Reinforcement Learning-Based Traffic Signal Control for IoT-Enabled Intersections
Urban traffic congestion remains a persistent challenge in car-dependent cities, imposing significant economic and societal costs. Traffic signal systems are increasingly deployed as networked cyber-physical components within smart-city infrastructures, where distributed sensing and edge intelligence enable adaptive traffic management. This paper investigates reinforcement learning (RL) as an edge-intelligent approach for adaptive traffic signal operation at a signalized urban intersection in Kuwait. A Proximal Policy Optimization (PPO)-based controller is developed to dynamically allocate green-phase durations using locally observed traffic states, without relying on future demand inform...
| Authors | Yousef AlSaqabi |
|---|---|
| Primary category | eess.SY |
| Published | 20 June 2026 |
| Last updated at source | 6 October 2026 |
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