用多头多尺度注意力提升交通流量预测精度
MSCMHMST: A traffic flow prediction model based on Transformer
- 引入多头多尺度注意力机制并行处理数据
- 在PeMS04/08数据集上实现长中短期高精度预测
- 适合交通管理与智能导航系统研发人员参考
本研究提出一种基于Transformer的混合模型MSCMHMST，旨在解决交通流量预测中的关键挑战。传统单一方法在预测任务中存在局限，而混合方法通过融合不同模型的优势，可提供更准确、更鲁棒的预测结果。MSCMHMST引入多头多尺度注意力机制，使模型能并行处理数据的不同部分，并从多个角度学习其内在表示，从而增强应对复杂情况的能力。该机制有效捕捉多尺度特征，理解短时变化与长期趋势。在PeMS04/08数据集上，经特定实验设置验证，该模型在长、中、短期交通流量预测中均表现出优异的鲁棒性与准确性。结果表明，该模型具有显著潜力，为交通流量预测领域提供了新的有效解决方案。
原文摘要 · Abstract (English)
This study proposes a hybrid model based on Transformers, named MSCMHMST, aimed at addressing key challenges in traffic flow prediction. Traditional single-method approaches show limitations in traffic prediction tasks, whereas hybrid methods, by integrating the strengths of different models, can provide more accurate and robust predictions. The MSCMHMST model introduces a multi-head, multi-scale attention mechanism, allowing the model to parallel process different parts of the data and learn its intrinsic representations from multiple perspectives, thereby enhancing the model's ability to handle complex situations. This mechanism enables the model to capture features at various scales effectively, understanding both short-term changes and long-term trends. Verified through experiments on the PeMS04/08 dataset with specific experimental settings, the MSCMHMST model demonstrated excellent robustness and accuracy in long, medium, and short-term traffic flow predictions. The results indicate that this model has significant potential, offering a new and effective solution for the field of traffic flow prediction.
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