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Journal of Risk Research Volume 17, 2014 - Issue 10
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Articles

Aircraft safety analysis using clustering algorithms

Olja ČokoriloFaculty of Transport and Traffic Engineering, Air Transport Department, University of Belgrade, Belgrade, SerbiaCorrespondence[email protected]
,
Mario De LucaDepartment of Civil, Construction and Environmental Engineering, University of Naples Federico II, Naples, Italy
&
Gianluca Dell’AcquaDepartment of Civil, Construction and Environmental Engineering, University of Naples Federico II, Naples, Italy
Pages 1325-1340 | Received 05 Oct 2013, Accepted 04 Nov 2013, Published online: 06 Feb 2014

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Serdar Dindar, Sakdirat Kaewunruen & Min An. (2018) Identification of appropriate risk analysis techniques for railway turnout systems. Journal of Risk Research 21:8, pages 974-995.
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Mario De Luca. (2017) A comparison between prediction power of artificial neural networks and multivariate analysis in road safety management. Transport 32:4, pages 379-385.
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Mario De Luca, Francesco Abbondati, Thomas J. Yager & Gianluca Dell’Acqua. (2016) Field measurements on runway friction decay related to rubber deposits. Transport 31:2, pages 177-182.
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James T. Luxhøj. (2015) A conceptual Object-Oriented Bayesian Network (OOBN) for modeling aircraft carrier-based UAS safety risk. Journal of Risk Research 18:10, pages 1230-1258.
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Nan Liu, Chuanqi Sun, Haofeng Wang, Xiang Li, Xiuting Zhou & Ying'er Zheng. (2022) Analytic hierarchy clustering research of all cargo airlines based on safety information. Analytic hierarchy clustering research of all cargo airlines based on safety information.
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Yaser Yousefi, Nader Karballaeezadeh, Dariush Moazami, Amirhossein Sanaei Zahed, Danial Mohammadzadeh S. & Amir Mosavi. (2020) Improving Aviation Safety through Modeling Accident Risk Assessment of Runway. International Journal of Environmental Research and Public Health 17:17, pages 6085.
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Olja Cokorilo. (2020) Urban Air Mobility: Safety Challenges. Transportation Research Procedia 45, pages 21-29.
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Guangji Hu, Manjot Kaur, Kasun Hewage & Rehan Sadiq. (2018) Fuzzy clustering analysis of hydraulic fracturing additives for environmental and human health risk mitigation. Clean Technologies and Environmental Policy 21:1, pages 39-53.
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Jing-Qiang Li, Hong-Yan Zhang, Yan Zhang & Hai-Tao Liu. (2018) Systematic assessment of intrinsic factors influencing visual attention performances in air traffic control via clustering algorithm and statistical inference. PLOS ONE 13:10, pages e0205334.
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Mustafa Jahangoshai Rezaee & Samuel Yousefi. (2018) An intelligent decision making approach for identifying and analyzing airport risks. Journal of Air Transport Management 68, pages 14-27.
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L. Moretti, P. Di Mascio, S. Nichele & O. Cokorilo. (2018) Runway veer-off accidents: Quantitative risk assessment and risk reduction measures. Safety Science 104, pages 157-163.
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Natalia Distefano & Salvatore Leonardi. (2018) Aircraft runway excursion features: a multiple correspondence analysis. Aircraft Engineering and Aerospace Technology 91:1, pages 197-203.
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Salvatore Antonio Biancardo, Francesca Russo, Daiva Žilionienė & Weibin Zhang. (2017) Rural two-lane two-way three-leg and four-leg stop-controlled intersections: predicting road safety effects. The Baltic Journal of Road and Bridge Engineering 12:2, pages 117-126.
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Aleksandar Knezevic, Ljubisa Vasov, Slavisa Vlacic & Cedomir Kostic. (2017) Imperfect maintenance model for estimating aircraft fleet availability. Aircraft Engineering and Aerospace Technology 89:2, pages 338-346.
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Ivana Čavka, Olja Čokorilo & Ljubiša Vasov. (2016) Energy efficiency in aircraft cabin environment: Safety and design. Energy and Buildings 115, pages 63-68.
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Mario De Luca, Francesco Abbondati, Monica Pirozzi & Daiva Žilionienė. (2016) Preliminary Study on Runway Pavement Friction Decay Using Data Mining. Transportation Research Procedia 14, pages 3751-3760.
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A. Ian Glendon. 2015. The Wiley Blackwell Handbook of the Psychology of Occupational Safety and Workplace Health. The Wiley Blackwell Handbook of the Psychology of Occupational Safety and Workplace Health 506 529 .

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