Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic 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	2014-03-01T01:22:53Z
dc.date.available	2014-03-01T01:22:53Z
dc.date.issued	2005	en
dc.identifier.issn	0968-090X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16706
dc.subject	Genetic optimization	en
dc.subject	Multivariate time series	en
dc.subject	Neural networks	en
dc.subject	Short-term predictions	en
dc.subject	Traffic flow	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	Feedforward neural networks	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Mathematical models	en
dc.subject.other	Multilayer neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Time series analysis	en
dc.subject.other	Genetic optimization	en
dc.subject.other	Intelligent transportation systems	en
dc.subject.other	Multivariate time series	en
dc.subject.other	Short term predictions	en
dc.subject.other	Traffic flow	en
dc.subject.other	Highway traffic control	en
dc.subject.other	intelligent transportation system	en
dc.title	Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.trc.2005.04.007	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.trc.2005.04.007	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	Short-term forecasting of traffic parameters such as flow and occupancy is an essential element of modern Intelligent Transportation Systems research and practice. Although many different methodologies have been used for short-term predictions, literature suggests neural networks as one of the best alternatives for modeling and predicting traffic parameters. However, because of limited knowledge regarding a network's optimal structure given a specific dataset, researchers have to rely on time consuming and questionably efficient rules-of-thumb when developing them. This paper extends past research by providing an advanced, genetic algorithm based, multilayered structural optimization strategy that can assist both in the proper representation of traffic flow data with temporal and spatial characteristics as well as in the selection of the appropriate neural network structure. Further, it evaluates the performance of the developed network by applying it to both univariate and multivariate traffic flow data from an urban signalized arterial. The results show that the capabilities of a simple static neural network, with genetically optimized step size, momentum and number of hidden units, are very satisfactory when modeling both univariate and multivariate traffic data. (c) 2005 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.2005.04.007	en
dc.identifier.isi	ISI:000231897000002	en
dc.identifier.volume	13	en
dc.identifier.issue	3	en
dc.identifier.spage	211	en
dc.identifier.epage	234	en