A method for assessing the resilience of urban road traffic networks under the influence of emergencies

Abstract

The present invention provides a method for assessing the resilience of an urban road traffic network under the influence of emergencies, the method comprising: collecting road traffic network GIS data and vehicle GPS trajectory data; constructing a network topology structure by using the Space L method, and matching traffic status information with road network and topology information by using a map matching algorithm; respectively calculating structural resilience and functional resilience indicators of the road traffic network, including node degree, betweenness centrality, closeness centrality, and relative operating speed of road sections, and calculating an absolute resilience indicator of the network based on the indicators; calculating the relative operating speed of road sections under the influence of the network topology structure, calculating the percolation threshold of the road network at different connectivity levels by using the percolation theory, and using the percolation threshold as the relative resilience indicator of the network; and comprehensively evaluating the absolute resilience and relative resilience indicators to establish a comprehensive index for assessing the resilience of the road traffic network.

Description

Technical Field

[0001] The present invention belongs to the technical field of road traffic network assessment, and in particular relates to a method for assessing the resilience of an urban road traffic network impacted by emergencies. Background Art

[0002] Studying how modern urban transportation systems can reduce urban disaster risks and improve urban transportation risk management capabilities has become an important scientific issue in the transportation field. As a key component of resilient cities, road transportation networks are an important vehicle for ensuring the efficiency of urban transportation operations. However, various external events (natural disasters such as floods, typhoons, and fires, and emergencies such as major traffic accidents and system failures) and internal disturbances (such as morning and evening rush hours, road maintenance, and facility repairs) can easily have a significant impact on the function and structure of road transportation networks. Therefore, it is urgent to establish a comprehensive road transportation network resilience indicator system to provide a reference for modeling and evaluating road transportation network resilience under emergencies.

[0003] Existing research (CN202010778694.7) provides a method for assessing the resilience of urban rail transit, including calculating the vulnerability index, robustness index, and resilience index of the urban rail transit network, and determining the weight and score of each index based on these indicators. Finally, the total resilience index of the urban rail transit network is calculated and a resilience assessment is performed. Another study (CN201910426909.6) involves the technical field of safety resilience assessment of urban transportation systems under rainy weather, and proposes a method for assessing the resilience of urban road transportation systems for rainstorm waterlogging. Specifically, it includes: constructing a road transportation operation system resilience index system from four levels: drainage, roads, transportation, and emergency response; using the fuzzy hierarchical analysis method to carry out judgment matrix, membership relationship, and evaluation operations, respectively, to form a quantitative comprehensive evaluation of the resilience of the road transportation system under rainstorm waterlogging scenarios; finally, combining the maximum membership principle to obtain the quantitative assessment results of the resilience of the road transportation operation system. In addition, there is also research (CN202111370538.8) focusing on the evaluation method of urban road traffic operation status from the perspective of resilience, specifically including: based on the urban road network topology model and floating vehicle GPS data, from the "macro-meso-micro" level, generating key performance indicators of road resilience; using percolation theory to explore the inherent mechanism of urban traffic congestion diffusion, and determine the minimum required performance of the road network; combined with the road resilience evolution curve, using the loss of road resilience at each moment in the time series to characterize the road operation status, and identify the critical threshold of congestion diffusion.

[0004] Currently, there is a certain research foundation for urban transportation network resilience assessment, but there are still the following shortcomings:

[0005] First, most research focuses on evaluating the resilience of urban rail transit networks under the impact of emergencies. However, due to the more complex structure and difficulty in obtaining operational status information of urban road transportation networks, there are currently few resilience assessment methods for urban road transportation networks.

