CN116229706A - Lane-dividing variable speed limiting control method based on intelligent network-connected special lane environment - Google Patents
Lane-dividing variable speed limiting control method based on intelligent network-connected special lane environment Download PDFInfo
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Abstract
The invention relates to a lane-dividing variable speed-limiting control method based on an intelligent network-connected special lane environment, which is used for predicting cell traffic flow parameters by utilizing a mixed traffic flow state prediction model based on an improved cell transmission model aiming at a highway in a mixed traffic flow state; then, constructing a multi-target optimization model of variable speed limit control of different lanes, acquiring and executing variable speed limit control strategies of different lanes, and completing variable speed limit control of different lanes. Compared with the prior art, the invention has the remarkable advantages that: setting up a scene of an intelligent network vehicle-connected special lane facing to a main line section of a highway, and matching and meeting the hybrid running characteristic of vehicles by improving cell transmission parameters under different road traffic flows; after constraint conditions of the optimized multi-objective model are determined, variable speed limit control strategies of different lanes are executed, so that stability of a driver in a driving process is improved, and independence and safety of intelligent network vehicle linkage and human driving are guaranteed.
Description
Technical Field
The invention relates to the technical field of intelligent traffic system control, in particular to a lane-dividing variable speed-limiting control method based on an intelligent network-connected special lane environment.
Background
The rapid development of the current intelligent networking technology makes the common application of intelligent networking vehicles day-to-day. According to a series of experiments of intelligent network vehicles, negative interaction exists in the mixed running process of the intelligent network vehicles and human driving vehicles, and unstable and sudden driving behaviors in the running process of the human driving vehicles bring certain potential safety hazards to the intelligent network vehicles. The construction of the intelligent network vehicle-connected special lane becomes one of measures for isolating two types of vehicles, reducing unfavorable conflict among vehicles and improving road passing efficiency. After the intelligent network vehicle-connected special lanes are applied to the expressway, the mixed traffic flow still can face the problem of congestion caused by traffic behaviors such as large flow, accidents, frequent entrance and exit of the special lanes and the like.
The variable speed limit control is mostly applied to bottleneck road sections such as lane reduction, accident frequency and the like or main line road sections of expressways and expressways under extreme weather conditions, and is an effective active traffic management control mode. For example:
document 1: the Chinese patent No. 202110079828.0 discloses an improvement method of a cell transmission model oriented to a cooperative environment of a vehicle road, which only aims at improving the cell transmission model in the running process of the vehicle on a main line section to improve the variable speed limit control efficiency and cannot completely cope with the running problem of the vehicle possibly encountered by a traffic route.
Document 2: the patent CN202110333583.X discloses a dynamic collaborative control method for variable speed limit of an automatic driving special lane and a general lane in a confluence area on a highway, which is characterized in that a feature data set with a traffic congestion state label is obtained to generate a traffic running state classifier, and the ratio of overflowed automatic driving vehicles distributed on the general lane is calculated to complete the correction of a variable speed limit model. The method is suitable for section speed management and control under single vehicle flow, and when mixed vehicle flow occurs, the calculation and distribution ratio error is larger.
Disclosure of Invention
The invention aims to provide a lane-dividing variable speed limit control method based on an intelligent network-connected special lane environment, which is used for constructing a lane-dividing variable speed limit control strategy by combining a multi-objective optimization function based on a mixed traffic flow state prediction model and determining constraint conditions aiming at the lane-dividing variable speed limit control strategy so as to accurately analyze actual traffic conditions of roads and running characteristics of different types of vehicles.
