Hybrid flow under double linkage variable speed limit and network vehicle collaborative control strategy

Abstract

This invention discloses a dual-linkage variable speed limit and connected vehicle collaborative management strategy for mixed traffic flows. Taking the optimal operation and management of mixed flows involving intelligent connected vehicles as its starting point, it constructs a hierarchical start-trigger model theory and control strategy based on speed fluctuation theory. It proposes a motion wave elimination (JAD) strategy for variable speed control under the influence of real-time fluctuation parameters of intelligent connected vehicles. Furthermore, it proposes a collaborative control strategy based on a collaborative hybrid control algorithm to reverse the shock wave formation process, mitigate the increase in headway caused by traffic flow start-stop waves, and optimize global traffic efficiency and safety. Based on this, it achieves optimal spatiotemporal effect output based on a genetic algorithm learning framework, providing a more scientific reference scheme for traffic management under mixed traffic flows.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation technology, specifically relating to a dual-linkage variable speed limit and connected vehicle collaborative management strategy under mixed flow conditions. Background Technology

[0002] Traffic congestion and reduced capacity caused by traffic ripples are common on expressways and lack a systematic solution. The formation mechanism of traffic ripples is due to vehicle speed oscillations caused by localized traffic bottlenecks resulting from unforced deceleration, random lane-changing behavior, or linear road conditions. Generally, the occurrence of ripples is accompanied by adverse consequences such as decreased traffic efficiency, increased vehicle emissions and energy consumption, and an increased potential risk of traffic accidents. Furthermore, once traffic oscillations form and propagate downstream, in high-volume localized traffic environments, the traffic ripples cannot dissipate automatically; instead, they propagate upstream and spread, eventually evolving into localized traffic congestion and paralysis. As research into traffic ripple phenomena deepens, their motion characteristics are becoming clearer, and how to reduce wave amplitude to slow their upstream propagation has become a key research question.

[0003] In recent years, Connected Autonomous Vehicles (CAVs) have demonstrated significant advantages and research importance in improving traffic efficiency, promoting traffic safety, and achieving energy conservation and emission reduction due to their strong controllability, high operational efficiency, and high level of intelligence. For a considerable period to come, the widespread adoption of CAVs will require continuous development and evolution, inevitably leading to mixed traffic flows involving human drivers and vehicles with different levels of automation. To fully leverage the advantages of CAVs in energy conservation, emission reduction, efficient execution, and network collaboration, and to effectively utilize their driving advantages to address current traffic problems, research on CAV-based model building, mixed traffic flow efficiency analysis, and traffic management methods involving single-vehicle optimization and coordinated control of CAVs has gained increasing attention from academia.

[0004] Meanwhile, the emergence of connected vehicles (CAVs) has provided new solutions to traffic congestion and the formation and transmission of motion waves, with increasingly sophisticated methods for optimizing local traffic segments through coordinated and controllable CAVs. However, current active traffic management technologies for CAVs often fail to consider the characteristics of mixed traffic flow itself. Research focuses primarily on using the perception capabilities of CAVs to control individual vehicle behavior to optimize traffic flow, without considering the impact of microscopic changes in vehicle behavior within mixed traffic flow, or the collaborative implementation model of active traffic control technologies altering overall traffic conditions in macroscopic scenarios and adjusting specific vehicle operations in conjunction with CAV control. Therefore, there is still a need to find an appropriate active traffic management strategy that allows for system-wide decision-making based on real-time traffic information, collaboratively controlling the optimized behavior of connected vehicles and active traffic management facilities, and providing better theoretical reference for traffic decision-makers when implementing traffic management methods. Summary of the Invention

[0005] The technical problem to be solved by this invention is that current active traffic control technologies for intelligent connected vehicles often do not take into account the characteristics of mixed traffic flow itself. The research focus is only on using the perception characteristics of CAV to control the behavior of individual vehicles to optimize traffic flow, without considering the impact of micro-level vehicle behavior changes in mixed traffic flow, and the collaborative implementation mode of active traffic control technology changing the overall traffic operation in macro-level scenarios and adjusting the operation of specific vehicles in combination with CAV control.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A hybrid flow dual-linkage variable speed limit and connected vehicle collaborative management strategy includes the following steps:

[0008] Step S1: For road sections with mixed traffic flow of intelligent connected vehicles and human-driven vehicles, determine whether there is a motion wave area in the mixed traffic flow road section. If there is no motion wave area, vehicles in the mixed traffic flow road section drive in a free flow state; if there is a motion wave area, proceed to step S2.

[0009] Step S2: For the motion wave region in the mixed traffic flow segment, based on the motion wave duration and the vehicle speed fluctuation model of the motion wave region, construct a gradient hierarchical control strategy to eliminate the motion wave region in the mixed traffic flow segment.

[0010] As a preferred embodiment of the present invention, in step S1, when the mixed traffic flow segment simultaneously meets the following conditions, it is determined that a motion wave region exists in the mixed traffic flow segment:

[0011] Condition 1: If the average speed of vehicles at a certain cross section in a mixed traffic flow section is lower than a preset percentage of the average speed of vehicles upstream and downstream, it is considered a speed slope event and a marker for the generation of motion waves. This cross section is the starting point of the motion waves.

[0012] Condition 2: The duration of the speed ramp event exceeds the preset duration.