[0006] Second, existing resilience assessment methods mainly characterize road network resilience by simply weighting structural resilience and functional resilience when calculating resilience, reflecting the absolute resilience capacity of the road traffic network as a whole. However, under the influence of sudden events, the connectivity of the road network and the traffic flow status will affect the resilience of the road network. Therefore, it is still necessary to consider the impact of major events and evaluate the relative resilience of the road network at different connectivity levels in order to establish a comprehensive resilience assessment indicator. Summary of the Invention

[0007] Technical issues: In view of the impact of emergencies on traffic flow, in order to accurately evaluate the network resilience of the road network when the traffic flow state is unevenly distributed, the relative resilience index is introduced to compensate for the estimation bias of the traditional resilience assessment method; the percolation theory is introduced to consider indicators such as node degree, betweenness centrality, closeness centrality and the relative operating speed of road sections to calculate the percolation threshold of the road network, thereby constructing a comprehensive resilience index that includes the absolute resilience and relative resilience of the road network.

[0008] Technical solution: To solve the above technical problems, the present invention proposes a method for assessing the resilience of urban road traffic networks under the influence of emergencies. The method comprises the following steps:

[0009] S1. Collect road traffic network GIS data and vehicle GPS trajectory data;

[0010] S2. Based on the data collected in step S1, the network topology is constructed by the Space L method, and the traffic status information is matched with the road network and topology information using a map matching algorithm;

[0011] S3. Calculate the structural and functional resilience indicators of the road traffic network, including node degree, betweenness centrality, closeness centrality, and relative speed of road sections, and calculate the absolute resilience indicator of the network based on these indicators.

[0012] S4. Calculate the relative operating speed of road sections considering the influence of network topology. Use percolation theory to calculate the percolation threshold of the road network at different connectivity levels, and use the percolation threshold as an indicator of the network's relative resilience.

[0013] S5. Comprehensively integrate absolute resilience and relative resilience indicators to establish comprehensive indicators for road traffic network resilience assessment to evaluate road network resilience.

[0014] Furthermore, the absolute toughness index calculation process in step S3 specifically includes the following steps:

[0015] Table 1 Multidimensional evaluation indicators

[0016]

[0017] S31. Using the duality method, we abstract road segments m in the road traffic network into nodes i and calculate the structural resilience of the urban road traffic network. The calculation formulas for node degree I1(i), betweenness centrality I2(i), and closeness centrality I3(i) are as follows:

[0018]

[0019]

[0020]

[0021] If node i is connected to node j, then δ ij =1, otherwise δ ij =0;P jk is the number of shortest paths between node j and node k; P jk (i) represents the number of shortest paths between node j and node k passing through node i; d(i,j) represents the shortest path distance between node i and node j; M is the number of nodes in the dual network, that is, the number of road segments in the original road network;

[0022] S32. Calculate the functional resilience of the urban road traffic network, using the relative operating speed of a road section to reflect its traffic operation efficiency. The relative operating speed of a road section is defined as the ratio of the actual operating speed of the road section at a certain moment to its maximum speed. The calculation formula is as follows:

[0023]

[0024] Among them, V m (t) is the speed of road section m at time t; V m (limit) is the speed limit of the road section m, which is generally taken as the 95th percentile speed of the road section throughout the day, r m (t) represents the relative running speed of section m at time t;

[0025] S33. Calculate the absolute resilience index R of the road network abs ,

[0026]

[0027] Among them, ω irepresents the weight coefficient of the i-th indicator, which is determined by principal component analysis; I1(l′), I2(l′), I3(l′) respectively represent the degree, betweenness centrality, and closeness centrality of the l-th road segment corresponding to the node l′ in the dual road network; I4(l,t) represents the relative speed of the l-th road segment in the road network at time t; M is the number of nodes in the dual network, that is, the number of road segments in the original road network.

