Temporal evolution of short-term urban traffic flow: A nonlinear dynamics approach

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	2014-03-01T01:29:18Z
dc.date.available	2014-03-01T01:29:18Z
dc.date.issued	2008	en
dc.identifier.issn	1093-9687	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19206
dc.subject	Nonlinear Dynamics	en
dc.subject	Traffic Flow	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Construction & Building Technology	en
dc.subject.classification	Engineering, Civil	en
dc.subject.other	Chlorine compounds	en
dc.subject.other	Cluster analysis	en
dc.subject.other	Dynamics	en
dc.subject.other	Flow of solids	en
dc.subject.other	Neural networks	en
dc.subject.other	Statistical methods	en
dc.subject.other	Traffic surveys	en
dc.subject.other	Deterministic structure	en
dc.subject.other	Multi layering	en
dc.subject.other	Non-linear dynamics	en
dc.subject.other	Non-linear evolutions	en
dc.subject.other	Statistical characteristics	en
dc.subject.other	Statistical treatment	en
dc.subject.other	Temporal evolutions	en
dc.subject.other	Temporal pattern identification	en
dc.subject.other	Temporal patterning	en
dc.subject.other	Traffic conditions	en
dc.subject.other	Traffic flowing	en
dc.subject.other	Traffic forecasting	en
dc.subject.other	Traffic patterns	en
dc.subject.other	Traffic volumes	en
dc.subject.other	Urban traffic	en
dc.subject.other	Traffic control	en
dc.subject.other	artificial neural network	en
dc.subject.other	cluster analysis	en
dc.subject.other	forecasting method	en
dc.subject.other	temporal evolution	en
dc.subject.other	urban transport	en
dc.title	Temporal evolution of short-term urban traffic flow: A nonlinear dynamics approach	en
heal.type	journalArticle	en
heal.identifier.primary	10.1111/j.1467-8667.2008.00554.x	en
heal.identifier.secondary	http://dx.doi.org/10.1111/j.1467-8667.2008.00554.x	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	Recognizing temporal patterns in traffic flow has been an important consideration in short-term traffic forecasting research. However, little work has been conducted on identifying and associating traffic pattern occurrence with prevailing traffic conditions. We propose a multilayer strategy that first identifies patterns of traffic based on their structure and evolution in time and then clusters the pattern-based evolution of traffic flow with respect to prevailing traffic flow conditions. Temporal pattern identification is based on the statistical treatment of the recurrent behavior of jointly considered volume and occupancy series; clustering is done via a two-level neural network approach. Results on urban signalized arterial 90-second traffic volume and occupancy data indicate that traffic pattern propagation exhibits variability with respect to its statistical characteristics such as deterministic structure and nonlinear evolution. Further, traffic pattern clustering uncovers four distinct classes of traffic pattern evolution, whereas transitional traffic conditions can be straightforwardly identified. © 2008 Computer-Aided Civil and Infrastructure Engineering.	en
heal.publisher	BLACKWELL PUBLISHING	en
heal.journalName	Computer-Aided Civil and Infrastructure Engineering	en
dc.identifier.doi	10.1111/j.1467-8667.2008.00554.x	en
dc.identifier.isi	ISI:000258597400005	en
dc.identifier.volume	23	en
dc.identifier.issue	7	en
dc.identifier.spage	536	en
dc.identifier.epage	548	en