Incorporating environmental knowledge embedding and spatial-temporal graph attention networks for inland vessel traffic flow prediction

Abstract

Accurate prediction of vessel traffic flow is crucial for maritime regulatory authorities and transportation planners. However, existing methods for inland vessel traffic flow prediction often overlook spatial correlation and environmental influences, leading to suboptimal accuracy. To address this issue, we propose an innovative model that incorporates environmental knowledge embedding and a spatial-temporal information extraction module. Our approach involves constructing a vessel traffic knowledge graph, embedding traffic flow through knowledge representation learning. The spatial-temporal information extraction module is leveraged to analyze inherent periodicity and external spatial relationships in vessel traffic flow. Extensive experiments on real-world datasets demonstrate that our approach significantly enhances predictive accuracy. In comparison to the second-ranked model, our approach achieves a decrease of 0.46 in mean absolute error, a decrease of 0.64 in root mean squared error, an increase of 3.06% in accuracy, and an increase of 0.07 in R-squared. Furthermore, our approach excels in upstream, downstream and long-term prediction, and displays robustness in handling noisy data.