Unobserved Component Model for Predicting Monthly Traffic Volume
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 145, Issue 12
Abstract
Traffic volume prediction plays a critical role in transportation system and infrastructure management. This paper develops the first application of an unobserved component model (UCM) for monthly traffic volume forecasting. We compare the UCM model with simple linear regression, autoregressive integrated moving average (ARIMA), support vector machine (SVM), and artificial neural network (ANN) models based on monthly traffic volume data from a key corridor in New Jersey. As a general econometric method, the UCM decomposes the time series into trend, seasonal, and irregular components, exhibiting superiority for statistically modeling traffic data with cyclic or seasonal fluctuations. The numerical analysis shows that the UCM outperforms all of the other four models and generates reasonably accurate prediction results. This research indicates that UCM can be considered as an alternative approach to modeling traffic volumes.
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Journal of Transportation Engineering, Part A: Systems
Volume 145 • Issue 12 • December 2019
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©2019 American Society of Civil Engineers.
History
Received: Jul 25, 2017
Accepted: Apr 17, 2019
Published online: Oct 4, 2019
Published in print: Dec 1, 2019
Discussion open until: Mar 4, 2020
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