Double-level toughness evaluation and classification method based on heterogeneous flow demand

Abstract

The invention discloses a double-level toughness evaluation and classification method based on heterogeneous flow requirements, and belongs to the technical field of calculation, reckoning or counting. The method comprises the following steps: acquiring traffic demand data and road link quality data, constructing a time-varying network diagram of an urban road traffic system, establishing an accessibility matrix based on a link quality matrix to obtain a demand weighting coefficient, further integrating a demand weighting coefficient curve in a threshold interval [0, 1], and averaging in a given time interval to obtain a global toughness index; calculating a key score of each link based on identification of restrictive links in the O-D demand path; calculating a toughness index of the link hierarchy based on the key score and quality of each link; carrying out evaluation consistency verification on a link level and a global level; and classifying the links according to the key scores of the links and the distribution of the link quality data. By adopting the method, the toughness evaluation precision and the management decision-making capability of the urban road traffic system can be improved when the urban road traffic system deals with high-frequency slight daily disturbance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban traffic management and intelligent transportation system, and particularly discloses a method and device for realizing multi-level resilience quantification and classification from the whole network to the local link in the urban road traffic system, a computer device and a storage medium, belonging to the technical field of calculation, estimation or counting. BACKGROUND

[0002] As one of the core infrastructures of the city, the urban road transportation system (URTS) plays an important role in meeting the daily travel needs of residents, emergency response, urban evacuation and resource allocation. With the rapid expansion of urban agglomerations, the increase of traffic network connectivity and the complexity of traffic flow, the URTS is vulnerable to various disturbances, which may come from internal factors such as mechanical failure, equipment aging, signal system failure, or external factors such as natural disasters, large public events, traffic accidents, etc. Disturbances will cause traffic network interruption and congestion, weaken network connectivity, and hinder the daily travel of residents.

[0003] In existing research, resilience is usually defined as the ability of the transportation system to resist, absorb disturbances and maintain operation and recover in a short time when facing disturbances. For the resilience evaluation of URTS, the main research methods include: topological structure-based method, attribute-based method, performance-based method and integrated method. However, existing research mainly focuses on major disturbances such as natural disasters, serious accidents, large public events, etc., and has a deep understanding of the impact on the system level; but there is insufficient attention to daily disturbances such as equipment failure, weather changes, passenger flow fluctuations, etc. The existing resilience indicators are difficult to capture the continuous performance changes of the daily transportation system. At the same time, unlike sudden destruction, daily disturbances more often manifest as repeated traffic congestion and small-scale delays, with limited impact on a single occasion, but long-term accumulation can significantly reduce network operating efficiency and harm passenger travel experience. In addition, existing researches are mostly focused on the evaluation of the whole traffic network, lacking classification and analysis of the resilience level of single road link, making it difficult to reveal the specific influence of local link on the overall network resilience. SUMMARY

[0004] The present application aims to solve the technical problems of lack of time-varying, locality and operability in the resilience evaluation of the prior art, and achieve the purpose of quantitative evaluation and classification management of the resilience of the traffic network.

[0005] The application is realized by adopting the following technical scheme to achieve the above-mentioned application purposes.

[0006] In a first aspect, the application provides a double-level resilience evaluation and classification method based on heterogeneous traffic demand, comprising:

[0007] Step 1, obtaining traffic demand data and road link quality data, constructing a time-varying network graph of the urban road traffic system, calculating a traffic demand data weighting coefficient based on the reachability matrix established based on the road link quality data, and calculating a global resilience index according to the traffic demand data weighting coefficient;

[0008] Step 2, identifying a restrictive link of an O-D pair, and calculating a criticality score of each link in the urban road traffic network according to the identification result of the restrictive link;

[0009] Step 3, calculating a resilience index of a link level according to the criticality score and quality of each link;

[0010] Step 4, performing evaluation consistency verification on the global resilience index and the resilience index of the link level;

[0011] Step 5, classifying each link according to the distribution of the criticality score and the link quality data of each link.

