Fuzzy rule-based system approach to combining traffic count forecasts

Stathopoulos, A; Karlaftis, MG; Dimitriou, L

dc.contributor.author	Stathopoulos, A	en
dc.contributor.author	Karlaftis, MG	en
dc.contributor.author	Dimitriou, L	en
dc.date.accessioned	2014-03-01T01:33:34Z
dc.date.available	2014-03-01T01:33:34Z
dc.date.issued	2010	en
dc.identifier.issn	0361-1981	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20462
dc.subject.classification	Engineering, Civil	en
dc.subject.classification	Transportation	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	Athens , Greece	en
dc.subject.other	Fuzzy adaptive	en
dc.subject.other	Fuzzy rule-based systems	en
dc.subject.other	Heuristic optimization	en
dc.subject.other	Knowledge base	en
dc.subject.other	Mamdani	en
dc.subject.other	Meta-analysis	en
dc.subject.other	New opportunities	en
dc.subject.other	Qualitative information	en
dc.subject.other	Real-time forecasting	en
dc.subject.other	Surveillance systems	en
dc.subject.other	Traffic counts	en
dc.subject.other	Traffic flow	en
dc.subject.other	Transportation management	en
dc.subject.other	Urban networks	en
dc.subject.other	Urban road	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Forecasting	en
dc.subject.other	Knowledge based systems	en
dc.subject.other	Optimization	en
dc.subject.other	Traffic surveys	en
dc.subject.other	Data handling	en
dc.title	Fuzzy rule-based system approach to combining traffic count forecasts	en
heal.type	journalArticle	en
heal.identifier.primary	10.3141/2183-13	en
heal.identifier.secondary	http://dx.doi.org/10.3141/2183-13	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	Current advances in artificial intelligence are providing new opportunities for utilizing the enormous amount of data available in contemporary urban road surveillance systems. Several approaches, methodologies, and techniques have been presented for analyzing and forecasting traffic counts because such information has been identified as vital for the deployment of advanced transportation management and information systems. In this paper, a meta-analysis framework is presented for improving forecasted information of traffic counts, based on an adaptive data processing scheme. In particular, a framework for combining traffic count forecasts within a Mamdani-type fuzzy adaptive optimal control scheme is presented and analyzed. The proposed methodology treats the uncertainty pertaining to such circumstances by augmenting qualitative information of future traffic flow states (and values) with a knowledge base and a heuristic optimization routine that provides dynamic training capabilities, resulting in an efficient real-time forecasting mechanism. Results from the application of the proposed framework on data acquired from realistic signalized urban network data (of Athens, Greece) and for a diversity of locations exhibit its potential.	en
heal.publisher	NATL ACAD SCIENCES	en
heal.journalName	Transportation Research Record	en
dc.identifier.doi	10.3141/2183-13	en
dc.identifier.isi	ISI:000287296000013	en
dc.identifier.issue	2183	en
dc.identifier.spage	120	en
dc.identifier.epage	128	en