The technical solution for realizing the purpose of the invention is as follows:
the lane-dividing variable speed limiting control method based on the intelligent network-connected special lane environment is characterized by dividing a set total control period into a plurality of time steps for lane-dividing variable speed limiting control aiming at the expressway in a mixed traffic state, and comprises the following specific steps of:
step 2, constructing a multi-objective optimization model of lane-dividing variable speed-limiting control, and obtaining an optimal variable speed-limiting control parameter of the current time step based on the cell traffic flow parameter of the current time step and the variable speed-limiting control parameter of the last time step obtained by prediction in the step 1;
step 3, carrying out lane-dividing variable speed limiting control of the next time step according to the optimal variable speed limiting control parameters obtained in the step 2, and returning to the step 1;
and step 4, outputting an optimal variable speed limiting control parameter set in the total control period when the set total control period is ended.
Furthermore, the method divides the highway main line section in the mixed traffic state into a special lane and a general lane, wherein the special lane is a single intelligent network traffic flow, and the general lane allows the mixture of the human driving traffic flow and the intelligent network traffic flow.
Further, the variable speed limit control parameter includes variable speed limit values of cells on different lanes.
Further, the mixed traffic state prediction model based on the improved cell transmission model is as follows:
wherein: k is the number of lanes special for intelligent network vehicle connection, and J is the number of general lanes; s is(s) k,i (t) and r k,i (t) vehicle outflow rate and inflow rate of the ith cell in the kth time step on the kth lane, respectively; s is(s) j,i (t) and r j,i (t) vehicle outflow rate and inflow rate of the ith cell in the jth time step on the jth general lane respectively; v vsl1,i (t) and v vsl2,i (t) variable speed limit values for the ith cell in the t-th time step on the special lane and the general lane respectively; ρ k,i (t) and ρ j,i (t) road traffic flow in the t time step for the ith cell on the special lane and the universal lane respectively;
q 1,max and q 3,max The single-lane road traffic capacity of single intelligent network traffic flow and single manual driving vehicle flow respectively; w is the traffic flow reverse blocking wave speed of the cell in the non-free flow state, ρ jam In order to achieve a blocking density,
the traffic capacity of the single-lane road after the variable speed limit control is achieved.
Further, the cellular traffic flow parameters include traffic flow, traffic flow density and travel speed of each cellular on different lanes, wherein:
the traffic flow of each cell on different lanes is as follows:
wherein: q k,i (t) is the road traffic flow of the cell i in the t-th time step on the special lane, q j,i (t) is the road traffic flow of the cell i in the t-th time step on the common lane;
the traffic flow density of each cell on different lanes is as follows:
wherein: t is the time step, ρ k,i (t+1) is the traffic flow density, ρ, of the cell i in the t-th time step on the lane j,i (t+1) is the traffic flow density of the cell i in the t-th time step on the common lane; q k,i+1 (t) is the road traffic flow of the (i+1) th cell in the t time step on the special lane; q j,i+1 (t) is the road traffic flow of the (i+1) th cell in the t time step on the common lane; l (L) i Length of the cell i;
the running speeds of cells on different lanes are as follows:
in the formula, v k,i (t) is that the cell i on the special lane is at the t-th under the control of variable speed limitTravel speed in time step, v j,i (t) is the running speed of the cell i in the t-th time step on the common lane under the variable speed limit control,
for the cell critical density value of the lane under different variable speed limit control values, +.>
A cell critical density value of a general lane under different variable speed limit control values; v f For free flow velocity, v vsl1,i (t-1) variable speed limit value, v, for the t-1 th time step dedicated lane cell i vsl2,i (t-1) is a variable speed limit value for the t-1 th time step common lane cell i.
Further, the channel changing proportion coefficient is introduced to update the traffic flow density of each cell on different lanes after channel changing guidance:
wherein: p is p off Is the channel changing proportionality coefficient.
Further, the multi-objective optimization model for the lane-dividing variable speed limit control is constructed as follows:
objective function:
J(x,y)＝min(-T TTF +T TTT +T TSD )
constraint conditions:
v min ≤v vsl1,i (t)、v vsl2,i (t)≤v max
|v vsl1,i+1 (t)-v vsl1,i (t)|<v cell,change
|v vsl2,i+1 (t)-v vsl2,i (t)|<v cell,change
|v vsl1,i (t+1)-v vsl1,i (t)|<v t,change
|v vsl2,i (t+1)-v vsl2,i (t)|<v t,change
|v vsl1,i (t)-v vsl2,i (t)|<v road,change
|v vsl1,i (t)-v vsl1,i (t-T)|＝C·Δv change
|v vsl2,i (t)-v vsl2,i (t-T)|＝C·Δv change
wherein: t (T) TTF For the total turnover of the variable speed limit control road section, T TTT For the total transit time of the variable speed limit control road section, T TSD Penalty term for total speed difference between lanes, V max Is the maximum static speed limit value of the expressway, V min The minimum static speed limit value of the expressway; v (V) cell,change For the first speed limit value set, V t,change In order to set the second speed limit value,
V road,change for the third speed limit value, deltaV change C is a constant, which is the difference between the variable speed limit values of adjacent time steps;
wherein:
wherein alpha is F1 、α F2 The total turnover coefficient of the road on the special lane and the general lane is respectively; alpha T1 、α T2 The total traffic time coefficient and alpha of the roads on the special lane and the general lane are respectively D Punishment term coefficient for total speed difference among lanes, N is variable speed limitControlling the total number of cells on a special lane and a general lane of a road section T t Is the total number of time steps in the total control period.
In addition, the present invention provides a computer-readable storage medium in which: the computer readable storage medium stores a computer program which is executed by a processor to realize the steps of the lane-dividing variable speed limit control method based on the intelligent network-connected special lane environment.
The invention also provides lane-dividing variable speed-limiting control equipment based on the intelligent network-connected special lane environment, which comprises the following steps:
a memory for storing a computer program;
and the processor is used for realizing the lane-dividing variable speed limit control method based on the intelligent network-connected special lane environment when executing the computer program.
Compared with the prior art, the invention has the remarkable advantages that: setting up a scene of an intelligent network vehicle-connected special lane facing to a main line section of a highway, and matching and meeting the hybrid running characteristic of vehicles by improving cell transmission parameters under different road traffic flows; after the constraint conditions of the optimized multi-target model are calculated and determined, a lane-dividing variable speed limit control strategy is executed, so that the stability of a driver in the driving process is improved, and the independence and safety of intelligent network vehicle linkage and human driving are ensured.
Drawings
Fig. 1 is a schematic diagram of a lane-division variable speed limit control method based on an intelligent network-connected special lane environment.
FIG. 2 is a schematic diagram of a hybrid traffic state prediction model based on an improved cellular transmission model in accordance with the present invention.
Fig. 3 is a schematic traffic flow diagram of the intelligent network vehicle and human driving vehicle cell transmission model of the present invention.
Fig. 4 is a schematic diagram of a lane setup for intelligent network coupling of a main line section of a multi-lane highway according to the present invention.
Fig. 5 is a schematic diagram of the operation flow of the lane-dividing variable speed limit control strategy of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
A lane-dividing variable speed-limiting control method based on an intelligent network-connected special lane environment is characterized in that aiming at a highway in a mixed traffic flow state, a set total control period is divided into a plurality of time steps to perform lane-dividing variable speed-limiting control. As shown in fig. 1:
step A: within any time step, predicting cell traffic flow parameters of the current time step based on a mixed traffic flow state prediction model of an improved cell transmission model;
and (B) step (B): constructing a multi-objective optimization model of lane variable speed limit control, setting a multi-objective optimization function, and obtaining an optimal variable speed limit control parameter of the current time step based on the predicted cellular traffic flow parameter and the variable speed limit control parameter of the last time step;
step C: constructing a lane-dividing variable speed limit control strategy based on a model predictive control algorithm, and carrying out lane-dividing variable speed limit control of the next time step according to the obtained optimal variable speed limit control parameter, and continuously predicting cell traffic flow parameters of the next time step; and outputting the optimal variable speed limiting control parameter set in the total control period when the set total control period is ended.
