Adaptive traffic signal control using deep reinforcement learning: Toward smarter and safer urban mobility

Abstract

In today’s rapidly evolving Intelligent Transportation Systems (ITS), traditional systems for controlling traffic signals are often inadequate in optimizing real-time traffic flow due to their dependency on preset schedules and lack of adaptability to dynamically changing traffic signal phases. These systems cannot analyze dynamic signal timing changes, especially at multiple intersections, resulting in inefficient vehicle flow, longer queues, and higher levels of congestion. Thus, the need arises to develop intelligent systems capable of optimizing traffic flow in real time, reducing delays, and addressing the growing challenges of intelligent transportation systems. To address these requirements, a novel deep reinforcement learning framework that combines the Twin Delayed Deep Deterministic Policy Gradient (TD3) with prioritization-based Intelligent Traffic Control (P-ITC) is proposed for real-time traffic signal optimization using stability techniques. The module focuses on TD3’s stability-enhancing techniques, including clipped Q-learning, delayed and targeted policy updates, and smoothing. The system ensures robust signal timing decisions across intersection networks. PER prioritizes critical traffic signal experiences, ensuring the system learns from key events that influence real-time traffic flow. The proposed TD3P-ITC framework achieves maximum reductions in queue length (up to 22 at transport hub intersections and 25 at highways) and a 17.9 percent decrease (compared to baseline approaches) in simulated accident rates.