[0028] Furthermore, the relative toughness index calculation process in step S4 specifically includes the following steps:

[0029] S41. Considering node degree, betweenness centrality, closeness centrality, and relative speed of road sections, the structural resilience of the network is used to predict the relative speed of road sections r. m (t) Correction is performed to obtain the corrected relative running speed r' m (t), to describe the coupling relationship between functional resilience and absolute resilience of the road network;

[0030]

[0031] Among them, ξ represents the correction coefficient of the road network topological structure characteristics to the relative speed, which represents the redundant resilience capacity of the road network;

[0032] S42. Based on the percolation theory, a relative speed value is first given, i.e., the control variable q; then the relative speed r′ of the modified road section is used. m The relative size of (t) and the control variable q defines the valid and invalid sections of the road network; then, the network connected sub-clusters are obtained by removing the invalid sections in the road network, where the network connected sub-clusters are defined as the sub-graph of the road network where any two vertices have a connected path; finally, the percolation threshold q is obtained in the process of simulating the percolation of the road network c (t), and use it to characterize the relative resilience of the road network at different connectivity levels, that is, the relative resilience of the road network at time t:

[0033] R rel (t) = q c (t).

[0034] Furthermore, the process of calculating the seepage threshold based on the seepage theory to characterize the relative toughness of the road network in step S42 specifically includes the following:

[0035] 1) Define valid and invalid road sections in the road network, and calculate the relative speed r′ of the road section according to the corrected relative speed r′. m (t), under the control variable q (range of 0 to 1), if the actual road relative speed r′ m (t)≥q, the road section is defined as a valid road section, otherwise it is an invalid road section. Then, under different control variables q, the entire road network will be composed of valid road sections (r′ m(t) ≥ q) and invalid road segments (r′ m (t) < q) are composed of:

[0036]

[0037] 2) Simulate the seepage process of the road network. Set the initial value of the control variable q to 0, and gradually increase q to 1 with a step size of 0.01. At each control variable q, remove the invalid road segments in the road network to obtain a new road network corresponding to the q value. Use the Tarjan algorithm to calculate the largest connected sub-clique and the second-largest connected sub-clique in the road network. Thus, when q increases from 0 to 1, the urban traffic network will gradually change from a globally connected state to a fragmented state, and the change curves of the largest and second-largest connected sub-cliques of the road network can be obtained;

[0038] 3) Obtain the seepage threshold of the road network. According to the seepage theory, during the seepage process of the road network, the scale (number of valid road segments included) of the largest connected sub-clique will gradually decrease, while the scale of the second-largest connected sub-clique will first increase and then decrease. When it reaches the maximum value, a seepage phase transition occurs, and the corresponding q value is the seepage threshold. Thus, the maximum value point of the change curve of the second-largest connected sub-clique of the road network can be calculated, and the corresponding q value is the seepage threshold of the road network at time t, denoted as q c (t), which also reflects the relative resilience of the road traffic network at different connectivity levels, that is, R rel (t).

[0039] Furthermore, the specific steps of the comprehensive resilience calculation method and resilience evaluation process in step S5 are as follows:

[0040] S51. Calculate the comprehensive resilience index, and obtain the comprehensive resilience index of the road traffic network through the linear weighted sum of relative resilience and absolute resilience:

[0041]

[0042] S52. According to the calculated comprehensive resilience index, monitor the change of the resilience curve of the road network in real time when it is disturbed by emergencies, and comprehensively evaluate the resilience of the road network.

[0043] Furthermore, the specific aspects of the change of the resilience curve of the road network evaluated in step S52 when it is disturbed by emergencies are as follows:

[0044] 1) Taking the resilience curve without emergency interference as the benchmark, use its lowest resilience in the period from 00:00 to 06:00 at night to represent the minimum acceptable resilience, denoted as R(t0);

[0045] 2) If the real-time monitoring comprehensive resilience index R(t) is lower than the minimum acceptable resilience R(t0), the disturbance of the sudden event is detected, and the corresponding time is recorded as t1. The resilience value corresponding to the lowest point of the resilience monitoring curve represents the lowest resilience of the road network, which is recorded as R(t2), and the corresponding time is recorded as t2. The road network's resistance or absorption capacity Q1 under sudden event disturbance can be represented by the area of ​​the resilience triangle corresponding to the resilience curve in the period t1 to t2, that is:

[0046]

