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<p>Rapid urbanization and increasing vehicle ownership have intensified traffic congestion, creating serious social, economic, and environmental challenges. Accurate short-term traffic flow prediction is therefore essential for effective urban traffic management and congestion mitigation. While traditional models struggle with the non-linear, dynamic nature of traffic data, our approach proposes an optimized synthesis named the Meteorological Spatio-Temporal Transformer Network with Multi-Head Attention and Whale Optimization Algorithm (MSTT-MHA-WOA). This framework enhances prediction by uniquely integrating non-linear meteorological features and, critically, by using the Whale Optimization Algorithm (WOA) for autonomous hyper parameter tuning. We evaluate the model’s robust and consistent performance across six diverse, real-world traffic scenarios–a crucial test of its generalization capability under complex conditions.</p>

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Enhanced short-term traffic flow forecasting using a meteorological spatio-temporal transformer with multi-head attention and whale optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月8日

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  • S. Revathi,
  • M. P. Paulraj,
  • R. N. Devendra Kumar

摘要

Rapid urbanization and increasing vehicle ownership have intensified traffic congestion, creating serious social, economic, and environmental challenges. Accurate short-term traffic flow prediction is therefore essential for effective urban traffic management and congestion mitigation. While traditional models struggle with the non-linear, dynamic nature of traffic data, our approach proposes an optimized synthesis named the Meteorological Spatio-Temporal Transformer Network with Multi-Head Attention and Whale Optimization Algorithm (MSTT-MHA-WOA). This framework enhances prediction by uniquely integrating non-linear meteorological features and, critically, by using the Whale Optimization Algorithm (WOA) for autonomous hyper parameter tuning. We evaluate the model’s robust and consistent performance across six diverse, real-world traffic scenarios–a crucial test of its generalization capability under complex conditions.