A traffic resilience evaluation method based on virtual traffic signal control

Abstract

The application discloses a kind of traffic resilience evaluation methods based on virtual traffic signal control, comprising: S1, build road network, and set signal control method;S2, set special event for road network;S3, under special event, the signal control method of setting is used to carry out traffic signal simulation control to road network, and corresponding road network change data is collected;S4, according to road network change data determines traffic resilience evaluation index, and builds multidimensional resilience evaluation system;S5, based on the multidimensional resilience evaluation system built, assesses and compares traffic resilience under different signal control methods.The application simulates autonomous driving technology scene with virtual traffic signal control, then compares the influence of three traffic signal control methods on traffic resilience in the face of increasing demand and decreasing supply, and the resilience evaluation system obtained comprehensively covers the ability of road network to resist traffic accidents and interference and the ability of rapid recovery.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of intelligent transportation, and particularly relates to a traffic resilience evaluation method based on virtual traffic signal control, which is used for evaluating a simulated autonomous driving scene under a virtual traffic signal control mode and researching a change trend of traffic resilience. BACKGROUND

[0002] In the past decade, the concept of resilience has become increasingly important in the transportation system, including transportation planning, design, operation and maintenance. The resilience of a system refers to the ability of the system to absorb shocks and disturbances while minimizing functional losses and quickly recovering full operation. Although there are various methods for implementing and evaluating road network resilience, one key element for significantly enhancing the resilience of urban road networks is traffic signals. By adjusting traffic signal indications, such as assigning directional priority and right-of-way, interruptions can be effectively managed, maintaining efficient traffic flow and minimizing delays. Although recent transportation research has mainly focused on the impact of infrastructure on traffic resilience, research on traffic control methods is relatively limited. Studying the impact of different signal control methods on the resilience of urban road networks can greatly help improve the management and operation of urban road traffic and enhance the overall efficiency of urban road networks.

[0003] CAVs have the potential to significantly change various transportation applications. This technology can make a significant contribution to improving driving safety, increasing road network efficiency, reducing traffic congestion, and providing transportation opportunities for individuals who cannot drive themselves. One of the notable advantages of CAVs is their ability to communicate with each other and with infrastructure, enabling coordinated movement and thus minimizing the risk of secondary accidents. This approach is particularly effective in emergency situations. By taking advantage of this communication capability, CAVs have the potential to optimize traffic flow, thereby reducing congestion and delays and ultimately improving the overall resilience of road networks. The advantages provided by CAVs are critical to ensuring the effective operation of the network when faced with high demand or unexpected events such as accidents and road closures. Their potential to enhance road resilience and adaptability is essential to maintaining an efficient transportation system.

[0004] The introduction of CAVs embodies several new factors that can significantly impact the resilience of road networks. Factors such as inter-vehicle communication, coordination and the implementation of intelligent traffic management systems play a crucial role in shaping this impact. The potential impact of CAVs on resilience can be positive or negative, depending on their implementation. However, CAVs have the potential to greatly enhance the overall adaptability and resilience of road networks. By facilitating real-time data exchange and intelligent traffic optimization, CAVs can quickly adapt to changing traffic conditions. This adaptability is achieved by adjusting route selection, speed and spacing. As a result, road networks become more sensitive and efficient in managing disruptions. SUMMARY

[0005] In view of the above problems in the prior art, the traffic resilience evaluation method based on virtual traffic signal control provided by the present application studies the changes of various indexes of the traffic system under VTS control through the method of simulation and data analysis, and enhances the understanding of the potential influence of CAV on the resilience of road network.

[0006] In order to achieve the above-mentioned purpose of the application, the technical scheme adopted by the present application is as follows: a traffic resilience evaluation method based on virtual traffic signal control, comprising the following steps:

[0007] S1, building a road network corresponding to a traffic signal control system to be evaluated, and setting a signal control method;

[0008] S2, setting a special event for the road network;

[0009] S3, under the special event, using the set signal control method to perform traffic signal simulation control of the road network for connected and autonomous vehicles (CAV), and collecting corresponding road network change data;

[0010] S4, determining traffic resilience evaluation indexes according to the road network change data, and performing normalization processing to build a multi-dimensional resilience evaluation system;

[0011] S5, based on the built multi-dimensional resilience evaluation system, evaluating and comparing the traffic resilience under different signal control methods.

