Going South?

Abstract

The recent increase in flight delays in the U.S. domestic system is analyzed by estimating an econometric model of average daily delay that incorporates 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 are not attributable to major causal factors. Results suggest that when these factors are controlled for, delays decreased steadily from 2000 through early 2003, but that the trend reversed thereafter. Of the total delay increase between early 2003 and early 2004, half to two-thirds can be attributed to specific sources.

Applications of Business Analytics in Predicting Flight On-time Performance in a Complex and Dynamic System

“…In flight‐delay studies, multiple regression analysis is used to predict the average delay (dependent variable). Independent variables could include factors such as arrival queuing, convective weather, terminal weather, clear time holding, flight operations, passenger loads, and demand (Hansen and Hsiao 2005; Hansen and Zhang 2005; Zhang and Nayak 2012). This method requires nonmissing values and has very restricted assumptions including normality, linearity, homoscedasticity, and nonmulticollinearity, since ordinary least squares (OLS) is used for estimating parameters.…”

“…In another paper, Abdelghany et al (2004) developed a flight-delay model using the classical, shortest-path algorithm to predict down-line flight delays and alert for crew/aircraft operation breaks occurring due to any operation irregularity. Hansen and Hsiao (2005) constructed an econometric model of average daily delay to estimate delays influenced by factors such as arrival queuing, convective weather, and terminal weather. In addition to these aggregated models, other studies examined flight-delay situations at the individual flight level.…”

Wireless Network-Aided Delay Information System Correlation with Airport Grid Distribution Based on Multideterminants Big Data

“…About correlation between delay information system and spatial airport grid distribution, Hansen and Hsiao used the econometric model to examine the daily mean of 32 airports' take-of delays in the United States from a time dimension. Te trend efects, including aircraft queues, fight schedule, and meteorological conditions, are statistically analyzed [12]. Tey found that the increase in total fight and operation demand would aggravate airport delays in the airport grid distribution.…”