A method for CAV speed cooperative passage in bottleneck sections

Abstract

This invention belongs to the field of intelligent transportation and relates to a method for coordinated vehicle-vehicle (CAV) speed travel in bottleneck sections. The method includes: determining the following conflict zone based on the traffic characteristics of the vehicle group and flow in the bottleneck section inside the highway tunnel, and dividing the following conflict zone into road segments; comprehensively considering the impact of multiple traffic factors on the bottleneck capacity, quantifying the bottleneck capacity based on the safe headway index, and modeling the maximum bottleneck capacity; acquiring traffic flow and collecting vehicle headway data through roadside equipment, and then applying a CAV speed coordination control algorithm to apply speed control values ​​to the CAVs within the speed guidance section; and using Model Predictive Control (MPC) to optimize the speed control quantity. This invention addresses the scenario of sudden bottlenecks inside highway tunnels, providing a new approach to mitigate the continuous impact of upstream traffic flow on the bottleneck, improve the coordination and stability of longitudinal following traffic in waiting traffic flows, and improve vehicle passage efficiency in the following conflict zone.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation, specifically relating to a method for CAV (Carrier-Assisted Vehicle) speed cooperative passage in bottleneck road sections. Background Technology

[0002] As a crucial component of my country's highway transportation infrastructure, expressways serve as vital arteries connecting major cities. Expressway tunnels, as typical sections of these expressways, directly impact the overall operational efficiency of the expressway network through their traffic efficiency and safety. As key nodes in the expressway network, the traffic conditions in these sections significantly limit the maximum capacity of the transportation system. However, the relatively uniform construction standards and traffic regulations governing expressway tunnels facilitate the research and implementation of advanced traffic strategies.

[0003] Scholars have found that in road bottleneck problems, using connected autonomous vehicles in mixed traffic as carriers and introducing relevant intelligent driving algorithms (such as adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC)) can effectively improve traffic efficiency. Currently, bottleneck road traffic control strategies based on connected and intelligent driving algorithms can be broadly categorized into two types: optimal control strategies and rule-based control strategies. Optimal control strategies collect traffic data from roadside equipment and connected autonomous vehicles, and then calculate and analyze the optimal sequence or path for these vehicles. LI et al. believe that the core problem in bottleneck road passage order is finding the optimal solution between system load and performance to improve bottleneck traffic efficiency. Pei et al. formulated a road bottleneck passage order and path strategy based on dynamic programming algorithms, and simulation results show that this strategy has good performance in terms of efficiency and safety optimization. Jing designed an optimization algorithm considering the multi-driver cooperative game problem, and verified the effectiveness of the model through simulation. Li Xin considered the impact of active lane changing on the bottleneck traffic efficiency of the merging area, and used particle swarm optimization to seek the optimal active lane changing ratio, designing an active lane changing control strategy. Ding et al. proposed a rule-based adjustment algorithm for merging traffic on bottleneck sections. Simulations under both balanced and unbalanced traffic flow scenarios verified the effectiveness of this method in improving efficiency and saving energy. Hu considered setting up a dedicated control zone upstream of the bottleneck road for lane-changing merging, implementing a traffic balancing strategy for trajectory optimization for all vehicles within this zone. Experimental results show that the above method outperforms the cooperative merging strategy to some extent.

[0004] However, when a sudden bottleneck occurs inside a highway tunnel, the upstream traffic flow lacks sufficient perception of road capacity, and the above methods suffer from concentrated lane-changing conflicts. As a result, these methods are unable to mitigate the continuous impact of the upstream traffic flow on the bottleneck. The coordination and stability of the longitudinal following traffic flow are poor, which can easily affect traffic efficiency. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method for coordinated CAV (Carrier Aisle and Vehicle) speeds in bottleneck sections. This invention aims to solve the problems of existing methods failing to mitigate the continuous impact of upstream traffic flow on bottlenecks, and the poor coordination and stability of longitudinal following traffic flow, which easily affects traffic efficiency.

[0006] This invention provides a method for CAV (Cooperative Availability) speed-cooperative passage in bottleneck road sections, comprising the following steps:

[0007] S1. Based on the traffic characteristics of the vehicle group and traffic flow in the bottleneck section inside the highway tunnel, determine the car-following conflict zone and divide the car-following conflict zone into road segments.

[0008] S2. Taking into account the impact of multiple traffic factors on bottleneck capacity, the bottleneck capacity is quantified based on the safe headway index, and the maximum capacity of the bottleneck is modeled.

