Work

Predicting inland waterway freight demand with a dynamic spatio-temporal graph attention-based multi attention network

Year: 2025

Type: article

Abstract: 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.

Source: Transportation Research Part E Logistics and Transportation Review

DOI: 10.1016/j.tre.2025.104139

Authors: Lingyu Zhang, Oliver Schacht, Qing Liu, Adolf K.Y. Ng

Institutions: Universität Hamburg, Beijing Normal-Hong Kong Baptist University

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Climate action

Open Access status: hybrid

Estimated APC paid: $4,240