Influencing factors on airplane boarding times

• Published in: Omega (Oxford) 2019-09, Vol.87, p.177-190
• Main Authors: Hutter, Leonie, Jaehn, Florian, Neumann, Simone
• Format: Article
• Language: English
• Subjects: Airplane boarding; Airport operations; Econometric analysis; Field study
• ISSN: 0305-0483; 1873-5274
• DOI: 10.1016/j.omega.2018.09.002

•The collection of empirical data on airplane boarding times and its statistical analysis are presented.•The structural equation modelling reveals that some (obvious) hypotheses hold true, e.g., concerning the number of passengers or the aircraft capacity.•Counterintuitively, the hypotheses testing did not allow for keeping the assumption that the amount of hand luggage per person increases boarding time.•A regression model for predicting boarding times is provided, which explains over 80% of the variations in the boarding times. The topic of airplane boarding is receiving increasing attention in practice and in the scientific literature. Shorter boarding times can reduce the time an airplane spends at the gate (the airplane turn-around time), resulting in annual cost savings of several hundred thousand dollars per airplane. Although several researchers have analyzed the boarding process purely theoretically or with simulation models, little empirical research has been performed, even though empirical research is the basis for any theoretical or simulation model. In this paper, we provide the fundamentals for this research area by presenting the results of an empirical study conducted at a large European airport. The aim of this study is to determine whether and to what extent certain factors, such as the number of passengers, the capacity of the airplane, and the amount of carry-on baggage, influence boarding times. Boarding times and additional data for short- and medium-haul flights with single-aisle airplanes have been manually collected in a field study and analyzed. The analyses yield the counter-intuitive result that a significant effect on the boarding time of a flight by the average amount of carry-on baggage per passenger cannot be demonstrated. Finally, we develop a regression model to predict boarding times based on the number of passengers and the capacity of the airplane. This straightforward model explains more than 85% of the variance in the boarding time and could therefore easily be used in the daily business of an airline to estimate the expected boarding times per flight. Furthermore, we compare our regression model to various simulation and analytical models as well as other empirical data for validation and out-of-sample testing.