An empirically grounded agent based model for modeling directs, conflict detection and resolution operations in air traffic management

Authored by Christian Bongiorno, Salvatore Micciche, Rosario N Mantegna

Date Published: 2017

DOI: 10.1371/journal.pone.0175036

Sponsors: No sponsors listed

Platforms: C

Model Documentation: Other Narrative Flow charts

Model Code URLs: https://github.com/cbongiorno/ELSA-ABM-Tactical-Layer/tree/CDR-ABM_UNIPA

Abstract

We present an agent based model of the Air Traffic Management socio-technical complex system aiming at modeling the interactions between aircraft and air traffic controllers at a tactical level. The core of the model is given by the conflict detection and resolution module and by the directs module. Directs are flight shortcuts that are given by air controllers to speed up the passage of an aircraft within a certain airspace and therefore to facilitate airline operations. Conflicts between flight trajectories can occur for two main reasons: either the planning of the flight trajectory was not sufficiently detailed to rule out all potential conflicts or unforeseen events during the flight require modifications of the flight plan that can conflict with other flight trajectories. Our model performs a local conflict detection and resolution procedure. Once a flight trajectory has been made conflict-free, the model searches for possible improvements of the system efficiency by issuing directs. We give an example of model calibration based on real data. We then provide an illustration of the capability of our model in generating scenario simulations able to give insights about the air traffic management system. We show that the calibrated model is able to reproduce the existence of a geographical localization of air traffic controllers' operations. Finally, we use the model to investigate the relationship between directs and conflict resolutions (i) in the presence of perfect forecast ability of controllers, and (ii) in the presence of some degree of uncertainty in flight trajectory forecast.

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