[0009] S3. Traffic flow is obtained through roadside equipment and vehicle headway data is collected. Then, the CAV speed coordination control algorithm is used to apply speed control values ​​to the CAVs in the speed guidance section.

[0010] S4. Model predictive control (MPC) is used to optimize the speed control input.

[0011] Furthermore, in step S1, the car-following conflict zone is functionally divided into CAV speed control sections, free evolution sections, and monitoring sections;

[0012] CAV speed control sections are set up in the middle and upper reaches of the car-following conflict zone. The CAV speed control sections are far from road bottlenecks and have relatively low traffic congestion, which is used to implement CAV speed control.

[0013] The free-evolution section is set in the downstream area near the bottleneck of the road to buffer and adjust the vehicle speed, allowing the traffic flow speed and following distance to evolve freely to a stable state;

[0014] The monitoring section is set up in the downstream area adjacent to the road bottleneck to detect the traffic capacity at the road bottleneck and transmit the traffic changes at the road bottleneck to the control system, providing a reference for the mixed vehicle group control target in the upstream CAV speed control section.

[0015] Furthermore, the bottleneck maximum throughput model constructed in step S2 is as follows:

[0016]

[0017] In the formula, P iLet represent the probability that the i-th vehicle in the traffic flow of the monitored road segment is a CAV. If the CAV penetration rate in the traffic flow of the monitored road segment is P0, then the probability that the i-th vehicle in the monitored road segment is a CAV is P0, and the probability that the i-th vehicle is a CHV is P1, where P0 + P1 = 1; ij t represents the probability that the preceding car is type i and the following car is type j among two adjacent cars; ij The headway between two adjacent vehicles is represented by the vehicle in front being type i and the vehicle behind being type j; S represents the state space corresponding to the mixed traffic flow of the monitored road segment.

[0018] Furthermore, step S3 includes the following sub-steps:

[0019] S3.1 Collects traffic flow and vehicle headway data, with road segment traffic flow f as the main indicator. By comparing the current traffic flow of the monitored road segment with the theoretical maximum traffic flow of the road segment, it is determined whether the critical conditions for implementing control are met.

[0020] S3.2 Under the premise of satisfying the critical control conditions, implement the CAV speed coordinated control strategy, and optimize the design of the control speed V using the model predictive control algorithm. lb Then, a control speed V is applied to the CAV in the speed guidance section. lb The same control speed V is applied to the CAV in each control cycle. lb ;

[0021] S3.3 Predicts the next moment's traffic flow f using a macro-level traffic flow CNN evolution model. * (t+1).

[0022] Furthermore, in step S3, the critical condition for implementing control is: the flow rate f(t) is greater than the minimum stable capacity of the bottleneck (1-δ)F(t) and less than the maximum capacity F(t), as specifically expressed below:

[0023] f(t)∈[(1-δ)F(t),F(t)]

[0024] In the formula, f(t) is the real-time traffic flow collected by the roadside of the monitored section; (1-δ)F(t) is the minimum stable traffic capacity at the bottleneck after the traffic flow is disturbed; δ represents the fluctuation influence coefficient.

[0025] Furthermore, in step S3, the speed V is controlled. lb The expression is:

[0026]

[0027]

[0028] In the formula, K represents the expected traffic volume for the road segment. lThis is the control factor.

[0029] Furthermore, in step S4, the step of optimizing the design speed control quantity using model predictive control (MPC) is as follows:

[0030] The CAV speed control section is divided into three control zones: a, b, and c, with different control speeds applied to each zone. The optimal solution is to comprehensively optimize the objective function, minimize the objective function value, and enumerate the speed combinations using a permutation algorithm.

[0031] The objective function to be optimized is:

[0032]

[0033] In the formula, f * (t+1) represents the applied control speed V lb (t) after which the speed guides the traffic flow of the road segment at the next moment.

[0034] Furthermore, the constraints are as follows:

[0035] V lb (t)∈[0,80]

[0036] In the formula, the control speed V lb (t) takes a speed of 10 or 5 times.

[0037] Beneficial effects:

[0038] 1. This invention addresses the problems of insufficient perception of road capacity by upstream traffic and concentrated lane-changing conflicts when sudden bottlenecks occur inside highway tunnels. Utilizing the concept of Cyber-Physical Systems (CPS) and considering the difficulties in navigating car-following conflict zones, a CAV (Car-Ahead Vehicle) speed coordination strategy is designed, incorporating bottleneck capacity. Using headway as a quantitative indicator and considering differences in safe headway between vehicles, the bottleneck capacity is modeled, providing a reliable model basis for the research of coordination algorithms.

