*Article* **Long-Term Trajectory Prediction for Oil Tankers via Grid-Based Clustering**

**Xuhang Xu 1, Chunshan Liu 1,\*, Jianghui Li 2,\*, Yongchun Miao <sup>3</sup> and Lou Zhao <sup>1</sup>**


**Abstract:** Vessel trajectory prediction is an important step in route planning, which could help improve the efficiency of maritime transportation. In this article, a high-accuracy long-term trajectory prediction algorithm is proposed for oil tankers. The proposed algorithm extracts a set of waymark points that are representative of the key traveling patterns in an area of interest by applying DBSCAN clustering to historical AIS data. A novel path-finding algorithm is then developed to sequentially identify a subset of waymark points, from which the predicted trajectory to a fixed destination is produced. The proposed algorithm is tested using real data offered by the Danish Maritime Authority. Numerical results demonstrate that the proposed algorithm outperforms state-of-the-art vessel trajectory prediction algorithms and is able to make high-accuracy long-term trajectory predictions.

**Keywords:** trajectory prediction; AIS data; clustering
