Coordination and Analysis of Connected and Autonomous Vehicles in Freeway On-Ramp Merging Areas
Doctoral thesis, 2022
Freeway on-ramps are typical bottlenecks in the freeway network, where the merging maneuvers of ramp vehicles impose frequent disturbances on the traffic flow and cause negative impacts on traffic safety and efficiency. The emerging Connected and Autonomous Vehicles (CAVs) hold the potential for regulating the behaviors of each individual vehicle and are expected to substantially improve the traffic operation at freeway on-ramps. The aim of this research is to explore the possibilities of optimally facilitating freeway on-ramp merging operation through the coordination of CAVs, and to discuss the impacts of CAVs on the traffic performance at on-ramp merging.
In view of the existing research efforts and gaps in the field of CAV on-ramp merging operation, a novel CAV merging coordination strategy is proposed by creating large gaps on the main road and directing the ramp vehicles into the created gaps in the form of platoon. The combination of gap creation and platoon merging jointly facilitates the mainline and ramp traffic and targets at the optimal performance at the traffic flow level. The coordination consists of three components: (1) mainline vehicles proactively decelerate to create large merging gaps; (2) ramp vehicles form platoons before entering the main road; (3) the gaps created on the main road and the platoons formed on the ramp are coordinated with each other in terms of size, speed, and arrival time. The coordination is analytically formulated as an optimization problem, incorporating the macroscopic and microscopic traffic flow models. The model uses traffic state parameters as inputs and determines the optimal coordination plan adaptive to real-time traffic conditions.
The impacts of CAV coordination strategies on traffic efficiency are investigated through illustrative case studies conducted on microscopic traffic simulation platforms. The results show substantial improvements in merging efficiency, throughput, and traffic flow stability. In addition, the safety benefits of CAVs in the absence of specially designed cooperation strategies are investigated to reveal the CAV’s ability to eliminate critical human factors in the ramp merging process.
In view of the existing research efforts and gaps in the field of CAV on-ramp merging operation, a novel CAV merging coordination strategy is proposed by creating large gaps on the main road and directing the ramp vehicles into the created gaps in the form of platoon. The combination of gap creation and platoon merging jointly facilitates the mainline and ramp traffic and targets at the optimal performance at the traffic flow level. The coordination consists of three components: (1) mainline vehicles proactively decelerate to create large merging gaps; (2) ramp vehicles form platoons before entering the main road; (3) the gaps created on the main road and the platoons formed on the ramp are coordinated with each other in terms of size, speed, and arrival time. The coordination is analytically formulated as an optimization problem, incorporating the macroscopic and microscopic traffic flow models. The model uses traffic state parameters as inputs and determines the optimal coordination plan adaptive to real-time traffic conditions.
The impacts of CAV coordination strategies on traffic efficiency are investigated through illustrative case studies conducted on microscopic traffic simulation platforms. The results show substantial improvements in merging efficiency, throughput, and traffic flow stability. In addition, the safety benefits of CAVs in the absence of specially designed cooperation strategies are investigated to reveal the CAV’s ability to eliminate critical human factors in the ramp merging process.
Operational efficiency
Safety impacts
Freeway on-ramp merging
Microscopic traffic simulation
Connected and autonomous vehicles
Coordinative merging strategy
SB-H5, Sven Hultins Gata 6, Campus Johanneberg, Chalmers (zoom password: 504487)
Opponent: Professor Shimul Haque, Queensland University of Technology, Australia
Author
Jie Zhu
Transportgruppen
Merging control strategies of connected and autonomous vehicles at freeway on-ramps: a comprehensive review
Journal of Intelligent and Connected Vehicles,;Vol. 5(2022)p. 99-111
Review article
Improving Freeway Merging Efficiency via Flow-Level Coordination of Connected and Autonomous Vehicles
IEEE Transactions on Intelligent Transportation Systems,;Vol. In Press(2024)
Journal article
Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas
Multimodal Transportation,;Vol. 1(2022)
Journal article
Zhu, J., Gao, K. Bi-level Ramp Merging Coordination in Congested Mixed Traffic Conditions 1 with Human-driven and Connected Autonomous Vehicles
Safety analysis of freeway on-ramp merging with the presence of autonomous vehicles
Accident Analysis and Prevention,;Vol. 152(2021)
Journal article
Entering a freeway from on-ramps is not an easy task. The merging maneuver requires a lot of negotiations between the mainline and ramp vehicles. Safety concerns may arise due to high traffic speed on freeways, implicit driving intentions of conflicting vehicles, blind spots introduced by the physical separation of roads, and increased attention of drivers in the merging process. Further, under dense traffic, it is difficult for the on-ramp vehicles to find space for merging, and their frequent cut-in behaviors may disturb the mainline traffic and induce traffic congestions in such bottleneck areas.
