CN115063990A - Dynamic speed limit control method for bottleneck section of highway in mixed traffic flow environment - Google Patents
Dynamic speed limit control method for bottleneck section of highway in mixed traffic flow environment Download PDFInfo
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
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- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
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Abstract
The invention discloses a dynamic speed-limiting control method for a bottleneck road section of a highway in a mixed traffic flow environment, which comprises the following steps: s1, identifying the bottleneck section of the highway by using traffic incident detection equipment or a construction operation reporting system; s2 setting a speed limit control period and a model prediction period; s3, dividing control road sections according to the area where the bottleneck road sections are located, and S4 collecting traffic flow data of the road sections to be controlled of the expressway by using traffic flow monitoring equipment; s5, optimizing the cellular transmission model according to the acquired traffic flow data and the traffic flow characteristics of the normal and speed-limited and bottleneck road sections of the highway under the mixed traffic flow environment to obtain an improved cellular transmission model; s6, selecting the optimal speed limit value according to the improved cell transmission model, and issuing the optimal speed limit value through the dynamic speed limit control system.
Description
Technical Field
The invention belongs to the field of traffic safety and intelligent traffic control, and particularly relates to a dynamic speed-limiting control method for a bottleneck section of a highway in a mixed traffic flow environment.
Background
With the increase of traffic demands, the traffic flow of the highway is continuously increased, and the traffic jam of the highway is more and more frequent. It is well known that traffic congestion causes an increase in the level of road service and traffic safety. In recent years, the automatic driving technology is rapidly developed, the automatic driving vehicle improves the dynamic characteristics of the vehicle and shortens the following distance from the vehicle microcosmic level, the basic attribute of the traditional traffic flow is expected to be fundamentally changed, and a new thought is provided for improving the traffic safety and the traffic efficiency. However, a long development process is required from the manual driving era to the fully automatic driving era, and the simultaneous driving of the manual driving vehicle and the automatic driving vehicle on the road becomes an important characteristic of a future traffic system for a long time in the future.
Bottleneck road sections generated by accidents, construction and other events on the expressway are key areas of traffic jam, the traffic flow of the bottleneck road sections of the expressway has the adverse characteristics of frequent acceleration and deceleration of vehicles and the like, and the traffic flow characteristics are more complex in a mixed traffic flow environment and can cause serious influence on the driving safety of the expressway. Therefore, the method for reasonably and effectively controlling the bottleneck road section of the expressway under the environment of manual-automatic driving mixed traffic flow has certain significance for improving the driving safety of the bottleneck road section.
Under the traditional traffic flow environment, the application of using the dynamic speed limit control method to relieve the traffic jam of the highway is very wide, and a better feedback result is obtained. Most of the existing dynamic speed limiting methods are designed based on the traditional macroscopic or microscopic traffic flow models, and for most of dynamic speed limiting control systems focusing on highway sections, the macroscopic models are more suitable for providing shorter control intervals. The traditional macroscopic traffic flow model does not consider the driving characteristics of automatic driving vehicles, the traffic flow characteristics of a highway are fundamentally changed in the environment of mixing manual and automatic driving vehicles, and the bottleneck road section dynamic speed limiting method based on the traditional traffic flow model is difficult to continuously apply. Therefore, it is required to develop a dynamic speed-limiting control method for the bottleneck section of the highway in the environment of manually and automatically driving the mixed traffic flow to improve the driving safety and efficiency of the bottleneck section of the highway.
