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Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

Published online by Cambridge University Press:  30 July 2020

Remzi Fiskin Open the ORCID record for Remzi Fiskin [Opens in a new window] ,
Erkan Cakir  and
Coşkan Sevgili
Remzi Fiskin*
Affiliation:
(Department of Marine Transportation Engineering, Fatsa Faculty of Marine Sciences, Ordu University, Ordu, Turkey)
Erkan Cakir
Affiliation:
(Department of Marine Transportation Engineering, Maritime Faculty, Recep Tayyip Erdogan University, Rize, Turkey)
Coşkan Sevgili
Affiliation:
(Department of Marine Transportation Engineering, Maritime Faculty, Dokuz Eylül University, İzmir, Turkey)
*
(E-mail: [email protected])
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Abstract

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.

Keywords

TugboatsDecision TreeData MiningAccident Severity
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 1 , January 2021 , pp. 79 - 104
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Copyright
Copyright © The Royal Institute of Navigation 2020

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References

REFERENCES

Aksoy, İ, Badur, B. and Mardikyan, S. (2010). Finding hidden patterns of hospital infections on newborn: a data mining approach. İstanbul University Journal of the School of Business, 39(2), 210–226.Google Scholar
Balin, A., Şener, B. and Demirel, H. (2019). Application of fuzzy VIKOR method for the evaluation and selection of a suitable tugboat. Proceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment, 1–8.Google Scholar
Bland, J. M. and Altman, D. G. (2000). The odds ratio. British Medical Journal, 320, 1648.CrossRefGoogle ScholarPubMed
Bodunov, O., Schmidt, F., Martin, A., Brito, A. and Fetzer, C. (2018). Grand Challenge: Real-Time Destination and ETA Prediction for Maritime Traffic. DEBS 2018 - Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems, 198–201.Google Scholar
Bogalecka, M. and Popek, M. (2008). Analysis of sea accidents in 2006. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 2(2), 179–182.Google Scholar
Breiman, L., Freidman, J. H., Olshen, R. A. and Stone, C. J. (1984). Classification and Regression Trees. New York: Taylor & Francis Group.Google Scholar
Bui, V. P. and Kim, Y. B. (2011). Development of constrained control allocation for ship berthing by using autonomous tugboats. International Journal of Control, Automation and Systems, 9(6), 1203–1208.Google Scholar
Bye, R. J. and Aalberg, A. L. (2018). Maritime navigation accidents and risk indicators: an exploratory statistical analysis using AIS data and accident reports. Reliability Engineering and System Safety, 176, 174–186.CrossRefGoogle Scholar
Çakíroğlu, G., Şener, B. and Balín, A. (2018). Applying a fuzzy-AHP for the selection of a suitable tugboat based on propulsion system type. Brodogradnja, 69(4), 1–13.CrossRefGoogle Scholar
Chan, Y. H. (2004). Biostatistics 202: logistic regression analysis. Singapore Medical Journal, 45(4), 149–153.Google ScholarPubMed
Chen, M. Y. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38(9), 11261–11272.CrossRefGoogle Scholar
Cheng, C. W., Leu, S. S., Cheng, Y. M., Wu, T. C. and Lin, C. C. (2012). Applying data mining techniques to explore factors contributing to occupational injuries in taiwan's construction industry. Accident Analysis and Prevention, 48, 214–222.CrossRefGoogle ScholarPubMed
Coraddu, A., Oneto, L., Baldi, F. and Anguita, D. (2017). Vessels fuel consumption forecast and trim optimisation: a data analytics perspective. Ocean Engineering, 130, 351–370.CrossRefGoogle Scholar
