A DRL-Based Lane-Changing Decision Model for Connected and Automated Trucks to Reduce Congestion in Freeway Merging Areas

Abstract

Trucks, which primarily travel in the rightmost lane on freeways, are in direct conflict with on-ramp vehicles and constitute a key factor contributing to congestion in merging areas. To address this challenge, this study proposes an adaptive information-fusion and stability-driven deep reinforcement learning (ASR) model for connected and automated trucks (CATs) lane-changing decision-making. Specifically, a graph convolutional network (GCN) is employed to encode topological relationships and interaction strengths into the state space, capturing the dynamic interactions between CATs and other vehicles in the connected traffic environment. In addition, to improve traffic system stability, a reward function based on the Lyapunov stability principle is designed to suppress fluctuations in traffic flow speed and acceleration, thereby mitigating merging conflicts. Simulation experiments conducted in Simulation of Urban MObility (SUMO) at varying traffic volumes demonstrate that the proposed model outperforms all benchmark models in alleviating merging area congestion, improving traffic stability, and reducing total travel time, total delay time, fuel consumption, and NOx emissions on the studied freeway segment. Moreover, the effectiveness of the ASR model is further validated under different CAT penetration rates, different speed limits, and unseen traffic volumes. The proposed model is also shown to improve truck lane change safety based on time-to-collision (TTC) comparisons. The findings of this study may provide useful guidance for traffic operations in freeway merging areas.