Disclosure of Invention
The invention aims to solve the technical problem that the main means of dynamic speed limit in the prior art does not consider mixed traffic flow environment to cause unreasonable speed limit value, and provides a dynamic speed limit control method for a bottleneck road section of a highway in the mixed traffic flow environment.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a dynamic speed limit control method for a bottleneck section of a highway in a mixed traffic flow environment comprises the following steps:
s1, under the environment of manual-automatic driving mixed traffic flow, identifying the bottleneck section of the highway by using traffic event detection equipment or a construction operation reporting system;
s2, after the recognition is finished, setting a speed limit control period and a model prediction period, wherein the model prediction period is generally an integral multiple of the speed limit control period, and the speed limit control period is 5-10 min;
s3, dividing control sections according to the area where the bottleneck section is located, and generally recording 500-700m upstream of the bottleneck section as a buffer section; setting the upstream 5-20km of the buffer road section as a speed-limiting road section, and segmenting the speed-limiting road section at intervals of 1-5 km;
s4, collecting traffic flow data of a highway section to be controlled by using traffic flow monitoring equipment, wherein the traffic flow data comprises manual and automatic driving vehicle proportion, lane occupation condition, speed, flow, density and current speed limit value, and the collection frequency is generally set according to a model control cycle and needs to be less than or equal to the model control cycle;
s5, optimizing the cellular transmission model according to the collected traffic flow data and the traffic flow characteristics of the normal, speed-limiting and bottleneck road sections of the highway under the mixed traffic flow environment to obtain an improved cellular transmission model, and predicting the traffic flow data in the next period under the control of each speed-limiting scheme under the mixed traffic flow environment based on the collected real-time traffic flow data;
s6, selecting an optimal speed limit value according to the improved cellular transmission model, and issuing the optimal speed limit value through a dynamic speed limit control system;
and S7, repeating the steps S4, S5 and S6 to determine the optimal speed limit value again, and realizing dynamic speed limit control of the bottleneck road section of the highway in the mixed traffic flow environment.
Further, in step S5, the optimizing the cellular transmission model according to the collected traffic flow data and the traffic flow characteristics of the normal, speed-limited and bottleneck sections of the highway in the mixed traffic flow environment to obtain an improved cellular transmission model includes:
marking a bottleneck road section, a buffer road section and a speed limit road section as a control road section m according to the requirements of the cellular transmission model, and taking the unit length L of the road section m as the unit length of the road section m m Is divided into N m A plurality of unit cells;
(1) building a manual-automatic driving mixed traffic flow cellular transmission model, namely an MCTM model
In the manual driving following model, the distance s between the car heads r The relation with the average vehicle speed v is:
s r ＝t r v+d r
t r delaying the response of manual driving; d r Is an artificial driving displacement difference term;
in the automatic driving following model, t a Expecting headway for autonomous driving; d a For automatically driving the head space in a crowded state, the traffic flow constraint relationship is as follows:
s a ＝t a v+d a
the traffic flow in the mixed state of manual driving and automatic driving can analyze the basic map characteristic of the mixed traffic flow in different automatic driving vehicle proportions by averaging the distances between heads of all vehicles, and p (p-is) is used 1 ,p 2 ) Respectively, the ratio of two types of traffic flow, p 1 +p 2 When the proportion of each type of vehicle is p, the blocking density is expressed as:
the critical density is expressed as:
the backward wave velocity is expressed as:
the maximum capacity is expressed as:
the cellular transmission model needs to carry out space-time discretization processing on the traffic flow, and m road sections are processed by unit length L m Is divided into N m The unit cell, under the condition of mixed traffic flow, the unit cell i belongs to {1,2, …, N ∈ [ ] m Parameter of traffic flow at kth ∈ {0,1,2, …, K } time interval (time interval Δ t)The number of the components comprises: average velocity v m,i (k) The cellular entry flow rate q m,i-1 (k) The outgoing flow rate q m,i (k) Traffic density ρ m,i (k) According to the cellular transmission model, the average velocity can be expressed as:
