Short-term prediction of urban traffic variability: Stochastic volatility modeling approach

Tsekeris, T; Stathopoulos, A

dc.contributor.author	Tsekeris, T	en
dc.contributor.author	Stathopoulos, A	en
dc.date.accessioned	2014-03-01T01:34:36Z
dc.date.available	2014-03-01T01:34:36Z
dc.date.issued	2010	en
dc.identifier.issn	0733-947X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20765
dc.subject	Forecasting	en
dc.subject	Intelligent transportation systems	en
dc.subject	Traffic flow	en
dc.subject	Traffic models	en
dc.subject.classification	Engineering, Civil	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	Conditional variance	en
dc.subject.other	Data input	en
dc.subject.other	Discrete-time	en
dc.subject.other	GARCH models	en
dc.subject.other	Generalized autoregressive conditional heteroscedasticity	en
dc.subject.other	Intelligent transportation systems	en
dc.subject.other	Measurement locations	en
dc.subject.other	Out-of-sample forecast	en
dc.subject.other	Performance measure	en
dc.subject.other	Predictive performance	en
dc.subject.other	Short term prediction	en
dc.subject.other	Stochastic volatility	en
dc.subject.other	Traffic flow	en
dc.subject.other	Traffic flow levels	en
dc.subject.other	Traffic model	en
dc.subject.other	Transportation management systems	en
dc.subject.other	Urban traffic	en
dc.subject.other	Urban traffic flow	en
dc.subject.other	Forecasting	en
dc.subject.other	Intelligent systems	en
dc.subject.other	Stochastic systems	en
dc.subject.other	Traffic surveys	en
dc.subject.other	Stochastic models	en
dc.subject.other	forecasting method	en
dc.subject.other	intelligent transportation system	en
dc.subject.other	numerical model	en
dc.subject.other	stochasticity	en
dc.subject.other	traffic management	en
dc.subject.other	urban transport	en
dc.title	Short-term prediction of urban traffic variability: Stochastic volatility modeling approach	en
heal.type	journalArticle	en
heal.identifier.primary	10.1061/(ASCE)TE.1943-5436.0000112	en
heal.identifier.secondary	http://dx.doi.org/10.1061/(ASCE)TE.1943-5436.0000112	en
heal.identifier.secondary	004007QTE	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	This paper addresses the problem of modeling and predicting urban traffic flow variability, which involves considerable implications for the deployment of dynamic transportation management systems. Traffic variability is described in terms of a volatility metric, i.e., the conditional variance of traffic flow level, as a latent stochastic (low-order Markov) process. A discrete-time parametric stochastic model, referred to as stochastic volatility (SV) model is employed to provide short-term adaptive forecasts of traffic (speed) variability by using real-time detector measurements of volumes and occupancies in an urban arterial. The predictive performance of the SV model is compared to that of the generalized autoregressive conditional heteroscedasticity (GARCH) model, which has been recently used for the traffic variability forecasting, with regard to different measurement locations, forms of data input, lengths of forecasting horizon and performance measures. The results indicate the potential of the SV model to produce out-of-sample forecasts of speed variability with significantly higher accuracy, in comparison to the GARCH model. © 2010 ASCE.	en
heal.publisher	ASCE-AMER SOC CIVIL ENGINEERS	en
heal.journalName	Journal of Transportation Engineering	en
dc.identifier.doi	10.1061/(ASCE)TE.1943-5436.0000112	en
dc.identifier.isi	ISI:000278907400003	en
dc.identifier.volume	136	en
dc.identifier.issue	7	en
dc.identifier.spage	606	en
dc.identifier.epage	613	en