Volume 9, Issue 2, April 2020, Page: 42-47
Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis
Le Qi, School of Navigation, Wuhan University of Technology, Wuhan, China; Hubei Key Laboratory of Inland Shipping Technology, Wuhan, China
Yuanyuan Ji, College of Information Science Technology, Dalian Maritime University, Dalian, China; Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, USA
Received: May 6, 2020;       Accepted: May 26, 2020;       Published: Jun. 8, 2020
DOI: 10.11648/j.wros.20200902.11      View  43      Downloads  34
With the development of Internet of Things (IoT) technology and its vast applications in ship transportation systems, such as the Automatic Identification System (AIS), a large quantity of ship trajectory data have been recorded and stored. Nowadays ship transportation has also entered the age of big data, which can support IoT applications in Intelligent Transportation System (ITS), e.g. traffic monitoring, fleet management and traffic safety enhancement. However, the redundancy of ship trajectory data considerably reduces the effectiveness and efficiency of large scale traffic data storage, mining and visualization. Therefore, compression processing of the data becomes a very important issue for these applications. Because ship trajectory is a type of vector data, employing the vector data compression algorithms is an efficient way to solve the data redundancy problem. In this paper, the pseudo-code of five typical vector data compression algorithms for ship trajectory data compression is introduced. The performances of these algorithms were tested by the compression experiments of actual ship trajectories in the Qiongzhou Strait. The results show that ships’ speeds and rate of turns, the requirement of real time processing can affect the option of the most appropriate algorithm, and the algorithm selection in different applications is suggested. The results and conclusions lay the foundation for the future development of ship transportation intelligentization.
Automatic Identification System, Big Data, Data Compression Algorithms, Ship Trajectory
To cite this article
Le Qi, Yuanyuan Ji, Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis, Journal of Water Resources and Ocean Science. Vol. 9, No. 2, 2020, pp. 42-47. doi: 10.11648/j.wros.20200902.11
Copyright © 2020 Authors retain the copyright of this article.
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Bell, M. G., Meng, Q. Special issue in Transportation Research Part B-Shipping, port and maritime logistics. Transportation Research Part B: Methodological 2016, 93, 697–699. Maritime Logistics.
United Nations Conference on Trade and Development (UNCTAD), 50 years of Review of Maritime Transport, 1968–2018, New York, 2018.
Moffitt, K. C., Vasarhelyi, M. A. AIS in an Age of Big Data. Journal of Information Systems 2013, 27, 1–19.
Dittmar, C., Die nächste Evolutionsstufe von AIS: Big Data. In Analytische Informationssysteme: Business Intelligence-Technologien und -Anwendungen, Gluchowski, P., Chamoni, P., Eds., Springer Berlin Heidelberg: Berlin, Heidelberg, 2016, pp. 55–65.
Zhang, L., Meng, Q., Fwa, T. F. Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters. Transportation Research Part E: Logistics and Transportation Review. 2017.
Isenor, A. W., 342 St-Hilaire, M. O., Webb, S., Mayrand, M. MSARI: A Database for Large Volume Storage and Utilisation of Maritime Data. Journal of Navigation 2017, 70, 276–290.
Bole, A., Wall, A., Norris, A. Chapter 5 - Automatic Identification System (AIS). In Radar and ARPA Manual (Third Edition), Third Edition ed., Bole, A., Wall, A., Norris, A., Eds., Butterworth-Heinemann: Oxford, 2014, pp. 255–275.
Zhi Sang, L., Wall, A., Mao, Z., ping Yan, X., Wang, J. A novel method for restoring the trajectory of the inland waterway ship by using AIS data. Ocean Engineering 2015, 110, 183-194.
Qi, L., & Zheng, Z. (2016). Trajectory prediction of vessels based on data mining and machine learning. J. Digit. Inf. Manage, 14 (1), 33-40.
Qi, L., & Zheng, Z. (2016). Vessel Trajectory Data Compression based on Course Alteration Recognition. A A, 1 (2), 1.
Ji, Y., Xu, W., Deng, A. A Study of Vessel Trajectory Compression Based on Vector Data Compression Algorithms. International Conference on Business Information Systems. Springer, 2019, pp. 473–484.
