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

Abstract

The invention relates to the technical field of expressway active control, in particular to a ten-lane expressway lane-level dynamic variable speed limit control method and system, and the method comprises the steps: obtaining traffic parameters, infrastructure states and vehicle driving data of an expressway in real time, and constructing an expressway real-time data set; according to the real-time data set, on the basis of a safety and efficiency game theory and a fuzzy mathematics theory, a multi-lane expressway divided lane maximum limiting speed calculation model under the safety and efficiency game is constructed; according to the calculated maximum limiting speed and the real-time data set, a lane-level speed limiting value model of ten lanes is constructed; and calculating the speed limit value of each lane according to the lane-level speed limit value model of the ten lanes, and issuing the speed limit value to a driver in real time. According to the invention, dynamic and accurate speed limit control of the ten-lane expressway is realized, traffic flow difference and vehicle speed change between lanes are effectively coped with, and the traffic efficiency and safety of the whole road are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of active highway control, and in particular to a lane-level dynamically variable speed limit control method and system for a ten-lane highway. Background Art

[0002] With the rapid development of the national economy, the number of motor vehicles in the population has increased year by year, and the traffic activities between cities have become more and more frequent. As the main artery of road traffic, expressways will carry more and more travel traffic. This has brought huge difficulties and challenges to expressway management. In order to meet the needs of increasing traffic volume, it is particularly necessary to widen and reconstruct existing expressways. At present, some provinces and cities across the country have carried out the reconstruction and expansion of ten-lane expressways, such as the expansion project of the Taichang section of the Jiangsu-Huwu Expressway, the Longxi hub to the Lukou hub section of the Hefei Ring Expressway, the Tangxia to Dongcheng section of the Pearl River Delta Ring Line Dongguan to Shenzhen Expressway and the reconstruction and expansion project of the Longlin branch line.

[0003] The difficulty of traffic management on ten-lane highways has increased significantly. The traditional fixed speed limit control method lacks personalized regulation of different lanes and cannot effectively cope with the differences in traffic flow and vehicle speed changes between lanes, which can easily lead to traffic congestion and safety hazards.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure, and should not be regarded as an admission or any suggestion that the information constitutes the prior art known to those skilled in the art. Summary of the invention

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

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

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

[0008] Obtain highway traffic parameters, infrastructure status, and vehicle driving data in real time to build a real-time highway data set;

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

[0010] Constructing a lane-level speed limit value model for ten lanes according to the maximum speed limit calculated by the lane-by-lane maximum speed limit calculation model for the multi-lane highway and the real-time data set;

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

[0012] Furthermore, the safety and efficiency game theory includes:

[0013] Taking the Lyapunov traffic flow stability index as the operation safety target, the disturbance terms of the Lyapunov traffic flow stability index are speed difference and lane-changing behavior;

[0014] The section traffic operation efficiency is taken as the operation efficiency target, and the section traffic operation efficiency is the number of kilometers traveled by vehicles passing through a section within a unit time in the time period.

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

[0016] Defining the traffic flow state, and introducing 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 mathematical expectation of the disturbance term;

[0018] The traffic flow state is represented in combination with 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 state change rate;

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

[0020] Furthermore, when the Lyapunov index is minimized, an optimal mathematical model for safety efficiency is constructed, and the optimal mathematical model for safety efficiency is expressed as follows:

[0021]

[0022] maxE=qvT

[0023] Among them, minS is the Lyapunov exponent that the goal is to minimize, is the absolute value of the natural logarithm of the Jacobian matrix of the state change rate of the i-th vehicle, that is, maxE is the goal to maximize the traffic flow efficiency, q is the traffic volume, v is the average travel speed, and T is the unit time.

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

[0025] Grouping the design variables and designing a set of distribution factors corresponding to the design variables one by one, wherein the design variables are the running speed, the average speed and the maximum speed limit;

[0026] Normalize the distribution factors corresponding to the design variables, and calculate the fuzzy similarity matrix and fuzzy equivalence matrix;

[0027] Changing the original variable grouping method, and defining the strategy vector of each group according to the changed grouping method, wherein the running speed and the average speed are grouped together, and the maximum speed limit is grouped together;

[0028] The safety and efficiency values ​​calculated under different situations are used as the numerator of the utility function, and the optimal safety and efficiency values ​​are used as the denominator of the utility function to obtain the normalized utility function of safety and efficiency.

