Spatio-temporal short-term urban traffic volume forecasting using genetically optimized modular networks

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	2014-03-01T01:27:16Z
dc.date.available	2014-03-01T01:27:16Z
dc.date.issued	2007	en
dc.identifier.issn	1093-9687	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18374
dc.subject	Genetics	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	Forecasting	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Multilayer neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Statistical methods	en
dc.subject.other	Genetically optimized modular networks	en
dc.subject.other	Modular neural predictor	en
dc.subject.other	Highway traffic control	en
dc.subject.other	Forecasting	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Highway traffic control	en
dc.subject.other	Multilayer neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Statistical methods	en
dc.subject.other	forecasting method	en
dc.subject.other	methodology	en
dc.subject.other	optimization	en
dc.subject.other	spatiotemporal analysis	en
dc.title	Spatio-temporal short-term urban traffic volume forecasting using genetically optimized modular networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1111/j.1467-8667.2007.00488.x	en
heal.identifier.secondary	http://dx.doi.org/10.1111/j.1467-8667.2007.00488.x	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	Current interest in short-term traffic volume forecasting focuses on incorporating temporal and spatial volume characteristics in the forecasting process. This article addresses the problem of integrating and optimizing predictive information from multiple locations of an urban signalized arterial and proposes a modular neural predictor consisting of temporal genetically optimized structures of multilayer perceptrons (MLP) that are fed with volume data from sequential locations to improve the accuracy of short-term forecasts. The results show that the proposed methodology provides more accurate forecasts compared to the conventional statistical methodologies applied, as well as to the static forms of neural networks. © 2007 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.1467-8667.2007.00488.x	en
dc.identifier.isi	ISI:000246433400001	en
dc.identifier.volume	22	en
dc.identifier.issue	5	en
dc.identifier.spage	317	en
dc.identifier.epage	325	en