of the transportation network by simulating the network performance recovery curves under different recovery strategies.

[0153] It also includes result visualization, visualizing the evaluation results through the Geographic Information System (GIS) platform, intuitively presenting the damaged areas of the transportation network, the damage probability of key nodes, and the recovery progress. Provide a decision support module based on the dynamic risk assessment results to provide a scientific basis for the transportation management department to formulate emergency response strategies and optimize resource allocation.

[0154] It also includes secondary disaster risk analysis. By combining the potential impacts of secondary disasters triggered by earthquakes (such as fires and landslides) on the transportation network, the dynamic risk assessment system is improved. Using historical earthquake data and disaster simulation results, the additional risks of secondary disasters to the transportation network are quantified and incorporated into the dynamic risk assessment system.

[0155] The total failure probability is the sum of the failure probability directly caused by the earthquake and the failure probability caused by secondary disasters; it is expressed as:

[0156] P 总 =P 地震 +P 次生

[0157] Wherein, P 总 is the total failure probability, P 地震 is the failure probability directly caused by the earthquake, and P 次生 is the failure probability caused by secondary disasters.

[0158] The present invention comprehensively considers the physical damage of earthquakes, the impacts of secondary disasters, and the dynamic impacts of social factors on the transportation network, and can more comprehensively evaluate the risks of regional transportation networks under earthquakes. By introducing a dynamic risk assessment model and Monte Carlo simulation technology, it can reflect the performance changes of the transportation network during the earthquake process in real time and provide real-time guidance for emergency response. The method combines geographic information system (GIS) and traffic simulation technology to realize the visual display of evaluation results, which is convenient for decision-makers to quickly understand and apply. The present invention is applicable to the seismic resilience assessment and emergency response management of transportation networks in earthquake-prone areas. Through dynamic risk assessment, the seismic capacity of the transportation system can be effectively improved, the long-term impacts of earthquakes on regional transportation can be reduced, and strong support can be provided for urban planning, transportation infrastructure construction, and emergency rescue. Provide decision-making support based on the risk assessment results, and provide a scientific basis for the traffic management department to formulate emergency response strategies and optimize resource allocation. According to the risk assessment results, emergency response suggestions are automatically generated, including traffic control measures, rescue team dispatching, and repair priority ranking, etc.

[0159] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A method for dynamic risk assessment of regional transportation network under the influence of sudden earthquake, characterized by: include: Data collection and preprocessing: collect earthquake parameter data, socioeconomic data, regional transportation network data when an earthquake occurs, and real-time detection data after an earthquake occurs, and preprocess the collected data; Construct a transportation network model using graph theory and use regional transportation network data and socio-economic data to weight the transportation network model; Construct an earthquake vulnerability model. According to the type and structural characteristics of transportation network facilities, establish a corresponding earthquake vulnerability model, substitute earthquake parameter data into the earthquake vulnerability model, and calculate the damage probability and degree of damage of transportation facilities at each node in an earthquake; Construct a dynamic risk assessment system, use the Monte Carlo simulation method, combined with the earthquake vulnerability model, simulate the earthquake damage scenario on the transportation network, and evaluate the damage probability and functional loss of transportation facilities; calculate the connectivity of the transportation network model, and based on the connectivity indicators and weights of the transportation network, dynamically calculate the performance changes of the transportation network after the earthquake; then evaluate the resilience of the transportation network based on the network performance recovery curve under the recovery strategy.

2. According to claim 1, a method for dynamic risk assessment of regional transportation network under the influence of sudden earthquakes is characterized by: It also includes result visualization, which visualizes the assessment results through the Geographic Information System (GIS) platform to intuitively present the damaged areas of the transportation network, the probability of damage to key nodes, and the recovery progress.

3. The method for dynamic risk assessment of regional transportation network under the influence of sudden earthquake according to claim 1 is characterized by: It also includes secondary disaster risk analysis, combining the potential impact of secondary disasters caused by earthquakes on the transportation network to improve the dynamic risk assessment system; using historical earthquake data and disaster simulation results to quantify the additional risks of secondary disasters to the transportation network and incorporate them into the dynamic risk assessment system; The total damage probability is the sum of the damage probability caused directly by the earthquake and the damage probability caused by secondary disasters; it is expressed as: P 总 =P 地震 +P 次生 Among them, P 总 is the total failure probability, P 地震 is the probability of damage caused directly by earthquake, P 次生 is the probability of damage caused by secondary disasters.

4. The method for dynamic risk assessment of regional transportation network under the influence of sudden earthquake according to claim 1 is characterized by: The earthquake parameter data include earthquake intensity, epicenter location, focal depth and geological conditions; The socio-economic data include the population density, economic activity distribution and key facility locations of the region; Regional transportation network data include road network topology, road types, traffic flow and distribution of transportation facilities; Real-time detection data after an earthquake includes road damage, traffic flow changes, and the operating status of transportation facilities.

