Abstract
Air transport is considered to be the safest mode of mass transportation. Air traffic management (ATM) systems constitute one of the fundamental pillars that contribute to these high levels of safety. In this paper we wish to answer two questions: (i) What is the underlying safety level of ATM systems in Europe? and (ii) What is the dispersion, that is, how far does each ATM service provider deviate from this underlying safety level? To do this, we develop four hierarchical Bayesian inference models that allow us to infer and predict the common rate of occurrence of SMIs, as well as the specific rates of occurrence for each air navigation service provider (ANSP). This study shows the usefulness of hierarchical structures when it comes to obtaining parameters that enable risk to be quantified effectively. The models developed have been found to be useful in explaining and predicting the safety performance of 29 European ATM systems with common regulations and work procedures, but with different circumstances and numbers of aircraft, each managing traffic of differing complexity.
Highlights
This study shows the usefulness of hierarchical structures when it comes to obtaining parameters that enable risk to be quantified effectively
This study makes use of the advantages of hierarchical Bayesian inference models to quantify the levels of safety in European airspace
Estimate how much each of the European air navigation service provider (ANSP) deviates from this general value
Summary
Hierarchicalmodels modelsare aremathematical mathematical representations involve multiple paramHierarchical representations thatthat involve multiple parameters in in such such aa way way that that credible credible values valuesof ofsome someof ofthe theparameters parametersdepend dependsignificantly significantly on eters on values of other parameters. Can be factored into a chain of dependencies, as seen in Equation (2): p(θs , ω | D ) α p( D | θs , ω ) p(θs , ω ) = p( D | θs ) p(θs |ω ) p(ω ) According to this refactoring, the data, D, depends only on each parameter θs. Any model that can be factored or decomposed into a chain of dependencies like that given in Equation (2) is a hierarchical model. Hierarchical dependencies between parameters enable all available data to be used to jointly inform the estimated values of the parameters. The data from all providers can be used together to estimate the ω parameter, which gives the rate of occurrence of SMIs of a generic ATM system
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
