Macro-micro integration of game-theoretic trajectory planning and tactical lane control for mixed traffic control at motorway bottlenecks

This paper presents an integrated approach to mitigate traffic congestion and improve road utilization at motorway bottlenecks in a mixed traffic environment with human-driven vehicles and connected automated vehicles (CAVs). The proposed methodology consists of two essential elements: a tactical lane controller and a game theory-based trajectory planner. The lane controller, based on macroscopic traffic characteristics, proactively redistributes traffic flow into an optimal configuration prior to reaching and activating potential bottlenecks, thereby identifying and directing suitable CAVs to execute lane-changing maneuvers. To achieve this, the (microscopic) trajectory planner anticipates vehicle interactions and quantifies the loss induced by lane-changing maneuvers, feeding this information back to the lane controller. Considering the heterogeneous nature of mixed traffic, game theory models are designed for realistic prediction and assessment. Numerical experiments demonstrate the proposed approach’s effectiveness in reducing traffic congestion and travel delays, considering lane-drop and diverging scenarios. Moreover, results show that the efficacy and robustness improve as the CAV penetration rate increases.