Modeling Aircraft Departure at a Runway Using a Time-Varying Fluid Queue

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Eri Itoh - , The University of Tokyo, National Institute of Maritime, Port and Aviation Technology (Author)
  • Mihaela Mitici - , Delft University of Technology (Author)
  • Michael Schultz - , Chair of Air Transport Technology and Logistics (Author)

Abstract

Reducing the length of departure queues at runway entry points is one of the most important requirements for reducing aircraft traffic congestion and fuel consumption at airports. This study designs an aircraft departure model at a runway using a time-varying fluid queue. The proposed model enables us to determine the aircraft waiting time in the departure queue and to evaluate effective control approaches for assigning suitable holds at gates rather than runway entry points. As a case study, this study modeled the departure queue at runway 05 of Tokyo International Airport for an entire day of operations. Using actual traffic data of departures at the airport, the model estimates that aircraft spend a total of 2.5 h departure waiting time in a day at runway 05. Considering the stochastic nature of actual departure traffic, the relevance of the proposed model is discussed using validation criteria. The model estimation shows a reasonable, expected order of magnitude compared with the departure queue recorded in the actual traffic data. Furthermore, ecological and economic benefits are quantitatively evaluated assuming a reduction in the departure queue length. Our results show that about one kiloton of fuel oil per year is wasted due to aircraft waiting to depart from a single departure runway.

Details

Original languageEnglish
Article number119
JournalAerospace
Volume9
Issue number3
Publication statusPublished - 25 Feb 2022
Peer-reviewedYes

External IDs

Scopus 85125778928

Keywords

Keywords

  • airport operations, data analysis, departure air traffic, queuing theory, runway management