[0012] Further, in the step S1, the signal control method includes a fixed time control method, an inductive signal control method and a VTS control method;

[0013] The VTS control method refers to autonomously coordinating and synchronizing the arrival and departure of vehicles at the intersection of the road network under the set operation rules, making the vehicles continuously move and ensuring a safe distance, and eliminating the need for traffic signals.

[0014] Further, in the step S2, the special event includes demand increase and supply decrease;

[0015] The demand increase refers to increasing the traffic flow of the road network within a set time range; the supply decrease refers to reducing the maximum speed limit of the vehicles in the road network within a set time range.

[0016] Further, in the step S3, the road network change data includes average speed, resistance, recovery, resilience triangle area and average fuel consumption of each vehicle.

[0017] Further, in the step S4, the traffic resilience evaluation index includes the average speed V before the special event, the difference H between the average speed before the special event and the minimum average speed point, the time difference L from the start of the special event to the complete recovery of the road network, the resilience triangle area S, and the average fuel consumption C of the vehicle.

[0018] Further, in the step S4, the normalization processing of the traffic resilience evaluation index refers to the standardization processing of the value of the traffic resilience evaluation index, and then using the sigmoid function to map the processing result value from [-∞, +∞] to [0, 1].

[0019] Further, the step S5 is specifically:

[0020] S51, extract the average speed of each road segment in the road network under different signal control methods, and perform singular spectrum analysis, filtering analysis, and then data visualization;

[0021] S52, analyze the visualized average speed curve using the MK trend test method, and identify the special points where the upward trend stagnates or starts to decline;

[0022] S53, taking the special point as the end time of the resilience triangle integral, and then drawing the traffic resilience change area curve under different signal control methods;

[0023] S54, using the MK trend test function to analyze the traffic resilience change area curve, and determining the values of each traffic resilience evaluation index in the multi-dimensional resilience evaluation system under different signal control methods;

[0024] S55, according to the values of the traffic resilience evaluation index after normalization processing, draw the radar chart corresponding to different signal control methods, and evaluate and compare the traffic resilience under different signal control methods according to the evaluation index.

[0025] Further, in the step S55, the evaluation index of traffic resilience evaluated according to the radar chart includes resilience, efficiency, and fuel consumption;

[0026] Among them, the resilience is embodied by the difference H and the time difference L, the difference H embodies the resistance, and the L embodies the recovery ability; the efficiency is embodied by the average speed V and the resilience triangle area S; and the fuel consumption is embodied by the average fuel consumption C of the vehicle.

[0027] The beneficial effects of the present application are:

[0028] (1) The present application establishes a traffic resilience evaluation system based on five-dimensional information, considers the optimization objectives of traffic flow parameters, road network robustness, and overall fuel consumption during modeling, and the obtained resilience evaluation system comprehensively covers the ability of the road network to resist traffic accidents and interference and the ability of rapid recovery.

[0029] (2) The present application simulates the automatic driving scene with virtual traffic signal control mode. Compared with other two traffic signal control modes, the simulation is carried out on the sumo platform in the case of supply increase and demand decrease, so as to study the influence of virtual traffic signal control on traffic resilience, and then verify the effectiveness of the multi-dimensional traffic resilience evaluation method of the present application.

[0030] (3) The present application can affect short-term decisions related to traffic management strategies and the integration of autonomous driving technology into existing traffic systems. Considering the VTS control system into future road network design can enhance resilience, reduce congestion and improve overall traffic flow.

[0031] (4) The present application reduces unnecessary vehicle start and stop, and the reduction of fuel consumption that follows, which helps to establish a more environmentally friendly traffic system. The adoption of VTS control conforms to the broader sustainable development goals and initiatives aimed at reducing carbon emissions and promoting sustainable transportation. By improving the resistance and recovery capacity of the traffic network, VTS control can help cities better cope with emergencies and increased demand. This enhances the overall resilience of urban infrastructure, ensures smoother traffic, improves safety, and reduces disruptions during emergencies or peak periods. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The traffic resilience evaluation method flow chart based on virtual traffic signal control provided by the present application.

