Multilane freeway merging control via trajectory optimization in a mixed traffic environment

Article; Early Access

关键词: ADAPTIVE CRUISE CONTROL;    AUTONOMOUS VEHICLES;    AUTOMATED VEHICLES;    FLOW;    MODEL;    ASSIGNMENT;    SYSTEMS;   

DOI  :  10.1049/itr2.12382

来源: SCIE

【 摘 要 】

Merging activities at freeway merging areas can cause significant recurrent and non-recurrent bottleneck congestion due to vehicles' mandatory lateral conflicts. The Connected and Automated Vehicles (CAVs), with their capabilities of real-time communication and precise trajectory control, hold great potential to prevent or mitigate such critical conflicts at merging areas. However, the performance of CAVs may be impaired by the imbalance of lane flow distribution, and non-cooperative movements of Human-driven Vehicles (HDVs) in the mixed traffic environment (i.e. traffic mixed with CAVs and HDVs). In this paper, a novel two-level hierarchical traffic control framework for multilane merging areas under the mixed traffic environment is developed. Note that this paper assumes that the merging sequence is determined by the high control level and focuses on the low level of the control framework. The low control level not only establishes a trajectory optimization strategy for CAVs with lane-changing optimization and cooperative merging control, but includes a human-like merging strategy for HDVs. First, to balance downstream lane flow distribution and provide sufficient merging space for on-ramp vehicles, a lane-changing optimization method is proposed to choose a certain number of designated mainline CAVs to perform early lane changes at the upstream mainline. Second, a cooperative merging control method is presented to optimize the longitudinal trajectories of both mainline and on-ramp CAVs while accounting for the movement of HDVs. Third, Gipps car-following model and heuristic control are combined to represent the HDVs' merging maneuvers. The proposed algorithm simulates and performs merging maneuvers at a typical two-lane freeway merging area and verifies it in various scenarios considering demand level, demand splits and CAV Penetration Rate (PR). The simulation results show that the proposed algorithm can effectively facilitate merging operations, reduce the Total Travel Time (TTT), and increase the Average Travel Speeds (ATS) compared to other merging algorithms. Specifically, compared to the case of using only cooperative merging control method, the proposed algorithm can further reduce TTT by 25.5% and increase ATS by 33.38%. When PR gradually increased, the control performance of the proposed algorithm can be further improved.