Hostname: page-component-7dd5485656-2pp2p Total loading time: 0 Render date: 2025-10-30T07:08:29.083Z Has data issue: false hasContentIssue false
  • English
  • Français

Modelling Port Water Collision Risk Using Traffic Conflicts

Published online by Cambridge University Press:  12 September 2011

Ashim Kumar Debnath ,
Hoong Chor Chin  and
Md. Mazharul Haque
Ashim Kumar Debnath*
Affiliation:
(Department of Civil Engineering, National University of Singapore)
Hoong Chor Chin
Affiliation:
(Department of Civil Engineering, National University of Singapore)
Md. Mazharul Haque
Affiliation:
(Department of Civil Engineering, National University of Singapore)
*
(Email: ashim@alumni.nus.edu.sg)
Get access Rights & Permissions [Opens in a new window]

Abstract

Navigational collisions are one of the major safety concerns for many seaports. Despite the extent of work recently done on collision risk analysis in port waters, little is known about the influential factors of the risk. This paper develops a technique for modelling collision risks in port waterways in order to examine the associations between the risks and the geometric, traffic, and regulatory control characteristics of waterways. A binomial logistic model, which accounts for the correlations in the risks of a particular fairway at different time periods, is derived from traffic conflicts and calibrated for the Singapore port fairways. Results show that the fairways attached to shoreline, traffic intersection and international fairway attribute higher risks, whereas those attached to confined water and local fairway possess lower risks. Higher risks are also found in the fairways featuring higher degree of bend, lower depth of water, higher numbers of cardinal and isolated danger marks, higher density of moving ships and lower operating speed. The risks are also found to be higher at night.

Keywords

Navigational collision riskPort fairwaysTraffic conflictsBinomial logistic model

Information

Type
Research Article
Information
The Journal of Navigation , Volume 64 , Issue 4 , October 2011 , pp. 645 - 655
Copyright
Copyright © The Royal Institute of Navigation 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. Proceedings of the Second International Symposium on Information Theory, Academiai Kiado, Budapest.Google Scholar
Akten, N. (2004). Analysis of Shipping Casualties in the Bosphorus. The Journal of Navigation 57(03), 345–356.CrossRefGoogle Scholar
Allison, P. D. (1999). Logistic regression using the SAS system: Theory and application. Cary, NC, USA, SAS Institute.Google Scholar
Chin, H. C. and Debnath, A. K. (2009). Modeling perceived collision risk in port water navigation. Safety Science 47(10), 1410–1416.CrossRefGoogle Scholar
Darbra, R.-M. and Casal, J. (2004). Historical analysis of accidents in seaports. Safety Science 42 (2), 85–98.CrossRefGoogle Scholar
Debnath, A. K. and Chin, H. C. (2009a). Hierarchical Modeling of Perceived Collision Risks in Port Fairways. Transportation Research Record: Journal of the Transportation Research Board 2100, 68–75.CrossRefGoogle Scholar
Debnath, A. K. and Chin, H. C. (2009b). Perceived Collision Risks in Anchorages: A Hierarchical Ordered Probit Analysis. Proceedings of the The 8th EASTS Conference, Surabaya, Indonesia.Google Scholar
Debnath, A. K. and Chin, H. C. (2010). Navigational Traffic Conflict Technique: A Proactive Approach to Quantitative Measurement of Collision Risks in Port Waters. The Journal of Navigation 63 (1), 137–152.CrossRefGoogle Scholar
Faulkner, D. (2003). Shipping Safety: A Matter of Concern. Proceedings - Institute of Marine Engineering Science and Technology Part B Journal of Marine Design and Operations 5, 37–56.Google Scholar
Goossens, L. H. J. and Glansdorp, C. C. (1998). Operational Benefits and Risk Reduction of Marine Accidents. The Journal of Navigation 51(03), 368–381.CrossRefGoogle Scholar
Hardin, J. W. and Hilbe, J. M. (2007). Generalized Linear Models and Extensions, Second Edition. College Station, Texas, USA, Stata Press.Google Scholar
IALA (1980). IALA Maritime Buoyage System, International Association of Marine Aids to Navigation and Lighthouse Authorities, France.Google Scholar
IMO (1998). Joint Nordic Project on Safety Assessment of High-Speed Craft Operations, International Maritime Organization.Google Scholar
Liu, C.-P., Liang, G.-S., et al. (2006). Navigation Safety Analysis in Taiwanese Ports. The Journal of Navigation 59(02), 201–211.CrossRefGoogle Scholar
Liu, Q., Pedersen, E., et al. (2006). Direct perception interface for ship-ship collision avoidance. Proceedings of the IEEE 2006 International Conference on Systems, Man and Cybernetics, Taipei, Taiwan, Institute of Electrical and Electronics Engineers Inc.CrossRefGoogle Scholar
Long, J. S. and Freese, J. (2006). Regression Models for Categorical Dependent Variables Using Stata, Stata Press, Texas, USA.Google Scholar
MPA (2006). Singapore Port Information (2006/2007 Edition). Singapore, Maritime and Port Authority of Singapore.Google Scholar
Roeleven, D., Kok, M., et al. (1995). Inland Waterway Transport: Modeliing the Probability of Accidents. Safety Science 19, 191–202.CrossRefGoogle Scholar
Sarioz, K., Kukner, A., et al. (2000). A Real-Time Ship Manoeuvring Simulation Study for the Strait of Istanbul (Bosporus). The Journal of Navigation 52(03), 394–410.CrossRefGoogle Scholar
Soares, C. G. and Teixeira, A. P. (2001). Risk assessment in maritime transportation. Reliability Engineering & System Safety 74(3), 299–309.CrossRefGoogle Scholar
SOLAS (1974). International Convention for the Safety of Life at Sea 1974. I. M. Organization. London, UK. Chapter V - Regulation 23.Google Scholar
Yip, T. L. (2008). Port traffic risks - A study of accidents in Hong Kong waters. Transportation Research Part E: Logistics and Transportation Review 44 (5), 921–931.CrossRefGoogle Scholar