[0012] As a further optimization scheme of the double-level resilience evaluation and classification method based on heterogeneous traffic demand, in step 1, the time-varying network graph of the urban road traffic system is specifically constructed as: wherein, is a city road traffic network graph in a time period t, denotes a road intersection node set, denotes a road set, denotes an adjacency matrix, denotes an O-D demand matrix in the time period t, denotes a network link quality matrix in the time period t, , is a travel demand from a starting point o to a terminal point d in the time period t, , is a link quality index of a link (i, j) in the time period t, , is a minimum travel time required through the link (i, j) in all time periods of a day, is a travel time through the link (i, j) in the time period t.

[0013] As a further optimization scheme of the two-level resilience evaluation and classification method based on heterogeneous traffic demand, in step 1, the specific method for calculating the traffic demand data weighting coefficient based on the reachability matrix established based on the road link quality data is:

[0014] Calculate the percolation threshold under the percolation theory framework The reachability matrix in the time period t , , The percolation threshold is The reachability index of the O-D pair, and n is the number of road intersection nodes, , The percolation threshold is The urban road traffic network diagram in the time period t;

[0015] The reachability matrix in the time period The reachability matrix in the time period is multiplied element by element and accumulated to obtain the traffic demand data weighting coefficient in the time period The total demand that can still be serviced in the time period . ;

[0016] The ratio of the total demand that can still be serviced in the time period The total demand D in the time period , t , the percolation threshold is obtained The traffic demand data weighting coefficient in the time period .

[0017] As a further optimization scheme of the two-level resilience evaluation and classification method based on heterogeneous traffic demand, in step 1, the global resilience index is calculated according to the traffic demand data weighting coefficient, specifically: the traffic demand data weighting coefficient is integrated in the percolation threshold interval [0, 1], and the average is taken in the given time window [t1, t2], wherein, The global resilience index is

[0018] As a further optimization scheme of the two-level resilience evaluation and classification method based on heterogeneous traffic demand, in step 2,

[0019] The specific method for identifying the restrictive link of the O-D pair is: for each O-D node, identify the bottleneck link on all paths, and select the bottleneck link with the highest link quality as the restrictive link of the O-D pair; ​​​

[0020] The criticality score of each link in the urban road traffic network is calculated according to the restrictive link identification result, specifically as follows: wherein, is the criticality score of link (i, j), 1 n is a unit vector, indicates that link (i, j) is a restrictive link.

[0021] As a further optimization scheme of the dual-level resilience evaluation and classification method based on heterogeneous traffic demand, step 3 calculates the resilience index of the link level according to the criticality score and quality of each link, specifically as follows: wherein, is the resilience index of the link level.

[0022] As a further optimization scheme of the dual-level resilience evaluation and classification method based on heterogeneous traffic demand, step 5 classifies each link according to the distribution of the criticality score and link quality of each link, specifically as follows:

[0023] When the link quality q ≥ 0.6 and the criticality score of the link is greater than or equal to the 75th percentile of its statistical distribution, the link is classified as a highest resilience link;

[0024] When the link quality q ≥ 0.6 and the criticality score of the link is less than the 75th percentile of its statistical distribution, the link is classified as a high resilience link;

[0025] When the link quality q < 0.6 and the criticality score of the link is greater than or equal to the 75th percentile of its statistical distribution, the link is classified as a low resilience link;

[0026] When the link quality q < 0.6 and the criticality score of the link is less than the 75th percentile of its statistical distribution, the link is classified as a lowest resilience link;

[0027] When the criticality score of the link is 0, the link is classified as a non-restrictive link.

[0028] In a second aspect, the application further provides a dual-level resilience evaluation and classification device based on heterogeneous traffic demand, comprising: a time-varying traffic network construction module, a link criticality calculation module, a resilience evaluation and consistency verification module, and a link classification and optimization module; wherein,

[0029] ​​​​​Time-varying traffic network construction module: used to acquire traffic demand data and road link quality data, construct a time-varying network diagram of the urban road traffic system, calculate the weighting coefficient of traffic demand data based on the accessibility matrix established based on road link quality data, and calculate the global resilience index based on the weighting coefficient of traffic demand data.

