Signal timing at an isolated intersection under mixed traffic environment with self-organizing connected and automated vehicles

This study provides a signal timing model for isolated intersections under the mixed traffic environment consisting of connected and human-driven vehicles (CHVs) and connected and automated vehicles (CAVs). Different from existing studies, CAVs are not controlled by traffic controllers and conduct trajectory planning themselves, which are called self-organizing CAVs (SOCAVs). The specific trajectory planning strategies of SOCAVs are not accessible to traffic controllers either. The signal optimization problem is formulated as a mixed integer linear programming (MILP) model for total vehicle delay minimization. The states of SOCAVs and CHVs passing the stop bar are predicted without prior information of the trajectory planning strategies of SOCAVs. SOCAVs can lead approaching platoons to pass the intersection effectively, and such "leading effects" of SOCAVs are utilized. Phase sequence and duration are optimized with the "structure-free" phasing scheme. A parallel particle swarm optimization algorithm with a grouping strategy is designed to solve the optimization model at a reduced scale for computational efficiency. Numerical studies validate that (1) the proposed algorithm significantly outperforms the benchmark method, which directly solves the proposed MILP model using the solver Gurobi 9.0, under medium and high traffic demand; and (2) the proposed model significantly outperforms fixed-time and vehicle-actuated signal control in terms of vehicle delay and throughput. Sensitivity analysis shows that the SOCAV penetration rate of 30% is sufficient to guarantee satisfactory performance of the proposed signal timing model.