A multi-scale congestion suppression method for mixed traffic with slow vehicles in a near-signal region

Abstract

The application belongs to the technical field of mixed traffic control, and discloses a mixed traffic multi-scale congestion suppression method mixed with slow-moving vehicles in a near-signal area, which comprises the following steps: S1. setting a three-lane mixed traffic scene mixed with network-connected automatic vehicles, human-driven vehicles and slow-moving vehicles in a near-signal area; S2. respectively analyzing the laws and influences of the red-green light interruption effect, the lane-changing behavior in the near-signal area and the slow-moving vehicle moving bottleneck effect; S3. establishing a mixed traffic flow model mixed with slow-moving vehicles in the near-signal area; S4. designing a network-connected automatic vehicle congestion feedback control method considering the flow difference; and S5. designing a speed guiding method of the network-connected automatic vehicle, so as to make speed suggestions and speed guidance for the network-connected automatic vehicle at the road section and vehicle scales. The application can effectively suppress the traffic congestion and parking problems caused by slow-moving vehicles and red-green lights, reduce fuel consumption, reduce the average travel and waiting time, improve the traffic efficiency, and provide a new idea for the traffic cooperative control problem in the near-signal area.

Description

Technical Field

[0001] This invention belongs to the field of mixed traffic control technology, specifically relating to a multi-scale congestion suppression method for mixed traffic with slow-moving vehicles near the signal zone. Background Technology

[0002] In actual urban traffic, due to differences in drivers, vehicle performance, and driving purposes, different drivers on the same road segment have different expected speeds. Drivers with lower expected speeds are more likely to drive slower than surrounding vehicles, causing other vehicles to accelerate, decelerate, change lanes, and overtake. The moving bottleneck effect caused by slow-moving vehicles leads to increased speed dispersion, reduces the orderliness of traffic flow, and deviates from the stable equilibrium state of traffic flow, resulting in traffic congestion. These problems are even more pronounced in signal-prone areas, which are bottleneck areas and "throat areas" in urban traffic. On the one hand, vehicles in signal-prone areas are constrained by their driving purposes and road alignment; drivers must observe both the driving status of surrounding vehicles and the transitions between signal states. On the other hand, continuous traffic flow can be interrupted due to changes in signal light phases. Finally, as vehicles and transportation systems become increasingly intelligent, automated, and connected, the mixing of connected autonomous vehicles with traditional human-driven vehicles transforms traditional homogeneous traffic flow into heterogeneous traffic flow. The differences between CAVs (Consumer-Action Vehicles) and HVs (Hardware-Vehicles) in information acquisition and control decision-making exacerbate information asymmetry and uneven intelligence levels in near-signal zone traffic, making the coupling relationships between heterogeneous vehicles in mixed traffic more complex. Therefore, leveraging the advantages of connected autonomous vehicles in perception and controllability is key to solving the mixed traffic problem of slow-moving vehicles near signal zones.

[0003] By reviewing relevant literature and patents, some scholars have proposed corresponding control methods for the near-signal zone scenario, which can reduce fuel consumption and improve traffic efficiency to some extent. However, their research mainly focuses on single-lane scenarios near the signal zone, and few scholars have considered the traffic problems caused by slow-moving vehicles in the near-signal zone scenario. In addition, few scholars have considered the changes in cyber-physical space during vehicle movement. Summary of the Invention

[0004] In view of this, the present invention provides a multi-scale congestion suppression method for mixed traffic with slow-moving vehicles in the near-signal zone. This invention addresses a three-lane scenario in the near-signal zone and proposes a mixed traffic congestion suppression method at both the road segment and vehicle scales. This method can effectively suppress traffic congestion caused by slow-moving vehicles and traffic lights, reduce energy consumption, and improve traffic efficiency in the near-signal zone.

