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Collision risk analysis on ferry ships in Jiangsu Section of the Yangtze River based on AIS data

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  • Cai, Mingyou
  • Zhang, Jinfen
  • Zhang, Di
  • Yuan, Xiaoli
  • Soares, C. Guedes

Abstract

To assess the collision risk of ferries in the Yangtze River during crossing, the collision risk modeling is conducted based on AIS data. Risk Influencing Factors (RIFs) including Distance to Closest Point of Approach (DCPA), Time to Closest Point of Approach (TCPA), distance and relative velocity are involved. First, the historical multi-ship encounter scenarios involved ferry during crossing are identified from AIS data. Then, the value of RIFs is calculated according to their cumulative distribution, and their corresponding weights are determined using entropy theory. Next, the Collision Risk Index of Ferry (CRIF) is proposed considering the behavior of ferry and multiple target ships, which makes it possible to assess real-time collision risk during crossing and to integrate collision risk of each voyage based on historical encounter scenarios. The performance of the proposed model is evaluated according to the analysis on several encounter scenarios with different collision risk. Furthermore, the areas with higher collision risk are identified. The results bring some new insights to enhancing navigation safety of ferries.

Suggested Citation

  • Cai, Mingyou & Zhang, Jinfen & Zhang, Di & Yuan, Xiaoli & Soares, C. Guedes, 2021. "Collision risk analysis on ferry ships in Jiangsu Section of the Yangtze River based on AIS data," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
  • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021004191
    DOI: 10.1016/j.ress.2021.107901
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    as
    1. Du, Lei & Goerlandt, Floris & Kujala, Pentti, 2020. "Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    2. Hänninen, Maria & Kujala, Pentti, 2012. "Influences of variables on ship collision probability in a Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 102(C), pages 27-40.
    3. Martins, Marcelo Ramos & Maturana, Marcos Coelho, 2013. "Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 89-109.
    4. Marcelo Ramos Martins & Marcos Coelho Maturana, 2010. "Human Error Contribution in Collision and Grounding of Oil Tankers," Risk Analysis, John Wiley & Sons, vol. 30(4), pages 674-698, April.
    5. Goerlandt, Floris & Montewka, Jakub, 2015. "Maritime transportation risk analysis: Review and analysis in light of some foundational issues," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 115-134.
    6. Bye, Rolf J. & Aalberg, Asbjørn L., 2018. "Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 174-186.
    7. Jinfen Zhang & Ângelo P Teixeira & C. Guedes Soares & Xinping Yan & Kezhong Liu, 2016. "Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks," Risk Analysis, John Wiley & Sons, vol. 36(6), pages 1171-1187, June.
    8. Antão, Pedro & Guedes Soares, C., 2008. "Causal factors in accidents of high-speed craft and conventional ocean-going vessels," Reliability Engineering and System Safety, Elsevier, vol. 93(9), pages 1292-1304.
    9. Sotiralis, P. & Ventikos, N.P. & Hamann, R. & Golyshev, P. & Teixeira, A.P., 2016. "Incorporation of human factors into ship collision risk models focusing on human centred design aspects," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 210-227.
    10. Zhang, Jinfen & Wan, Chengpeng & He, Anxin & Zhang, Di & Soares, C. Guedes, 2021. "A two-stage black-spot identification model for inland waterway transportation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    11. Montewka, Jakub & Hinz, Tomasz & Kujala, Pentti & Matusiak, Jerzy, 2010. "Probability modelling of vessel collisions," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 573-589.
    12. Shelmerdine, Richard L., 2015. "Teasing out the detail: How our understanding of marine AIS data can better inform industries, developments, and planning," Marine Policy, Elsevier, vol. 54(C), pages 17-25.
    13. Zhang, D. & Yan, X.P. & Yang, Z.L. & Wall, A. & Wang, J., 2013. "Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze River," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 93-105.
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    Cited by:

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    7. Mazurek, J. & Lu, L. & Krata, P. & Montewka, J. & Krata, H. & Kujala, P., 2022. "An updated method identifying collision-prone locations for ships. A case study for oil tankers navigating in the Gulf of Finland," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    8. Gil, Mateusz & Kozioł, Paweł & Wróbel, Krzysztof & Montewka, Jakub, 2022. "Know your safety indicator – A determination of merchant vessels Bow Crossing Range based on big data analytics," Reliability Engineering and System Safety, Elsevier, vol. 220(C).

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