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Open AccessArticle

L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis

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Department of Information Science and Engineering, Ocean University of China, Qingdao 266000, China
2
Department of Science, Qingdao University of Technology, Qingdao 266000, China
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Department of Information Technology, Wenzhou Vocational College of Science and Technology, Wenzhou 325006, China
*
Author to whom correspondence should be addressed.
This paper is an extended version of conference paper: Guo, S.; Liu, C.; Guo, Z.; Feng, Y.; Hong, F.; Huang, H. Trajectory Prediction for Ocean Vessels Base on K-order Multivariate Markov Chain. In Proceedings of the International Conference on Wireless Algorithms, Systems, and Applications, Tianjin, China, 20–22 June 2018.
Sensors 2019, 19(20), 4365; https://doi.org/10.3390/s19204365
Received: 16 September 2019 / Revised: 3 October 2019 / Accepted: 3 October 2019 / Published: 9 October 2019
(This article belongs to the Special Issue Smart Sensing: Leveraging AI for Sensing)
With the rapid development of marine IoT (Internet of Things), ocean MDTN (Mobile Delay Tolerant Network) has become a research hot spot. Long-term trajectory prediction is a key issue in MDTN. There are no long-term fine-grained trajectory prediction methods proposed for ocean vessels because a vessel’s mobility pattern lacks map topology support and can be easily influenced by the fish moratorium, sunshine duration, etc. A traditional on-land trajectory prediction algorithm cannot be directly utilized in this field because trajectory characteristics of ocean vessels are far different from that on land. To address the problem above, we propose a novel long-term trajectory prediction algorithm for ocean vessels, called L-VTP, by utilizing multiple sailing related parameters and K-order multivariate Markov Chain. L-VTP utilizes multiple sailing related parameters to build multiple state-transition matrices for trajectory prediction based on quantitative uncertainty analysis of trajectories. Trajectories’ sparsity of ocean vessels results in a critical state missing problem of a high-order state-transition matrix. L-VTP automatically traverses other matrices in a specific sequence in terms of quantitative uncertainty results to overcome this problem. Furthermore, the different mobility models of the same vessel during the day and the night are also exploited to improve the prediction accuracy. Privacy issues have been taken into consideration in this paper. A quantitative model considering Markov order, training metadata and privacy leak degree is proposed to help the participant make the trade-off based on their customized requirements. We have performed extensive experiments on two years of real-world trajectory data that include more than two thousand vessels. The experiment results demonstrate that L-VTP can realize fine-grained long-term trajectory prediction with the consideration of privacy issues. The average error of 4.5-hour fine-grained prediction is less than 500 m. In addition, the proposed method can be extended to 10-hour prediction with an average error of 2.16 km, which is also far less than the communication range of ocean vessel communication devices. View Full-Text
Keywords: vessel trajectory prediction; entropy analysis; marine IoT; ocean MDTN; K-order Markov chain vessel trajectory prediction; entropy analysis; marine IoT; ocean MDTN; K-order Markov chain
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Liu, C.; Guo, S.; Feng, Y.; Hong, F.; Huang, H.; Guo, Z. L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis. Sensors 2019, 19, 4365.

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