The method is characterized in that a highway main line section in a mixed traffic state is divided into a special lane and a general lane, wherein the special lane is a single intelligent network traffic flow, and the general lane allows the mixture of a human driving traffic flow and the intelligent network traffic flow; the variable speed limit control parameter includes variable speed limit values of cells on different lanes.
As shown in fig. 2, the mixed traffic state prediction model based on the improved cell transmission model in step a.1 is:
wherein: k is the number of lanes special for intelligent network vehicle connection, and J is the number of general lanes; s is(s) k,i (t) and r k,i (t) vehicle outflow rate and inflow rate of the ith cell in the kth time step on the kth lane, respectively; s is(s) j,i (t) and r j,i (t) vehicle outflow rate and inflow rate of the ith cell in the jth time step on the jth general lane respectively; v vsl1,i (t) and v vsl2,i (t) variable speed limit values for the ith cell in the t-th time step on the special lane and the general lane respectively; ρ k,i (t) and ρ j,i (t) road traffic flow in the t time step for the ith cell on the special lane and the universal lane respectively;
q 1,max and q 3,max The single-lane road traffic capacity of single intelligent network traffic flow and single manual driving vehicle flow respectively; w is the traffic flow reverse blocking wave speed of the cell in the non-free flow state, ρ jam In order to achieve a blocking density,
the traffic capacity of the single-lane road after the variable speed limit control is achieved.
As shown in fig. 3, the cell traffic flow parameters in step a.2 include cell traffic flow, traffic flow density and running speed on different lanes, wherein:
the traffic flow of each cell on different lanes is as follows:
wherein: q k,i (t) is the road traffic flow of the cell i in the t-th time step on the special lane, q j,i (t) is the road traffic flow of the cell i in the t-th time step on the common lane;
as shown in fig. 4, the traffic densities of each cell on different lanes in step a.3 are as follows:
wherein: t is the time step, ρ k,i (t+1) is the traffic flow density, ρ, of the cell i in the t-th time step on the lane j,i (t+1) is the traffic flow density of the cell i in the t-th time step on the common lane; q k,i+1 (t) is the road traffic flow of the (i+1) th cell in the t time step on the special lane; q j,i+1 (t) is the road traffic flow of the (i+1) th cell in the t time step on the common lane; l (L) i Length of the cell i;
the running speeds of cells on different lanes are as follows:
in the formula, v k,i (t) is the running speed of the cell i in the t-th time step on the special lane under the variable speed limit control, v j,i (t) is the running speed of the cell i in the t-th time step on the common lane under the variable speed limit control,
for the cell critical density value of the lane under different variable speed limit control values, +.>
To control values at different variable speed limitsCell critical density value of the lower general lane; v f For free flow velocity, v vsl1,i (t-1) variable speed limit value, v, for the t-1 th time step dedicated lane cell i vsl2,i (t-1) is a variable speed limit value for the t-1 th time step common lane cell i.
Specifically, when the variable speed limit control is implemented, the intelligent network vehicle connection guiding lane changing on the general lane is oriented, the lane changing proportion coefficient is introduced to update the cell density on the lane dividing after the lane changing guiding, and the lane changing proportion coefficient is the traffic flow proportion of the lane changing behavior in the lane dividing variable speed limit control process:
wherein: p is p off Is the channel changing proportionality coefficient.
Specifically, the optimization targets in the invention comprise related indexes of road traffic efficiency and vehicle traffic safety, the multi-target optimization function of lane-dividing variable speed limit control takes the minimum road total traffic time, the total speed difference penalty term among lanes and the maximum road total turnover as the optimization targets, and the step B.2 is to construct a multi-target optimization model of lane-dividing variable speed limit control as follows:
objective function:
J(x,y)＝min(-T TTF +T TTT +T TSD )
wherein:
wherein T is TTF For the total turnover of the variable speed limit control road section, T TTT For the total transit time of the variable speed limit control road section, T TSD Punishment term for total speed difference between lanes, alpha F1 、α F2 The total turnover coefficient of the road on the special lane and the general lane is respectively; alpha T1 、α T2 The total traffic time coefficient and alpha of the roads on the special lane and the general lane are respectively D Penalty term coefficients for the total speed difference between lanes.
Specifically, based on the setting of the special lane and the variable speed limit control strategy of the proposed split lane, the constraint condition of the multi-objective optimization model is determined in the step B.3, and the safety and the stability of the driver in the driving process are ensured.
Constraint 1: constraint of static speed limit value. V with variable limit control value less than maximum static limit value max And the variable limit control value is greater than the minimum static limit value v min The method comprises the following steps:
v min ≤v vsl1,i (t)、v vsl2,i (t)≤v max
constraint 2: adjacent interval variable speed limit value fluctuation constraint. The difference between the variable speed limit values between adjacent sections is smaller than v cell,change The traffic accident risk is reduced; v cell,change The speed limit value is set according to the actual road traffic condition, namely:
|v vsl1,i+1 (t)-v vsl1,i (t)|<v cell,change
|v vsl2,i+1 (t)-v vsl2,i (t)|<v cell,change
constraint 3: adjacent time variable speed limit value fluctuation constraint. The variation amplitude of the adjacent time variable speed limit value is smaller than v t,change The impact on driving behavior and traffic flow stability caused by overlarge variation amplitude is reduced; v t,change The speed limit value set according to the feedback of the driver is that:
|v vsl1,i (t+1)-v vsl1,i (t)|<v t,change
|v vsl2,i (t+1)-v vsl2,i (t)|<v t,change
constraint 4: adjacent lane variable speed limit value difference constraint. When the lane-dividing variable speed limiting control is carried out, the variable speed limiting value difference among lanes of different use types is smaller than v road,change The independence and the safety of the intelligent network vehicle and the running of the human driving vehicle are ensured; v road,change The speed limit value is set according to the actual road traffic condition and the running characteristics of different types of vehicles, namely:
|v vsl1,i (t)-v vsl2,i (t)|<v road,change
constraint 5: constraint of variable speed limit value variation amplitude. The difference between the front and rear time variable speed limit values is too large or too small to reduce the sensitivity of the driver, so the difference between the front and rear time variable speed limit values is usually taken as Deltav change Integer multiples of (2), namely:
|v vsl1,i (t)-v vsl1,i (t-T)|＝C·Δv change
|v vsl2,i (t)-v vsl2,i (t-T)|＝C·Δv change
wherein: deltav change Typically, the value is 10km/h, C is a constant, and the value range of C is C= {1,2}.
Specifically, as shown in fig. 5, the lane-dividing variable speed limit control strategy based on the model predictive control algorithm in step C is:
step one: acquiring a traffic flow state data set; the traffic flow state data set x (t) comprises cell traffic flow q in different lanes in the t-th time step in the improved cell transmission model k,i (t)、q j,i (t) and cell travel speed v k,i (t)、v j,i (t) calculating the average speed difference between the current time step and the last time step, if the average speed difference exceeds the variable speed limit control starting threshold v d Implementing variable speed limit control; wherein v is d Determined by feedback from the human vehicle driver.
Step two: updating the cell density of lane-dividing cells under the guidance of lane change to obtain a traffic interference parameter data set; the traffic disturbance parameter data set d (t) contains upstream cell transfer traffic and inter-lane change traffic.
Step three: respectively inputting the traffic flow state data set and the traffic interference parameter data set into a multi-objective optimization function of variable speed limit control of the lane-dividing system to obtain traffic control parameters; the traffic control parameter u (t) comprises variable speed limit values v of cells on different lanes within the t-th time step vsl1,i (t)、v vsl2,i (t)。
Step four: determining constraint conditions according to the traffic control parameters, and optimizing the traffic control parameters according to the constraint conditions; selecting multi-objective optimization function, and evaluating input variables including cell traffic flow state in next time step in step one
And traffic control parameter of variable speed limit control period +.>
The traffic control parameters include the variable speed limit possible value v in the next time step vsl1,i (t+1)、v vsl2,i (t+1) outputting the optimal control signal parameter +.1 in the t+1 time step according to the evaluation result of the multi-objective optimization function>
Step five: outputting a variable speed limit control value, and returning to the step one; and outputting the optimal variable speed limit control value set when the total control period is finished, and finishing simulation.