Coraddu, A., Oneto, L., Baldi, F. and Anguita, D. (2018). In soft computing for sustainability science. In: Corona, C. C. (ed.). Vessels Fuel Consumption: A Data Analytics Perspective to Sustainability, Berlin Heidelberg: Springer, 11–48.Google Scholar
Couce, L. C., Couce, J. C. C. and Formoso, JÁF. (2015). Operation and handling in escort tugboat manoeuvres with the aid of automatic towing winch systems. The Journal of Navigation, 68(1), 71–88.CrossRefGoogle Scholar
Dachev, Y. and Panov, A. (2017). Traditional Navigation in e-Navigation Context. 18th Annual General Assembly (AGA) 2018, 2, 106–115.Google Scholar
Darbra, R. M. and Casal, J. (2004). Historical analysis of accidents in seaports. Safety Science, 42(2), 85–98.CrossRefGoogle Scholar
De Oña, J., López, G. and Abellán, J. (2013). Extracting decision rules from police accident reports through decision trees. Accident Analysis and Prevention, 50, 1151–1160.CrossRefGoogle ScholarPubMed
Eleftheria, E., Apostolos, P. and Markos, V. (2016). Statistical analysis of ship accidents and review of safety level. Safety Science, 85, 282–292.Google Scholar
Erol, S. and Başar, E. (2015). The analysis of ship accident occurred in Turkish search and rescue area by using decision tree. Maritime Policy and Management, 42(4), 377–388.CrossRefGoogle Scholar
Erol, S., Demir, M., Çetişli, B. and Eyüboğlu, E. (2018). Analysis of ship accidents in the Istanbul strait using neuro-fuzzy and genetically optimised fuzzy classifiers. The Journal of Navigation, 71(2), 419–436.CrossRefGoogle Scholar
Fernández, L., Mediano, P., García, R., Rodríguez, J. M. and Marín, M. (2016). Risk factors predicting infectious lactational mastitis: decision tree approach versus logistic regression analysis. Maternal and Child Health Journal, 20(9), 1895–1903.CrossRefGoogle ScholarPubMed
Fiskin, R. (2019). Route planning and optimization for maritime collision avoidance. Ph.D. thesis. Dokuz Eylül University, İzmir, Turkey.Google Scholar
Forsell, K., Eriksson, H., Järvholm, B., Lundh, M., Andersson, E. and Nilsson, R. (2017). Work environment and safety climate in the Swedish merchant fleet. International Archives of Occupational and Environmental Health, 90(2), 161–168.CrossRefGoogle ScholarPubMed
Guo, W., Xia, X. and Wang, X. (2015). A remote sensing ship recognition method of entropy-based hierarchical discriminant regression. Optik, 126(20), 2300–2307.CrossRefGoogle Scholar
Gysel, N. R., Russell, R. L., Welch, W. A. and Cocker, D. R. (2016). Impact of aftertreatment technologies on the in-use gaseous and particulate matter emissions from a tugboat. Energy and Fuels, 30(1), 684–689.CrossRefGoogle Scholar
Han, J., Pei, J. and Kamber, M. (2011). Data Mining: Concepts and Techniques. Waltham, USA: Elsevier.Google Scholar
Hashemi, R. R., Le Blanc, L. A., Rucks, C. T. and Shearry, A. (1995). A neural network for transportation safety modeling. Expert Systems with Applications, 9(3), 247–256.CrossRefGoogle Scholar
International Maritime Organization (IMO). (2005). Casualty-Related Matters Reports on Marine Casualties and Incidents. http://www.imo.org/en/OurWork/MSAS/Casualties/Documents/MSC-MEPC.3-Circ.3.pdf. Accessed 12 February 2020.Google Scholar
Jin, D. (2014). The determinants of fishing vessel accident severity. Accident Analysis and Prevention, 66, 1–7.CrossRefGoogle ScholarPubMed
Jin, D., Kite-Powell, H. and Talley, W. (2001). The safety of commercial fishing: determinants of vessel total losses and injuries. Journal of Safety Research, 32(2), 209–228.CrossRefGoogle Scholar