[0039] 2. Furthermore, based on the design of the CAV speed coordination control algorithm, and combined with the traffic flow macro-evolution model, the MPC algorithm is used to optimize the target control speed. The speed control section is divided into three control intervals, and the same control speed is applied to the CAVs in the same interval. This reduces the difficulty and time consumption of optimization while ensuring control effectiveness. This strategy provides a new approach to mitigate the continuous impact of upstream traffic flow on bottlenecks and improve the coordination and stability of longitudinal following traffic flow, thereby improving vehicle passage efficiency in following conflict zones.

[0040] 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

[0041] Figure 1 This is a flowchart of a CAV speed-coordinated passage method for bottleneck road sections according to the present invention;

[0042] Figure 2 A schematic diagram of the functional area division of the CAV speed coordination control scheme;

[0043] Figure 3 A diagram showing the headway of different front and rear vehicle combinations;

[0044] Figure 4 Execution logic design for CAV speed coordination control scheme;

[0045] Figure 5 This is a schematic diagram showing the division of speed-controlled road sections. Detailed Implementation

[0046] To make the technical solutions, advantages, and objectives of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the protection scope of this application.

[0047] like Figure 1 As shown, the present invention provides a method for CAV (Cooperative Availability and Vehicle) speed-cooperative passage in bottleneck road sections, comprising the following steps:

[0048] S1. Based on the traffic characteristics of the vehicle group and traffic flow in the bottleneck section inside the highway tunnel, determine the car-following conflict zone and divide the car-following conflict zone into road segments.

[0049] The rules for determining the "car-following conflict zone" are as follows: a no-entry sign is set up 200 meters upstream of the road bottleneck, and the only passable lane on the left downstream of the no-entry sign is called the "car-following conflict zone".

[0050] like Figure 2 As shown, the car-following conflict zone is functionally divided into CAV speed control sections L. b Free evolution section L f and monitoring section L m ;

[0051] CAV speed control section L b Located in the middle and upper reaches of the car-following conflict zone, the CAV speed control section is far from the road bottleneck and the vehicle congestion level is relatively low. When CAV speed control is implemented, it can slow down the flow of traffic towards the road bottleneck in advance, and provide sufficient time to make speed adjustment decisions, which can effectively improve the macro traffic coordination of the car-following conflict zone.

[0052] Free evolution section L f Located downstream of a road bottleneck, this section is designed to mitigate the risk of vehicles over-adjusting and frequently accelerating or decelerating due to the proximity of the bottleneck. Therefore, this section primarily serves as a buffer for speed adjustments, allowing traffic flow and following distance to evolve freely to a more stable state.

[0053] Monitoring section L m It is set up in the downstream area adjacent to the road bottleneck to detect the traffic capacity at the road bottleneck and transmit the traffic changes at the road bottleneck to the control system, providing a reference for the mixed vehicle group control target in the upstream CAV speed control section.

[0054] S2. Taking into account the impact of multiple traffic factors on bottleneck capacity, the bottleneck capacity is quantified based on the safe headway index, and the maximum capacity of the bottleneck is modeled.

[0055] The specific model for the bottleneck's maximum throughput capacity is as follows:

[0056] The lane saturation flow rate s under uniform flow conditions is calculated using the following expression:

[0057]

[0058] In the formula, F(t) represents the maximum road capacity; saturation headway. Meaning: Average headway under maximum lane capacity conditions. The time interval between two vehicles passing the same road marker is expressed as... The headway is calculated as the ratio of the distance between the heads of two adjacent vehicles to the speed of the following vehicle, and its expression is as follows:

[0059]

[0060] In the formula, The distance between vehicle i and the front vehicle is indicated by l. i V represents the distance between the front ends of vehicle i and the vehicle in front. i v i Let be the average speed of vehicle i during the monitoring period. The saturation headway of a road segment is derived from the mean of the weighted sum of the safe headways of all vehicles in the segment and the vehicle type, and its expression is as follows:

[0061]

[0062] In the formula, t i w represents the headway of the i-th vehicle in the monitored road segment. i Let be the weighting coefficient of the i-th vehicle, N be the total number of vehicles in the monitored road segment, and T be the sum of the head-to-head time distances of N-1 vehicles in the monitored road segment.

[0063] Considering the impact of heterogeneous traffic flow characteristics and their penetration rates on safe headway, the minimum safe headway for different types of vehicles is weighted by their penetration rates and used as the road saturation headway, as expressed below:

[0064]

[0065] In the formula, T CAVs P0 and T represent the safe headway and penetration rate of the CAV, respectively. CHVs P1 represents the safe headway and penetration rate of CHV, where P0 + P1 = 1.

