Comprehensive traffic network toughness evaluation method based on multiple indexes

Abstract

The invention relates to the technical field of traffic emergency evaluation, and discloses a comprehensive traffic network toughness evaluation method based on multiple indexes, which comprises the following steps of: in a pre-stage, selecting a plurality of key indexes for evaluating the traffic network toughness; an in-event stage: when an emergency occurs, acquiring real-time traffic data based on the evaluation model; and a post-event stage: after the emergency event is ended, analyzing the influence of the event on the traffic network, and evaluating the recovery capability of the traffic network based on the evaluation model in combination with the restoration time of the facility, the traffic flow recovery condition and the emergency resource allocation record. A topological structure index, a traffic flow index, a facility reliability index and an emergency response capability index are adopted, the robustness of the traffic network under different dimensions can be comprehensively evaluated, through multi-dimensional quantitative analysis, the impact resistance, the recovery capability and the emergency management capability of the network can be comprehensively evaluated, and the evaluation accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic emergency assessment, and in particular to a comprehensive traffic network resilience assessment method based on multiple indicators. Background Art

[0002] In emergencies, the continuity and rapid recovery capabilities of transportation networks directly affect urban emergency response, material transportation, and social stability. Existing transportation network management and evaluation methods are often limited to one aspect, making it difficult to comprehensively evaluate the global resilience of transportation networks.

[0003] The existing technology mainly has the following deficiencies:

[0004] Single-dimensional assessment: Current transportation network assessment methods mostly focus on analyzing a specific aspect, such as traffic flow, road conditions, etc., and lack a comprehensive assessment of the impact resistance of the entire network system. There are few comprehensive analyses of multiple dimensions such as the topological structure, facility reliability, and emergency response capabilities of the transportation network, and it is impossible to measure the resilience of the transportation network from a global perspective.

[0005] Lack of advance prediction and optimization: Existing technologies are mostly passive responses to emergencies, lacking effective advance prediction and preventive measures. When faced with natural disasters or man-made accidents, traffic managers often lack the ability to identify vulnerable nodes and paths in advance, resulting in the inability of emergency plans to fully play their role and affecting the rational allocation of emergency resources.

[0006] Insufficient dynamic adjustment: When an emergency occurs, the traffic network needs to be adjusted according to real-time data, but the existing technologies are not efficient enough in path optimization and resource scheduling in emergency response, making it difficult to achieve intelligent and dynamic management. This results in longer arrival time of emergency resources, slower rescue and recovery efficiency, and prolonged traffic interruption.

[0007] Weak recovery capacity assessment: Existing traffic assessment systems often lack detailed analysis of the recovery capacity of the traffic network after an incident. Core data such as facility repair time and traffic flow recovery progress are not fully integrated, resulting in traffic managers being unable to fully understand the efficiency and bottlenecks of network recovery and unable to make optimized plans for the next emergency.

[0008] To this end, those skilled in the art provide a comprehensive transportation network resilience assessment method based on multiple indicators to solve the problems raised in the above background technology. Summary of the invention

[0009] In view of the shortcomings of the existing technology, the present invention provides a comprehensive transportation network resilience assessment method based on multiple indicators, which solves the problem that the existing technology is unable to comprehensively monitor and optimize the resilience of the transportation network before, during and after the event, resulting in low management efficiency, slow emergency response and slow post-disaster recovery.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: a comprehensive transportation network resilience assessment method based on multiple indicators, comprising the following steps:

[0011] Pre-event stage: by selecting a number of key indicators for evaluating the resilience of the transportation network, including topological structure indicators, traffic flow indicators, facility reliability indicators and emergency response capability indicators, an evaluation model of the comprehensive transportation network is constructed based on these indicators;

[0012] During the incident: When an emergency occurs, based on the evaluation model, real-time traffic data is obtained, including real-time monitoring data of infrastructure such as roads and bridges, traffic flow monitoring data, and emergency response records, and these data are used to evaluate the immediate performance of the network;

[0013] Post-event stage: After the emergency is over, analyze the impact of the incident on the transportation network, and evaluate the recovery capacity of the transportation network based on the evaluation model, taking into account the repair time of facilities, traffic flow recovery and emergency resource allocation records.

