The adaptive dynamic programming signal control system for person in a connected vehicle environment

Urban person delay and congestion remain persistent challenges in modern traffic systems. Leveraging Connected Vehicle (CV) data, this study proposes a novel Person-Based Adaptive Control Algorithm (PB-ACA) to minimize average person delay at isolated urban intersections. Unlike traditional vehicle-based controls, PB-ACA integrates vehicle occupancy data with real-time trajectory and speed information to assign signal priorities based on person-level delay impacts. A three-layered dynamic programming approach is adopted in PB-ACA with the objective of minimizing person delay with real time occupancy data. A signal phase transition exploration mechanism is also developed to explore all possible signal timing plans according to non-conflicting phase rules and efficient principles. The generalized vehicle trajectory and car-following model is adopted for predicting the platoon discharge times considering different cases and fleet trajectories to enhance the responsiveness to CV data. Performance evaluations using microsimulation in SUMO compare PB-ACA against three benchmark approaches: fixed-time control (FTCA), inductive loop-actuated control (ILACA), and vehicle-based adaptive CV signal control (VBACVSC). Results show that PB-ACA reduces average person delay by up to 55% compared to FTCA, by 42% compared to ILACA and by 11% relative to VBACVSC, especially benefiting high-occupancy vehicles. These findings demonstrate PB-ACA's potential to improve individual mobility and promote equitable traffic signal control in connected environments.