Modeling Seasonal Heteroscedasticity in Vehicular Traffic Condition Series Using a Seasonal Adjustment Approach
This article has been corrected.
VIEW CORRECTIONPublication: Journal of Transportation Engineering
Volume 140, Issue 5
Abstract
Heteroscedasticity modeling in transportation engineering is primarily conducted in short-term traffic condition forecasting to generate time varying prediction intervals around the point forecasts through quantitatively predicting the conditional variance of traffic condition series. Until recently, the generalized autoregressive conditional heteroscedasticity (GARCH) model and the stochastic volatility model have been two major approaches adopted from the field of financial time series analysis for traffic heteroscedasticity modeling. In this paper, recognizing the pronounced seasonal pattern in traffic condition data, a simple seasonal adjustment approach is explored for modeling seasonal heteroscedasticity in traffic-flow series, and four types of seasonal adjustment factors are proposed with respect to daily or weekly patterns. Using real-world traffic-flow data collected from highway systems in the United Kingdom and the United States, the proposed seasonal adjustment approach is implemented and validated. Empirical results show that the proposed model can effectively capture and hence model the seasonal heteroscedasticity in traffic-flow series. In addition, through a comparison with the conventional GARCH model, the proposed approach is shown to consistently generate improved performances in terms of prediction interval construction. Potential applications are discussed to explore the value of heteroscedasticity modeling in transportation engineering studies.
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Acknowledgments
The authors thank the Minnesota Department of Transportation and the United Kingdom Highways Agency for providing the traffic data used in this research. The authors hold all the responsibility for the analyses and views presented in this work. This work is supported by the National Science Foundation of China under grant No. 71101025, the National Key Technology R&D Program under grant No. 2011BAK21B01, and the Fundamental Research Funds for the Central Universities.
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Published In
Journal of Transportation Engineering
Volume 140 • Issue 5 • May 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Jun 25, 2013
Accepted: Dec 16, 2013
Published online: Feb 24, 2014
Published in print: May 1, 2014
Discussion open until: Jul 24, 2014
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