Deconstructing delay: A non-parametric approach to analyzing delay changes in single server queuing systems |
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| Institution: | 1. Department of Civil and Environmental Engineering, University of Alberta, Canada;2. Department of Civil and Environmental Engineering, University of California, Berkeley, United States;1. Department of Civil and Environmental Engineering, University of Alberta, Canada;2. Transportation Services, City of Edmonton, Canada;3. Engineering Services Department, City of St. Albert, Canada;1. Department of Civil and Environmental Engineering, University of Alberta, Canada;2. Office of Traffic Safety, City of Edmonton, Canada;1. Department of Civil and Environmental Engineering, University of Alberta, 6-362 Donadeo Innovation Centre for Engineering, Edmonton, AB T6G 1H9, Canada;2. Department of Civil and Environmental Engineering, University of Alberta, 6-269 Donadeo Innovation Centre for Engineering, Edmonton, AB T6G 1H9, Canada;3. Department of Resource Economics and Environmental Sociology, University of Alberta, 515 General Services Building, Edmonton, AB T6G 2H1, Canada;1. Department of Civil and Environmental Engineering, Donadeo Innovation Centre for Engineering, University of Alberta, Edmonton, AB, Canada, T6G 1H9;2. Department of Urban and Regional Planning, College of Architecture and Planning, University of Colorado Denver, Campus Box 126, PO Box 173364, Denver, CO, United States |
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| Abstract: | This paper introduces an empirically driven, non-parametric method to isolate and estimate the effects that changes in demand and changes in throughput have on delay – in particular, arrival and departure flight delay at airport runways. Classic queuing concepts were used to develop a method by which an intermediate, or counterfactual, queuing scenario could be constructed, 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 has three key features. Firstly, the function relies on non-parametric, empirically-based probability distributions of throughput counts. Secondly, facility capacity needs not be explicitly defined, as it is implicitly included in the probability distributions of throughput. Thirdly, the throughput performance function preserves the effect of factors that cause capacity (and, therefore, throughput) to fluctuate over a given period. Temporal sequences of high, moderate, and low capacity are maintained between the observed and counterfactual scenarios. The method was applied to a case study of the three major New York area airports of LaGuardia (LGA), Newark Liberty (EWR), and John F. Kennedy (JFK), using operational data extracted from the Federal Aviation Administration’s (FAA’s) Aviation System Performance Metrics (ASPM) database. The focus was on the peak summer travel seasons of 2006 and 2007, as these airports experienced record levels of delay in 2007. The results indicate that decreases in both demand and throughput were experienced at LGA and EWR, although the decreases in throughput had more significant effects on operational delays as they increased overall at these airports. At JFK, the increase in departure throughput was not sufficient to offset the increase in departure demands. For arrivals, demand increased and throughput decreased. These trends caused a significant growth in delay at JFK between 2006 and 2007. |
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| Keywords: | Counterfactual delay scenario Empirical probabilistic simulation Airport runway operations Aviation Systems Performance Metrics (ASPM) database |
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