A method for simplifying ship trajectory based on improved Douglas–Peucker algorithm

Abstract Automatic identification system (AIS) can provide massive ship trajectory data that is valuable for mining information in water traffic. However, large sizes lead to difficulties in storing, querying, and processing the aforementioned data. In the present study, to better compress ship trajectory data regarding compression time and efficiency, a method based on the improved Douglas–Peucker (DP) algorithm is presented. In the process of compression, the proposed method considers the shape of vessel trajectory derived from course information of track points. Parallel experiments are conducted based on AIS data gathered over the duration of a month in the Chinese Zhou Shan islands. The results indicate that this method can effectively compress ship trajectory information. Additionally, when compared with the traditional DP algorithm, this method can significantly reduce the compression time and exhibits better performance at high compression strengths. Also, the proposed method outperforms other existing trajectory compression algorithms in term of compression time.

[1]  Zhiwei Xu,et al.  Mapping Global Shipping Density from AIS Data , 2016, Journal of Navigation.

[2]  Konstantinos Chatzikokolakis,et al.  Knowledge extraction from maritime spatiotemporal data: An evaluation of clustering algorithms on Big Data , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[3]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[4]  Yu Huang,et al.  Trajectory compression-guided visualization of spatio-temporal AIS vessel density , 2016, 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP).

[5]  Eamonn J. Keogh,et al.  An online algorithm for segmenting time series , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[6]  S. S. Ravi,et al.  Compression of trajectory data: a comprehensive evaluation and new approach , 2014, GeoInformatica.

[7]  Michele Vespe,et al.  Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction , 2013, Entropy.

[8]  Nikos Pelekis,et al.  Online event recognition from moving vessel trajectories , 2016, GeoInformatica.

[9]  Nirvana Meratnia,et al.  Spatiotemporal Compression Techniques for Moving Point Objects , 2004, EDBT.

[10]  S. S. Ravi,et al.  Algorithms for compressing GPS trajectory data: an empirical evaluation , 2010, GIS '10.

[11]  Wen Liu,et al.  Research on Vessel Trajectory Multi-Dimensional Compression Algorithm Based on Douglas-Peucker Theory , 2014 .

[12]  Maria Riveiro,et al.  A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance , 2017 .

[13]  Maarten van Someren,et al.  Machine learning for vessel trajectories using compression, alignments and domain knowledge , 2012, Expert Syst. Appl..

[14]  H. Ligteringen,et al.  Study on collision avoidance in busy waterways by using AIS data , 2010 .

[15]  Konstantinos Chatzikokolakis,et al.  A Big Data Driven Approach to Extracting Global Trade Patterns , 2017, MATES@VLDB.

[16]  Urs Ramer,et al.  An iterative procedure for the polygonal approximation of plane curves , 1972, Comput. Graph. Image Process..

[17]  Jiaxuan Yang,et al.  Ship Trajectories Pre-processing Based on AIS Data , 2018, Journal of Navigation.

[18]  Guoyou Shi,et al.  AIS Trajectories Simplification and Threshold Determination , 2015, Journal of Navigation.

[19]  Thomas Devogele,et al.  Spatio-temporal trajectory analysis of mobile objects following the same itinerary , 2010 .