[0005] This invention provides a multi-scale congestion suppression method for mixed traffic with slow-moving vehicles near the signal zone, comprising the following steps:

[0006] S1. Set up a three-lane mixed traffic scenario near the signal zone, which includes connected autonomous vehicles, human-driven vehicles, and slow-moving vehicles;

[0007] Among them, connected autonomous vehicles can obtain traffic flow, density and traffic light information within their perception range, while human-driven vehicles and slow-moving vehicles can only obtain traffic information through the driver's perception.

[0008] S2. Based on the mixed traffic scenario set in step S1, analyze the patterns and impacts of traffic light interruption effect, lane changing behavior near signal zone, and slow vehicle movement bottleneck effect;

[0009] S3. Considering the differences between heterogeneous traffic entities at the cyber-physical level, establish a mixed traffic flow model for near-signal zones that includes slow-moving vehicles;

[0010] S4. Based on the changes of vehicles in the cyber-physical space near the signal zone, the near signal zone is divided into a speed suggestion zone and a speed guidance zone. Within the speed suggestion zone, based on the hybrid traffic flow model constructed in step S3, a congestion feedback control method for connected autonomous vehicles that considers the flow difference is designed.

[0011] S5. Within the speed guidance zone, design a speed guidance method for connected autonomous vehicles by combining vehicle speed, vehicle position, traffic light phase and time information, so as to provide speed suggestions and speed guidance for connected autonomous vehicles at the road segment and vehicle scale.

[0012] Furthermore, the three-lane mixed traffic scenario near the signal zone set in step S1 is 500m long, and lane changes are not allowed in the last 50m of the guidance zone. The three lanes are left-turn, straight-ahead, and right-turn lanes.

[0013] Furthermore, step S2 includes the following sub-steps:

[0014] S2.1 Calculate the maximum traffic flow on roads near the signal zone. The calculation expression is as follows:

[0015]

[0016] In the formula, Q represents the throughput under the traffic light interruption effect; T represents the length of one traffic light cycle; T g Indicates the length of the green light; Q in Q represents the actual traffic flow into the road; max This indicates the maximum traffic flow on the road without traffic lights; c represents the time lost due to stopping and starting.

[0017] S2.2 Analyzes the free lane-changing behavior of vehicles by road density and the forced lane-changing behavior of vehicles by lane-changing urgency. The closer the vehicle is to the solid line, the higher the lane-changing urgency.

[0018]

[0019] In the formula, x i (t) represents the vehicle position; X s X0 represents the starting position of the solid line; X0 represents the starting position of the near-signal zone; n represents the number of lane changes;

[0020] The bottleneck effect created by slow-moving vehicles in S2.3 causes other vehicles to slow down, change lanes, and overtake, thereby reducing road traffic flow. The formula for calculating the reduced flow is as follows:

[0021]

[0022] In the formula, Q represents the decreasing flow rate; ρ z Represents road traffic density; ρ s This represents the density corresponding to the maximum traffic flow on the road.

[0023] Furthermore, in step S3, the penetration rate p of human-vehicle interaction is considered respectively. h slow-moving vehicle penetration rate p s Human-vehicle penetration rate p c , and p h +p s +p c =1, then the mixed traffic flow model of the near-signal zone with slow-moving vehicles can be expressed as:

[0024]

[0025] In the formula, ρ represents density; v represents velocity; γ represents the lane-changing rate near the signal zone; β m This indicates that the greater the distance between lattice elements, the smaller the influence; l c ρ represents vehicle length; ρ0 represents average road density; ρ j The flow rate of the j-th lattice is represented by 'a'; the driver sensitivity coefficient is represented by 'm'; the lattice number in front of the CAV is represented by 'm'; the maximum number of communicating lattices is represented by 'M'; and T represents the maximum number of communicating lattices. r V(ρ) represents the red light duration; c represents the time lost by vehicles starting or stopping during the transition between traffic lights; λ represents the compliance rate of slower vehicles, where 0 < λ < 1. The smaller λ is, the slower the slower the vehicles, indicating that they are less likely to follow the optimal speed; V(ρ) j The optimal velocity function is given by the calibrated parameters: V1 = -2 m / s, V2 = 8.234 m / s, and C1 = 0.1092 m. -1 , C2=3.414, V1'=14.32m / s, V2'=4.916m / s, C1'=0.3456m -1 C2' = 1.446.