The present invention provides a computer-readable storage medium in which: the computer readable storage medium stores a computer program which is executed by a processor to realize the steps of the lane-dividing variable speed limit control method based on the intelligent network-connected special lane environment.
The invention also provides lane-dividing variable speed limit control equipment based on the intelligent network-connected special lane environment, which comprises a memory for storing a computer program and a processor for realizing the steps of the lane-dividing variable speed limit control method based on the intelligent network-connected special lane environment when executing the computer program.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program, which may be stored on a non-transitory computer readable storage medium and which, when executed, may comprise the steps of the above-described embodiments of the methods. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (9)
1. A lane-dividing variable speed limit control method based on an intelligent network-connected special lane environment is characterized by comprising the following steps of: the method aims at the expressway in a mixed traffic flow state, divides a set total control period into a plurality of time steps to perform lane-division variable speed limiting control, and specifically comprises the following steps:
step 1, within any time step, predicting a cell traffic flow parameter of the current time step by using a mixed traffic flow state prediction model based on an improved cell transmission model;
step 2, constructing a multi-objective optimization model of lane-dividing variable speed-limiting control, and obtaining an optimal variable speed-limiting control parameter of the current time step based on the cell traffic flow parameter of the current time step and the variable speed-limiting control parameter of the last time step obtained by prediction in the step 1;
step 3, carrying out lane-dividing variable speed limiting control of the next time step according to the optimal variable speed limiting control parameters obtained in the step 2, and returning to the step 1;
and step 4, outputting an optimal variable speed limiting control parameter set in the total control period when the set total control period is ended.
2. The lane-splitting variable speed limit control method based on the intelligent network-connected special lane environment according to claim 1, wherein the method is characterized in that: the method divides the highway main line section in the mixed traffic state into a special lane and a general lane, wherein the special lane is a single intelligent network traffic flow, and the general lane allows the mixture of the human driving traffic flow and the intelligent network traffic flow.
3. The lane-splitting variable speed limit control method based on the intelligent network-connected special lane environment according to claim 1, wherein the method is characterized in that: the variable speed limit control parameter comprises variable speed limit values of cells on different lanes.
4. The lane-splitting variable speed limit control method based on the intelligent network-connected special lane environment according to claim 2, wherein the method is characterized in that: the mixed traffic flow state prediction model based on the improved cell transmission model is as follows:
wherein: k is the number of lanes special for intelligent network vehicle connection, and J is the number of general lanes; s is(s) k,i (t) and r k,i (t) vehicle outflow rate and inflow rate of the ith cell in the kth time step on the kth lane, respectively; s is(s) j,i (t) and r j,i (t) vehicle outflow rate and inflow rate of the ith cell in the jth time step on the jth general lane respectively; v vsl1,i (t) and v vsl2,i (t) variable speed limit values for the ith cell in the t-th time step on the special lane and the general lane respectively;
ρ k,i (t) and ρ j,i (t) road traffic flow in the t time step for the ith cell on the special lane and the universal lane respectively; q 1,max And q 3,max The single-lane road traffic capacity of single intelligent network traffic flow and single manual driving vehicle flow respectively; w is the traffic flow reverse blocking wave speed of the cell in the non-free flow state, ρ jam In order to achieve a blocking density,
the traffic capacity of the single-lane road after the variable speed limit control is achieved.
