CN114627647B - Mixed traffic flow optimal control method based on combination of variable speed limit and lane change - Google Patents

Abstract

The present invention discloses a hybrid traffic flow optimization control method based on a combination of variable speed limit and lane change, comprising the following steps: S1, dividing the road network into a VSL control area, an LC control area and a bottleneck area; S2, calculating the expected output flow of each lane in the VSL control area; S3, calculating the expected speed of each lane in the VSL control area and the single-vehicle speed limit of each vehicle; S4, performing lane change control on vehicles in the LC control area. The present invention customizes variable speed limit values for each vehicle at different locations through vehicle networking technology and directly sends the speed limit to the corresponding single vehicle, overcoming the impact of the traditional speed limit signboards that the number and position are fixed and cannot be flexibly changed. At the same time, when designing the variable speed limit algorithm, both the penetration rate of autonomous driving vehicles and the compliance of human-driven vehicles are considered, so that the effect of variable speed limit control is improved.

Description

A mixed traffic flow optimization control method based on variable speed limit and lane change

Technical Field

The present invention belongs to the field of intelligent traffic information technology, and in particular relates to a mixed traffic flow optimization control method based on a combination of variable speed limit and lane change.

Background technique

During the peak travel period across the country, large-scale traffic congestion ensues, especially in bottleneck sections prone to traffic congestion, such as the reduction in the number of expressway lanes caused by road design, construction and maintenance, and traffic incidents, and the intersection of main roads and ramps. The traffic demand upstream of the bottleneck area is greater than the maximum capacity of the bottleneck area. Vehicles start to queue up from the bottleneck area, and traffic congestion is easily formed in the bottleneck area. Traffic congestion often leads to a reduction in road capacity, that is, a decrease in capacity, which further aggravates the degree of vehicle congestion. In addition, traffic congestion can also cause many negative effects, including higher fuel consumption and exhaust emissions, increased risk of vehicle collisions, and severe driving discomfort.

At present, most scholars use variable speed limit (VSL) control and lane change control strategies to solve the problem of reduced traffic capacity in bottleneck areas of urban expressways. Most variable speed limit controls obtain real-time data of various traffic parameters of road sections through fixed sensor equipment such as road monitoring and detectors, and adjust the speed limit of vehicles on the lane by limiting the speed of the road section, thereby effectively alleviating traffic congestion and achieving the purpose of improving driving safety and improving the traffic environment. However, this variable speed limit has many disadvantages. The traditional variable speed limit estimates the traffic state through historical data measured by fixed sensors arranged on the road, which cannot reflect the real-time nature of the traffic state, and the road state between fixed sensors is unknown. In addition, factors such as the placement of the variable speed limit sign, the rate of change of the speed limit value, and the driver's compliance with the speed limit value have a significant impact on the control effect of the variable speed limit. Lane change control is to send lane change information to the vehicle in advance, so that the vehicle completes the lane change operation a distance before the bottleneck, prevent most vehicles from changing lanes at the bottleneck, alleviate traffic congestion at the bottleneck, and thus make the traffic capacity at the bottleneck reach the maximum capacity.

With the development of Internet of Vehicles and autonomous driving technology, the emerging networked autonomous vehicles (AV) can not only transmit real-time information, but also drive strictly according to given instructions. It can not only overcome the disadvantages of fixed sensors collecting data, but also eliminate the problem of human drivers' compliance with speed limits. However, in the future, the roads will inevitably be a mixed traffic flow state of autonomous vehicles and human vehicles (HV). Therefore, when designing a variable speed limit and lane change control method, it is necessary to consider the mixed traffic flow state of autonomous vehicles and human vehicles.

Patent document CN113450583A discloses a method for variable speed limit and lane change cooperative control on highways under vehicle-road collaboration. This method is based on real-time information sharing of vehicle-road collaboration. It predicts whether the traffic density of each control section on the main line of the highway is greater than the critical density, and performs variable speed limit control and cooperative lane change control on each control section on the main line of the highway to regulate the flow into the downstream, thereby improving the traffic efficiency of the highway. However, this method performs speed limit control on each lane of each control section on the main line of the highway, and can only apply the same speed limit value to vehicles in the same section, and it is impossible to determine a more accurate and reasonable speed limit value based on the status of each vehicle itself. At the same time, the control effect of the variable speed limit method is affected by the driver's compliance with the speed limit value, but this method does not propose a control method for this factor.

