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Development of safety performance functions (SPFs) to analyse and predict aircraft loss of separation in accordance with the characteristics of the airspace

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  • Valdés, Rosa Maria Arnaldo
  • Comendador, Victor Fernando Gómez
  • Castán, Javier Alberto Perez
  • Sanz, Alvaro Rodriguez
  • Sanz, Luis Perez
  • Ayra, Eduardo Sanchez
  • Nieto, Francisco Javier Saez

Abstract

The occurrence of Losses Of Separation between aircraft in an airspace that could lead to Mid Air Collisions (MAC) is of major concern to Air Traffic Management. This paper uses an innovative approach to develop Safety Performance Functions to predict the occurrence of losses of separation. This will improve the safety conditions under which airlines operate. A data driven approach is used to characterise the Losses Of Separation between aircraft as count data with an excess of zeros and over dispersion. Subsequently, the relationships between the number of aircraft conflicts in a particular route segment and the airspace design and traffic flow characteristics are modelled using Zero-inflated models. Based on the characteristics of the route segment, the distribution that most closely matches observations of the number of conflicts in airspace segments is a Zero-inflated negative binomial probability distribution. It also takes account of the large amount of null values that characterise safety occurrences in aviation.

Suggested Citation

  • Valdés, Rosa Maria Arnaldo & Comendador, Victor Fernando Gómez & Castán, Javier Alberto Perez & Sanz, Alvaro Rodriguez & Sanz, Luis Perez & Ayra, Eduardo Sanchez & Nieto, Francisco Javier Saez, 2019. "Development of safety performance functions (SPFs) to analyse and predict aircraft loss of separation in accordance with the characteristics of the airspace," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 143-161.
    • Handle: RePEc:eee:reensy:v:186:y:2019:i:c:p:143-161
    DOI: 10.1016/j.ress.2019.02.007
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    References listed on IDEAS

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    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273.
    2. Iain L. MacDonald, 2017. "Models for count data," The American Statistician, Taylor & Francis Journals, vol. 71(2), pages 187-190, April.
    3. Xiaoqian Sun & Sebastian Wandelt & Xianbin Cao, 2017. "On Node Criticality in Air Transportation Networks," Networks and Spatial Economics, Springer, vol. 17(3), pages 737-761, September.
    4. Arnab Majumdar & Washington Ochieng & Robert B. Noland, 2003. "An Analysis of the Spatio-Temporal Factors Affecting Aircraft Conflicts Based on Simulation Modelling," ERSA conference papers ersa03p389, European Regional Science Association.
    5. Li Zhu & Kimberly F. Sellers & Darcy Steeg Morris & Galit Shmueli, 2017. "Bridging the Gap: A Generalized Stochastic Process for Count Data," The American Statistician, Taylor & Francis Journals, vol. 71(1), pages 71-80, January.
    6. Zhang, Yimin & Shortle, John & Sherry, Lance, 2015. "Methodology for collision risk assessment of an airspace flow corridor concept," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 444-455.
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    Cited by:

    1. Pang, Bizhao & Hu, Xinting & Dai, Wei & Low, Kin Huat, 2022. "UAV path optimization with an integrated cost assessment model considering third-party risks in metropolitan environments," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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