Urban traffic network toughness evaluation method in intelligent network connection environment

Abstract

The invention relates to an urban traffic network toughness evaluation method in an intelligent network connection environment, which belongs to the field of urban traffic evaluation and comprises the following steps: inputting road network basic data; obtaining travel requirements of manual driving automobiles and intelligent network connection automobiles among all OD pairs in the current network; the traffic flow of the manually-driven automobile is distributed based on a user balance principle; traffic flows of the intelligent network connection automobiles are distributed based on a system optimization principle; repeating the previous step to obtain new manual driving vehicle flow and intelligent network connection vehicle flow of each road section, and performing iterative updating through a continuous weighted average method; checking whether the current flow state converges or not; checking whether the flow of each road section meets the traffic capacity constraint or not; and searching the minimum traffic capacity maintenance rate. According to the invention, a new perspective is provided for traffic network toughness evaluation in an intelligent network connection environment.

Description

Technical Field

[0001] This invention belongs to the field of urban traffic assessment technology and relates to a method for assessing the resilience of urban traffic networks in an intelligent connected environment. Background Technology

[0002] The existence of intelligent connected vehicles, and the impact of mixed traffic flow on transportation networks, is a noteworthy issue that warrants further investigation. In real-world transportation systems, traffic networks are often viewed as degraded networks. This is because, in actual operating environments, road capacity is affected by random events such as severe weather, traffic accidents, and road construction, leading to actual capacity on various road segments falling below their design or expected levels.

[0003] Transportation network resilience refers to the ability of a transportation system to maintain its basic service level and quickly recover to normal operation after being damaged by external disturbances or internal failures. It reflects the system's ability to withstand shocks, recover, and adapt to uncertain events. The capacity maintenance rate θ of a transportation network reflects the proportion of the network's current available capacity to its maximum capacity, i.e., the level of capacity the network retains in its current state. A lower maintenance rate indicates that the network can still meet predetermined travel demands under lower capacity conditions, possessing higher redundancy and the ability to withstand emergencies, i.e., stronger resilience. Conversely, a higher maintenance rate requires more available capacity to meet travel demands, indicating weaker resilience.

[0004] Currently, existing methods measure urban transportation network resilience based on maximum capacity, without considering capacity degradation or seeking the minimum capacity maintenance rate to assess transportation network resilience. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method for assessing the resilience of urban traffic networks in an intelligent connected environment, which is used to evaluate the ability of traffic networks to meet predetermined requirements in the mixed traffic conditions of intelligent connected vehicles and manually driven vehicles.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for assessing the resilience of urban transportation networks in an intelligent connected environment includes the following steps: S1: Input basic road network data; S2: Obtain the travel demand of manually driven cars and intelligent connected cars among each OD pair in the current network; S3: Allocate traffic flow for manually driven vehicles based on the user balance principle; S4: Allocate traffic flow for intelligent connected vehicles based on the principle of system optimization; S5: Repeat step S3 to calculate the new traffic flow of manually driven vehicles on each road segment, and iteratively update it using the continuous weighted average method; S6: Repeat step S4 to calculate the new intelligent connected vehicle traffic flow for each road segment, and iteratively update it using the continuous weighted average method; S7: Check if the current traffic status has converged; S8: Check whether the traffic flow in each road segment meets the traffic capacity constraints; S9: Find the minimum capacity maintenance rate.

[0007] Furthermore, in step S1, the basic data includes the traffic network topology G(N,A), where N = {1, 2, …,n} is the set of nodes, A = {1, 2, …, m} is the set of road segments, and the expected maximum capacity C of each road segment. a and free-flow travel time , capacity maintenance rate θ and capacity maintenance rate change Δθ, travel demand Q, intelligent connected vehicle penetration rate λ, counter z.

