Drafting Route Plan Templates for Ships on the Basis of AIS Historical Data

The paper provides a description of a method of drafting route plan templates on the basis of AIS (automatic identification system) historical data. The first section features a brief background on the problem of drafting route plan templates in the light of international regulations. The main section contains a description of the methods and tools used for processing AIS data into a GRID reference system: ship traffic intensity, average COG (course over ground) and average SOG (speed over ground) as well as route plan templates. The final section includes a presentation of the research method and an analysis of the results, conducted on the basis of maps with charted paths of drafted route plan templates. The summary constitutes a synthesis of general conclusions, the advantages and disadvantages of the solution as well as areas for further research to enhance the solution.

[1]  Naixue Xiong,et al.  A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis , 2017, Sensors.

[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]  I. Obradovic,et al.  Machine Learning Approaches to Maritime Anomaly Detection , 2014 .

[4]  Xinping Yan,et al.  CPA Calculation Method based on AIS Position Prediction , 2016, Journal of Navigation.

[5]  Akeai Moceiwai The revision of the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW) 1978 : its implications and impact on maritime education and training in Fiji and the South Pacific , 1996 .

[6]  Bradley J. Rhodes,et al.  Adaptive Mixture-Based Neural Network Approach for Higher-Level Fusion and Automated Behavior Monitoring , 2009, 2009 IEEE International Conference on Communications.

[7]  Göran Falkman,et al.  Online Learning and Sequential Anomaly Detection in Trajectories , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Andrea Copping,et al.  Maritime Route Delineation using AIS Data from the Atlantic Coast of the US , 2016, Journal of Navigation.

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

[10]  Anton Bardera,et al.  SHNN-CAD+: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection , 2018, Sensors.

[11]  M. Wąż,et al.  The idea of using the A* algorithm for route planning an unmanned vehicle "Edredon" , 2013 .

[12]  Jurgen Beyerer,et al.  Evaluation and comparison of anomaly detection algorithms in annotated datasets from the maritime domain , 2015, 2015 SAI Intelligent Systems Conference (IntelliSys).

[13]  Richard O. Lane,et al.  Maritime anomaly detection and threat assessment , 2010, 2010 13th International Conference on Information Fusion.

[14]  Piotr Borkowski,et al.  The Ship Movement Trajectory Prediction Algorithm Using Navigational Data Fusion , 2017, Sensors.

[15]  Etienne Martineau,et al.  Maritime Anomaly Detection: Domain Introduction and Review of Selected Literature , 2011 .

[16]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[17]  Keon-Myung Lee,et al.  Deep Learning-Based Caution Area Traffic Prediction with Automatic Identification System Sensor Data , 2018, Sensors.

[18]  Tarunraj Singh,et al.  Anomaly Detection using Context-Aided Target Tracking , 2011, J. Adv. Inf. Fusion.

[19]  Göran Falkman,et al.  Sequential Conformal Anomaly Detection in trajectories based on Hausdorff distance , 2011, 14th International Conference on Information Fusion.