The prediction of ship location has become an increasingly popular research hotspot in the field of maritime transportation engineering, which benefits maritime safety supervision and security. Existing methods of ship location prediction based on motion characteristics have a large uncertainty and cannot guarantee trajectory prediction accuracy of the target ship. An improved method of location prediction using k-nearest neighbor (KNN) is proposed in this paper. An expanded circle area of the latest point of the target ship is first generated to find the reference points with similar movement characteristics in the constraints of distance and time intervals. Then, the top k-nearest neighbors are determined based on the degree of similarity. Relationships between the reference point of each neighbor and the latest points of the target ship are calculated. The predicted location of the target ship can then be determined by a weighted calculation of the locations of all neighbors at the predicted time and their relationships with the target ship. Experiments of ship location prediction in 10 min, 20 min, and 30 min were conducted. The correlation coefficient of the location prediction error for the three experiments was 0.992, 0.99, and 0.9875, respectively. The results show that ship location prediction with reference to multiple nearest neighbors with similar movements can provide better accuracy.@en

At present, many maritime applications, such as ship traffic statistics, require fast spatiotemporal query of ship traj ectory data. The existing research is weak in supporting the trajectory spatiotemporal query, and can only realize the spatiotemporal query based on circular and rectangular regions. Moreover, complex spatial indexes and computer clusters are usually required to achieve efficient spatiotemporal query, which undoubtedly increases the storage cost. To solve above problems, this article designs the storage structure and partition strategy of ship trajectory data, and builds attributes multilevel index and H3 spatial index on ship traj ectory data based on ClickHouse database, which not only achieves spatiotemporal query based on the oval, round, and arbitrary polygon area, and improve the efficiency of the spatiotemporal query; At the same time, the data storage space is reduced, so that a common server can manage 10 billion ship trajectory data. The empirical study on a large number of data shows that the storage scheme designed in this paper not only enhances the ability of spatiotemporal query, improves the efficiency of data storage and spatiotemporal query, but also reduces the cost of data storage to a large extent.@en