[0008] Furthermore, in step S2, based on the travel demand Q input in step S1 and the penetration rate of intelligent connected vehicles, the travel demand of manually driven vehicles and intelligent connected vehicles is calculated. The specific steps are as follows:

[0009]

[0010] in, It represents the total travel demand on the OD (Operation Demand) and is also an element of travel demand Q. This refers to the demand for human-driven vehicles on the OD (Operational Development) platform. It is the travel demand of intelligent connected vehicles on r–s from OD, where R is the set of starting points and S is the set of ending points; Set the counter z = 1.

[0011] Furthermore, step S3 includes the following steps: S31: Calculate the travel time for each road segment using the BPR function:

[0012] in, and These are the parameters of the BPR function. and These are the traffic flows for manually driven cars and intelligent connected vehicles on road segment a. During the initial traffic allocation, there was no traffic on the network. and All are 0; S32: Calculate the travel time for each route based on the travel time of each road segment and the relationship between road segments and routes:

[0013]

[0014] in, It is the travel time of the i-th path on rs from OD. It is a set of OD to rs paths; Allocate all demand from each OD pair to the path with the shortest travel time:

[0015] in, It is the path number with the shortest travel time on rs for OD; S33: Calculate the traffic flow of manually driven vehicles on each road segment based on the relationship between road segments and routes, and the traffic flow of manually driven vehicles on each route. :

[0016] in, It represents the traffic flow of manually driven vehicles on the i-th path of OD on rs.

[0017] Furthermore, step S4 specifically includes the following steps: S41: Calculate the marginal travel time of each road segment based on the BPR function:

[0018] S42: Calculate the marginal travel time for each path based on the marginal travel time of each road segment:

[0019]

[0020] in, It is the marginal travel time of OD on the j-th path of rs; S43: Assign all demands from each OD pair to the path with the shortest marginal travel time:

[0021] in, It is the path number with the shortest marginal travel time on rs for OD; S44: Calculate the traffic flow of intelligent connected vehicles on each road segment based on the relationship between road segments and routes, and the traffic flow of manually driven vehicles on each route. : .

[0022] Furthermore, step S5 specifically includes the following steps: S51: Repeat step S3 to calculate the new traffic flow for manually driven vehicles on each road segment. .

[0023] S52: Update traffic flow for manually driven vehicles:

[0024] Step S6 specifically includes the following steps: S61: Repeat step S4 to calculate the new intelligent connected vehicle traffic flow for each road segment. ; S62: Update traffic for connected vehicles:

[0025] Step S7 specifically includes the following steps: S71: The total traffic flow for each road segment is obtained by summing the traffic flow of manually driven vehicles and the traffic flow of intelligent connected vehicles on the corresponding road segment.

[0026]

[0027] S72: Check for convergence, if... If this is the case, then the current traffic flow has converged. To determine the total traffic flow of each road segment under equilibrium conditions, let Conversely, let z = z + 1, and return to step S5 to continue running.

[0028] Furthermore, in step S8, the traffic capacity state vector C = (C1, C2,..., C m Let θ represent the maximum network capacity state. Due to the capacity maintenance rate, the current capacity state vector is C(θ) = (θC1, θC2, ..., θC). m ), where θC i (1 ≤ i ≤ m) represents the actual traffic capacity of each road segment. The traffic flow of each road segment is compared one by one to see if it meets the traffic capacity constraint. .

[0029] Furthermore, in step S9, if the traffic flow of all road segments meets the capacity constraint, it indicates that the network still has redundancy. The minimum capacity maintenance rate is found by iteratively decreasing the capacity: let θ = θ - Δθ, and return to step S2; otherwise, the network has no redundancy, meaning the current capacity state cannot meet the predetermined demand, and the minimum capacity maintenance rate is output: θ min = θ+Δθ.

[0030] The beneficial effects of this invention are as follows: This invention uses the network capacity maintenance rate θ to measure the resilience of the transportation network, and is the minimum capacity maintenance rate corresponding to fulfilling the predetermined travel demand under the condition of meeting capacity constraints; this invention gradually reduces the network capacity maintenance rate θ, and solves the operating state of intelligent connected vehicles and manually driven vehicles in the transportation network based on a hybrid equilibrium model, until the minimum capacity maintenance rate θ that can still maintain network operation under capacity constraints is found. min θ min The lower the value, the higher the network redundancy, the stronger the ability to resist random events, and the better the resilience; conversely, the higher the value, the worse the resilience. This invention, from the perspective of capacity degradation, aims to assess traffic network resilience by finding the minimum capacity maintenance rate, filling a gap in existing technologies.

