Pre-tactical prediction of atfm delay for individual flights
| Autor: | Sergi Mas-Pujol, Paolino De Falco, Esther Salami, Luis Delgado |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Ciència i Tecnologia Aeroespacials, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. ICARUS - Intelligent Communications and Avionics for Robust Unmanned Aerial Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC) 41st Digital Avionics Systems Conference (DASC) |
| Popis: | Airlines develop an operation plan, during the day prior to operations (D-1), to identify potential network issues and prepare potential pre-tactical preventing measures such as aircraft tail swapping or crew reassignment to be applied on D0. Flights might experience discrepancies between their plan and execution due to many different factors, and in particular demand-capacity imbalances in the network leading to Air Traffic Flow Management (ATFM) regulations. Dispatcher3, a Clean Sky 2 innovation action, focuses on the use of machine learning techniques to support the airlines processes prior departure: dispatching, understood as the broad flight planning from the day prior to operations to the flight plan definition and selection, and advisories to pilot. This paper focuses on the estimation of ATFM delay for individual flights during the pre-tactical phase (D-1), which could help airspace users apply mitigation actions. Four machine learning models are developed to produce individual independent estimations with different level of granularity. The first two are binary classifier models that provide information on the probability of a given flight being affected by ATFM delay, and the reason for this delay (airport or airspace congestion). These models reported an accuracy between 75% and 88%. The later two models estimate the impact of the delay (amount of delay assigned to the flight if regulated), with a Mean Absolute Error close to 9.35 minutes. This work has been performed as part of Dispatcher3 innovation action which has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreements No 886461. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union. The opinions expressed herein reflect the authors’ views only. Under no circumstances shall the Clean Sky 2 Joint Undertaking be responsible for any use that may be made of the information contained herein. |
| Databáze: | OpenAIRE |
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