CTM-based traffic signal optimization of mixed traffic flow with connected automated vehicles and human-driven vehicles

Abstract

This paper proposes a cell transmission model (CTM)-based traffic signal timing model of mixed traffic flow composed of connected automated vehicles (CAVs) and human-driven vehicles (HDVs). Firstly, the CTM of mixed traffic flow is derived from considering the influence of the market penetration rates (MPRs) of CAVs. Secondly, the dynamic evolution is developed to capture the queue accumulation and the congestion dissipation at the entrance of the intersection. Then, the optimization model is proposed based on the constraints of traffic signals and the relationship of flow transmission between adjacent cells. Moreover, the simultaneous perturbation stochastic approximation (SPSA) algorithm is adopted to solve the proposed model. The evolution laws of the density of each entrance with time and space are compared under the fixed and the optimized traffic signals. Finally, the vehicle’s delay is selected as the evaluation index, and the superiority of the optimization model is discussed. The results show that the proposed model can effectively reduce the range and dissipation time of traffic congestion. The average dissipation efficiency of each entrance is increased by 11.11%. Furthermore, the traffic delay gradually decreases with the MPRs of CAVs, and the delay of homogeneous CAVs is 14.81% lower than that of homogeneous HDVs traffic flow. Therefore, the large-scale application of CAVs can alleviate traffic congestion and improve the traffic capacity of the signalized intersection.