A variable speed limit control method for mixed traffic near signal areas based on macroscopic model

Abstract

The present invention discloses a method for controlling a variable speed limit for mixed traffic in a near-signal area based on a macro model, comprising the following steps: dividing the road sections in the near-signal area by function and length; periodically feeding back the traffic flow information of each road section to vehicles within the control area; constructing a variable speed limit model for mixed traffic in the near-signal area based on the macro model; determining the objective function and constraints according to the control objective, and designing a method for controlling a variable speed limit for mixed traffic based on the macro model; and controlling the vehicles in each controlled road section within a set time interval to execute a speed adjustment process according to the calculated optimal speed. The present invention provides a variable speed limit control method for effectively controlling mixed vehicle groups in the near-signal area under new mixed traffic conditions, providing a new approach to alleviating the adverse effects of periodic regulation of traffic lights and lane changes of vehicles in the near-signal area.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent traffic information technology, and in particular relates to a variable speed limit control method for mixed traffic in a near-signal area based on a macro model. Background Art

[0002] The near-signal zone refers to all areas upstream of road intersections that are affected by traffic lights. It plays a vital role in the normal operation of urban transportation systems. Within this zone, vehicles are prone to frequent acceleration, deceleration, and starts and stops due to the influence of periodic traffic light regulation and lane-changing behavior. This often leads to traffic congestion and severely impacts overall traffic efficiency. With the development of intelligent connected vehicles (ICVs), new mixed traffic scenarios consisting of connected autonomous vehicles and traditional human drivers will coexist for a long time. How to fully utilize connected autonomous vehicles to constrain and guide traditional human drivers, thereby optimizing overall traffic flow, is a topic worthy of research and exploration.

[0003] By reviewing relevant literature and patents, existing research on the intersection area near the signal area is relatively mature, mainly focusing on the centralized control of traffic lights and the coordinated control of networked vehicles. Patent publication CN110164152A has developed a traffic light control system for a single intersection, which uses machine vision technology to obtain traffic flow information at the intersection and combines it with a neural network algorithm to intelligently control the traffic lights. This can improve the traffic efficiency of the intersection to a certain extent, but the control ability is also weakened under conditions of excessive traffic flow; Patent publication CN115985119A discloses a method for intersection vehicle control and signal optimization under fully networked conditions, which uses the vehicle's network communication capabilities to adjust vehicle behavior in a timely manner, which can effectively reduce vehicle energy consumption at the intersection and improve traffic efficiency. However, this cannot adapt to the limitations of perception and information transmission of traditional human-driven vehicles under mixed traffic conditions.

[0004] Therefore, there is an urgent need for a mixed vehicle group speed control method that can effectively suppress the negative effect of the bottleneck near the signal area. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method for controlling variable speed limits in mixed traffic near signal areas based on a macro model, comprising the following steps:

[0006] S1. The road extending upstream from the signal area by a set distance is divided into a control area and a release area in order from upstream to downstream;

[0007] Control area: the dotted lane-changing area located upstream of the near-signal area;

[0008] Release zone: The solid line prohibited lane change area located downstream of the near signal area;

[0009] S2. Divide the controlled area sections into different control lengths according to vehicle types;

[0010] S3 set the time interval, obtain the time interval near the signal area of ​​each section and vehicle traffic status information;

[0011] S4. Based on the real-time road and vehicle traffic status information in the near-signal area, a mixed traffic variable speed limit model applicable to the near-signal area is constructed based on the macro model;

[0012] S5. Design objective functions and constraints based on the control objectives, and design a variable speed limit control method for mixed traffic based on a macro-model;

[0013] S6. Control the vehicles in each controlled section within the set time interval to perform a speed adjustment process according to the calculated optimal speed.

[0014] Furthermore, in step S1, the length of the control zone is 200 to 1000 m, and the length of the release zone is 40 to 100 m.

[0015] Furthermore, in step S2, the vehicle types are divided into connected autonomous vehicles (CAVs) and traditional human-driven vehicles (HVs), and the control length of the connected autonomous vehicle (CAV) is L cav , the control length of the traditional human-driven HV is L hv .

[0016] Furthermore, in step S3, the traffic status information of the road section includes the average traffic flow speed v of the road section i. i , traffic flow density ρ i , flow rate q i and the phase information μ of the signal light; the vehicle traffic status information includes vehicle type p, networked automatic vehicle penetration rate β, current lane c, target turning lane c t .