[0026] Furthermore, in step S4, the speed suggestion zone is 300m long and the speed guidance zone is 200m long.

[0027] Furthermore, in step S4, the model of the designed connected autonomous vehicle congestion feedback control method is as follows:

[0028]

[0029] Among them, u j It is a control item, in the following form:

[0030]

[0031] In the formula, ω represents the control gain. If the flow rate of the preceding lattice is greater than the flow rate of the current lattice, it indicates that the path ahead is clear, and the control term u... j A positive value indicates an increase in the optimal flow rate for the current road segment, while a negative value indicates congestion. m This means that the closer a lattice is to itself, the greater its influence.

[0032] Based on the optimal speed function and the average density of the road segment, the optimal speed of the road segment can be obtained conversely, thereby providing speed suggestions for vehicles on the current road segment and suppressing traffic congestion.

[0033] Furthermore, the speed guidance method in step S5 includes the following speed guidance strategy:

[0034] x cav (t0+t real )=x stop

[0035]

[0036] In the formula, t0 represents the current time; x cav (t0) is the current position of the CAV vehicle; v cav (t0) is the current CAV speed; v represents the planned speed of the vehicle; a decel Represents deceleration; t represents the remaining time of the green light phase; r Represents the duration of the red light; t real It is the programmable time of the current phase; x stop It is the position of the stop line; v lim It is the maximum speed limit on the road; This indicates the remaining time for the red light phase.

[0037] Beneficial effects:

[0038] This invention addresses the issue that existing control strategies do not consider traffic problems caused by slow-moving vehicles near signal zones or the changes in the cyber-physical space of vehicles during their journey. It proposes a multi-scale slow-moving vehicle congestion suppression method at both the road segment and vehicle scales. This method can effectively suppress traffic congestion and parking problems caused by slow-moving vehicles and traffic lights, reduce fuel consumption, lower average travel and waiting time, and improve traffic efficiency, providing a new approach to traffic cooperative control problems in signal zones.

[0039] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0040] Figure 1 This is a flowchart of a multi-scale congestion suppression method for mixed traffic with slow-moving vehicles near the signal zone according to the present invention.

[0041] Figure 2 This diagram illustrates the control scheme for a multi-scale congestion suppression method for mixed traffic with slow-moving vehicles near the signal zone, as described in this invention. Detailed Implementation

[0042] To make the technical solutions, advantages, and objectives of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the protection scope of this application.

[0043] like Figure 1 and Figure 2 As shown, this invention provides a multi-scale congestion suppression method for mixed traffic with slow-moving vehicles near the signal zone, comprising the following steps:

[0044] S1. Set up a three-lane mixed traffic scenario near the signal zone, which includes connected autonomous vehicles, human-driven vehicles, and slow-moving vehicles;

[0045] In the near-signal zone, the three-lane scenario covers a total distance of 500m, with lane changes prohibited in the last 50m of the guidance zone. The three lanes are designated for left turns, straight ahead, and right turns. Connected autonomous vehicles can acquire traffic status information such as flow rate and density, as well as traffic light information, within their perception range. Human drivers and slow-moving vehicles can only obtain traffic information through driver perception.

[0046] S2. Based on the mixed traffic scenario set in step S1, analyze the patterns and impacts of traffic light interruption effect, lane changing behavior near signal zone, and slow vehicle movement bottleneck effect;

[0047] S2.1 Because the periodic changes of traffic lights can cause interruptions in continuous traffic flow, affecting the maximum traffic capacity of the road, and considering the time loss of vehicles during the start-stop process, the maximum traffic capacity of the road near the signal zone is calculated as follows:

[0048]

[0049] In the formula, Q represents the throughput under the traffic light interruption effect; T represents the length of one traffic light cycle; T g Indicates the length of the green light; Q in Q represents the actual traffic flow into the road; max This indicates the maximum traffic flow on the road without traffic lights; c represents the time lost due to stopping and starting.