Jin, D., Kite-Powell, H. and Talley, W. K. (2012). Safety in Shipping. In: Talley, W. K. (ed) Maritime Economics. West Sussex, UK: Blackwell Publishing, 333–345.Google Scholar
Karakasnaki, M., Vlachopoulos, P., Pantouvakis, A. and Bouranta, N. (2018). ISM code implementation: an investigation of safety issues in the shipping industry. WMU Journal of Maritime Affairs, 17(3), 461–474.CrossRefGoogle Scholar
Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29(2), 119–127.CrossRefGoogle Scholar
Kim, K. I. and Jeong, J. S. (2016). Visualization of Ship Collision Risk Based on Near-Miss Accidents. Proceedings - 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016, 323–327. IEEE, Sapporo, Japan.CrossRefGoogle Scholar
Kokotos, D. X. and Linardatos, D. S. (2011). An application of data mining tools for the study of shipping safety in restricted waters. Safety Science, 49(2), 192–197.CrossRefGoogle Scholar
Lewis, R. J. and Street, W. C. (2000). An Introduction to Classification and Regression Tree (CART) Analysis. 2000 Annual Meeting of the Society for Academic Emergency Medicine, 310, 14.Google Scholar
Li, K. X., Yin, J., Bang, H. S., Yang, Z. and Wang, J. (2014). Bayesian network with quantitative input for maritime risk analysis. Transportmetrica A: Transport Science, 10(2), 89–118.CrossRefGoogle Scholar
Lin, C. L. and Fan, C. L. (2019). Evaluation of CART, CHAID, and QUEST algorithms: a case study of construction defects in Taiwan. Journal of Asian Architecture and Building Engineering, 18(6), 539–553.CrossRefGoogle Scholar
Lin, L. H., Chen, K. K. and Chiu, R. H. (2017). Predicting customer retention likelihood in the container shipping industry through the decision tree approach. Journal of Marine Science and Technology (Taiwan), 25(1), 23–33.Google Scholar
Liu, Z. (2009). Hybrid Evolutionary Strategy Optimization for Port Tugboat Operation Scheduling. 3rd International Symposium on Intelligent Information Technology Application, IITA 2009, 511–515. IEEE, Shanghai, China.CrossRefGoogle Scholar
Lu, C. S. and Tsai, C. L. (2008). The effects of safety climate on vessel accidents in the container shipping context. Accident Analysis and Prevention, 40(2), 594–601.CrossRefGoogle ScholarPubMed
Luna, J. H., Mar-Ortiz, J., Gracia, M. D. and Morales-Ramírez, D. (2018). An efficiency analysis of cargo-handling operations at container terminals. Maritime Economics and Logistics, 20(2), 190–210.CrossRefGoogle Scholar
Mansson, J. T., Lutzhoft, M. and Brooks, B. (2017). Joint activity in the maritime traffic system: perceptions of ship masters, maritime pilots, tug masters, and vessel traffic service operators. The Journal of Navigation, 70(3), 547–560.CrossRefGoogle Scholar
Maragkogianni, A. and Papaefthimiou, S. (2015). Evaluating the social cost of cruise ships air emissions in major ports of Greece. Transportation Research Part D: Transport and Environment, 36, 10–17.CrossRefGoogle Scholar
Mistikoglu, G., Gerek, I. H., Erdis, E., Mumtaz Usmen, P. E., Cakan, H. and Kazan, E. E. (2015). Decision tree analysis of construction fall accidents involving roofers. Expert Systems with Applications, 42(4), 2256–2263.CrossRefGoogle Scholar
Moenv, B. E., Riise, T. and Helseth, A. (1994). Mortality among seamen with special reference to work on tankers. International Journal of Epidemiology, 23(4), 737–741.CrossRefGoogle Scholar
Nas, S., Özkan, E. D. and Uçan, E. (2016). The determination of the number of tugboats in the area of towage service authorization by using simulation modelling technique. Journal of ETA Maritime Science, 4(1), 91–99.CrossRefGoogle Scholar
