Conference
Dispatcher3 – Machine learning for efficient flight planning: approach and challenges for data-driven prototypes in air transport
| Τίτλος: | Dispatcher3 – Machine learning for efficient flight planning: approach and challenges for data-driven prototypes in air transport |
|---|---|
| Συγγραφείς: | Delgado Muñoz, Luis, Mas Pujol, Sergi, Skorobogatov, Georgy, Argerich, Clara, Gregori, Ernesto |
| Συνεισφορές: | Universitat Politècnica de Catalunya. Doctorat en Ciència i Tecnologia Aeroespacials, Universitat Politècnica de Catalunya. ICARUS - Intelligent Communications and Avionics for Robust Unmanned Aerial Systems |
| Έτος έκδοσης: | 2022 |
| Συλλογή: | Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |
| Θεματικοί όροι: | Àrees temàtiques de la UPC::Aeronàutica i espai, Engineering--Data processing, Decision making, Machine learning, Decision making--Data processing, Challenges, Pre-departure, Aprenentatge automàtic, Decisió, Presa de--Informàtica |
| Περιγραφή: | Machine learning techniques to support decisionmaking processes are in trend. These are particularly relevant in the context of flight management where large datasets of planned and realised operations are available. Current operations experience discrepancies between planned and executed flight plan, these might be due to external factors (e.g. weather, congestion) and might lead to sub-optimal decisions (e.g. recovering delay (burning extra fuel) when no holding is expected at arrival and therefore it was no needed). Dispatcher3 produces a set of machine learning models to support flight crew pre-departure, with estimations on expected holding at arrival, runway in use and fuel usage, and the airline’s duty manager on pre-tactical actions, with models trained with a larger look ahead time for ATFM and reactionary delay estimations. This paper describes the prototype architecture and approach of Dispatcher3 with particular focus on the challenges faced by this type of data-driven machine learning models in the field of air transport ranging: from technical aspects such as data leakage to operational requirements such as the consideration and estimation of uncertainty. These considerations should be relevant for projects which try to use machine learning in the field of aviation in general. ; This work is 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 Topic Manager is Thales AVS France SAS. 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. ; Postprint (published version) |
| Τύπος εγγράφου: | conference object |
| Περιγραφή αρχείου: | 12 p.; application/pdf |
| Γλώσσα: | English |
| Relation: | https://www.3af-tsas.com/; info:eu-repo/grantAgreement/EC/H2020/886461/EU/Innovative processing for flight practices/Dispatcher3; info:eu-repo/grantAgreement/EC/H2020/101007858/EU/Clean Sky 2 Technologies for Greener Airports by 2050/GREENPORT2050; https://hdl.handle.net/2117/387292 |
| Διαθεσιμότητα: | https://hdl.handle.net/2117/387292 https://westminsterresearch.westminster.ac.uk/item/vzq09/dispatcher3-machine-learning-for-efficient-flight-planning-approach-and-challenges-for-data-driven-prototypes-in-air-transport |
| Rights: | Open Access |
| Αριθμός Καταχώρησης: | edsbas.45398C3D |
| Βάση Δεδομένων: | BASE |
| Η περιγραφή δεν είναι διαθέσιμη |