Deconstructing Delay: A Case Study of Demand and Throughput at the New York Airports

• Author(s) / Creator(s)

  • Kim, A.
  • Hansen, M.

• This paper introduces an empirically driven, nonparametric method to isolate and estimate the effects of demand and throughput changes to observed changes in flight delay. Classical queuing model concepts were used to develop a method by which an intermediate queuing scenario could be constructed, in order to isolate the delay effects due to shifts in demand and throughput. This method includes the development of a stochastic throughput function that is based entirely on data and as a result has two advantages: it uses non-parametric, empirically-based probability distributions, and capacity need not be estimated explicitly. The method was applied to a case study of the three major New York airports of LaGuardia (LGA), John F. Kennedy (JFK), and Newark Liberty (EWR), for the peak summer travel seasons of 2006 and 2007, using data extracted from ASPM. This case study was of particular interest given that these airports experienced record levels of delay in 2007. The simulation results were consistent with both OPSNET and ASPM data, and were successful in quantifying the delay effects of demand and throughput changes from 2006 to 2007.

• Date created

  2019-11-18

• Subjects / Keywords

  • Delay
  • Opsnet
  • Demand
  • ASPM
  • Runway Operations
  • New York Airports
  • Simulation
  • Capacity
  • Throughput
  • Probability
  • New York

• Type of Item

  Conference/Workshop Presentation

• DOI

  https://doi.org/10.7939/r3-4ag3-at58

• License

  Attribution-NonCommercial 4.0 International