[0050] In the near-signal zone scenario (S2.2), on the one hand, vehicles will engage in free lane-changing behavior as they pursue higher speeds and efficiency. On the other hand, constrained by the solid line area near the signal zone and the driving purpose, vehicles will engage in forced lane-changing behavior. The free lane-changing behavior of vehicles can be represented by road density, while the forced lane-changing behavior can be represented by the urgency of the lane change; the closer the vehicle is to the destination, the higher the urgency of the lane change.

[0051]

[0052] In the formula, x i (t) represents the vehicle position; X s X0 represents the starting position of the solid line; X0 represents the starting position of the near-signal zone; n represents the number of lane changes;

[0053] The bottleneck effect created by slow-moving vehicles in S2.3 causes other vehicles to slow down, change lanes, and overtake, thereby reducing road traffic flow. The formula for calculating the reduced flow is as follows:

[0054]

[0055] In the formula, Q represents the decreasing flow rate; ρ z Represents road traffic density; ρ s This represents the density corresponding to the maximum traffic flow on the road.

[0056] S3. Considering the differences between heterogeneous traffic entities at the cyber-physical level, establish a mixed traffic flow model for near-signal zones that includes slow-moving vehicles;

[0057] Because connected and autonomous vehicles can acquire richer road traffic information at the information level, while human-driven and slow-moving vehicles can only obtain traffic information through driver observation, and the physical differences between human-driven and slow-moving vehicles will affect traffic efficiency. Considering the penetration rate p of human-driven vehicles separately... h slow-moving vehicle penetration rate p s Human-vehicle penetration rate p c , and p h +p s +p c =1, then the mixed traffic flow model of the near-signal zone with slow-moving vehicles can be expressed as:

[0058]

[0059] In the formula, ρ represents density; v represents speed; γ represents the lane-changing rate near the signal zone. The closer to the solid line, the higher the urgency for vehicles to change lanes, and the lower the lane-changing rate, because vehicles will change lanes as early as possible to avoid stopping and waiting; β m The physical meaning is that the farther away a crystal lattice is, the smaller its influence on it; c ρ represents vehicle length; ρ0 represents average road density; ρ j The flow rate of the j-th lattice is represented by 'a'; the driver sensitivity coefficient is represented by 'm'; the lattice number in front of the CAV is represented by 'm'; the maximum number of communicating lattices is represented by 'M'; and T represents the maximum number of communicating lattices. r V(ρ) represents the red light duration; c represents the time lost by vehicles starting or stopping during the transition between traffic lights; λ represents the compliance rate of slower vehicles, where 0 < λ < 1. The smaller λ is, the slower the slower the vehicles, indicating that they are less likely to follow the optimal speed; V(ρ) j The optimal velocity function is given by the calibrated parameters: V1 = -2 m / s, V2 = 8.234 m / s, and C1 = 0.1092 m. -1 , C2=3.414, V1'=14.32m / s, V2'=4.916m / s, C1'=0.3456m -1 C2' = 1.446.

[0060] S4. Based on the changes of vehicles in the cyber-physical space near the signal zone, the near signal zone is divided into a speed suggestion zone and a speed guidance zone. Within the speed suggestion zone, based on the hybrid traffic flow model constructed in step S3, a congestion feedback control method for connected autonomous vehicles that considers the flow difference is designed.