[0047] 3) If the real-time monitored comprehensive resilience index R(t) recovers from the lowest resilience to the normal resilience level for the first time, the road network is monitored to have fully recovered from the emergency, and the recovery time is recorded as t3. The road network's recovery capacity Q2 under the disturbance of the emergency can be represented by the area of ​​the resilience triangle corresponding to the resilience curve in the period t2 to t3, that is:

[0048]

[0049] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0050] 1. Aiming at the need for resilience assessment of urban road traffic networks affected by emergencies, the paper considers the connectivity of the road traffic network and the changes in traffic flow characteristics under the influence of emergencies, and introduces a relative resilience index to assess the network resilience when the traffic flow state is unevenly distributed.

[0051] 2. Introducing the percolation theory, the complex coupling relationship between the structural resilience and functional resilience of road traffic networks under the influence of sudden events is revealed, and the relative resilience of the road network is characterized by the percolation threshold.

[0052] 3. Based on the connectivity perspective, this paper proposes the concept of relative resilience, improves the traditional resilience assessment method, and proposes a road traffic network resilience assessment method oriented to the impact of emergencies, which makes up for the estimation bias of the traditional resilience assessment method.

[0053] 4. This invention introduces the percolation theory, integrates functional resilience and structural resilience to construct a relative resilience evaluation index for road traffic networks, and conducts quantitative analysis of the index through the percolation threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Toughness curve graph;

[0055] Figure 2 Flow chart of the method of the present invention;

[0056] Figure 3 Resilience curve of the study area;

[0057] Figure 4 Comparison chart of the comprehensive toughness index proposed by the present invention and the traditional absolute toughness index;

[0058] Figure 5 Resilience change diagram of the road network under the regional attack strategy (emergency);

[0059] Figure 6 The resilience change diagram of the road network under the intensity increasing disturbance strategy (emergency event). DETAILED DESCRIPTION

[0060] The present invention proposes a method for evaluating the resilience of urban road traffic networks under the influence of emergencies, which includes the following steps:

[0061] S1. Collect road traffic network GIS data and vehicle GPS trajectory data;

[0062] S2. Based on the data collected in step S1, the network topology is constructed by the Space L method, and the traffic status information is matched with the road network and topology information using a map matching algorithm;

[0063] S3. Calculate the structural and functional resilience indicators of the road traffic network, including node degree, betweenness centrality, closeness centrality, and relative speed of road sections, and calculate the absolute resilience indicator of the network based on these indicators.

[0064] S4. Calculate the relative operating speed of road sections considering the influence of network topology. Use percolation theory to calculate the percolation threshold of the road network at different connectivity levels, and use the percolation threshold as an indicator of the network's relative resilience.

[0065] S5. Comprehensively integrate absolute resilience and relative resilience indicators to establish comprehensive indicators for road traffic network resilience assessment to evaluate road network resilience.

[0066] Furthermore, the absolute toughness index calculation process in step S3 specifically includes the following steps:

[0067] Table 1 Multidimensional evaluation indicators

[0068]

[0069]

[0070] S31. Using the duality method, we abstract road segments m in the road traffic network into nodes i and calculate the structural resilience of the urban road traffic network. The calculation formulas for node degree I1(i), betweenness centrality I2(i), and closeness centrality I3(i) are as follows:

[0071]

[0072]

[0073]

[0074] If node i is connected to node j, then δ ij =1, otherwise δ ij =0;P jk is the number of shortest paths between node j and node k; P jk (i) represents the number of shortest paths between node j and node k passing through node i; d(i,j) represents the shortest path distance between node i and node j; M is the number of nodes in the dual network, that is, the number of road segments in the original road network;

[0075] S32. Calculate the functional resilience of the urban road traffic network, using the relative operating speed of a road section to reflect its traffic operation efficiency. The relative operating speed of a road section is defined as the ratio of the actual operating speed of the road section at a certain moment to its maximum speed. The calculation formula is as follows:

[0076]

