Deep Reinforcement Learning Models for Smart Traffic Management

Abstract

Traffic congestion remains a critical challenge in urban areas, leading to increased travel time, fuel consumption, and environmental pollution. Traditional traffic management systems often rely on fixed-time control or heuristic-based methods, which lack adaptability to dynamic traffic conditions. Recent advancements in Deep Reinforcement Learning (DRL) offer promising solutions by enabling intelligent, adaptive, and scalable traffic signal control. This paper explores the design and implementation of DRL models tailored for smart traffic management systems.

The proposed approach leverages state-of-the-art DRL algorithms such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) to optimize traffic signal timings dynamically based on real-time traffic flow data. A multi-agent framework is introduced, allowing decentralized decision-making at intersections, which enhances scalability and robustness. The environment is modeled using traffic simulators such as SUMO, incorporating realistic traffic scenarios including varying vehicle densities, pedestrian flows, and incident occurrences.

Simulation results demonstrate that the DRL-based controllers outperform traditional fixed-time and actuated control systems by reducing average waiting times, queue lengths, and total travel times significantly. The models also adapt efficiently to unexpected traffic disruptions, showcasing superior generalization capabilities. Furthermore, the integration of reward shaping and attention mechanisms improves convergence speed and policy stability.

This research contributes a novel DRL framework for smart traffic management that balances traffic efficiency, environmental sustainability, and user convenience. The findings highlight the potential of DRL in revolutionizing urban traffic control, paving the way for intelligent transportation systems in smart cities.