Hierarchical Cooperative Lane-Change Decision for CAVs to Enhance Mainline Traffic Resilience at Freeway Merging Areas

Cooperative lane-change decisions at freeway merging areas can resolve local vehicle conflicts. A locally acceptable interaction does not necessarily ensure the maintenance and recovery of mainline traffic performance after a lane-change disturbance. Individual utility-oriented cooperation may even induce excessive yielding and amplify disturbances to upstream traffic. To address this problem, a hierarchical cooperative lane-change decision model for connected and automated vehicles (CAVs) is developed to enhance mainline traffic resilience. A coupled resilience indicator system of safety and efficiency is first established to characterize mainline performance maintenance and recovery throughout the lane-change disturbance process. The hierarchical cooperative lane-change decision model consists of a behavior-decision layer and a system-optimization layer. The behavior-decision layer develops a Stackelberg game to determine the equilibrium strategy between lane change and yielding, with resilience incorporated into the vehicle payoff. The system-optimization layer optimizes the lane-change speed and acceleration according to mainline speed fluctuations and vehicle delay. The coupled resilience of the mainline system is further used to determine the final execution parameters from the Pareto solution set. The results show that the proposed hierarchical cooperative lane-change model increases the minimum mean safety performance of the mainline system from approximately 0.66 to 0.78. System resilience varies nonmonotonically with the CAV penetration rate: the coupled resilience decreases to approximately 0.872 at a 60% penetration rate and recovers to approximately 0.887 at an 80% penetration rate. In addition, the increase in cooperation intensity from 0.3 to 0.9 improves system safety resilience by up to 2.6%. These results demonstrate the effectiveness of hierarchical coordination and reveal the influence of heterogeneous mixed traffic on mainline traffic resilience.