Visual analytic based ship collision probability modeling for ship navigation safety

Abstract This study presents a tangible visual analytic tool to analyse maritime traffic in spatio-temporal basis using AIS data. This novel approach helps in understanding the macroscopic safety structure of both fairways and individual ships with evidences in microscopic level. Qualification of our system is demonstrated with 7-days AIS trajectory collected from Mexican Gulf. We find out that spatio-temporal position pattern of encountered ships in Port Houston varies over time, significantly. In addition, the spatial distribution of ship accidents coincide with proposed near-miss density areas. Furthermore, proposed tool is capable of capturing real accident cases. Field experiments with domain experts have demonstrated that our approach helps in making realistic inferences about navigational safety behaviour of both individual vessel and water area.

[1]  Liang Jin,et al.  Visual Analytics Approach to Vessel Behaviour Analysis , 2018 .

[2]  Sheng-Long Kao,et al.  A Fuzzy Logic Method for Collision Avoidance in Vessel Traffic Service , 2007 .

[3]  Feng Lu,et al.  A quantitative approach for delineating principal fairways of ship passages through a strait , 2015 .

[4]  Tamara Munzner,et al.  Design Study Methodology: Reflections from the Trenches and the Stacks , 2012, IEEE Transactions on Visualization and Computer Graphics.

[5]  Jakub Montewka,et al.  Maritime transportation risk analysis: Review and analysis in light of some foundational issues , 2015, Reliab. Eng. Syst. Saf..

[6]  Ning Wang,et al.  Intelligent Evaluation System of Ship Management , 2010 .

[7]  Jakub Montewka,et al.  A framework for risk analysis of maritime transportation systems: A case study for oil spill from tankers in a ship–ship collision , 2015 .

[8]  Maribel Yasmina Santos,et al.  Automated Traffic Route Identification Through the Shared Nearest Neighbour Algorithm , 2012, AGILE Conf..

[9]  Torgeir Moan,et al.  Estimating Navigation Patterns from AIS , 2009, Journal of Navigation.

[10]  Y. Fujii,et al.  Design of VTS systems for water with bridges , 2017 .

[11]  K Marcjan,et al.  Vessel Traffic Stream Analysis in Vicinity of The Great Belt Bridge , 2013 .

[12]  T Macduff,et al.  THE PROBABILITY OF VESSEL COLLISIONS , 1974 .

[13]  Juan-Chen Huang,et al.  Risk assessment of ships maneuvering in an approaching channel based on AIS data , 2019, Ocean Engineering.

[14]  Michael Baldauf,et al.  A common risk model for the assessment of encounter situations on board ships , 1997 .

[15]  Qing Wu,et al.  Information visualization of AIS data , 2016, 2016 International Conference on Logistics, Informatics and Service Sciences (LISS).

[16]  Aldo Napoli,et al.  Data integrity assessment for maritime anomaly detection , 2020, Expert Syst. Appl..

[17]  Luca Cazzanti,et al.  Automated port traffic statistics: From raw data to visualisation , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[18]  Ioannis Kopanakis,et al.  Visual Analytics Methods for Movement Data , 2008, Mobility, Data Mining and Privacy.

[19]  Richard Bucknall,et al.  Cooperative path planning algorithm for marine surface vessels , 2013 .

[20]  Jarke J. van Wijk,et al.  Interactive visualization of multivariate trajectory data with density maps , 2011, 2011 IEEE Pacific Visualization Symposium.

[21]  Fowler,et al.  Modeling Ship Transportation Risk , 2000, Risk analysis : an official publication of the Society for Risk Analysis.

[22]  T. Fwa,et al.  Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters , 2017, Transportation Research Part E: Logistics and Transportation Review.

[23]  Helwig Hauser,et al.  Interactive visualization of streaming data with Kernel Density Estimation , 2011, 2011 IEEE Pacific Visualization Symposium.

[24]  Ryan Wen Liu,et al.  Maritime Traffic Data Visualization: A Brief Review , 2019, 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA).

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

[26]  David S. Ebert,et al.  TraSeer: A visual analytics tool for vessel movements in the coastal areas , 2017, 2017 IEEE International Symposium on Technologies for Homeland Security (HST).

[27]  Tamara Munzner,et al.  Process and Pitfalls in Writing Information Visualization Research Papers , 2008, Information Visualization.

[28]  Ahmad C. Bukhari,et al.  An intelligent real-time multi-vessel collision risk assessment system from VTS view point based on fuzzy inference system , 2013, Expert Syst. Appl..

[29]  Zihao Liu,et al.  A novel framework for regional collision risk identification based on AIS data , 2019, Applied Ocean Research.

[30]  Ray R. Hashemi,et al.  A Neural Network for Transportation Safety Modeling , 1995 .