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

[0030] The optimal combination theory is used and R language programming is adopted to calculate the maximum speed limit values ​​of various types of vehicles under the condition of balance between safety and efficiency.

[0031] Furthermore, the normalized utility function of safety and efficiency obtained according to the strategy 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 efficiency utility value at speed v, respectively; minS(v) and minE(v) are the minimum safety utility value and the minimum efficiency utility value at all speeds v, respectively.

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

[0035] According to the speed distribution of vehicle types on the ten lanes, the distribution matrix is ​​set and the density of each type of vehicle on each lane is obtained;

[0036] Simulate the driver's free choice to drive at the maximum speed, calculate the allocation variables, and obtain the density after allocation;

[0037] According to the density after distribution, the speed relationship under different conditions is determined, and the speed-distance function of each type of vehicle is obtained;

[0038] According to the speed-distance function of each type of vehicle, the lane-level speed limit value model of the ten lanes for each type of vehicle is obtained respectively.

[0039] Furthermore, the vehicle class density of the ten-lane road is:

[0040]

[0041] Among them, the class density of type 1 vehicles (small cars) is The class density of type 2 vehicles (large vehicles) is ρ i is the class density of the i-th vehicle and , ρ1 and ρ2 are the class densities of small cars and large cars respectively, is the lane distribution variable of the i-th vehicle, and are the occupancy ratios of small and large vehicles on the ten lanes, S i,j is the optimal driving distance of each type of vehicle in different lanes, S 1,j and S 2,j are the optimal driving distances for small cars and large cars in the jth lane respectively.

[0042] Furthermore, according to the speed-distance function of each type of vehicle, the lane-level speed limit value model of the ten lanes of each type of vehicle is obtained respectively, and the lane-level speed limit value model of the ten lanes is:

[0043]

[0044] Among them, V1 is the vehicle speed under which the speed and density present a linear relationship. is the minimum speed after improvement under the logarithmic relationship between speed and density, is the minimum speed after improvement under the exponential relationship between speed and density, v f is the free flow velocity, v imin is the minimum speed of the vehicle, v imax is the maximum speed of the vehicle, ρ is the current traffic flow density, ρ j is the critical density of traffic flow, ρ i,j is the vehicle density of the i-th type of vehicle in the j-th lane.

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

[0046] Real-time traffic parameter acquisition module, which acquires the traffic parameters, infrastructure status and vehicle driving data of the expressway in real time and builds a real-time data set of the expressway;

[0047] A maximum speed limit model building module, which builds a lane-by-lane maximum speed limit calculation model for a multi-lane highway under the safety and efficiency game based on the real-time data set and based on the safety and efficiency game theory and fuzzy mathematics theory;

[0048] A lane-level speed limit model construction module, which constructs a lane-level speed limit value model for ten lanes according to the maximum speed limit calculated by the lane-by-lane maximum speed limit calculation model for the multi-lane highway and the real-time data set;

[0049] 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 it to the driver in real time.

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

[0051] Through the present invention, dynamic and precise speed limit control of a ten-lane highway is achieved, effectively coping with traffic flow differences and vehicle speed changes between lanes, and improving the overall road traffic efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 A flow chart of a lane-level dynamic variable speed limit control method for a ten-lane highway;

[0054] Figure 2 A schematic diagram of the process for determining the maximum speed limit;

[0055] Figure 3 Schematic diagram of the process of constructing a lane-level speed limit value model for ten lanes. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0058] Embodiment 1

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

[0060] S1: Obtain highway traffic parameters, infrastructure status, and vehicle driving data in real time to build 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 flow monitoring equipment, radar speed guns and video detection systems installed on each lane. At the same time, basic information such as road conditions, weather conditions and the status of traffic facilities is obtained. Vehicle type and load data are identified through video recognition systems or induction coils. All data are 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 the real-time data set, and based on the safety and efficiency game theory and fuzzy mathematics theory, a calculation model for the maximum speed limit of multi-lane highway lanes under the safety and efficiency game is constructed;

