A CPS control strategy for optimizing high-volume traffic flow on highways

Abstract

This invention belongs to the field of cyber-physical system control and discloses a CPS control strategy for optimizing high-volume traffic efficiency on highways. The strategy includes the following steps: S1. Determining the system-level objective function based on the communication overhead, computational overhead, and cluster correlation required for the road segment cluster structure; S2. Solving for the optimal road segment cluster structure under the current traffic conditions; S3. Establishing five subsystem-level objective functions to create a multi-objective optimization problem; S4. Solving the multi-objective optimization problem to obtain the optimal control decision; S5. Determining whether to initiate active control measures and update the control decision; S6. Calculating the desired speed of the vehicle; S7. The main lane ICV travels at the desired speed, and the ramp ICV travels according to the ramp control signals. The strategy proposed in this invention can dynamically adjust the network connection relationship between roadside equipment, achieving targeted information sharing and collaborative control, saving communication resource overhead, and realizing distributed collaborative control of highways under limited cyber resource constraints.

Description

Technical Field

[0001] This invention belongs to the field of cyber-physical system control, specifically relating to a CPS control strategy for optimizing high-volume traffic flow on highways. Background Technology

[0002] Due to limited road capacity, sudden surges in traffic volume can easily cause congestion, especially on closed highways where congestion is difficult to dissipate, causing not only inconvenience but also economic losses and environmental pollution. Furthermore, cyber resources (computing and communication resources) are finite; therefore, optimizing highway traffic efficiency under cyber resource constraints is a crucial issue.

[0003] Existing research on traffic control approaches includes Variable Speed ​​Limit (VSL) and Ramp Metering (RM), and often employs three Model Predictive Control (MPC) methods to solve for the optimal control signal: centralized MPC, local MPC, and fixed-structure distributed MPC. Each has its advantages, but none can simultaneously guarantee control effectiveness and real-time performance under limited cyber resources. Centralized MPC offers better control performance, but struggles to meet real-time requirements under limited computational resources. Local MPC reduces computational overhead, but its effectiveness in controlling overall highway traffic efficiency is poor due to a lack of information sharing between road segments. Fixed-structure distributed MPC can guarantee real-time performance, but with the increase in Intelligent Connected Vehicles (ICVs) and traffic volume, the demand for cyber resources is gradually increasing, making this method insufficient for highway traffic control under cyber resource constraints. Furthermore, highway systems exhibit increasingly complex distributed and networked characteristics, involving not only the physical relationships between roads and vehicles, but also cyber relationships such as ICVs and roadside equipment, and the information processing and interaction between them, constituting a complex Cyber-Physical System (CPS). Therefore, it is necessary to further explore the distributed cooperative control problem of highways under the constraint of limited cyber resources from a CPS perspective. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a CPS control strategy for optimizing the traffic efficiency of highways with high traffic volume. This invention aims to solve the problem of low traffic efficiency caused by high traffic volume under the constraints of limited computing and communication resources in each section of the highway.

[0005] The technical solution adopted by this invention to solve its technical problem is:

[0006] A three-layer cyber-physical system architecture based on ICV (Integrated Vehicle Vehicle) comprises three levels: system level, subsystem level, and vehicle level. At the system level, based on the traffic density of each highway segment, road segments are dynamically grouped into clusters, i.e., subsystems, through network connections. At the subsystem level, each subsystem, based on shared traffic information and traffic flow patterns, triggers optimized speed recommendations for vehicles on the main road and control signals for ramps within its jurisdiction. At the vehicle level, based on the recommended speeds, the system guides the speed of ICVs on the main road and simultaneously controls ICVs on ramps to operate according to ramp flow control signals.

[0007] Based on the above system architecture, this invention provides a CPS control strategy for optimizing high-volume traffic flow efficiency on highways, including a system-level dynamic clustering strategy, a subsystem-level cooperative control strategy, and a vehicle-level speed guidance strategy. The implementation steps of this strategy are as follows:

[0008] S1. Determine the system-level objective function based on the optimization objectives of the communication overhead, computational overhead, and cluster correlation required for the road segment cluster structure;

[0009] S2. The optimal segment cluster structure of the highway under the current traffic conditions is solved by using the optimal cluster partitioning algorithm;

[0010] S3. Based on the optimal road segment cluster structure obtained in step S2, five subsystem-level objective functions are established, taking the total vehicle travel time, total travel distance, entrance ramp queue length, speed limit change within the road segment, and ramp release rate change as optimization objectives. Furthermore, a multi-objective optimization problem is established, using the upper and lower bounds of the road segment speed limit and the upper and lower bounds of the ramp release rate as constraints.