using λ m Representing the number of lanes, the outflow rate can be expressed as:
q m,i (k)＝ρ m,i (k)v m,i (k)λ m
the number of vehicles of cell i can be expressed as:
n m,i (k+1)＝n m,i (k)+y m,i-1 (k)-y m,i (k)
wherein n is m,i (k) Is the number of original vehicles in the cell, y m,i-1 (k) Number of incoming vehicles, y m,i (k) Driving the road section without inflow and outflow ramps for the number of the outflow vehicles, wherein the inflow and outflow calculation formulas are as follows:
y m,i (k)＝q m,i (k)Δt
according to the traffic flow theory, the number of vehicles of the cell i can also be expressed as:
n m,i (k+1)＝ρ m,i (k+1)L m λ m
the density of cell i at time k +1 can be expressed as:
deducing according to the formula, calculating traffic flow parameters such as vehicle number, density and the like of the current cell of the mixed traffic flow at the next moment under the condition of different manual and automatic vehicle driving proportions, traversing the whole cell layer and the time layer, calculating the time-space traffic state evolution of the mixed traffic flow, and completing MCTM modeling;
(2) building dynamic speed limit MCTM model, namely DMCTM model
Using v dsl Representing the dynamic speed limit, the average speed can be expressed as:
maximum traffic capacity under dynamic speed-limiting conditions
And critical density
The relationship between can be expressed as:
the flow rate into the cell can be expressed as:
the density is obtained by deduction according to the vehicle number conservation, and any time-space traffic flow basic parameter can be obtained by recursion according to the DMCTM model formula;
(3) building MCTM (road condition model) model of bottleneck road section, namely BMCTM model
Outflow from bottleneck section:
wherein the content of the first and second substances,
gamma is the descending range of traffic capacity, and according to the traffic flow theory, the traffic flow speed v of the bottleneck road section m,i (k) Expressed as:
the MCTM, the DMCTM and the BMCTM can represent the space-time evolution process of the traffic flow of a normal road section, a dynamic speed-limiting road section and a bottleneck road section in a mixed traffic flow environment, and can be used for predicting the traffic flow parameters of different speed-limiting schemes at future time.
Further, in step S6, the selecting an optimal speed limit value according to the improved cellular transmission model, and issuing the optimal speed limit value through a dynamic speed limit control system includes:
(1) determining an objective function
The main objective is the traffic safety and the traffic efficiency of the bottleneck section of the highway, and in the aspect of safety, the traffic safety objective is realized by using the minimization of the sum of the speed differences at the same moment between adjacent cells:
in terms of efficiency, the maximum total traffic volume is used as the traffic efficiency target:
converting a multi-target problem into a single-target problem by adopting a linear weighting method, respectively carrying out normalization processing on two evaluation index values in order to eliminate the influence of non-uniform evaluation index value dimensions on a result, and carrying out weighted combination on the processed evaluation indexes to form an objective function:
wherein alpha is 1 ，α 2 Are the weights, alpha, of two targets, respectively 1 +α 2 ＝1；
Respectively, the normalized values of the two targets.
(2) Determining constraints
(2.1) maximum and minimum constraint, wherein the maximum speed limit of the speed limit cells cannot be greater than the maximum safe vehicle speed of the road section, generally the maximum speed limit value of the road section is 120km/h or 100km/h, and the minimum speed limit cannot be less than the minimum passing speed corresponding to the minimum passing efficiency, generally 20 km/h;
(2.2) time change constraint, wherein in order to reduce adverse effects of speed change on driving operation and traffic flow stability caused by speed change reduction, the change of adjacent control periods of the speed limit value of the same cellular cannot exceed 10 km/h;
(2.3) space change constraint, in order to enable the speed to smoothly drop and reduce potential safety hazards caused by violent speed change, the difference of the speed limit values between two adjacent cells is less than 20km/h, and the speed limit value of an upstream cell is more than or equal to that of a downstream cell;
(2.4) displaying convenient constraint, wherein the speed limit value is a multiple of 10;
(3) solving optimization model
Solving the speed limit control model according to the objective function and the constraint conditions of the optimization model, wherein the essence is to find the optimal speed limit value of each speed limit cell in the control period so that the total objective function is optimal;
(4) speed limiting scheme for issuing
And issuing the optimal speed limit value through a dynamic speed limit control system for a control period.
