Possibilities of quantification of factors influencing the aircraft ground handling process and TOBT prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F23%3A00372103" target="_blank" >RIV/68407700:21260/23:00372103 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.trpro.2023.12.009" target="_blank" >https://doi.org/10.1016/j.trpro.2023.12.009</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.trpro.2023.12.009" target="_blank" >10.1016/j.trpro.2023.12.009</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Possibilities of quantification of factors influencing the aircraft ground handling process and TOBT prediction

Popis výsledku v původním jazyce

The subject of this paper is to summarize current research in the area of aircraft departure delay prediction based on machine learning algorithms and to confirm the relevancy of the identified variables (factors) whose implementation into predictive models could improve their accuracy and thus the ability to accurately predict the Target Off Block Time (TOBT) at Collaborative Decision Making (CDM) airport. In order to predict delays, several prediction models have been developed. One of the large categories of mathematical models are machine learning methods. The article includes a comprehensive literature review focused on machine learning algorithms confirming that none of those approaches used data from aircraft ground handling to predict aircraft departure delays, mainly due to ground handling data availability and scope of the research. The paper describes variables that could extend the existing machine learning prediction models. This research is supported with the real operational data from Václav Havel Airport Prague. The case study at Prague airport verifies a correlation of proposed variables with TOBT time. In several cases, a strong correlation between the proposed variables and TOBT was confirmed.

Název v anglickém jazyce

Possibilities of quantification of factors influencing the aircraft ground handling process and TOBT prediction

Popis výsledku anglicky

The subject of this paper is to summarize current research in the area of aircraft departure delay prediction based on machine learning algorithms and to confirm the relevancy of the identified variables (factors) whose implementation into predictive models could improve their accuracy and thus the ability to accurately predict the Target Off Block Time (TOBT) at Collaborative Decision Making (CDM) airport. In order to predict delays, several prediction models have been developed. One of the large categories of mathematical models are machine learning methods. The article includes a comprehensive literature review focused on machine learning algorithms confirming that none of those approaches used data from aircraft ground handling to predict aircraft departure delays, mainly due to ground handling data availability and scope of the research. The paper describes variables that could extend the existing machine learning prediction models. This research is supported with the real operational data from Václav Havel Airport Prague. The case study at Prague airport verifies a correlation of proposed variables with TOBT time. In several cases, a strong correlation between the proposed variables and TOBT was confirmed.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

21100 - Other engineering and technologies

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Transportation Research Procedia - INAIR 2023

ISBN

—

ISSN

2352-1457

e-ISSN

2352-1465

Počet stran výsledku

9

Strana od-do

68-76

Název nakladatele

Elsevier BV

Místo vydání

Linz

Místo konání akce

Tartu

Datum konání akce

15. 11. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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