A ten-lane highway lane-level dynamic variable speed limit control method and system

Abstract

The application relates to the field of expressway active control technology, in particular to a ten-lane expressway lane-level dynamic variable speed limit control method and system, which comprises the following steps: acquiring traffic parameters, infrastructure states and vehicle driving data of an expressway in real time to construct an expressway real-time data set; constructing a multi-lane expressway lane-by-lane highest limit speed calculation model under safety and efficiency game based on the real-time data set and based on safety and efficiency game theory and fuzzy mathematics theory; constructing a ten-lane lane-level speed limit value model according to the calculated highest limit speed and the real-time data set; and calculating lane speed limit values according to the ten-lane lane-level speed limit value model and publishing the speed limit values to drivers in real time. Through the application, dynamic and accurate speed limit control of the ten-lane expressway is realized, traffic flow differences between lanes and vehicle speed changes are effectively coped with, and the overall road traffic efficiency and safety are improved.

Description

Technical Field

[0001] This invention relates to the field of active control technology for highways, specifically to a lane-level dynamic variable speed limit control method and system for a ten-lane highway. Background Technology

[0002] With the rapid development of the national economy, the number of motor vehicles owned by residents has been increasing year by year, and intercity travel has become increasingly frequent. As the main artery of highway transportation, expressways will bear an increasing amount of traffic flow. This has brought enormous difficulties and challenges to expressway management. In order to meet the needs of traffic growth, it is particularly necessary to widen and reconstruct existing expressways. At present, some provinces and cities across the country have carried out reconstruction and expansion projects of ten-lane expressways, such as the expansion project of the Taiyuan-Changde section of the Shanghai-Wuhan Expressway in Jiangsu Province, the Longxi Interchange to Lukou Interchange section of the Hefei Ring Expressway, and the reconstruction and expansion project of the Tangxia to Dongcheng section and Longlin branch line of the Dongguan-Shenzhen Expressway in the Pearl River Delta.

[0003] Traffic management on ten-lane highways is significantly more difficult. Traditional fixed speed limit control methods lack personalized control over different lanes and cannot effectively cope with differences in traffic flow and vehicle speed between lanes, which can easily lead to traffic congestion and safety hazards.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of this disclosure and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a lane-level dynamic variable speed limit control method and system for a ten-lane highway, thereby effectively solving the problems pointed out in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for lane-level dynamic variable speed limit control on a ten-lane highway, the method comprising:

[0008] Real-time acquisition of highway traffic parameters, infrastructure status, and vehicle driving data to construct a real-time highway data set;

[0009] Based on the real-time data set and on the theory of safety and efficiency game theory and fuzzy mathematics theory, a calculation model for the maximum speed limit of lanes in a multi-lane highway under the safety and efficiency game is constructed.

[0010] Based on the maximum speed limit calculated by the multi-lane highway lane maximum speed limit calculation model and the real-time data set, a lane-level speed limit value model for ten lanes is constructed.

[0011] The speed limit value for each lane is calculated based on the lane-level speed limit value model of the ten lanes and published to the driver in real time.

[0012] Furthermore, the aforementioned game theory of security and efficiency includes:

[0013] The Lyapunov traffic flow stability index is used as the operational safety target, and the disturbance terms of the Lyapunov traffic flow stability index are speed difference and lane changing behavior.

[0014] The cross-sectional traffic operation efficiency is used as the operation efficiency target, where the cross-sectional traffic operation efficiency is the number of kilometers traveled by vehicles passing through a certain cross-section within a unit of time.

[0015] Furthermore, the steps for solving the Lyapunov traffic flow stability index include:

[0016] Define traffic flow state and introduce the speed difference and lane-changing behavior as disturbance terms;

[0017] Define the probability and number of lane changes for each vehicle in the current lane, and calculate the expected value of the disturbance term;

[0018] The traffic flow state is represented by the disturbance term, and the rate of change of the speed, position and lane of each vehicle over time is calculated to obtain the Jacobian matrix of the rate of change of the state.

[0019] The overall Lyapunov index of traffic flow state is obtained from the Jacobian matrix, and the overall Lyapunov index of traffic flow state is expressed as follows: Where S is the Lyapunov index, t is the time span, n is the number of vehicles, and ln|J i | represents the absolute value of the natural logarithm of the Jacobian matrix representing the rate of change of state of vehicle i.