[0063] Specifically, a calculation model for the maximum speed limit of each lane on a multi-lane highway is constructed by using real-time data sets and combining safety and efficiency game theory with fuzzy mathematics theory. Through this model, it is possible to optimize traffic efficiency and reasonably set the maximum speed limit for each lane while ensuring traffic safety. The model analyzes real-time traffic data, identifies the traffic flow characteristics of each lane, and dynamically balances safety and efficiency requirements, thereby providing accurate speed limit recommendations, effectively responding to traffic flow differences and vehicle speed changes between different lanes, reducing traffic accidents, and improving road capacity.

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

[0065] Specifically, by combining real-time collected traffic parameters such as traffic flow, vehicle speed, lane occupancy, and 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. The model manages the traffic flow and vehicle speed of each lane in a refined manner, effectively responds to traffic flow differences and speed changes between lanes, reduces traffic accidents and congestion, and improves the overall road capacity.

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

[0067] Through the present invention, dynamic and precise speed limit control of a ten-lane highway is achieved, effectively coping with traffic flow differences and vehicle speed changes between lanes, and improving the overall road traffic efficiency and safety.

[0068] As a preferred embodiment of the above, the safety 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 section traffic operation efficiency is taken as the operation efficiency target. The section traffic operation efficiency is the number of kilometers traveled by vehicles passing through a section in a unit time during that time period.

[0071] Specifically, in traffic flow, the Lyapunov index can be used to evaluate the non-periodicity and unpredictability of vehicle movement, as well as the strength of traffic flow stability. If the value of S is negative, it means that the traffic flow state is stable and the vehicle operation state is safe; if the value of S is positive, it means that the traffic flow state is unstable and the vehicle operation state is unsafe; if the value of S is 0, it means that the traffic flow state is critical and the vehicle operation state is safe but may be affected by disturbances. Section traffic operation efficiency is used to reflect the service level and service traffic volume of the section.

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

[0073] A11: Define the 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 mathematical expectation of the disturbance term;

[0075] A13: The traffic flow state is expressed in combination with the disturbance term, and the rate of change of each vehicle's speed, position, and lane over time is calculated to obtain the Jacobian matrix of the state change rate.

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

[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 the position x i , speed v i and the lane you are in i If the disturbance term is not considered, the traffic flow state is represented as the state x = [x1,x2,…,x1,v1,v2,…,v N ,l1,l2,…l N ], since there are many lanes and conflict points on a ten-lane highway, the probability of traffic flow disorder in the horizontal and vertical directions is relatively high. The present invention considers adding two disturbance terms, speed difference and lane-changing behavior, to the Lyapunov index, and represents the lane-changing behavior and speed difference of each vehicle as Δd i (x,t) and Δv i (x, t), where t represents time; at the same time, in the lane-changing behavior disturbance term, the value is determined by the probability and number of lane changes in the current lane by the vehicle. The probability of each vehicle changing lanes in the current lane is p i (x, t), the number of lane changes in a period of time is N i (x, t), then the mathematical expectation of the disturbance term is expressed as E[Δd i (x,t)]=p i (x,t)N i (x, t), these disturbance terms may be random. Therefore, the present invention represents the two disturbance terms as a random vector

[0078] 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 expressed as follows. Assume that the expectation of the disturbance term is 0, that is, E[Δw(t)] = 0; at this time, the speed, position and lane change rate of each vehicle over time f i =[dx i / dt,dx i / dt,dl i / dt], where dx i / dt indicates position x i rate of change over time; dvi / dt represents the speed v i The rate of change over time; dl i / dt indicates lane l i The rate of change over time, the state change rate Jacobian matrix J i It is expressed as follows:

[0079]

[0080] According to the Jacobian matrix, the overall Lyapunov index of the traffic flow state

[0081] As a preferred embodiment of the above, an optimal mathematical model for safety efficiency is constructed when the Lyapunov index is minimized. The optimal mathematical model for safety efficiency is expressed as follows:

[0082]

[0083] maxE=qvT

[0084] Among them, minS is the Lyapunov exponent that the goal is to minimize, is the absolute value of the natural logarithm of the Jacobian matrix of the state change rate of the i-th vehicle, that is, maxE is the goal to maximize the traffic flow efficiency, q is the traffic volume, v is the average travel speed, and T is the unit time.

