AI-based Traffic Prediction and Control
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I6P104Keywords:
AI-based traffic prediction, traffic control, deep learning, spatio-temporal modeling, reinforcement learning, intelligent transportation systems, traffic flow forecasting, congestion management, smart cities, dynamic signal optimizationAbstract
Urban traffic congestion remains a critical challenge for modern cities, leading to increased travel time, fuel consumption, and environmental pollution. Traditional traffic management techniques, which rely on fixed-time or reactive control strategies, often fail to adapt to rapidly changing traffic conditions. This research presents an advanced Artificial Intelligence (AI)-based framework for accurate traffic prediction and intelligent traffic control. The proposed system integrates deep learning models for spatio-temporal traffic forecasting with reinforcement learning algorithms for dynamic signal optimization. Using real-world and simulated traffic datasets, the framework predicts traffic flow, speed, and density with high accuracy while autonomously adjusting signal timings to reduce congestion at intersections. Experimental results demonstrate significant improvements in prediction performance and traffic efficiency, including reduced queue lengths, minimized delays, and optimized travel times. The findings highlight the potential of AI-driven approaches to transform conventional transportation systems into adaptive, efficient, and intelligent traffic management solutions suitable for next-generation smart cities
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