[0014] Preferably, the topology index includes node connectivity and link redundancy, and the calculation formula of the node connectivity is:

[0015]

[0016] Among them, C is the connectivity, N is the total number of nodes, and A ij is the connection between node i and node j, A ij =1 indicates connection, A ij =0 means no connection.

[0017] Preferably, the traffic flow index includes daily traffic distribution and peak load, and the saturation of traffic flow is calculated by the following formula:

[0018]

[0019] Among them, S is the traffic flow saturation, V i is the actual traffic flow of road i, C i is the design capacity of road i, and n is the total number of road sections.

[0020] Preferably, the facility reliability index calculates the facility damage probability by the following formula:

[0021]

[0022] Among them, P f is the overall damage probability of the facility, p i is the damage probability of the ith facility, and n is the total number of facilities.

[0023] Preferably, the emergency response capability index is evaluated by an emergency resource allocation model, and the model includes the following calculation formula:

[0024]

[0025] Among them, T r is the shortest time for resource arrival, d ij is the distance from resource point j to accident point i, v j is the vehicle speed of the resource point, and R is the set of available resource points.

[0026] Preferably, in the prior stage, the traffic network is stress-tested by simulating natural disasters, and the stress-test model is constructed based on a Bayesian network, and the calculation formula is:

[0027]

[0028] Among them, P(T f ) is the prior probability of traffic system failure, E i is a possible event, P(E i |T f ) is the conditional probability of an event occurring in the event of a system failure, and n is the total number of events that affect the transportation network.

[0029] Preferably, in the process stage, the network path is dynamically optimized by real-time monitoring of node connectivity and traffic flow changes combined with the Dijkstra algorithm, and the calculation formula of the algorithm is:

[0030] d(u)=min(d(u),d(v)+w(u,v))

[0031] Among them, d(u) is the shortest path to node u, d(v) is the shortest path to node v, and w(u,v) is the path weight connecting nodes u and v.

[0032] Preferably, in the post-event phase, by analyzing the facility repair time and traffic flow recovery situation, the traffic flow recovery rate is calculated by the following formula

[0033]

[0034] Among them, R v(t) is the traffic flow recovery rate at time t, V(t) is the traffic flow at time t, V0 is the lowest traffic flow after the event, V b is the baseline traffic flow before the incident.

[0035] Preferably, the coordination between the facility repair time and the emergency resources is evaluated in the post-event phase by the following formula:

[0036]

[0037] Among them, C f is the coordination index between facility repair and emergency resources, R i is the repair resource usage of the i-th facility, T i is the repair time of the ith facility, and n is the total number of facilities.

[0038] Preferably, the assessment model combines the indicators for quantitative analysis in the pre-, mid- and post-stages, and generates a resilience assessment report for the comprehensive transportation network based on the calculation results.

[0039] The present invention provides a comprehensive transportation network resilience assessment method based on multiple indicators. It has the following beneficial effects:

[0040] 1. The present invention uses topological structure indicators, traffic flow indicators, facility reliability indicators and emergency response capability indicators to comprehensively evaluate the robustness of the transportation network in different dimensions. Through multi-dimensional quantitative analysis, it can comprehensively evaluate the network's impact resistance, recovery ability and emergency management ability, thereby improving the scientificity and accuracy of the evaluation.

[0041] 2. The evaluation model of the present invention covers the pre-event, in-event and post-event stages, and conducts a full-cycle analysis of the resilience of the transportation network through stress testing, real-time data monitoring and recovery capacity assessment. Predicting vulnerable links in advance, dynamically optimizing paths and resource scheduling during the event, and evaluating recovery speed and resource coordination after the event will help improve the response efficiency of the transportation network throughout the entire process.

[0042] 3. The present invention can dynamically optimize the emergency response in emergencies by introducing the Dijkstra algorithm and the emergency resource allocation model. The model calculates the shortest path and resource scheduling time based on real-time data to ensure that emergency resources can quickly arrive at the scene and minimize the impact of the incident on the transportation network.