[0077] Among them, V m (t) is the speed of road section m at time t; V m (limit) is the speed limit of the road section m, which is generally taken as the 95th percentile speed of the road section throughout the day, r m (t) represents the relative running speed of section m at time t;

[0078] S33. Calculate the absolute resilience index R of the road network abs ,

[0079]

[0080] Among them, ω i represents the weight coefficient of the i-th indicator, which is determined by principal component analysis; I1(l′), I2(l′), I3(l′) respectively represent the degree, betweenness centrality, and closeness centrality of the l-th road segment corresponding to the node l′ in the dual road network; I4(l,t) represents the relative speed of the l-th road segment in the road network at time t; M is the number of nodes in the dual network, that is, the number of road segments in the original road network.

[0081] Furthermore, the relative toughness index calculation process in step S4 specifically includes the following steps:

[0082] S41. Considering node degree, betweenness centrality, closeness centrality, and relative speed of road sections, the structural resilience of the network is used to predict the relative speed of road sections r. m (t) Correction is performed to obtain the corrected relative running speed r' m(t) to describe the coupling relationship between the functional resilience and the absolute resilience of the road network;

[0083]

[0084] Among them, ξ represents the correction coefficient of the road network topological structure characteristics to the relative speed, characterizing the surplus resilience ability of the road network;

[0085] S42. Based on the percolation theory, first, a relative speed value, that is, the control variable q, is given; then, the effective sections and ineffective sections of the road network are defined by the relative magnitude of the corrected relative running speed r′ m (t) of the section and the control variable q; then, the connected sub-clusters of the road network are obtained by removing the ineffective sections in the road network, where the connected sub-clusters of the road network are defined as the sub-graphs of the road network in which there is a connected path between any two vertices; finally, the percolation threshold q c (t) is obtained during the process of simulating the percolation of the road network, and is used to characterize the relative resilience of the road network at different connectivity levels, that is, the relative resilience of the road network at time t:

[0086] R rel (t) = q c (t).

[0087] Furthermore, the specific process of calculating the percolation threshold based on the percolation theory in step S42 to characterize the relative resilience of the road network includes the following:

[0088] 1) Define the effective sections and ineffective sections of the road network. According to the corrected relative speed r′ m (t) of the section, under the control variable q (the value range is 0 to 1), if the actual relative speed r′ m (t) ≥ q of the section, the section is defined as an effective section, otherwise it is an ineffective section. Then, under different control variables q, the entire road network will consist of effective sections (r′ m (t) ≥ q) and ineffective sections (r′ m (t) < q):

[0089]

[0090] 2) Simulate the percolation process of the road network. Set the initial value of the control variable q to 0, and gradually increase q to 1 with a step of 0.01; at each control variable q, remove the ineffective sections in the road network to obtain a new road network corresponding to the q value; use the Tarjan algorithm to calculate the maximum connected sub-cluster and the second-largest connected sub-cluster in the road network; thus, when q increases from 0 to 1, the urban traffic network will gradually change from a globally connected state to a fragmented state, and the change curves of the maximum and second-largest connected sub-clusters of the road network will be obtained;

[0091] 3) Obtain the percolation threshold of the road network. According to percolation theory, during the percolation process of the road network, the size of the largest connected subcluster (the number of valid road sections it contains) will gradually decrease, while the size of the second largest connected subcluster will first increase and then decrease. When it reaches the maximum value, a percolation phase transition occurs, and the corresponding q value is the percolation threshold. Therefore, the maximum point can be calculated based on the change curve of the second largest connected subcluster of the road network. The corresponding q value is the percolation threshold of the road network at time t, which is recorded as q c (t), and also reflects the relative resilience of the road traffic network at different connectivity levels, namely, R rel (t).

[0092] Furthermore, the comprehensive toughness calculation method and toughness evaluation process of step S5 specifically includes the following steps:

[0093] S51. Calculate the comprehensive resilience index. The comprehensive resilience index of the road traffic network is obtained by linearly weighted summation of relative resilience and absolute resilience:

[0094]

[0095] S52. Based on the calculated comprehensive resilience index, monitor in real time the changes in the resilience curve of the road network when it is disturbed by an emergency event, and comprehensively evaluate the resilience of the road network.