[0030] Link criticality calculation module: used to identify restricted links in OD pairs, and calculate the criticality score of each link in the urban road traffic network based on the restricted link identification results;

[0031] Resilience Assessment and Consistency Verification Module: This module is used to calculate the resilience index at the link level based on the criticality score and quality of each link, and to evaluate and verify the consistency between the global resilience index and the link-level resilience index.

[0032] Link classification and optimization module: It is used to classify each link according to the criticality score and the distribution of link quality data, and generate traffic management and optimization strategies for each link based on the classification results.

[0033] Thirdly, this application also provides a computer device for two-level resilience assessment and classification based on heterogeneous flow demand, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the two-level resilience assessment and classification method.

[0034] Fourthly, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described two-level resilience assessment and classification method.

[0035] The present invention, by adopting the above technical solution, has the following beneficial effects:

[0036] 1. The two-level resilience assessment and classification method based on heterogeneous traffic demand proposed in this invention assesses the resilience of urban road traffic systems from the perspective of daily disturbances. It focuses on the time-varying resilience characteristics of the overall network and the resilience classification of local links, providing a comprehensive resilience assessment framework for urban road traffic systems. By combining time-varying network diagrams with link criticality scores, it can accurately capture daily resilience changes and classify links into five categories to help optimize traffic management strategies. In addition, this research can conduct hierarchical assessments from the overall to the local, providing strong support for traffic network planning and emergency response.

[0037] 2. To address the heterogeneity of daily traffic demand, this invention proposes a link criticality scoring method and establishes a two-level resilience assessment framework based on this method. This framework combines heterogeneous traffic flow demand with network topology, analyzes the overall network and local links, and assesses the resilience characteristics of daily URTS. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating a two-level resilience assessment and classification method based on heterogeneous flow demand, provided in one embodiment of the present invention.

[0039] Figure 2 This is a schematic diagram illustrating the equivalence verification of global resilience indicators and link-level resilience indicators provided in one embodiment of the present invention.

[0040] Figure 3 This is a schematic diagram illustrating the classification based on link quality parameters and link criticality scores in one embodiment of the present invention. Detailed Implementation

[0041] To overcome the shortcomings of existing urban road traffic system resilience assessments, such as insufficient attention to daily disturbances and a lack of detailed link-level analysis, this invention proposes a two-tiered resilience assessment and classification method oriented towards daily disturbances. This method introduces a link criticality score, combining the heterogeneity of daily traffic demand with network topology to achieve multi-tiered resilience assessment from the overall network to local links. Based on link quality and criticality scores, road links are classified into five categories: highest resilience, high resilience, low resilience, lowest resilience, and unrestricted. This allows for the rapid identification of critical, redundant, and vulnerable links, providing decision support for traffic management, dynamic control, and emergency response, and improving the overall resilience of urban traffic systems under daily disturbances.

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0043] In one exemplary embodiment, such as Figure 1 As shown, a two-level resilience assessment and classification method based on heterogeneous flow demand includes the following five steps.

[0044] Step 1: Obtain traffic demand data and road link quality data to construct a time-varying network diagram of the urban road traffic system. Under the seepage theory framework, as the seepage threshold gradually increases, establish an accessibility matrix based on the link quality matrix, calculate the proportion of unaffected demand in the urban road traffic network to the total network demand, and obtain the demand weighting coefficient (DWC). Further, integrate the DWC curve in the threshold interval [0,1] and average it over a given time interval to obtain the global resilience index. It is used to characterize the functional maintenance capability of urban road traffic networks under different operating conditions;

[0045] Step 2: Based on the identification of restrictive links in the OD demand path, calculate the criticality score of each link;

[0046] Step 3: Calculate the link-level resilience index based on the criticality score and quality of each link. ;

[0047] Step 4: Prove through mathematical derivation This ensures consistency in evaluation between the link level and the global level;

[0048] Step 5, further analyze the criticality scores of each link. Based on the distribution of link quality q, links are classified into five categories: highest resilience links, high resilience links, low resilience links, lowest resilience links, and unrestricted links. Corresponding traffic management and optimization strategies are proposed based on the classification results.