[0043] 4. The present invention generates a comprehensive resilience assessment report by quantifying assessment indicators, helping traffic managers identify potential risks, develop optimization plans, and improve emergency plans. The report provides a scientific basis for daily management and emergency response, and promotes the continuous optimization of the transportation network and the enhancement of risk resistance.

[0044] 5. By analyzing the facility repair time and traffic flow recovery, the model of the present invention effectively evaluates the recovery speed and efficiency of the transportation network after an emergency, provides data support for the rapid recovery of the transportation network, and helps to minimize the traffic interruption time and its negative impact on the social economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Please refer to the attached Figure 1 The embodiment of the present invention provides a comprehensive transportation network resilience assessment method based on multiple indicators, comprising the following steps:

[0048] Pre-event stage: By selecting multiple key indicators for evaluating the resilience of transportation networks, including topological structure indicators, traffic flow indicators, facility reliability indicators, and emergency response capability indicators, an evaluation model for the comprehensive transportation network is constructed based on these indicators;

[0049] Specifically, a scientific evaluation model was constructed by selecting multiple key indicators to comprehensively evaluate the resilience of the integrated transportation network. These indicators include: topological structure indicators, traffic flow indicators, facility reliability indicators, and emergency response capability indicators. Each indicator quantitatively analyzes the resilience of the transportation network from different dimensions. First, topological structure indicators such as node connectivity and link redundancy can effectively measure the ability of the transportation network to maintain normal operation under emergencies, ensuring that when some nodes or roads are damaged, traffic flows can be smoothly connected through other paths. By calculating the connectivity and path redundancy of nodes in the network, possible weak links in the network can be identified, so that corresponding redundant designs can be planned in advance to improve the robustness of the network.

[0050] Secondly, traffic flow indicators can help managers understand the carrying capacity of the transportation network under normal and high-pressure load conditions by monitoring daily traffic distribution and peak-time carrying capacity. By calculating traffic flow saturation and flow / capacity ratio, bottleneck areas that may cause traffic congestion can be identified in a timely manner, helping to optimize the distribution of traffic flow and improve the operating efficiency of the network. Furthermore, facility reliability indicators monitor the status of transportation infrastructure (such as roads, bridges, etc.) and assess the probability of damage in emergencies, thereby ensuring the stability of the network's infrastructure when disasters occur. Using the facility health index (FHI) or damage probability model, the operating status of the facility can be quantified, and maintenance or renewal measures can be planned in advance, thereby reducing the impact of infrastructure damage on the overall operation of the transportation network.

[0051] Finally, the emergency response capability index evaluates the ability of the transportation network to respond to emergencies by analyzing the deployment of emergency resources and response time. By real-time monitoring of the dispatch path and time of emergency vehicles, it can ensure that emergency resources arrive at the accident site in the shortest time, thereby minimizing the impact of the incident on the transportation network. Based on these indicators, the evaluation model constructed integrates multi-dimensional data and uses advanced calculation methods such as Bayesian networks and shortest path algorithms (such as Dijkstra algorithm). It can accurately simulate the impact of emergencies on the transportation network and provide systematic quantitative results to help managers identify potential weak links in advance, optimize emergency plans, and improve the overall response capability and recovery speed of the network in emergencies.

[0052] The working principle of this evaluation model is based on the dual combination of data-driven and model-driven. It can dynamically adjust the network evaluation results according to different scenario simulations and actual data to ensure that it adapts to different types of emergencies. The beneficial effect is that this method can not only comprehensively analyze the resilience of the transportation network, but also improve the decision-making efficiency of managers in practical applications, help identify weak areas in advance, formulate optimization plans, and respond quickly when emergencies occur, thereby greatly improving the impact resistance and recovery capacity of the transportation network, ensuring the continuous and stable operation of urban transportation, and effectively reducing the negative impact of emergencies on social stability and economic development.