[0031] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0032] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a flowchart of the traffic network resilience assessment method under the intelligent connected environment described in this invention; Figure 2 This is a network diagram for a specific embodiment. Detailed Implementation

[0033] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0034] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0035] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0036] Example 1: This invention provides a method for assessing the resilience of transportation networks in an intelligent connected environment, the flowchart of which is shown below. Figure 1 As shown, it includes the following steps: 1) Input basic road network data.

[0037] The basic data includes the traffic network topology G(N,A), where N = {1, 2, …, n} is the set of nodes, A = {1, 2, …, m} is the set of road segments, and the expected maximum capacity C of each road segment. a and free-flow travel time , capacity maintenance rate θ and capacity maintenance rate change Δθ, travel demand Q, intelligent connected vehicle penetration rate λ, counter z.

[0038] 2) Obtain the travel demand of each OD in the current network for different types of vehicles.

[0039] In mixed traffic environments, manually driven vehicles and intelligent connected vehicles have significantly different driving characteristics and behaviors, resulting in varying impacts on road capacity and traffic flow patterns. To accurately characterize the travel characteristics of different vehicle types within the network, it is necessary to categorize the travel demands of the two types of vehicles based on overall travel demand and the penetration rate of intelligent connected vehicles. Based on the travel demand Q input in step 1) and the penetration rate of intelligent connected vehicles, the travel demand of each OD (Original Demand) for both manually driven and intelligent connected vehicles is calculated. The specific steps are as follows:

[0040]

[0041] in, It represents the total travel demand on the OD-r-s axis, and is also an element of travel demand Q. It is the demand for human-driven cars on the OD (Operational Development) platform. It represents the travel demand of intelligent connected vehicles on the r-s axis, where R is the set of starting points and S is the set of ending points.

[0042] Set the counter z=1. The counter is used to assist in flow convergence.

[0043] 3) Allocate traffic flow for manually driven vehicles based on the User Equilibrium (UE) principle.

[0044] Human-driven cars, acting as non-cooperative individual decision-makers, self-interestedly choose paths that minimize their own travel time (travel cost) based on current or perceived road conditions and travel time information. In this non-cooperative, self-interested selection process, when equilibrium is reached, no single driver can further shorten their travel time by unilaterally changing the route; this corresponds to the Wardrop first equilibrium principle, or User Equilibrium (UE). The process involves first calculating the travel time for each path in the current network, and then allocating all demand from each Origin Point (OD) to the path with the shortest travel time. The specific steps are as follows: First, use the BPR function to calculate the travel time for each road segment:

[0045] in, and These are the parameters of the BPR function. and These are the traffic flows for manually driven cars and intelligent connected vehicles on road segment a. During the initial traffic allocation, there was no traffic on the network. and All are 0.

[0046] Secondly, based on the travel time of each road segment and the relationship between road segments and routes, the travel time of each route is calculated:

[0047]

[0048] in, It is the travel time of the i-th path on rs from OD. It is a set of OD to rs paths.

[0049] Allocate all demand from each OD pair to the path with the shortest travel time:

[0050] in, It is the path number with the shortest travel time on rs for OD.

[0051] Finally, based on the relationship between road segments and routes, and the traffic flow of manually driven vehicles on each route, the traffic flow of manually driven vehicles on each road segment is calculated. :

[0052] in, It represents the traffic flow of manually driven vehicles on the i-th path of OD on rs.

[0053] 4) Allocate traffic flow for intelligent connected vehicles based on the system optimality (SO) principle.

