Network-wide assessment of ATM mechanisms using an agent-based model

Delgado, L.; Gurtner, G.; Mazzarisi, Piero; Zaoli, Silvia; Valput, D.; Cook, Andrew J.; Lillo, Fabrizio

doi:10.1016/j.jairtraman.2021.102108

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…Recently, fast-time gate-to-gate agent-based modelling (ABM) environments grew more comprehensive such as AgentFly [10]. Recent work in this area such as ELSA [11] and Mercury [12,13] provide a simulations environment, which allows us to investigate different operational mechanisms, based on probabilistic implementations of trajectories or ground operation. This is performed by extending the temporal horizon through including strategic and pre-tactical levels and incorporating a variety of relevant stakeholders, such as AOCs or Air Traffic Flow Management (ATFM).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Gate-to-Gate Simulation Environment for the Air Traffic System

Yildiz,

Förster,

Langner

et al. 2023

Aerospace

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This paper presents a concept of a fast-time gate-to-gate simulation environment. The implementation is divided into an air traffic part that uses BADA performance parameters and a simulation of ground processes. The main objective of the flow-based hybrid simulation environment is to cover commercial European air traffic, in order to investigate network-related effects when exposed to disturbances. Based on historic traffic scenarios, the hybrid simulation platform enables the investigation of the local and global effects of a variety of disruptions. With respect to current flow-based models, it is intended to gain better insights into the underlying interdependencies by modelling higher levels of detail for selected parts, whilst covering the whole European air traffic network. After a validation and first calibration of the approach, Monte Carlo simulations, based on flight plans, are performed as proof of concept. This aims to illustrate the local effects of network-wide disturbances and is applied by means of stochastic influences of ground processes, gained from real operational data.

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Section: Introductionmentioning

confidence: 99%