Furthermore, the dynamic speed limit control system comprises detection equipment, a background center computer, an electronic information board, an automatic driving vehicle OBU and third-party navigation software.
Due to the adoption of the technical scheme, the invention has the following beneficial effects:
the invention displays the current speed limit value in real time through a roadside variable speed limit board, an automatic driving vehicle-mounted terminal and third-party trip software by using an improved cellular transmission model and a dynamic speed limit optimization algorithm based on highway traffic flow data obtained by real-time detection under the environment of artificial-automatic driving mixed traffic flow, thereby realizing the dynamic control of the driving speed of vehicles in a bottleneck region of a highway, improving the driving safety and the passing efficiency of the vehicles and relieving traffic jam.
The invention effectively realizes the dynamic speed control under the environment of manual-automatic driving mixed traffic flow, prevents the sudden change of the speed limit value from causing traffic accidents, and improves the safety and the convenience of the bottleneck area of the highway under the environment of mixed traffic flow.
Drawings
FIG. 1 is a control flow diagram of the present invention;
FIG. 2 is a schematic illustration of a highway control segment division according to the present invention;
Detailed Description
The technical solution of the present invention will be further described in detail with reference to the accompanying drawings and examples.
A dynamic speed limit control method for a mixed traffic flow environment expressway bottleneck section based on an improved cellular transmission model comprises the following steps:
s1, under the environment of manual-automatic driving mixed traffic flow, identifying the bottleneck section of the highway by using traffic event detection equipment or a construction operation reporting system;
s2, after the recognition is finished, setting a speed limit control period and a model prediction period to be both 5 minutes (the speed limit value is updated once in 5 minutes, and the length of the model for predicting the next moment is 5 minutes);
s3, dividing control road sections according to the area where the bottleneck road sections are located, and recording 500m upstream of the bottleneck road sections as buffer road sections; setting 20km at the upstream of the buffer road section as a speed-limiting road section, wherein the speed-limiting road section is divided into segments at intervals of 5km, and the division is schematically shown in figure 2;
s4, collecting traffic flow data of a highway section to be controlled by using traffic flow monitoring equipment, wherein the traffic flow data comprises manual and automatic driving vehicle proportion, lane occupation condition, speed, flow, density and current speed limit value, and the collection frequency is once every 5 minutes;
s5, optimizing the cellular transmission model according to the collected traffic flow data and the traffic flow characteristics of the normal, speed-limiting and bottleneck road sections of the highway under the mixed traffic flow environment to obtain an improved cellular transmission model, and predicting the traffic flow data in the next period under the control of each speed-limiting scheme under the mixed traffic flow environment based on the collected real-time traffic flow data:
(1) building a manual-automatic driving mixed traffic flow cellular transmission model, namely, in the manual driving following model of the MCTM model, the distance s between the vehicle heads r The relation with the average vehicle speed v is:
s r ＝t r v+d r
t r delaying the response of manual driving; d r For the artificial driving displacement difference term, the Newell artificial driving following model is calibrated according to related research, t r ＝1.61s，d r ＝8.53m；
In the automatic driving following model, t a Anticipating headway for autonomous driving; d a For automatically driving the head space in a crowded state, the traffic flow constraint relationship is as follows:
s a ＝t a v+d a
according to the PATH laboratory automatic driving vehicle following model, the parameter calibration result is t a ＝0.6s，d a ＝7m；
The traffic flow in the manual driving and automatic driving mixed state can analyze the basic map characteristic of the mixed traffic flow in different automatic driving vehicle proportions by averaging the distances between heads of all vehicles, and p ═ (p ═ is used 1 ,p 2 ) Respectively, the ratio of two types of traffic flow, p 1 +p 2 When the proportion of each type of vehicle is p, the blocking density is expressed as:
the critical density is:
the backward wave velocity is:
the maximum traffic capacity is:
the average velocity can be expressed as:
using λ m Representing the number of lanes, the outflow rate can be expressed as:
q m,i (k)＝ρ m,i (k)v m,i (k)λ m
the number of vehicles of cell i can be expressed as:
n m,i (k+1)＝n m,i (k)+y m,i-1 (k)-y m,i (k)
wherein n is m,i (k) Is the number of original vehicles in the cell, y m,i-1 (k) Number of vehicles flowing in, y m,i (k) Driving the road section without inflow and outflow ramps for the number of the outflow vehicles, wherein the inflow and outflow calculation formulas are as follows:
y m,i (k)＝q m,i (k)Δt
according to the traffic flow theory, the number of vehicles of the cell i can also be expressed as:
n m,i (k+1)＝ρ m,i (k+1)L m λ m
the density of cell i at time k +1 can be expressed as:
(2) building dynamic speed limit MCTM model, namely DMCTM model
According to DMCTM, using v dsl Representing the dynamic speed limit, the average speed can be expressed as:
maximum capacity
And critical density
The relationship between can be expressed as:
the flow rate into the cell can be expressed as:
the density can be derived from vehicle number conservation, consistent with MCTM.
