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Article: A self-learning short-term traffic forecasting system
Title | A self-learning short-term traffic forecasting system |
---|---|
Authors | |
Keywords | Self-Learning Traffic Forecasting |
Issue Date | 2012 |
Publisher | Pion 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? |
Abstract | A 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traffic patterns to short-term traffic forecasting are profound and this can easily make an existing forecasting system outdated.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 Identifier | http://hdl.handle.net/10722/176304 |
ISSN | 2016 Impact Factor: 1.527 2019 SCImago Journal Rankings: 1.109 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
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dc.contributor.author | Zhu, J | en_US |
dc.contributor.author | Yeh, AGO | en_US |
dc.date.accessioned | 2012-11-26T09:08:19Z | - |
dc.date.available | 2012-11-26T09:08:19Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.citation | Environment And Planning B: Planning And Design, 2012, v. 39 n. 3, p. 471-485 | en_US |
dc.identifier.issn | 0265-8135 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/176304 | - |
dc.description.abstract | A 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.language | eng | en_US |
dc.publisher | Pion Ltd.. The Journal's web site is located at http://www.envplan.com/B.html | en_US |
dc.relation.ispartof | Environment and Planning B: Planning and Design | en_US |
dc.subject | Self-Learning | en_US |
dc.subject | Traffic Forecasting | en_US |
dc.title | A self-learning short-term traffic forecasting system | en_US |
dc.type | Article | en_US |
dc.identifier.email | Yeh, AGO: hdxugoy@hkucc.hku.hk | en_US |
dc.identifier.authority | Yeh, AGO=rp01033 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1060/b36174 | en_US |
dc.identifier.scopus | eid_2-s2.0-84864034446 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-84864034446&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 39 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.spage | 471 | en_US |
dc.identifier.epage | 485 | en_US |
dc.identifier.isi | WOS:000306244600005 | - |
dc.publisher.place | United Kingdom | en_US |
dc.identifier.scopusauthorid | Zhu, J=35147395700 | en_US |
dc.identifier.scopusauthorid | Yeh, AGO=7103069369 | en_US |
dc.identifier.issnl | 0265-8135 | - |