[0054] Intelligent connected vehicles possess global information perception and collaborative decision-making capabilities, enabling them to comprehensively consider overall network operating efficiency during route selection, thereby achieving system optimal equilibrium (SO) that minimizes the total system travel cost. Traffic is allocated to intelligent connected vehicles based on minimizing marginal travel time, with the following specific steps: First, the marginal travel time for each road segment is calculated based on the BPR function:

[0055] Secondly, the marginal travel time of each path is calculated based on the marginal travel time of each road segment:

[0056]

[0057] in, It is the marginal travel time of the j-th path on OD to rs. Allocate all demand from each OD pair to the path with the shortest marginal travel time:

[0058] in, It is the path number with the shortest marginal travel time on rs for OD.

[0059] Finally, based on the relationship between road segments and routes, and the traffic flow of manually driven vehicles on each route, the traffic flow of intelligent connected vehicles on each road segment is calculated. :

[0060] 5) Update traffic flow for manually driven vehicles: Repeat step 3) to calculate the new traffic flow for manually driven vehicles on each road segment. To gradually approximate the UE state, the traffic flow of manually driven vehicles on each road segment needs to be updated. This process uses the Method of Successive Averages (MSA) for iterative calculation to smooth traffic changes and ensure the convergence of the algorithm. The specific steps are as follows:

[0061] 6) Update traffic for intelligent connected vehicles: Repeat step 4) to calculate the new intelligent connected vehicle traffic flow for each road segment. To gradually approach the SO state, the traffic flow of intelligent connected vehicles on each road segment needs to be updated. This process uses a continuous weighted average method for iterative calculation to smooth traffic changes and ensure the convergence of the algorithm. The specific steps are as follows:

[0062] 7) Check if the current traffic status has converged: First, based on the traffic flow of manually driven vehicles and intelligent connected vehicles obtained in the current iteration and the previous iteration, the total traffic flow of each road segment is calculated. Then, the results of the two iterations are compared to determine whether the system has reached the convergence condition. If the change in the total traffic flow of road segments between two adjacent iterations is lower than a preset threshold, the network traffic state is considered to have basically stabilized and the algorithm has converged; otherwise, the next iteration continues until the convergence criterion is met.

[0063] First, the total traffic flow for each road segment is obtained by summing the traffic flow of manually driven vehicles and the traffic flow of intelligent connected vehicles for that segment.

[0064]

[0065] Secondly, if If this is the case, then the current traffic flow has converged. To determine the total traffic flow of each road segment under equilibrium conditions, let Conversely, let z = z + 1, return to step 5) and continue running.

[0066] 8) Check whether the traffic flow on each road segment meets the traffic capacity constraints: The capacity state vector C = (C1, C2,..., C m The capacity maintenance rate (θ) represents the maximum capacity state of each road segment in the network under ideal conditions, and is an important indicator for measuring the overall operational potential of the network. Introducing the capacity maintenance rate (θ), the current capacity state vector can be represented as C(θ) = (θC1, θC2, ..., θC...). m ), where θC i(1 ≤ i ≤ m) represents the actual traffic capacity of each road segment. By comparing the actual traffic flow of each road segment with its corresponding traffic capacity one by one, we check whether the traffic flow of the road segment meets the traffic capacity constraint.

[0067] 9) Reduce capacity maintenance rate to find the minimum capacity maintenance rate: If the traffic flow on all road segments meets the capacity constraints, it indicates that the network still has some redundancy under the current capacity maintenance rate. In this case, the capacity maintenance rate can be appropriately reduced, and the calculation process can be re-executed to gradually approach the minimum capacity maintenance rate required for feasible operation, thereby determining the minimum capacity level the network can withstand. Let θ = θ - Δθ, then return to step 2).