5. The method for dynamic risk assessment of regional transportation network under the influence of sudden earthquake according to claim 1 is characterized by: In the transportation network model, nodes represent transportation facilities in the regional transportation network data, and edges represent connection facilities in the regional transportation network basic data; transportation facilities include bridges, tunnels and transportation hubs, and connection facilities include roads and railways; weighted processing includes traffic flow weighting, road topology weighting and population density weighting; The weight of traffic flow is obtained by the ratio of the traffic flow of the ith road to the maximum flow among the roads in the region; Traffic flow weight calculation is expressed as: Among them, the flow i represents the traffic flow of the ith road, W 流量，i is the corresponding weight; The weight of the road topology is calculated by the ratio of the road length, number of lanes and speed limit of the ith road to the maximum road length, maximum number of lanes and maximum speed limit of the roads in the area, and the corresponding weight coefficients are set for them respectively; The weighted calculation of road topology is expressed as: Among them, a, b and c are adjustable weight coefficients, W 拓扑，i is the corresponding weight; The weight of population density is calculated by the ratio of the population density of the region where the i-th node is located to the maximum population density of the nodes in the region; The population density weight calculation is expressed as: Among them, population density i represents the population density of the area where the i-th node is located, W 人口，i is the corresponding weight; Then, corresponding weight coefficients are set for the traffic flow weight, road topology weight and population density weight, and the sum of them is used to obtain the comprehensive weight; The comprehensive weighted processing is expressed as: IN 综合,i,j =α×W 流量，i，j +β×W 拓扑，i，j +γ×W 人口，i，j Among them, a, β and γ are adjustable weight coefficients, W 综合,i,j Represents the comprehensive weight from node i to node j.

6. The method for dynamic risk assessment of regional transportation network under the influence of sudden earthquake according to claim 1 is characterized by: The probability of damage to the transportation facilities at each node in an earthquake is calculated by using the earthquake intensity and structural fragility curve method; the fragility curve represents the probability of the transportation facilities being in different damage states under different earthquake intensities; Assume that the fragility curve is P(D|I), which represents the probability of a transportation facility being damaged in state D under an earthquake intensity I. The damage probability is calculated by the following formula: Where x is the logarithm of earthquake intensity, μ and σ are the parameters of the fragility curve; According to the type and structural characteristics of each transportation facility, a damage probability function is established to calculate the damage degree of each node's transportation facility in an earthquake. The damage probability function is expressed as: Where P(D≥d|I) represents the probability that the damage degree of the facility is greater than or equal to d under a given earthquake intensity I, Φ represents the cumulative distribution function of the standard normal distribution, and μ d and σ d They represent the mean and standard deviation of the damage degree d, respectively; Then the destruction probability P(D|I) and damage degree P(D≥d|I) are visualized through the geographic information system GIS platform.

7. The method for dynamic risk assessment of regional transportation network under the influence of sudden earthquake according to claim 5 is characterized by: In the calculation of the connectivity of the traffic network model, the average network efficiency weighted by passenger flow is used to represent the network connectivity performance. That is, the reciprocal of the average shortest path length between all pairs of nodes in the network; The calculation formula of the average network efficiency before the earthquake is: Among them, E avg(0) is the average network efficiency before the earthquake, n is the number of nodes in the initial network before the earthquake, d ij is the shortest path length between node i and node j; The calculation formula for the average network efficiency after an earthquake is: Among them, E avg(t) is the average network efficiency at time t after the earthquake, N is the number of remaining valid nodes in the network after the earthquake damage, d ij is the shortest path length between node i and node j; N=n×(1-P(D|I)) Then E avg(t) With E avg(0) By comparison, the change of connectivity index at time t after the earthquake is obtained, and the connectivity change curve is drawn.

8. The method for dynamic risk assessment of regional transportation network under the influence of sudden earthquake according to claim 7 is characterized by: In the dynamic calculation of post-earthquake network performance, the network efficiency formula is used to calculate the performance of the network at time t after the earthquake: Among them, W i and W j are the comprehensive weights of node i and node j respectively; Then calculate the performance E of multiple post-earthquake networks according to different times t weighted(t) , and draw the network performance change curve to obtain the performance changes of the transportation network after the earthquake.

9. The method for dynamic risk assessment of regional transportation network under the influence of sudden earthquake according to claim 8 is characterized by: The specific operation of evaluating the resilience of the transportation network includes quantitatively evaluating the seismic resilience index and resilience loss of the transportation network based on the network performance curve and resilience triangle at different times after the earthquake; The calculation formula of toughness index RI is: Among them, RI is the toughness index, E weighted(t) is the network performance response function at time t, E avg(0) is the initial system network connectivity performance during normal operation, t0 is the time when the earthquake occurs, and t E The moment when post-earthquake restoration is completed; The calculation formula for toughness loss is: Among them, RL is the toughness loss, E weighted(t) is the network performance response function at time t, E avg(0) is the initial system network connectivity performance during normal operation, t0 is the time when the earthquake occurs, and t E The moment when post-earthquake restoration is completed; The resilience index RI and resilience loss RL are visualized and displayed dynamically through the geographic information system GIS platform.

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