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Article: A self-learning short-term traffic forecasting system

TitleA self-learning short-term traffic forecasting system
Authors
KeywordsSelf-Learning
Traffic Forecasting
Issue Date2012
PublisherPion Ltd.. The Journal's web site is located at http://www.envplan.com/B.html
Citation
Environment And Planning B: Planning And Design, 2012, v. 39 n. 3, p. 471-485 How to Cite?
AbstractA reliable and accurate short-term traffic forecasting system is crucial for the successful deployment of any intelligent transportation system. A lot of forecasting models have been developed in recent years but none of them could consistently outperform the others. In real-world applications, traffic forecasting accuracy can be affected by a lot of factors. Impacts of long-term changes to traffic patterns to short-term traffic forecasting are profound and this can easily make an existing forecasting system outdated. Therefore, it is very important for forecasting systems to detect long-term changes in traffic patterns and make updates accordingly. This paper presents a new forecasting mechanism, in which a dynamic hybrid approach is taken and self-learning ability is enhanced. Results of a case study show the proposed approach is feasible in enhancing the adaptability of traffic forecasting systems. © 2012 Pion and its Licensors.
Persistent Identifierhttp://hdl.handle.net/10722/176304
ISSN
2015 Impact Factor: 1.195
2015 SCImago Journal Rankings: 0.582
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZhu, Jen_US
dc.contributor.authorYeh, AGOen_US
dc.date.accessioned2012-11-26T09:08:19Z-
dc.date.available2012-11-26T09:08:19Z-
dc.date.issued2012en_US
dc.identifier.citationEnvironment And Planning B: Planning And Design, 2012, v. 39 n. 3, p. 471-485en_US
dc.identifier.issn0265-8135en_US
dc.identifier.urihttp://hdl.handle.net/10722/176304-
dc.description.abstractA reliable and accurate short-term traffic forecasting system is crucial for the successful deployment of any intelligent transportation system. A lot of forecasting models have been developed in recent years but none of them could consistently outperform the others. In real-world applications, traffic forecasting accuracy can be affected by a lot of factors. Impacts of long-term changes to traffic patterns to short-term traffic forecasting are profound and this can easily make an existing forecasting system outdated. Therefore, it is very important for forecasting systems to detect long-term changes in traffic patterns and make updates accordingly. This paper presents a new forecasting mechanism, in which a dynamic hybrid approach is taken and self-learning ability is enhanced. Results of a case study show the proposed approach is feasible in enhancing the adaptability of traffic forecasting systems. © 2012 Pion and its Licensors.en_US
dc.languageengen_US
dc.publisherPion Ltd.. The Journal's web site is located at http://www.envplan.com/B.htmlen_US
dc.relation.ispartofEnvironment and Planning B: Planning and Designen_US
dc.subjectSelf-Learningen_US
dc.subjectTraffic Forecastingen_US
dc.titleA self-learning short-term traffic forecasting systemen_US
dc.typeArticleen_US
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hken_US
dc.identifier.authorityYeh, AGO=rp01033en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1060/b36174en_US
dc.identifier.scopuseid_2-s2.0-84864034446en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84864034446&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume39en_US
dc.identifier.issue3en_US
dc.identifier.spage471en_US
dc.identifier.epage485en_US
dc.identifier.isiWOS:000306244600005-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridZhu, J=35147395700en_US
dc.identifier.scopusauthoridYeh, AGO=7103069369en_US

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