Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-11T08:44:51.808Z Has data issue: false hasContentIssue false
  • English
  • Français

A Bayesian-Network-based Approach to Risk Analysis in Runway Excursions

Published online by Cambridge University Press:  27 March 2019

Fernando Calle-Alonso Open the ORCID record for Fernando Calle-Alonso [Opens in a new window] ,
Carlos J. Pérez  and
Eduardo S. Ayra
Fernando Calle-Alonso*
Affiliation:
(Universidad de Málaga, Spain)
Carlos J. Pérez
Affiliation:
(Universidad de Extremadura, Spain)
Eduardo S. Ayra
Affiliation:
(Universidad Politécnica de Madrid, Spain)
*
(E-mail: fernandocalle@uma.es)
Get access Rights & Permissions [Opens in a new window]

Abstract

Aircraft accidents are extremely rare in the aviation sector. However, their consequences can be very dramatic. One of the most important problems is runway excursions, when an aircraft exceeds the end (overrun) or the side (veer-off) of the runway. After performing exploratory analysis and hypothesis tests, a Bayesian-network-based approach was considered to provide information from risk scenarios involving landing procedures. The method was applied to a real database containing key variables related to landing operations on three runways. The objective was to analyse the effects over runway overrun excursions of failing to fulfil expert recommendations upon landing. For this purpose, the most influential variables were analysed statistically, and several scenarios were built, leading to a runway ranking based on the risk assessed.

