Maritime accidents in the estuary of the River Seine from 2009–2019
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Abstract
Background: In confined waters, ships run a high risk of groundings, contact, sinkings and near misses. In
such waters the maritime traffic is dense, the waterway is narrow, the depth is limited, and tides and currents
are constantly changing.
Materials and methods: From 2009–2019, 75 accidents were investigated in the estuary of the Seine. Weather conditions and perceived fatigue were studied. From May to June 2020, 114 seafarers, 34 pilots and 80 captains, responded to a questionnaire focusing on the use of Pilot Portable Units (PPU) and Electronic Chart Display Information Systems (ECDIS).
Results: The 75 accidents corresponded to an average of 6.8 ± 3.2 accidents per year. Groundings were the most frequent accidents (35%, n = 26) followed by contact accidents with the quayside (25%, n = 19), between ships or tugs while manoeuvring (8%, n = 6) or while sailing (1%, n = 1). There was no loss of vessels nor fatalities of crew members. In poor weather conditions, there were 76% more accidents than in normal conditions (4.4 ± 2.5 accidents/10,000 movements versus 2.5 ± 1.9 accidents/10,000 movements, p < 0.03). Almost all the accidents (96%) were related to human errors of judgment (81%), or negligence (53%), or both (39). Perceived fatigue was probably in cause in 6 accidents. Only 3 accidents were related to mechanical causes. Through the questionnaires, 69% of the pilots complained of difficulties in mastering the devices and software. They felt distracted by alarms which affected their attention while navigating. They requested training on a simulator. Concerning ship captains, 83% felt comfortable with ECDIS devices yet only 20% were able to configure the ECDIS correctly.
Conclusions: In the Seine estuary, 75 accidents occurred within the 11 year-study. Risk factors were poor weather conditions and human error. PPU and ECDIS were considered as useful tools in the prevention of accidents. However, pilots and captains requested more thorough training in their use.
Keywords: confined waterscollisiongroundingPilot Portable UnitElectronic Chart Display Information SystemECDIS
References
- Gould K, Røed B, Koefoed V, et al. Performance-Shaping factors associated with navigation accidents in the Royal Norwegian Navy. Military Psychology. 2009; 18(sup1): S111–S129.
- Akten N. Analysis of shipping casualties in the bosphorus. Journal of Navigation. 2004; 57(3): 345–356.
- Kujala P, Hänninen M, Arola T, et al. Analysis of the marine traffic safety in the Gulf of Finland. Reliability Engineering & System Safety. 2009; 94(8): 1349–1357.
- Aydogdu Y, Yurtoren C, Park JS, et al. A study on local traffic management to improve marine traffic safety in the Istanbul Strait. J Navig. 2011; 65(1): 99–112.
- Di Ciaccio F, Menegazzo P, Troisi S. Optimization of the maritime signaling system in the Lagoon of Venice. Sensors (Basel). 2019; 19(5).
- Zhang J, Teixeira Â, Soares CG, et al. Quantitative assessment of collision risk influence factors in the Tianjin port. Safety Science. 2018; 110: 363–371.
- Acejo IL, Sampson H, Turgo NN, et al. The causes of maritime accidents in the period 2002-2016. 2018.
- Weng J, Yang D, Weng J, et al. Investigation of shipping accident injury severity and mortality. Accid Anal Prev. 2015; 76: 92–101.
- Chen P, Huang Y, Mou J, et al. Ship collision candidate detection method: A velocity obstacle approach. Ocean Engineering. 2018; 170: 186–198.
- Chai T, Xue H, Chai T, et al. A study on ship collision conflict prediction in the Taiwan Strait using the EMD-based LSSVM method. PLoS One. 2021; 16(5): e0250948.
- Aydin M, Uğurlu Ö, Boran M, et al. Assessment of human error contribution to maritime pilot transfer operation under HFACS-PV and SLIM approach. Ocean Engineering. 2022; 266: 112830.
- Liu Z, Wu Z, Zheng Z, et al. A novel framework for regional collision risk identification based on AIS data. Applied Ocean Research. 2019; 89: 261–272.
- Wang Y, Fu S, Wang Y, et al. Framework for process analysis of maritime accidents caused by the unsafe acts of seafarers: a case study of ship collision. J Mar Sci Eng. 2022; 10(11): 1793.
- Chauvin C, Lardjane S, Morel G, et al. Human and organisational factors in maritime accidents: analysis of collisions at sea using the HFACS. Accid Anal Prev. 2013; 59: 26–37.
- Weng J, Liao S, Li G. Bayesian regression model for estimating economic loss resulting from two-ship collisions. Transportation Research Record: Journal of the Transportation Research Board. 2019; 2673(1): 164–172.
- Hetherington C, Flin R, Mearns K. Safety in shipping: the human element. J Safety Res. 2006; 37(4): 401–411.
