A hybrid deep learning method for the prediction of ship time headway using automatic identification system data

Quandang Ma, Xu Du, Cong Liu*, Yuting Jiang, Zhao Liu, Zhe Xiao, Mingyang Zhang

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Ship Time Headway (STH) is used in maritime navigation to describe the time interval between the arrivals of two consecutive ships in the same water area. This measurement may offer a straightforward way to gauge the frequency of ship traffic and the likelihood of congestion in a particular area. STH is an important factor in understanding and managing the dynamics of ship movements in busy waterways. This paper introduces a hybrid deep learning method for predicting STH in time domain. The method integrates the Seasonal-Trend Decomposition using Loess (STL), Multi-head Self-Attention (MSA) mechanism into Long Short-Term Memory (LSTM) neural network. The STH dataset was extracted from the Automatic Identification System (AIS) through ship trajectory spatial motion, and the seasonal, trend and residual components of the decomposition were then determined from the STH dataset using the STL algorithms. MSA-LSTM is adopted to comprehensively capture the evolving patterns of STH from the sequence. Comparison studies with existing methods demonstrate the accuracy and robustness of the predictions provided by this method, indicating that the proposed method outperforms other models in terms of prediction performance and learning capabilities. By predicting STH, the method offers potential to assist maritime traffic managers and navigators in assessing ship flow, thereby enabling them to make informed decisions on navigation safety and efficiency.

Original languageEnglish
Article number108172
Number of pages14
JournalEngineering Applications of Artificial Intelligence
Volume133, Part B
DOIs
Publication statusPublished - Jul 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • Attention mechanism
  • Hybrid deep learning
  • Maritime traffic
  • Ship time headway prediction
  • Time series decomposition

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