[0013] As a preferred technical solution of the present invention, step S2, for the motion wave region in the mixed traffic flow section, based on the motion wave duration and the vehicle speed fluctuation model of the motion wave region, includes the following steps: constructing a gradient hierarchical control strategy to eliminate the motion wave region in the mixed traffic flow section:

[0014] Step S2.1: Based on the starting point of the motion wave, obtain the preset road segment in the direction of motion wave propagation as the motion wave region, and then obtain the vehicle speed fluctuation model F(X) of the motion wave region through the following formula;

[0015]

[0016]

[0017] In the formula, X = (X1, X2...X) i ...X n ), n represents the total number of vehicles in the motion wave region, i represents the i-th vehicle, and s i u represents the headway between the i-th vehicle and the vehicle in front within the motion wave region. i Let Δv represent the acceleration of the i-th vehicle within the motion wave region. i This represents the speed difference between the (i-1)th vehicle and the ith vehicle within the motion wave region;

[0018] Step S2.2: Based on the vehicle speed fluctuation model, obtain the standard average form value under basic traffic capacity. Thus, F and The ratio is calculated as β;

[0019] Step S2.3: Based on the vehicle speed fluctuation model of the motion wave duration and motion wave region, construct the gradient hierarchical control strategy shown below to eliminate the motion wave region in the mixed traffic flow segment:

[0020]

[0021] In the formula, t represents the duration of the motion wave, t0 < t1, t0 represents the first preset duration, t1 represents the second preset duration, β0 represents the first preset ratio, and β1 represents the second preset ratio.

[0022] As a preferred embodiment of the present invention, the speed control motion wave elimination strategy includes the following steps:

[0023] Step A1: Based on the starting point of the moving wave, obtain the distance x between the starting point and the direction of the moving wave propagation using the following formula. e The position is used as the starting position for the motion wave elimination strategy of the speed control.

[0024]

[0025] In the formula, v a Speed ​​control is achieved through a motion wave elimination strategy for variable speed control; t w v is the time interval during which the motion wave propagates upstream to the starting position. f v is the free-flow speed. w f is the slow-moving speed downstream of the starting position. ρ The penetration rate influencing factor is controlled by a preset table corresponding to the penetration rate of intelligent connected vehicles on the current road section;

[0026] Step A2: Based on the starting position of the motion wave elimination strategy for transmission control, select an intelligent connected vehicle as the optimal lead vehicle in the direction of motion wave propagation. The optimal lead vehicle satisfies the following formula:

[0027] Δt i，i-1 ≤t w

[0028] In the formula, Δt i，i-1 =t i -t i-1 , Δt i，i-1 This represents the difference in the time it takes for the motion waves of vehicle i to meet when it is designated as the lead vehicle, compared to when its preceding vehicle (i-1) is designated as the lead vehicle; t w The time interval for the motion wave to propagate upstream to the starting position;

[0029] Step A3: Based on the current speed v of the lead vehicle, if v ≥ v a Then the optimal speed of the lead vehicle decreases to v. a ′, v a ′=K1v a K1 represents a preset percentage. The optimal speed of the lead vehicle is increased to v until the distance between the lead vehicle and the vehicle in front meets the preset headway. a If v < v a Then the optimal speed of the lead vehicle increases to v. a The vehicles behind the lead vehicle follow the preset car-following model.

[0030] As a preferred embodiment of the present invention, the cooperative control strategy includes the following steps:

[0031] Step B1: Based on the origin of the moving wave, obtain the distance x between the origin of the moving wave and the direction of its propagation using the following formula. e The position is used as the starting position for the motion wave elimination strategy of the speed control.

[0032]

[0033] In the formula, v a Speed ​​control is achieved through a motion wave elimination strategy for variable speed control; t w v is the time interval during which the motion wave propagates upstream to the starting position. f v is the free-flow speed. w f is the slow-moving speed downstream of the starting position. ρ The penetration rate influencing factor is controlled by a preset table corresponding to the penetration rate of intelligent connected vehicles on the current road section;

[0034] Step B2: Based on the starting position of the motion wave elimination strategy for transmission control, select an intelligent connected vehicle as the optimal lead vehicle in the direction of motion wave propagation. The optimal lead vehicle satisfies the following formula:

[0035] Δt i，i-1 ≤t w

[0036] In the formula, Δt i，i-1 =t i -t i-1 , Δt i，i-1 This represents the difference in the time it takes for the motion waves of vehicle i to meet when it is designated as the lead vehicle, compared to when its preceding vehicle (i-1) is designated as the lead vehicle; t w The time interval for the motion wave to propagate upstream to the starting position;

[0037] Step B3: For the optimal lead vehicle, based on its current speed v, if v ≥ v a Then the optimal speed of the lead vehicle decreases to v. a ′, v a ′=K1v a K1 represents a preset percentage. The optimal speed of the lead vehicle is increased to v until the distance between the lead vehicle and the vehicle in front meets the preset headway. a If v < v a The optimal speed of the lead vehicle is increased to v. a For intelligent connected vehicles that have driven out of the motion wave area, they will accelerate back to free-flow driving state when the distance between them and the vehicle in front meets the preset headway.

[0038] Step B4: The variable speed limit sign closest to the optimal lead vehicle position in the direction of motion wave transmission is taken as the variable speed limit control start position. Vehicles entering the variable speed limit control start position control their speed according to the optimal variable speed limit control value VSL(t), and vehicles behind the lead vehicle follow the preset car-following model.

[0039] VSL(t)=(1+α)*V opt (t)

[0040]

[0041] In the formula, V opt VSL(t) represents the preset speed limit corresponding to the variable speed limit control at time node t, VSL(t) represents the optimal variable speed limit control value at time node t, α represents the real-time compliance rate of vehicles in the mixed traffic flow segment, and V avg (t-1) represents the average speed of the mixed traffic flow segment under basic capacity at the previous time node (t-1), VSL(t-1) represents the average speed of the mixed traffic flow segment under the cooperative control strategy at the previous time node (t-1), and VSL(t-1) is the optimal variable speed limit control value at the previous time node (t-1).

