MPC-based dynamic speed control of CAVs in multiple sections upstream of the bottleneck area within a mixed vehicular environment

The traffic congestion phenomenon in bottleneck areas is always a difficult problem that occurs in many freeways. To reduce traffic congestion and improve the safety of bottleneck areas, a dynamic speed control method is proposed using connected and autonomous vehicle (CAV) technology. First, the traditional cell transmission model (CTM) is improved to describe the capacity drop phenomenon at the freeway bottleneck in mixed CAV and human-driven vehicle (HV) traffic flow. Then, a model predictive control (MPC)-based variable speed control (MVSC) is proposed to limit the dynamic speed of CAVs in multiple minor sections separately considering the dynamic transmission of the congestion area. Subsequently, a particle swarm optimization (PSO) algorithm is adopted to solve the control model. Finally, the proposed multiple-sections MVSC and two other methods, the no variable speed control (NVSC) and the feedback variable speed control (FVSC), are analysed and compared. The results showed that the MVSC strategy proposed in this paper could effectively improve traffic efficiency and safety compared to NVSC and FVSC, even in a low CAV penetration rate environment.