Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume

Vlahogianni, EI; Karlaftis, MG; Golias, JC

dc.contributor.author	Vlahogianni, EI	en
dc.contributor.author	Karlaftis, MG	en
dc.contributor.author	Golias, JC	en
dc.date.accessioned	2014-03-01T01:25:11Z
dc.date.available	2014-03-01T01:25:11Z
dc.date.issued	2006	en
dc.identifier.issn	0968-090X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17587
dc.subject	Non-stationarity	en
dc.subject	Nonlinearity	en
dc.subject	Recurrence plots	en
dc.subject	Recurrence quantification analysis	en
dc.subject	Short-term prediction	en
dc.subject	Signalized arterials	en
dc.subject	State-space reconstruction	en
dc.subject	Traffic volume	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	Boundary conditions	en
dc.subject.other	Highway traffic control	en
dc.subject.other	Mathematical models	en
dc.subject.other	Statistical methods	en
dc.subject.other	Traffic signals	en
dc.subject.other	Recurrence plots	en
dc.subject.other	Recurrence quantification analysis	en
dc.subject.other	Signalized arterials	en
dc.subject.other	State-space reconstruction	en
dc.subject.other	Traffic forecasting	en
dc.subject.other	Traffic volume	en
dc.subject.other	Traffic surveys	en
dc.subject.other	boundary condition	en
dc.subject.other	forecasting method	en
dc.subject.other	nonlinearity	en
dc.subject.other	recurrence interval	en
dc.subject.other	stochasticity	en
dc.subject.other	time series analysis	en
dc.subject.other	traffic congestion	en
dc.subject.other	traffic management	en
dc.title	Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.trc.2006.09.002	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.trc.2006.09.002	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	Short-term traffic volume data are characterized by rapid and intense fluctuations with frequent shifts to congestion. Currently, research in short-term traffic forecasting deals with these phenomena either by smoothing them or by accounting for them by nonlinear models. But, these approaches lead to inefficient predictions particularly when the data exhibit intense oscillations or frequent shifts to boundary conditions (congestion). This paper offers a set of tools and methods to assess on underlying statistical properties of short-term traffic volume data, a topic that has largely been overlooked in traffic forecasting literature. Results indicate that the statistical characteristics of traffic volume can be identified from prevailing traffic conditions; for example, volume data exhibit frequent shifts from deterministic to stochastic structures as well as transitions between cyclic and strongly nonlinear behaviors. These findings could be valuable in the implementation of a variable prediction strategy according to the statistical characteristics of the prevailing traffic volume states. (c) 2006 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Transportation Research Part C: Emerging Technologies	en
dc.identifier.doi	10.1016/j.trc.2006.09.002	en
dc.identifier.isi	ISI:000242311500004	en
dc.identifier.volume	14	en
dc.identifier.issue	5	en
dc.identifier.spage	351	en
dc.identifier.epage	367	en