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Article: Multivariate traffic forecasting technique using cell transmission model and SARIMA model

TitleMultivariate traffic forecasting technique using cell transmission model and SARIMA model
Authors
KeywordsData collections
Forecasting
Intersections
Seasonal variations
Traffic flow
Traffic models
Issue Date2009
PublisherAmerican Society of Civil Engineers. The Journal's web site is located at http://www.pubs.asce.org/journals/te.html
Citation
Journal Of Transportation Engineering, 2009, v. 135 n. 9, p. 658-667 How to Cite?
AbstractThe paper develops a short-term space-time traffic flow forecasting strategy integrating the empirical-based seasonal autoregressive integrated moving average (SARIMA) time-series forecasting technique with the theoretical-based first-order macroscopic traffic flow model-cell transmission model. A case study in Dublin city center which has serious traffic congestion is performed to test the effectiveness of the proposed multivariate traffic forecasting strategy. The results show that the forecasts at the junctions only deviate around 10% at a maximum from the original observations and seem to indicate that the proposed strategy is one of the effective approaches to predict the real-time traffic flow level in a congested network especially at the locations where no continuous data collection takes place. © 2009 ASCE.
Persistent Identifierhttp://hdl.handle.net/10722/91218
ISSN
2015 Impact Factor: 0.801
2015 SCImago Journal Rankings: 0.627
ISI Accession Number ID
Funding AgencyGrant Number
National University of SingaporeR-264-000-229-112
Funding Information:

This research is jointly sponsored by the start-up grant (Grant No. R-264-000-229-112) from the National University of Singapore and the Program for Research in Third-Level Institutions (PRTLI) administered by the Irish Higher Education Authority. The writers are grateful for the constructive comments of the referees and editors.

References

 

DC FieldValueLanguage
dc.contributor.authorSzeto, WYen_HK
dc.contributor.authorGhosh, Ben_HK
dc.contributor.authorBasu, Ben_HK
dc.contributor.authorO'Mahony, Men_HK
dc.date.accessioned2010-09-17T10:15:04Z-
dc.date.available2010-09-17T10:15:04Z-
dc.date.issued2009en_HK
dc.identifier.citationJournal Of Transportation Engineering, 2009, v. 135 n. 9, p. 658-667en_HK
dc.identifier.issn0733-947Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/91218-
dc.description.abstractThe paper develops a short-term space-time traffic flow forecasting strategy integrating the empirical-based seasonal autoregressive integrated moving average (SARIMA) time-series forecasting technique with the theoretical-based first-order macroscopic traffic flow model-cell transmission model. A case study in Dublin city center which has serious traffic congestion is performed to test the effectiveness of the proposed multivariate traffic forecasting strategy. The results show that the forecasts at the junctions only deviate around 10% at a maximum from the original observations and seem to indicate that the proposed strategy is one of the effective approaches to predict the real-time traffic flow level in a congested network especially at the locations where no continuous data collection takes place. © 2009 ASCE.en_HK
dc.languageengen_HK
dc.publisherAmerican Society of Civil Engineers. The Journal's web site is located at http://www.pubs.asce.org/journals/te.htmlen_HK
dc.relation.ispartofJournal of Transportation Engineeringen_HK
dc.subjectData collectionsen_HK
dc.subjectForecastingen_HK
dc.subjectIntersectionsen_HK
dc.subjectSeasonal variationsen_HK
dc.subjectTraffic flowen_HK
dc.subjectTraffic modelsen_HK
dc.titleMultivariate traffic forecasting technique using cell transmission model and SARIMA modelen_HK
dc.typeArticleen_HK
dc.identifier.emailSzeto, WY:ceszeto@hku.hken_HK
dc.identifier.authoritySzeto, WY=rp01377en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1061/(ASCE)0733-947X(2009)135:9(658)en_HK
dc.identifier.scopuseid_2-s2.0-69249196057en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-69249196057&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume135en_HK
dc.identifier.issue9en_HK
dc.identifier.spage658en_HK
dc.identifier.epage667en_HK
dc.identifier.isiWOS:000269062200008-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridSzeto, WY=7003652508en_HK
dc.identifier.scopusauthoridGhosh, B=15925454400en_HK
dc.identifier.scopusauthoridBasu, B=36027501000en_HK
dc.identifier.scopusauthoridO'Mahony, M=7102575274en_HK

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