[0011] S4. Using a subsystem-level multi-objective optimization algorithm, solve the multi-objective optimization problem established in step S3 to obtain the optimal control decision, namely the vehicle speed recommendation for the main road of each road segment and the ramp control signal;

[0012] The subsystem-level multi-objective optimization algorithm uses a non-dominated sorting evolutionary algorithm to solve the multi-objective optimization problem established in step S3, and obtains the Pareto optimal solution set under the multi-objective function. Then, based on the approximation ideal solution sorting method, an optimal control decision is selected from the Pareto optimal solution set.

[0013] S5. Design the congestion level index of the road segment, and use the congestion level index to calculate the congestion level of each road segment in the subsystem. Then, each road segment determines whether to activate active control measures and whether to update control decisions based on its own traffic conditions and the congestion level of adjacent road segments in its subsystem.

[0014] S6. Based on the recommended speed for the road segment and the distance between the vehicle and the vehicle in front, calculate the vehicle's expected speed;

[0015] S7. The main road ICV travels at the desired speed, while the ramp ICV travels according to the ramp control signals.

[0016] Furthermore, step S1 includes the following sub-steps:

[0017] S1.1 Calculate the communication overhead E of the system at time kT. c (k);

[0018]

[0019] In the formula, C i (k) represents the i-th subsystem; C(k) is the set of subsystems; m represents the value of C. i The m-th road segment in (k); c m The amount of data sent for road segment m within the cluster; E elec The energy consumed to send or receive each bit of data; ε amp The energy consumption per unit square meter for transmitting each bit of data; d m,m′ Let m be the distance between road segments m and m′; Υ be the propagation attenuation index under the communication environment; and b be the bandwidth occupied by communication between the two road segments. i (k) represents the number of road segments within the cluster;

[0020] S1.2 Calculate the computational cost T of the system at time kT cal ;

[0021]

[0022] In the formula, T evo To optimize the evolutionary operation time of the algorithm; t1 and t2 are constants to be fitted; M represents the total number of road segments;

[0023] S1.3 Calculate the cluster correlation R of the system at time kT d (k);

[0024]

[0025]

[0026] In the formula, ρ m,i (k) represents the density of the i-th interval of road segment m at time kT; Let m be the critical density of road segment m;

[0027] S1.4 Based on steps S1.1 to S1.3, the system-level objective function is determined as follows:

[0028]

[0029] In the formula, κc κ t The weights represent the communication overhead and computational overhead, respectively; k t =kT / T t T t The system-level control cycle is represented by kT; kT represents the end time of the kth segment information sampling cycle, where k is a constant and T represents the segment information sampling cycle.

[0030] Furthermore, the optimal cluster partitioning algorithm in step S2 is based on dynamic programming and includes a main algorithm and sub-algorithms;

[0031] The main algorithm is used to initially segment sub-regions that may form road segment clusters based on the congestion level of each road segment within a multi-segment area of ​​the highway, and to call sub-algorithms.

[0032] The sub-algorithm is used to calculate the optimal collaborative road segment cluster partitioning decision within the sub-region.

[0033] Furthermore, step S3 includes the following sub-steps:

[0034] S3.1 Establish five subsystem-level objective functions;

[0035] I. Establish a system based on the total travel time of vehicles within the subsystem. TTS (k c Let be the objective function for optimizing the objective.

[0036]

[0037] In the formula, U m For road segment m∈C j A control decision, including the speed limit value (VSL) of the main road control area. m,i and the ramp release rate r in the entrance ramp control area m,i N p For the number of time-domain steps to predict; Z = T c / T,k c =k / Z,T c For subsystem-level control cycles; w m′,i (Zk c +p) indicates that the i-th segment of road segment m′ is in Zk c +p corresponds to the queue length of the entrance ramp; p is an integer and p∈{Zk} c ,Zk c +1,···,Z(k c +N p )}; L represents the set of all segments of road segment m′; m′,i λ represents the length of the i-th segment of road segment m′; m′,i ρ represents the number of lanes in the i-th segment of road segment m′;m′,i (Zk c +p) indicates that the i-th segment of road segment m′ is in Zk c Density at time +p;

[0038] II. Establish a system based on the total driving distance J TTD (k c Let be the objective function for optimizing the objective.