[0049] In one embodiment, step 1 involves constructing a time-varying network diagram of the urban road traffic system by acquiring traffic demand data and road link quality data, including:

[0050] Based on population distribution data, resident travel characteristic data, open street map (OSM), and traffic analysis zone (TAZ) data of the study area, an OD demand matrix for time period t is generated using a gravity model. ,in This represents the travel demand from origin o to destination d within time period t;

[0051] Based on the Baidu Maps API, the average travel speed of each OOD pair within that time interval is crawled every 5 minutes, and finally aggregated into link average speed data with a 1-hour time interval; a link quality index is constructed based on travel time. Used to quantify over a time period The impact of congestion on the inner link (i,j) on traffic flow through that link is expressed by the following formula:

[0052] (1)

[0053] In equation (1), This represents the minimum travel time required to traverse link (i,j) throughout all time periods of the day. This represents the travel time through link (i,j) within time period t.

[0054] Based on this, a network link quality matrix is ​​constructed. .

[0055] In each non-overlapping hour interval [t, t+1), based on the time-varying urban road traffic network map Construct urban road traffic network maps for different time periods, where V represents the set of road intersection nodes, E represents the set of roads, and A represents the adjacency matrix. This represents the OD demand matrix within time t. This represents the network link quality matrix within time period t.

[0056] In an exemplary embodiment, step 1 establishes an reachability matrix based on the link quality matrix, and within the framework of seepage theory, calculates the proportion of unaffected demand in the urban road traffic network to the total network demand as the seepage threshold gradually increases, obtaining the demand weighting coefficient (DWC). Furthermore, the DWC curve is integrated over the threshold interval [0,1] and averaged over a given time interval to obtain the global resilience index. Characterized by the resilience of transportation networks under different operating conditions, including:

[0057] Indicates the seepage threshold The reachability matrix within the next time interval t, and

[0058] (2)

[0059] In formula (2): seepage threshold The accessibility index for the lower OD pair, where n is the number of road intersection nodes. , seepage threshold The urban road traffic network map for the next time period t. The reachability matrix and OD demand matrix are multiplied element-by-element and then summed to obtain the value at the seepage threshold. The total demand for service can still be maintained within the next time period t. And calculate the seepage threshold accordingly. Traffic demand weighting coefficient in the next time period t The formula is as follows:

[0060] (3)

[0061] In equation (3), This represents the total demand across the entire network within time period t;

[0062] DWC represents the proportion of demand that the network can still meet at a given seepage threshold. As the seepage threshold increases, DWC decreases monotonically, forming a seepage curve, i.e., the DWC curve.

[0063] The DWC curve is integrated over the threshold interval [0,1] and averaged over a given time window [t1,t2], as shown in the following formula:

[0064] (4)

[0065] Global resilience indicators The value range is [0,1], representing the resilience of the urban road traffic network under different operating conditions. (Indicator) The larger the value, the more OD demand the network can maintain without being affected when it is subjected to disturbances such as link degradation or failure, and the stronger its resistance to disturbances.

[0066] From the perspective of the resilience triangle theory, The average closed area corresponding to the seepage curve reflects the degree of cumulative performance loss of the network during the disturbance-recovery process.

[0067] In one instance, step 2 includes:

[0068] First, the OD demand matrix within time period t. With network link quality matrix As input data, for each OD pair, identify the bottleneck link on all possible paths, i.e., the link with the lowest quality in the path; from the set of bottleneck links, select the link with the highest quality and define it as the restrictive link of the OD pair.

[0069] If the same link is identified as a restrictive link by multiple OD pairs, its importance is accumulated according to demand. Based on this, a criticality score for link (i,j) is defined. The formula is as follows:

[0070] (5)

[0071] In equation (5), This represents the travel demand from origin o to destination d within time period t. This represents the network-wide OD demand matrix within time period t. It is a unit vector. This indicates that link (i,j) is a restricted link. If link (i,j) is not identified as a restricted link by any OD pair, i.e., link (i,j) is an unrestricted link, then the criticality score of link (i,j) is... It is 0.