[0085] Specifically, by combining the two formulas of minimizing the Lyapunov exponent and maximizing traffic flow efficiency, a comprehensive optimization model is constructed to improve the overall traffic flow efficiency while ensuring the stability of the traffic system. This comprehensive optimization model helps to achieve efficient and safe traffic management, adapt to changing traffic conditions, maximize road utilization efficiency, and provide a scientific basis for formulating reasonable speed limits and traffic management strategies.

[0086] As a preferred embodiment of the above, Figure 2 As shown, determine the maximum speed limit, including:

[0087] B11: Group the design variables and design a set of distribution factors corresponding to the design variables one by one. The design variables are the running speed, average speed and maximum speed limit;

[0088] B12: Normalize the distribution factors corresponding to the design variables and calculate the fuzzy similarity matrix and fuzzy equivalence matrix;

[0089] B13: Change the original variable grouping method, and define the strategy vector of each group according to the changed grouping method, the grouping method is that the running speed and the average speed are one group, and the maximum speed limit is another group;

[0090] B14: The safety and efficiency values ​​calculated under different situations are used as the numerator of the utility function, and the optimal safety and efficiency values ​​are used as the denominator of the utility function to obtain the normalized utility function of safety and efficiency;

[0091] B15: Based on the strategy vector and the normalized utility function of safety and efficiency, the maximum speed limit calculation model of lanes on multi-lane highways under the game of safety and efficiency is obtained;

[0092] B16: Using the optimal combination theory and R language programming, we can calculate the maximum speed limit for each type of vehicle while balancing safety and efficiency.

[0093] Specifically, the design variables are grouped using fuzzy clustering theory, and the classified design variables are Assume v = V1, vi=V3, so the total number of classified design variables is V={V1,V2,V3}, and each design variable is characterized by a set of distribution factors (V i,1 ,V i,2 ), where V i,1 and V i,2 They represent the influence of design variables on safety and efficiency respectively. The running speed v is the speed of the vehicle under actual conditions, which generally corresponds to the 85% percentile. The running speed is inversely proportional to safety and directly proportional to traffic efficiency. Therefore, V is taken as 1,1 =-0.8, V 1,2 =0.9. According to relevant research, the higher the average speed, the higher the casualty rate per million vehicle kilometers, so V 2,1 =-0.75, V 2,2 =0.8, the maximum speed limit is set by the management department, and in this embodiment, V 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 )=(-1,0.95) The data were normalized by standard deviation transformation to eliminate the influence of dimension. The normalized distribution coefficient is as follows: (V 1,1 ,V 1,2 )=(0.8,0.66667),(V 2,1 ,V 2,2 )=(1.0,0),(V 3,1 ,V 3,2)=(0,1.0) and use the absolute value subtraction method (take M=0.1) for correction. The fuzzy similarity matrix is ​​expressed as follows: The fuzzy equivalent matrix R of R is obtained by using the square self-synthesis method * as follows:

[0094] As a preferred embodiment of the above embodiment, the normalized utility function of security and efficiency obtained according to the strategy vector includes:

[0095]

[0096] 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 efficiency utility value at speed V, respectively. minS(V) and minE(V) are the minimum safety utility value and the minimum efficiency utility value at all speeds v, respectively.

[0097] Specifically, in this embodiment, according to the fuzzy clustering theory, when λ is 0.9, R λ The elements in the row direction that are equal to 1 are grouped together, so the final design variable grouping is {V1, V2}, {V3}. Since the design variable V3 is the maximum speed limit 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, which is expressed 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.