[0033] Figure 2 The road network simulation diagram provided by the present application.

[0034] Figure 3 The road network simulation diagram under the supply reduction scenario provided by the present application.

[0035] Figure 4 The traffic resilience curve diagram provided by the present application.

[0036] Figure 5 The average speed diagram under the supply reduction scenario provided by the present application.

[0037] Figure 6 The resilience triangle area (entire road network) diagram of the three signal control methods under the improved supply reduction scenario provided by the present application.

[0038] Figure 7 The average speed diagram under the supply reduction scenario of the speed limit section provided by the present application.

[0039] Figure 8 The resilience triangle area (only speed limit road) diagram of the three signal control methods under the supply reduction scenario provided by the present application.

[0040] Figure 9 Fig. 2 is a schematic diagram of the resilience evaluation result in the supply reduction scenario (entire road network) provided by the present application.

[0041] Figure 10 Fig. 3 is a schematic diagram of the resilience evaluation result in the supply reduction scenario (only speed-limit road) provided by the present application. DETAILED DESCRIPTION

[0042] The specific embodiments of the present application are described below to facilitate the understanding of the present application for those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and determined by the appended claims, and all the inventions utilizing the concept of the present application are within the scope of protection.

[0043] The embodiment of the present application provides a traffic resilience evaluation method based on virtual traffic signal control, as shown in the figure, comprising the following steps: Figure 1

[0044] S1, build a road network corresponding to a traffic signal control system to be evaluated, and set a signal control method;

[0045] S2, set a special event for the road network;

[0046] S3, under the special event, the set signal control method is used to control the traffic signal simulation of the road network for connected and autonomous vehicles (CAV), and the corresponding road network change data is collected;

[0047] S4, determine the traffic resilience evaluation index according to the road network change data, and normalize it to build a multi-dimensional resilience evaluation system;

[0048] S5, based on the built multi-dimensional resilience evaluation system, evaluate and compare the traffic resilience under different signal control methods.

[0049] In step S1 of the embodiment of the present application, as an example, a road network of 3x3 and each intersection has a traffic signal control system is built. Figure 2

[0050] In step S1 of the embodiment of the present application, the signal control method includes a fixed time control method, an inductive signal control method and a VTS control method.

[0051] ​​The fixed time control method in the embodiment refers to adjusting traffic flow by using predetermined signal timing. The parameters related to fixed time control in the embodiment are shown in Table 1. The fixed time control method is a simple method that requires minimal infrastructure, but it lacks the ability to adapt to changing traffic conditions and cannot effectively reduce congestion.

[0052] Table 1 Signal control scheme

[0053]

[0054]

[0055] The adaptive signal control method in the embodiment is a highly adaptive traffic management strategy that continuously adjusts traffic signal durations using real-time traffic data. This method has been proven to improve traffic efficiency and reduce congestion. However, it requires additional infrastructure and has high implementation costs. In the embodiment, for the adaptive control condition, the maximum green time is 30 seconds, the minimum green time is 6 seconds, and the extension time is 3 seconds.

[0056] The VTS control method in the embodiment is a method that uses direct communication between vehicles and traffic signals to optimize signal timing. It refers to autonomously coordinating and synchronizing the arrival and departure of vehicles at intersections in a road network under certain operating rules, allowing vehicles to move continuously and maintain a safe distance, eliminating the need for traffic signals. This method can significantly improve traffic flow and alleviate congestion, but its implementation requires a large number of integrated facilities and widespread adoption of connected vehicle technology. Specifically, since the implementation of the VTS control method relies heavily on CAVs, and the VTS control method can demonstrate most of the characteristics of CAVs, analyzing the control mode of VTS on traffic resilience allows us to infer the impact of CAVs on traffic resilience to some extent.

[0057] In step S2 of the embodiment of the present application, the special event includes demand increase and supply decrease;

[0058] wherein the demand increase refers to increasing the traffic flow of the road network within a certain time range; and the supply decrease refers to reducing the maximum speed limit of vehicles in the road network within a certain time range.