[0053] Topological structure indicators include node connectivity and link redundancy. The calculation formula of node connectivity is:

[0054]

[0055] Among them, C is the connectivity, N is the total number of nodes, and A ij is the connection between node i and node j, A ij =1 indicates connection, A ij =0 means no connection.

[0056] Specifically, as an important part of the comprehensive transportation network resilience assessment, the topological structure index covers two core indicators, node connectivity and link redundancy, to measure the ability of the transportation network to maintain effective connection in emergencies. First, node connectivity can describe the overall connectivity of nodes in the network and measure the connection between any two nodes in the network, thereby reflecting the robustness of the network when some nodes or paths fail. The calculation formula for node connectivity is:

[0057]

[0058] Among them, C is the connectivity, N is the total number of nodes in the network, and A i j represents the connection between node i and node j. If A i j=1, it means there is a direct connection between node i and node j; if A ij = 0, it means there is no direct connection between the two nodes. By quantifying the connectivity between all nodes in the traffic network, we can accurately measure the overall connectivity of the network and identify whether there is a phenomenon of over-reliance on a single node or path in the network. In practice, high connectivity means that even if some nodes or paths fail, the network can still maintain normal traffic flow through other paths, thereby enhancing the network's ability to resist shocks.

[0059] In addition, the link redundancy index can be combined to further enhance the robustness assessment capability of the network. Link redundancy reflects whether there are redundant and alternative paths in the network, helping to ensure that when certain connections or nodes fail, traffic can still flow through redundant links, thereby avoiding network paralysis. High link redundancy can not only alleviate the impact of emergencies, but also reduce the pressure on a single link during daily peak traffic, thereby improving the overall efficiency of the transportation network.

[0060] The working principle of this algorithm is based on the quantification of network connectivity in graph theory. By analyzing each node and its connection path in the traffic network, the connectivity and redundancy of the entire network in various scenarios can be calculated. The calculation of node connectivity and link redundancy can not only reflect the current status of the network, but also simulate the network performance under different failure or emergency scenarios. For example, after some nodes fail, whether the network can still maintain flow through alternative paths.

[0061] The beneficial effect is that the use of node connectivity and link redundancy as core evaluation indicators can comprehensively and systematically evaluate the robustness and impact resistance of the transportation network. The quantitative evaluation of these indicators can help traffic managers discover potential weak nodes and paths in the network in advance, and take measures to optimize the structural design of the network, increase redundant paths, and enhance the network's ability to respond to emergencies. Especially in the event of natural disasters or major accidents, high connectivity and high redundancy can significantly reduce the possibility of traffic interruption, ensure the smooth flow of emergency rescue channels, and thus improve the overall emergency response efficiency.

[0062] Traffic flow indicators include daily flow distribution and peak load. The saturation of traffic flow is calculated by the following formula:

[0063]

[0064] Among them, S is the traffic flow saturation, V i is the actual traffic flow of road i, C i is the design capacity of road i, and n is the total number of road sections.

[0065] Specifically, traffic flow indicators, as an important parameter for measuring the resilience of the comprehensive transportation network, mainly include two core contents: daily traffic distribution and peak load capacity. By monitoring and analyzing traffic flow, the performance of the transportation network under normal operating conditions and high load conditions can be accurately evaluated. Especially when emergencies occur, these indicators can provide key data for traffic management departments to help evaluate the stability and congestion risks of the network. Specifically, the saturation of traffic flow is an important indicator reflecting the load of the transportation network, measuring whether the road is close to or exceeds its design carrying capacity. The calculation formula for traffic flow saturation is:

[0066]

[0067] Among them, S is the traffic flow saturation, V i is the actual traffic flow of road i, C iis the design capacity of road i, and n is the total number of road sections. The closer the traffic flow saturation value is to 1, the closer the load of the road is to its design limit, the greater the flow pressure is, and there is a risk of traffic congestion; when the saturation value is greater than 1, it means that the road flow has exceeded the design capacity, which may cause serious traffic jams or even traffic system collapse. Dynamically calculating the traffic flow and design capacity of multiple roads can accurately reflect the load level of the entire transportation network. Especially during peak hours, the saturation of each road section can help managers identify traffic bottleneck areas and formulate corresponding diversion measures to prevent local congestion from spreading to the entire network. The working principle of this algorithm is to monitor the actual flow of each road in real time, compare it with its design carrying capacity, and calculate its saturation. By taking a weighted average of the saturation of all relevant road sections, the overall load of the entire region or transportation network can be obtained, and then the flow pressure of the network can be evaluated. Especially during peak hours, by calculating the saturation of each road section, it is possible to predict which areas are prone to congestion, and then take optimization measures in advance such as signal light adjustment, route replanning, and vehicle diversion.