(3) Building an MCTM model, namely a BMCTM model, of a bottleneck road section
According to BMCTM, outflow of bottleneck section:
wherein the content of the first and second substances,
gamma is the descending range of traffic capacity, and according to the traffic flow theory, the traffic flow speed v of the bottleneck road section m,i (k) Can be expressed as:
MCTM, DMCTM and BMCTM can represent the time-space evolution process of the traffic flow of a normal road section, a dynamic speed-limiting road section and a bottleneck road section in a mixed traffic flow environment, and the traffic flow parameters of different speed-limiting schemes at future time can be predicted according to the improved cellular transmission model and the acquired real-time traffic flow data;
s6, selecting an optimal speed limit value according to the improved cellular transmission model, and issuing the optimal speed limit value through a dynamic speed limit control system, wherein the method comprises the following steps:
(1) determining an objective function
The main objective is the traffic safety and the traffic efficiency of the bottleneck section of the highway, and in the aspect of safety, the traffic safety objective is realized by using the minimization of the sum of the speed differences at the same moment between adjacent cells:
in terms of efficiency, the maximum total traffic volume is used as the traffic efficiency target:
converting the multi-target problem into a single-target problem by adopting a linear weighting method; in order to eliminate the influence of the non-uniform evaluation index value dimension on the result, the two evaluation index values are respectively subjected to normalization processing, the processed evaluation indexes are combined in a weighting mode, and an objective function is as follows:
wherein alpha is 1 ，α 2 Are the weights, alpha, of two targets, respectively 1 +α 2 ＝1；
Respectively taking values after two targets are normalized;
(2) determining constraints
In practical application, some constraint conditions need to be added to ensure the smooth implementation of the dynamic speed-limiting strategy;
(2.1) maximize, minimize constraints; the maximum speed limit of the speed limit cells cannot be larger than the maximum safe vehicle speed of the road section, and is generally 120km/h or 100km/h of the maximum speed limit value of the road section. The minimum speed limit cannot be smaller than the minimum passing speed corresponding to the minimum passing efficiency, and is generally 20 km/h;
(2.2) time change constraint, wherein in order to reduce adverse effects of speed change on driving operation and traffic flow stability caused by speed change reduction, the change of adjacent control periods of the speed limit value of the same cellular cannot exceed 10 km/h;
(2.3) space change constraint, in order to enable the speed to be reduced smoothly and reduce potential safety hazards caused by severe speed change, the difference of the speed limit values between two adjacent cells is less than 20km/h, and the speed limit value of an upstream cell is required to be greater than or equal to that of a downstream cell;
(2.4) displaying convenient constraint, wherein the speed limit value is a multiple of 10;
(3) solving optimization model
Solving the speed limit control model according to the optimization model objective function and the constraint condition, wherein the essence is that the optimal speed limit value of each speed limit cell in the control period is found, so that the total objective function is optimal, and the solution can be carried out by adopting a genetic algorithm;
(4) scheme for giving speed limit
Issuing the optimal speed limit value through a dynamic speed limit control system for a control period;
and S7, repeating the steps S4, S5 and S6 to determine the optimal speed limit value again, and realizing dynamic speed limit control of the bottleneck section of the expressway in the mixed traffic flow environment.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (4)
1. A dynamic speed limit control method for a bottleneck section of a highway in a mixed traffic flow environment is characterized by comprising the following steps:
