Collision risk assessment in Jiangsu section of the Yangtze River based on evidential reasoning

Collision between ships is one of the dominant types of accidents in the Jiangsu section of the Yangtze River, accounting for over 60% of the accidents. An evidential reasoning (ER) approach is introduced to perform a quantitative assessment of the safety of the whole waterway by dividing it into 17 sub-sections. The Risk Influencing Factors (RIFs) including channel condition, navigation environment and navigation aids conditions are considered and further decomposed into several sub-factors. The expert knowledge is used to quantify the relative importance of the RIFs to the collision risk. The historical data is used to make the Basic Probability Assignments (BPAs) of the belief structures. The hazard index (HI) is used as a measure of collision risk. The results indicate that Kouanzhi Waters and Jiaoshan Waters carry much higher collision risk than other waters, whereas Nanjing Waters and Fanjiafan Waters have the lowest collision risk. The results are useful for the maritime safety management in the Jiangsu section of the Yangtze River.

[1]  Sohag Kabir,et al.  Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review , 2019, Safety Science.

[2]  Min Xie,et al.  Accident risk assessment in marine transportation via Markov modelling and Markov Chain Monte Carlo simulation , 2014 .

[3]  Antti Talvitie,et al.  Reasoning-Building Process for Transportation Project Evaluation and Decision Making , 2014 .

[4]  Taiwei Wang,et al.  Navigation risk assessment method based on flow conditions: A case study of the river reach between the Three Gorges Dam and the Gezhouba Dam , 2019, Ocean Engineering.

[5]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[6]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[7]  Wencheng Huang,et al.  Fault Tree and Fuzzy D-S Evidential Reasoning combined approach: An application in railway dangerous goods transportation system accident analysis , 2020, Inf. Sci..

[8]  Jinxian Weng,et al.  Development of a quantitative risk assessment model for ship collisions in fairways , 2017 .

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

[10]  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..

[11]  Emre Akyuz,et al.  A marine accident analysing model to evaluate potential operational causes in cargo ships , 2017 .

[12]  Lokukaluge P. Perera,et al.  Collision risk detection and quantification in ship navigation with integrated bridge systems , 2015 .

[13]  Abolfazl Mohammadzadeh Moghaddam,et al.  Identification of accident-prone sections in roadways with incomplete and uncertain inspection-based information: A distributed hazard index based on evidential reasoning approach , 2018, Reliab. Eng. Syst. Saf..

[14]  Wei Liu,et al.  Research on risk assessment and control of inland navigation safety , 2018, Int. J. Syst. Assur. Eng. Manag..

[15]  Xi Yongtao,et al.  Unascertained measure model for port traffic risk assessment , 2009 .

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

[17]  Jin Wang,et al.  Bayesian network with quantitative input for maritime risk analysis , 2014 .

[18]  M. Singh,et al.  An Evidential Reasoning Approach for Multiple-Attribute Decision Making with Uncertainty , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[19]  Dan Zhang,et al.  Use of Cusp Catastrophe for Risk Analysis of Navigational Environment: A Case Study of Three Gorges Reservoir Area , 2016, PloS one.