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.

KEYWORDS:

• downstream discharge routes
• traffic control policy
• adaptive signal controller
• deep Q-learning
• traffic network management
• intersection capacity

Acknowledgment

The authors acknowledge the collaboration with JA Project Consultant Sdn. Bhd., Kuala Lumpur, Malaysia to acquire the needed traffic data for the case study in this research project.

Disclosure statement

No potential conflict of interest was reported by the author(s).