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Conference Paper: Short-term traffic speed forecasting based on data recorded at irregular intervals

TitleShort-term traffic speed forecasting based on data recorded at irregular intervals
Authors
Issue Date2010
PublisherIEEE.
Citation
The 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), 19-22 September 2010. In Proceedings of the 13th IEEE ITSC, 2010, p. 1541-1546 How to Cite?
AbstractAs demand for proactive real-time transportation management systems has grown, major developments have been seen in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, genetic algorithms, etc., to short-time traffic forecasting to make forecasts more reliable, efficient and accurate. However, most of these methods can only deal with data recorded at regular time intervals, thereby restricting the range of data collection tools to loop detectors or other equipment that generate regular data. The study reported here represents an attempt to expand on several existing time series forecasting methods to accommodate data recorded at irregular time intervals, thus ensuring these methods can be used to obtain predicted traffic speeds through intermittent data sources such as the GPS. The study tested several methods using the GPS data from 480 Hong Kong taxis. The results show that the best performance is obtained using a neural network model with acceleration information predicted by ARIMA model. ©2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/124247
References

 

DC FieldValueLanguage
dc.contributor.authorYe, Qen_HK
dc.contributor.authorWong, SCen_HK
dc.contributor.authorSzeto, WYen_HK
dc.date.accessioned2010-10-31T10:23:22Z-
dc.date.available2010-10-31T10:23:22Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), 19-22 September 2010. In Proceedings of the 13th IEEE ITSC, 2010, p. 1541-1546en_HK
dc.identifier.urihttp://hdl.handle.net/10722/124247-
dc.description.abstractAs demand for proactive real-time transportation management systems has grown, major developments have been seen in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, genetic algorithms, etc., to short-time traffic forecasting to make forecasts more reliable, efficient and accurate. However, most of these methods can only deal with data recorded at regular time intervals, thereby restricting the range of data collection tools to loop detectors or other equipment that generate regular data. The study reported here represents an attempt to expand on several existing time series forecasting methods to accommodate data recorded at irregular time intervals, thus ensuring these methods can be used to obtain predicted traffic speeds through intermittent data sources such as the GPS. The study tested several methods using the GPS data from 480 Hong Kong taxis. The results show that the best performance is obtained using a neural network model with acceleration information predicted by ARIMA model. ©2010 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofProceedings of the 13th IEEE International Conference on Intelligent Transportation Systems, ITSC 2010en_HK
dc.rightsInternational Conference on Intelligent Transportation. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2010 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.-
dc.titleShort-term traffic speed forecasting based on data recorded at irregular intervalsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailSzeto, WY:ceszeto@hku.hken_HK
dc.identifier.authoritySzeto, WY=rp01377en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ITSC.2010.5625184en_HK
dc.identifier.scopuseid_2-s2.0-78650482496en_HK
dc.identifier.hkuros183128en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78650482496&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage1541en_HK
dc.identifier.epage1546en_HK
dc.description.otherThe 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), 19-22 September 2010. In Proceedings of the 13th IEEE ITSC, 2010, p. 1541-1546-
dc.identifier.scopusauthoridYe, Q=36740482400en_HK
dc.identifier.scopusauthoridWong, SC=36599753900en_HK
dc.identifier.scopusauthoridSzeto, WY=7003652508en_HK

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