The emerging Connected Autonomous Vehicles (CAVs) are capable of timely communication with other road users and infrastructures, so that their driving tasks can be planned in advance based on a better awareness of surrounding traffic conditions. Further, the planned driving tasks can be executed in a timely and accurate manner with less delays and errors related to human drivers. These advanced capabilities of CAVs provide a potential to improve traffic operation at freeway on-ramp entries.
In this research, a novel CAV merging strategy is proposed to coordinate the mainline and ramp traffic at freeway on-ramps. The strategy first collects the merging needs of on-ramp vehicles and requests the mainline vehicles to decelerate accordingly to create on-demand gaps on the main road. Then, the ramp vehicles are formed into groups and guided into the created mainline gaps at the appropriate time and speed. The proposed merging coordination is adapted and tested in microscopic simulations in different scenarios, including: (1) a basic scenario of single-lane freeways with full CAV penetration rate, (2) an extended scenario with mixed traffic consisting of CAVs and Human-Driven Vehicles (HDVs), and (3) an extended scenario with multiple lanes on the freeway. The simulation results show that the proposed merging coordination prevents the occurrence of traffic congestions and leads to increased traffic efficiency at on-ramp merging. Further, it reveals that the introduction of CAVs reduces the conflicting risk at on-ramp merging through the well-designed cooperation between vehicles and the elimination of critical human factors in driving.
The emerging Connected Autonomous Vehicles (CAVs) are capable of timely communication with other road users and infrastructures, so that their driving tasks can be planned in advance based on a better awareness of surrounding traffic conditions. Further, the planned driving tasks can be executed in a timely and accurate manner with less delays and errors related to human drivers. These advanced capabilities of CAVs provide a potential to improve traffic operation at freeway on-ramp entries.
In this research, a novel CAV merging strategy is proposed to coordinate the mainline and ramp traffic at freeway on-ramps. The strategy first collects the merging needs of on-ramp vehicles and requests the mainline vehicles to decelerate accordingly to create on-demand gaps on the main road. Then, the ramp vehicles are formed into groups and guided into the created mainline gaps at the appropriate time and speed. The proposed merging coordination is adapted and tested in microscopic simulations in different scenarios, including: (1) a basic scenario of single-lane freeways with full CAV penetration rate, (2) an extended scenario with mixed traffic consisting of CAVs and Human-Driven Vehicles (HDVs), and (3) an extended scenario with multiple lanes on the freeway. The simulation results show that the proposed merging coordination prevents the occurrence of traffic congestions and leads to increased traffic efficiency at on-ramp merging. Further, it reveals that the introduction of CAVs reduces the conflicting risk at on-ramp merging through the well-designed cooperation between vehicles and the elimination of critical human factors in driving.
Areas of Advance
Transport
Subject Categories
Transport Systems and Logistics
Infrastructure Engineering
Vehicle Engineering
ISBN
978-91-7905-643-8
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5109
Publisher
Chalmers
SB-H5, Sven Hultins Gata 6, Campus Johanneberg, Chalmers (zoom password: 504487)
Opponent: Professor Shimul Haque, Queensland University of Technology, Australia