Original scientific paper
https://doi.org/10.21278/brod77309
Spatio-temporal prediction of vessel traffic flow based on GL-STFormer
Quandang Ma
; HubeiKey Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan, China
Qihong Shao
; HubeiKey Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan, China
Xu Du
; HubeiKey Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan, China
Zhao Liu
; HubeiKey Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan, China
Chi Zhang
; Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Sweden
Yongjin Guo
; State Key Laboratory of Submarine Geoscience, Shanghai Jiao Tong University, Shanghai, China
*
Mingyang Zhang
; State Key Laboratory of Submarine Geoscience, Shanghai Jiao Tong University, Shanghai, China
* Corresponding author.
Abstract
Accurate prediction of vessel traffic flow is crucial for ensuring the safety of inland river shipping and enhancing the efficiency of traffic operations. Inland vessel traffic flow typically exhibits significant complexity and spatio-temporal dynamic characteristics. To address these challenges, this paper proposes a Global-Local Spatiotemporal Transformer (GL-STFormer) deep learning model. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is utilized to decompose the original data into multi-feature inputs, effectively mitigating data non-stationarity. The model integrates Gated Recurrent Units (GRU) with a self-attention mechanism to extract temporal features of traffic patterns. The multi-head attention and local masking mechanisms of the Transformer model are employed to extract global and local spatial dependencies. Furthermore, the Whale Optimization Algorithm (WOA) is applied to optimize the model’s hyperparameters. This study employs real-world Automatic Identification System (AIS) data from the Nantong waters of the Yangtze River for experimental validation. The results show that the proposed method significantly outperforms various baseline models in inland vessel traffic flow prediction. This study provides scientific support for precise traffic prediction and offers novel insights for the intelligent development of dynamic waterway traffic management.
Keywords
AIS data; vessel traffic flow prediction; spatio-temporal features; CEEMDAN
Hrčak ID:
345644
URI
Publication date:
1.7.2026.
Visits: 286 *