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<p>Deep learning has empowered traffic prediction models to integrate diverse auxiliary data sources, such as weather and temporal features, for enhanced forecasting accuracy. However, existing approaches often suffer from limited generality and scalability, and the field lacks a unified benchmark for fair model comparison. This absence hinders consistent performance evaluation, slows the development of robust and adaptable models, and makes it challenging to quantify the incremental benefits of different auxiliary data sources. To address these issues, we present MltAuxTSPP, a unified benchmark framework for deep learning-based traffic state prediction with multi-source auxiliary data. The framework features a standardized data container and a fusion embedding module, enabling consistent utilization of heterogeneous data and improving scalability. It produces unified hidden representations that can be seamlessly adopted by various downstream models, ensuring fair and reproducible comparisons under identical conditions. Extensive experiments on real-world datasets demonstrate that MltAuxTSPP effectively leverages weather and temporal features to improve long-term forecast performance and offers a practical and reproducible foundation for advancing research in traffic state prediction.</p>

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MltAuxTSPP: a unified benchmark for deep learning-based traffic state prediction with multi-source auxiliary data

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • Yusong Zhou,
  • Xiaoyu Jiang,
  • Shu Sun,
  • Xinmin Zhang,
  • Yuanqiu Mo,
  • Zhihuan Song

摘要

Deep learning has empowered traffic prediction models to integrate diverse auxiliary data sources, such as weather and temporal features, for enhanced forecasting accuracy. However, existing approaches often suffer from limited generality and scalability, and the field lacks a unified benchmark for fair model comparison. This absence hinders consistent performance evaluation, slows the development of robust and adaptable models, and makes it challenging to quantify the incremental benefits of different auxiliary data sources. To address these issues, we present MltAuxTSPP, a unified benchmark framework for deep learning-based traffic state prediction with multi-source auxiliary data. The framework features a standardized data container and a fusion embedding module, enabling consistent utilization of heterogeneous data and improving scalability. It produces unified hidden representations that can be seamlessly adopted by various downstream models, ensuring fair and reproducible comparisons under identical conditions. Extensive experiments on real-world datasets demonstrate that MltAuxTSPP effectively leverages weather and temporal features to improve long-term forecast performance and offers a practical and reproducible foundation for advancing research in traffic state prediction.