[0059] Specifically, in the embodiment, for the special event of demand increase, the road network is as shown in Figure 2As shown, in order to simulate the case of demand increase, the traffic flow of the road network in this embodiment is set to be different in each time period, from the beginning to 1800 seconds, the traffic flow is 200 vehicles per hour. In this embodiment, it is assumed that due to peak hours and the like, the traffic flow increases to 500 vehicles per hour at 1800 seconds. Then at 3600 seconds, the traffic flow returns to 200 vehicles per hour.

[0060] Table 2 Traffic flow of each lane in different time periods

[0061]

[0062] In this embodiment, for the special event of supply reduction, the road network is as shown in the figure Figure 3 As shown, in order to simulate the case of demand increase, the traffic flow of the road network in this embodiment is set to be different in each time period, from the beginning to 1800 seconds, the traffic flow is 200 vehicles per hour. In this embodiment, it is assumed that due to peak hours and the like, the traffic flow increases to 500 vehicles per hour at 1800 seconds. Then at 3600 seconds, the traffic flow returns to 200 vehicles per hour.

[0063] Table 3 Maximum speed limit in different time periods

[0064]

[0065] In step S3 of the embodiment of the present application, the traffic network in the two scenarios of demand increase and supply reduction described above is simulated on the SUMO simulation platform. During the simulation process, three signal control methods are compared, and in particular in the scenario of supply reduction, the changes in the lane where the emergency event occurs and the entire road network are analyzed respectively.

[0066] In step S3 of the embodiment of the present application, the road network change data includes average speed, resistance, resilience, triangle area and average fuel consumption per vehicle.

[0067] In step S4 of the embodiment of the present application, the traffic resilience evaluation index includes the average speed V before the special event, the difference H between the average speed before the special event and the lowest average speed point, the time difference L from the start of the special event to the complete recovery of the road network, the resilience triangle area S and the average fuel consumption C of the vehicle.

[0068] In step S4 of the embodiment of the present application, during the normalization process, since the units of the five indexes are different, the data needs to be normalized to obtain the final multi-dimensional resilience evaluation system; a widely used data normalization method is the outlier normalization process, which performs linear transformation on the original data to ensure that the result value is mapped to the range of [0, 1].

[0069] But in some cases, the extreme value (too large or too small) may distort the overall data, and the extreme difference of each case is different, resulting in different evaluation criteria; based on this, the traffic resilience evaluation index is normalized in step S4 of the embodiment of the application, that is, the value of the traffic resilience evaluation index is standardized, and then the sigmoid function is used to map the processed result value from [-∞, +∞] to [0, 1].

[0070] Specifically, the data is preliminarily processed and standardized according to the mean and standard deviation of the original data to obtain a normal distribution with a mean of 0 and a standard deviation of 1; the standardized result is mapped from [-∞, +∞] to [0, 1] using the sigmoid function; wherein the formula involved is as follows:

[0071]

[0072] In the formula, μ represents the sample mean, and σ represents the sample standard deviation; the advantage of this method is that it is less sensitive to extreme values compared to linear normalization. After obtaining the normalized result, the sigmoid function is used for further processing, as shown below:

[0073]

[0074] In this embodiment, data normalization is performed on the above five kinds of data, but some parameter values are large, indicating that the resilience of the road section is low, such as the height of the resilience curve, the time from the beginning to the end of the resilience curve, and the resilience area. But this is contrary to the trend of the model function of this embodiment; in order to achieve the effect that a smaller data value corresponds to a larger function value, the negative value of x can be used instead of x, and the above formula is replaced by:

[0075]

[0076] In the embodiment of the application, in the process of evaluating the road network under different signal control methods, the abnormal situation occurring in the road network is regarded as a disturbance of different degrees to the network, and the response of the road network after being disturbed reflects the resistance and recovery ability of the network, which is regarded as the resilience of the network. If the resistance of the road network is high, the decrease of the road network capacity after disturbance is minimal. If the recovery ability of the road network is high, the time required for the network to recover to the initial capacity after being disturbed will be less.

[0077] Therefore, in this embodiment, the resistance of the road network is quantified by measuring the degree of decrease of the communication capacity of the road network, and the recovery ability of the road network is quantified by measuring the time required for the road network to recover to the original state after the decrease.

