This study compares the performance of ensemble machine learning, ordinary least-squared and penalized algorithms to predict taxi-out time at two different periods of NextGen capability implementation. In the pre-sample, ordinary least-squared and ridge models performed better than other ensemble learning models. However, the gradient boosting model provided the lowest root mean squared errors in the post-sample. No algorithm fits data better in all cases. This paper recommends selecting the model that provides the best balance between bias and variance.
This paper proposes to use confirmatory factor analysis (CFA) to evaluate the relationship between six observed variables (arrival and departure counts, arrival and departure demand, taxi-out and airborne delays) and their underlying latent (unobserved) constructs (operations, demand, and delays) at six of the most delayed airports (EWR, JFK, LGA, MIA, ORD, and SFO) during the calendar years of 2006e2008. The CFA revealed a good fit between the six observed variables and the three factors that may explain ontime performance except in the case of JFK. The use of CFA can help analysts validate constructs when theory supports a priori predictions and relationships between observed and unobserved variables.
This study uses survival models to evaluate how selected operational factors affect the duration of aircraft taxi-out times at John F. Kennedy Airport, New York. Frailty models help assess whether fixed or random effects are likely to explain differences between two summers, 2006 and 2007. The hourly departure records for summer are censored when operations occurred below the airport's ceiling and visibility minima, that is, in instrument meteorological conditions. Cox regression models showed that block delay and the percent of airport utilized capacity are most likely to increase the risk of longer taxiout times in instrument meteorological conditions compared with other factors such as departure delays, arrival delays and the volume of departures. Frailty analysis reveals that taxi-out times are not significantly affected by either fixed or random effects.Published by Elsevier Ltd.
a b s t r a c t An airport is efficient if it can handle operations on-time by minimizing overall demand and maximizing available airport capacity. Grangercausality tests determined the factors that may cause changes in key components and indicators of airport performance. Compared with the other airports, JFK experienced the greatest improvement in technical efficiency. The Granger-causality tests stressed the significance of airport operations and en route factors in supporting efficiency.
a b s t r a c tThis study, based Newark Liberty International, New York John F. Kennedy, New York LaGuardia, Chicago O'Hare International and San Francisco International in the US, provides a perspective on the predictability of on-time performance evaluation by applying an actuarial and financial method called copula. It is common for the percentage of on-time gate arrivals to be portrayed in daily news as an indicator of schedule reliability. However, the degree of interdependency between gate arrival delays and block delays may provide some indication of schedule reliability.
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