5. The lane-splitting variable speed limit control method based on the intelligent network-connected special lane environment according to claim 4, wherein the method is characterized in that: the cellular traffic flow parameters comprise cellular traffic flow, traffic flow density and running speed on different lanes, wherein:
the traffic flow of each cell on different lanes is as follows:
wherein: q k,i (t) is the road traffic flow of the cell i in the t-th time step on the special lane, q j,i (t) is the road traffic flow of the cell i in the t-th time step on the common lane;
the traffic flow density of each cell on the different lanes is as follows:
wherein: t is the time step, ρ k,i (t+1) is the traffic flow density, ρ, of the cell i in the t-th time step on the lane j,i (t+1) is the traffic flow density of the cell i in the t-th time step on the common lane; q k,i+1 (t) is the road traffic flow of the (i+1) th cell in the t time step on the special lane; q j,i+1 (t) is the road traffic flow of the (i+1) th cell in the t time step on the common lane; l (L) i Length of the cell i;
the running speeds of the cells on different lanes are as follows:
in the formula, v k,i (t) is the running speed of the cell i in the t-th time step on the special lane under the variable speed limit control, v j,i (t) is the time step t of the cell i on the common lane under the variable speed limit controlThe running speed in the long-run period,
for the cell critical density value of the lane under different variable speed limit control values, +.>
A cell critical density value of a general lane under different variable speed limit control values; v f For free flow velocity, v vsl1,i (t-1) variable speed limit value, v, for the t-1 th time step dedicated lane cell i vsl2,i (t-1) is a variable speed limit value for the t-1 th time step common lane cell i.
6. The lane-splitting variable speed limit control method based on the intelligent network-connected special lane environment according to claim 5, wherein the method is characterized in that: introducing a lane change proportion coefficient to update the traffic flow density of each cell on different lanes after lane change guidance:
wherein: p is p off Is the channel changing proportionality coefficient.
7. The lane-splitting variable speed limit control method based on the intelligent network-connected special lane environment according to claim 6, wherein the method is characterized in that: the multi-objective optimization model for the lane-dividing variable speed limit control is constructed as follows:
objective function:
J(x，y)＝min(-T TTF +T TTT +T TSD )
constraint conditions:
v min ≤v vsl1，i (t)、v vsl2，i (t)≤v max
|v vsl1，i+1 (t)-v vsl1，i (t)|＜v cell，change
|v vsl2，i+1 (t)-v vsl2，i (t)|＜v cell，change
|v vsl1，i (t+1)-v vsl1，i (t)|＜v t，change
|v vsl2，i (t+1)-v vsl2，i (t)|＜v t，change
|v vsl1，i (t)-v vsl2，i (t)|＜v road，chanhe
|v vsl1，i (t)-v vsl1，i (t-T)|＝C·Δv change
|v vsl2，i (t)-v vsl2，i (t-T)|＝C·Δv chang e
wherein: t (T) TTF For the total turnover of the variable speed limit control road section, T TTT For the total transit time of the variable speed limit control road section, T TSD Penalty term for total speed difference between lanes, V max Is the maximum static speed limit value of the expressway, V min The minimum static speed limit value of the expressway; v (V) cell，change For the first speed limit value set, V t，change For setting the second speed limit value, V road，change For the third speed limit value, deltaV change C is a constant, which is the difference between the variable speed limit values of adjacent time steps;
wherein:
wherein alpha is F1 、α F2 The total turnover coefficient of the road on the special lane and the general lane is respectively; alpha T1 、α T2 The total traffic time coefficient and alpha of the roads on the special lane and the general lane are respectively D The punishment term coefficient for the total speed difference between lanes is that N is the total number of cells on a special lane and a general lane of a variable speed limit control road section, T t Is the total number of time steps in the total control period.
8. A computer-readable storage medium, characterized by: the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the lane-splitting variable speed limit control method in an intelligent networking dedicated lane-based environment according to any one of claims 1 to 7.
9. Lane-dividing variable speed limiting control equipment based on intelligent network-connected special lane environment is characterized by comprising the following components:
a memory for storing a computer program;
a processor for implementing the steps of the lane-splitting variable speed limit control method in an intelligent networking-based dedicated lane environment according to any one of claims 1 to 7 when executing the computer program.
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