A Macro-Micro Lane Changing Strategy for Congestion Mitigation in Mixed Traffic

This paper presents an integrated approach to mitigate congestion and improve road utilization near bottlenecks in mixed traffic environments with human-driven vehicles and connected automated vehicles (CAVs). This approach integrates two core elements: a macroscopic tactical lane controller and a microscopic trajectory planner. The objective of the tactical lane controller is to proactively redistribute traffic flow into the designed optimal configuration before reaching congestion bottlenecks, thereby minimizing disruptions to CAVs movements. Bézier curves are employed within the trajectory planner to define both lateral and longitudinal trajectories during the lane-changing process. Additionally, diverse models of game theory are utilized to anticipate interactions between vehicles in various scenarios. The proposed approach's effectiveness is validated through numerical simulation in a lane-drop scenario, demonstrating significant improvements in congestion mitigation.