AI-Based Real-Time Traffic Congestion Prediction and Signal Optimization System

Abstract: Traffic congestion has become a major challenge in urban areas due to rapid population growth and increasing vehicle density. Conventional traffic signal systems operate on fixed timing schedules and fail to adapt to real-time traffic conditions, leading to inefficient traffic flow and increased delays. This paper proposes an AI-based real-time traffic congestion prediction and adaptive signal optimization system to address these limitations. The system utilizes techniques from Machine Learning to analyze traffic parameters such as vehicle density, time of day, and historical traffic patterns. A predictive model is developed to classify congestion levels, and based on the predicted output, signal timings are dynamically adjusted to optimize traffic flow. The proposed approach improves traffic efficiency by reducing vehicle waiting time and minimizing congestion at intersections. Experimental results demonstrate that the AI-based system outperforms traditional fixed-time signal control methods in terms of accuracy and overall traffic management performance. This work contributes to the development of intelligent and scalable solutions aligned with modern Smart City initiatives.

Keywords: Artificial Intelligence, Traffic Congestion Prediction, Adaptive Signal Control, Real-Time Traffic Management, Machine Learning, Smart Cities, Intelligent Transportation Systems, Data Analytics