[0039]

[0040] In the formula, v m′,i (Zk c +p) indicates that the i-th segment of road segment m′ is in Zk c The average speed of vehicles within the segment at time +p;

[0041] III. Objective function with the entrance ramp queue length as the optimization objective;

[0042]

[0043]

[0044] In the formula, J w For the prediction time-domain steps N p Internal subsystem C j Mid-entry ramp queue length penalty; w m',i (k) represents the queue length of the entrance ramp at time kT; This represents the maximum number of vehicles queuing at the corresponding entrance ramp;

[0045] IV. The objective function is to optimize the sudden changes in speed limits within a road segment.

[0046]

[0047] In the formula, J ΔVSL To control the number of time-domain steps N c Internal penalty for sudden changes in speed limit values ​​within a certain range (m); VSL m,i This represents the speed limit value of the i-th interval of road segment m; c is an integer and c∈{0,1,...,N} c -1)};

[0048] V. The objective function that optimizes the sudden changes in ramp release rate within a road segment;

[0049]

[0050] In the formula, J Δr To control the number of time-domain steps N c Internal penalty for sudden changes in ramp release rate within a road segment m; r m,iThis represents the ramp release rate of the i-th interval of road segment m;

[0051] S3.2 Based on the five subsystem-level objective functions established in step S3.1, and using the upper and lower bounds of the speed limit and the upper and lower bounds of the ramp release rate as constraints, a multi-objective optimization model is established as shown below:

[0052]

[0053] In the formula, VSL min and VSL max These are the upper and lower bounds of the speed limit, respectively. r min and r max These are the upper and lower limits of the ramp release rate, respectively.

[0054] Furthermore, step S5 includes the following sub-steps:

[0055] S5.1 Calculate the congestion level of the road segment;

[0056]

[0057] In the formula, MC m An index indicating the degree of congestion in road segment m; Let be the critical density of the i-th interval of road segment m;

[0058] S5.2 Definition The control trigger time sequence and the control decision update time sequence are respectively, and their calculation expressions are as follows:

[0059]

[0060]

[0061] In the formula, θ c σ u and θ u All of these are hyperparameters.

[0062] Furthermore, in step S6, the desired speed The calculation expression is:

[0063]

[0064]

[0065] In the formula, This is the distance between your vehicle and the vehicle in front in lane l of the current road segment; Recommended speed; This represents the vehicle spacing when ICVs are evenly distributed in lane l of the current road segment; L vehThe average length of the ICV.

[0066] Beneficial effects:

[0067] 1. The strategy proposed in this invention can dynamically adjust the network connection relationship between roadside equipment according to the traffic conditions of each road segment, realize targeted information sharing and collaborative control, avoid unnecessary communication resource overhead and computational resource overhead that is ineffective in improving traffic efficiency, and realize distributed collaborative control of highways under the constraint of limited cyber resources.

[0068] 2. Based on the vehicle-level speed guidance strategy, ICV can achieve single-vehicle speed control according to the suggested speed and the surrounding traffic conditions, which can ensure the uniform distribution of vehicles within the road segment and avoid the negative impact of random and non-uniform distribution of ICV in traffic flow on highway traffic efficiency.

[0069] 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

[0070] Figure 1 This demonstrates a three-layer cyber-physical system architecture based on ICV;

[0071] Figure 2 A schematic diagram of a highway scene is shown;

[0072] Figure 3 This paper presents a CPS dynamic centralized-distributed control system architecture aimed at improving highway traffic efficiency.

[0073] Figure 4 The pseudocode of the main algorithm in the optimal cluster partitioning algorithm is shown;

[0074] Figure 5 The pseudocode of the sub-algorithm in the optimal cluster partitioning algorithm is shown;

[0075] Figure 6 The flowchart of the subsystem-level multi-objective optimization algorithm is shown. Detailed Implementation

[0076] 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.

[0077] Figure 1 This invention demonstrates a three-layer cyber-physical system architecture based on ICV (Integrated Vehicle Vehicle), comprising three levels: system level, subsystem level, and vehicle level. The system level, designed for highways composed of multiple road segments, dynamically adjusts network connections to group road segments into clusters (subsystems) based on traffic density. The subsystem level includes multiple road segment clusters, each capable of triggering optimized speed recommendations for vehicles on its main road and control signals for ramps based on traffic flow information from its respective cluster. The vehicle level, based on the recommended speed, guides the speed of ICVs on the main road while simultaneously controlling ICVs on ramps to operate according to ramp flow control signals.

