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Port traffic risks - A study of accidents in Hong Kong waters

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  • Yip, Tsz Leung

Abstract

This paper investigates port traffic risk issues by discussing historic accidents in Hong Kong port. A total of 2012 marine accidents with 94 deaths were reported in Hong Kong waters in year 2001-2005, while 660,427 oceanic ships visited the port. A negative binomial regression model is used to analyze the record of dataset. It is found that port traffic risks are of certain pattern and collision accidents are the most popular incidents when port traffic is heavy. Passenger-type vessels have higher potential for injuries during accidents. The findings have identified statistically significant factors for improvements in managing port traffic risks.

Suggested Citation

  • Yip, Tsz Leung, 2008. "Port traffic risks - A study of accidents in Hong Kong waters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(5), pages 921-931, September.
    • Handle: RePEc:eee:transe:v:44:y:2008:i:5:p:921-931
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    Citations

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    Cited by:

    1. Cao, Xinhu & Lam, Jasmine Siu Lee, 2019. "A fast reaction-based port vulnerability assessment: Case of Tianjin Port explosion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 128(C), pages 11-33.
    2. Bing Wu & Huibin Tian & Xinping Yan & C. Guedes Soares, 2020. "A probabilistic consequence estimation model for collision accidents in the downstream of Yangtze River using Bayesian Networks," Journal of Risk and Reliability, , vol. 234(2), pages 422-436, April.
    3. 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).
    4. Jiang, Dan & Wu, Bing & Cheng, Zhiyou & Xue, Jie & van Gelder, P.H.A.J.M., 2021. "Towards a probabilistic model for estimation of grounding accidents in fluctuating backwater zone of the Three Gorges Reservoir," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    5. Wencheng Huang & Yueyang Li & Xingyi Kou & Wenzhe Wang & Yifei Xu, 2021. "Using a FMEA–TIFIAD Approach to Identify the Risk of Railway Dangerous Goods Transportation System," Group Decision and Negotiation, Springer, vol. 30(1), pages 63-95, February.
    6. Zhiyuan Sun & Jianyu Wang & Yanyan Chen & Huapu Lu, 2018. "Influence Factors on Injury Severity of Traffic Accidents and Differences in Urban Functional Zones: The Empirical Analysis of Beijing," IJERPH, MDPI, vol. 15(12), pages 1-16, December.
    7. 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.
    8. Liye Zhang & Hua Wang & Qiang Meng & Hongbin Xie, 2019. "Ship accident consequences and contributing factors analyses using ship accident investigation reports," Journal of Risk and Reliability, , vol. 233(1), pages 35-47, February.
    9. Wang, Huanxin & Liu, Zhengjiang & Wang, Xinjian & Graham, Tony & Wang, Jin, 2021. "An analysis of factors affecting the severity of marine accidents," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    10. Gino J. Lim & Jaeyoung Cho & Selim Bora & Taofeek Biobaku & Hamid Parsaei, 2018. "Models and computational algorithms for maritime risk analysis: a review," Annals of Operations Research, Springer, vol. 271(2), pages 765-786, December.
    11. Jomon Aliyas Paul & Leo MacDonald, 2017. "An empirical analysis of US vessel-related port accidents (2002–2012): Impact of union membership and port efficiency on accident incidence and economic damage," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(4), pages 723-748, December.

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