Bi-directional information fusion-driven deep network for ship trajectory prediction in intelligent transportation systems

Huanhuan Li, Wenbin Xing, Hang Jiao, Kum Fai Yuen, Ruobin Gao, Yan Li, Christian Matthews and Zaili Yang

Transportation Research Part E: Logistics and Transportation Review, 2024, vol. 192, issue C

Abstract: Accurate ship trajectory prediction (STP) is crucial to realise the early warning of ship collision and ensure maritime safety. Driven by advancements in artificial intelligence technology, deep learning-based STP has become a predominant approach in the research field of ship collision avoidance. This paper, based on a state-of-the-art survey of the existing STP research progress, aims to develop a new bi-directional information fusion-driven prediction model that enables the achievement of more accurate STP results by addressing the drawbacks of the classical methods in the field. In this context, a cascading network model is developed by combining two bi-directional networks in a specific order. It incorporates the Bi-directional Long Short-Term Memory (BiLSTM) and the Bi-directional Gated Recurrent Unit (BiGRU) neural network into a single three-layer, information-enhanced network. It takes advantage of both networks to realise more accurate prediction of ship trajectories. Furthermore, the performance of the proposed model is comprehensively evaluated using Automatic Identification System (AIS) data from three water areas representing traffic scenarios of different safety concerns. The superiority of the proposed model is verified through comparative analysis with twenty other methods, including the state-of-the-art STP in the literature. The finding reveals that the new model is better than all the benchmarked ones, and thus, the new STP solution in this paper makes new contributions to improving autonomous navigation and maritime safety.

Keywords: Ship trajectory prediction; Deep learning; Cascading network; Maritime safety; AIS data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.tre.2024.103770

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