With the rapid development of the global economy, transport safety and security have become the key issues in maritime transportation all over the world. In practical applications, the Automatic Identification System (AIS)-based measurement of similarities between different vessel trajectories plays an important role in improving maritime transportation, e.g., maritime navigation, maritime supervision and management. However, the received AIS datasets are usually composed of a large amount of redundant information which could significantly increase the computational complexity. To deal with this problem, a Douglas-Peucker (DP)-based calculation method is introduced in this paper to accurately compress the spatio-temporal AIS trajectories while preserving the main geometrical structures. Based on the compressed trajectories, it is able to accelerate the Dynamic Time Warping (DTW) algorithm for the measurement of similarities between different vessel trajectories. In particular, the combination of DP and DTW has the capacity of significantly reducing the computational cost and guaranteeing the accuracy of similarity measures. The experimental results have demonstrated the superior performance of the proposed method in terms of computational cost and accuracy of similarity measures.

Abstract
1. Introduction
    1.1 Background and related work
    1.2 Organization
2. Basic Mathematics Theories
    2.1. Douglas-Peucker algorithm
    2.2. Dynamic time warping algorithm
3. The Two-Step Framework for Measuring AISTrajectory Similarity
4. Experiment Results and Discussion
    4.1. The influences of DP compression thresholds on AIStrajectories
    4.2. DTW-based AIS trajectory similarity measures
    4.3.MATLAB-based software for AIS trajectory
5. Conclusion
References

• Kai WANG(School of Navigation, Wuhan University of Technology)
• Ryan Wen LIU(Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology) Corresponding Author
• Yan LI(Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology)
• Maohan LIANG(Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology)
• Yi LIU(Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology)
• Jianhua WU(Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology)
• Jingxian LIU(Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology)