Predicting inland waterway freight demand with a dynamic spatio-temporal graph attention-based multi attention network
提出一种动态图注意力多注意力网络模型，用于预测内河货运需求，该模型能捕捉时空动态变化，在对比实验中显著优于传统方法，有助于港口规划和支持可持续运输转型。
Inland waterway transport (IWT) has gained significant attention for its environmental sustainability. Consequently, there is an increasing focus on boosting IWT’s market share to reduce transportation emissions. Accurate forecasting of IWT freight demand is crucial for ports to plan long-term targets and support a mode shift towards sustainable transport. However, forecasting IWT demand is challenging due to the complexity of external environments. This paper introduces a Dynamic Graph Attention Multi-attention Network (DGAT-MAN) model designed to forecast IWT freight demand by capturing evolving spatial and temporal dynamics. Our comparative analysis demonstrates that this model significantly outperforms established baseline approaches. As one of the first studies to apply spatio-temporal deep learning models to IWT demand forecasting, this work contributes a novel approach to enhancing sustainable transport planning.