[0020] Furthermore, a mathematical model for optimal safety efficiency is constructed under the condition of minimizing the Lyapunov exponent. This optimal safety efficiency mathematical model is expressed as follows:

[0021]

[0022] maxE=qvT

[0023] Where minS is the Lyapunov exponent that the objective is to minimize. Let be the absolute value of the natural logarithm of the Jacobian matrix representing the rate of change of state of the i-th vehicle, i.e. maxE represents the traffic flow efficiency that is maximized, q represents the traffic volume, v represents the average travel speed, and T represents the time per unit.

[0024] Furthermore, the maximum speed limit is determined, including:

[0025] The design variables are grouped and a set of distribution factors corresponding one-to-one with the design variables, wherein the design variables are operating speed, average speed and maximum speed limit;

[0026] The distribution factors corresponding to the design variables are normalized, and the fuzzy similarity matrix and fuzzy equivalence matrix are calculated.

[0027] The original variable grouping method is changed, and a strategy vector for each group is defined according to the changed grouping. The grouping method is that the running speed and average speed are in one group, and the maximum speed limit is in another group.

[0028] By using the safety and efficiency values ​​calculated under different conditions as the numerator of the utility function and the optimal safety and efficiency values ​​as the denominator of the utility function, a normalized utility function for safety and efficiency is obtained.

[0029] Based on the strategy vector and the normalized utility function of safety and efficiency, a calculation model for the maximum speed limit of lanes on a multi-lane highway under the game of safety and efficiency is obtained.

[0030] Using optimal combination theory, the maximum speed limit values ​​for various types of vehicles under the condition of balancing safety and efficiency are calculated using R language programming.

[0031] Furthermore, the normalized utility function for security and efficiency obtained from the policy vector includes:

[0032]

[0033] Where u1 and u2 are the normalized values ​​of the safety utility function and the efficiency utility function, respectively; S(v) and E(v) are the safety utility value and the efficiency utility value at velocity v, respectively; and minS(v) and minE(v) are the minimum safety utility value and the minimum efficiency utility value among all velocities v, respectively.

[0034] Furthermore, a lane-level speed limit model for the ten lanes is constructed, including:

[0035] Based on the speed distribution of vehicle types across the ten lanes, an allocation matrix is ​​set up, and the density of each type of vehicle in each lane is obtained.

[0036] The simulated driver freely chooses to drive at the maximum speed, calculates the assigned variables, and obtains the density after assignment;

[0037] Based on the allocated density, the speed relationships under different conditions are determined, and the speed-space function for various types of vehicles is obtained;

[0038] Based on the speed-distance function of each type of vehicle, lane-level speed limit models for each of the ten lanes are obtained.

[0039] Furthermore, the vehicle type density for the ten-lane road is:

[0040]

[0041] Among them, the class density of vehicles of type 1 is The class density of vehicles of type 2 is ρ i Let ρi represent the class density of the i-th type of vehicle, and ρ1 and ρ2 represent the class densities of small cars and large cars, respectively. Let i be the lane distribution variable for the i-th type of vehicle. and The percentages of vehicles occupying the ten lanes are for small cars and large cars, respectively. i,j S is the optimal driving distance for each type of vehicle in different lanes. 1,j and S 2,j These represent the optimal driving distances for small cars and large cars in lane j, respectively.

[0042] Furthermore, based on the speed-distance function of each type of vehicle, lane-level speed limit models for the ten lanes are obtained for each type of vehicle. The lane-level speed limit models for the ten lanes are as follows:

[0043]

[0044] Among them, v1 represents the vehicle speed under the condition that the velocity and density have a linear relationship. The improved minimum velocity is given by the logarithmic relationship between velocity and density. v is the improved minimum velocity under the condition that velocity and density have an exponential relationship. f v is the free-flow velocity. imin v is the minimum speed of the vehicle. imax ρ is the vehicle's maximum speed, ρ is the displacement, and ρ is the current traffic flow density. j ρ is the critical density of traffic flow. i,j Let be the vehicle density of the i-th type of vehicle in the j-th lane.

[0045] A lane-level dynamic variable speed limit control system for a ten-lane highway, the system comprising:

[0046] The real-time traffic parameter acquisition module acquires real-time traffic parameters, infrastructure status, and vehicle driving data of the highway to build a real-time highway data set.

[0047] The maximum speed limit model construction module constructs a calculation model for the maximum speed limit of a multi-lane highway under the safety and efficiency game theory based on the real-time data set and the safety and efficiency game theory and fuzzy mathematics theory.

[0048] The lane-level speed limit model construction module constructs a lane-level speed limit value model for ten lanes based on the maximum speed limit calculated by the multi-lane highway lane maximum speed limit calculation model and the real-time data set.

