Reference : A Comparison of Departure Time of Day Formulations
E-prints/Working papers : First made available on ORBi
Business & economic sciences : Quantitative methods in economics & management
http://hdl.handle.net/2268/192498
A Comparison of Departure Time of Day Formulations
English
Lurkin, Virginie mailto [Université de Liège > HEC-Ecole de gestion de l'ULg : UER > UER Opérations : Informatique de gestion >]
Garrow, Laurie mailto [Georgia Institute of Technology > School of Civil and Environmental Engineering > > >]
Higgins, Matthew mailto [Georgia Institute of Technology > Ernest Scheller Jr. College of Business > > >]
Newman, Jeffrey mailto [Georgia Institute of Technology > School of Civil and Environmental Engineering > > >]
Schyns, Michael mailto [Université de Liège > HEC-Ecole de gestion de l'ULg : UER > UER Opérations : Informatique de gestion >]
27-Jan-2016
Journal of Air Transport Management
15
Yes (verified by ORBi)
0969-6997
[en] Departure time preferences ; itinerary choice
[en] Airline passengers’ itinerary choices are influenced by many factors including carriers, prices, the number of connections, and departure times. This paper compares three different methods that have been used to model departure time of day preferences. The first is a discrete formulation that uses indicator variables to represent the hour of departure. The next two methods are based on a continuous formulation that uses a series of sine and cosine functions. One assumes departure time preferences over a 24-hour cycle and the other uses shorter cycle lengths that account for fewer departures during certain hours of the day. We compare models using itineraries in the Continental U.S. that are separated by two time zones. Although the discrete formulation fits the data better, the two continuous time of day formulations are preferred as they provide more intuitive predictions and require fewer parameters. Results between the two continuous time of day formulations are similar but differ in how strongly they weight itineraries that depart very early or very late in the day. Based on empirical results, we recommend testing both 24-hour and less than 24-hour cycle lengths for a particular dataset.
QuanTOM, Georgia Institute of Technology
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Researchers ; Professionals
http://hdl.handle.net/2268/192498

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