[0068] The facility reliability index calculates the probability of facility damage using the following formula:

[0069]

[0070] Among them, P f is the overall damage probability of the facility, p i is the damage probability of the ith facility, and n is the total number of facilities.

[0071] Specifically, in the present invention, the facility reliability index is used to evaluate the robustness and damage resistance of various types of infrastructure (such as roads, bridges, tunnels, etc.) in the transportation network in emergencies, to ensure that the infrastructure can maintain normal or partial functions when natural disasters or man-made accidents occur, and to ensure the operation of the transportation network. Facility reliability quantifies the damage probability of each infrastructure and ultimately calculates the damage probability of the entire facility in the transportation network, thereby providing managers with accurate information on the status of the facility and helping to formulate maintenance and repair strategies. The calculation formula for the facility damage probability is:

[0072]

[0073] Among them, P f is the overall damage probability of the facility, p i is the damage probability of the ith facility, and n is the total number of facilities. This formula calculates the comprehensive damage risk of the entire network by considering the individual damage probability of each facility in the network. The damage probability of each facility p iIt can be evaluated based on its historical failure data, environmental conditions (such as earthquakes, floods, etc.) and structural status (such as aging, defects, etc.). The independent damage probability of each facility in the formula is used for multiplication, and then the overall damage probability is obtained by subtracting the multiplication result from 1, indicating the possibility that at least one facility in the entire transportation network is damaged. The working principle of this algorithm is to integrate the independent damage risk of each facility to obtain the comprehensive probability of facility failure in the entire transportation network in an emergency. By modeling the independent damage probability of all facilities in the network, managers can analyze the damage risk of each facility, and then repair or reinforce the weak links in the network (i.e., facilities with high damage probability) in advance to reduce traffic interruptions caused by facility failures.

[0074] The beneficial effect is that the calculation of the probability of facility damage provides managers with a tool to quantitatively analyze the robustness and risk resistance of transportation infrastructure. This method can not only help predict the possibility of failure of individual facilities in disasters, but also conduct a comprehensive assessment of the overall damage risk of the entire network, helping managers to prioritize the identification and treatment of high-risk facilities, thereby improving the network's ability to resist shocks. Before an emergency occurs, managers can plan the maintenance and repair of infrastructure in advance based on the results of the model, optimize facility management strategies, and minimize traffic interruptions and economic losses when accidents occur. After an emergency occurs, through real-time updates and assessments of the probability of facility damage, managers can quickly determine the extent of damage and prioritize the restoration of key facilities, ensuring that the transportation network can resume normal operations as soon as possible, thereby improving the resilience and sustainability of the comprehensive transportation network.

[0075] The emergency response capability index is evaluated through the emergency resource allocation model, which includes the following calculation formula:

[0076]

[0077] Among them, T r is the shortest time for resource arrival, d ij is the distance from resource point j to accident point i, v j is the vehicle speed of the resource point, and R is the set of available resource points.

[0078] Specifically, the model calculates the time from each resource point to the accident point based on the location, distance and speed of all available resource points, selects the resources with the shortest time for priority scheduling, and ensures that the fastest rescue resources can arrive at the scene as soon as possible.

[0079] How it works: The model dynamically calculates the arrival time of each emergency resource point and selects the optimal path to dispatch resources, ensuring that emergency needs at the accident site can be responded to in the shortest possible time. By evaluating traffic conditions and resource locations in real time, the model can respond quickly.