[0049] The real-time speed limit data publishing module calculates the speed limit value of each lane based on the lane-level speed limit value model of the ten lanes and publishes it to the driver in real time.

[0050] The technical solution of this invention can achieve the following technical effects:

[0051] This invention enables dynamic and precise speed limit control for ten-lane highways, effectively addressing differences in traffic flow and vehicle speed between lanes, and improving overall road traffic efficiency and safety. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart illustrating a lane-level dynamic variable speed limit control method for a ten-lane highway.

[0054] Figure 2 A flowchart illustrating the process for determining the maximum speed limit;

[0055] Figure 3 A flowchart illustrating the process of constructing a lane-level speed limit model for a ten-lane road. Detailed Implementation

[0056] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0058] Example 1

[0059] like Figure 1 As shown, this invention provides a lane-level dynamic variable speed limit control method for a ten-lane highway, the method comprising:

[0060] S1: Real-time acquisition of highway traffic parameters, infrastructure status, and vehicle driving data to construct a real-time highway data set;

[0061] Specifically, traffic parameters such as traffic flow, vehicle speed, and lane occupancy are collected in real time through traffic monitoring equipment, radar speedometers, and video detection systems installed on each lane; at the same time, basic information such as road conditions, weather conditions, and traffic facility status is obtained; and data such as vehicle type and load are identified through video recognition systems or induction coils. All data is transmitted to the data processing center through a dedicated network, and after processing, analysis, and fusion, a real-time updated highway data set is formed.

[0062] S2: Based on real-time data sets and on the theory of safety and efficiency game theory and fuzzy mathematics theory, construct a calculation model for the maximum speed limit of lanes on multi-lane highways under the safety and efficiency game theory.

[0063] Specifically, by utilizing real-time datasets and combining safety-efficiency game theory and fuzzy mathematics, a calculation model for the maximum speed limits of each lane on multi-lane highways is constructed. This model can optimize traffic efficiency while ensuring traffic safety, and rationally set the maximum speed limits for each lane. By analyzing real-time traffic data, the model identifies the traffic flow characteristics of each lane, dynamically balances safety and efficiency requirements, and thus provides accurate speed limit recommendations. This effectively addresses differences in traffic flow and vehicle speed changes between different lanes, reduces traffic accidents, and improves road capacity.

[0064] S3: Based on the maximum speed limit calculated by the multi-lane highway lane maximum speed limit calculation model and the real-time data set, construct a lane-level speed limit value model for ten lanes;

[0065] Specifically, by combining real-time traffic parameters such as traffic flow, vehicle speed, and lane occupancy rate with basic information such as road conditions and weather conditions, the optimal speed limit for each lane is dynamically determined to improve road traffic efficiency and safety. This model effectively addresses differences in traffic flow and speed variations between lanes through refined management of traffic flow and vehicle speed in each lane, reducing traffic accidents and congestion, and improving overall road capacity.

[0066] S4: Calculate the speed limit value for each lane based on the lane-level speed limit value model of the ten lanes and publish it to the driver in real time.

[0067] This invention enables dynamic and precise speed limit control for ten-lane highways, effectively addressing differences in traffic flow and vehicle speed between lanes, and improving overall road traffic efficiency and safety.

[0068] As a preferred embodiment of the above, the security and efficiency game theory includes:

[0069] The Lyapunov Traffic Flow Stability Index is used as the operational safety target, and the disturbance terms of the Lyapunov Traffic Flow Stability Index are speed difference and lane changing behavior.

[0070] The cross-sectional traffic operation efficiency is taken as the operation efficiency target. The cross-sectional traffic operation efficiency is the number of kilometers traveled by vehicles passing through a certain cross-section within a unit of time.

[0071] Specifically, in traffic flow, the Lyapunov index can be used to assess the non-periodicity and unpredictability of vehicle motion, as well as the strength of traffic flow stability. A negative S value indicates a stable traffic flow and safe vehicle operation; a positive S value indicates an unstable traffic flow and unsafe vehicle operation; and a S value of 0 indicates a critical traffic flow and safe vehicle operation, but potentially susceptible to disturbances. Cross-sectional traffic efficiency reflects the level of service and service traffic volume of a cross-section.

[0072] As a preferred embodiment of the above, the steps for solving the Lyapunov traffic flow stability index include:

[0073] A11: Define traffic flow state and introduce speed difference and lane changing behavior as disturbance terms;

[0074] A12: Define the probability and number of lane changes for each vehicle in the current lane, and calculate the expected value of the disturbance term;

[0075] A13: Represent the traffic flow state by combining the disturbance term, and calculate the rate of change of the speed, position and lane of each vehicle over time to obtain the Jacobian matrix of the rate of change of the state.