[0017] Furthermore, step S4 includes the following sub-steps:

[0018] S4.1 Based on the density information ρ of road section i at time t i (t), flow information q i (t), and the traffic information q of the previous segment i-1 i-1 (t), determine the expected density ρ of road segment i at time t+1 i (t+1), expressed as:

[0019]

[0020] Where T represents the prediction time step, λ i represents the number of lanes in road section i, Li represents the length of road segment i;

[0021] S4.2 Based on the average vehicle speed information v of the road section i and the previous road section i-1 at time t i (t) and v i-1 (t), and the density information ρ of the road segment i and the next road segment i-1 i (t) and ρ i+1 (t), determine the expected speed v of road section i at time t+1 i (t+1), expressed as:

[0022]

[0023] Where η represents the speed density relationship coefficient, τ represents the driver speed adjustment function, ξ represents the compensation coefficient, and s l，i represents the flow transfer rate of section i on lane l; in addition, H[ρ i (t)] represents the expected velocity function, which can be expressed as:

[0024] H[ρ i (t)]=βV[ρ i (t)] CAV +(1-β)V[ρ i (t)] HV

[0025]

[0026]

[0027] Where, V[ρ i (t)] CAV represents the expected speed function of the connected autonomous vehicle, V[ρ i (t)] HV represents the expected speed function of traditional human driving, μ is the intensity response coefficient of the current signal light phase, and v f Indicates the free-flow speed of the road section, p c represents the critical traffic flow density, α i represents the model parameters, v max represents the maximum speed of the vehicle, p represents the number of sections between the current section and the section where the traffic light is located, ρ0 represents the safety density, ρ s Indicates the traffic flow density in the signal area;

[0028] S4.3 Determine the expected flow rate of road section i at time t+1 based on the expected density and expected speed of road section i at time t calculated at time t, expressed as follows:

[0029] q i (t+1)=λi ρ i (t+1)v i (t+1).

[0030] Furthermore, step S5 includes the following sub-steps:

[0031] S5.1 determines the control objectives from three aspects: traffic volume, fuel consumption, and travel time. The weight distribution of the three control objectives is 2:1:1. After normalization, the objective function is finally expressed as:

[0032] min J=TFC+TTT-TTC

[0033] Where TFC represents the fuel consumption in the time interval, TTT represents the travel time, and TTC represents the traffic volume;

[0034] S5.2 Set the constraints of the objective function:

[0035] ① The speed limit value obtained does not exceed the maximum speed limit value in the near-signal area;

[0036] ② The difference in posted speed limits between adjacent road sections and adjacent paces shall not exceed 10 km / h;

[0037] ③ The calculated posted speed limit value is rounded to an integer multiple of 5 km / h;

[0038] S5.3 Set up a roadside variable message board, which updates the speed limit value every 60 seconds to provide section-level speed guidance for passing traditional human-driven HVs; and use V2X communication technology to update lane-level control instructions for connected autonomous vehicles (CAVs) every 0.1 seconds.

[0039] Furthermore, step S6 includes the following sub-steps:

[0040] S6.1, based on the vehicle type, determines whether the vehicle has reached the update time for the control command;

[0041] If the update time is reached, the vehicle speed limit value is calculated according to the variable speed limit model of mixed traffic in the near-signal area based on the macro model;

[0042] If the update time is not reached, the vehicle will continue to run at the original speed;

[0043] S6.2 Determine whether the lane-level speed recommendation obtained by the connected automated vehicle (CAV) is greater than the speed limit displayed on the variable message board;

[0044] If the speed limit is higher than the speed limit posted on the variable message board, then drive according to the speed limit posted on the road section.

[0045] If the speed limit is not greater than the speed limit posted on the variable message board, the vehicle will drive according to the calculated lane speed limit;

[0046] S6.3 All vehicles shall execute the speed adjustment process according to the calculated speed recommendation value, release the speed control after reaching the release zone, and remain in the current lane to freely pass through the signalized section.

[0047] The beneficial effects of the present invention are:

[0048] This paper designs a mixed vehicle group speed control method to suppress the negative bottleneck effect near the signal area. By coordinating the road section-level control and vehicle-level control of vehicles, it realizes the control of the upstream traffic flow near the signal area, and effectively suppresses the adverse effects of the periodic control of traffic lights and vehicle lane changing behavior in the signal area.