[0072] Formula (5) outputs the criticality score for each link, which measures the contribution of each link to the overall network traffic carrying capacity. The higher the criticality score of a link, the more important the link is in maintaining network connectivity and meeting demand, and the more likely it is to be a potentially vulnerable link in the transportation system.

[0073] In one instance, step 3 includes:

[0074] Based on link criticality score With link quality Calculate the resilience index at the link level. The formula is as follows:

[0075] (6)

[0076] Since the data is collected hourly, the above formula (6) can be calculated on a discrete hourly scale, and the corresponding result can be expressed as the sum of the hourly resilience values, as shown in the following formula:

[0077] (7)

[0078] In one instance, step 4 includes:

[0079] First, from the perspective of link hierarchy, the weighted sum of the hourly resilience values ​​of the link can be expressed as:

[0080] (8)

[0081] in, This indicates the quality of the restrictive link in the od pair. During seepage, when the seepage threshold... When od pairs remain connected, otherwise they lose connectivity, therefore:

[0082] (9)

[0083] Therefore, we can conclude that:

[0084]

[0085] (10)

[0086] Substitution The definitions are as follows:

[0087] (11)

[0088] This proves the link-level resilience index. Numerical comparison with global resilience index Equivalently, this conclusion ensures methodological consistency when assessing resilience from both a global and a link-based perspective, meaning that the resilience results are the same regardless of whether the contribution is measured from the perspective of the network as a whole or from the contribution of local links.

[0089] To more intuitively demonstrate the global resilience indicators Link-level resilience metrics The correspondence is given in the present invention as follows: Figure 2 The diagram shows the equivalence derivation.

[0090] In one exemplary embodiment, step 5 includes:

[0091] Based on the link quality parameter q and link criticality score of urban road traffic network The distribution of road links is used to classify their resilience, such as... Figure 3 As shown, the link classification results can be intuitively divided using a two-dimensional coordinate plane, where the horizontal axis represents the link quality parameter q, and the vertical axis represents the link criticality score. Based on q and... Different combinations of these elements can be used to classify links into the following five categories:

[0092] Highest resilience link: When the link quality q ≥ 0.6 and the link criticality score is... Links that are greater than or equal to the 75th percentile of their statistical distribution are considered the most resilient links; it is recommended to maintain dynamic monitoring to ensure smooth traffic flow and prevent congestion.

[0093] High-resilience links: When the link quality q ≥ 0.6 and the link criticality score is high. If the link is below the 75th percentile, meaning it plays a low role in traffic demand transmission, it is considered a high-resilience link and is recommended as an emergency route or diversion route to alleviate traffic pressure on main roads.

[0094] Low resilience link: When the link quality q < 0.6 and the link criticality score is low. Links at or above the 75th percentile are classified as low-resilience links; it is recommended to improve road quality and alleviate congestion by implementing traffic control measures, and to divert traffic as necessary.

[0095] Minimum resilience link: When the link quality q < 0.6 and the link criticality score is low. Links ranked below the 75th percentile, meaning those with low traffic demand capacity, are considered the least resilient links. It is recommended that they be upgraded, decommissioned, or primarily used for static network purposes.

[0096] Unrestricted Link: When the link criticality score is... In this case, it is determined to be a non-restrictive link; it is recommended not to use it as a restrictive factor for the transportation network, and no additional management measures are required.