[0078] Figure 2 The scenario showcases a highway consisting of five road segments: R1, R2, R3, R4, and R5. Each segment includes one on-ramp and one off-ramp, and all segments are equipped with identical roadside infrastructure. This scenario serves as an embodiment of the present invention. It should be noted that all vehicles in this scenario are ICVs (Independent Vehicles).

[0079] Based on a three-layer cyber-physical system architecture, this invention discloses a CPS control strategy for optimizing high-volume traffic efficiency on highways under cyber resource constraints, including a system-level dynamic clustering strategy, a subsystem-level cooperative control strategy, and a vehicle-level speed guidance strategy. Figure 3 This paper demonstrates a CPS dynamic centralized-distributed control system architecture for highway traffic efficiency built based on this strategy. The system architecture is... Figure 1 The system architecture shown is further refined, detailing the architecture of the system-level, subsystem-level, and vehicle-level systems, as well as the specific relationships between these three levels. Next, we will... Figure 2 The scenario shown is an example, based on Figure 1 The basic system architecture shown is Figure 3 The detailed system architecture shown provides a detailed introduction to the specific implementation methods of the three strategies.

[0080] The system-level dynamic clustering strategy includes steps S1 and S2 in the implementation steps of this invention:

[0081] S1. Determine the system-level objective function based on the optimization objectives of the communication overhead, computational overhead, and cluster correlation required for the road segment cluster structure;

[0082] That is, the communication overhead is quantified by network bandwidth usage and communication energy consumption, and the computational overhead of road segments is evaluated by computation time. Then, the evaluation index of the correlation between road segments within the cluster is designed. Then, the communication overhead, computational overhead and cluster correlation (the sum of the evaluation index of the correlation between road segments within the cluster) required for communication and computation between roadside devices in each road segment are used as optimization objectives. The system-level objective function is determined by weighting, and a single-objective optimization problem is established.

[0083] S1.1 Calculate the communication overhead E of the system at time kT. c (k);

[0084]

[0085] In the formula, C i (k) represents the i-th subsystem; For a set of subsystems; m represents C i The m-th road segment in (k); c m The amount of data sent for road segment m within the cluster; E elec The energy consumed to send or receive each bit of data; ε amp The energy consumption per unit square meter for transmitting each bit of data; d m,m′ Let m be the distance between road segments m and m′; Υ be the propagation attenuation index under the communication environment; and b be the bandwidth occupied by communication between the two road segments. i (k) represents the number of road segments within the cluster;

[0086] S1.2 Calculate the computational cost T of the system at time kT cal ;

[0087]

[0088] In the formula, T evo To optimize the evolutionary operation time of the algorithm; t1 and t2 are constants to be fitted; M represents the total number of road segments, and in this embodiment, M = 5;

[0089] S1.3 Calculate the cluster correlation R of the system at time kT d (k);

[0090]

[0091]

[0092] In the formula, ρ m,i (k) represents the density of the i-th interval of road segment m at time kT; Let m be the critical density of road segment m;

[0093] S1.4 Based on steps S1.1 to S1.3, the system-level objective function is determined as follows:

[0094]

[0095] In the formula, κ c κ t The weights for communication overhead and computation overhead are represented by κ, respectively, and are chosen based on multiple experimental results. c =0.1, κ t =0.01; k t =kT / T t T t The system-level control cycle is represented by kT; kT represents the end time of the kth segment information sampling cycle, where k is a constant and T represents the segment information sampling cycle.

[0096] S2. The optimal cluster partitioning algorithm designed is used to solve the single-objective optimization problem established in step S1, to obtain the optimal road segment cluster structure of the highway under the current traffic conditions, and to determine the communication connection relationship (communication / non-communication) between the roadside equipment of each road segment and the roadside equipment of other road segments.

[0097] The optimal cluster partitioning algorithm in step S2 is based on Dynamic Programming (DP) and includes a main algorithm and sub-algorithms. The main algorithm initially divides the highway into sub-regions that may form road segment clusters based on the congestion level of each road segment within a multi-segment area. Further, for each sub-region, the main algorithm calls the sub-algorithms to calculate the optimal cooperative road segment cluster partitioning decision within that sub-region. The pseudocode of the main algorithm is as follows: Figure 4 As shown, the pseudocode for the sub-algorithm is as follows: Figure 5 As shown.

