Bayesian Time-Series Model for Short-Term Traffic Flow Forecasting
Publication: Journal of Transportation Engineering
Volume 133, Issue 3
Abstract
The seasonal autoregressive integrated moving average (SARIMA) model is one of the popular univariate time-series models in the field of short-term traffic flow forecasting. The parameters of the SARIMA model are commonly estimated using classical (maximum likelihood estimate and/or least-squares estimate) methods. In this paper, instead of using classical inference the Bayesian method is employed to estimate the parameters of the SARIMA model considered for modeling. In Bayesian analysis the Markov chain Monte Carlo method is used to solve the posterior integration problem in high dimension. Each of the estimated parameters from the Bayesian method has a probability density function conditional to the observed traffic volumes. The forecasts from the Bayesian model can better match the traffic behavior of extreme peaks and rapid fluctuation. Similar to the estimated parameters, each forecast has a probability density curve with the maximum probable value as the point forecast. Individual probability density curves provide a time-varying prediction interval unlike the constant prediction interval from the classical inference. The time-series data used for fitting the SARIMA model are obtained from a certain junction in the city center of Dublin.
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Acknowledgments
The writers would like to thank Dr. Sourabh Bhattacharya for his helpful discussions and advice on this paper. The research work is funded under the Programme for Research in Third-Level Institutions (PRTLI), administered by the HEA.
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Published In
Journal of Transportation Engineering
Volume 133 • Issue 3 • March 2007
Pages: 180 - 189
Copyright
© 2007 ASCE.
History
Received: Oct 5, 2005
Accepted: Jul 26, 2006
Published online: Mar 1, 2007
Published in print: Mar 2007
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