Summary of the invention

The purpose of the present invention is to provide a mixed traffic flow optimization control method based on a combination of variable speed limit and lane change. Under mixed traffic conditions of automatic driving vehicles and human driving, through the vehicle networking technology, when traffic congestion occurs in the bottleneck area of an urban expressway, the mixed traffic flow optimization control method based on a combination of variable speed limit and lane change is used to limit the speed and change lanes of individual vehicles upstream of the bottleneck area, thereby avoiding the reduction of road capacity in the bottleneck area caused by traffic congestion, so that the bottleneck area can reach the maximum road capacity even when the road is congested, thereby improving road traffic efficiency.

To achieve the above object, the present invention provides the following technical solution: a mixed traffic flow optimization control method based on a combination of variable speed limit and lane change, comprising the following steps:

S1, dividing the road network into a VSL control area, a LC control area and a bottleneck area along the vehicle travel direction; the VSL control area and the LC control area are both upstream of the bottleneck area, and the LC control area is located between the VSL control area and the bottleneck area;

Number the lanes of the road network from right to left along the lane width as m, m∈{0,1,…,N};

Deploy an RSU in the bottleneck area, and collect the position, speed and lane information of each vehicle in the VSL control area and the LC control area through the RSU;

Transmitting the information collected by the RSU to a central server for calculation;

S2. Calculate the expected output flow of each lane in the VSL control area according to the average speed of each lane in the LC control area;

S3, calculating the expected speed of each lane in the VSL control area according to the expected output flow and average density of each lane in the VSL control area;

Calculate the single-vehicle speed limit of each autonomous vehicle and human-driven vehicle according to the expected speed, density and maximum speed limit of each lane in the VSL control area;

S4, performing lane change control on the vehicles in the LC control area;

Calculating the density of each lane of the LC control area according to the number of vehicles in each lane of the LC control area;

Calculating the expected number of lane-changing vehicles in each lane according to the actual density of each lane in the LC control area and the average density of the non-narrowed lanes;

A suitable vehicle is selected for lane changing according to the expected number of lane-changing vehicles in each lane of the LC control area, the number of autonomous driving vehicles, and the compliance of human-driven vehicles.

Furthermore, the RSU in S1 transmits information to the vehicle using wireless connection signals, and transmits information to the central server using wired connection; the vehicles are all equipped with positioning systems and wireless communication equipment.

Further, the S2 comprises the following steps:

S21: Introduce parameter r∈[0,1] to describe the impact of lane changes in the LC control area on the traffic efficiency of the bottleneck area. The value of parameter r is related to the number of lanes that narrow in the bottleneck area. The more lanes narrow, the larger the value of r. For example, in the case of 4 lanes changing to 3 lanes, it is recommended that r be 0.2;

S22: Calculate the maximum allowable output flow of each lane in the VSL control area according to the number of lanes upstream of the bottleneck area, the number of lanes downstream of the bottleneck area, the parameter r, and the maximum capacity of the bottleneck area, using the formula;

Where r∈[0,1]; ^NU represents the number of lanes upstream of the bottleneck (including the LC control area and the VSL control area); ^ND represents the number of lanes downstream of the bottleneck; and C represents the maximum traffic capacity downstream of the bottleneck area (bottleneck section).

S23: Calculate the expected output flow of each lane in the VSL control area according to the average speed of each lane in the LC control area, the maximum allowable output flow, and the maximum speed limit of the vehicle, and express it using a formula.

in represents the average speed of lane m in the LC control area in the previous cycle, where 0≤m≤N; sl _Max represents the maximum speed limit of the vehicle;

Further, the S3 comprises the following steps:

S31: setting a speed limit update time u _i for each vehicle i, indicating the next speed limit update time of vehicle i;

S32: Calculate the average vehicle density d _m,k-1 of each lane m in the VSL control area in the previous cycle based on the vehicle speed and lane information collected by the RSU;

S33: Based on the expected flow rate of each lane in the VSL control area in S2 and the average vehicle density of the lane, the expected speed of each lane in the VSL control area is calculated according to the relationship between lane flow rate, density and speed, and is expressed using a formula.

