TS-PVL: Two-Stage Deep-Reinforcement-Learning-Based Traffic Light With Pedestrian-Vehicle Control in Mixed-Autonomy Traffic

Deep reinforcement learning (DRL) has become a proactive control strategy at intersections in mixed-autonomy traffic of connected autonomous vehicles (CAV) and humandriven vehicles (HDV). Despite the superior performance of DRL in intersection application and vehicle efficiency optimization compared to traditional traffic signal control (TSC) methods, scant research focused on pedestrians, one key and vulnerable component of traffic participants, especially pedestrian safety. Based on DRL techniques and protected/prohibited right-turn (PPRT) policy that regulates vehicular right-turn movement for pedestrian protection, this study proposes a two-stage pedestrianvehicle TSC system called TS-PVL, which cooperative control the traffic signal and pedestrian-vehicle flows, aiming to minimize pedestrian-vehicle conflicts and delays. The first stage adopts the DRL algorithm frameset, comprising the state, action, and reward designed for pedestrians and vehicles, to schedule the green time of PPRT signals. The second stage implements a speed planning model based on the DRL-solved signal timing to manage pedestrian and vehicle movement. To evaluate TS-PVL effectiveness, a real-world intersection with daylong traffic flow data in Changsha City is simulated as the case study in mixedautonomy traffic using various CAV and HDV car-following models. Experimental results show that the proposed TS-PVL method reduces total delay by 15.6%, stop frequency by 17.2%, pedestrian-vehicle conflicts by 28.3%, and jaywalking incidents by 31.5% compared to current TSC and DRL-based TSC methods. Besides, as the market penetration rates of CAVs in mixedautonomy traffic increase, the efficacy of TS-PVL reaches its optimal level.