[0080] The beneficial effect is that the model significantly reduces emergency response time and improves rescue efficiency through the optimal emergency resource scheduling method. Regardless of traffic congestion or complex road conditions, the model can dynamically adjust the scheduling plan to ensure that emergency resources are quickly and accurately allocated, minimize the impact of emergencies on the traffic network, and ensure the timeliness of traffic restoration and emergency rescue.

[0081] In the pre-event stage, the traffic network is stress-tested by simulating natural disasters. The stress-test model is built based on the Bayesian network, and the calculation formula is:

[0082]

[0083] Among them, P(T f ) is the prior probability of traffic system failure, E i is a possible event, P(E i |T f ) is the conditional probability of an event occurring in the event of a system failure, and n is the total number of events that affect the transportation network.

[0084] Specifically, through this formula, the system first evaluates the basic probability of traffic network failure based on historical data and environmental conditions, and then combines the impact of each emergency on the network to derive the overall failure probability of the network in a disaster scenario.

[0085] Working principle: The Bayesian network model integrates the impact of different events (such as earthquakes, floods, etc.) on the transportation network, transforms complex risk factors into systematic probability analysis, and dynamically calculates the impact of each event on the overall operation of the network.

[0086] The beneficial effect is that the model can predict the vulnerable links of the transportation network in advance by simulating different emergency scenarios, help transportation managers formulate prevention and optimization strategies, enhance the network's disaster resistance, reduce the interruption and damage caused by sudden disasters to the transportation network, and improve the network's robustness and emergency response efficiency.

[0087] During the incident: When an emergency occurs, based on the evaluation model, real-time traffic data is obtained, including real-time monitoring data of infrastructure such as roads and bridges, traffic flow monitoring data, and emergency response records. These data are used to evaluate the immediate performance of the network.

[0088] During the process, the network path is dynamically optimized by real-time monitoring of node connectivity and traffic flow changes combined with the Dijkstra algorithm. The calculation formula of the algorithm is:

[0089] d(u)=min(d(u),d(v)+w(u,v))

[0090] Among them, d(u) is the shortest path to node u, d(v) is the shortest path to node v, and w(u,v) is the path weight connecting nodes u and v.

[0091] Specifically, in the mid-event stage of the present invention, when an emergency occurs, real-time traffic data is obtained based on a pre-built evaluation model to conduct an immediate network performance evaluation. The data includes real-time monitoring data of infrastructure such as roads and bridges, traffic flow monitoring data, and emergency response records. These data can accurately reflect the current operating status of the transportation network, the condition of facilities, and the distribution of traffic flow, facilitating immediate decision-making and emergency response. At this stage, the node connectivity and traffic flow changes of the transportation network are tracked through a real-time monitoring system. In order to ensure smooth traffic flow and that emergency rescue vehicles can quickly reach the scene of the accident, the Dijkstra algorithm is used to dynamically optimize the paths of the transportation network. The algorithm calculates the shortest path, and the formula is:

[0092] d(u)=min(d(u),d(v)+w(u,v))

[0093] Among them, d(u) is the shortest path of node u, d(v) is the shortest path of node v, and w(u,v) is the path weight between nodes u and v. This formula optimizes the vehicle's travel path by dynamically updating the shortest path between nodes, ensuring that the transportation network can efficiently respond to changes in the event of an emergency or traffic interruption. The beneficial effect is that through real-time monitoring and dynamic path optimization, the dispatch of emergency resources and vehicle paths can be adjusted in a timely manner, traffic delays and congestion can be reduced, and the emergency response speed and dynamic adaptability of the transportation network can be effectively improved.

[0094] Post-event stage: After the emergency is over, analyze the impact of the incident on the transportation network, and evaluate the recovery capacity of the transportation network based on the evaluation model, taking into account the repair time of facilities, traffic flow recovery and emergency resource allocation records.

[0095] In the post-event phase, by analyzing the facility repair time and traffic flow recovery, the traffic flow recovery rate is calculated using the following formula:

[0096]

[0097] Among them, R v (t) is the traffic flow recovery rate at time t, V(t) is the traffic flow at time t, V0 is the lowest traffic flow after the event, V b is the baseline traffic flow before the incident.

