Decision Support Tool Employing Bayesian Risk Framework for Environmentally Safe Shipping

Due to the significant increase of tanker traffic from and to the Black Sea that pass through narrow straits formed by the 1600 Greek islands, the Aegean Sea is characterized by an extremely high marine environmental risk. Therefore it is vital to all socio-economic and environmental sectors to reduce the risk of a ship accident in that area. In this chapter a web tool for environmentally safe shipping is presented. The proposed tool focuses on extracting aggregated statistics using spatial analysis of multilayer information: vessel trajectories, vessel data as well as information regarding environmentally important areas. The decision support system includes preprocessing, clustering of trajectories (based on their spatial similarity) and risk assessment employing probabilistic models (Bayesian network). Applications of the web tool are presented in areas such as marine traffic monitoring in environmentally protected areas, and influence of restricted areas in marine traffic. Results demonstrate that the web tool can provide essential information for maritime policy makers.

[1]  S. Perantonis,et al.  LONG-TERM MARINE TRAFFIC MONITORING FOR ENVIRONMENTAL SAFETY IN THE AEGEAN SEA , 2015 .

[2]  Maria Hänninen,et al.  Bayesian networks for maritime traffic accident prevention: benefits and challenges. , 2014, Accident; analysis and prevention.

[3]  Theodoros Giannakopoulos,et al.  Bayesian network to predict environmental risk of a possible ship accident , 2014, PETRA '14.

[4]  Nikos Pelekis,et al.  HERMES: aggregative LBS via a trajectory DB engine , 2008, SIGMOD Conference.

[5]  Charou Eleni,et al.  A dynamic model for environmentally safe shipping through the Aegean Sea , 2015, 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA).

[6]  Jakub Montewka,et al.  A framework for risk assessment for maritime transportation systems - A case study for open sea collisions involving RoPax vessels , 2014, Reliab. Eng. Syst. Saf..

[7]  Stavros J. Perantonis,et al.  Accelerating multi-objective ship routing using a novel grid structure and a simple heuristic , 2015, 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA).

[8]  Kai W. Wirtz,et al.  Total oil spill costs and compensations , 2006 .

[9]  P. Silveira,et al.  Use of AIS Data to Characterise Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal , 2013, Journal of Navigation.

[10]  Stavros J. Perantonis,et al.  AMINESS: a platform for environmentally safe shipping , 2014, PETRA.

[11]  Daniel Müllner,et al.  fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python , 2013 .

[12]  Maria Hänninen,et al.  Bayesian network modeling of Port State Control inspection findings and ship accident involvement , 2014, Expert Syst. Appl..

[13]  Pan Jia-cai,et al.  An AIS data Visualization Model for Assessing Maritime Traffic Situation and its Applications , 2012 .

[14]  Catherine J. Grey.  The Cost of Oil Spills from Tankers: An Analysis of IOPC Fund Incidents , 1999 .

[15]  I. C. White,et al.  Factors that Determine the Cost of Oil Spills , 2003 .

[16]  Carlos Guedes Soares,et al.  Methods and Tools , 2009 .

[17]  D. S. Etkin,et al.  Estimating Cleanup Costs for Oil Spills , 1999 .

[18]  Eleni Charou,et al.  A maritime data analytics platform for policy recommendation , 2015, 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA).

[19]  Nikos Pelekis,et al.  HERMES: A Trajectory DB Engine for Mobility-Centric Applications , 2015, Int. J. Knowl. Based Organ..

[20]  Jarke J. van Wijk,et al.  Eurographics/ Ieee-vgtc Symposium on Visualization 2009 Visualization of Vessel Movements , 2022 .

[21]  Slava Kisilevich,et al.  Spatio-temporal clustering , 2010, Data Mining and Knowledge Discovery Handbook.

[22]  Christos A. Kontovas,et al.  An empirical analysis of IOPCF oil spill cost data. , 2010, Marine pollution bulletin.