[0078] As Figure 4As shown, the traffic capacity of the road network decreases at the beginning of the disturbance and gradually recovers over time, and the resistance of the road network in this embodiment is represented as H, and the recovery force is represented as L; from the perspective of traffic resilience, a multi-dimensional resilience evaluation system in step S4 is constructed, and in the traffic resilience evaluation index of the multi-dimensional resilience evaluation system, in addition to the parameters H and L, the average speed V and the average fuel consumption C of the vehicle can be directly obtained from the original simulation data results. After establishing the multi-dimensional evaluation method, we can evaluate the resilience of the road network through the simulation results. After modeling the traffic resilience curve, the MK trend test method is used to find special points to fit the resilience curve, and then the area S of the resilience triangle is calculated; after obtaining the five index data, the traffic resilience can be evaluated.

[0079] Based on this, step S5 of the embodiment of the application is specifically:

[0080] S51, extract the average speed of each road segment in the road network under different signal control methods, and perform singular spectrum analysis, filtering analysis, and then data visualization;

[0081] S52, analyze the visualized average speed curve using the MK trend test method, and identify special points where the upward trend stagnates or starts to decline;

[0082] S53, take the special point as the end time of the resilience triangle integral, and then draw the traffic resilience area change curve under different signal control methods;

[0083] S54, analyze the traffic resilience area change curve using the MK trend test function to determine the values of each traffic resilience evaluation index in the multi-dimensional resilience evaluation system under different signal control methods;

[0084] S55, draw the radar chart corresponding to different signal control methods according to the normalized values of the traffic resilience evaluation index, and evaluate and compare the traffic resilience under different signal control methods according to the radar chart.

[0085] In step S55 of the embodiment, the evaluation indexes for evaluating traffic resilience according to the radar chart include recovery force, efficiency, and fuel consumption;

[0086] Among them, the recovery force is embodied by the difference H and the time difference L, the difference H represents the resistance, and L represents the recovery ability; the efficiency is embodied by the average speed V and the resilience triangle area S; the fuel consumption is embodied by the average fuel consumption C of the vehicle.

[0087] Based on the above method, the embodiment gives a specific traffic resilience evaluation example:

[0088] In this embodiment, experiments are conducted under different scenarios and the results are analyzed and evaluated. In each scenario, the speed of each road segment is extracted, and the data is visualized after performing singular spectrum analysis and filtering analysis. The average speed of the road network under the three signal control modes when the supply is reduced is shown in Figure 5 As shown in Figure 5 It can be seen that the average speed curves of the three signal control methods show similar shapes and trends. Without reducing supply, the average speed of VTS is about 14.5 m / s, which is much higher than that of the controlled execution mode and the timing control mode, which are about 11.6 m / s and 11.5 m / s, respectively. When the supply is reduced at 1800 s, the average speed of the three control modes is affected to varying degrees, and the average speed decreases. First, there is a significant difference in the reduction amplitude of the average speed of the three control modes, and the average speed reduction amplitude of VTS is much smaller than that of the inductive control and timing control modes. Second, there is a large difference in the time required for the average speed of the three control modes to recover, and the average speed recovery time of VTS is about 5580 s, while the inductive control mode and the timing control mode require about 7620 s and 7560 s, respectively.

[0089] The specific points where the upward trend stagnates or begins to decline are identified by the MK trend test method, and then these points are used as the endpoint time of the resilience triangular integral. The traffic resilience change area curves of the three signal control methods can be plotted, as shown in Figure 6 By analyzing them and using the MK trend test function, the results with a confidence level of 0.05 are obtained, as shown in Table 4;

[0090] Table 4 Parameters of three control modes

[0091]

[0092] In addition, by analyzing the supply-reduced road segments in the road network, the simulation data results of SUMO can be obtained, and the average speed curves under the three signal control methods can also be obtained, as shown in Figure 7As shown in the figure, the average speed change curves of the three control methods have similar shape and trend. Without reducing the supply, the average speed of VTS is about 14.5 m / s, which is significantly higher than the average speed of about 10.5 m / s of the execution control and the average speed of 10.3 m / s of the timing control. When the supply is reduced at 1800 s, the average speed of the three control modes is affected to different degrees, and the average speed is reduced at 1800 s. First, there is a significant difference in the average speed drop of the three control modes, and the drop of VTS is much smaller than that of inductive control and timing control. Second, there is also a significant difference in the average speed recovery time of the three control modes, and the average speed recovery time of VTS is about 5640 s, the start-up control is about 7680 s, and the timing control is about 7620 s.