[0098] The subsystem-level collaborative control strategy includes steps S3, S4, and S5 in the implementation steps of this invention:

[0099] S3. Based on the optimal road segment cluster structure obtained in step S2, five subsystem-level objective functions are established, with the total travel time (TTS), total travel distance (TTD), entrance ramp queue length, speed limit change within the road segment, and ramp release rate change as optimization objectives. The upper and lower bounds of the road segment speed limit and the ramp release rate are used as constraints. Combining the five subsystem-level objective functions, a multi-objective optimization problem is established.

[0100] S3.1 Establish five subsystem-level objective functions;

[0101] I. Establish a system based on the total travel time of vehicles within the subsystem.TTS (k c Let be the objective function for optimizing the objective.

[0102]

[0103] In the formula, U m For road segment m∈C j A control decision, including the speed limit value (VSL) of the main road control area. m,i and the ramp release rate r in the entrance ramp control area m,i N p For the number of time-domain steps to predict; Z = T c / T,k c =k / Z,T c For subsystem-level control cycles; w m′,i (Zk c +p) indicates that the i-th segment of road segment m′ is in Zk c +p corresponds to the queue length of the entrance ramp; p is an integer and p∈{Zk} c ,Zk c +1,···,Z(k c +N p )}; L represents the set of all segments of road segment m′; m′,i λ represents the length of the i-th segment of road segment m′; m′,i ρ represents the number of lanes in the i-th segment of road segment m′; m′,i (Zk c +p) indicates that the i-th segment of road segment m′ is in Zk c Density at time +p;

[0104] II. Establish a system based on the total driving distance J TTD (k c Let be the objective function for optimizing the objective.

[0105]

[0106] In the formula, v m′,i (Zk c +p) indicates that the i-th segment of road segment m′ is in Zk c The average speed of vehicles within the segment at time +p;

[0107] III. Objective function with the entrance ramp queue length as the optimization objective;

[0108]

[0109]

[0110] In the formula, J w For the prediction time-domain steps Np Internal subsystem C j Mid-entry ramp queue length penalty; w m',i (k) represents the queue length of the entrance ramp at time kT; This represents the maximum number of vehicles queuing at the corresponding entrance ramp;

[0111] IV. The objective function is to optimize the sudden changes in speed limits within a road segment.

[0112]

[0113] In the formula, J ΔVSL To control the number of time-domain steps N c Internal penalty for sudden changes in speed limit values ​​within a certain range (m); VSL m,i This represents the speed limit value of the i-th interval of road segment m; c is an integer and c∈{0,1,...,N} c -1)};

[0114] V. The objective function that optimizes the sudden changes in ramp release rate within a road segment;

[0115]

[0116] In the formula, J Δr To control the number of time-domain steps N c Internal penalty for sudden changes in ramp release rate within a road segment m; r m,i This represents the ramp release rate of the i-th interval of road segment m;

[0117] S3.2 Based on the five subsystem-level objective functions established in step S3.1, and using the upper and lower bounds of the speed limit and the upper and lower bounds of the ramp release rate as constraints, a multi-objective optimization model is established as shown below:

[0118]

[0119] In the formula, VSL min and VSL max These are the upper and lower bounds of the speed limit, respectively. r min and r max These are the upper and lower limits of the ramp release rate, respectively.

[0120] S4. Using a subsystem-level multi-objective optimization algorithm, solve the multi-objective optimization problem established in step S3 to obtain the optimal control decision, namely the vehicle speed recommendation for the main road of each road segment and the ramp control signal;

[0121] The flowchart of the subsystem-level multi-objective optimization algorithm is as follows: Figure 6As shown, the algorithm first uses the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) to solve the multi-objective optimization problem established in step 3, obtaining a Pareto optimal solution set under the multi-objective function, which contains multiple optimal solutions. Then, based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), an optimal control decision is selected from the Pareto optimal solution set. Specifically:

[0122] The Pareto solutions in the Pareto optimal solution set are represented as control decision objects with multiple objective function values. When the number of Pareto solutions in the Pareto optimal solution set is p, assume the optimal solution U... + And the worst solution U - The Pareto optimal solution set consists of all Pareto solutions in each objective function J. z The optimal value of z∈{TTS,TTD,ΔVSL,Δr,o} and worst value Composition. At this point, the Euclidean distances between any Pareto solution p in the Pareto optimal solution set and the optimal and worst solutions are respectively expressed as:

[0123]

[0124] Among them, D + D represents the Euclidean distance between the Pareto solution p and the optimal solution. - Let p represent the Euclidean distance between the Pareto solution p and the worst solution. Then, the relative distance between the Pareto solution p and the best solution is expressed as:

[0125]

[0126] Where D*∈[0,1] is used to evaluate how close the Pareto solution p is to the optimal solution. The closer D* is to 1, the closer the Pareto solution p is to the optimal solution. Each Pareto solution is scored by calculating its relative distance to the optimal solution. Finally, the solutions are sorted according to their scores to select the optimal control decision for the current road segment. It should be noted that since the dimensions of different objective function values ​​are not uniform, the objective function values ​​need to be standardized before calculation.

[0127] The recommended vehicle speed for the main road and the ramp control signals for each road segment can be obtained by using a subsystem-level multi-objective optimization algorithm.

[0128] S5. Design congestion level indicators for road segments, and calculate the congestion level of each road segment within the subsystem based on these indicators. Then, each road segment, based on its own traffic conditions and the congestion levels of adjacent road segments within its subsystem, determines whether to initiate active control measures and whether to update its control decisions. Typically, road segments only need to update their current control decisions when there is a significant discrepancy between the traffic conditions of the road segments under the subsystem's jurisdiction or the congestion levels of other cooperating road segments and the information obtained during the last control decision update.

[0129] S5.1 Calculate the congestion level of the road segment;

[0130]

[0131] In the formula, MC m An index indicating the degree of congestion in road segment m; Let be the critical density of the i-th interval of road segment m;

[0132] S5.2 Definition The control trigger time sequence and the control decision update time sequence are respectively, and their calculation expressions are as follows:

[0133]

[0134]

[0135] In the formula, θ c σ u and θ u All of these are hyperparameters.

[0136] The vehicle-level speed guidance strategy includes steps 6 and 7 in the implementation steps of this invention:

[0137] S6. Based on the recommended speed for the road segment and the distance between the vehicle and the vehicle in front, calculate the vehicle's expected speed;

[0138] In step S6, the desired speed The calculation expression is:

[0139]

[0140]

[0141] In the formula, This is the distance between your vehicle and the vehicle in front in lane l of the current road segment; Recommended speed; This represents the vehicle spacing when ICVs are evenly distributed in lane l of the current road segment; L veh The average length of the ICV.

[0142] S7. The main road ICV travels at the desired speed, while the ramp ICV travels according to the ramp control signals.

[0143] Therefore, by following the detailed steps outlined above, you can obtain... Figure 2 The control decisions for each section of the highway in the illustrated embodiment optimize the high-volume traffic efficiency of the highway under cyber resource constraints.

[0144] It is hereby declared that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A CPS control strategy for optimizing high-volume traffic flow efficiency on highways, characterized in that, Includes the following steps: S1. Determine the system-level objective function based on the optimization objectives of the communication overhead, computational overhead, and cluster correlation required for the road segment cluster structure; S2. The optimal segment cluster structure of the highway under the current traffic conditions is solved by using the optimal cluster partitioning algorithm; S3. Based on the optimal road segment cluster structure obtained in step S2, five subsystem-level objective functions are established, taking the total vehicle travel time, total travel distance, entrance ramp queue length, speed limit change within the road segment, and ramp release rate change as optimization objectives. Furthermore, a multi-objective optimization problem is established, using the upper and lower bounds of the road segment speed limit and the upper and lower bounds of the ramp release rate as constraints. S4. Using a subsystem-level multi-objective optimization algorithm, solve the multi-objective optimization problem established in step S3 to obtain the optimal control decision, namely the vehicle speed recommendation for the main road of each road segment and the ramp control signal; The subsystem-level multi-objective optimization algorithm uses a non-dominated sorting evolutionary algorithm to solve the multi-objective optimization problem established in step S3, and obtains the Pareto optimal solution set under the multi-objective function. Then, based on the approximation ideal solution sorting method, an optimal control decision is selected from the Pareto optimal solution set. S5. Design the congestion level index of the road segment, and use the congestion level index to calculate the congestion level of each road segment in the subsystem. Then, each road segment determines whether to activate active control measures and whether to update control decisions based on its own traffic conditions and the congestion level of adjacent road segments in its subsystem. S6. Based on the recommended speed for the road segment and the distance between the vehicle and the vehicle in front, calculate the vehicle's expected speed; S7. The main road ICV travels at the desired speed, while the ramp ICV travels according to the ramp control signals.