[0096] Furthermore, the step S52 of evaluating the change in the resilience curve of the road network when it is disturbed by an emergency event specifically includes the following aspects:

[0097] 1) Taking the resilience curve without sudden event interference as the benchmark, the lowest resilience during the nighttime period of 00:00-06:00 represents the minimum acceptable resilience, which is recorded as R(t0);

[0098] 2) If the real-time monitoring comprehensive resilience index R(t) is lower than the minimum acceptable resilience R(t0), the disturbance of the sudden event is detected, and the corresponding time is recorded as t1. The resilience value corresponding to the lowest point of the resilience monitoring curve represents the lowest resilience of the road network, which is recorded as R(t2), and the corresponding time is recorded as t2. The road network's resistance or absorption capacity Q1 under sudden event disturbance can be represented by the area of ​​the resilience triangle corresponding to the resilience curve in the period t1 to t2, that is:

[0099]

[0100] 3) If the real-time monitored comprehensive resilience index R(t) recovers from the lowest resilience to the normal resilience level for the first time, the road network is monitored to have fully recovered from the emergency, and the recovery time is recorded as t3. The road network's recovery capacity Q2 under the disturbance of the emergency can be represented by the area of ​​the resilience triangle corresponding to the resilience curve in the period t2 to t3, that is:

[0101]

[0102] Example Results

[0103] 1. Results of real-world road traffic network resilience assessment:

[0104] The local road network of Kunshan City, Jiangsu Province was selected as the study area. There are 209 road sections and 144 intersections in this area. After establishing the topological structure of the road network, the GPS trajectory data of taxis in the study area for three days from January 4 to January 6, 2018 were extracted. The GPS trajectory points were matched to the corresponding road sections using a map matching algorithm. The relative operating speed of the road sections was then calculated at every 15-minute time interval and the seepage evolution process of the road network was explored. Finally, the absolute resilience index, relative resilience index and comprehensive resilience index of the road network were calculated respectively, so as to evaluate the road network resilience in this area.

[0105] As attached Figure 3 As shown in Figure 2, the resilience of the road traffic network will fluctuate with the disturbance of some events on the road network (such as the morning and evening rush hour phenomenon here) and show a typical "resilience triangle" trend. Figure 4 It further shows that although the traditional absolute resilience evaluation index can also reflect the changing trend of this resilience, compared with the comprehensive resilience evaluation method invented in this paper, the traditional resilience evaluation method still uses the absolute resilience of the road network to characterize the resilience level of the road network, resulting in deviations in resilience evaluation in certain periods.

[0106] 2. Results of road network resilience assessment under the impact of emergencies:

[0107] In order to reflect the impact of emergencies, the present invention designed a network attack experiment to simulate the impact of emergencies on real road networks. The specific network attack strategy and resilience evaluation results are shown in the attached figure. Figure 5 and attached Figure 6 shown.

[0108] 1) Figure 5 (b) Figure 5 (d) represents the regional attack strategy with the center point of the road network and the edge point of the road network as the initial attack target, respectively simulating the impact of emergencies on the core area and edge area of ​​the road network;

[0109] 2) Figure 5 (a) Figure 5(c) corresponds to the changes in resilience indicators in the core and peripheral areas of the road network under the influence of sudden events. The results show that both the traditional absolute resilience indicator and the proposed comprehensive resilience indicator reflect the characteristic that the network's resilience level gradually decreases as the scope of the regional attack continues to expand. However, under the regional attack strategy, the connectivity of the entire network will drop sharply due to severe damage to the road network, resulting in a decrease in the comprehensive resilience of the road network. The traditional absolute resilience indicator ignores the overall connectivity of the road network, making it difficult to accurately assess the resilience level of the road network at this stage.

