Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results
Publication: Journal of Transportation Engineering
Volume 129, Issue 6
Abstract
This article presents the theoretical basis for modeling univariate traffic condition data streams as seasonal autoregressive integrated moving average processes. This foundation rests on the Wold decomposition theorem and on the assertion that a one-week lagged first seasonal difference applied to discrete interval traffic condition data will yield a weakly stationary transformation. Moreover, empirical results using actual intelligent transportation system data are presented and found to be consistent with the theoretical hypothesis. Conclusions are given on the implications of these assertions and findings relative to ongoing intelligent transportation systems research, deployment, and operations.
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Information
Published In
Journal of Transportation Engineering
Volume 129 • Issue 6 • November 2003
Pages: 664 - 672
Copyright
Copyright © 2003 American Society of Civil Engineers.
History
Received: Dec 3, 2001
Accepted: Sep 27, 2002
Published online: Oct 15, 2003
Published in print: Nov 2003
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