[0076] A14: The overall Lyapunov index of traffic flow state is obtained from the Jacobian matrix. The overall Lyapunov index of traffic flow state is expressed as... Where S is the Lyapunov index, t is the time span, n is the number of vehicles, and ln|J i | represents the absolute value of the natural logarithm of the Jacobian matrix representing the rate of change of state of vehicle x.

[0077] Specifically, according to the definition of traffic flow state (assuming there are N vehicles in the traffic flow, the state of the i-th vehicle can be represented by its position x), i Speed ​​v i and the lane i To describe it), if we disregard disturbance terms, the traffic flow state can be represented as a state vector x = [x1, x2, ..., x...]. N ,v1,v2,…,v N ,l1,l2,…l N Because ten-lane highways have many lanes and conflict points, the probability of traffic flow turbulence is relatively high in both the lateral and longitudinal directions. Therefore, this invention incorporates two disturbance terms—speed difference and lane-changing behavior—into the Lyapunov exponent, expressing each vehicle's lane-changing behavior and speed difference as Δd. i (x,t) and Δv i (x,t), where t represents time; meanwhile, the lane-changing behavior disturbance term is determined by the probability and frequency of a vehicle changing lanes in the current lane, with the probability of each vehicle changing lanes in the current lane being p. i (x,t), the number of lane changes within a certain time period is N. i (x,t), then the mathematical expectation of the disturbance term is represented as E[Δd i [x,t)]=p i (x,t)N i (x,t), these perturbation terms may be random. Therefore, this invention represents the two perturbation terms as a random vector Δw(t)=[Δd1(x,t),Δd2(x,t),…,Δd N (x,t),Δv1(x,t),Δv2(x,t),…,Δv N [x,t], therefore the traffic flow state is represented as follows, assuming the expectation of the disturbance term is 0, i.e., E[Δw(t)]=0; at this time, the rate of change of each vehicle's speed, position, and lane over time is f i =[dxi / dt,dx i / dt,dl i / dt], where dx i / dt represents position x i rate of change over time; dv i / dt represents velocity v irate of change over time; dl i / dt indicates lane number i Rate of change over time, Jacobian matrix of rate of change of state J i It is expressed as follows:

[0078]

[0079] According to the Jacobian matrix, the overall Lyapunov index of traffic flow status...

[0080] As a preferred embodiment of the above, a mathematical model for optimal safety efficiency is constructed when the Lyapunov exponent is minimized. The mathematical model for optimal safety efficiency is expressed as follows:

[0081]

[0082] maxE=qvT

[0083] Where minS is the Lyapunov exponent that the objective is to minimize. Let be the absolute value of the natural logarithm of the Jacobian matrix representing the rate of change of state of the i-th vehicle, i.e. maxE represents the traffic flow efficiency that is maximized, q represents the traffic volume, v represents the average travel speed, and T represents the time per unit.

[0084] Specifically, by combining the two formulas of minimizing the Lyapunov exponent and maximizing traffic flow efficiency, a comprehensive optimization model is constructed. While ensuring the stability of the traffic system, it improves the overall traffic flow efficiency. This comprehensive optimization model helps to achieve efficient and safe traffic management, adapt to constantly changing traffic conditions, maximize road utilization efficiency, and provide a scientific basis for formulating reasonable speed limits and traffic management strategies.

[0085] As a preferred embodiment of the above, such as Figure 2 As shown, the maximum speed limit is determined by:

[0086] B11: Group the design variables and design a set of distribution factors that correspond one-to-one with the design variables. The design variables are operating speed, average speed and maximum speed limit.

[0087] B12: Normalize the distribution factors corresponding to the design variables and calculate the fuzzy similarity matrix and fuzzy equivalence matrix;

[0088] B13: Change the original variable grouping method and define the strategy vector for each group according to the changed grouping. The grouping method is to group the running speed and average speed together, and the maximum speed limit together.

[0089] B14: By using the safety and efficiency values ​​calculated under different conditions as the numerator of the utility function and the optimal safety and efficiency values ​​as the denominator of the utility function, a normalized utility function for safety and efficiency is obtained.

[0090] B15: Based on the strategy vector and the normalized utility function of safety and efficiency, a calculation model for the maximum speed limit of lanes on a multi-lane highway under the game of safety and efficiency is obtained.

