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 transportation. 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 1 The code and data of our experiments is available on GitHub https://github.com/AIR-SkyForecast/METO-S2S. • The paper releases a toolkit for the AIS data preprocessing and a processed AIS dataset of US coastal waters which is more suitable for deep learning methods. • The paper develops a novel long-term vessel trajectory prediction method METO-S2S based on a Seq2Seq structure which makes good use of track information and diversified sailing features. • The paper proposes a Multiple-semantic Encoder that combines coordinate, speed, course, and sailing distance characteristics. Furthermore, the paper also proposes a Type-Oriented Decoder taking the combined vectors of vessel type and departure time as guidance information. • METO-S2S is evaluated on our released trajectory dataset. Compared with other baselines, METO-S2S can generate more accurate and robust prediction results. [ABSTRACT FROM AUTHOR]