[0098] In the post-event phase, the coordination between facility repair time and emergency resources is evaluated using the following formula:

[0099]

[0100] Among them, C f is the coordination index between facility repair and emergency resources, R i is the repair resource usage of the i-th facility, T i is the repair time of the ith facility, and n is the total number of facilities.

[0101] Specifically, in the post-event phase of the present invention, after the emergency is over, it is necessary to conduct a comprehensive assessment of the impact of the incident on the transportation network, analyze the damage to the facilities, the progress of repairs, the recovery of traffic flow, and the deployment records of emergency resources. Through these data, the recovery capacity of the transportation network is quantitatively evaluated based on the evaluation model. The focus is on evaluating the efficiency of facility repairs and the speed at which the network returns to normal after the incident.

[0102] In terms of traffic flow recovery, the traffic flow recovery rate is calculated by analyzing the changes in traffic flow after the incident. The formula is:

[0103]

[0104] Among them, R v (t) is the traffic flow recovery rate at time t, V(t) is the actual traffic flow at time t, V0 is the lowest traffic flow after the event, and V b is the baseline traffic flow before the event. This formula evaluates the traffic flow status of the network at each moment during the recovery process by comparing the traffic flow before and after the event.

[0105] In addition, it is also necessary to evaluate the coordination between facility repair time and emergency resources, which can be calculated using the following formula:

[0106]

[0107] Among them, C f is the coordination index between facility repair and emergency resources, R i is the repair resource usage of the i-th facility, T i is the repair time of the ith facility, and n is the total number of facilities. This formula can quantify the matching degree between facility repair and emergency resources in terms of time and resources, thereby judging the efficiency of the repair process. The beneficial effect is that through detailed analysis in the post-event stage, the recovery progress of the transportation network after the emergency can be accurately understood, providing data support for future facility improvement and emergency management, helping to optimize resource allocation and repair plans, and ensuring that the transportation network can be quickly and effectively restored after the event.

[0108] The assessment model combines indicators for quantitative analysis in the pre-, mid- and post-stages, and generates a resilience assessment report for the comprehensive transportation network based on the calculation results.

[0109] Specifically, in the pre-event stage, the model mainly relies on topological structure indicators, traffic flow indicators, facility reliability indicators, and emergency response capability indicators to simulate scenarios of emergencies (such as natural disasters or accidents) to conduct stress tests, identify vulnerable points in the network in advance, and provide reference data for optimized design and emergency preparedness. In this stage, methods such as Bayesian networks are used to calculate the failure probability of the transportation network under different emergencies, predict possible risks, and generate preliminary prevention and improvement suggestions.

[0110] During the incident phase, the model obtains real-time health monitoring data of infrastructure such as roads and bridges, traffic flow monitoring data, and emergency response dispatch data. Through path optimization tools such as the Dijkstra algorithm, the model can dynamically adjust node connectivity and traffic flow changes according to the current network status to ensure that key channels of the transportation network remain unobstructed during emergencies. Emergency resource allocation ensures the efficient implementation of emergency rescue or repair operations based on the shortest time and path calculated in real time, reducing the impact of the incident on the transportation network.

[0111] In the post-event stage, the model comprehensively analyzes the recovery capacity of the transportation network by combining the repair time of facilities, the recovery of traffic flow, and the records of emergency resource deployment. By calculating the recovery rate of traffic flow and the coordination index between facility repair and emergency resources, the model evaluates the recovery speed and efficiency of the network after the emergency and generates a detailed repair and recovery report. The quantitative analysis results of each stage are finally summarized into a comprehensive transportation network resilience assessment report, which provides managers with a comprehensive assessment of the network's robustness, resilience, and shock resistance, helping to optimize future network planning, emergency response, and resource scheduling. This report not only helps with daily management, but also provides a scientific basis for the continuous improvement of the transportation network in emergencies, ensuring that the network has a high response capability and recovery speed in different scenarios.

