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Article: Urban traffic flow prediction using a fuzzy-neural approach

TitleUrban traffic flow prediction using a fuzzy-neural approach
Authors
KeywordsFuzzy-neural model
Online rolling training procedure
Time series forecasting
Traffic flow prediction
Urban traffic control system
Issue Date2002
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/trc
Citation
Transportation Research Part C: Emerging Technologies, 2002, v. 10 n. 2, p. 85-98 How to Cite?
AbstractThis paper develops a fuzzy-neural model (FNM) to predict the traffic flows in an urban street network, which has long been considered a major element in the responsive urban traffic control systems. The FNM consists of two modules: a gate network (GN) and an expert network (EN). The GN classifies the input data into a number of clusters using a fuzzy approach, and the EN specifies the input-output relationship as in a conventional neural network approach. While the GN groups traffic patterns of similar characteristics into clusters, the EN models the specific relationship within each cluster. An online rolling training procedure is proposed to train the FNM, which enhances its predictive power through adaptive adjustments of the model coefficients in response to the real-time traffic conditions. Both simulation and real observation data are used to demonstrative the effectiveness of the method. © 2002 Elsevier Science Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/71551
ISSN
2015 Impact Factor: 3.075
2015 SCImago Journal Rankings: 2.062
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYin, Hen_HK
dc.contributor.authorWong, SCen_HK
dc.contributor.authorXu, Jen_HK
dc.contributor.authorWong, CKen_HK
dc.date.accessioned2010-09-06T06:33:01Z-
dc.date.available2010-09-06T06:33:01Z-
dc.date.issued2002en_HK
dc.identifier.citationTransportation Research Part C: Emerging Technologies, 2002, v. 10 n. 2, p. 85-98en_HK
dc.identifier.issn0968-090Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/71551-
dc.description.abstractThis paper develops a fuzzy-neural model (FNM) to predict the traffic flows in an urban street network, which has long been considered a major element in the responsive urban traffic control systems. The FNM consists of two modules: a gate network (GN) and an expert network (EN). The GN classifies the input data into a number of clusters using a fuzzy approach, and the EN specifies the input-output relationship as in a conventional neural network approach. While the GN groups traffic patterns of similar characteristics into clusters, the EN models the specific relationship within each cluster. An online rolling training procedure is proposed to train the FNM, which enhances its predictive power through adaptive adjustments of the model coefficients in response to the real-time traffic conditions. Both simulation and real observation data are used to demonstrative the effectiveness of the method. © 2002 Elsevier Science Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/trcen_HK
dc.relation.ispartofTransportation Research Part C: Emerging Technologiesen_HK
dc.subjectFuzzy-neural modelen_HK
dc.subjectOnline rolling training procedureen_HK
dc.subjectTime series forecastingen_HK
dc.subjectTraffic flow predictionen_HK
dc.subjectUrban traffic control systemen_HK
dc.titleUrban traffic flow prediction using a fuzzy-neural approachen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0968-090X&volume=10&spage=85 &epage= 98&date=2002&atitle=Urban+traffic+flow+prediction+using+a+fuzzy-neural+approachen_HK
dc.identifier.emailWong, SC:hhecwsc@hku.hken_HK
dc.identifier.authorityWong, SC=rp00191en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/S0968-090X(01)00004-3en_HK
dc.identifier.scopuseid_2-s2.0-0036532655en_HK
dc.identifier.hkuros66168en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036532655&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume10en_HK
dc.identifier.issue2en_HK
dc.identifier.spage85en_HK
dc.identifier.epage98en_HK
dc.identifier.isiWOS:000173105800001-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYin, H=55007283200en_HK
dc.identifier.scopusauthoridWong, SC=24323361400en_HK
dc.identifier.scopusauthoridXu, J=8850249000en_HK
dc.identifier.scopusauthoridWong, CK=24475830600en_HK

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