There are two weaknesses in current researches into human casualty of ship collision. One is that the
range of injuries or fatalities is restricted to the maximum number of casualties in a particular sample, which
may not cover all the possible numbers of casualties in the future. International Maritime Organization (IMO)
employed the injured or dead percentage of all the persons on board to represent casualties, but it only provided
several discrete values to quantify human losses in different scenarios. The other is that the assumption that
the distributions of the injuries or fatalities follow certain distribution, such as negative binomial and Poisson
distributions is left to be statistically tested. Firstly, this study considers casualty rate, including injury and
fatality rates, as random variables; the interval of the variables are from 0 to 1. Then, the distributions of the
variables are investigated using historical data. From historical data, we can find that there are many zeros. Zeroinflated
models are proved to be effective in processing data with inflated zeros. Furthermore, the probability
density of the variables decreases rapidly as the casualty rate becomes larger. Thus, zero-inflated exponential
distribution is assumed to fit the data. The parameters of zero-inflated exponential distribution are calibrated by
maximum likelihood estimation (MLE) method. Finally, the assumption is tested by chi-square test. The zeroinflated
exponential distribution can be used to generate human losses as a part of consequences in the simulation
of ship collision risk.
HUANG Daozheng (黄道正), HU Hao (胡昊), LI Yizhou (李逸舟)
. Zero-Inflated Exponential Distribution of Casualty Rate in Ship Collision[J]. Journal of Shanghai Jiaotong University(Science), 2019
, 24(6)
: 739
-744
.
DOI: 10.1007/s12204-019-2121-3
[1] ASARIOTIS R, BENAMARA H, HOFFMANN J, etal. Review of maritime transport 2013 [R]. New York:United Nations Conference on Trade and Development,2013.
[2] YIP T L. Port traffic risks: A study of accidents inHong Kong waters [J]. Transportation Research PartE, 2008, 44(5): 921-931.
[3] HUANG D Z, HU H, LI Y Z. Spatial analysis of maritimeaccidents using the geographic information system[J]. Transportation Research Record, 2013 (2326):39-44.
[4] QU X B, MENG Q, LI S Y. Analyses and implicationsof accidents in Singapore Strait [J]. Transportation ResearchRecord, 2012 (2273): 106-111.
[5] IMO. Guidelines for formal safety assessment(FSA) for use in the IMO rule-making process:MSC/Circ.1023 [S]. London: IMO, 2002.
[6] FUJII Y, YAMANOUCHI H,MIZUKI N. Some factorsaffecting the frequency of accidents in marine traffic[J]. Journal of Navigation, 1974, 27(2): 235-252.
[7] HELCOM ExpertWorking Group. Risk analysis of seatraffic in the area around Bornholm [R]. Copenhagen:COWI, 2008.
[8] IMO. Formal safety assessment FSA of general cargoships: MSC 93/15/2 [S]. London: IMO, 2011.
[9] IMO. Formal safety assessment FSA-container vessels:MSC 83/INF.8 [S]. London: IMO, 2007.
[10] IMO. Formal safety assessment FSA-liquefied naturalgas carriers: MSC 83/INF.3 [S]. London: IMO, 2007.
[11] IMO. Formal safety assessment FSA-RoPax ships:MSC 85/INF.3 [S]. London: IMO, 2008.
[12] IMO. Formal safety assessment of bulk carrier safety:MSC 75/5/2 [S]. London: IMO, 2002.
[13] PEDERSEN P T. Review and application of ship collisionand grounding analysis procedures [J]. MarineStructure, 2010, 23(3): 241-262.
[14] ALMAZ O A, ALTIOK T, GHAFOORI A.Risk analysis of vessel traffic in Delaware River[C]//Transportation Research Board’s 91st AnnualMeeting. Washington: [s.n.], 2012: 1-12.
[15] WENG, J X, MENG Q, LI Y S. Quantitative riskassessment model for ship collisions in the SingaporeStrait [C]//Transportation Research Board’s 93rd AnnualMeeting. Washington: [s.n.], 2014: 1-14.
[16] WENG J X, YANG D. Investigation of shipping accidentinjury severity and mortality [J]. Accident Analysisand Prevention, 2015, 76: 92-101.
[17] WENG J X, LI G R, CHAI T, et al. Evaluation of twoshipcollision severity using ordered probit approaches[J]. The Journal of Navigation, 2018, 71: 822-836.
[18] TALLEY W K, JIN D, POWELL-KITE H. Determinantsof the severity of passenger vessel accidents [J].Maritime Policy Management, 2006, 33(2): 173-186.
[19] LUO M, SHIN S H. Half-century research developmentsin maritime accidents: Future directions [J]. AccidentAnalysis and Prevention, 2019, 123: 448-460.
[20] CHUNG Y S, LEE P T W, LEE, J K. Burnout in seafarers:Its antecedents and effects on incidents at sea.Maritime Policy & Management, 2017, 44(7): 916-931.
[21] ZHENG Y S, TALLEY W K, JIN D, et al. Crew injuriesin container vessel accidents [J]. Maritime Policy& Management, 2016, 43(5): 541-551.
[22] LUO M, SHIN S, CHANG Y, et al. Duration analysisfor recurrent ship accidents [J]. Maritime Policy &Management, 2017, 44(5): 603-622.
[23] WENG J X, YANG D, DU G. Generalized F distributionmodel with random parameters for estimatingproperty damage cost in maritime accidents. MaritimePolicy & Management, 2018, 45(8): 963-978.
[24] TALLEY W K, JIN D, POWELL-KITE H. Determinantsof the severity of cruise vessel accidents [J].Transportation Research Part D, 2008, 13(2): 86-94.
[25] LAMBERT D. Zero inflated Poisson regression withan application to defects in manufacturing [J]. Technometrics,1992, 34(1): 1-14.