Where d _m,k-1 represents the average vehicle density on lane m in the VSL control area in the previous cycle; f _m,k represents the expected output flow of lane m.

Furthermore, in S31, the vehicle i is divided into an autonomous driving vehicle and a human-driven vehicle; the speed limit of the autonomous driving vehicle is set to the expected speed of lane m, that is, The method for calculating the speed limit value of a human-driven vehicle comprises the following steps:

1) According to the average speed and expected speed of the VSL control area, calculate the degree to which vehicles in the VSL control area comply with the speed limit value in the previous control cycle, and express it using the formula;

where v _m,k-1 and They represent the average speed and expected speed on lane m in the VSL control area in the last update cycle respectively;

2) The average speed that human-driven vehicles that do not comply with the speed limit in the VSL control area can travel is calculated based on the penetration rate of autonomous vehicles, the density of each lane in the VSL control area, the critical density of the lane, and the maximum speed limit of the vehicle, which can be expressed using the formula;

Where β represents the penetration rate of AVs; d _Max represents the maximum density of a single lane; d _c represents the critical density of a single lane, that is, the road density corresponding to the maximum capacity; p represents the compliance of HVs; v represents the average speed of HVs that do not comply with the speed limit; B represents the average speed of vehicles that comply with the speed limit in the VSL control area; SL represents the maximum speed that HVs that do not comply with the speed limit can reach in the VSL control area.

For HVs that do not obey speed control in VSL control areas, if the road is not congested (ie, 0＜d _m,k ≤d _c ), they can accelerate to the maximum speed limit sl _Max (such as free flow). However, the HV may not be able to accelerate to the maximum speed limit sl _Max because its speed is limited by neighboring vehicles, such as its preceding vehicle. Therefore, when 0＜d _m,k ≤d _c , Given by the formula, it represents the average speed of vehicles that comply with the speed limit in the VSL control area. When d _c ＜d _m,k ≤d _Max , the HV that does not comply with the speed limit cannot accelerate to the maximum speed limit sl _Max . For this purpose, use/> It indicates the maximum speed that a HV that does not comply with the speed limit can reach in the VSL control area, calculated using the formula. Therefore, when d _c ＜d _m,k ≤d _Max ,

3) Calculate the speed limit sl _i,k of human-driven vehicles according to the expected speed of each lane in the VSL control area, the average speed at which human-driven vehicles that do not comply with the speed limit can travel, and the degree to which vehicles in the VSL control area comply with the speed limit. The speed limit sl i, _k of human-driven vehicles can be obtained by solving the formula;

4) Round down the speed limit value to the nearest integer. An integer multiple of , where /> is a given constant, usually 5km/h or 10km/h, which can be calculated according to the formula;

After finding the speed limit of the autonomous vehicle or human-driven vehicle, the time required for vehicle i to accelerate or decelerate to the given speed limit is calculated based on the maximum comfortable acceleration, minimum comfortable deceleration and instantaneous speed of vehicle i, which can be expressed using the formula;

where _vi,t represents the instantaneous speed of vehicle i at time t; ai _,max and _bi,max represent the maximum comfortable acceleration and minimum comfortable deceleration of vehicle i, respectively.

The next speed limit update time of vehicle i is calculated based on the current time t and the time required for vehicle i to accelerate or decelerate to a given speed limit value. The next speed limit update time of vehicle i can be expressed by the formula;

u _i = t + _ti,k (1.11)

To avoid frequent acceleration and deceleration of vehicles, a new speed limit is calculated for the vehicle only when the vehicle accelerates or decelerates to its speed limit. That is, the speed limit update time for each vehicle is independent.