[0098] As a preferred embodiment of the above, Figure 3 As shown in the figure, a lane-level speed limit value model for ten lanes is constructed, including:

[0099] C11: According to the speed distribution of vehicle types on the ten lanes, set the distribution matrix and obtain the density of each type of vehicle on each lane;

[0100] C12: Simulate the driver's free choice to drive at the maximum speed, calculate the allocation variables, and obtain the density after allocation;

[0101] C13: According to the allocated density, determine the speed relationship under different conditions and obtain the speed-distance function of each type of vehicle;

[0102] C14: Based on the speed-distance function of each type of vehicle, the lane-level speed limit value model of ten lanes for each type of vehicle is obtained.

[0103] Specifically, according to the speed distribution of vehicle types on ten lanes, the distribution matrix is ​​set and the density of each type of vehicle on each lane is obtained; the driver is simulated to freely choose the lane at the maximum speed, the distribution variables are calculated and the density after distribution is obtained; the speed relationship under different conditions is determined according to the density after distribution, and the speed-distance functions of linear relationship, logarithmic relationship and exponential relationship are constructed respectively; finally, according to these speed-distance functions, the lane-level speed limit value model for small and large vehicles on ten lanes is obtained respectively, including the speed limit model for public lanes under mixed driving conditions. This method ensures that the speed limit value can dynamically adapt to the actual traffic conditions of different lanes, improve road traffic efficiency and safety, and reduce traffic accidents and congestion through scientific speed limit management, providing a smoother and safer driving experience.

[0104] As a preferred embodiment of the above, the vehicle class density of ten lanes is:

[0105]

[0106] Among them, the class density of type 1 vehicles (small cars) is The class density of type 2 vehicles (large vehicles) is ρ i is the class density of the i-th vehicle and , ρ1 and ρ2 are the class densities of small cars and large cars respectively, is the lane distribution variable of the i-th vehicle, and are the occupancy ratios of small and large vehicles on the ten lanes, S i,j is the optimal driving distance of each type of vehicle in different lanes, S 1,j and S 2,j are the optimal driving distances for small cars and large cars in the jth lane respectively.

[0107] In this embodiment, the class density represents the distribution of different types of vehicles (such as small cars and large cars) on each lane on a ten-lane highway. It measures the density of each type of vehicle on each lane. By calculating the class density, the density distribution of each type of vehicle on the ten lanes can be obtained, which provides an important basis for subsequent speed limit calculation and traffic management. By reasonably allocating lane occupancy ratios and optimizing driving spacing, the overall efficiency and safety of traffic flow can be effectively improved.

[0108] As a preferred embodiment of the above, according to the speed-distance function of each type of vehicle, a lane-level speed limit value model of ten lanes for each type of vehicle is obtained respectively, and the lane-level speed limit value model of ten lanes is:

[0109]

[0110]

[0111] Among them, V1 is the vehicle speed under which the speed and density present a linear relationship. is the minimum speed after improvement under the logarithmic relationship between speed and density, is the minimum speed after improvement under the exponential relationship between speed and density, v f is the free flow velocity, v imin is the minimum speed of the vehicle, v imax is the maximum speed of the vehicle, ρ is the current traffic flow density, ρ j is the critical density of traffic flow, ρ i,j is the vehicle density of the i-th type of vehicle in the j-th lane.

[0112] Specifically, input the real-time traffic flow density ρ and the vehicle density of each lane ρ i,j , use the above model formula to calculate the speed limit value of each lane, select the appropriate relationship model (linear, logarithmic or exponential) for calculation according to the actual situation of traffic flow density, and publish the calculated speed limit value to the driver to ensure that the speed limit value can dynamically adapt to the actual traffic conditions of different lanes and improve road traffic efficiency and safety. Through the above specific steps, according to the speed-spacing function of each type of vehicle, a lane-level speed limit value model for ten lanes is constructed, and accurate speed limit control for different lanes and different types of vehicles is achieved. This method can effectively cope with the differences in traffic flow and vehicle speed changes in different lanes, improve the traffic efficiency and safety of highways, reduce traffic accidents and congestion, and provide drivers with a smoother and safer driving experience. At the same time, consider that in actual road traffic, if small cars and large cars are mixed, that is, two different types of vehicles have a shared lane (i.e. is not an integer), then the speeds of the two vehicles on this lane are equal, and the speed limit model for vehicles on the public lane is:

[0113] Embodiment 2

[0114] Based on the same inventive concept as the lane-level dynamic variable speed limit control method for a ten-lane highway in the aforementioned embodiment, the present invention also provides a lane-level dynamic variable speed limit control system for a ten-lane highway, the system comprising:

[0115] Real-time traffic parameter acquisition module, which acquires the traffic parameters, infrastructure status and vehicle driving data of the expressway in real time and builds a real-time data set of the expressway;

[0116] The maximum speed limit model building module builds a maximum speed limit calculation model for lanes on multi-lane highways under the safety and efficiency game based on real-time data sets and based on safety and efficiency game theory and fuzzy mathematics theory;

[0117] The lane-level speed limit model construction module builds a lane-level speed limit value model for ten lanes based on the maximum speed limit calculated by the lane-by-lane maximum speed limit calculation model for multi-lane highways and the real-time data set;

[0118] 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 ten lanes and publishes it to the driver in real time.

[0119] The above-mentioned control system in the present invention can effectively implement the lane-level dynamic variable speed limit control method for a ten-lane highway. The technical effects that can be achieved are as described in the above-mentioned embodiments and will not be repeated here.

[0120] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined therein, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A lane-level dynamic variable speed limit control method for a ten-lane highway, characterized in that: include: Obtain highway traffic parameters, infrastructure status, and vehicle driving data in real time to build a real-time highway data set; According to the real-time data set, and based on the safety and efficiency game theory and fuzzy mathematics theory, a calculation model for the maximum speed limit of lanes on a multi-lane highway under the safety and efficiency game is constructed; Constructing a lane-level speed limit value model for ten lanes according to the maximum speed limit calculated by the lane-by-lane maximum speed limit calculation model for the multi-lane highway and the real-time data set; The speed limit value of each lane is calculated according to the lane-level speed limit value model of the ten lanes and is issued to the driver in real time.

2. The method for controlling lane-level dynamic variable speed limit on a 10-lane highway according to claim 1 is characterized in that: The security and efficiency game theory includes: Taking the Lyapunov traffic flow stability index as the operation safety target, the disturbance terms of the Lyapunov traffic flow stability index are speed difference and lane-changing behavior; The section traffic operation efficiency is taken as the operation efficiency target, and the section traffic operation efficiency is the number of kilometers traveled by vehicles passing through a section within a unit time in the time period.

3. The lane-level dynamic variable speed limit control method for a ten-lane highway according to claim 2 is characterized in that: The steps for solving the Lyapunov traffic flow stability index include: Defining the traffic flow state, and introducing the speed difference and lane-changing behavior as disturbance terms; Define the probability and number of lane changes for each vehicle in the current lane, and calculate the mathematical expectation of the disturbance term; The traffic flow state is represented in combination with 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 state change rate; The overall Lyapunov index of the traffic flow state is obtained according to the Jacobian matrix. The overall Lyapunov index of the traffic flow state is expressed as: Where S is the Lyapunov index, t is the time span, n is the number of vehicles, ln|J j | is the absolute value of the natural logarithm of the Jacobian matrix of the state change rate of the i-th vehicle.

4. The method for controlling lane-level dynamic variable speed limit on a ten-lane highway according to claim 2 is characterized in that: When the Lyapunov index is the smallest, the optimal mathematical model for safety efficiency is constructed. The optimal mathematical model for safety efficiency is expressed as follows: maxE=qvT; Among them, mminS is the Lyapunov index that the goal is to minimize, is the absolute value of the natural logarithm of the Jacobian matrix of the state change rate of the i-th vehicle, that is, maxE is the goal to maximize the traffic flow efficiency, q is the traffic volume, v is the average travel speed, and T is the unit time.