[0091] B16: Using optimal combination theory and R language programming, the maximum speed limit values ​​for various types of vehicles under the condition of balancing safety and efficiency are calculated.

[0092] Specifically, using fuzzy clustering theory to group design variables, all the design variables to be classified are... Let v = V1, vi = V3, therefore the set of all design variables to be classified is V = {V1, V2, V3}, and each design variable has a set of distribution factors to represent it (V i,1 V i,2 ), where V i,1 and V i,2 These represent the degree of influence of design variables on the two objectives of safety and efficiency, respectively. Operating speed v is the vehicle's speed under actual conditions, generally corresponding to the 85th percentile. Operating speed is inversely proportional to safety and directly proportional to traffic efficiency; therefore, V is chosen. 1,1 =-0.8, V 1,2 =0.9. According to relevant research, the higher the average speed, the higher the injury rate per million vehicle kilometers, therefore V 2,1 = -0.75, V 2,2 =0.8, the maximum speed limit is stipulated by the management department, and in this embodiment, V is taken as 0.8. 1,1 =-1, V 1,2 =0.95. In summary, the distribution coefficients of all model variables are as follows: (V 1,1 V 1,2 ) = (-0.8, 0.9), (V 2,1 V 2,2 ) = (-0.75, 0.8), (V 3,1 ,v 3,2 The data (V) = (-1, 0.95) was normalized by transforming the standard deviation to eliminate the influence of dimensions. The normalized distribution coefficients are shown below: 1,1 V 1,2 )=(0.8,0.66667),(V 2,1 V 2,2 )=(1.0,0), (V 3,1 V 3,2The fuzzy similarity matrix of (0, 1.0) is corrected using the absolute value subtraction method (with M = 0.1). The matrix is ​​represented as follows: The fuzzy equivalent matrix R of R is obtained using the square self-synthesis method. * as follows:

[0093] As a preferred embodiment of the above embodiments, the normalized utility function for security and efficiency obtained from the policy vector includes:

[0094]

[0095] Where u1 and u2 are the normalized values ​​of the safety utility function and the efficiency utility function, respectively; S(v) and E(v) are the safety utility value and the efficiency utility value at velocity v, respectively; and minS(v) and minE(v) are the minimum safety utility value and the minimum efficiency utility value among all velocities v, respectively.

[0096] Specifically, in this embodiment, according to fuzzy clustering theory, when λ is 0.9, R... λ Elements equal to 1 in one row are grouped together, resulting in the final design variable groups {V1,V2} and {V3}. Since design variable V3 represents the maximum limiting speed and only appears in the efficiency objective function, the design variable group {V3} is associated with the efficiency game player P2, and the design variable group {V1,V2} is associated with P1, as follows: s1=(s 11 ,s 12 )=(V1,V2), s2=(s 21 ) = V3, where s1 is the strategy vector of player P1 and s2 is the strategy vector of player P2, thus obtaining the final normalized function.

[0097] As a preferred embodiment of the above, such as Figure 3 As shown, a lane-level speed limit model for a ten-lane road is constructed, including:

[0098] C11: Based on the speed distribution of vehicle types across ten lanes, set up the distribution matrix and obtain the density of each type of vehicle in each lane;

[0099] C12: Simulate a driver freely choosing to drive at the maximum speed, calculate the allocation variables, and obtain the density after allocation;

[0100] C13: Based on the allocated density, determine the speed relationship under different conditions and obtain the speed-space function for various types of vehicles;

[0101] C14: Based on the speed-distance function of various types of vehicles, obtain the lane-level speed limit model for ten lanes for each type of vehicle.

[0102] Specifically, based on the speed distribution of vehicle types across ten lanes, an allocation matrix is ​​established to obtain the density of each vehicle type in each lane. Simulating a driver freely choosing lanes at maximum speed, allocation variables are calculated to obtain the allocated density. Based on the allocated density, speed relationships under different conditions are determined, constructing linear, logarithmic, and exponential speed-space functions respectively. Finally, based on these speed-space functions, lane-level speed limit models for small and large vehicles across the ten lanes are obtained, including speed limit models for public lanes under mixed traffic conditions. This method ensures that speed limits dynamically adapt to the actual traffic conditions of different lanes, improving road efficiency and safety, and reducing traffic accidents and congestion through scientific speed limit management, providing a smoother and safer driving experience.