[31]  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.

[32]  Kadir Cicek,et al.  Individual collision risk assessment in ship navigation: A systematic literature review , 2019, Ocean Engineering.

[33]  Li Ke,et al.  Ship Automatic Collision Avoidance by Altering Course Based on Ship Dynamic Domain , 2016, 2016 IEEE Trustcom/BigDataSE/ISPA.

[34]  Tamara Munzner Visualization analysis and design: keynote address , 2016 .

[35]  Rafal Szlapczynski,et al.  Determining and visualizing safe motion parameters of a ship navigating in severe weather conditions , 2018, Ocean Engineering.

[36]  Jung Sik Jeong,et al.  Visualization of Ship Collision Risk Based on Near-Miss Accidents , 2016, 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS).

[37]  Floris Goerlandt,et al.  Traffic simulation based ship collision probability modeling , 2011, Reliab. Eng. Syst. Saf..

[38]  Christophe Hurter,et al.  Visualization, Selection, and Analysis of Traffic Flows , 2016, IEEE Transactions on Visualization and Computer Graphics.

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

[40]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[41]  Göran Falkman,et al.  Interactive Visualization of Normal Behavioral Models and Expert Rules for Maritime Anomaly Detection , 2009, 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization.

[42]  R. Smierzchalski,et al.  Ships' Domains As A Collision Risk At Sea In TheEvolutionary Trajectory Planning , 2000 .

[43]  Wen-Chih Peng,et al.  RouteMiner: Mining Ship Routes from a Massive Maritime Trajectories , 2014, 2014 IEEE 15th International Conference on Mobile Data Management.

[44]  Jakub Montewka,et al.  Probability modelling of vessel collisions , 2010, Reliab. Eng. Syst. Saf..

[45]  Ashim Kumar Debnath,et al.  Modeling perceived collision risk in port water navigation , 2009 .

[46]  Rong Wen,et al.  Spatio-temporal route mining and visualization for busy waterways , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[47]  Hyun Gook Kang,et al.  Probabilistic risk assessment on maritime spent nuclear fuel transportation (Part II: Ship collision probability) , 2017, Reliab. Eng. Syst. Saf..

[48]  Preben Terndrup Pedersen,et al.  Collision risk for fixed offshore structures close to high-density shipping lanes , 2002 .

[49]  Po-Ruey Lei,et al.  Mining maritime traffic conflict trajectories from a massive AIS data , 2019, Knowledge and Information Systems.

[50]  K Inoue,et al.  Innovative Probabilistic Prediction of Accident Occurrence , 2007 .

[51]  Jean-François Balmat,et al.  MAritime RISk Assessment (MARISA), a fuzzy approach to define an individual ship risk factor , 2009 .

[52]  C. Guedes Soares,et al.  Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions , 2011 .

[53]  Ashim Kumar Debnath,et al.  Navigational traffic conflict technique: a proactive approach to quantitative measurement of collision risks in port waters , 2010 .

[54]  Gennady L. Andrienko,et al.  Composite Density Maps for Multivariate Trajectories , 2011, IEEE Transactions on Visualization and Computer Graphics.

[55]  Sara Irina Fabrikant,et al.  Geovisualization of Dynamics, Movement and Change: Key Issues and Developing Approaches in Visualization Research , 2008, Inf. Vis..

[56]  Jakub Montewka,et al.  A method for detecting possible near miss ship collisions from AIS data , 2015 .

[57]  Pengfei Chen,et al.  Probabilistic risk analysis for ship-ship collision: State-of-the-art , 2019, Safety Science.

[58]  Floris Goerlandt,et al.  On the Reliability and Validity of Ship–Ship Collision Risk Analysis in Light of Different Perspectives on Risk , 2014 .

[59]  Agnieszka Lazarowska,et al.  A new deterministic approach in a decision support system for ship's trajectory planning , 2017, Expert Syst. Appl..

[60]  Key-Pyo Rhee,et al.  A study on the collision avoidance of a ship using neural networks and fuzzy logic , 2012 .

[61]  Andy Norris,et al.  AIS implementation -- success or failure? , 2007 .

[62]  Jarke J. van Wijk,et al.  Evaluation of the Visibility of Vessel Movement Features in Trajectory Visualizations , 2011, Comput. Graph. Forum.

[63]  Li Suyi,et al.  Ship collision risk assessment for the Singapore Strait. , 2011, Accident; analysis and prevention.

[64]  Fujio Kaneko,et al.  Methods for probabilistic safety assessments of ships , 2002 .

[65]  Zbigniew Michalewicz,et al.  Adaptive modeling of a ship trajectory in collision situations at sea , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[66]  Jakub Montewka,et al.  A Risk-Informed Ship Collision Alert System: Framework and Application , 2015 .