Further, the S4 comprises the following steps:

S41: Calculate the road density of all lanes in the LC control area based on the collected vehicle lane and position information. m∈{0,1,…,N} represents the road density of lane m in the LC control area; the average road density of the non-narrowed lanes is calculated based on the road density of each lane and the number of non-narrowed lanes/> For ease of understanding, assume that only the right lane narrows and the number of narrowed lanes is δ, then the average road density/> It can be calculated using the formula;

S42: Calculate the expected number of lane-changing vehicles for each non-narrowed lane in the LC control area, so that the density of the non-narrowed lanes is equal to the average road density. Equal, need to be based on the average road density of the un-narrowed lanes/> To calculate the expected number of lane-changing vehicles in each lane in the LC control area, use /> |mw|=1, represents the number of vehicles that change lanes from lane m to lane w in the LC control area, and |mw|=1 means that vehicles cannot change lanes continuously. Since only the right lane is narrowed, vehicles in the narrowed lane need to change lanes to the left lane, resulting in more vehicles in the lanes closer to the right narrowed lane and greater lane density. Therefore, it can be considered that vehicles in the right lane need to change lanes to the left lane in order to make the road density of the non-narrowed lane reach the average road density/> Based on this assumption, the expected number of lane-changing vehicles in each lane in the LC control area/> It can be determined by the following method:

First, starting from the leftmost lane N, based on the road density of the leftmost lane N, the number of vehicles changing lanes from lane N-1 to lane N can be calculated. Use the formula to calculate:

Where L ^LC represents the length of the LC control zone. Next, change the density of lane N-1 to Use the formula to calculate:

For lanes N-1 to lane δ, the above method can be used to calculate the expected number of lane-changing vehicles for each remaining non-narrowed lane. After determining the expected number of lane-changing vehicles for each non-narrowed lane, step S43 is used to select vehicles that meet the conditions for lane change;

S43: selecting a suitable vehicle for lane changing according to the expected number of lane changing vehicles in each lane of the LC lane changing control area;

When selecting a lane-changing vehicle, an autonomous driving vehicle is preferred; when the number of autonomous driving vehicles is less than the expected number of lane-changing vehicles, the number of human-driven vehicles that need to change lanes is calculated based on the compliance of human-driven vehicles and the difference between the expected number of lane-changing vehicles and the number of autonomous driving vehicles, and human-driven vehicles that meet the expected number of lane-changing vehicles are randomly selected from the human-driven vehicles for lane changing. It can be calculated using the formula:

Where p represents the driving compliance of the person.

S44: For vehicles in narrowing lanes, when entering the lane change control area, they should try to change lanes to non-narrowing lanes while ensuring lane change safety, thereby reducing lane changing behavior at bottleneck positions.

Beneficial effects:

In view of the problem of reduced traffic capacity in bottleneck areas in the prior art, the present invention provides a mixed traffic flow optimization control method based on a combination of variable speed limit and lane change, and the beneficial effects include the following points:

1. Compared with the traditional variable speed limit, the hybrid traffic flow optimization control method based on the combination of variable speed limit and lane change is not only based on real-time macro traffic information (average speed and average density of the road), but also based on real-time micro vehicle information (acceleration, deceleration and speed of a single vehicle) to determine the speed limit of a single vehicle. Since real-time micro information can better reflect the behavior of a single vehicle, the speed limit determined for a single vehicle is more accurate and reasonable than the speed limit determined for a road section.

2. Compared with traditional speed limit technology, the Internet of Vehicles technology customizes variable speed limit values for each vehicle at different locations and directly sends the speed limit to the corresponding single vehicle, overcoming the impact of the traditional speed limit sign placement number and location being fixed and unable to change flexibly. At the same time, when designing the variable speed limit algorithm, both the penetration rate of autonomous driving vehicles and the compliance of human-driven vehicles are taken into account, which improves the effect of variable speed limit control.