[0097] This invention proposes a two-tiered resilience assessment and classification method based on heterogeneous traffic demand. Addressing the shortcomings of existing urban road traffic system resilience studies, which lack sufficient attention to daily disturbances and detailed link-level analysis, this invention establishes a comprehensive assessment framework that considers both the overall network and local links. On one hand, it generates an origin-destination (OD) demand matrix based on population distribution and travel characteristic data of the study area, and calculates link quality parameters by combining road link operating speeds, thereby constructing time-varying traffic network snapshots for different time periods. On the other hand, by identifying restrictive links in the OD path, it proposes a link criticality scoring method to quantify the role of each link in traffic demand transmission. Based on this, it calculates system resilience indicators and overall resilience indicators, and classifies road links into five categories—highest resilience, high resilience, low resilience, lowest resilience, and unrestricted links—according to the link quality and criticality scores, forming a two-tiered resilience assessment and classification framework. This invention can accurately capture the time-varying resilience trends of urban traffic networks under daily disturbances, quickly identify critical links, redundant links, and vulnerable links, thus providing a scientific basis for traffic planning, dynamic management, and emergency dispatch. Unlike traditional studies that focus solely on major disturbances or the overall network, this invention can improve the accuracy and practicality of traffic resilience assessment at a fine-grained level, providing effective support for decisions on sustainable urban traffic operation and management.

[0098] Based on the same inventive concept, an exemplary embodiment also provides an apparatus for implementing the above-described two-level resilience assessment and classification method based on heterogeneous flow demand. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations of one or more apparatus embodiments for the two-level resilience assessment and classification method based on heterogeneous flow demand provided below can be found in the limitations of the two-level resilience assessment and classification method described above, and will not be repeated here.

[0099] In one exemplary embodiment, a two-level resilience assessment and classification device based on heterogeneous traffic demand is provided, comprising:

[0100] The time-varying traffic network construction module is used to acquire traffic demand data and road link quality data, and construct a time-varying network diagram of the urban road traffic system. Under the seepage theory framework, as the seepage threshold gradually increases, an accessibility matrix is ​​established based on the link quality matrix, and the demand weighting coefficient (DWC) is calculated to obtain the global resilience index. This is used to characterize the network's ability to maintain functionality under different operating states;

[0101] Link Criticality Calculation Module: Used to identify restrictive links based on OD demand paths and calculate the criticality score of each link. To quantify the importance of links in maintaining traffic demand satisfaction;

[0102] Resilience assessment and conformity verification module: used for link criticality score-based assessment. Calculate the link-level resilience index in conjunction with the link quality q. And prove it through mathematical derivation. This ensures consistency between the evaluation results at the link level and the global level.

[0103] Link classification and optimization module: used to classify links based on their criticality scores. Based on the link quality q, road links are classified into five categories: highest resilience links, high resilience links, low resilience links, lowest resilience links, and unrestricted links. Corresponding traffic management and optimization strategies are proposed based on the classification results.

[0104] Each module in the aforementioned two-level resilience assessment and classification device based on heterogeneous flow demand can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0105] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described two-level resilience assessment and classification method.

[0106] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described two-level resilience assessment and classification method.

[0107] For those skilled in the art, various modifications and improvements can be made without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this invention should be determined by the appended claims.

Claims

1. A two-level resilience assessment and classification method based on heterogeneous flow demand, characterized in that, include: Step 1: Obtain traffic demand data and road link quality data, construct a time-varying network diagram of the urban road traffic system, calculate the traffic demand data weighting coefficient based on the accessibility matrix established based on the road link quality data, and calculate the global resilience index based on the traffic demand data weighting coefficient. Step 2: Identify the restrictive links of OD pairs, and calculate the criticality score of each link in the urban road traffic network based on the results of the restrictive link identification; Step 3: Calculate the resilience index of the link layer based on the criticality score and quality of each link; Step 4: Evaluate and verify the consistency of the global resilience index and the link-level resilience index; Step 5: Classify each link according to its criticality score and the distribution of link quality data.

2. The two-level resilience assessment and classification method based on heterogeneous flow demand according to claim 1, characterized in that, In step 1, the time-varying network diagram of the urban road traffic system constructed is specifically as follows: ,in, Time period Urban road traffic network map, Represents the set of road intersection nodes. Represents a set of roads. Represents the adjacency matrix. Indicates time period Internal OD demand matrix Indicates time period Internal network link quality matrix , Time period Travel demand from origin o to destination d , Time period The link quality metric for the inner link (i,j). , Let (i,j) be the minimum travel time required to traverse link (i,j) throughout all time periods of the day. Time period The travel time through link (i,j) within the network.

