Predictive Analytics in Aviation Management: Passenger Arrival Prediction

Maximilian Moll (), Thomas Berg, Simon Ewers and Michael Schmidt
Additional contact information
Maximilian Moll: Universität der Bundeswehr München
Thomas Berg: Universität der Bundeswehr München
Simon Ewers: Universität der Bundeswehr München
Michael Schmidt: Flughafen München

A chapter in Operations Research Proceedings 2019, 2020, pp 667-674 from Springer

Abstract: Abstract Due to increasing passenger and flight numbers, airports need to plan and schedule carefully to avoid wasting their resources, but also congestion and missed flights. In this paper, we present a deep learning framework for predicting the number of passengers arriving at an airport within a 15-min interval. To this end, a first neural network predicts the number of passengers on a given flight. These results are then being used with a second neural network to predict the number of passengers in each interval.

Keywords: Predictive analytics; Aviation management; Deep learning; Neural networks (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-030-48439-2_81

Ordering information: This item can be ordered from
http://www.springer.com/9783030484392

DOI: 10.1007/978-3-030-48439-2_81

Access Statistics for this chapter

More chapters in Operations Research Proceedings from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().