3. The present invention combines variable speed limit and lane changing. On the one hand, it eliminates the influence of lane changing behavior of vehicles near the bottleneck on the performance of variable speed limit. On the other hand, through lane changing control, vehicles on the narrow lane complete lane changing at a distance upstream of the bottleneck, eliminating the concentrated lane changing behavior of vehicles at the bottleneck, so that the traffic capacity of the bottleneck area can be maximized as much as possible, greatly reducing the decline in traffic capacity in the bottleneck area and improving road traffic efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a mixed traffic flow optimization control method based on a combination of variable speed limit and lane change;

Figure 2 is a schematic diagram of the road network structure.

Detailed ways

The present invention is further described below in conjunction with specific embodiments, examples of which are shown in the accompanying drawings, wherein the same or similar numbers throughout represent the same or similar elements or elements with the same or similar functions.

Example 1

As shown in FIG. 1 and FIG. 2 , this embodiment provides a mixed traffic flow optimization control method based on a combination of variable speed limit and lane change, comprising the following steps:

Step A: The upstream of the bottleneck area is divided into a VSL control area and a lane change (Lane Change, LC) control area. The position, speed, lane and other information of each vehicle in the VSL and LC control areas are collected through communication between vehicles and vehicles (V2V) and vehicles and infrastructure (V2I). This information is transmitted to the central server through the RSU, and the hybrid traffic flow optimization control method combining variable speed limit and lane change is run on the server;

Step B: Calculate the expected output flow of each lane in the VSL control area according to the average speed information of each lane in the LC control area;

Step C: Calculate the expected speed of each lane in the VSL control area according to the expected output flow of each lane in the VSL control area and the average density of each lane, and then calculate the speed limit of each autonomous vehicle and human-driven vehicle according to the expected speed of the lane, the density of the lane, the maximum speed limit and other information;

Step D: Perform lane change control on vehicles in the LC control area. First, calculate the average road density of each lane in the LC control area based on the number of vehicles in each lane in the LC control area. Then calculate the expected number of lane change vehicles in each lane based on the actual road density of each lane and the average road density information of the LC control area. Finally, select the appropriate vehicle for lane change based on the expected number of lane change vehicles in each lane, the number of AV vehicles, and the compliance of the HV.

Further, in step A, the entire road network is divided into three parts, namely, the VSL control area, the LC control area, and the bottleneck area, as shown in FIG2 , and the lanes are numbered m from right to left along the road width direction, m∈{0,1,…,N}. The hybrid traffic flow optimization control method combining variable speed limit and lane change runs on a central server, and the central server is connected to the roadside unit (RSU) through a wired connection. The RSU is deployed in the bottleneck section, and there are two types of network connections. The first is a wireless connection, where the RSU and the vehicle transmit information through a wireless connection, and the other is a wired connection, where the RSU can transmit information through a wired connection with the central server. It is assumed that different types of sensors, global positioning systems, and wireless communication devices are already embedded in the vehicle. Therefore, when the vehicle is traveling on the road, the vehicle can collect real-time traffic information (such as the speed and location information of the vehicle). The real-time traffic information is uploaded to the central server. Specifically, the real-time traffic information is first sent to the RSU through wireless communication, and then sent to the central server through wired communication.

In step B, in order to minimize the decrease in bottleneck area capacity, the purpose of the present invention is to increase the flow rate in the LC control area to the maximum capacity C. However, due to lane closures, vehicles change lanes upstream of the bottleneck, resulting in a decrease in the bottleneck area's capacity. To this end, the present invention first introduces a parameter r∈[0,1] to characterize the impact of vehicle lane changes in the LC control area on the bottleneck area's traffic efficiency. The value of the parameter r is related to the number of lanes that are narrowed in the bottleneck area. The more lanes that are narrowed, the larger the value of r. For example, in the case of 4 lanes becoming 3 lanes, it is recommended that r be taken as 0.2. Then, the maximum allowable output flow rate for each lane in the VSL control area is calculated based on the number of lanes upstream of the bottleneck N ^U , the number of lanes downstream of the bottleneck N ^D , the parameter r, and the capacity C of the bottleneck section, using the formula:

Finally, according to the average speed of each lane in the LC control area The maximum permissible output flow Φ ₁ and the maximum speed limit of the vehicle sl _Max are used to calculate the expected output flow of each lane in the VSL control area using the formula:

