Deep Reinforcement Learning for Smart Traffic Management in Indian Cities

Abstract

Indian cities are plagued by traffic congestion, leading to significant economic and environmental costs. This paper explores the application of Deep Reinforcement Learning (DRL) to develop smart traffic management systems capable of optimizing traffic flow in real-time. The study evaluates the performance of DRL algorithms such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) in managing traffic signals, routing vehicles, and reducing congestion in cities like Delhi, Mumbai, and Bangalore. The research utilizes traffic simulation models that integrate DRL with real-time traffic data from sensors and cameras. Case studies from smart traffic management pilot projects in Indian cities are presented to highlight the effectiveness of these systems in reducing travel time, fuel consumption, and air pollution. The paper also discusses the challenges of deploying DRL-based traffic management systems in India, including infrastructure limitations, data accuracy, and scalability. Additionally, the ethical implications of using AI for traffic management, such as privacy concerns and algorithmic bias, are considered