[0049] In addition, the control method proposed in the present invention takes into account fuel consumption and traffic stability while ensuring the traffic efficiency in the near-signal area. It can regulate the speed of upstream vehicles in advance based on the real-time traffic flow changes in the near-signal area, which not only improves the driving experience of the vehicle, but also improves the overall traffic efficiency of the near-signal area.

[0050] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The figure is a schematic diagram of the overall process of the variable speed limit control method for mixed traffic near signal areas based on a macro model;

[0052] Figure 2 This is an application scenario diagram of the variable speed limit control method for mixed traffic near signal areas based on a macro model;

[0053] Figure 3 This is the algorithm flow chart of the variable speed limit control method for mixed traffic in the near signal area. DETAILED DESCRIPTION

[0054] To make the technical solutions, advantages, and purposes of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0055] like Figure 1 and Figure 3As shown, the present invention provides a variable speed limit control method for mixed traffic near a signal area based on a macro model, comprising the following steps:

[0056] S1. A road extending upstream from a near-signal area by a set distance is divided into a control area and a release area in order from upstream to downstream. The three lanes in the near-signal area are numbered from left to right as Lane 1, Lane 2, and Lane 3.

[0057] Control Zone: The dashed lane-changing zone located upstream of the near-signal zone. The control zone is 200 to 1000 meters long. In this embodiment, the control zone is 1000 meters long. Release Zone: The solid lane-changing prohibited zone located downstream of the near-signal zone. The release zone is 40 to 100 meters long. In this embodiment, the release zone is 50 meters long.

[0058] Figure 2 This is a diagram of an application scenario of the macro-model-based variable speed limit control method for mixed traffic near signal areas shown in this embodiment. Figure 2 This is merely exemplary, and in other embodiments, the total number of lanes may be 2, 4, 5, or other values.

[0059] S2. Divide the control area into different control lengths according to vehicle type and determine the length of each L of the connected autonomous vehicle (CAV). cav m is a control length, such as L cav =100m, traditional people driving HV per L hv m is a control length, such as L hv =500m.

[0060] Vehicle types are divided into connected autonomous vehicles (CAVs) and traditional human-driven vehicles (HVs). The control length of the connected autonomous vehicle (CAV) is L. cav , the control length of the traditional human-driven HV is L hv .

[0061] S3 set the time interval, obtain the time interval near the signal area of ​​each section and vehicle traffic status information;

[0062] In step S3, the traffic status information of the road section includes the average traffic flow speed v of the road section i i , traffic flow density ρ i , flow rate q i and the phase information μ of the signal light; the vehicle traffic status information includes vehicle type p, networked automatic vehicle penetration rate β, current lane c, target turning lane c t The time interval can be any time period, such as 1 second, 2 seconds, 5 seconds, 10 seconds, 30 seconds, 60 seconds, etc.

[0063] S4. Based on the real-time road and vehicle traffic status information within the near-signal area, a mixed traffic variable speed limit model applicable to the near-signal area is constructed based on the macro model. This specifically includes the following sub-steps:

[0064] S4.1 Based on the density information ρ of road section i at time t i (t), flow information q i (t), and the traffic information q of the previous segment i-1 i-1 (t), determine the expected density ρ of road segment i at time t+1 i (t+1), expressed as:

[0065]

[0066] Where T represents the prediction time step, λ i represents the number of lanes in road section i, L i represents the length of road segment i;

[0067] S4.2 Based on the average vehicle speed information v of the road section i and the previous road section i-1 at time t i (t) and v i-1 (t), and the density information ρ of the road segment i and the next road segment i-1 i (t) and ρ i+1 (t), determine the expected speed v of road section i at time t+1 i (t+1), expressed as:

[0068]

[0069] Where η represents the speed density relationship coefficient, τ represents the driver speed adjustment function, ξ represents the compensation coefficient, and s l，i represents the flow transfer rate of section i on lane l; in addition, H[ρ i (t)] represents the expected velocity function, which can be expressed as:

[0070] H[ρ i (t)]=βV[ρ i (t)] CAV +(1-β)V[ρ i (t)] HV

[0071]

[0072]

[0073] Where, V[ρ i (t)] CAV represents the expected speed function of the connected autonomous vehicle, V[ρ i (t)]HV represents the expected speed function of traditional human driving, μ is the intensity response coefficient of the current signal light phase, and v f Indicates the free-flow speed of the road section, p c represents the critical traffic flow density, α i represents the model parameters, v max represents the maximum speed of the vehicle, p represents the number of sections between the current section and the section where the traffic light is located, ρ0 represents the safety density, ρ s Indicates the traffic flow density in the signal area;

[0074] S4.3 Determine the expected flow rate of road section i at time t+1 based on the expected density and expected speed of road section i at time t calculated at time t, expressed as follows:

[0075] q i (t+1)=λ i ρ i (t+1)v i (t+1).

