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Article: An aggregation approach to short-term traffic flow prediction

TitleAn aggregation approach to short-term traffic flow prediction
Authors
KeywordsAutoregressive moving average (ARIMA) model
Data aggregation (DA)
Exponential smoothing (ES)
Moving average (MA)
Neural network (NN)
Time series
Traffic flow prediction
Issue Date2009
PublisherI E E E. The Journal's web site is located at http://www.ewh.ieee.org/tc/its/trans.html
Citation
Ieee Transactions On Intelligent Transportation Systems, 2009, v. 10 n. 1, p. 60-69 How to Cite?
AbstractIn this paper, an aggregation approach is proposed for traffic flow prediction that is based on the moving average (MA), exponential smoothing (ES), autoregressive MA (ARIMA), and neural network (NN) models. The aggregation approach assembles information from relevant time series. The source time series is the traffic flow volume that is collected 24 h/day over several years. The three relevant time series are a weekly similarity time series, a daily similarity time series, and an hourly time series, which can be directly generated from the source time series. The MA, ES, and ARIMA models are selected to give predictions of the three relevant time series. The predictions that result from the different models are used as the basis of the NN in the aggregation stage. The output of the trained NN serves as the final prediction. To assess the performance of the different models, the nave, ARIMA, nonparametric regression, NN, and data aggregation (DA) models are applied to the prediction of a real vehicle traffic flow, from which data have been collected at a data-collection point that is located on National Highway 107, Guangzhou, Guangdong, China. The outcome suggests that the DA model obtains a more accurate forecast than any individual model alone. The aggregation strategy can offer substantial benefits in terms of improving operational forecasting. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/58498
ISSN
2015 Impact Factor: 2.534
2015 SCImago Journal Rankings: 1.300
ISI Accession Number ID
Funding AgencyGrant Number
Council of the Hong Kong Special Administrative Region, ChinaHKU 7176/07E
University of Hong Kong10207394
National Natural Science Foundation ofChina50578064
70629001
Natural Science Foundation of Guangdong Province, China06025219
National Basic Research Program of China2006CB705500
Funding Information:

This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region, China, under Project HKU 7176/07E, by the University of Hong Kong under Grant 10207394, by the National Natural Science Foundation ofChina under Grant 50578064 and Grant 70629001, by the Natural Science Foundation of Guangdong Province, China, under Grant 06025219, and by the National Basic Research Program of China under Grant 2006CB705500. The Associate Editor for this paper was H. Mahmassam.

References

 

DC FieldValueLanguage
dc.contributor.authorTan, MCen_HK
dc.contributor.authorWong, SCen_HK
dc.contributor.authorXu, JMen_HK
dc.contributor.authorGuan, ZRen_HK
dc.contributor.authorZhang, Pen_HK
dc.date.accessioned2010-05-31T03:31:29Z-
dc.date.available2010-05-31T03:31:29Z-
dc.date.issued2009en_HK
dc.identifier.citationIeee Transactions On Intelligent Transportation Systems, 2009, v. 10 n. 1, p. 60-69en_HK
dc.identifier.issn1524-9050en_HK
dc.identifier.urihttp://hdl.handle.net/10722/58498-
dc.description.abstractIn this paper, an aggregation approach is proposed for traffic flow prediction that is based on the moving average (MA), exponential smoothing (ES), autoregressive MA (ARIMA), and neural network (NN) models. The aggregation approach assembles information from relevant time series. The source time series is the traffic flow volume that is collected 24 h/day over several years. The three relevant time series are a weekly similarity time series, a daily similarity time series, and an hourly time series, which can be directly generated from the source time series. The MA, ES, and ARIMA models are selected to give predictions of the three relevant time series. The predictions that result from the different models are used as the basis of the NN in the aggregation stage. The output of the trained NN serves as the final prediction. To assess the performance of the different models, the nave, ARIMA, nonparametric regression, NN, and data aggregation (DA) models are applied to the prediction of a real vehicle traffic flow, from which data have been collected at a data-collection point that is located on National Highway 107, Guangzhou, Guangdong, China. The outcome suggests that the DA model obtains a more accurate forecast than any individual model alone. The aggregation strategy can offer substantial benefits in terms of improving operational forecasting. © 2006 IEEE.en_HK
dc.languageengen_HK
dc.publisherI E E E. The Journal's web site is located at http://www.ewh.ieee.org/tc/its/trans.htmlen_HK
dc.relation.ispartofIEEE Transactions on Intelligent Transportation Systemsen_HK
dc.rights©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectAutoregressive moving average (ARIMA) modelen_HK
dc.subjectData aggregation (DA)en_HK
dc.subjectExponential smoothing (ES)en_HK
dc.subjectMoving average (MA)en_HK
dc.subjectNeural network (NN)en_HK
dc.subjectTime seriesen_HK
dc.subjectTraffic flow predictionen_HK
dc.titleAn aggregation approach to short-term traffic flow predictionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1524-9050&volume=10&spage=60&epage=69&date=2009&atitle=An+aggregation+approach+to+short-term+traffic+flow+predictionen_HK
dc.identifier.emailWong, SC:hhecwsc@hku.hken_HK
dc.identifier.authorityWong, SC=rp00191en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TITS.2008.2011693en_HK
dc.identifier.scopuseid_2-s2.0-61849156325en_HK
dc.identifier.hkuros154633en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-61849156325&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume10en_HK
dc.identifier.issue1en_HK
dc.identifier.spage60en_HK
dc.identifier.epage69en_HK
dc.identifier.isiWOS:000263919800007-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridTan, MC=7401464871en_HK
dc.identifier.scopusauthoridWong, SC=24323361400en_HK
dc.identifier.scopusauthoridXu, JM=26423381000en_HK
dc.identifier.scopusauthoridGuan, ZR=26029785200en_HK
dc.identifier.scopusauthoridZhang, P=9242315800en_HK

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