[0103] As a preferred embodiment of the above, the vehicle class density for the ten-lane road is:

[0104]

[0105] Among them, the class density of type 1 vehicles (small cars) is The class density of type 2 vehicles (large vehicles) is ρ i Let ρi represent the class density of the i-th type of vehicle, and ρ1 and ρ2 represent the class densities of small cars and large cars, respectively. Let i be the lane distribution variable for the i-th type of vehicle. and The percentages of vehicles occupying the ten lanes are for small cars and large cars, respectively. i,j S is the optimal driving distance for each type of vehicle in different lanes. 1,j and S 2,j These represent the optimal driving distances for small cars and large cars in lane j, respectively.

[0106] In this embodiment, class density represents the distribution of different types of vehicles (such as small cars and large vehicles) in each lane on a ten-lane highway. It measures the density of each type of vehicle in each lane. By calculating class density, the density distribution of each type of vehicle in the ten lanes can be obtained, providing an important basis for subsequent speed limit calculation and traffic management. By reasonably allocating lane occupancy ratios and optimizing driving distances, the overall efficiency and safety of traffic flow can be effectively improved.

[0107] As a preferred embodiment of the above, based on the speed-distance function of various types of vehicles, lane-level speed limit models for ten lanes are obtained for each type of vehicle. The lane-level speed limit models for ten lanes are as follows:

[0108]

[0109] Among them, V1 represents the vehicle speed under a linear relationship between velocity and density. The improved minimum velocity is given by the logarithmic relationship between velocity and density. v is the improved minimum velocity under the condition that velocity and density have an exponential relationship. f v is the free-flow velocity. imin v is the minimum speed of the vehicle. imax ρ is the vehicle's maximum speed, ρ is the displacement, and ρ is the current traffic flow density. j ρ is the critical density of traffic flow. i,j Let be the vehicle density of the i-th type of vehicle in the j-th lane.

[0110] Specifically, the inputs are the real-time traffic flow density ρ and the vehicle density ρ of each lane. i,j The speed limit for each lane is calculated using the aforementioned model formula. An appropriate relationship model (linear, logarithmic, or exponential) is selected based on the actual traffic flow density. The calculated speed limit is then published to drivers, ensuring that the speed limit dynamically adapts to the actual traffic conditions of different lanes, improving road efficiency and safety. Through these specific steps, a lane-level speed limit model for ten lanes is constructed based on the speed-distance function of various vehicle types. This achieves precise speed limit control for different lanes and different types of vehicles. This method effectively addresses differences in traffic flow and vehicle speed changes in different lanes, improving highway efficiency and safety, reducing traffic accidents and congestion, and providing drivers with a smoother and safer driving experience. It also considers the situation in actual road traffic where small cars and large vehicles share lanes (i.e., two different types of vehicles share lanes). If the value is not an integer, then the speeds of the two vehicles are equal in this lane. The speed limit model for the public lane in this case is:

[0111] Example 2

[0112] Based on the same inventive concept as the lane-level dynamic variable speed limit control method for a ten-lane highway in the foregoing embodiments, the present invention also provides a lane-level dynamic variable speed limit control system for a ten-lane highway, the system comprising:

[0113] The real-time traffic parameter acquisition module acquires real-time traffic parameters, infrastructure status, and vehicle driving data of the highway to build a real-time highway data set.

[0114] The maximum speed limit model construction module constructs a calculation model for the maximum speed limit of each lane on a multi-lane highway under the game of safety and efficiency, based on real-time data sets and the theory of safety and efficiency game and fuzzy mathematics.

[0115] The lane-level speed limit model construction module constructs a lane-level speed limit value model for ten lanes based on the maximum speed limit calculated by the multi-lane highway lane maximum speed limit calculation model and the real-time data set.

[0116] The real-time speed limit data publishing module calculates the speed limit value for each lane based on the lane-level speed limit value model of the ten lanes and publishes it to the driver in real time.

[0117] The control system described above in this invention can effectively realize the lane-level dynamic variable speed limit control method for ten-lane highways, and the technical effects it can achieve are as described in the above embodiments, and will not be repeated here.