[0042] As a preferred technical solution of the present invention, the parameter x e With v a The objective function was constructed as follows, and a genetic algorithm was used to minimize the objective function to obtain the solution:

[0043]

[0044] In the formula,

[0045]

[0046]

[0047] Where w1 represents the evaluation gradient coefficient of the first preset indicator, w2 represents the evaluation gradient coefficient of the second preset indicator, and x i，t v represents the position of the i-th car at time t. i，t TTC represents the speed of the i-th vehicle at time t. i，t Let m represent the collision time of the i-th vehicle at time t, m be the total number of vehicles in the mixed traffic flow segment, and T represent the total time domain. T This represents the total travel time of all vehicles within time period T. This represents the normalized cardinality corresponding to the collision time. This represents the normalized base corresponding to the total travel time.

[0048] A system for coordinated management and control of hybrid flow dual-linkage variable speed limit and connected vehicles includes a motion wave discrimination module and a motion wave elimination module.

[0049] The motion wave discrimination module is used to determine whether there is a motion wave area in a mixed traffic flow segment involving intelligent connected vehicles and manually driven vehicles. If there is no motion wave area, vehicles in the mixed traffic flow segment travel in a free-flow state; if there is a motion wave area, the motion wave elimination module is activated.

[0050] The motion wave elimination module is used to eliminate motion wave areas in mixed traffic flow road sections. Based on the duration of motion waves and the vehicle speed fluctuation model of motion wave areas, it constructs a gradient-level control strategy to eliminate motion wave areas in mixed traffic flow road sections.

[0051] The beneficial effects of this invention are as follows: This invention provides a dual-linkage variable speed limit and connected vehicle collaborative management strategy for mixed traffic flow. Taking the optimal operation and management of mixed traffic flow involving intelligent connected vehicles as the research starting point, it analyzes the operational characteristics and models of microscopic mixed traffic flow within a fluctuating framework. Based on the mixed traffic flow management involving intelligent connected vehicles, it studies the motion wave transmission scenario formed under high-flow conditions in typical bottleneck sections, and designs a hierarchical decision control strategy based on real-time traffic information elements to collaboratively control the optimized behavior of connected vehicles and active traffic management facilities. It proposes a variable speed control motion wave elimination (JAD) strategy under the influence of real-time fluctuating parameters of intelligent connected vehicles, supplementing the optimized fitted state robust control motion wave dissipation algorithm by analyzing the possible steady state and wave dynamics of the current scenario and motion state. It also proposes a dual-linkage feedback system control based on the collaborative control strategy, which mitigates the increase in headway caused by traffic flow start-stop waves through system activation, macroscopic deceleration, active control, and reverse elimination of the shock wave formation process, thus optimizing global traffic efficiency and safety. The collaborative control strategy dissipates motion waves while ensuring high-flow throughput as much as possible, optimizing local traffic conflicts. This invention, through a quantitative data structure management approach, can better respond to driving participants in vehicle-road cooperative networks, while providing better theoretical reference for traffic decision-makers when implementing traffic management methods. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0053] Figure 2 This is a schematic diagram of the spatiotemporal trajectory of the vehicle in the scenario of this invention;

[0054] Figure 3 This is a simulation experiment result diagram from the verification process of this invention. Detailed Implementation

[0055] The present invention will be further described below with reference to the accompanying drawings. The following embodiments will enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way.

[0056] A hybrid flow dual-linkage variable speed limit and connected vehicle collaborative management strategy, such as Figure 1 As shown, it includes the following steps:

[0057] Step S1: For road sections with mixed traffic flow of intelligent connected vehicles and manually driven vehicles, determine whether there is a motion wave area in the mixed traffic flow road section. If there is no motion wave area, vehicles in the mixed traffic flow road section travel in a free-flow state; if there is a motion wave area, proceed to step S2. In this embodiment, it is a one-way control for a mixed traffic flow road section on a single lane.

[0058] In step S1, a motion wave region is determined to exist in a mixed traffic flow segment when the following conditions are met simultaneously:

[0059] When designing control strategies, it is crucial to ensure that the control methods do not negatively impact other parts of the network (including delays, security issues, etc.) or increase overall regional travel time. Therefore, it is important to set trigger conditions to justify the activation of the strategy. In this study, a trigger condition based on a sudden decrease in speed of a specific road segment relative to its upstream and downstream segments was used. Thus, if the average speed of a specific road segment suddenly decreases relative to its upstream and downstream segments, the cooperative control mechanism is triggered because queued stations are subsequently affected by traffic congestion from bottlenecks.

[0060] Therefore, when designing an adaptive start-up algorithm, two conditions must be met: (i) the average speed of vehicles in the motion wave region decreases sharply relative to the upstream and downstream areas; (ii) the slowing process is continuous, that is, the vehicle speed changes significantly within a certain time period, thus proving that the motion wave is being transmitted.

[0061] The two conditions that translate into this embodiment are:

[0062] Condition 1: If the average speed of vehicles at a certain cross section in a mixed traffic flow section is lower than a preset percentage of the average speed of vehicles upstream and downstream, it is considered a speed slope event and a marker for the generation of motion waves. This cross section is the starting point of the motion waves.

[0063] Condition 2: The duration of the triggered steep speed slope event exceeds the preset duration. That is, the first preset duration t0.

[0064] Step S2: For the motion wave region in the mixed traffic flow segment, based on the motion wave duration and the vehicle speed fluctuation model of the motion wave region, construct a gradient hierarchical control strategy to eliminate the motion wave region in the mixed traffic flow segment.

