VIA AI: Reliable Deep Reinforcement Learning for Traffic Signal Control

Matvey Gerasyov, Dmitrii Kiselev, Maxim Beketov, Ilya Makarov

Abstract: Traffic signal control optimization is an integral part of any modern transportation system. However, modern traffic signal control systems often rely on predetermined fixed rules to adjust traffic signal timings. This paper presents VIA AI - an intelligent traffic signal control system that leverages deep reinforcement learning (RL) applied to count-based traffic data. Our solution offers additional adaptability and flexibility by allowing the system to learn and adjust its strategies based on real-time feedback and environmental changes. We test our approach using real-world traffic data and show that it outperforms classical methods of intersection control.