[0076] S5. Design an objective function and constraints based on the control objectives, and design a variable speed limit control method for mixed traffic based on a macro-model. This method includes the following sub-steps:

[0077] S5.1 determines the control objectives from three aspects: traffic volume, fuel consumption, and travel time. The weight distribution of the three control objectives is 2:1:1. After normalization, the objective function is finally expressed as:

[0078] min J=TFC+TTT-TTC

[0079] Where TFC represents the fuel consumption in the time interval, TTT represents the travel time, and TTC represents the traffic volume;

[0080] S5.2 To ensure the rationality of the speed limit, set the constraints of the objective function:

[0081] ① The speed limit value obtained shall not exceed the maximum speed limit of 60km / h in the near-signal area;

[0082] ② To ensure smooth operation of HVs, the difference in posted speed limits between adjacent road sections and adjacent speed steps shall not exceed 10 km / h;

[0083] ③ Taking into account the operability of HVs, the calculated posted speed limit value is rounded to an integer multiple of 5 km / h.

[0084] S5.3 Set up a roadside variable message board, which updates the speed limit value every 60 seconds to provide section-level speed guidance for passing traditional human-driven HVs; and use V2X communication technology to update lane-level control instructions for connected autonomous vehicles (CAVs) every 0.1 seconds.

[0085] S6. Control the vehicle within each control section within the set time interval to perform the speed adjustment process according to the calculated optimal speed; specifically comprising the following sub-steps:

[0086] S6.1, based on the vehicle type, determines whether the vehicle has reached the update time for the control command;

[0087] If the update time is reached, the vehicle speed limit value is calculated according to the variable speed limit model of mixed traffic in the near-signal area based on the macro model;

[0088] If the update time is not reached, the vehicle will continue to run at the original speed;

[0089] S6.2 Determine whether the lane-level speed recommendation obtained by the connected automated vehicle (CAV) is greater than the speed limit displayed on the variable message board;

[0090] If the speed limit is higher than the speed limit posted on the variable message board, then drive according to the speed limit posted on the road section.

[0091] If the speed limit is not greater than the speed limit posted on the variable message board, the vehicle will drive according to the calculated lane speed limit;

[0092] S6.3 All vehicles shall execute the speed adjustment process according to the calculated speed recommendation value, release the speed control after reaching the release zone, and remain in the current lane to freely pass through the signalized section.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. 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, which should all be included in the scope of protection of the present invention.