[0065] In step S2, for the motion wave region in the mixed traffic flow segment, based on the motion wave duration and the vehicle speed fluctuation model of the motion wave region, the following steps are included to construct a gradient hierarchical control strategy to eliminate the motion wave region in the mixed traffic flow segment:

[0066] Step S2.1: Based on the starting point of the motion wave, obtain the preset road segment in the direction of motion wave propagation as the motion wave region, and then obtain the vehicle speed fluctuation model F(X) of the motion wave region through the following formula;

[0067]

[0068]

[0069] In the formula, X = (X1, X2...X) i ...X n ), n represents the total number of vehicles in the motion wave region, i represents the i-th vehicle, and s i u represents the headway between the i-th vehicle and the vehicle in front within the motion wave region. i Let Δv represent the acceleration of the i-th vehicle within the motion wave region. i This represents the speed difference between the (i-1)th vehicle and the ith vehicle within the motion wave region; the parameters are generally obtained from the onboard transmitter of the CAV vehicle and the roadside monitoring equipment. The changes in vehicle state within the motion wave region are derived from the trigger function F(X), which is mainly influenced and controlled by parameters such as headway, speed, and acceleration of the current road segment.

[0070] Step S2.2: Based on the vehicle speed fluctuation model, obtain the standard average form value under basic traffic capacity. Thus, F and The ratio is calculated as β;

[0071] Step S2.3: Based on the vehicle speed fluctuation model of the motion wave duration and motion wave region, construct the gradient hierarchical control strategy shown below to eliminate the motion wave region in the mixed traffic flow segment:

[0072]

[0073] In the formula, t represents the duration of the motion wave, t0 < t1, t0 represents the first preset duration, t1 represents the second preset duration, β0 represents the first preset ratio, and β1 represents the second preset ratio.

[0074] In this embodiment, according to the above algorithm, as long as conditions 1 and 2 are met, and the average trigger function F of the specific road segment is lower than the standard function value during average driving, If the speed limit β0 (the default value in this embodiment is β0 = 40%) and the trigger time exceeds time t0 (the default value in this embodiment is t0 = 20 steps), the road segment is considered to have a motion wave, and the control strategy is triggered. Furthermore, when no triggering condition exists, the strategy will automatically deactivate, and the system will gradually revert to the default speed limit value, meaning that vehicles on mixed traffic flow sections will travel in a free-flow state. During the triggering process, the sensitivity of speed reduction and its duration need to be graded using gradient classification. The main control parameters are β1 and t1 (the default values ​​in this experiment are β1 = 60% and t1 = 40 steps).

[0075] The variable speed control motion wave elimination strategy includes the following steps:

[0076] Step A1: Based on the starting point of the moving wave, obtain the distance x between the starting point and the direction of the moving wave propagation using the following formula. e The position is used as the starting position for the motion wave elimination strategy of the speed control.

[0077]

[0078] In the formula, v a Speed ​​control is achieved through a motion wave elimination strategy for variable speed control; t w v is the time interval during which the motion wave propagates upstream to the starting position. f v is the free-flow speed. w f is the slow-moving speed downstream of the starting position. ρ This is a penetration rate influencing factor, controlled by a preset table corresponding to the current road segment's intelligent connected vehicle penetration rate.

[0079] from Figure 2 According to the spatiotemporal diagram, the further upstream the candidate vehicle is from the motion wave, the greater the absorption speed it can achieve. However, this also leads to an increase in the duration of the downstream motion wave. Figure 2 In the middle, S f S represents the deceleration curve of the motion wave region. w1 The acceleration curve represents the motion wave region, W represents the motion wave region, E represents the JAD control region, A represents the VSL control region, and S represents the acceleration curve of the motion wave region. t S represents the acceleration curve of the JAD control region. h This represents the deceleration curve in the JAD control region, v vstThis represents the optimal variable speed limit control value, and VCA represents the intersection section of the traffic wave under JAD control and VSL control. The congestion dissipation control strategy (JAD), i.e., the variable speed control motion wave elimination strategy, requires determining the leading vehicle while ensuring no secondary waves are generated, achieving a balance between absorbing motion wave speed and control duration. In the specific implementation of the motion wave dissipation strategy, to maximize overall benefits, the position x during JAD execution is... e With velocity v a We perform optimization. During execution, we assume an optimized full JAD strategy for execution distance x. e With velocity v a Through derivation, the ideal control position is precisely the point where the JAD vehicle's speed changes. Under this condition, the controlled JAD vehicle does not need to change gears again when approaching the motion wave, and returns to the most ideal motion state (maintaining the preset headway) after leaving the motion wave region, thus achieving the final optimization effect. Based on this, assume that the controlled JAD vehicle is at a distance x from the position where it normally follows the motion wave. e When the JAD control strategy is enabled, the control start position x can be derived from the trajectory quantization relationship during full JAD control. e .

[0080] Step A2: Based on the starting position of the motion wave elimination strategy for transmission control, select an intelligent connected vehicle as the optimal lead vehicle in the direction of motion wave propagation. The optimal lead vehicle satisfies the following formula:

[0081] Δt i，i-1 ≤t w

[0082] In the formula, Δt i，i-1 =t i -t i-1 , Δt i，i-1 t represents the difference in the time of encounter between the motion waves of vehicle i when it is designated as the lead vehicle and that of its preceding vehicle (i-1) when it is designated as the lead vehicle. w This is the time interval during which the motion wave propagates upstream to the starting position.

[0083] When the algorithm controls the optimal speed v a Then it will immediately determine the position x at which the speed significantly decreases from the point where the bottleneck is reached. eThe first selectable CAV vehicle appearing upstream is controlled. Each time an upstream candidate vehicle is moved one vehicle upstream and designated as the JAD (Jump Action Execution) lead vehicle, each additional vehicle is caught in the downstream motion wave impact, increasing the stopping delay by one vehicle. Therefore, when a vehicle is moved backward as the JAD lead vehicle, the JAD lead vehicle selection process ends and the optimal lead vehicle is determined when the reduced motion wave encounter time compared to the preceding vehicle meets the above conditions.

