Probabilistic Traffic Models for Occupancy Counting
(2017) Seventh SESAR Innovation Days — Location: Belgrade,Serbia (28.November.2017)
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- Abstract
- Air Traffic Management is subject to many uncertainties. These uncertainties drastically reduce overall predictability and force the introduction of margins having a negative impact on the capacity of the system. Moreover, as of today, uncertainties are not explicitly accounted for and human operator judgement and experience is relied upon to assess the “quality” of the estimations provided by the support tools. One way to make it explicit is to convey uncertainty through probability distributions. Building on this approach, the paper describes how to derive probabilistic traffic models from historical data. These models are used as input to the algorithm developed by Gonze et al. [1] in order to compute occupancy count distributions. The application of the approach to one sector of EUROCONTROL’s MUAC airspace is presented to show how uncertainty is captured by the proposed models.
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Citations
Boucquey, J., Hately, a., Irvine, R., Steurs, S., Gonze, F., Huens, E., & Jungers, R. (2017). Probabilistic Traffic Models for Occupancy Counting. Seventh SESAR Innovation Days, Belgrade,Serbia. https://hdl.handle.net/2078.5/252385