In step C, the present invention calculates the single-vehicle speed limit of the vehicle in the VSL control area. Since the acceleration and deceleration capabilities and traffic conditions of different vehicles may be different, the present invention sets a speed limit update time u _i for each vehicle i. The speed limit update time u _i represents the next speed limit update time of vehicle i. The present invention first calculates the average vehicle density of each lane m in the VSL control area in the previous cycle based on the collected vehicle speed and lane information. Then, based on the expected flow f _m,k of each lane in the VSL control area in step B and the average density d _m,k-1 of the lane, the expected speed of each lane in the VSL control area is calculated according to the relationship between flow, density and speed, and the formula is used for calculation:

Finally, for vehicle i in lane m of the VSL control area, if vehicle i is an autonomous vehicle, its speed limit can be assumed to be the expected speed of lane m, that is, If vehicle i is a human-driven vehicle, the calculation method of its speed limit sli _,k is described as follows:

Step C1: Calculate the degree to which the vehicles in the VSL control area comply with the speed limit value in the previous control cycle. This can be done based on the average speed v _m,k-1 and the expected speed in the VSL control area. To calculate, use the formula;

Step C2: Calculate the average speed that a person-driven vehicle that does not comply with the speed limit can travel in the VSL control area using the formula:

Where β represents the penetration rate of AV; d _Max represents the maximum density of a single lane; d _c represents the critical density of a single lane, that is, the road density corresponding to the maximum traffic capacity; p represents the compliance of HV; Indicates the average speed of HVs that do not comply with speed limits; /> Represents the average speed of vehicles complying with the speed limit in the VSL control area, which can be calculated using the formula; /> The maximum speed that an HV that does not comply with the speed limit can reach in the VSL control area can be calculated using the formula.

Step C3: Calculate the speed limit of human-driven vehicles based on the expected speed of each lane in the VSL control area, the average speed at which human-driven vehicles that do not comply with the speed limit can travel, the degree to which vehicles in the VSL control area comply with the speed limit, and the AV penetration rate information, using the formula:

Step C4: Round down the obtained speed limit value to the nearest An integer multiple of , where /> is a given constant, usually 5km/h or 10km/h, which can be calculated according to the formula;

After obtaining the speed limit value sl _i,k of vehicle i, the present invention calculates the time required for vehicle i to accelerate or decelerate to the given speed limit value according to the maximum comfortable acceleration, minimum comfortable deceleration and instantaneous speed of vehicle i. The calculation is performed using the formula:

where _vi,t represents the instantaneous speed of vehicle i at time t; ai _,max and _bi,max represent the maximum comfortable acceleration and minimum comfortable deceleration of vehicle i, respectively.

In order to avoid frequent acceleration and deceleration of vehicles, a new speed limit will be recalculated for the vehicle only when the vehicle accelerates or decelerates to its speed limit value, that is, the speed limit update time of each vehicle is different. The next speed limit update time of vehicle i can be calculated based on the current speed limit update time and the time required to accelerate to a given speed limit value, using the formula:

u _i = t + _ti,k (1.26)

In step D, the present invention performs lane change control on the vehicle in the LC control area. Further, for ease of understanding, it is assumed that only the right lane is narrowed and the number of narrowed lanes is δ. The lane change control method in the LC control area can be described as the following steps:

Step D1: Calculate the road density of all lanes in the LC control area and the average road density of all vehicles after changing lanes to the non-narrowed lanes. First, calculate the road density of each lane based on the collected vehicle lane and position information, and use m∈{0,1,…,N} represents the road density of lane m in the LC control area. Then, the average road density of the non-narrowed lanes in the LC control area is calculated based on the road density of each lane and the number of non-narrowed lanes./> Use the formula to calculate;

