Discharge control policy based on density and speed for deep Q-learning adaptive traffic signal

Abstract

This study introduces a control strategy based on intersection capacity. The optimisation technique is formulated from available space at discharge routes. The downstream policy utilises density and speed (k-v) measurements to guide a deep Q-learning agent (DQLA) in managing a signalised junction using a constrained local communication protocol. Testing of the DQLA k-v strategy against other control methods is carried out in a simulated micro-model of a real urban traffic network. Though the adaptive signal system design is decentralised, statistical analyses explicitly prove the effectiveness of the discharge-based controller in mitigating operation at a global scale. The DQLA k-v controller has achieved significant cost savings in waiting time (10%−36%) and travel time (5%−25%) and asserted the highest mean travel speed (3.4 m/s). Consequently, vehicular traffic experienced the least time loss when traversing routes and witnessed fewer stops leading to close to optimum network operation at a 0.80 clearance ratio.