METO-S2S: A S2S based vessel trajectory prediction method with Multiple-semantic Encoder and Type-Oriented Decoder

Vessel trajectory prediction plays a vital role in maintaining a safe and effective status in maritime transporta-tion. The development of deep learning provides appropriate mechanisms to predict vessel trajectory using Automatic Identification System (AIS) data. However, besides limited historical position information, much information can be used to enhance deep models, including speed, course, departure time, sailing distance and vessel type. Realistically speaking, these are related to the ships' endurance, oil storage, tide and work-rest habits. In order to address these problems, we propose a novel vessel trajectory prediction model METO-S2S based on a sequence-to-sequence structure, consisting of the Multiple-semantic Encoder and the Type-Oriented Decoder. After comprehensive data preprocessing and feature engineering, the Multiple-semantic Encoder embeds the sequential inputs of the voyage information into higher-dimensional latent vectors. In addition, the combined vector of vessel type and departure time of a particular ship is fed into the Type-Oriented Decoder treated as guidance information. To verify the efficiency and efficacy of METO-S2S, we employ an AIS dataset in US coastal waters which is more suitable for deep learning models, and implement comparative experiments with several baseline models. The experimental results show that METO-S2S is superior to them both quantitatively and qualitatively.1