Work

Dynamic Spatio-Temporal Modeling for Vessel Traffic Flow Prediction with FSTformer

Year: 2025

Type: article

Abstract: With the rapid growth of global shipping, accurate vessel traffic prediction is essential for waterway management and navigation safety. This study proposes the Fusion Spatio-Temporal Transformer (FSTformer) to address non-Gaussianity, non-stationarity, and spatiotemporal heterogeneity in traffic flow prediction. FSTformer incorporates a Weibull–Gaussian Transformation for distribution normalization, a hybrid Transformer encoder with Heterogeneous Mixture-of-Experts (HMoE) to model complex dependencies, and a Kernel MSE loss function to enhance robustness. Experiments on AIS data from the Fujiangsha waters of the Yangtze River show that FSTformer consistently outperforms baseline models across multiple horizons. Compared with the best baseline (STEAformer), it reduces MAE, RMSE, and MAPE by 3.9%, 1.8%, and 6.3%, respectively. These results demonstrate that FSTformer significantly improves prediction accuracy and stability, offering reliable technical support for intelligent shipping and traffic scheduling in complex waterways.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse13091822

Authors: Dong Zhang, Haichao Xu, Yongfeng Guo, Shaoxi Li, Yinyin Lu, Mingyang Pan

Institutions: Dalian Maritime University, Wuhan University of Technology, Sanjiang University

Language: English

Topic: Maritime Navigation and Safety

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137