[0084] Step A3: Based on the current speed v of the lead vehicle, if v ≥ v a Then the optimal speed of the lead vehicle decreases to v. a ′, v a ′=K1v a K1 represents a preset percentage. The optimal speed of the lead vehicle is increased to v until the distance between the lead vehicle and the vehicle in front meets the preset headway. a If v < v a Then the optimal speed of the lead vehicle increases to v. a Vehicles following the lead vehicle follow a preset car-following model. In this embodiment, intelligent connected vehicles follow the Cooperative Adaptive Cruise Control (CACC) car-following model, while manually driven vehicles follow the Intelligent Driver Car-Following Model (IDM).

[0085] In actual traffic operations, due to the intermittent nature of vehicle arrivals, the control process needs to incorporate a fitted-state control strategy: that is, when the intelligent connected vehicle satisfies Δt... i，i-1 ≤t w Within the optimal execution range of the lead car, when the running speed v ≥ v a If this happens, it will eventually encounter a congested delivery queue before reaching the bottleneck. To address this issue, a robust control method is introduced, where the CAV is controlled at the optimal JAD control speed v at the start of control. a ′, v a ′=K1v a (K1 adopts the default value of 80%) until sufficient voids are formed in front, and then restore the optimal dissipation rate v. a To mitigate the shock wave. Furthermore, if the CAV is further away from the ideal optimal position x... e Furthermore, when approaching the end of the queue, a more stringent v is required. a The value of ' or a longer state duration t i Only in this way can a sufficient gap be formed in front. This method controls the gradual dissipation of the shock wave, creating an ideal, unobstructed space; that is, a cooperative control strategy is employed.

[0086] The collaborative control strategy includes the following steps:

[0087] Step B1: Based on the origin of the moving wave, obtain the distance x between the origin of the moving wave and the direction of its propagation using the following formula. e The position is used as the starting position for the motion wave elimination strategy of the speed control.

[0088]

[0089] In the formula, v a Speed ​​control is achieved through a motion wave elimination strategy for variable speed control; t w v is the time interval during which the motion wave propagates upstream to the starting position. f v is the free-flow speed, and v is the average speed. w f is the speed of the vehicle moving slowly downstream of the starting position, and f is the average speed. ρ This is a penetration rate influencing factor, controlled by a preset table corresponding to the current road segment's intelligent connected vehicle penetration rate.

[0090] Step B2: Based on the starting position of the motion wave elimination strategy for transmission control, select an intelligent connected vehicle as the optimal lead vehicle in the direction of motion wave propagation. The optimal lead vehicle satisfies the following formula:

[0091] Δt i，i-1 ≤t w

[0092] In the formula, Δt i，i-1 =t i -t i-1 , Δt i，i-1 This represents the difference in the time it takes for the motion waves of vehicle i to meet when it is designated as the lead vehicle, compared to when its preceding vehicle (i-1) is designated as the lead vehicle; t w This is the time interval during which the motion wave propagates upstream to the starting position.

[0093] Step B3: For the optimal lead vehicle, based on its current speed v, if v ≥ v a Then the optimal speed of the lead vehicle decreases to v. a ′, v a ′=K1v a K1 represents a preset percentage. The optimal speed of the lead vehicle is increased to v until the distance between the lead vehicle and the vehicle in front meets the preset headway. a If v < v a The optimal speed of the lead vehicle is increased to v. a For intelligent connected vehicles that have driven out of the motion wave area, the vehicle will accelerate back to its initial speed when the distance between it and the vehicle in front meets the preset headway.

[0094] Step B4: The variable speed limit sign closest to the optimal lead vehicle position in the direction of motion wave transmission is taken as the variable speed limit control start position. That is, the variable speed limit sign closest to the optimal lead vehicle position upstream of the optimal lead vehicle position is taken as the variable speed limit control start position. Vehicles entering the variable speed limit control start position control their speed according to the optimal variable speed limit control value VSL(t). The optimal variable speed limit control value VSL(t) is the maximum speed of the vehicle after entering the variable speed limit control section. Vehicles behind the lead vehicle follow the vehicle according to the preset car-following model. In this embodiment, intelligent connected vehicles follow the Cooperative Adaptive Cruise Control (CACC) car-following model, and manually driven vehicles follow the Intelligent Driver Car-Following Model (IDM).

[0095] VSL(t)=(1+α)*V opt (t)

[0096]

[0097] In the formula, V opt VSL(t) represents the preset speed limit corresponding to the variable speed limit control at time node t, VSL(t) represents the optimal variable speed limit control value at time node t, α represents the real-time compliance rate of vehicles in the mixed traffic flow segment, and V avg (t-1) represents the average speed of the mixed traffic flow segment under basic capacity at the previous time node (t-1), VSL(t-1) represents the average speed of the mixed traffic flow segment under the cooperative control strategy at the previous time node (t-1), and VSL(t-1) is the optimal variable speed limit control value at the previous time node (t-1).

[0098] In this embodiment, it is assumed that the compliance rate follows the "desired speed distribution" curve assigned to each vehicle category. In other words, the driver adheres to the corresponding desired speed distribution curve. During the variable speed limit control process, all vehicles entering the variable speed limit control area will gradually return to the current speed limit based on the comfort acceleration (deceleration) calibrated in the vehicle dynamics coefficients, i.e., satisfying the above optimal variable speed limit control value VSL(t), i.e., VSL control. The optimal solution of the parameters will be derived through the spatiotemporal trajectory diagram of vehicle operation and obtained according to the optimization solution process in the genetic algorithm. Simultaneously, in the process of controlling the traffic flow through the JAD strategy, when the current CAV vehicle is connected to the control speed, the traffic flow behind will follow the car-following model to carry out a coordinated deceleration process. During the control process, the speed change and parameter change of the mixed flow satisfy the speed fluctuation model and the car-following lane-changing theory.