[0061] The suggested speed zone is 300m long, and the speed guidance zone is 200m long. Since both slow-moving vehicles and green lights cause changes in road traffic flow, a feedback control method considering the traffic flow differences across multiple road segments ahead is designed. The model is as follows:

[0062]

[0063] Among them, u j It is a control item, in the following form:

[0064]

[0065] In the formula, ω represents the control gain. If the flow rate of the preceding lattice is greater than the flow rate of the current lattice, it indicates that the path ahead is clear, and the control term u... j A positive value indicates an increase in the optimal flow rate for the current road segment, while a negative value indicates congestion. m This means that the closer a lattice is to itself, the greater its influence.

[0066] Based on the optimal speed function and the average density of the road segment, the optimal speed of the road segment can be obtained conversely, thereby providing speed suggestions for vehicles on the current road segment and suppressing traffic congestion.

[0067] S5. Within the speed guidance zone, design a speed guidance method for connected autonomous vehicles by combining vehicle speed, vehicle position, traffic light phase and time information, so as to provide speed suggestions and speed guidance for connected autonomous vehicles at the road segment and vehicle scale.

[0068] Within the speed guidance zone, vehicles can obtain not only traffic status information but also traffic light information, enabling more refined planning at the vehicle scale. This requires vehicles not to cross the stop line when the light is red and to cross the stop line when the light is green. The speed guidance strategy can be represented as:

[0069] x cav (t0+t real )=x stop

[0070]

[0071] In the formula, t0 represents the current time; x cav (t0) is the current position of the CAV vehicle; v cav (t0) is the current CAV speed; v represents the planned speed of the vehicle; a decel Represents deceleration; t represents the remaining time of the green light phase; r Represents the duration of the red light; t real It is the programmable time of the current phase; x stop It is the position of the stop line; v lim It is the maximum speed limit on the road; This indicates the remaining time for the red light phase.

[0072] When a CAV (Car Access Vehicle) enters the speed recommendation area, it calculates a recommended speed based on the proportions of pedestrians, connected vehicles, and slow-moving vehicles in the three road segments ahead, as well as road traffic information. Within the speed guidance area, it calculates a guidance speed based on vehicle and traffic light information, thereby adopting different control methods according to scale changes in the cyber-physical space.

[0073] It is hereby declared that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-scale congestion suppression method for mixed traffic with slow-moving vehicles near the signal zone, characterized in that, Includes the following steps: S1. Set up a three-lane mixed traffic scenario near the signal zone, which includes connected autonomous vehicles, human-driven vehicles, and slow-moving vehicles; Among them, connected autonomous vehicles can obtain traffic flow, density and traffic light information within their perception range, while human-driven vehicles and slow-moving vehicles can only obtain traffic information through the driver's perception. S2. Based on the mixed traffic scenario set in step S1, analyze the patterns and impacts of traffic light interruption effect, lane changing behavior near signal zone, and slow vehicle movement bottleneck effect; S3. Considering the differences between heterogeneous traffic entities at the cyber-physical level, establish a mixed traffic flow model for near-signal zones that includes slow-moving vehicles; In step S3, the penetration rate of human-vehicle interaction p is considered respectively. h slow-moving vehicle penetration rate p s Human-vehicle penetration rate p c , and p h +p s +p c =1, then the mixed traffic flow model of the near-signal zone with slow-moving vehicles can be expressed as: In the formula, ρ represents density; v represents velocity; γ represents the lane-changing rate near the signal zone; β m This indicates that the greater the distance between lattice elements, the smaller the influence; l c ρ represents vehicle length; ρ0 represents average road density; ρ j The flow rate of the j-th lattice is represented by 'a'; the driver sensitivity coefficient is represented by 'm'; communication between the CAV and the m-th lattice ahead is represented by 'M'; the maximum number of communication lattices is represented by 'T'. r V(ρ) represents the red light duration; c represents the time lost by vehicles starting and stopping during the transition between traffic lights; λ represents the compliance rate of slower vehicles, where 0 < λ < 1. The smaller λ is, the slower the vehicle's speed, indicating that the vehicle is less likely to follow the optimal speed limit; V(ρ) j The optimal velocity function is given by the calibrated parameters: V1 = -2 m / s, V2 = 8.234 m / s, and C1 = 0.1092 m. -1 , C2=3.414, V1'=14.32m / s, V2'=4.916m / s, C1'=0.3456m -1 C2' = 1.446; S4. Based on the changes of vehicles in the cyber-physical space near the signal zone, the near signal zone is divided into a speed suggestion zone and a speed guidance zone. Within the speed suggestion zone, based on the hybrid traffic flow model constructed in step S3, a congestion feedback control method for connected autonomous vehicles that considers the flow difference is designed. In step S4, the model of the designed connected autonomous vehicle congestion feedback control method is as follows: Among them, u j It is a control item, in the following form: ω represents the control gain. If the flux of the preceding lattice is greater than the flux of the current lattice, it means the path ahead is clear, then the control term u... j A positive value indicates an increase in the optimal flow rate for the current road segment, while a negative value indicates congestion. m This means that the closer a lattice is to itself, the greater its influence. Based on the optimal speed function and the average density of the road segment, the optimal speed of the road segment can be obtained, thereby providing speed suggestions for vehicles on the current road segment and suppressing traffic congestion. S5. Within the speed guidance zone, design a speed guidance method for connected autonomous vehicles by combining vehicle speed, vehicle position, traffic light phase and time information, so as to provide speed suggestions and speed guidance for connected autonomous vehicles at the road segment and vehicle scale. Where T represents the length of one traffic light cycle; x i (t) represents the vehicle position; X s X0 represents the starting position of the solid line; X0 represents the starting position of the near-signal zone; n represents the number of lane changes; ρ z Represents road traffic density; ρ s This represents the density corresponding to the maximum traffic flow on the road.

