Advancing Machine Learning for Real-Time Traffic Prediction

Noor Staky, Abdhul Hasan

Abstract


Real-time traffic prediction is critical for intelligent transportation systems (ITS) and urban mobility management. This paper explores advancements in machine learning (ML) techniques for accurate and scalable traffic forecasting. By analyzing historical and real-time traffic data, we assess the performance of various ML models, including traditional regression techniques, neural networks, and hybrid approaches. Our findings highlight the importance of spatiotemporal data integration, feature engineering, and model interpretability. These insights contribute to enhancing urban traffic efficiency and reducing congestion.

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