Optimization of hybrid neural network predictor for spatial traffic flow data treatment: Genetic phase-space reconstruction

Vlahogianni, EI; Karlaftis, MG

dc.contributor.author	Vlahogianni, EI	en
dc.contributor.author	Karlaftis, MG	en
dc.date.accessioned	2014-03-01T02:49:52Z
dc.date.available	2014-03-01T02:49:52Z
dc.date.issued	2004	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34761
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-15844383460&partnerID=40&md5=783ca42f434755636e10bb25599dc330	en
dc.subject.other	Correlation methods	en
dc.subject.other	Data acquisition	en
dc.subject.other	Error analysis	en
dc.subject.other	Functions	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Information analysis	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Statistical methods	en
dc.subject.other	Hybrid neural network predictors	en
dc.subject.other	Multilayer perceptrons (MLP)	en
dc.subject.other	Sequential information	en
dc.subject.other	Spatial flow data treatment	en
dc.subject.other	Traffic control	en
dc.title	Optimization of hybrid neural network predictor for spatial traffic flow data treatment: Genetic phase-space reconstruction	en
heal.type	conferenceItem	en
heal.publicationDate	2004	en
heal.abstract	The present paper proposes a hybrid neural predictor that consists of temporal structures of Multilayer Perceptrons for encompassing traffic flow series from sequential points in a traffic network to improve short-term traffic flow prediction. Each of the temporal structures is genetically optimized to provide the optimum embedding of the series attached.	en
heal.journalName	Proceedings of the International Conference on Applications of Advanced Technologies in Transportation Engineering	en
dc.identifier.spage	18	en
dc.identifier.epage	22	en