[0110] 3) In addition, compared with Figure 5 (c) reflects the change in resilience of the edge area of ​​the road network when it is affected by an emergency. Under the regional attack strategy with the center of the road network as the initial attack target ( Figure 5 (b)), the resilience of the road network will decrease more quickly to a lower level (e.g. Figure 5 (a) shows that the core area of ​​the road network will be quickly paralyzed if the road network is affected by an emergency.

[0111] 4) Figure 6 (a) represents the road network resilience monitoring curve when the intensity of regional attacks changes over time, reflecting the entire process of road network resilience changes under the influence of emergencies; Figure 6 (b) reflects the changes in the resilience of the road network when it is subjected to different attack intensities during peak hours, reflecting the maximum attack intensity that the road network can withstand and reflecting the road network's ability to resist different emergencies.

Claims

1. A method for assessing the resilience of urban road traffic networks under the influence of emergencies, characterized in that: The method comprises the following steps: S1. Collect road traffic network GIS data and vehicle GPS trajectory data; S2. Based on the data collected in step S1, the network topology is constructed by the Space L method, and the traffic status information is matched with the road network and topology information using a map matching algorithm; S3. Calculate the structural and functional resilience indicators of the road traffic network, including node degree, betweenness centrality, closeness centrality, and relative speed of road sections, and calculate the absolute resilience indicator of the network based on these indicators. S4. Calculate the relative operating speed of road sections considering the influence of network topology. Use percolation theory to calculate the percolation threshold of the road network at different connectivity levels, and use the percolation threshold as an indicator of the network's relative resilience. S5. Combine absolute and relative resilience indicators to establish a comprehensive road network resilience assessment index to evaluate road network resilience. The relative toughness index calculation process in step S4 specifically includes the following steps: S41. Considering node degree, betweenness centrality, closeness centrality, and relative speed of road sections, the structural resilience of the network is used to predict the relative speed of road sections r. m (t) Correction is performed to obtain the corrected relative velocity r' m (t), to describe the coupling relationship between functional resilience and absolute resilience of the road network; Among them, ξ represents the correction coefficient of the road network topological structure characteristics to the relative speed, which represents the redundant resilience capacity of the road network; S42. Based on the percolation theory, a relative speed value is first given, i.e., the control variable q; then the relative speed r′ of the modified road section is used. m The relative size of (t) and the control variable q defines the valid and invalid sections of the road network; the connected sub-cluster of the road network is obtained by removing the invalid sections in the road network, where the connected sub-cluster of the road network is defined as a sub-graph of the road network in which any two vertices have a connected path, and the percolation threshold q is obtained in the process of simulating the percolation of the road network c (t), and use it to characterize the relative resilience of the road network at different connectivity levels, that is, the relative resilience of the road network at time t: R rel (t)=q c (t)。 2. The urban road traffic network resilience assessment method for emergency events according to claim 1 is characterized in that: The absolute toughness index calculation step S3 includes the following steps: S31. Using the duality method, we abstract road segments m in the road traffic network into nodes i and calculate the structural resilience of the urban road traffic network. The calculation formulas for node degree I1(i), betweenness centrality I2(i), and closeness centrality I3(i) are as follows: If node i is connected to node j, then δ ij =1, otherwise δ ij =0;P jk is the number of shortest paths between node j and node k; P jk (i) represents the number of shortest paths between node j and node k passing through node i; d(i,j) represents the shortest path distance between node i and node j; M is the number of nodes in the dual network, that is, the number of road segments in the original road network; S32. Calculate the functional resilience of the urban road traffic network, using the relative operating speed of a road section to reflect its traffic operation efficiency. The relative operating speed of a road section is defined as the ratio of the actual operating speed of the road section at a certain moment to its maximum speed. The calculation formula is as follows: Among them, V m (t) is the speed of road section m at time t; V m (limit) is the speed limit of the road section m, which is taken as the 95th percentile speed of the road section throughout the day, r m (t) represents the relative running speed of section m at time t; S33. Calculate the absolute resilience index R of the road network abs , Among them, ω i represents the weight coefficient of the i-th indicator, which is determined by principal component analysis; I1(l′), I2(l′), I3(l′) respectively represent the degree, betweenness centrality, and closeness centrality of the l-th road segment corresponding to the node l′ in the dual road network; I4(l,t) represents the relative speed of the l-th road segment in the road network at time t; M is the number of nodes in the dual network, that is, the number of road segments in the original road network.