[0099] During the variable speed control motion wave elimination strategy and the cooperative control strategy, the parameter x e With v aThe objective function was constructed as follows, and a genetic algorithm was used to minimize the objective function to obtain the solution; the core parameter x in the control strategy of this invention. e With v a The objective function optimization process utilizes a genetic algorithm (GA). This is because GA can effectively address multi-criteria optimization problems arising during traffic control. Furthermore, GA provides discrete variable outputs (i.e., speed limits). For the mixed traffic flow optimized by the algorithm, the primary consideration is the optimization effect of the control strategy on traffic efficiency and safety. Therefore, the objective function selected mainly includes efficiency indicators such as total travel time (TTT) and collision time (TTC). Thus, the parameter x... e With v a The objective function was constructed as follows, and a genetic algorithm was used to minimize the objective function to obtain the solution:

[0100]

[0101] In the formula,

[0102]

[0103]

[0104] Where w1 represents the evaluation gradient coefficient of the first preset indicator, w2 represents the evaluation gradient coefficient of the second preset indicator, and x i，t v represents the position of the i-th car at time t. i，t TTC represents the speed of the i-th vehicle at time t. i，t Let m represent the collision time of the i-th vehicle at time t, m be the total number of vehicles in the mixed traffic flow segment, and T represent the total time domain. T This represents the total travel time of all vehicles within time period T. This represents the normalized cardinality corresponding to the collision time. This represents the normalized base corresponding to the total travel time.

[0105] The parameters in the above objective function can be derived from the following formula. Based on the analysis of the coupling structure relationship between factors such as road scene environment, autonomous driving hybrid traffic flow penetration rate, and speed fluctuation state, an optimized cooperative adaptive cruise control and intelligent driver car-following model are used to evaluate the basic car-following mode of the overall traffic flow, while explaining the strategy logic and performing optimization numerical calculations. In this process, a general discretized longitudinal model of vehicle motion equations is used. At this stage, this paper only considers the longitudinal kinematic behavior of the vehicle. Since the subsequent optimization solution process involves discrete equation models, speed fluctuation theory is considered in the hybrid flow model. The general discretized motion of the vehicle along the lane can be described by the following equations:

[0106] vi (t+1)=v i (t)+a i (t)T m

[0107]

[0108] When the traffic flow penetration rate is ρ, the headway in the overall traffic flow should be:

[0109] h ρ (Q)=ρ 2 h av-av +2ρ*(1-p)h aw-hv +(1-ρ)(1-ρ)h hv-hv

[0110] Where x i (t), v i (t) and a i (t) represents the position, velocity, and acceleration of the i-th vehicle in the current lane at time t; T m It is the time interval between two consecutive moments, h av-av h represents the headway between intelligent connected vehicles. av-hv The headway (h) represents the time difference between a connected vehicle and a manually driven vehicle. hv-hv The headway represents the distance between vehicles driven by humans, and Q represents the total flow rate of the mixed traffic flow segment. This headway is the preset headway for the mixed traffic flow segment in this invention and the headway satisfied by the car-following model. In the above formula, the speed v and position x of any vehicle at the current time t are... i It can be obtained from vehicle trajectory data, acceleration term a i (t) mainly refers to the function affected by the corresponding VSL (Variable Speed ​​Limiting) and CAV (Continuous Actuation Control) deceleration control in the control strategy. The acceleration term a is involved in the motion wave elimination strategy of variable speed control. i (t) is a function influenced by the speed control of the lead vehicle based on the car-following model; the acceleration term a in the cooperative control strategy process. i (t) is a function based on the car-following model that is affected by the speed control of the lead vehicle and the variable speed limit control.

[0111] The cooperative control strategy of this invention responds to control based on a strategy activation mechanism. When the activation conditions of the cooperative control strategy are met, it first finds the optimal vehicle to execute the JAD strategy, i.e., the lead vehicle, and then calculates the optimal variable speed limit control value and the JAD execution speed. The JAD strategy logic is illustrated in the reference spatiotemporal diagram. Figure 2As shown, the slow-moving speed and slow-moving area length at the execution moment are obtained based on the propagation process of the motion wave. JAD control is then applied to the lead vehicle. First, the speed is reduced to a specified speed, i.e., adjusted to the JAD execution speed, to prevent the downstream motion wave from propagating upstream. Then, at the endpoint, the vehicle accelerates back to its initial speed to quickly leave the motion wave fluctuation area. Upstream of the JAD execution, speed control is performed based on the optimal variable speed limit (VSL) control value. During this process, following vehicles will adjust their following behavior accordingly based on the changes in the lead vehicle's driving behavior. Simultaneously, upstream speed control mitigates the significant deceleration caused by JAD speed changes, with the speed change gradually propagating upstream to the following vehicles. The movement of the following vehicles is sequentially affected by the control speed limit (VSL) and the JAD slow-moving speed. Furthermore, compression and dissipation waves are formed at the starting and ending points, respectively, gradually digesting the impact of the motion wave.

