AI-Driven Traffic Congestion Management: A Predictive Analytics Approach for Smart Cities

Rapid urbanization, increased vehicle usage, and the growth of developing cities have resulted in severe traffic congestion, leading to economic losses, environmental harm, and a decline in quality of life. Traditional traffic management systems tend to be reactive, focusing on addressing traffic flow issues after they occur, rather than proactively managing them. To tackle this challenge, this paper proposes an AI-driven system designed to predict and manage traffic congestion. The system leverages continuous traffic data from IoT devices, such as images, GPS, and inductive loop sensors, to monitor real-time traffic conditions. Traffic predictions are made using a combination of CNN and LSTM networks. Additionally, Geographic Information System (GIS) technology is integrated for spatial analysis and dynamic traffic control, enabling real-time traffic light adjustments and providing drivers with alternative routes to avoid congestion. Preliminary experiments show that intelligent traffic management systems can significantly improve traffic flow and reduce congestion in large cities. The paper also explores challenges related to data privacy, scalability, and future advancements, including the integration of self-driving vehicles and the application of reinforcement learning. This system offers a practical solution for alleviating congestion and optimizing urban transportation systems.