3. The urban road traffic network resilience assessment method for emergency events according to claim 1 is characterized in that: The process of calculating the seepage threshold based on the seepage theory to characterize the relative toughness of the road network in step S42 specifically includes the following: 1) Define the effective and ineffective road segments of the road network. According to the corrected relative speed of the road segment r m ′m(t), under the control variable q, the value range is 0 to 1. If the actual relative speed of the road segment r m ′(t) ≥ q, the road segment is defined as an effective road segment; otherwise, it is an ineffective road segment. Then, under different control variables q, the entire road network will consist of effective road segments r m ′(t) ≥ q and ineffective road segments r m ′m(t) < q: 2) Simulate the percolation process of the road network by setting the initial value of the control variable q to 0 and gradually increasing q to 1 in steps of 0.

01. Under each control variable q, remove the invalid road sections in the road network to obtain the new road network under the corresponding q value. Use the Tarjan algorithm to calculate the largest connected subcluster and the second largest connected subcluster in the road network. Therefore, when q increases from 0 to 1, the urban transportation network gradually changes from a globally connected state to a fragmented state, and the change curves of the largest and second largest connected subclusters of the road network are obtained; 3) Obtain the percolation threshold of the road network. According to the percolation theory, during the percolation process of the road network, the size of the largest connected subcluster gradually decreases, while the size of the second largest connected subcluster first increases and then decreases. When it reaches the maximum value, the percolation phase transition occurs, and the corresponding q value is the percolation threshold. Therefore, the maximum point is calculated according to the change curve of the second largest connected subcluster of the road network. The corresponding q value is the percolation threshold of the road network at time t, which is recorded as q c (t), reflects the relative resilience of the road traffic network at different connectivity levels, namely R rel (t).

4. The urban road traffic network resilience assessment method for emergency events according to claim 3 is characterized in that: The comprehensive toughness calculation method and toughness evaluation process of step S5 specifically include the following steps: S51. Calculate the comprehensive resilience index. The comprehensive resilience index of the road traffic network is obtained by linearly weighted summation of relative resilience and absolute resilience: S52. Based on the calculated comprehensive resilience index, monitor in real time the changes in the resilience curve of the road network when it is disturbed by an emergency event, and comprehensively evaluate the resilience of the road network.

5. The urban road traffic network resilience assessment method for emergency events according to claim 4 is characterized in that: The step S52 of evaluating the change of the resilience curve of the road network when it is disturbed by an emergency event specifically includes the following aspects: 1) Taking the resilience curve without sudden event interference as the benchmark, the lowest resilience during the nighttime period of 00:00-06:00 represents the minimum acceptable resilience, which is recorded as R(t0); 2) If the real-time monitoring comprehensive resilience index R(t) is lower than the minimum acceptable resilience R(t0), the disturbance of the sudden event is detected, and the corresponding time is recorded as t1. The resilience value corresponding to the lowest point of the resilience monitoring curve represents the lowest resilience of the road network, which is recorded as R(t2), and the corresponding time is recorded as t2. The resistance or absorption capacity Q1 of the road network under sudden event disturbance is represented by the area of ​​the resilience triangle corresponding to the resilience curve in the period t1 to t2, that is: 3) If the real-time monitoring comprehensive resilience index R(t) recovers from the lowest resilience to the normal resilience level for the first time, the road network is monitored to have fully recovered from the emergency, and the recovery time is recorded as t3. The road network's recovery capacity Q2 under the disturbance of the emergency is represented by the area of ​​the resilience triangle corresponding to the resilience curve in the period t2 to t3, that is:

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