[0068] Otherwise, it indicates that the network lacks redundancy, meaning that it cannot meet the given traffic demand under the current capacity conditions. In this case, the corresponding minimum capacity maintenance rate is output: θ min = θ+Δθ θ min This directly reflects the resilience level of the network under road conditions where intelligent connected vehicles and manually driven vehicles coexist. Specifically, θ min The larger the value, the higher the network capacity required to meet the given traffic demand. When the network is affected by a sudden random event and the capacity drops to θ, the network becomes more vulnerable to sudden changes in traffic flow. min In the following situations, the predetermined service level cannot be maintained; conversely, θ min The smaller the value, the better the network can still meet traffic demands even with lower throughput, indicating stronger redundancy to withstand capacity reductions caused by random events, thus demonstrating greater resilience.

[0069] Example 2: A specific example Figure 2 As shown, an abstraction of a transportation network yields... Figure 2 The network consists of 13 nodes, 19 road segments, and 4 origin-destination (OD) pairs. Nodes 1 and 4 represent origins, nodes 2 and 3 represent destinations, and the remaining nodes are intermediate nodes. The travel demands for OD pairs 1-2, 1-3, 4-2, and 4-3 are 1000, 1500, 1000, and 1500, respectively. The capacity maintenance rate θ = 1, and the capacity maintenance rate variation Δθ = 0.0001. Table 1 shows the maximum capacity and free-flow travel time for each road segment in the network.

[0070] Table 1

[0071] The minimum network throughput maintenance rate θ is calculated according to the method steps of this invention when the penetration rate of intelligent connected vehicles λ = 0.5. min =0.6342, meaning that when the network's throughput maintenance rate is less than 0.6342, the network will be unable to handle the current travel demand.

[0072] Example 3: An electronic device, comprising a memory and a processor; The memory is used to store computer programs; The processor is configured to implement the method described in Embodiment 1 when executing the computer program.

[0073] Example 4: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0074] Example 5: A computer program product includes a computer program that, when executed by a processor, implements the method described in Example 1.

[0075] In the above embodiments, the reference to "this embodiment" in the specification indicates that a specific feature, structure, or characteristic described in connection with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple appearances of "this embodiment" do not necessarily refer to the same embodiment.

[0076] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory structures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed. The embodiments of the invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims.

[0077] As will be understood by those skilled in the art, the computer-readable storage medium described in this embodiment allows for the implementation of all or part of the steps in the above method embodiments by computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0078] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication between them. The memory is used to store computer programs, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer programs, so that the electronic terminal performs the steps of the above method.

[0079] In this embodiment, the memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0080] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0081] This invention can be used in a wide range of general-purpose or special-purpose computing system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0082] This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for assessing the resilience of urban transportation networks in an intelligent connected environment, characterized in that: Includes the following steps: S1: Input basic road network data; S2: Obtain the travel demand of manually driven cars and intelligent connected cars among each OD pair in the current network; S3: Allocate traffic flow for manually driven vehicles based on the user balance principle; S4: Allocate traffic flow for intelligent connected vehicles based on the principle of system optimization; S5: Repeat step S3 to calculate the new traffic flow of manually driven vehicles on each road segment, and iteratively update it using the continuous weighted average method; S6: Repeat step S4 to calculate the new intelligent connected vehicle traffic flow for each road segment, and iteratively update it using the continuous weighted average method; S7: Check if the current traffic status has converged; S8: Check whether the traffic flow in each road segment meets the traffic capacity constraints; S9: Find the minimum capacity maintenance rate.

2. The method for assessing the resilience of urban transportation networks in an intelligent connected environment according to claim 1, characterized in that: In step S1, the basic data includes the traffic network topology G(N,A), where N = {1, 2, …, n} is the set of nodes, A = {1, 2, …, m} is the set of road segments, and the expected maximum capacity C of each road segment. a and free-flow travel time , capacity maintenance rate θ and capacity maintenance rate change Δθ, travel demand Q, intelligent connected vehicle penetration rate λ, counter z.

3. The method for assessing the resilience of urban transportation networks in an intelligent connected environment according to claim 1, characterized in that: In step S2, based on the travel demand Q input in step S1 and the penetration rate of intelligent connected vehicles, the travel demand of manually driven vehicles and intelligent connected vehicles is calculated. The specific steps are as follows: in, It represents the total travel demand on the OD (Operation Demand) and is also an element of travel demand Q. This refers to the demand for human-driven vehicles on the OD (Operational Development) platform. It is the travel demand of intelligent connected vehicles on r–s from OD, where R is the set of starting points and S is the set of ending points; Set the counter z = 1.