s1, under the environment of manual-automatic driving mixed traffic flow, identifying the bottleneck section of the highway by using traffic event detection equipment or a construction operation reporting system;
s2, after the recognition is finished, setting a speed limit control period and a model prediction period, wherein the model prediction period is an integral multiple of the speed limit control period, and the speed limit control period is 5-10 min;
s3, dividing control sections according to the area where the bottleneck section is located, and setting 500-700m upstream of the bottleneck section as a buffer section; setting the upstream 5-20km of the buffer road section as a speed-limiting road section, and segmenting the speed-limiting road section at intervals of 1-5 km;
s4, collecting traffic flow data of a highway section to be controlled by using traffic flow monitoring equipment, wherein the traffic flow data comprises manual and automatic driving vehicle proportion, lane occupation condition, speed, flow, density and current speed limit value, and the collection frequency is set according to a model control cycle and needs to be less than or equal to the model control cycle;
s5, optimizing the cellular transmission model according to the collected traffic flow data and the traffic flow characteristics of the normal, speed-limiting and bottleneck road sections of the highway under the mixed traffic flow environment to obtain an improved cellular transmission model, and predicting the traffic flow data in the next period under the control of each speed-limiting scheme under the mixed traffic flow environment based on the collected real-time traffic flow data;
s6, selecting an optimal speed limit value according to the improved cellular transmission model, and issuing the optimal speed limit value through a dynamic speed limit control system;
and S7, repeating the steps S4, S5 and S6 to determine the optimal speed limit value again, and realizing dynamic speed limit control of the bottleneck section of the expressway in the mixed traffic flow environment.
2. The method for controlling dynamic speed limit of a bottleneck section of a highway under a mixed traffic flow environment according to claim 1, wherein in step S5, the step of optimizing the cellular transmission model according to the collected traffic flow data and the traffic flow characteristics of the normal, speed-limit and bottleneck sections of the highway under the mixed traffic flow environment to obtain an improved cellular transmission model comprises the following steps:
marking a bottleneck road section, a buffer road section and a speed limit road section as a control road section m according to the requirements of the cellular transmission model, and taking the unit length L of the road section m as the unit length of the road section m m Is divided into N m A plurality of unit cells;
(1) building a manual-automatic driving mixed traffic flow cellular transmission model, namely an MCTM model
In the manual driving following model, the distance s between the car heads r The relation with the average vehicle speed v is:
s r ＝t r v+d r
t r delaying the response of manual driving; d is a radical of r Is an artificial driving displacement difference term;
in the automatic driving following model, t a Expecting headway for autonomous driving; d a For automatically driving the head space in a crowded state, the traffic flow constraint relationship is as follows:
s a ＝t a v+d a
the traffic flow in the manual driving and automatic driving mixed state can analyze the basic map characteristic of the mixed traffic flow in different automatic driving vehicle proportions by averaging the distances between heads of all vehicles, and p ═ (p ═ is used 1 ，p 2 ) Respectively, the ratio of two types of traffic flow, p 1 +p 2 When the proportion of each type of vehicle is p, the blocking density is expressed as:
the critical density is expressed as:
the backward wave velocity is expressed as:
the maximum capacity is expressed as:
