Maritime Safety in a Developing Country, Bangladesh: Where We Are and the Way Forward
Md. Imran Uddin, Bangladesh University of Engineering and TechnologyShow Abstract
Md Asif Raihan, Bangladesh University of Engineering and Technology
Zobair Awal, Bangladesh University of Engineering and Technology
Bangladesh is a developing country which is well-known as a land of rivers. The inland water transport allows quite easy access to certain zones of the country where it is not comfortable to travel through land transports i.e. roads and railways. In spite of being one of the most popular public transport modes, there remain substantial deficiencies concerning maritime safety. This study analyzes the maritime accidents of Bangladesh during 2005 to 2017. The accident data have been collected from the Department of Shipping of Bangladesh. Analysis of data reveals that the major accident type in Bangladesh is collision by the vessels. Moreover, a significant portion of casualty by overall maritime accidents is also caused due to collision. Cargo vessels and passenger vessels are found to be predominantly involved in the accidents. Furthermore, the most vulnerable route for the occurrence of collision accidents is found to be the Dhaka to Barisal route. The analysis also reveals that accidents mostly occur during fair weather and poor visibility condition. Besides, the temporal distribution of accidents suggests that most of the accidents occur during night, although, the death toll is significantly higher during daytime. Most remarkably, the accidents involving foundering of vessels after collision stands for almost 95% of overall fatalities. Scarcity of standard data is the major limitation of this study that signifies poor accident reporting system of the country. Finally, several policy implications are suggested for effective mitigation of collision accidents to improve overall maritime safety of Bangladesh.
Analysis of Port Accidents and Calibration of Heinrich’s Pyramid
Arnab Majumdar, Imperial College LondonShow Abstract
Iulia Manole, Imperial College London
Ryan Nalty, Imperial College London
Academics and industry have used the Heinrich Pyramid for decades to justify overall safety theory, risk assessments and accident prevention strategies. Most use Heinrich’s original severity ratios (1:29:300) for accident causation developed in a factory setting. However, to use the Pyramid effectively and mitigate risks/hazards, it must be calibrated to represent specific industry reality. This paper, for the first time, focuses on calibration of Heinrich’s Pyramid to maritime accident data, using databases from the Marine Accident Investigation Branch of the Department for Transport. This research clusters five years (2013-2017) of accident data using K-Means clustering on categorical variables and severity levels of accidents, similar logic to Heinrich’s analysis. This approach and descriptive statistics provide a new set of ratios between accident severity classifications for casualties with a ship (CS) and occupational accidents (OA) separately. Results show that the data does not appear to fall into Heinrich’s Pyramid shape and yields a vastly different and lower ratio to that of Heinrich’s. Especially concerning was that Very Serious and Serious accidents occurred at a 1:5 ratio for CS and 4:1 for OA, very different from Heinrich’s 1:29. While these results calculated a new ratio, it may not represent reality due to accident reporting/investigation requirements under UK law, a lack of an agreed taxonomy of risk and hazard definitions, and likely underreporting of less severe accidents. This is proven by the fact that, in 2017, CS data became pyramid-shaped, after a decrease in the number of accidents and a 17% increase in near-misses.
Stochastic Programming for Liner Ship Routing and Scheduling under Uncertain Sea Ice Conditions in the Northern Sea Route
Jiaxuan Ding, Tongji UniversityShow Abstract
Chi Xie, Tongji University
It is anticipated that in the foreseeable future the Northern Sea Route (NSR) will be able to serve commercial shipping as an alternative transportation shortcut between East Asia and Europe, especially in the summer season. The sailing time along this route, however, is heavily subject to the variation of sea ice conditions. Any participating shipping company must consider how to mitigate the ill effects of caused sailing time and cost uncertainty on itinerary planning. Finding a good trade-off between the benefit from a tight schedule and the risk caused by an unexpected delay is a key element in relevant routing and scheduling decisions and may be beyond the reach of traditional deterministic planning models. With the aim of maximizing profits over all possible shipping environment scenarios, this article proposes a two-stage stochastic nonlinear integer programming model for liner ship routing and scheduling with uncertain shipping time and cost, the nonlinearity of which arises from the coexistence of schedule-sensitive shipping demand and uncertain arrival time variables in the objective function. The model is converted into an equivalent linear integer programming counterpart by introducing a set of nominal delay variables and Benders decomposition is applied to solve this linear integer version of the problem. Numerical experiments and sensitivity analyses are conducted to validate the efficacy and effectiveness of the model, the results of which suggest several managerial insights that can be used to guide liner ship route and schedule planning under uncertain shipping conditions.
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