2. The multi-scale congestion suppression method for mixed traffic with slow-moving vehicles near the signal zone according to claim 1, characterized in that: The three-lane mixed traffic scenario set in step S1 near the signal zone is 500m long. Lane changes are not allowed in the last 50m of the guidance zone. The three lanes are left-turn, straight-ahead, and right-turn lanes.

3. The multi-scale congestion suppression method for mixed traffic with slow-moving vehicles near the signal zone according to claim 1, characterized in that: Step S2 includes the following sub-steps: S2.1 Calculate the maximum traffic flow on roads near the signal zone. The calculation expression is as follows: In the formula, Q represents the throughput under the traffic light interruption effect; T represents the length of one traffic light cycle; T g Indicates the length of the green light; Q in Q represents the actual traffic flow into the road; max This indicates the maximum traffic flow on the road without traffic lights; c represents the time lost due to stopping and starting. S2.2 Analyzes the free lane-changing behavior of vehicles by road density and the forced lane-changing behavior of vehicles by lane-changing urgency. The closer the vehicle is to the solid line, the higher the lane-changing urgency. The bottleneck effect created by slow-moving vehicles in S2.3 causes other vehicles to slow down, change lanes, and overtake, thereby leading to a decrease in road traffic flow.

4. The multi-scale congestion suppression method for mixed traffic with slow-moving vehicles near the signal zone according to claim 1, characterized in that: In step S4, the speed suggestion zone is 300m long and the speed guidance zone is 200m long.

5. A multi-scale congestion suppression method for mixed traffic with slow-moving vehicles near the signal zone according to claim 4, characterized in that: The speed guidance method in step S5 includes the following speed guidance strategy: x cav (t0+t real )=x stop t0 represents the current time; x cav (t0) is the current position of the CAV vehicle; v cav (t0) is the current CAV speed; v represents the planned speed of the vehicle; a decel Represents deceleration; This represents the remaining time of the green light phase; t r Represents the duration of the red light; t real It is the programmable time of the current phase; x stop It is the position of the stop line; v lim It is the maximum speed limit on the road; This indicates the remaining time for the red light phase.

Images