the cellular transmission model needs to carry out space-time discretization processing on the traffic flow, and m road sections are divided into unit lengths L m Is divided into N m The unit cell is in the condition of mixed traffic flow, the unit cell i belongs to {1,2, …, N ∈ [ ] m Traffic flow parameters at kth e {0,1,2, …, K } time intervals (time intervals Δ t) include: average velocity v m，i (k) The cellular entrance flow rate q m，i-1 (k) The outgoing flow rate q m，i (k) Traffic density ρ m，i (k) According to the cellular transmission model, the average velocity can be expressed as:
using λ m Representing the number of lanes, the outflow rate can be expressed as:
q m，i (k)＝ρ m，i (k)v m，i (k)λ m
the number of vehicles of cell i can be expressed as:
n m，i (k+1)＝n m，i (k)+y m，i-1 (k)-y m，i (k)
wherein n is m，i (k) Is the number of original vehicles in the cell, y m，i-1 (k) Number of incoming vehicles, y m，i (k) Driving the road section without inflow and outflow ramps for the number of the outflow vehicles, wherein the inflow and outflow calculation formulas are as follows:
y m，i (k)＝q m，i (k)Δt
according to the traffic flow theory, the number of vehicles of the cell i can also be expressed as:
n m，i (k+1)＝ρ m，i (k+1)L m λ m
the density of cell i at time k +1 can be expressed as:
deducing according to the formula, calculating traffic flow parameters such as vehicle number, density and the like of the current cell of the mixed traffic flow at the next moment under the condition of different manual and automatic vehicle driving proportions, traversing the whole cell layer and the time layer, calculating the time-space traffic state evolution of the mixed traffic flow, and finishing MCTM modeling;
(2) building dynamic speed limit MCTM model, namely DMCTM model
Using v dsl Representing the dynamic speed limit, the average speed can be expressed as:
maximum traffic capacity under dynamic speed-limiting conditions
And critical density
The relationship between can be expressed as:
the flow rate into the cell can be expressed as:
the density is obtained by deduction according to the vehicle number conservation, and any time-space traffic flow basic parameter can be obtained by recursion according to the DMCTM model formula;
(3) building an MCTM model, namely a BMCTM model, of a bottleneck road section
Outflow from bottleneck section:
wherein the content of the first and second substances,
gamma is the descending range of traffic capacity, and according to the traffic flow theory, the traffic flow speed v of the bottleneck road section m，i (k) Expressed as:
the MCTM, the DMCTM and the BMCTM can represent the time-space evolution process of the traffic flow of a normal road section, a dynamic speed-limiting road section and a bottleneck road section under the mixed traffic flow environment, and can be used for predicting the traffic flow parameters of different speed-limiting schemes at future time.
3. The method for controlling dynamic speed limit of a bottleneck section of a highway under a mixed traffic flow environment according to claim 1, wherein in step S6, the step of selecting an optimal speed limit value according to the improved cellular transmission model and issuing the optimal speed limit value through a dynamic speed limit control system comprises the steps of:
(1) determining an objective function
The main objective is the traffic safety and the traffic efficiency of the bottleneck section of the highway, and in the aspect of safety, the traffic safety objective is realized by using the minimization of the sum of the speed differences at the same moment between adjacent cells:
in terms of efficiency, the maximum total traffic volume is used as the traffic efficiency target:
converting a multi-target problem into a single-target problem by adopting a linear weighting method, respectively carrying out normalization processing on two evaluation index values in order to eliminate the influence of non-uniform evaluation index value dimensions on a result, and carrying out weighted combination on the processed evaluation indexes to form an objective function:
wherein alpha is 1 ，α 2 Are the weights, alpha, of two targets, respectively 1 +α 2 ＝1；
Respectively, the normalized values of the two targets.