Oltedal, H. A. and McArthur, D. P. (2011). Reporting practices in merchant shipping, and the identification of influencing factors. Safety Science, 49(2), 331–338.CrossRefGoogle Scholar
Rezaee, S., Pelot, R. and Finnis, J. (2016). The effect of extratropical cyclone weather conditions on fishing vessel incidents' severity level in Atlantic Canada. Safety Science, 85, 33–40.CrossRefGoogle Scholar
Rodríguez, G. D. M., Martin-Alcalde, E., Murcia-González, J. C. and Saurí, S. (2017). Evaluating air emission inventories and indicators from cruise vessels at ports. WMU Journal of Maritime Affairs, 16(3), 405–420.CrossRefGoogle Scholar
Saeed, F., Bury, A., Bonsall, S. and Riahi, R. (2017). A cost benefit analysis approach to identify improvements in merchant navy deck officers' HELM (Human Element Leadership and Management) training. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 10(4), 551–560.CrossRefGoogle Scholar
Sapri, F. E., Nordin, N. S., Hasan, S. M., Yaacob, W. F. W. and Nasir, S. A. M. (2017). Decision tree model for non-fatal road accident injury. International Journal on Advanced Science, Engineering and Information Technology, 7(1), 63–70.CrossRefGoogle Scholar
Sevgili, C. and Zorba, Y. (2018). Marine casualty analysis of bunker tankers between 1966 and 2017. Journal of Marine Technology and Environment, 2, 51–56.Google Scholar
Steinfort, D. P., Liew, D., Conron, M., Hutchinson, A. F. and Irving, L. B. (2010). Cost-benefit of minimally invasive staging of non-small cell lung cancer: a decision tree sensitivity analysis. Journal of Thoracic Oncology, 5(10), 1564–1570.CrossRefGoogle ScholarPubMed
Syaraswati, R. A., Slamet, I. and Winarno, B. (2017). Classication of Status of the Region on Java Island using C4.5, CHAID, and CART Methods. Journal of Physics: Conference Series, 855, 1.Google Scholar
Szumilas, M. (2010). Explaining odds ratios. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 19(3), 227.Google ScholarPubMed
Talley, W. K. (1995). Vessel damage severity of tanker accidents. Logistics and Transportation Review, 31(3), 191–208.Google Scholar
Talley, W. K. (1996). Determinants of cargo damage risk and severity: the case of containership accidents. Logistics and Transportation Review, 32(4), 377.Google Scholar
Talley, W. K. (1999a). Determinants of the property damage costs of tanker accidents. Transportation Research Part D: Transport and Environment, 4(6), 413–426.CrossRefGoogle Scholar
Talley, W. K. (1999b). Determinants of ship accident seaworthiness. International Journal of Maritime Economics, 1(2), 1–14.CrossRefGoogle Scholar
Talley, W. K., Jin, D. and Kite-Powell, H. (2006). Determinants of the severity of passenger vessel accidents. Maritime Policy & Management, 33(2), 173–186.CrossRefGoogle Scholar
Talley, W. K., Jin, D. and Kite-Powell, H. (2008). Determinants of the severity of cruise vessel accidents. Transportation Research Part D: Transport and Environment, 13(2), 86–94.CrossRefGoogle Scholar
Thill, J. C. and Venkitasubramanian, K. (2015). Multi-layered hinterland classification of Indian ports of containerized cargoes using GIS visualization and decision tree analysis. Maritime Economics and Logistics, 17(3), 265–291.CrossRefGoogle Scholar
Tsou, M. C. (2019). Big data analysis of port state control ship detention database. Journal of Marine Engineering & Technology, 18(3), 113–121.CrossRefGoogle Scholar
Tzannatos, E. (2010). Human element and accidents in Greek shipping. The Journal of Navigation, 63, 119–127.CrossRefGoogle Scholar