[0093] By MK trend test method, the inflection points where the upward trend turns into flat or downward trend are identified, and then these points are used as end time to integrate the toughness triangle, and then the graphical representation of the toughness triangle area of the three control modes can be generated, as shown in Figure 8 .

[0094] By analyzing the data shown in Figure 8 and using MK trend test function, the result list with 95% confidence is generated, as shown in Table 5;

[0095] Table 5 Three control mode parameters

[0096]

[0097] In this embodiment, the results of normalizing the above 5 evaluation indexes are visualized in the form of radar chart, and the results are as shown in Figure 9 and Figure 10 , and it can be concluded that Figure 9 and Figure 10 .

[0098] 1. In terms of resilience: H represents resistance, and L represents recovery. In terms of the entire road network, the average speed under VTS control decreased by 37.5% and 44.4% under the supply reduction and the other two demand increase control modes, respectively. Moreover, the traffic speed under VTS control was relatively stable during the recovery process. This indicates that the traffic network under VTS control has better resistance to demand changes. The time points of supply reduction and recovery under VTS control are earlier than those under the other two demand increase control modes, which are shortened by 62.7% and 67.5%, respectively. This indicates that VTS has stronger recovery ability than the other two control modes. The results of the damaged road section also show similar trends, with the average speed under VTS control decreasing by 20%, while the average speed under the other two control modes decreases by 15.8% and 20%. The time points of supply reduction and recovery under VTS control are earlier than those under the other two demand increase control modes, which are shortened by 34.7% and 34.0%, respectively. In particular, it can be found that the simulation results of the push control seem to be slightly worse than those of the fixed time control. Comparing the simulation results of the damaged road section and the entire road network, it can be found that the average speed under VTS control decreases by 540.0%, which is much higher than the 400.0% and 300.0% under the other two control modes. However, the recovery time of the three control modes increases by about 60 seconds.

[0099] 2. In terms of efficiency: V and S reflect the efficiency of the road network. For the entire network, the average speed under VTS control is 14.5 m / s, which is 25.0% and 26.1% higher than the other two control modes, respectively. At the same time, the rebound triangle area under VTS control is 790.3 m, which is much smaller than the 2119.0 m and 2433.7 m under the other two control modes, with a decrease of 62.7% and 67.5%, respectively. This indicates that under the VTS control mode, the average delay of vehicles is smaller, the speed of vehicles passing through the road network is higher, and the efficiency is higher. The results of the damaged road section are also roughly similar, with the average speed under VTS control being 14.5 m / s, which is 38.1% and 40.1% higher than the other two control modes, respectively. At the same time, the rebound triangle area under VTS control is 7613.1 m, which is much smaller than the 14623.5 m and 15255.1 m under the other two control modes, with a decrease of 47.9% and 50.1%, respectively. Comparing the simulation results of the damaged road section and the entire road network, it is worth noting that the average speed under VTS control is basically the same, while the average speed under the other two control modes decreases by 9.5% and 10.4%, respectively. At the same time, the resilience triangle area of the three control modes increases significantly by 863.3%, 590.1%, and 526.8%, respectively, indicating that the average delay of vehicles on the damaged road section increases, and the efficiency is lower.

[0100] 3. In terms of fuel consumption: C directly reflects the average fuel consumption of vehicles passing through the road network. In the network, the average fuel consumption under VTS control is 140281.6, which is 30.7% and 32.5% lower than the other two control modes, respectively. At the same time, in the damaged road section, the average fuel consumption under VTS control is 140356.1, which is 30.7% and 32.6% lower than the other two control modes, respectively. Comparing the simulation results of the damaged road section and the entire road network, it is found that the fuel consumption of the three control modes only increases slightly. Therefore, under the VTS control mode, the fuel consumption is significantly reduced due to the reduction of unnecessary engine start-stop, which is more environmentally friendly.

