Going South?: Econometric Analysis of U.S. Airline Flight Delays from 2000 to 2004

Hansen, Mark; Hsiao, Chieh Yu

doi:10.3141/1915-11

Cited by 11 publications

(11 citation statements)

References 1 publication

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“…According to Hansen and Hsiao (2005), the recent increase in flight delays in the U.S. domestic system was analyzed by estimating an econometric model of average daily delay that incorporated the effects of arrival queuing, convective weather, terminal weather conditions, seasonal effects, and secular effects (trends in delays not accounted for by other variables). From the estimation results it was possible to quantify some sources of higher delays in late 2003 and early 2004 and track changes in delays that were not attributable to major causal factors.…”

Section: Research Backgroundmentioning

confidence: 99%

Do market-concentrated airports propagate more delays than less concentrated ones? A case study of selected U.S. airports

Diana

2009

Journal of Air Transport Management

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a b s t r a c tAirport congestion and widespread passenger discontent with airlines' poor on-time performance have recently led the Federal government to reduce peak-time operations at large airports such as Chicago O'Hare and New York John F. Kennedy. This paper proposes a methodology to compute delay propagation based on the discrete Fourier transform (DFT) at a sample of ten U.S. airports in summer 2000, 2007 and 2008. The sampled airports are different in terms of size, location and index of concentration. In this research, a flight is considered to be late if it arrives more than fifteen minutes past its schedule. Delay propagation is defined as the hourly ratio of two amplitudes: the one of late arrivals at a sampled airport to that of late arrivals at the final destinations served from that same sampled airport. The purpose of this study is to determine whether delay propagation differs at market-concentrated airports from less concentrated ones. Based on nonparametric tests and proximity analysis, there is no clear evidence that market-concentrated airports are different from less concentrated ones in terms of propagated delays.Published by Elsevier Ltd.

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Section: Research Backgroundmentioning

confidence: 99%

Do market-concentrated airports propagate more delays than less concentrated ones? A case study of selected U.S. airports

Diana

2009

Journal of Air Transport Management

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“…Storms and hurricanes are almost synonymous with Florida; hence, it is logical to expect these weather events to impact air travel (Hansen and Hsiao 2005;Goh, Law, and Mok 2008) as included in equation ( 1) and shown in the estimates from Table 1. Storm variables HCATMED and HCATHGH were set to zero to represent the absence of tropical storms and hurricanes during an 11-year period (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006), thus giving some insight into the expected air travel in the absence of such events.…”

Section: Simulations For Hurricanesmentioning

confidence: 99%

Habit Persistence in Air Passenger Traffic Destined for Florida

Cazanova

Ward

Holland

2013

Journal of Travel Research

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At the core of travel demand theory is separating short-and long-term responses to events and determining how quickly potential travelers respond, if at all, to current events. Using habit persistence models applied to air travel to Florida, air travel demand models are estimated that measure habit persistence while accounting for economic and noneconomic travel demand variables. Along with the traditional economic demand drivers, the models account for weather, wildfires, and adjustments to post-9/11. Elasticities are shown and then simulations are used to fully illustrate the impacts of changing ticket prices, incomes, seasonality, Florida storms, and the recovery from 9/11. The degree of habit persistence is measured and then the short-and long-run adjustments in air traffic destined for Florida are shown.

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“…Airlines usually schedule for fair weather, so any decrease in AAR or ADR caused by weather can lead to delays. Factors related to weather are defined as convective weather, reduced ceiling and visibility, wind, and corresponding AAR or ADR at local and destination airport, respectively (5,(16)(17)(18). The quarter-hour airport report from the FAA database (ASPM) records weather conditions of wind, ceiling, visibility, temperature, AAR, and ADR for each 15-min epoch.…”

Section: Weathermentioning

confidence: 99%

“…Researchers formulated several kinds of variables to represent the demand, capacity, and relationship between demand and capacity in the NAS. These variables include departure demand and queue size (10), levels of traffic (19), arrival demand and total flight operations (18), arrival queue and volume (17), airport throughput (20), and rho (ρ), the ratio of number of scheduled operations and capacity (21). This research separated the scheduled operation into scheduled departure operation and arrival operation and capacity into departure throughput and arrival throughput.…”

Section: Ratio Of Operation Demand and Airport Capacity At Scheduled Departure Timementioning

confidence: 99%

Multifactor Model for Predicting Delays at U.S. Airports

Sherry

Laskey

2008

Transportation Research Record: Journal of the Transportation R

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Air transportation is provided by the movement of aircraft through a network of airports. Researchers have established that approximately 84% of delays are generated at airports. These delays propagate to downstream airports where they are absorbed, passed on or enhanced. Researchers have correlated delays with sets of causal factors and have created models to predict aggregate daily delays at airports. Airport Operation Center (AOC) personnel and Traffic Flow Management (TFM) Specialists have suggested that a model for predicting airport delay in 15 minute epochs would have utility. This paper describes multi-factor models for predicting airport delays in 15 minute epochs at each of the 34 OEP airports. The models are developed using piece-wise linear regressions, using Multi-Adaptive Regression Splines (MARS), for generated delays and for absorbed delays for each of the 34 OEP airports. The models were generated using historic individual airport data. Accuracy evaluation on separate test data shows mean absolute prediction error of 5.3 minutes for generated delay across all the airports, and 2.2 minutes for absorbed delay across all the airports. A summary of the factors that drive the performance of each airport is provided. The implications of these results are discussed.

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Going South?: Econometric Analysis of U.S. Airline Flight Delays from 2000 to 2004

Cited by 11 publications

References 1 publication

Do market-concentrated airports propagate more delays than less concentrated ones? A case study of selected U.S. airports

Do market-concentrated airports propagate more delays than less concentrated ones? A case study of selected U.S. airports

Habit Persistence in Air Passenger Traffic Destined for Florida

Multifactor Model for Predicting Delays at U.S. Airports

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