4. The method for assessing the resilience of urban transportation networks in an intelligent connected environment according to claim 1, characterized in that: Step S3 includes the following steps: S31: Calculate the travel time for each road segment using the BPR function: in, and These are the parameters of the BPR function. and These are the traffic flows for manually driven cars and intelligent connected vehicles on road segment a. During the initial traffic allocation, there was no traffic on the network. and All are 0; S32: Calculate the travel time for each route based on the travel time of each road segment and the relationship between road segments and routes: in, It is the travel time of the i-th path on rs from OD. It is a set of OD to rs paths; Allocate all demand from each OD pair to the path with the shortest travel time: in, It is the path number with the shortest travel time on rs for OD; S33: Calculate the traffic flow of manually driven vehicles on each road segment based on the relationship between road segments and routes, and the traffic flow of manually driven vehicles on each route. : in, It represents the traffic flow of manually driven vehicles on the i-th path of OD on rs.

5. The method for assessing the resilience of urban transportation networks in an intelligent connected environment according to claim 1, characterized in that: Step S4 Specifically, the following steps are included: S41: Calculate the marginal travel time of each road segment based on the BPR function: S42: Calculate the marginal travel time for each path based on the marginal travel time of each road segment: in, It is the marginal travel time of the j-th path on OD to rs; S43: Assign all demands from each OD pair to the path with the shortest marginal travel time: in, It is the path number with the shortest marginal travel time on rs for OD; S44: Calculate the traffic flow of intelligent connected vehicles on each road segment based on the relationship between road segments and routes, and the traffic flow of manually driven vehicles on each route. : 。 6. The method for assessing the resilience of urban transportation networks in an intelligent connected environment according to claim 1, characterized in that: Step S5 includes the following steps: S51: Repeat step S3 to calculate the new traffic flow of manually driven vehicles on each road segment. ; S52: Update traffic flow for manually driven vehicles: 。 7. The method for assessing the resilience of urban transportation networks in an intelligent connected environment according to claim 1, characterized in that: Step S6 includes the following steps: S61: Repeat step S4 to calculate the new intelligent connected vehicle traffic flow for each road segment. ; S62: Update traffic for connected vehicles: 。 8. The method for assessing the resilience of urban transportation networks in an intelligent connected environment according to claim 1, characterized in that: Step S7 includes the following steps: S71: The total traffic flow for each road segment is obtained by summing the traffic flow of manually driven vehicles and the traffic flow of intelligent connected vehicles on the corresponding road segment. S72: Check for convergence, if... If this is the case, then the current traffic flow has converged. To determine the total traffic flow of each road segment under equilibrium conditions, let Conversely, let z = z + 1, and return to step S5 to continue running.

9. The method for assessing the resilience of urban transportation networks in an intelligent connected environment according to claim 1, characterized in that: In step S8, the traffic capacity state vector C = (C1, C2,..., C m Let θ represent the maximum network capacity state. Due to the capacity maintenance rate, the current capacity state vector is C(θ) = (θC1, θC2, ..., θC). m ), where θC i This represents the actual traffic capacity of each road segment, 1 ≤ i ≤ m. We compare the traffic flow of each road segment to see if it meets the traffic capacity constraints. 。 10. The method for assessing the resilience of urban transportation networks in an intelligent connected environment according to claim 1, characterized in that: In step S9, if the traffic flow of all road segments meets the capacity constraint, it indicates that the network still has redundancy. The minimum capacity maintenance rate is found by iteratively decreasing the capacity: let θ = θ - Δθ, and return to step S2; otherwise, the network has no redundancy, meaning the current capacity state cannot meet the predetermined demand, and the minimum capacity maintenance rate is output: θ min = θ+Δθ.

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