Keywords

AviationSafetyBayesian estimationRisk MinimisationRunway excursion
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 5 , September 2019 , pp. 1121 - 1139
Copyright
Copyright © The Royal Institute of Navigation 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ahmed, M.M., Abdel-Aty, M., Lee, J. and Yu, R. (2014). Real-time assessment of fog-related crashes using airport weather data: A feasibility analysis. Accident Analysis & Prevention, 72, 309–317.Google Scholar
Akhtar, M.J. and Utne, I.B. (2014). Human fatigue's effect on the risk of maritime groundings - A Bayesian Network modeling approach. Safety science, 62, 427–440.Google Scholar
Andres, D.M., Luxhoj, J.T., and Coit, D.W. (2005). Modelling of human-system risk and safety: aviation case studies as exemplars. Human Factors and Aerospace Safety, 5, 137–167.Google Scholar
Arnaldo Valdes, R.M., Gomez Comendador, F., Mijares Gordun, L. and Saez Nieto, F. (2011). The development of probabilistic models to estimate accident risk (due to runway overrun and landing undershoot) applicable to the design and construction of runway safety areas. Safety Science, 49, 633–650.Google Scholar
ATAG Air Transport Action Group. (2018). Aviation Benefits Beyond Borders. Technical report. https://aviationbenefits.org/media/166344/abbb18_full-report_web.pdf Accessed 11 February 2019.Google Scholar
Ayres, M., Shirazi, H., Carvalho, R., Hall, J., Speir, R., Arambula, E., David, R., Gadzinski, J., Caves, R., Wong, D. and Pitfield, D. (2013). Modelling the location and consequences of aircraft accidents. Safety Science, 51, 178–186.Google Scholar
Banghart, M., Bian, L., Strawderman, L. and Babski-Reeves, K. (2017). Risk assessment on the EA-6B aircraft utilizing Bayesian networks. Quality Engineering, 29, 499–511.Google Scholar
Benedetto, A., D'amico, F. and Tosti, F. (2014). Improving safety of runway overrun through the correct numerical evaluation of rutting in cleared and graded areas. Safety Science, 62, 326–338.Google Scholar
Boeing. (2017). Statistical summary of commercial jet airplane accidents: Worldwide operations 1959–2016. Technical Report. https://www.skybrary.aero/bookshelf/books/4239.pdf Accessed 11 February 2019Google Scholar
Boyd, D.D. and Stolzer, A. (2016). Accident-precipitating factors for crashes in turbine-powered general aviation aircraft. Accident Analysis & Prevention, 86, 209–216.Google Scholar
Brooker, P. (2011). Experts, Bayesian Belief Networks, rare events and aviation risk estimates. Safety Science, 49, 1142–1155.Google Scholar
Burin, J.M. (2011). Keys to a safe arrival. AeroSafety World, 6, 14–17.Google Scholar
Byoung-Hee, K. (2014). Bayesian networks. Machine Learning, 10, 601B.Google Scholar
Chang, Y.-H., Yang, H.-H. and Hsiao, Y.-J. (2016). Human risk factors associated with pilots in runway excursions. Accident Analysis & Prevention, 94, 227–237.Google Scholar
Cooper, G.F. and Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309–347.Google Scholar
Cowell, R.G., Dawid, P., Lauritzen, S.L. and Spiegelhalter, D.J. (2006). Probabilistic networks and expert systems: Exact computational methods for Bayesian networks. Springer Science & Business Media.Google Scholar
Daidzic, N.E. and Shrestha, J. (2008). Airplane landing performance on contaminated runways in adverse conditions. Journal of Aircraft, 45, 2131–2144.Google Scholar
Dash, D. and Cooper, G.F. (2004). Model averaging for prediction with discrete Bayesian networks. The Journal of Machine Learning Research, 5, 1177–1203.Google Scholar
Drees, L., Wang, C. and Holzapfel, F. (2014). Using subset simulation to quantify stakeholder contribution to runway overrun. Proceedings of Probabilistic Safety Assessment and Management PSAM 12, Honolulu, Hawaii.Google Scholar
Dunn, O.J. (1961). Multiple Comparisons Among Means. Journal of the American Statistical Association, 56, 52–64.Google Scholar
Evans, J.K. (2014). Frequency of Specific Categories of Aviation Accidents and Incidents During 2001–2010. Technical Report 218184. NASA. https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20140012710.pdf Accessed 11 February 2019.Google Scholar
Feng, Q., Sahin, H. and Karson, M.J. (2009). Bayesian analysis models for aviation baggage screening. IIE Transactions, 41, 995–1006.Google Scholar
FSF. (2009). Runway Safety Initiative. Reducing the risk of runway excursions. Technical Report. Flight Safety Foundation. https://flightsafety.org/files/RERR/fsf-runway-excursions-report.pdf Accessed 11 February 2019.Google Scholar
Gerard, V.E. (2006). When a runway is not long enough to land on. Aviation Safety Letters, 1, 27–28.Google Scholar
Gu, Y. (2009). Risk Influence Modeling for Helicopter Safety. Ph.D. thesis Norwegian University of Science.Google Scholar
Heckerman, D. (2008). A tutorial on learning with Bayesian networks. Springer.Google Scholar
Hunter, D.R., Martinussen, M., Wiggins, M. and O'Hare, D. (2011). Situational and personal characteristics associated with adverse weather encounters by pilots. Accident Analysis & Prevention, 43, 176–186.Google Scholar
IATA. (2015). Runway Safety Accident Analysis Report. Technical Report. International Air Transport Association. https://www.iata.org/whatwedo/safety/runway-safety/Documents/RSAR-1st-2015-final-version.pdf Accessed 11 February 2019Google Scholar
ICAO. (2006). Aircraft Operations, Volume I, Flight Procedures (Doc 8168 OPS/611). Manual. International Civil Aviation Organization. http://www.chcheli.com/sites/default/files/icao_doc_8168_vol_1.pdf Accessed 11 February 2019.Google Scholar
ICAO. (2018). Aerodrome Design and Operations, Annex 14 to the Convention on International Civil Aviation. Volume 1 – Aerodromes – Design and Operations. Manual. International Civil Aviation Organization. https://www.bazl.admin.ch/dam/bazl/de/dokumente/Fachleute/Regulationen_und_Grundlagen/icao-annex/icao_annex_14_aerodromesvolumei-aerodromedesignandoperations.pdf.download.pdf/an14_v1_cons.pdf. Accessed 28 February 2019.Google Scholar
Jenkins, M. and Aaron, R.F. (2012). Reducing runway landing overruns. Aero Magazine, 3, 14–19.Google Scholar
Kim, J.-H. (2009). Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Computational statistics & data analysis, 53, 3735–3745.Google Scholar
Kirkland, I., Caves, R., Humphreys, I. and Pitfield, D. (2004). An improved methodology for assessing risk in aircraft operations at airports, applied to runway overruns. Safety Science, 42, 891–905.Google Scholar
Kruskal, W.H. and Wallis, W.A. (1952) Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47, 583–621.Google Scholar
Luxhøj, J.T. and Kauffeld, K. (2003). Evaluating the effect of technology insertion into the national airspace system. The Rutgers Scholar, 5.Google Scholar
Mann, H.B. and Whitney, D.R. (1947). On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. Annals of Mathematical Statistics, 18, 50–60.Google Scholar
Ronald, W. and Fabian, N. (2014). Bayesian model averaging: an application to the determinants of airport departure delay in Uganda. American Journal of Theoretical and Applied Statistics, 3, 1–5.Google Scholar
Rosenkrans, W. (2012). Overrun Breakdown. Report. Flight Safety Foundation. https://flightsafety.org/asw-article/overrun-breakdown/ Accessed 11 February 2019.Google Scholar
Ryan, D. and Brodegard, W. (2003). Runway overrun monitor and method for monitoring runway overruns. Google Patents US App. 10/442,147.Google Scholar
Silander, T., Roos, T., Kontkanen, P. and Myllymaki, P. (2008). Factorized normalized maximum likelihood criterion for learning Bayesian network structures. Proceedings of the 4th European workshop on probabilistic graphical models (PGM-08), Hirtshals, Denmark.Google Scholar
Van Es, G. (2005). Running out of runway: Analysis of 35 years of landing overrun accidents. Technical Report. 498 National Aerospace Laboratory NLR. https://skybrary.aero/bookshelf/books/1146.pdf Accessed 11 February 2019Google Scholar
Van Es, G., Tritschler, K. and Tauss, M. (2009). Development of a landing overrun risk index. Technical Report. 280 National Aerospace Laboratory NLR. https://reports.nlr.nl/xmlui/bitstream/handle/10921/235/TP-2009-280.pdf Accessed 11 February 2019.Google Scholar
Wang, L., Wu, C. and Sun, R. (2014). An analysis of flight quick access recorder (QAR) data and its applications in preventing landing incidents. Reliability Engineering & System Safety, 127, 86–96.Google Scholar
Wilke, S., Majumdar, A. and Ochieng, W.Y. (2014). Airport surface operations: A holistic framework for operations modeling and risk management. Safety Science, 63, 18–33.Google Scholar
Wong, D.K., Pitfield, D.E., Caves, R.E. and Appleyard, A.J. (2006). Quantifying and characterising aviation accident risk factors. Journal of Air Transport Management, 12, 352–357.Google Scholar
You, X., Ji, M. and Han, H. (2013). The effects of risk perception and flight experience on airline pilot's locus of control with regard to safety operation behaviors. Accident Analysis & Prevention, 57, 131–139.Google Scholar