2. The CPS control strategy for optimizing high-volume traffic flow efficiency on highways according to claim 1, characterized in that: The optimal cluster partitioning algorithm in step S2 is based on dynamic programming and includes a main algorithm and sub-algorithms. The main algorithm is used to initially segment sub-regions that may form road segment clusters based on the congestion level of each road segment within a multi-segment area of ​​the highway, and to call sub-algorithms. The sub-algorithm is used to calculate the optimal collaborative road segment cluster partitioning decision within the sub-region.

3. The CPS control strategy for optimizing high-volume traffic flow efficiency on highways according to claim 2, characterized in that: Step S3 includes the following sub-steps: S3.1 Establish five subsystem-level objective functions; I. Establish a system based on the total travel time of vehicles within the subsystem. TTS (k c Let be the objective function for optimizing the objective. In the formula, U m For road segment m∈C j A control decision, including the speed limit value (VSL) of the main road control area. m,i and the ramp release rate r in the entrance ramp control area m,i N p For the number of time-domain steps to predict; Z = T c / T,k c =k / Z,T c The subsystem-level control cycle; T represents the road segment information sampling cycle; k represents a constant; w m′,i (Zk c +p) indicates that the i-th segment of road segment m′ is in Zk c +p corresponds to the queue length of the entrance ramp; p is an integer and p∈{Zk} c ,Zk c +1,···,Z(k c +N p )}; L represents the set of all segments of road segment m′; m′,i λ represents the length of the i-th segment of road segment m′; m′,i ρ represents the number of lanes in the i-th segment of road segment m′; m′,i (Zk c +p) indicates that the i-th segment of road segment m′ is in Zk c Density at time +p; II. Establish a system based on the total driving distance J TTD (k c Let be the objective function for optimizing the objective. In the formula, v m′,i (Zk c +p) indicates that the i-th segment of road segment m′ is in Zk c The average speed of vehicles within the segment at time +p; III. Objective function with the entrance ramp queue length as the optimization objective; In the formula, J w For the prediction time-domain steps N p Internal subsystem C j Penalty for queue length at mid-entry ramps; w m',i (k) represents the queue length of the entrance ramp at time kT; This represents the maximum number of vehicles queuing at the corresponding entrance ramp; IV. The objective function is to optimize the sudden changes in speed limits within a road segment. In the formula, J ΔVSL To control the number of time-domain steps N c Internal penalty for sudden changes in speed limit values ​​within a certain range (m); VSL m,i This represents the speed limit value of the i-th interval of road segment m; c is an integer and c∈{0,1,...,N} c -1)}; V. The objective function that optimizes the sudden changes in ramp release rate within a road segment; In the formula, J Δr To control the number of time-domain steps N c Internal penalty for sudden changes in ramp release rate within a road segment m; r m,i This represents the ramp release rate of the i-th interval of road segment m; S3.2 Based on the five subsystem-level objective functions established in step S3.1, and using the upper and lower bounds of the speed limit and the upper and lower bounds of the ramp release rate as constraints, a multi-objective optimization model is established as shown below: In the formula, VSL min and VSL max These are the lower and upper bounds of the speed limit, respectively. r min and r max These are the lower and upper limits of the ramp release rate, respectively.

4. The CPS control strategy for optimizing high-volume traffic flow efficiency on highways according to claim 3, characterized in that: Step S5 includes the following sub-steps: S5.1 Calculate the congestion level of the road segment; In the formula, MC m An index indicating the degree of congestion in road segment m; Let be the critical density of the i-th interval of road segment m; S5.2 Definition The control trigger time sequence and the control decision update time sequence are respectively, and their calculation expressions are as follows: In the formula, θ c σ u and θ u All of these are hyperparameters.

5. A CPS control strategy for optimizing high-volume traffic flow efficiency on highways according to claim 4, characterized in that: In step S6, the desired speed The calculation expression is: In the formula, This is the distance between your vehicle and the vehicle in front in lane l of the current road segment; Recommended speed; This represents the vehicle spacing when ICVs are evenly distributed in lane l of the current road segment; L veh The average length of the ICV.

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