5. The lane-level dynamic variable speed limit control method for a ten-lane highway according to claim 1 is characterized in that: Determine the maximum speed limit, including: Grouping the design variables and designing a set of distribution factors corresponding to the design variables one by one, wherein the design variables are the running speed, the average speed and the maximum speed limit; Normalize the distribution factors corresponding to the design variables, and calculate the fuzzy similarity matrix and fuzzy equivalence matrix; Changing the original variable grouping method, and defining the strategy vector of each group according to the changed grouping method, wherein the running speed and the average speed are grouped together, and the maximum speed limit is grouped together; The safety and efficiency values ​​calculated under different situations are used as the numerator of the utility function, and the optimal safety and efficiency values ​​are used as the denominator of the utility function to obtain the normalized utility function of safety and efficiency. According to the strategy vector and the normalized utility function of safety and efficiency, a calculation model for the maximum speed limit of lanes of a multi-lane highway under the safety and efficiency game is obtained; The optimal combination theory is used and R language programming is adopted to calculate the maximum speed limit values ​​of various types of vehicles under the condition of balance between safety and efficiency.

6. The method for controlling lane-level dynamically variable speed limits on a 10-lane highway according to claim 5 is characterized in that: The normalized utility function of safety and efficiency obtained according to the strategy vector includes: 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 efficiency utility value at speed v, respectively; minS(v) and minE(v) are the minimum safety utility value and the minimum efficiency utility value at all speeds v, respectively.

7. The lane-level dynamic variable speed limit control method for a ten-lane highway according to claim 1 is characterized in that: Constructing the lane-level speed limit value model for the ten lanes, including: According to the speed distribution of vehicle types on the ten lanes, the distribution matrix is ​​set and the density of each type of vehicle on each lane is obtained; Simulate the driver's free choice to drive at the maximum speed, calculate the allocation variables, and obtain the density after allocation; According to the density after distribution, the speed relationship under different conditions 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 lanes for each type of vehicle is obtained respectively.

8. The lane-level dynamic variable speed limit control method for a ten-lane highway according to claim 1 is characterized in that: The vehicle class density for ten lanes is: Among them, the class density of type 1 vehicles (small cars) is The class density of type 2 vehicles (large vehicles) is ρ i is the class density of the i-th vehicle and , ρ1 and ρ2 are the class densities of small cars and large cars respectively, is the lane distribution variable of the i-th vehicle, and are the occupancy ratios of small and large vehicles on the ten lanes, S i，j is the optimal driving distance of each type of vehicle in different lanes, S 1，j and S 2，j are the optimal driving distances for small cars and large cars in the jth lane respectively.

9. The method for controlling lane-level dynamically variable speed limits on a 10-lane highway according to claim 7, characterized in that: According to the speed-distance function of each type of vehicle, the lane-level speed limit value model of the ten lanes of each type of vehicle is obtained respectively. The lane-level speed limit value model of the ten lanes is: Among them, V1 is the vehicle speed under which the speed and density present a linear relationship. is the minimum speed after improvement under the logarithmic relationship between speed and density, is the minimum speed after improvement under the exponential relationship between speed and density, v f is the free flow velocity, v imin is the minimum speed of the vehicle, v imax is the maximum speed of the vehicle, ρ is the current traffic flow density, ρ j is the critical density of traffic flow, ρ i，j is the vehicle density of the i-th type of vehicle in the j-th lane.

10. A lane-level dynamically variable speed limit control system for a ten-lane highway, characterized in that: The system comprises: Real-time traffic parameter acquisition module, which acquires the traffic parameters, infrastructure status and vehicle driving data of the expressway in real time and builds a real-time data set of the expressway; A maximum speed limit model building module, which builds a lane-by-lane maximum speed limit calculation model for a multi-lane highway under the safety and efficiency game based on the real-time data set and based on the safety and efficiency game theory and fuzzy mathematics theory; A lane-level speed limit model construction module, which constructs a lane-level speed limit value model for ten lanes according to the maximum speed limit calculated by the lane-by-lane maximum speed limit calculation model for the multi-lane highway and the real-time data set; 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 it to the driver in real time.

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