Zhang, S. k., Liu, Z. j., Cai, Y., Wu, Z. l., Shi, G. y. AIS Trajectories Simplification and Threshold Determination. Journal of Navigation 2015, -1, 1–16.
Zhao, L., Shi, G. A trajectory clustering method based on Douglas-Peucker compression and density for marine traffic pattern recognition. Ocean Engineering 2019, 172, 456–467.
Liu, J., Li, H., Yang, Z., Wu, K., Liu, Y., Liu, W. Adaptive Douglas-Peucker Algorithm with Automatic Thresholding for AIS-Based Vessel Trajectory Compression. IEEE Access 2019, PP, 1–1.
Liang, M., Liu, W., Zhong, Q., Liu, J., Zhang, J. Neural Network-Based Automatic Reconstruction of Missing Vessel Trajectory Data. 2019, pp. 426–430.
Zhang, L., Meng, Q., Xiao, Z., Fu, X. A novel ship trajectory reconstruction approach using AIS data. Ocean Engineering 2018, 159, 165–174.
Xiao, F., Ligteringen, H., van Gulijk, C., Ale, B. Comparison study on AIS data of ship traffic behavior. Ocean Engineering 2015, 95, 84-93.
Schmid, F., Richter, K. F., Laube, P. Semantic Trajectory Compression. Advances in Spatial and Temporal Databases, Mamoulis, N., Seidl, T., Pedersen, T. B., Torp, K., Assent, I., Eds., Springer Berlin Heidelberg: Berlin, Heidelberg, 2009, pp. 411–416. doi: 10.1007/978-3-642-02982-0\_30.
Ifrim, C., Iuga, I., Pop, F., Wallace, M., Poulopoulos, V. Data Reduction Techniques Applied on Automatic Identification System Data. Semantic Keyword-Based Search on Structured Data Sources, Szyman´ ski, J., Velegrakis, Y., Eds., Springer International Publishing: Cham, 2018, pp. 14–19.
Yang, B., Li, Q. Efficient compression of vector data map based on a clustering model. Geo-spatial Information Science 2009, 12, 13–17.
de Vries, G., van Someren, M. Clustering Vessel Trajectories with Alignment Kernels under Trajectory Compression. Machine Learning and Knowledge Discovery in Databases, Balcázar, J. L., Bonchi, Gionis, A., Sebag, M., Eds., Springer Berlin Heidelberg: Berlin, Heidelberg, 2010, pp. 296–311.
De Vries, G. K. D., van Someren, M. Machine learning for vessel trajectories using compression, alignments and domain knowledge. Expert Systems with Applications 2012, 39, 13426-13439.
Cao, W., Li, Y. DOTS: An online and near-optimal trajectory simplification algorithm. Journal of Systems and Software 2017, 126, 34–44.
Deng, Z., Han, 447 W., Wang, L., Ranjan, R., Zomaya, A. Y., Jie, W. An efficient online direction-preserving compression approach for trajectory streaming data. Future Generation Computer Systems 2017, 68, 150–162.
Gao, M., Shi, G. Y. Ship Spatiotemporal Key Feature Point Online Extraction Based on AIS Multi-Sensor Data Using an Improved Sliding Window Algorithm. Sensors 2019, 19.
Image Compression. In Data Compression: The Complete Reference, Springer New York: New York, NY, 2004, pp. 251–512.
Yang, B., Purves, R. S., Weibel, R. Variable-resolution Compression of Vector Data. GeoInformatica 2008, 12, 357–376.
Ji, H., Wang, Y. The Research on the Compression Algorithms for Vector Data. International Conference on Multimedia Technology, 2010, pp. 1–4.
Gudmundsson, J., Katajainen, J., Merrick, D., Ong, C., Wolle, T. Compressing spatio-temporal trajectories. Computational Geometry 2009, 42, 825–841.
Sandu Popa, I.; Zeitouni, K.; Oria, V.; Kharrat, A. Spatio-temporal compression of trajectories in road networks. GeoInformatica 2015, 19, 117–145.
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