Step D2: To make the road density of the un-narrowed lanes and the average road density Equal, need to be based on the average road density of the un-narrowed lanes/> To calculate the expected number of lane-changing vehicles in each lane in the LC control area, use /> |mw|=1, represents the number of vehicles that change lanes from lane m to lane w in the LC control area, and |mw|=1 means that vehicles cannot change lanes continuously. Since only the right lane is narrowed, vehicles in the narrowed lane need to change lanes to the left lane, resulting in more vehicles in the lanes closer to the right narrowed lane and greater lane density. Therefore, it can be considered that vehicles in the right lane need to change lanes to the left lane in order to make the road density of the non-narrowed lane reach the average road density/> Based on this assumption, the expected number of lane-changing vehicles in the non-narrowing lanes in the LC control area/> It can be determined by the following method:

First, starting from the leftmost lane N, based on the road density of the leftmost lane N, the number of vehicles changing lanes from lane N-1 to lane N can be calculated. Use the formula to calculate:

Where L ^LC represents the length of the LC control zone. Next, change the density of lane N-1 to Calculate using the formula:

For lanes N-2 to lane δ, the above method can be used to calculate the expected number of lane-changing vehicles for each remaining non-narrowed lane. After determining the expected number of lane-changing vehicles for each non-narrowed lane, step D3 is used to select vehicles that meet the conditions for lane change.

Step D3: Calculate the expected number of lane-changing vehicles in each lane of the LC control area After that, you need to select a suitable vehicle to change lanes. Since human drivers may not follow the lane change instructions they receive, when selecting a vehicle, autonomous driving vehicles are preferred for lane change. Less than the expected number of lane-changing vehicles/> When the driver's compliance and the expected number of lane-changing vehicles are The difference between the number of autonomous vehicles and the number of human drivers who need to change lanes is used to calculate the number of human drivers who need to change lanes/> Then randomly select from the people driving /> Number of people driving to change lanes. It can be calculated using the formula:

Where p represents the driving compliance of the person.

Step D4: For vehicles in the narrowing lane, when entering the lane change control area, they should change lanes to the non-narrowing lanes as much as possible while ensuring lane change safety, thereby reducing the lane change behavior of vehicles at bottleneck positions.

The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims (5)

1. The mixed traffic flow optimal control method based on the combination of variable speed limit and lane change is characterized by comprising the following steps:

S1, dividing a road network into a VSL control area, an LC control area and a bottleneck area along the running direction of a vehicle; the VSL control zone and the LC control zone are both upstream of the bottleneck zone, the LC control zone being located between the VSL control zone and the bottleneck zone;

Numbering lanes m, m=0, 1, and N along the lane width direction;

deploying an RSU in the bottleneck area, and collecting position, speed and lane information of each vehicle in the VSL control area and the LC control area through the RSU;

Dividing the upstream of the bottleneck area into a VSL control area and a lane change LC control area, and collecting the position, speed and lane information of each vehicle in the VSL and LC control areas through communication between the vehicles and the vehicles V2V and between the vehicles and the infrastructure V2I;

The RSU and the vehicle adopt wireless connection signals to transmit information, and adopt wired connection to transmit information; the vehicles are provided with a positioning system and wireless communication equipment;

transmitting the information collected by the RSU to a central server for operation;

s2, calculating expected output flow of each lane in the VSL control area according to the average speed of each lane in the LC control area;

S3, calculating the expected speed of each lane in the VSL control area according to the expected output flow and the average density of each lane in the VSL control area;

Calculating a single vehicle speed limit value of each automatic driving vehicle and each human driving vehicle according to the expected speed, the density and the maximum speed limit value of each lane in the VSL control area;

s4, lane change control is carried out on the vehicle in the LC control area;

calculating the density of each lane of the LC control area according to the number of vehicles of each lane of the LC control area;

calculating the expected lane change vehicle number of each lane according to the actual density and the average density of each lane in the LC control area;

And selecting a proper vehicle to change the lane according to the expected number of lane-changing vehicles of each lane of the LC control area, the number of vehicles of the automatic driving vehicle and the compliance degree of the human driving vehicle.