3. The two-level resilience assessment and classification method based on heterogeneous flow demand according to claim 2, characterized in that, In step 1, the specific method for calculating the weighting coefficients of traffic demand data based on the accessibility matrix established by the road link quality data is as follows: Calculating the seepage threshold within the framework of seepage theory Next time period The reachable matrix within , , seepage threshold The accessibility index for the lower OD pair, where n is the number of road intersection nodes. , seepage threshold Next time period Map of the city's road traffic network; The permeation threshold Next time period Within the reachability matrix and time period The seepage threshold is obtained by multiplying each element of the internal OD demand matrix and summing them. Next time period The total demand for services can still be maintained within the system. ; Then based on the seepage threshold Next time period The total demand for services can still be maintained within the system. With time period Total network demand D t The ratio is used to obtain the seepage threshold. Next time period Weighted coefficient of internal traffic demand data .

4. The two-level resilience assessment and classification method based on heterogeneous flow demand according to claim 3, characterized in that, In step 1, the global resilience index is calculated based on the weighted coefficients of the traffic demand data. Specifically, this involves integrating the weighted coefficients of the traffic demand data within the seepage threshold interval [0,1] and averaging them over a given time window [t1,t2]. ,in, It serves as a global resilience indicator.

5. The two-level resilience assessment and classification method based on heterogeneous flow demand according to claim 4, characterized in that, In step 2, The specific method for identifying the restrictive link of an OD pair is as follows: For each OD pair, identify the bottleneck link on all paths and select the bottleneck link with the highest link quality as the restrictive link of the OD pair. Based on the results of restrictive link identification, the criticality score of each link in the urban road traffic network is calculated, specifically as follows: ,in, For the criticality score of link (i,j), 1 n It is a unit vector. This indicates that link (i,j) is a restricted link.

6. The two-level resilience assessment and classification method based on heterogeneous flow demand according to claim 5, characterized in that, Step 3 calculates the resilience index of the link layer based on the criticality score and quality of each link, specifically as follows: ,in, This is a resilience indicator at the link layer.

7. The two-level resilience assessment and classification method based on heterogeneous flow demand according to claim 6, characterized in that, Step 5 classifies the links based on their criticality scores and link quality distribution, specifically as follows: When the link quality q ≥ 0.6 and the link criticality score is... Links are classified as the most resilient links when they are greater than or equal to the 75th percentile of their statistical distribution. When the link quality q ≥ 0.6 and the link criticality score is... When the link is below the 75th percentile of its statistical distribution, it is classified as a high-resilience link. When the link quality q < 0.6 and the link criticality score Links are classified as low-resilience links when they are greater than or equal to the 75th percentile of their statistical distribution. When the link quality q < 0.6 and the link criticality score When the link is below the 75th percentile of its statistical distribution, it is classified as the least resilient link. When the link criticality score When the value is 0, the link is classified as an unrestricted link.

8. A two-level resilience assessment and classification device based on heterogeneous flow demand, characterized in that, Its features include: The time-varying traffic network construction module is used to acquire traffic demand data and road link quality data, construct a time-varying network diagram of the urban road traffic system, calculate the traffic demand data weighting coefficient based on the accessibility matrix established by the road link quality data, and calculate the global resilience index based on the traffic demand data weighting coefficient. The link criticality calculation module is used to identify restricted links in OD pairs and calculate the criticality score of each link in the urban road traffic network based on the restricted link identification results. The resilience assessment and consistency verification module is used to calculate the link-level resilience index based on the criticality score and quality of each link, and to evaluate and verify the consistency between the global resilience index and the link-level resilience index; and, The link classification and optimization module is used to classify each link based on its criticality score and the distribution of link quality data, and to generate traffic management and optimization strategies for each link based on the classification results.

9. A computer device comprising a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the two-level resilience assessment and classification method of claim 1.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the two-level resilience assessment and classification method of claim 1.

Images