(2) Determining constraints
(2.1) maximum and minimum constraint, wherein the maximum speed limit of the speed limit cells cannot be greater than the maximum safe vehicle speed of the road section, generally the maximum speed limit value of the road section is 120km/h or 100km/h, and the minimum speed limit cannot be less than the minimum passing speed corresponding to the minimum passing efficiency, generally 20 km/h;
(2.2) time change constraint, wherein in order to reduce adverse effects of speed change on driving operation and traffic flow stability caused by speed change reduction, the change of adjacent control periods of the speed limit value of the same cellular cannot exceed 10 km/h;
(2.3) space change constraint, in order to enable the speed to be reduced smoothly and reduce potential safety hazards caused by severe speed change, the difference of the speed limit values between two adjacent cells is less than 20km/h, and the speed limit value of an upstream cell is required to be greater than or equal to that of a downstream cell;
(2.4) displaying convenient constraint, wherein the speed limit value is a multiple of 10;
(3) solving optimization model
Solving the speed limit control model according to the optimization model objective function and the constraint condition, wherein the essence is to find the optimal speed limit value of each speed limit cell in the control period so as to optimize the total objective function;
(4) speed limiting scheme for issuing
And issuing the optimal speed limit value through a dynamic speed limit control system for a control period.
4. The dynamic speed limit control method for the bottleneck section of the expressway in the mixed traffic flow environment according to claim 1, characterized by comprising the following steps of: the dynamic speed limit control system comprises detection equipment, a background center computer, an electronic information board, an automatic driving vehicle OBU and third-party navigation software.
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| PCT/CN2023/087564 WO2023216793A1 (en) | 2022-05-12 | 2023-04-11 | Dynamic speed limit control method for highway bottleneck section in mixed traffic flow environment |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115641734A (en) * | 2022-10-15 | 2023-01-24 | 河北省交通规划设计研究院有限公司 | Highway construction scene main line traffic control method and system |
| CN116229706A (en) * | 2022-12-07 | 2023-06-06 | 南京理工大学 | Lane-dividing variable speed limiting control method based on intelligent network-connected special lane environment |
| CN116238497A (en) * | 2023-05-11 | 2023-06-09 | 安徽蔚来智驾科技有限公司 | Vehicle speed limit control method, electronic device, storage medium and vehicle |
| WO2023216793A1 (en) * | 2022-05-12 | 2023-11-16 | 湖南纽狐科技有限公司 | Dynamic speed limit control method for highway bottleneck section in mixed traffic flow environment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US9666072B2 (en) * | 2014-12-29 | 2017-05-30 | Here Global B.V. | Dynamic speed limit |
| CN106021814B (en) * | 2016-06-13 | 2019-05-21 | 东南大学 | A kind of variable speed-limit optimal control method improved towards traffic efficiency |
| CN112435474B (en) * | 2020-11-20 | 2022-03-18 | 吉林大学 | Expressway variable speed limit control system and control method for rear-end collision risk prevention and control |
| CN113554875B (en) * | 2021-03-12 | 2023-01-13 | 北京航空航天大学 | Variable speed-limiting control method for heterogeneous traffic flow of expressway based on edge calculation |
| CN115063990A (en) * | 2022-05-12 | 2022-09-16 | 湖南纽狐科技有限公司 | Dynamic speed limit control method for bottleneck section of highway in mixed traffic flow environment |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023216793A1 (en) * | 2022-05-12 | 2023-11-16 | 湖南纽狐科技有限公司 | Dynamic speed limit control method for highway bottleneck section in mixed traffic flow environment |
| CN115641734A (en) * | 2022-10-15 | 2023-01-24 | 河北省交通规划设计研究院有限公司 | Highway construction scene main line traffic control method and system |
| CN116229706A (en) * | 2022-12-07 | 2023-06-06 | 南京理工大学 | Lane-dividing variable speed limiting control method based on intelligent network-connected special lane environment |
| CN116229706B (en) * | 2022-12-07 | 2024-03-26 | 南京理工大学 | Lane-dividing variable speed limiting control method based on intelligent network-connected special lane environment |
| CN116238497A (en) * | 2023-05-11 | 2023-06-09 | 安徽蔚来智驾科技有限公司 | Vehicle speed limit control method, electronic device, storage medium and vehicle |
| CN116238497B (en) * | 2023-05-11 | 2024-01-26 | 安徽蔚来智驾科技有限公司 | Vehicle speed limit control method, electronic device, storage medium and vehicle |
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