The Unsupervised Method of Vessel Movement Trajectory Prediction

• URL: http://arxiv.org/abs/2007.13712v3
• Date: Wed, 29 Jul 2020 15:42:43 GMT
• Title: The Unsupervised Method of Vessel Movement Trajectory Prediction
• Authors: Chih-Wei Chen, Charles Harrison, and Hsin-Hsiung Huang
• Abstract summary: This article presents an unsupervised method of ship movement trajectory prediction. It represents the data in a three-dimensional space which consists of time difference between points, the scaled error distance between the tested and its predicted forward and backward locations, and the space-time angle. Unlike most statistical learning or deep learning methods, the proposed clustering-based trajectory reconstruction method does not require computationally expensive model training.
• Score: 1.2617078020344619
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In real-world application scenarios, it is crucial for marine navigators and security analysts to predict vessel movement trajectories at sea based on the Automated Identification System (AIS) data in a given time span. This article presents an unsupervised method of ship movement trajectory prediction which represents the data in a three-dimensional space which consists of time difference between points, the scaled error distance between the tested and its predicted forward and backward locations, and the space-time angle. The representation feature space reduces the search scope for the next point to a collection of candidates which fit the local path prediction well, and therefore improve the accuracy. Unlike most statistical learning or deep learning methods, the proposed clustering-based trajectory reconstruction method does not require computationally expensive model training. This makes real-time reliable and accurate prediction feasible without using a training set. Our results show that the most prediction trajectories accurately consist of the true vessel paths.

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