O’Hare Airport Short-Term Ground Transportation Modal Demand Forecast Using Gaussian Processes
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 3
Abstract
The principal objective of this study is to analyze the spatial and temporal variation of ground transportation airport demand and provide demand forecast to inform planning capability and explore alternatives for investments to accommodate airport growth. Because of its good adaptability and strong generalization ability for dealing with high-dimensional input, small-sample, and nonlinear spatial data, Gaussian process (GP) regression is used to provide forecast estimates using data from transportation network company (TNC) trips and urban rail passengers at Chicago’s O’Hare International Airport. TNC airport trips differ significantly, with three times more distance, more than twice the travel time, and half of the share requests compared with nonairport trips. This highlights the need for separate demand models. Hourly analysis of the rail service indicates that this is likely heavily used by airport workers, whereas TNC services focus on travelers because of variations in the peak demand hours. Heteroscedastic GP regression is implemented because of differences in trip variance between night and day hours. Estimates are given for weekdays and weekend trips, and the 95% confidence intervals are calculated. The introduction of flight schedule information into the models shows marginal improvements in their performance. However, fitting a GP regression becomes computationally expensive with increased sample size and the introduction of spatial components. Transportation planners and policymakers can use the results and methods implemented in this study to optimize transportation assets and provide long-range simulations of the current and future conditions in the area.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party, specifically the Chicago Transit Authority and the City of Chicago. Direct requests for these materials may be made to the provider.
Acknowledgments
The submission has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a US Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. Argonne National Laboratory’s work was supported by the US Department of Energy, Office of Vehicle Technologies, under contract DE-AC02-06CH11357.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 3 • March 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 9, 2023
Accepted: Aug 28, 2023
Published online: Dec 21, 2023
Published in print: Mar 1, 2024
Discussion open until: May 21, 2024
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