Improving Traffic Operation of Bottleneck in a Connected and Automated Vehicles Environment: An Integrated Lane-Level Control Method

Aiming at improving the operation of the bottleneck area of the highway in the environment of a connected and automated vehicle, this paper proposes an integrated lane-level control (ILC) method by combining the variable speed limit control method and lane selection method into a comprehensive framework. A lane-level variable speed limit (LVSL) control method is proposed for the mixed traffic flow based on a deep deterministic policy gradient algorithm. Then, a lane-level short-term traffic prediction (LSTP) model based on hybrid deep learning is built to forecast the traffic state in a next control horizon. Finally, a lane selection method using a dynamic programming algorithm is established for the connected automated vehicle (CAV) to look for the optimal lane-level route by considering the estimated traffic speeds and limit speeds from LSTP and LVSL respectively. Comprehensive simulation-based evaluation experiments were conducted in various scenarios e.g., with different traffic demands, penetration rates of CAVs, length of control horizons, and the number of lanes. The evaluation results reveal that the proposed LSTP model outperforms the state-of-the-art traffic prediction models in terms of accuracy and stability. In addition, the proposed ILC method is capable of improving the traffic operation of the bottleneck efficiently by synthetically taking the advantage of the LVSL method and lane selection method. Compared with the no-control strategies, the ILC method can reduce total travel time by more than 30% in various traffic scenarios.