[0118] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A lane-level dynamic variable speed limit control method for a ten-lane highway, characterized in that, The application relates to a method for calculating the maximum speed limit of a multi-lane highway. The method comprises the following steps: acquiring traffic parameters, infrastructure states and vehicle driving data of a highway in real time to build a real-time data set of the highway; building a multi-lane highway lane-based maximum speed limit calculation model under a safety and efficiency game based on the real-time data set and based on a safety and efficiency game theory and a fuzzy mathematics theory, wherein the safety and efficiency game theory comprises the following steps: taking a Lyapunov traffic flow stability index as a running safety target, wherein a disturbance term of the Lyapunov traffic flow stability index is a speed difference and a lane-changing behavior, and a solving step of the Lyapunov traffic flow stability index comprises the following steps: defining a traffic flow state and introducing the speed difference and the lane-changing behavior as the disturbance term; defining a probability of lane-changing of each vehicle in a current lane and a lane-changing frequency, and calculating a mathematical expectation of the disturbance term; combining the traffic flow state with the disturbance term to represent the traffic flow state, and calculating a rate of change of speed, position and a lane of each vehicle with time to obtain a Jacobian matrix of a state change rate; and building a safety and efficiency optimal mathematical model under a condition that a Lyapunov index is minimum, wherein the safety and efficiency optimal mathematical model is respectively represented as: taking a section traffic running efficiency as a running efficiency target, wherein the section traffic running efficiency is a driving distance of a vehicle through a section in a time period in a unit time; building a ten-lane lane-based speed limit value model according to a maximum speed limit calculated by the multi-lane highway lane-based maximum speed limit calculation model and the real-time data set to determine a maximum speed limit value, wherein the ten-lane lane-based speed limit value model comprises the following steps: grouping design variables and designing a group of distribution factors corresponding to the design variables, wherein the design variables are a running speed, an average speed and a maximum limit speed; performing normalization processing on the distribution factors corresponding to the design variables, and calculating a fuzzy similarity matrix and a fuzzy equivalent matrix; changing an original variable grouping mode, and defining a strategy vector of each group according to the changed grouping, wherein the running speed and the average speed are a group, and the maximum limit speed is a group; taking safety and efficiency values calculated under different situations as a numerator of a utility function, and taking safety and efficiency optimal values as a denominator of the utility function to obtain a normalized utility function of safety and efficiency; obtaining the multi-lane highway lane-based maximum speed limit calculation model under the safety and efficiency game according to the strategy vector and the normalized utility function of safety and efficiency; using an optimal combination theory to calculate the maximum speed limit value of each type of vehicle under a safety and efficiency balance condition by using R language programming; and calculating lane speed limit values according to the ten-lane lane-based speed limit value model and publishing the lane speed limit values to drivers in real time. The normalized utility function of safety and efficiency obtained according to the strategy vector comprises the following steps: building the ten-lane lane-based speed limit value model, which comprises the following steps: setting an allocation matrix according to speed distribution of each type of vehicle on ten lanes to obtain densities of each type of vehicle on each lane; simulating that a driver freely selects to drive at a maximum speed, calculating an allocation variable and obtaining an allocated density; and calculating a maximum speed limit value of each type of vehicle on each lane according to the allocated density and the normalized utility function of safety and efficiency. The ten-lane lane-based speed limit value model comprises the following steps: setting an allocation matrix according to speed distribution of each type of vehicle on ten lanes to obtain densities of each type of vehicle on each lane; simulating that a driver freely selects to drive at a maximum speed, calculating an allocation variable and obtaining an allocated density; and calculating a maximum speed limit value of each type of vehicle on each lane according to the allocated density and the normalized utility function of safety and efficiency. ​ ​ ​ A global Lyapunov exponent of traffic flow state is obtained according to the Jacobian matrix, and the global Lyapunov exponent of traffic flow state is expressed as Wherein S is a Lyapunov exponent, t is a time span, n is a vehicle number, is a natural logarithm absolute value of a state change rate Jacobian matrix of the ith vehicle. ​ ； ； where minS is the Lyapunov exponent to be minimized, is the natural logarithm absolute value of the Jacobian matrix of the state rate of change of the ith vehicle, is the traffic flow efficiency to be maximized, q is the traffic volume, v is the average travel speed, and T is the unit time; ​ ​ ​ ​ ​ ​ ​ ​ ​ 2. The ten-lane highway lane-level dynamic variable speed limit control method of claim 1, wherein, ​ ； ； wherein , are the normalized value of the safety utility function and the normalized value of the efficiency utility function, respectively, , are the safety utility value and the efficiency utility value at speed , respectively, , are the minimum value of the safety utility value and the efficiency utility minimum value over all speeds , respectively.

3. The ten-lane highway lane-level dynamic variable speed limit control method of claim 1, wherein, ​ ​ ​ According to the density after the distribution, the speed relationship in different cases is determined, and the speed-distance function of each type of vehicle is obtained; According to the speed-distance function of each type of vehicle, the lane-level speed limit value model of the ten-lane is obtained respectively.