[0112] To test the effectiveness of this strategy in eliminating traffic waves, this section designed a 1km test road segment in the SUMO traffic simulation software and loaded mixed traffic flow based on CAV and HV micro-models. In this scenario, the free-flow speed was 72km / h, the total traffic demand was 3600veh / h, and the penetration rate of intelligent connected vehicles was 50%. To simulate local traffic waves, we designed designated vehicles to slow down or stop at different locations. Figure 3 As can be seen, both single intelligent connected vehicle control and variable speed limit control have limited effects on dissipating motion waves. Furthermore, maintaining large speed fluctuations over extended periods can easily lead to slow dissipation times and secondary ripples, resulting in adverse traffic phenomena. Therefore, a single control strategy offers relatively limited improvement to overall traffic safety. However, when we adopt the cooperative control algorithm described in this paper, we can clearly see that the motion waves are completely eliminated by this strategy in a short time, the vehicle speed stabilizes without generating secondary waves, and subsequent vehicles can freely pass through the area without interference from the motion waves.

[0113] Based on the above method, a system for the coordinated management and control strategy of hybrid flow dual-linkage variable speed limit and connected vehicles was also designed, including a motion wave discrimination module and a motion wave elimination module.

[0114] The motion wave discrimination module is used to determine whether there is a motion wave area in a mixed traffic flow segment involving intelligent connected vehicles and manually driven vehicles. If there is no motion wave area, vehicles in the mixed traffic flow segment travel in a free-flow state; if there is a motion wave area, the motion wave elimination module is activated.

[0115] The motion wave elimination module is used to eliminate motion wave areas in mixed traffic flow road sections. Based on the duration of motion waves and the vehicle speed fluctuation model of motion wave areas, it constructs a gradient-level control strategy to eliminate motion wave areas in mixed traffic flow road sections.

[0116] The combined use of CAV with Variable Speed ​​Limiting (VSL) offers significant advantages: (i) it is more efficient in achieving longitudinal dissipation of motion waves because it addresses existing queues by controlling vehicle deceleration rather than simply creating a low-flow state; (ii) it is safer, as speed limits intersect with acceleration changes less frequently than traditional VSLs; (iii) the strategy is simpler and more adaptable to various scenarios when CAVs reach a certain proportion; and (iv) it is potentially more cost-effective: short-area control is sufficient in this strategy, whereas multiple speed controllers along the control area are necessary in traditional integrated control strategies (including upper queue management). Furthermore, the CAV communication infrastructure can be used for various purposes and facilitates integration with other CAV-enabled controls, thereby reducing overall implementation costs.

[0117] This invention addresses the optimal operation and management of mixed traffic flows involving intelligent connected vehicles. It designs a dual-linkage variable speed limit and connected vehicle collaborative management strategy for mixed traffic flows. Based on the management of mixed traffic flows involving intelligent connected vehicles, this invention studies the propagation of motion waves formed under high-volume conditions in typical bottleneck sections. It makes system decisions based on real-time traffic information and designs a hierarchical decision-making control method to collaboratively control the optimized behavior of connected vehicles and active traffic management facilities. Through this collaborative strategy, motion waves are dissipated while ensuring high-volume traffic flow, thus optimizing local traffic conflicts. This invention, through a quantitative data structure management approach, can better respond to driver participants in vehicle-road cooperative networks and provides better theoretical reference for traffic decision-makers when formulating traffic management methods.

[0118] The above are merely preferred embodiments of the present invention, but do not limit the patent scope of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of the present invention specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of the present invention.

Claims

1. A hybrid flow-down double linkage variable speed limit and network vehicle cooperative control strategy, characterized in that: Includes the following steps: Step S1: For road sections with mixed traffic flow of intelligent connected vehicles and human-driven vehicles, determine whether there is a motion wave area in the mixed traffic flow road section. If there is no motion wave area, vehicles in the mixed traffic flow road section drive in a free flow state; if there is a motion wave area, proceed to step S2. Step S2: For the motion wave region in the mixed traffic flow section, based on the motion wave duration and the vehicle speed fluctuation model of the motion wave region, construct a gradient hierarchical control strategy to eliminate the motion wave region in the mixed traffic flow section; In step S2 above, for the motion wave region in the mixed traffic flow segment, based on the motion wave duration and the vehicle speed fluctuation model of the motion wave region, the following steps are included to construct a gradient hierarchical control strategy to eliminate the motion wave region in the mixed traffic flow segment: Step S2.1: Based on the motion wave starting point, a preset section of the motion wave transmission direction is obtained as a motion wave area, and then a vehicle speed fluctuation model of the motion wave area is obtained through the following formula ; ； ； In the formula, , , This represents the total number of vehicles in the motion wave region, where i represents the i-th vehicle. Indicates the first wave in the region of motion wave The distance between the front of the vehicle and the vehicle in front of it. Indicates the first wave in the region of motion wave Vehicle acceleration, Indicates the first wave in the region of motion wave Vehicles and the The speed difference between the vehicles; Step S2.2: Based on the vehicle speed fluctuation model, obtain the standard average form value under basic traffic capacity. Thus, F and The ratio is calculated as follows: ; Step S2.3: Based on the vehicle speed fluctuation model of the motion wave duration and motion wave region, construct the gradient hierarchical control strategy shown below to eliminate the motion wave region in the mixed traffic flow segment: ； In the formula, Indicates the duration of the motion wave. , Indicates the first preset duration. Indicates the second preset duration. This represents the first preset ratio. This indicates the second preset ratio.

2. The hybrid flow dual-linkage variable speed limit and connected vehicle collaborative management strategy according to claim 1, characterized in that: In step S1, a motion wave region is determined to exist in a mixed traffic flow segment when the following conditions are met simultaneously: Condition 1: If the average speed of vehicles at a certain cross section in a mixed traffic flow section is lower than a preset percentage of the average speed of vehicles upstream and downstream, it is considered a speed slope event and a marker for the generation of motion waves. This cross section is the starting point of the motion waves. Condition 2: The duration of the speed ramp event exceeds the preset duration.