[0112] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive transportation network resilience assessment method based on multiple indicators, characterized by: The following steps are involved: Pre-event stage: by selecting a number of key indicators for evaluating the resilience of the transportation network, including topological structure indicators, traffic flow indicators, facility reliability indicators and emergency response capability indicators, an evaluation model of the comprehensive transportation network is constructed based on these indicators; During the incident: When an emergency occurs, based on the evaluation model, real-time traffic data is obtained, including real-time monitoring data of infrastructure such as roads and bridges, traffic flow monitoring data, and emergency response records, and these data are used to evaluate the immediate performance of the network; Post-event stage: After the emergency is over, analyze the impact of the incident on the transportation network, and evaluate the recovery capacity of the transportation network based on the evaluation model, taking into account the repair time of facilities, traffic flow recovery and emergency resource allocation records.

2. The multi-index-based comprehensive transportation network resilience assessment method according to claim 1 is characterized in that: The topology index includes node connectivity and link redundancy. The calculation formula of the node connectivity is: Among them, C is the connectivity, N is the total number of nodes, and A ij is the connection between node i and node j, A ij =1 means there is a connection, A ij =0 means no connection.

3. The multi-index-based comprehensive transportation network resilience assessment method according to claim 1 is characterized in that: The traffic flow indicators include daily traffic distribution and peak load, and the traffic flow saturation is calculated by the following formula: Among them, S is the traffic flow saturation, V i is the actual traffic flow of road i, C i is the design capacity of road i, and n is the total number of road sections.

4. The multi-index-based comprehensive transportation network resilience assessment method according to claim 1 is characterized in that: The facility reliability index is calculated by the following formula to calculate the probability of facility damage: Among them, P f is the overall damage probability of the facility, p i is the damage probability of the ith facility, and n is the total number of facilities.

5. The multi-index-based comprehensive transportation network resilience assessment method according to claim 1 is characterized in that: The emergency response capability index is evaluated by an emergency resource allocation model, which includes the following calculation formula: Among them, T r is the shortest time for resource arrival, d ij is the distance from resource point j to accident point i, v j is the vehicle speed of the resource point, and R is the set of available resource points.

6. The comprehensive transportation network resilience assessment method based on multiple indicators according to claim 1 is characterized in that: In the prior stage, the traffic network is stress-tested by simulating natural disasters. The stress-testing model is constructed based on the Bayesian network, and the calculation formula is: Among them, P(T f ) is the prior probability of traffic system failure, E i is a possible event, P(E i |T f ) is the conditional probability of an event occurring in the event of a system failure, and n is the total number of events that affect the transportation network.

7. The multi-index-based comprehensive transportation network resilience assessment method according to claim 1 is characterized in that: During the process, the network path is dynamically optimized by real-time monitoring of node connectivity and traffic flow changes combined with the Dijkstra algorithm. The calculation formula of the algorithm is: d(u)=min(d(u),d(v)+w(u,v)) Among them, d(u) is the shortest path to node u, d(v) is the shortest path to node v, and w(u,v) is the path weight connecting nodes u and v.

8. The multi-index-based comprehensive transportation network resilience assessment method according to claim 1 is characterized in that: In the post-event phase, the facility repair time and traffic flow recovery are analyzed, and the traffic flow recovery rate is calculated using the following formula: Among them, R v (t) is the traffic flow recovery rate at time t, V(t) is the traffic flow at time t, V0 is the lowest traffic flow after the event, V b is the baseline traffic flow before the incident.

9. The multi-index-based comprehensive transportation network resilience assessment method according to claim 1 is characterized in that: The post-event phase evaluates the coordination between facility repair time and emergency resources through the following formula: Among them, C f is the coordination index between facility repair and emergency resources, R i is the repair resource usage of the i-th facility, T i is the repair time of the ith facility, and n is the total number of facilities.

10. The multi-index-based comprehensive transportation network resilience assessment method according to claim 1 is characterized in that: The assessment model combines the indicators for quantitative analysis in the pre-, mid- and post-stages, and generates a resilience assessment report for the comprehensive transportation network based on the calculation results.

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