4. The ten-lane highway lane-level dynamic variable speed limit control method of claim 1, wherein, According to the speed-distance function of each type of vehicle, the lane-level speed limit value model of the ten-lane is obtained respectively. ； wherein, the vehicle speed under which the speed-density relationship is linear, the improved minimum speed under which the speed-density relationship is logarithmic, the improved minimum speed under which the speed-density relationship is exponential, the free-flow speed, the minimum travel speed of the vehicle, the maximum travel speed of the vehicle, the current traffic flow density, the critical density of the traffic flow, the vehicle density of the i-th type of vehicle on the j-th lane.

5. The ten-lane highway lane-level dynamic variable speed limit control method of claim 3, wherein, According to the density after the distribution, the ten-lane vehicle class density is further calculated as: ； where is the class density of type 1 vehicles, is the class density of type 2 vehicles, , is the class density of the ith class of vehicles, and are the class densities of small and large vehicles, respectively, is the lane distribution variable of the ith class of vehicles, and are the occupancy proportions of small and large vehicles on a ten-lane road, respectively, is the optimal driving distance of each class of vehicles on different lanes, and are the optimal driving distances of small and large vehicles on the jth lane, respectively.

6. A ten-lane highway lane-level dynamic variable speed limit control system, characterized by, The system comprises: A real-time traffic parameter acquisition module acquires real-time traffic parameters, infrastructure states and vehicle driving data of the expressway, and constructs an expressway real-time data set; A highest speed limit model construction module constructs a multi-lane expressway lane-based highest limit speed calculation model under safety and efficiency game based on the real-time data set and based on safety and efficiency game theory and fuzzy mathematics theory, wherein the safety and efficiency game theory comprises: Taking Lyapunov traffic flow stability index as the running safety target, the disturbance term of the Lyapunov traffic flow stability index is the speed difference and lane changing behavior, and the solving steps of the Lyapunov traffic flow stability index comprise: Defining traffic flow state and introducing the speed difference and lane changing behavior as disturbance terms; Defining the probability and number of lane changing of each vehicle in the current lane, and calculating the mathematical expectation of the disturbance term; Combining the traffic flow state with the disturbance term, the rate of change of the speed, position and lane of each vehicle with time is calculated, and the Jacobian matrix of the state change rate is obtained; A global Lyapunov exponent of traffic flow state is obtained according to the Jacobian matrix, and the global Lyapunov exponent of traffic flow state is expressed as Wherein S is a Lyapunov exponent, t is a time span, n is a vehicle number, is a natural logarithm absolute value of a state change rate Jacobian matrix of the ith vehicle. A safety and efficiency optimal mathematical model is constructed under the condition that the Lyapunov index is minimum, and the safety and efficiency optimal mathematical model is respectively represented as: ； ； where minS is the Lyapunov exponent to be minimized, is the natural logarithm of the absolute value of the Jacobian matrix of the state rate of change of the ith vehicle, is the traffic flow efficiency to be maximized, q is the traffic volume, v is the average travel speed, and T is the unit time; Taking the cross-section traffic operation efficiency as the running efficiency target, the cross-section traffic operation efficiency is the driving distance per kilometer of vehicles passing a certain cross-section in a time period within a unit time; A lane-level speed limit model construction module constructs a ten-lane lane-level speed limit value model according to the highest limit speed calculated by the multi-lane expressway lane-based highest limit speed calculation model and the real-time data set, and determines the highest speed limit value, comprising: Grouping design variables and designing a group of distribution factors corresponding to the design variables, wherein the design variables are running speed, average speed and highest limit speed; Normalizing the distribution factors corresponding to the design variables, and calculating the fuzzy similarity matrix and the fuzzy equivalent matrix; Changing the original variable grouping method, and defining the strategy vector of each group according to the changed grouping, wherein the running speed and the average speed are a group, and the highest limit speed is a group; Taking the safety and efficiency values calculated under different situations as the numerator of the utility function, and taking the safety and efficiency optimal value as the denominator of the utility function, a normalized utility function of safety and efficiency is obtained; According to the strategy vector and the normalized utility function of safety and efficiency, the multi-lane expressway lane-based highest limit speed calculation model under safety and efficiency game is obtained. The optimal combination theory is used, and R language programming is adopted to calculate the highest speed limit value of each type of vehicle under the condition of safety and efficiency balance; The real-time speed limit data publishing module calculates the speed limit value of each lane according to the lane-level speed limit value model of the ten lanes and publishes the speed limit value to the driver in real time.

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