2. The hybrid traffic flow optimization control method based on the combination of variable speed limit and lane change according to claim 1, wherein S2 comprises the steps of:

S21: introducing parameters r E [0,1] to describe the influence of vehicle lane changing in the LC control area on the traffic efficiency of the bottleneck area;

S22: calculating the maximum allowable output flow of each lane in the VSL control area according to the number of lanes upstream of the bottleneck area, the number of lanes downstream of the bottleneck area and the traffic capacity of the bottleneck area;

s23: and calculating the expected output flow of each vehicle in the VSL control area according to the average speed of each lane in the LC control area, the maximum allowable output flow and the maximum speed limit value of the vehicle.

3. The hybrid traffic flow optimization control method based on the combination of variable speed limit and lane change according to claim 2, wherein S3 comprises the steps of:

s31: setting a speed limit value updating time u _i for each vehicle to represent the next speed limit value updating time of the vehicle i;

s32: calculating the average vehicle density of each lane m of the VSL control area in the last period according to the vehicle speed and lanes acquired by the RSU;

S33: and (2) calculating the expected speed of each lane of the VSL control area according to the relation among the traffic flow, the density and the speed of the lanes based on the expected traffic flow of each lane of the VSL control area and the average density of the lanes in the S2.

4. The hybrid traffic flow optimization control method based on the combination of variable speed limit and lane change according to claim 3, wherein the vehicles i in S31 are classified into autonomous vehicles and human driven vehicles; the speed limit value of the automatic driving vehicle is set to be the expected speed of the lane m; the speed limit value calculating method of the human-driven vehicle comprises the following steps:

1) Calculating the degree of compliance of the vehicle in the VSL control area with the speed limit value in the last control period according to the average speed and the expected speed of the VSL control area;

2) Calculating the average speed of the driving vehicle which does not follow the speed limit value in the VSL control area according to the permeability of the driving vehicle, the density of each lane in the VSL control area, the critical density of the lanes and the maximum limit of the vehicle;

3) Calculating the speed limit value of the driving vehicle according to the expected speed of each lane of the VSL control area, the average speed of the driving vehicle which does not accord with the speed limit value and the degree of the driving vehicle which accords with the speed limit value in the VSL control area;

4) Rounding down the calculated speed limit value to the integer multiple closest to A, wherein A is a given constant, and the value is 5km/h or 10km/h;

After the speed limit of the human-driven vehicle or the automatic-driven vehicle is calculated, calculating the time required for accelerating or decelerating the vehicle i to a given speed limit value according to the maximum comfortable acceleration, the minimum comfortable deceleration and the instantaneous speed of the vehicle i;

Calculating the next speed limit value updating time of the vehicle i according to the current time t and the time required by the vehicle i to accelerate or decelerate to a given speed limit value;

In order to avoid frequent acceleration and deceleration of the vehicle, a new speed limit value is recalculated for the vehicle only after the vehicle accelerates or decelerates to the speed limit value, i.e. the speed limit value updating time of each vehicle is independent.

5. The hybrid traffic flow optimization control method based on the combination of variable speed limit and lane change according to claim 4, wherein S4 comprises the steps of:

S41: calculating the road density of all lanes in the LC control area according to the collected vehicle position and lane information; calculating the average road density of the non-narrowed lanes according to the road density of each lane and the number of the non-narrowed lanes;

S42: calculating the expected lane change number of the non-narrowed lanes in the LC control area to make the density of the non-narrowed lanes equal to the average road density;

s43: according to the expected lane change number of each non-narrowed lane in the LC lane change control area, selecting a proper vehicle for lane change;

s44: for the vehicles on the narrowed lanes, after entering the lane change control area, the lane is changed to the non-narrowed lanes as far as possible on the premise of meeting lane change safety, so that lane change behaviors of the vehicles at bottleneck positions are reduced;

When the lane change vehicle is selected, an automatic driving vehicle is preferably selected; when the number of the automatic driving vehicles is smaller than the number of the expected lane change vehicles, the number of the driving vehicles needing lane change is calculated according to the difference between the compliance degree of the driving vehicles, the number of the expected lane change vehicles and the number of the automatic driving vehicles, and the driving vehicles meeting the number of the expected lane change vehicles are randomly selected from the driving vehicles to change lanes.