Multivariate Traffic Forecasting Technique Using Cell Transmission Model and SARIMA Model
Publication: Journal of Transportation Engineering
Volume 135, Issue 9
Abstract
The paper develops a short-term space-time traffic flow forecasting strategy integrating the empirical-based seasonal autoregressive integrated moving average (SARIMA) time-series forecasting technique with the theoretical-based first-order macroscopic traffic flow model—cell transmission model. A case study in Dublin city center which has serious traffic congestion is performed to test the effectiveness of the proposed multivariate traffic forecasting strategy. The results show that the forecasts at the junctions only deviate around 10% at a maximum from the original observations and seem to indicate that the proposed strategy is one of the effective approaches to predict the real-time traffic flow level in a congested network especially at the locations where no continuous data collection takes place.
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Acknowledgments
This research is jointly sponsored by the start-up grant (Grant No. UNSPECIFIEDR-264-000-229-112) from the National University of Singapore and the Program for Research in Third-Level Institutions (PRTLI) administered by the Irish Higher Education Authority. The writers are grateful for the constructive comments of the referees and editors.UNSPECIFIED
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Information & Authors
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Published In
Journal of Transportation Engineering
Volume 135 • Issue 9 • September 2009
Pages: 658 - 667
Copyright
© 2009 ASCE.
History
Received: Mar 1, 2007
Accepted: Apr 15, 2009
Published online: Aug 14, 2009
Published in print: Sep 2009
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