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Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow

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dc.contributor.author Dimitriou, L en
dc.contributor.author Tsekeris, T en
dc.contributor.author Stathopoulos, A en
dc.date.accessioned 2014-03-01T01:27:49Z
dc.date.available 2014-03-01T01:27:49Z
dc.date.issued 2008 en
dc.identifier.issn 0968-090X en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/18595
dc.subject Fuzzy rule-based systems en
dc.subject Global optimization en
dc.subject Short-term forecasting en
dc.subject Traffic flow modeling en
dc.subject Urban networks en
dc.subject.classification Transportation Science & Technology en
dc.subject.other Algorithms en
dc.subject.other Computer networks en
dc.subject.other Data structures en
dc.subject.other File organization en
dc.subject.other Forecasting en
dc.subject.other Function evaluation en
dc.subject.other Fuzzy logic en
dc.subject.other Fuzzy rules en
dc.subject.other Fuzzy sets en
dc.subject.other Genetic algorithms en
dc.subject.other Knowledge based systems en
dc.subject.other Mathematical models en
dc.subject.other Metropolitan area networks en
dc.subject.other Model structures en
dc.subject.other Network protocols en
dc.subject.other Statistical methods en
dc.subject.other Statistics en
dc.subject.other Traffic surveys en
dc.subject.other Variational techniques en
dc.subject.other (min ,max ,+) functions en
dc.subject.other Elsevier (CO) en
dc.subject.other hybrid fuzzy en
dc.subject.other Multivariate data en
dc.subject.other Off-line applications en
dc.subject.other On line applications en
dc.subject.other On line tuning en
dc.subject.other Short-term forecasting en
dc.subject.other Statistical techniques en
dc.subject.other Traffic conditions en
dc.subject.other Traffic data en
dc.subject.other traffic flowing en
dc.subject.other Univariate en
dc.subject.other Urban arterial networks en
dc.subject.other Urban traffic en
dc.subject.other Membership functions en
dc.subject.other adaptive management en
dc.subject.other forecasting method en
dc.subject.other fuzzy mathematics en
dc.subject.other genetic algorithm en
dc.subject.other modeling en
dc.subject.other multivariate analysis en
dc.subject.other optimization en
dc.subject.other traffic management en
dc.subject.other transportation planning en
dc.subject.other urban transport en
dc.title Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow en
heal.type journalArticle en
heal.identifier.primary 10.1016/j.trc.2007.11.003 en
heal.identifier.secondary http://dx.doi.org/10.1016/j.trc.2007.11.003 en
heal.language English en
heal.publicationDate 2008 en
heal.abstract This paper presents an adaptive hybrid fuzzy rule-based system (FRBS) approach for the modeling and short-term forecasting of traffic flow in urban arterial networks. Such an approach possesses the advantage of suitably addressing data imprecision and uncertainty, and it enables the incorporation of,expert's knowledge on local traffic conditions within the model structure. The model employs univariate and multivariate data structures and uses a Genetic Algorithm for the offline and online tuning of the FRBS membership functions according to the prevailing traffic conditions. The results obtained from the online application of the proposed FRBS are found to overperform those of the offline application and conventional statistical techniques, when modeling both univariate and multivariate traffic data corresponding to a real signalized urban arterial corridor. (c) 2007 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.2007.11.003 en
dc.identifier.isi ISI:000258344800003 en
dc.identifier.volume 16 en
dc.identifier.issue 5 en
dc.identifier.spage 554 en
dc.identifier.epage 573 en


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