Flight connections and their impacts on delay propagation

Wang, P.T.R.; Schaefer, Lisa A.; Wojcik, Leonard A.

doi:10.1109/dasc.2003.1245858

Cited by 36 publications

(33 citation statements)

References 3 publications

(3 reference statements)

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“…The authors found that the level of delays decreased as airline dominance increased, other things being equal. Wang et al (2003) presented a simple analytic model that explicitly separated the controllable factors that influenced delays and propagation of delays in the NAS from those factors that were random variables in a given scenario. The controllable type of factor was called ''fixed'' and the random type of factor was referred to as ''variable''.…”

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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“…Schaefer and Millner (2001) presented a Detailed Policy Assessment Tool (DPAT) analysis to demonstrate the impacts of simulated changes in capacity due to inclement weather. Wang et al (2003) presented a simple analytic model, which explicitly separates the controllable factors that impact delays and propagation of delays in the National Airspace System (NAS) from those items in a given scenario. They showed how the model could be implemented to better understand delay propagation from specific NAS airports, especially the impacts of flight schedule parameters on measured delay.…”

Section: Introductionmentioning

confidence: 99%

Ranking different factors influencing flight delay

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Zehi²,

Tash‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬³

et al. 2014

10.5267/j.msl

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Flight interruption is one of the most important issues in today's airline industry. Every year, most airlines spend significant amount of money to compensate flight delays. Therefore, it is important to detect important factors influencing on flight delays. This paper presents an empirical investigation to determine important factors on this issue. The study also asks some decision makers to make pairwise comparison and ranks various factors using the art of analytical hierarchy process. The study determines that technical defects and delayed entry were among the most important factors to blame for flight delays. In addition, announcing the postponement, replacement aircraft and path replacement are among the most important decisions facing managers in the aviation industry during the disruption of the flight.

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“…The author asserted that schedules can become become robust and reliable only if buffer times are appropriately embedded and designed during scheduling. Wang et al used queuing models to analyze how the responses to propagated delays varies by airport [24]. The same method was also used by Janic for the quantification of economic effects of flight delays [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Airline delay prediction by machine learning algorithms

Khaksar

Sheikholeslami

2017

Scientia Iranica

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Flight planning, as one of the challenging issue in the industrial world, is faced with many uncertain conditions. One such condition is delay occurrence, which stems from various factors and imposes considerable costs on airlines, operators, and travelers. With these considerations in mind, we implemented flight delay prediction through proposed approaches that are based on machine learning algorithms. Parameters that enable the effective estimation of delay are identified, after which Bayesian modeling, decision tree, cluster classification, random forest, and hybrid method are applied to estimate the occurrences and magnitude of delay in a network. These methods were tested on a U.S. flight dataset and then refined for a large Iranian airline network. Results showed that the parameters affecting delay in US networks are visibility, wind, and departure time, whereas those affecting delay in Iranian airline flights are fleet age and aircraft type. The proposed approaches exhibited an accuracy of more than 70% in calculating delay occurance and magnitude in both the whole-network US and Iranian. It is hoped that the techniques put forward in this work will enable airline companies to accurately predict delays, improve flight planning, and prevent delay propagation.

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Flight connections and their impacts on delay propagation

Cited by 36 publications

References 3 publications

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

Ranking different factors influencing flight delay

Airline delay prediction by machine learning algorithms

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