Abstract
Air travelers’ behavior is closely related to the operational performance of any airport terminal. Much of previous research has focused on how airport operators balance the number of facilities in a terminal and the Level of Service (LOS), while the behavior of passengers is less considered. Not much is known, however, about passenger’s behavior during the entire departure process in an airport. In this study, we analyze empirical departure passenger’s data to gain an insight into the regular patterns of their activities in an airport. We find that there exist two distinguished temporal patterns during two discretionary periods— post check-in and pre-security check , post security check and pre-boarding . The time that departure passengers spend in these two periods is well approximated by a double power-law distribution and an exponential truncated power-law distribution respectively. The two distinguished distributions suggest that there may be different mechanisms underlying passengers’ behavior as indicated by previous studies on human mobility. We introduce a stochastic model that considers traveling experience and time pressure to capture the decision dynamics of human behavior. Simulation results suggest that traveling experience and time pressure dominate passenger’s decisions before and after security respectively. Our findings contribute to a better understanding of human dynamics, and also offer the potential for optimizing and simulation of airport terminal operation.
Highlights
Air transport provided transportation service to more than 3.5 billion passenger segments in 2015, with an average of 5.5% growth rate since 2010 [1]
MODEL AND SIMULATION RESULTS To uncover the key mechanisms needed to reproduce passenger’s temporal patterns, we propose the following model to capture the stochastic nature of departure passengers
The time of a passenger spends between Check-in and Security, and between Security and Boarding are drawn from the following distributions, log-normal distribution, power-law distribution, two-Gaussian distribution, and log-normal distribution
Summary
Air transport provided transportation service to more than 3.5 billion passenger segments in 2015, with an average of 5.5% growth rate since 2010 [1]. At the frontiers of research in boarding, passenger boarding behavior is modeled as a one-dimensional, stochastic, and time/space discrete transition process, a set of indicators for prediction of boarding time is proposed in [18] To predict air travelers’ activity patterns in an airport, Liu et al developed a nested logit model based on passengers’ socio-demographical characteristics and travel-specific information (e.g. number of check-in baggage, flight time, etc.) [23]. A question may arise in airport passengers whether we can understand and predict passengers’ ‘‘mobility patterns’’ in the airport terminal This rapidly developing field in data science holds great promise for advancing research on passenger behavior in the airport terminal.
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