AIS Trajectories Simplification and Threshold Determination

Facilitated by recent establishment of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship trajectories are becoming increasingly available and the size of recorded trajectories is getting larger. Large sets of trajectories create problems of storing, transmitting and processing data. Using appropriate methods, an accurate representation of the original trajectories can be obtained by compressing redundant information, while maintaining the main characteristic elements. In this paper, a new scheme and the implementation of the Douglas-Peucker (DP) algorithm are presented, which can simplify AIS trajectories by extracting characteristic points. As for the simplification threshold, the solo parameter of the DP algorithm, a new AIS-based minimum ship domain evaluation method is proposed and acts as criteria for simplification threshold determination. Finally, a validation is made to examine the effectiveness of the DP simplification algorithm and the rationality of the simplification threshold. The result indicates that the DP algorithm can simplify AIS trajectories effectively; the simplification threshold is scientific and reasonable.

[1]  Elisabeth M. Goodwin,et al.  A Statistical Study of Ship Domains , 1973, Journal of Navigation.

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

[3]  George F. Jenks LINES, COMPUTERS, AND HUMAN FRAILTIES* , 1981 .

[4]  T. Coldwell Marine Traffic Behaviour in Restricted Waters , 1983, Journal of Navigation.

[5]  Yahei Fujii,et al.  SURVEY ON VESSEL TRAFFIC MANAGEMENT SYSTEM , 1984 .

[6]  E. R. White Assessment of Line-Generalization Algorithms Using Characteristic Points , 1985 .

[7]  Robert B McMaster,et al.  A Statistical Analysis of Mathematical Measures for Linear Simplification , 1986 .

[8]  J. D. Whyatt,et al.  Line generalisation by repeated elimination of points , 1993 .

[9]  Wu Zhao-lin,et al.  Comments on Ship Domains , 1993, Journal of Navigation.

[10]  Józef Lisowski,et al.  Neural network classifier for ship domain assessment , 2000 .

[11]  Xiaolin Zhu,et al.  DOMAIN AND ITS MODEL BASED ON NEURAL NETWORKS , 2001 .

[12]  Rafal Szlapczynski,et al.  A Unified Measure Of Collision Risk Derived From The Concept Of A Ship Domain , 2006, Journal of Navigation.

[13]  Wenzhong Shi,et al.  Positional error modeling for line simplification based on automatic shape similarity analysis in GIS , 2006, Comput. Geosci..

[14]  Study on Simplification of Contour Lines Preserving Topological Coherence , 2007 .

[15]  Michela Bertolotto,et al.  Efficient and consistent line simplification for web mapping , 2007, Int. J. Web Eng. Technol..

[16]  宋金平,et al.  美国地理学百年发展脉络分析―基于《Annals of the Association of American Geographers》学术论文的统计分析 , 2007 .

[17]  Zu-wen Wang,et al.  A Unified Analytical Framework for Ship Domains , 2009, Journal of Navigation.

[18]  Francisco Javier Ariza-López,et al.  Sinuosity pattern recognition of road features for segmentation purposes in cartographic generalization , 2009, Pattern Recognit..

[19]  Zbigniew Pietrzykowski,et al.  The Ship Domain : A Criterion of Navigational Safety Assessment in an Open Sea Area , 2009 .

[20]  Yueh-Min Huang,et al.  Extraction of characteristic points and its fractal reconstruction for terrain profile data , 2009 .

[21]  Joachim Gudmundsson,et al.  Compressing spatio-temporal trajectories , 2009, Comput. Geom..

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

[23]  Ning Wang,et al.  An Intelligent Spatial Collision Risk Based on the Quaternion Ship Domain , 2010, Journal of Navigation.

[24]  D. Peters,et al.  Estimating AIS Coverage from Received Transmissions , 2012 .

[25]  José L. G. Pallero,et al.  Robust line simplification on the plane , 2013, Comput. Geosci..

[26]  Toke Koldborg Jensen,et al.  Empirical Ship Domain based on AIS Data , 2013, Journal of Navigation.

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

[28]  M. Charlton,et al.  A new approach and procedure for generalising vector-based maps of real-world features , 2013 .