Tzannatos, E. and Kokotos, D. (2009). Analysis of accidents in Greek shipping during the pre- and post-ISM period. Marine Policy, 33(4), 679–684.CrossRefGoogle Scholar
UNCTAD. (2019). Review of Maritime Transport 2019. New York, USA: The United Nations Conference on Trade and Development.Google Scholar
Wang, L. and Yang, Z. (2018). Bayesian network modelling and analysis of accident severity in waterborne transportation: a case study in China. Reliability Engineering & System Safety, 180, 277–289.CrossRefGoogle Scholar
Wang, S., Kaku, I., Chen, G. and Zhu, M. (2012). Research on the modeling of tugboat assignment problem in container terminal. Advanced Materials Research, 433, 1957–1961.CrossRefGoogle Scholar
Wen, M. (2019). Research on Decision Tree Application in Data of Fire Alarm Receipt and Disposal. 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 37–41. IEEE, Hangzhou, China.Google Scholar
Weng, J. and Li, G. (2019). Exploring shipping accident contributory factors using association rules. Journal of Transportation Safety and Security, 11(1), 36–57.CrossRefGoogle Scholar
Weng, J., Li, G., Chai, T. and Yang, D. (2018). Evaluation of two-ship collision severity using ordered probit approaches. The Journal of Navigation, 71(4), 822–836.CrossRefGoogle Scholar
Weng, J., Meng, Q. and Wang, D. Z. W. (2013). Tree-based logistic regression approach for work zone casualty risk assessment. Risk Analysis, 33(3), 493–504.CrossRefGoogle ScholarPubMed
Weng, J. and Yang, D. (2015). Investigation of shipping accident injury severity and mortality. Accident Analysis and Prevention, 76, 92–101.CrossRefGoogle ScholarPubMed
Weng, J., Yang, D., Chai, T. and Fu, S. (2019). Investigation of occurrence likelihood of human errors in shipping operations. Ocean Engineering, 182, 28–37.CrossRefGoogle Scholar
Wu, P. J., Chen, M. C. and Tsau, C. K. (2017). The data-driven analytics for investigating cargo loss in logistics systems. International Journal of Physical Distribution and Logistics Management, 47(1), 68–83.CrossRefGoogle Scholar
Wu, B., Yip, T. L., Yan, X. and Mao, Z. (2020). A mutual information-based bayesian network model for consequence estimation of navigational accidents in the Yangtze River. The Journal of Navigation, 73(3), 559–580.CrossRefGoogle Scholar
Xu, X. and Bai, G. (2017). Fuzzy classification and implementation methods for tugboat main engine fault. MATEC Web of Conferences, 95, 2–6.CrossRefGoogle Scholar
Xue, J., Chen, Z., Papadimitriou, E., Wu, C. and Van Gelder, P. H. A. J. M. (2019). Influence of environmental factors on human-like decision-making for intelligent ship. Ocean Engineering, 186, 106060.CrossRefGoogle Scholar
Xue, J., Wu, C., Chen, Z., Van Gelder, P. H. A. J. M. and Yan, X. (2019). Modeling human-like decision-making for inbound smart ships based on fuzzy decision trees. Expert Systems with Applications, 115, 172–188.CrossRefGoogle Scholar
Yang, L. J., Hong, B. G., Inoue, K. and Sadakane, H. (2010). Experimental study on braking force characteristics of tugboat. Journal of Hydrodynamics, 22(5), 343–348.CrossRefGoogle Scholar
Yuan, L. C. W., Tjahjowidodo, T., Lee, G. S. G., Chan, R. and Adnanes, A. K. (2016). Equivalent Consumption Minimization Strategy for Hybrid All-Electric Tugboats to Optimize Fuel Savings. Proceedings of the American Control Conference, 6803–6808. Boston, USA.Google Scholar
Zytoon, M. A. (2012). Occupational injuries and health problems in the Egyptian Mediterranean fisheries. Safety Science, 50(1), 113–122.CrossRefGoogle Scholar