[0066] Suppose there is a mixed traffic flow of N vehicles in the monitored road segment. Let the state of vehicle i be A. i ∈{0,1}, A i = 0 indicates that the monitored vehicle i is a CAV, otherwise it is a CHV. In this embodiment of the invention, in the mixed traffic flow of the monitored road segment, the distribution relationship between the front and rear vehicles is independent, and the state variable A of the i-th vehicle is... i The corresponding state-space expression S is as follows:

[0067] S = {0, 1}

[0068] Assuming the CAV penetration rate in the traffic flow of the monitored road segment is P0, then the probability that the i-th vehicle in the monitored road segment is a CAV is P0, and the probability that the i-th vehicle is a CHV is P1, that is, Pr(A i =0)=P0,Pr(A i =1)=P1,P0+P1=1.

[0069] For the same vehicle platoon, the vehicle types and distributions at the front and rear may differ, and different combinations correspond to different vehicle response times and communication capabilities, such as... Figure 3 As shown.

[0070] Define the probability of the occurrence of the driving combination of the front and rear vehicles as the state variable a. ij Its transition matrix A is expressed as follows:

[0071]

[0072] Among them, a ij This represents the probability that, in a pair of adjacent vehicles, the preceding vehicle is type i and the following vehicle is type j.ij The expression is as follows:

[0073]

[0074] Therefore, there is a. 10 =a 00 =P0, a 11 =a 01 =P1=1-P0.

[0075] The overall distribution of headway spacing in the monitored road section can be represented as:

[0076] T = [t] 11 ,t 10 ,t 00 ,t 01 ]

[0077] In the formula, t ij This represents the headway between two adjacent vehicles, where the preceding vehicle is type i and the following vehicle is type j. The expression for the average headway of the monitored road segment is as follows:

[0078]

[0079] Therefore, the maximum throughput capacity at the bottleneck is expressed as follows:

[0080]

[0081] In the formula, P i Let S represent the probability that the i-th vehicle in the traffic flow of the monitored road segment is a CAV; S = {0, 1} represents the state space corresponding to the mixed traffic flow of the monitored road segment.

[0082] S3. Traffic flow and vehicle headway data are acquired through roadside equipment. Then, the CAV speed coordination control algorithm is used to apply speed control values ​​to the CAVs within the speed-guided road section, such as... Figure 4 As shown;

[0083] S3.1 Collects traffic flow and vehicle headway data, with road segment traffic flow f as the main indicator. By comparing the current traffic flow of the monitored road segment with the theoretical maximum traffic flow of the road segment, it is determined whether the critical conditions for implementing control are met.

[0084] The critical condition for implementing control is: the flow rate f(t) is greater than the minimum stable capacity of the bottleneck (1-δ)F(t) and less than the maximum capacity F(t), as shown in the following expression:

[0085] f(t)∈[(1-δ)F(t),F(t)]

[0086] In the formula, f(t) is the real-time traffic flow collected by the roadside of the monitored road section; (1-δ)F(t) is the minimum stable traffic capacity at the bottleneck after the traffic flow is disturbed; δ represents the fluctuation influence coefficient.

[0087] S3.2 Under the premise of satisfying the critical control conditions, implement the CAV speed coordinated control strategy, and optimize the design of the control speed V using the model predictive control algorithm. lb Then, a control speed V is applied to the CAV in the speed guidance section. lb The same control speed V is applied to the CAV in each control cycle. lb ;

[0088] Control speed V lb The expression is:

[0089]

[0090]

[0091] In the formula, K represents the expected traffic volume for the road segment. l For control coefficients;

[0092] S3.3 Predicts the next moment's traffic flow f using a macro-level traffic flow CNN evolution model. * (t+1); The optimization design objective is to determine the traffic flow f at the next moment after applying speed control. * The combined index is formed by (t+1) and the maximum traffic flow F(t).

[0093] S4. Model predictive control (MPC) is used to optimize the speed control quantity. The steps for optimizing the speed control quantity using MPC are as follows:

[0094] The CAV speed control section is divided into three control zones: a, b, and c, with different control speeds applied to each zone. like Figure 5 As shown, by comprehensively optimizing the objective function, the minimum objective function value is obtained by enumerating the speed combinations through a permutation algorithm, which is the optimal solution.

