Models to predict traffic volatility in transportation networks

Tsekeris, T; Stathopoulos, A

dc.contributor.author	Tsekeris, T	en
dc.contributor.author	Stathopoulos, A	en
dc.date.accessioned	2014-03-01T02:52:04Z
dc.date.available	2014-03-01T02:52:04Z
dc.date.issued	2009	en
dc.identifier.issn	14746670	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35825
dc.subject.other	Generalized autoregressive conditional heteroscedasticity	en
dc.subject.other	Real-time traffic datum	en
dc.subject.other	Realized volatility	en
dc.subject.other	Relative performance	en
dc.subject.other	Stochastic volatility	en
dc.subject.other	Transportation network	en
dc.subject.other	Urban arterial networks	en
dc.subject.other	Volatility forecasts	en
dc.subject.other	Forecasting	en
dc.subject.other	Stochastic models	en
dc.title	Models to predict traffic volatility in transportation networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.3182/20090902-3-US-2007.0053s	en
heal.identifier.secondary	http://dx.doi.org/10.3182/20090902-3-US-2007.0053s	en
heal.publicationDate	2009	en
heal.abstract	This paper describes the application and relative performance of three different models for predicting traffic volatility in transportation networks. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, the Stochastic Volatility (SV) model and the Realized Volatility (RV) model are implemented in a real urban arterial network using real-time traffic data of volumes and occupancies. The experimental results provide evidence of the superior performance of the SV model and, at a lesser extent, of the RV model to produce out-of-sample volatility forecasts, in comparison to the standard GARCH methodology © 2009 IFAC.	en
heal.journalName	IFAC Proceedings Volumes (IFAC-PapersOnline)	en
dc.identifier.doi	10.3182/20090902-3-US-2007.0053s	en
dc.identifier.spage	98	en
dc.identifier.epage	103	en