3. The hybrid flow dual-linkage variable speed limit and connected vehicle collaborative management strategy according to claim 1, characterized in that: The variable speed control motion wave elimination strategy includes the following steps: Step A1: Based on the origin of the moving wave, obtain the distance from the origin of the moving wave in the direction of wave propagation using the following formula. The position is used as the starting position for the motion wave elimination strategy of the speed control. ； In the formula, The speed is controlled by a motion wave elimination strategy for variable speed control; The time interval for the motion wave to propagate upstream to the starting position. This refers to the free-flow speed. The speed at which the vehicle slowly travels downstream of the starting position; The penetration rate influencing factor is controlled by a preset table corresponding to the penetration rate of intelligent connected vehicles on the current road section; Step A2: Based on the starting position of the motion wave elimination strategy for transmission control, select an intelligent connected vehicle as the optimal lead vehicle in the direction of motion wave propagation. The optimal lead vehicle satisfies the following formula: ； In the formula, , Indicates the first When a vehicle is designated as the lead vehicle, compared to the vehicle in front of it, it is ranked [number missing]. The difference in the time of encounter of the motion waves when the vehicle is designated as the lead vehicle; The time interval for the motion wave to propagate upstream to the starting position; Step A3: Based on the current speed of the lead vehicle, the optimal execution method is implemented. ,like Then the optimal speed of the lead vehicle is reduced to , , This represents a preset percentage. The optimal speed of the lead vehicle is increased until the distance between the lead vehicle and the vehicle in front meets the preset headway. ;like The optimal speed of the lead vehicle is increased to The vehicles behind the lead vehicle follow the preset car-following model.

4. The hybrid flow dual-linkage variable speed limit and connected vehicle collaborative management strategy according to claim 1, characterized in that: The collaborative control strategy includes the following steps: Step B1: Based on the origin of the moving wave, obtain the distance from the origin of the moving wave in the direction of wave propagation using the following formula. The position is used as the starting position for the motion wave elimination strategy of the speed control. ； In the formula, The speed is controlled by a motion wave elimination strategy for variable speed control; The time interval for the motion wave to propagate upstream to the starting position. This refers to the free-flow speed. The speed at which the vehicle slowly travels downstream of the starting position; The penetration rate influencing factor is controlled by a preset table corresponding to the penetration rate of intelligent connected vehicles on the current road section; Step B2: Based on the starting position of the motion wave elimination strategy for transmission control, select an intelligent connected vehicle as the optimal lead vehicle in the direction of motion wave propagation. The optimal lead vehicle satisfies the following formula: ； In the formula, , Indicates the first When a vehicle is designated as the lead vehicle, compared to the vehicle in front of it, it is ranked [number missing]. The difference in the time when the moving waves of a vehicle meet when it is designated as the lead vehicle; The time interval for the motion wave to propagate upstream to the starting position; Step B3: For the optimal lead vehicle, based on its current speed... ,like Then the optimal speed of the lead vehicle is reduced to , , This represents a preset percentage. The optimal speed of the lead vehicle is increased until the distance between the lead vehicle and the vehicle in front meets the preset headway. ;like The optimal speed of the lead vehicle is increased to For intelligent connected vehicles that have driven out of the motion wave area, they will accelerate back to free-flow driving state when the distance between them and the vehicle in front meets the preset headway. Step B4: The variable speed limit sign closest to the optimal lead vehicle position in the direction of the motion wave transmission is designated as the variable speed limit control activation position. Vehicles entering the variable speed limit control activation position will then operate according to the optimal variable speed limit control value. Speed ​​control is implemented, and vehicles behind the lead vehicle are required to follow the vehicle in accordance with the preset car-following model. ； ； In the formula, This indicates the preset speed limit corresponding to the variable speed control at time node t. This represents the optimal variable speed limit control value at time node t. This indicates the real-time compliance rate of vehicles in mixed traffic flow road sections. This represents the average speed of the mixed traffic flow segment under basic capacity at the previous time point (t-1). This represents the average speed of the mixed traffic flow segment under the cooperative control strategy at the previous time point (t-1). This is the optimal variable speed limit control value at the previous time node (t-1).

5. The hybrid flow dual-linkage variable speed limit and connected vehicle collaborative management strategy according to claim 3 or 4, characterized in that: The parameters and The objective function was constructed as follows, and a genetic algorithm was used to minimize the objective function to obtain the solution: ； In the formula, ； ； in, Indicates the evaluation gradient coefficient of the first preset index. This represents the evaluation gradient coefficient of the second preset index. Let represent the position of the i-th car at time t. This represents the speed of the i-th vehicle at time t. Let t represent the collision time of the i-th vehicle at time t, and m represent the total number of vehicles in the entire mixed traffic flow section. Represents time across the entire time domain. express Total travel time for all vehicles within the specified time period. This represents the normalized cardinality corresponding to the collision time. This represents the normalized base corresponding to the total travel time.

6. A system based on the hybrid flow dual-linkage variable speed limit and connected vehicle collaborative management strategy described in any one of claims 1 to 5, characterized in that: Includes a motion wave discrimination module and a motion wave elimination module. The motion wave discrimination module is used to determine whether there is a motion wave area in a mixed traffic flow segment involving intelligent connected vehicles and manually driven vehicles. If there is no motion wave area, vehicles in the mixed traffic flow segment travel in a free-flow state; if there is a motion wave area, the motion wave elimination module is activated. The motion wave elimination module is used to eliminate motion wave areas in mixed traffic flow road sections. Based on the duration of motion waves and the vehicle speed fluctuation model of motion wave areas, it constructs a gradient-level control strategy to eliminate motion wave areas in mixed traffic flow road sections.

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