- Akhtar M, Utne I. Human fatigue’s effect on the risk of maritime groundings – A Bayesian Network modeling approach. Safety Science. 2014; 62: 427–440.
- Hasanspahić N, Vujičić S, Frančić V, et al. The role of the human factor in marine accidents. J Mar Sci Eng. 2021; 9(3): 261.
- Weng J, Yang D, Chai T, et al. Investigation of occurrence likelihood of human errors in shipping operations. Ocean Engineering. 2019; 182: 28–37.
- Liu K, Yu Q, Yuan Z, et al. A systematic analysis for maritime accidents causation in Chinese coastal waters using machine learning approaches. Ocean & Coastal Management. 2021; 213: 105859.
- Xue J, Papadimitriou E, Reniers G, et al. A comprehensive statistical investigation framework for characteristics and causes analysis of ship accidents: A case study in the fluctuating backwater area of Three Gorges Reservoir region. Ocean Engineering. 2021; 229: 108981.
- Carter T, Williams JG, Roberts SE. Crew and passenger deaths from vessel accidents in United Kingdom passenger ships since 1900. Int Marit Health. 2019; 70(1): 1–10.
- Kum S, Sahin B. A root cause analysis for Arctic Marine accidents from 1993 to 2011. Safety Science. 2015; 74: 206–220.
- Soares C, Teixeira AP. Risk assessment in maritime transportation. Reliability Engineering & System Safety. 2001; 74(3): 299–309.
- Antão P, Soares CG. Causal factors in accidents of high-speed craft and conventional ocean-going vessels. Reliability Engineering & System Safety. 2008; 93(9): 1292–1304.
- Adumene S, Afenyo M, Salehi V, et al. An adaptive model for human factors assessment in maritime operations. International Journal of Industrial Ergonomics. 2022; 89: 103293.
- Zhang L, Wang H, Meng Q, et al. Ship accident consequences and contributing factors analyses using ship accident investigation reports. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 2019; 233(1): 35–47.
- Park Y, Yip T, Park H. An analysis of pilotage marine accidents in Korea. The Asian Journal of Shipping and Logistics. 2019; 35(1): 49–54.
- Lee JD, Sanquist TF. Augmenting the operator function model with cognitive operations: assessing the cognitive demands of technological innovation in ship navigation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans. 2000; 30(3): 273–285.
- Bye R, Aalberg A. Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports. Reliability Engineering & System Safety. 2018; 176: 174–186.
- Feng H, Grifoll M, Yang Z, et al. Collision risk assessment for ships’ routeing waters: An information entropy approach with Automatic Identification System (AIS) data. Ocean & Coastal Management. 2022; 224: 106184.
- Guo Y, Jin Y, Hu S, et al. Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN. Reliability Engineering & System Safety. 2023; 229: 108850.
- Kang L, Meng Q, Liu Q. Fundamental diagram of ship traffic in the Singapore Strait. Ocean Engineering. 2018; 147: 340–354.
- Li M, Mou J, Liu R, et al. Relational model of accidents and vessel traffic using AIS data and GIS: a case study of the western port of Shenzhen city. Journal of Marine Science and Engineering. 2019; 7(6): 163.
- Marino M, Cavallaro L, Castro E, et al. Analysis on a database of ship accidents in port areas. Data Brief. 2023; 48: 109127.
- Mou J, Tak C, Ligteringen H. Study on collision avoidance in busy waterways by using AIS data. Ocean Engineering. 2010; 37(5-6): 483–490.
- Ng T, Park YS, Smith M, et al. A comparison of ES and PARK maritime traffic risk assessment models in a korean waterway. Journal of the Korean Society of Marine Environment and safety. 2015; 21(3): 246–252.
- Purba PH, Dinariyana A, Handani DW, et al. Application of formal safety assessment for ship collision risk analysis in Surabaya west access channel. IOP Conference Series: Earth and Environmental Science. 2020; 557(1): 012034.
- Ugurlu H, Cicek I. Analysis and assessment of ship collision accidents using Fault Tree and Multiple Correspondence Analysis. Ocean Engineering. 2022; 245: 110514.
- Ung ST. Navigation risk estimation using a modified bayesian network modeling-a case study in Taiwan. Reliability Engineering & System Safety. 2021; 213: 107777.
- Wu X, Mehta A, Zaloom V, et al. Analysis of waterway transportation in Southeast Texas waterway based on AIS data. Ocean Engineering. 2016; 121: 196–209.
- Xin X, Liu K, Yang X, et al. A simulation model for ship navigation in the “Xiazhimen” waterway based on statistical analysis of AIS data. Ocean Engineering. 2019; 180: 279–289.
- Zulkifli, N, RI Raja Ibrahim, Fahmi, A Azlan, and A Yasid, Maritime Cooperation in the Straits of Malacca (2016-2020): Challenges and Recommend For a New Framework. Asian Journal of Research in Education and Social Sciences, 2020. 2: p. : 10–32.