Claims

1. A variable speed limit control method for mixed traffic near signal areas based on a macro model, characterized in that: The following steps are involved: S1. The road extending upstream from the signal area by a set distance is divided into a control area and a release area in order from upstream to downstream; Control area: the dotted lane-changing area located upstream of the near-signal area; Release zone: The solid line prohibited lane change area located downstream of the near signal area; S2. Divide the controlled area sections into different control lengths according to vehicle types; S3 set the time interval, obtain the time interval near the signal area of ​​each section and vehicle traffic status information; S4. Based on the real-time road and vehicle traffic status information in the near-signal area, a mixed traffic variable speed limit model applicable to the near-signal area is constructed based on the macro model; S4.1 Based on the density information ρ of road section i at time t i (t), flow information q i (t), and the traffic information q of the previous segment i-1 i-1 (t), determine the expected density ρ of road segment i at time t+1 i (t+1), expressed as: Where T represents the prediction time step, λ i Indicates the number of lanes in road section i, L i represents the length of road segment i; S4.2 Based on the average vehicle speed information v of the road section i and the previous road section i-1 at time t i (t) and v i-1 (t), and the density information ρ of the road segment i and the next road segment i+1 i (t) and ρ i+1 (t), determine the expected speed v of road section i at time t+1 i (t+1), expressed as: Where η represents the speed density relationship coefficient, τ represents the driver speed adjustment function, ξ represents the compensation coefficient, and s l,i represents the flow transfer rate of section i on lane l; in addition, H[ρ i (t)] represents the expected velocity function, which can be expressed as: H[r i (t)]=βV[ρ i (t)] CAV +(1-β)V[ρ i (t)] HV Where, V[ρ i (t)] CAV represents the expected speed function of the connected autonomous vehicle, V[ρ i (t)] HV represents the expected speed function of traditional human driving, μ is the intensity response coefficient of the current signal light phase, and v f represents the free-travel speed on the road section, ρ c represents the critical traffic flow density, α i represents the model parameters, v max represents the maximum speed of the vehicle, p represents the number of sections between the current section and the section where the traffic light is located, ρ0 represents the safety density, ρ s Indicates the traffic flow density in the signal area; S4.3 Determine the expected flow rate of road section i at time t+1 based on the expected density and expected speed of road section i at time t calculated at time t, expressed as follows: q i (t+1)=λ i ρ i (t+1)v i (t+1) S5. Design objective functions and constraints based on the control objectives, and design a variable speed limit control method for mixed traffic based on a macro-model; S6. Control the vehicles in each controlled section within the set time interval to perform a speed adjustment process according to the calculated optimal speed.

2. The macro-model-based variable speed limit control method for mixed traffic near signal areas according to claim 1 is characterized by: In step S1, the length of the control zone is 200-1000 m, and the length of the release zone is 40-100 m.

3. The macro-model-based variable speed limit control method for mixed traffic near signal areas according to claim 1 is characterized by: In step S2, the vehicle types are divided into connected autonomous vehicles (CAVs) and traditional human-driven vehicles (HVs). The control length of the connected autonomous vehicle (CAV) is L. cav , the control length of the traditional human-driven HV is L hv .

4. The macro-model-based variable speed limit control method for mixed traffic near signal areas according to claim 1 is characterized by: In step S3, the traffic status information of the road section includes the average traffic flow speed v of the road section i. i , traffic flow density ρ i , flow rate q i and the phase information μ of the signal light; the vehicle traffic status information includes the vehicle type p, the penetration rate of connected automatic vehicles β, the current lane c and the target turning lane c t .

5. The method for controlling variable speed limit in mixed traffic near signal areas based on a macro model according to claim 1 is characterized in that: The step S5 includes the following sub-steps: S5.1 determines the control objectives from three aspects: traffic volume, fuel consumption, and travel time. The weight distribution of the three control objectives is 2:1:

1. After normalization, the objective function is finally expressed as: min J=TFC+TTT-TTC Where TFC represents the fuel consumption in the time interval, TTT represents the travel time, and TTC represents the traffic volume; S5.2 Set the constraints of the objective function: ① The speed limit value obtained does not exceed the maximum speed limit value in the near-signal area; ② The difference in posted speed limits between adjacent road sections and adjacent paces shall not exceed 10 km / h; ③ The calculated posted speed limit value is rounded to an integer multiple of 5 km / h; S5.3 Set up a roadside variable message board, which updates the speed limit value every 60 seconds to provide section-level speed guidance for passing traditional human-driven HVs; and use V2X communication technology to update lane-level control instructions for connected autonomous vehicles (CAVs) every 0.1 seconds.

6. The method for controlling variable speed limit in mixed traffic near signal areas based on a macro model according to claim 1 is characterized in that: The step S6 includes the following sub-steps: S6.1, based on the vehicle type, determines whether the vehicle has reached the update time for the control command; If the update time is reached, the vehicle speed limit value is calculated according to the variable speed limit model of mixed traffic in the near-signal area based on the macro model; If the update time is not reached, the vehicle will continue to run at the original speed; S6.2 Determine whether the lane-level speed recommendation obtained by the connected automated vehicle (CAV) is greater than the speed limit displayed on the variable message board; If the speed limit is higher than the speed limit posted on the variable message board, then drive according to the speed limit posted on the road section. If the speed limit is not greater than the speed limit posted on the variable message board, the vehicle will drive according to the calculated lane speed limit; S6.3 All vehicles shall execute the speed adjustment process according to the calculated speed recommendation value, release the speed control after reaching the release zone, and remain in the current lane to freely pass through the signalized section.

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