[0101] Based on the above analysis, it can be seen that the traffic road network running under the VTS control mode shows excellent resistance and recovery ability when facing the pressure of reduced supply and increased demand. The average delay time of vehicles under VTS control is shorter, and they can travel at a higher speed on this road network. The VTS control mode effectively reduces unnecessary vehicle start-stop, thereby reducing fuel consumption and enhancing environmental friendliness. In contrast, VTS has multiple advantages over the fixed-time control and actuated signal control methods. These advantages cover various aspects, such as average speed, resistance, recovery, resilience triangle area, and average fuel consumption per vehicle. Therefore, implementing VTS in the traffic road network has multiple benefits in improving the response ability to emergencies and increasing demand. This in turn improves overall traffic efficiency, resilience, and safety, thereby forming a more efficient and sustainable transportation system.

[0102] The principles and implementation manners of the present application are described by using specific embodiments in the present application, and the above embodiment descriptions are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges can be changed, and the above descriptions should not be understood as limitations of the present application.

[0103] Those skilled in the art will realize that the embodiments described herein are for the purpose of understanding the principles of the present application and should be understood as not limiting the scope of protection of the present application to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations according to the technical inspiration disclosed in the present application without departing from the essence of the present application, and these modifications and combinations are still within the scope of protection of the present application.

Claims

1. A traffic resilience assessment method based on virtual traffic signal control, characterized in that, The method comprises the following steps: S1, a road network corresponding to a traffic signal control system to be evaluated is built, and a signal control method is set; S2, a special event is set for the road network; S3, under the special event, the road network is controlled by the traffic signal simulation of the connected and automated vehicle (CAV), and the corresponding road network change data is collected; S4, the traffic resilience evaluation index is determined according to the road network change data, and a multi-dimensional resilience evaluation system is built by normalizing the traffic resilience evaluation index; S5, based on the built multi-dimensional resilience evaluation system, the traffic resilience under different signal control methods is evaluated and compared; In the step S3, the road network change data includes average speed, resistance, recovery, resilience triangle area and average fuel consumption of each vehicle; In the step S4, the traffic resilience evaluation index includes average speed V before the special event, difference H between the average speed before the special event and the lowest average speed point, time difference L from the special event to the complete recovery of the road network, resilience triangle area S and average fuel consumption C of the vehicle; In the step S4, the normalization processing of the traffic resilience evaluation index refers to the standardization processing of the value of the traffic resilience evaluation index, and then using the sigmoid function to map the processing result value from [-∞, +∞] to [0, 1]; The step S5 is specifically: S51, the average speed of each road section in the road network under different signal control methods is extracted, and data visualization is performed after singular spectrum analysis and filtering analysis; S52, the average speed curve is analyzed by using the MK trend test method, and the special points of rising trend stagnation or beginning to decline are identified; S53, the special points are used as the end time of the resilience triangle integral, and then the traffic resilience change area curve under different signal control methods is drawn; S54, the traffic resilience change area curve is analyzed by using the MK trend test function, and the values of each traffic resilience evaluation index in the multi-dimensional resilience evaluation system under different signal control methods are determined; S55, according to the values of the traffic resilience evaluation index after normalization processing, the radar chart corresponding to different signal control methods is drawn, and the traffic resilience under different signal control methods is evaluated and compared according to the radar chart; In the step S55, the evaluation indexes of the traffic resilience evaluated according to the radar chart include recovery, efficiency and fuel consumption; Among them, the recovery is embodied by the difference H and the time difference L, the difference H embodies the resistance, and the L embodies the recovery ability; the efficiency is embodied by the average speed V and the resilience triangle area S; the fuel consumption is embodied by the average fuel consumption C of the vehicle.

2. The traffic resilience assessment method based on virtual traffic signal control according to claim 1, characterized in that, In the step S1, the signal control method includes fixed time control method, induction signal control method and VTS control method; Among them, the VTS control method refers to the autonomous coordination and synchronization of vehicles arriving and leaving at the intersection of the road network under the set operation rules, so that the vehicles continuously move and ensure the safety distance, and eliminate the demand for traffic signals. 3.The traffic resilience assessment method based on virtual traffic signal control according to claim 1, wherein, In the step S2, the special event includes demand increase and supply reduction; The demand increase refers to increasing the traffic flow of the road network in a set time range; the supply decrease refers to decreasing the maximum speed limit of the vehicle in the road network in a set time range.

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