Abstract:The study examines the determinants of fishing vessel accident severity in the Northeastern United States using vessel accident data from the U.S. Coast Guard for [2001][2002][2003][2004][2005][2006][2007][2008]. Vessel damage and crew injury severity equations were estimated separately utilizing the ordered probit model. The results suggest that fishing vessel accident severity is significantly affected by several types of accidents. Vessel damage severity is positively associated with loss of stability, sink… Show more
“…Overturning is a decisive factor in the total loss of a shipwrecked commercial fishing vessel (Jin et al, 2001). Later, Jin (2014) collected the data on ship accidents from the U.S. Coast Guard from 2001 to 2008 and the result showed that the severity of fishing vessel damage was positively associated with destabilization, foundering, daytime wind speed, ship age and distance from the shore. In terms of oil tankers, oil spills seriously damage the marine economy development such as fishery and cause significant damages to marine ecological resources (Aguilera et al, 2010).…”
The world shipping industry is risky involving high uncertainties, and maritime safety has a direct bearing on human life, property and health of the marine environment. Therefore, safety has always been the focus of maritime transportation. Total-loss marine accidents present the most serious accident type in terms of economic cost. It is therefore crucial for decision-making and guidance on rational safety resource allocation through the analysis and evaluation of the influential factors of total-loss marine accidents. Its novelty lies in the pioneering analysis of all the factors solely influencing total-loss accidents in the whole world region, hence aid the development of a big database on total-loss marine accidents for rational safety policy making. This paper involves 16 ship types and 13 main navigation sea regions and analyses the data on the total-loss marine accidents that occurred in the world from 1998 to 2018. As a result, 11 main influential factors are selected and evaluated by an improved entropy weight-TOPSIS model. The results show that, in the both models with respect to ship type and sea region, the main influential factors are foundering, stranding and fires/explosions. Based on such findings, this paper proposes a series of countermeasures with respect to different factors respectively, which will aid the relevant maritime safety authorities such as the International Maritime Organization and ship owners/operations to take effective risk control actions to avoid/reduce the occurrence of total-loss marine accidents in future.
“…Overturning is a decisive factor in the total loss of a shipwrecked commercial fishing vessel (Jin et al, 2001). Later, Jin (2014) collected the data on ship accidents from the U.S. Coast Guard from 2001 to 2008 and the result showed that the severity of fishing vessel damage was positively associated with destabilization, foundering, daytime wind speed, ship age and distance from the shore. In terms of oil tankers, oil spills seriously damage the marine economy development such as fishery and cause significant damages to marine ecological resources (Aguilera et al, 2010).…”
The world shipping industry is risky involving high uncertainties, and maritime safety has a direct bearing on human life, property and health of the marine environment. Therefore, safety has always been the focus of maritime transportation. Total-loss marine accidents present the most serious accident type in terms of economic cost. It is therefore crucial for decision-making and guidance on rational safety resource allocation through the analysis and evaluation of the influential factors of total-loss marine accidents. Its novelty lies in the pioneering analysis of all the factors solely influencing total-loss accidents in the whole world region, hence aid the development of a big database on total-loss marine accidents for rational safety policy making. This paper involves 16 ship types and 13 main navigation sea regions and analyses the data on the total-loss marine accidents that occurred in the world from 1998 to 2018. As a result, 11 main influential factors are selected and evaluated by an improved entropy weight-TOPSIS model. The results show that, in the both models with respect to ship type and sea region, the main influential factors are foundering, stranding and fires/explosions. Based on such findings, this paper proposes a series of countermeasures with respect to different factors respectively, which will aid the relevant maritime safety authorities such as the International Maritime Organization and ship owners/operations to take effective risk control actions to avoid/reduce the occurrence of total-loss marine accidents in future.
“…Yip (2008) employed the negative binomial regression technique to describe the injuries and fatalities caused by ship accidents in Hong Kong Waters. Jin (2014) developed an ordered Probit model to evaluate the vessel damage and crew injury severity in fishing vessel accidents. However, it should be pointed out that the majority of shipping accidents may cause no fatalities or injuries.…”
This study develops a Zero-Inflated Negative Binomial (ZINB) regression model to evaluate the factors influencing the loss of human life in shipping accidents using ten years' ship accident data in the South China Sea. The ZINB regression model results show that the expected loss of human life is higher for collision, fire/explosion, contact, grounding, hull damage, machinery damage/failure and capsizing accidents occurring in adverse weather conditions during night periods. Sinking can cause the highest loss of life compared to all other accident types. There are fewer fatalities and missing people when the ship involved in an accident is moored or docked. The results also reveal that the loss of human life is associated with shipping accidents occurring far away from the coastal area/harbour/ports. The results of this study are beneficial for policy-makers in proposing efficient strategies to reduce shipping accident casualties in the South China Sea. K E Y WO R D S 1. Zero Inflated Negative Binomial Model.2. Safety.
“…Hull type and propulsion variables, however, are mostly used in studies examining a specific type of vessel. For example, Jin (2014) and Jin et al (2001) focused solely on fishing vessels in their research on accident severity. Figure 2.Conceptual framework for the analysis of harbour tugboat accident severity.…”
Section: Methodsmentioning
confidence: 99%
“…Figure 2.Conceptual framework for the analysis of harbour tugboat accident severity. References: Talley (1995, 1996, 1999a, 1999b), Jin et al (2001), Talley et al (2006, 2008), Jin (2014), Wang and Yang (2018), Weng et al (2018), Weng and Li (2019). …”
As tugboats interact very closely with ships in restricted waters, the possibility of accidents increases in these operations. Despite the high accident possibility, there is a gap in studies on tugboat accidents. This study aims to analyse accidents involving tugboats using data mining. For this purpose, a tugboat accidents dataset consisting of a total of 496 accident records for the period from 2008 to 2019 was collected. Logistic regression and decision tree algorithms were implemented to the dataset. The results revealed that tugboat propulsion type is the most important and influential factor in the severity of tugboat accidents. The inferences drawn from these results could be beneficial for tugboat operators and port authorities in enhancing their awareness of the factors affecting tugboat accidents. In addition, the outputs of this study can be a reference for management units in developing strategies for preventing tugboat accidents and can also be used in effective planning for practicable prevention programmes and practices.
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