[0095] The objective function is:

[0096]

[0097] In the formula, f * (t+1) represents the applied control speed V lb (t) after which the speed guides the traffic flow of the road segment at the next moment.

[0098] The constraint is: set the control speed V. lb(t) is taken as a multiple of 10 or 5. In this embodiment, 10 km / h is taken as the control speed V. lb The adjustment step size of (t) and the control speed value can be expressed as: V lb (t)∈[0,80], and Mod[V lb [(t),10]=0.

[0099] 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 protection scope of the present invention.

Claims

1. A method for CAV (Carrier-Assisted Vehicle) speed-coordinated passage in bottleneck road sections, characterized in that, Includes the following steps: S1. Based on the traffic characteristics of the vehicle group and traffic flow in the bottleneck section inside the highway tunnel, determine the car-following conflict zone and divide the car-following conflict zone into road segments. In step S1, the car-following conflict zone is divided into CAV speed control section, free evolution section and monitoring section according to function. CAV speed control sections are set up in the middle and upper reaches of the car-following conflict zone. The CAV speed control sections are far from road bottlenecks and have relatively low traffic congestion, which is used to implement CAV speed control. The free-evolution section is set in the downstream area near the bottleneck of the road to buffer and adjust the vehicle speed, allowing the traffic flow speed and following distance to evolve freely to a stable state; The monitoring section is set in the downstream area adjacent to the road bottleneck to detect the traffic capacity at the road bottleneck and transmit the traffic changes at the road bottleneck to the control system, providing a reference for the mixed vehicle group control target in the upstream CAV speed control section; S2. Taking into account the impact of multiple traffic factors on bottleneck capacity, the bottleneck capacity is quantified based on the safe headway index, and the maximum capacity of the bottleneck is modeled. The bottleneck maximum throughput model constructed in step S2 is as follows: In the formula, P i Let represent the probability that the i-th vehicle in the traffic flow of the monitored road segment is a CAV. If the CAV penetration rate in the traffic flow of the monitored road segment is P0, then the probability that the i-th vehicle in the monitored road segment is a CAV is P0, and the probability that the i-th vehicle is a CHV is P1, where P0 + P1 = 1; ij t represents the probability that the preceding car is type i and the following car is type j among two adjacent cars; ij The headway between two adjacent vehicles is represented by the vehicle in front being type i and the vehicle behind being type j; S represents the state space corresponding to the mixed traffic flow of the monitored road segment. S3. Traffic flow is obtained through roadside equipment and vehicle headway data is collected. Then, the CAV speed coordination control algorithm is used to apply speed control values ​​to the CAVs in the speed control section. S3.1 Collects traffic flow and vehicle headway data, with road segment traffic flow as the main indicator. By comparing the current traffic flow of the monitored road segment with the theoretical maximum traffic flow of the road segment, it determines whether the critical conditions for implementing control are met. The critical condition for implementing control is that the flow rate f(t) is greater than or equal to the minimum stable capacity of the bottleneck (1-δ)F(t) and less than or equal to the maximum capacity of the bottleneck F(t), as shown in the following expression: f(t)∈[(1-δ)F(t),F(t)] In the formula, f(t) is the real-time traffic flow collected by the roadside of the monitored road section; (1-δ)F(t) is the minimum stable traffic capacity at the bottleneck after the traffic flow is disturbed; δ represents the fluctuation influence coefficient. S3.2 Under the premise of satisfying the critical control conditions, implement the CAV speed coordinated control strategy, and optimize the design of the control speed V using the model predictive control algorithm. lb Then, a control speed V is applied to the CAV in the speed control section. lb The same control speed V is applied to the CAV in each control cycle. lb ; Control speed V lb The expression is: In the formula, K represents the expected traffic volume for the road segment. l For control coefficients; S3.3 Predicts the next moment's traffic flow f using a macro-level traffic flow CNN evolution model. * (t+1); S4. Model predictive control (MPC) is used to optimize the speed control parameters. The steps for model predictive control (MPC) optimization design of speed control variables are as follows: The CAV speed control section is divided into three control zones: a, b, and c, with different control speeds applied to each zone. By comprehensively optimizing the objective function and minimizing its value, the optimal solution is obtained by enumerating the speed combinations using a permutation algorithm. The objective function to be optimized is: In the formula, f * (t+1) represents the applied control speed V lb After (t), the traffic flow of the speed-controlled section at the next moment.

2. The method for CAV speed-coordinated passage in a bottleneck section according to claim 1, characterized in that: The constraints are: V lb (t)∈[0,80] In the formula, the control speed V lb (t) takes a speed of 10 or 5 times.

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