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Article: 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 Date2012
Citation
IEEE Transactions on Intelligent Transportation Systems, 2012, v. 13 n. 4, p. 1727-1737 How to Cite?
AbstractRecent growth in demand for proactive real-time transportation management systems has led to major advances in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, and genetic algorithms to short-term 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, which restricts the range of data collection tools to presence-type detectors or other equipment that generates regular data. The study reported here is an attempt to extend several existing time series forecasting methods to accommodate data recorded at irregular time intervals, which would allow transportation management systems to obtain predicted traffic speeds from intermittent data sources such as Global Positioning System (GPS). To improve forecasting performance, acceleration information was introduced, and information from segments adjacent to the current forecasting segment was adopted. The study tested several methods using GPS data from 480 Hong Kong taxis. The results show that the best performance in terms of mean absolute relative error is obtained by using a neural network model that aggregates speed information and acceleration information from the current forecasting segment and adjacent segments.
Persistent Identifierhttp://hdl.handle.net/10722/152648
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYe, Qen_US
dc.contributor.authorSzeto, WYen_US
dc.contributor.authorWong, SCen_US
dc.date.accessioned2012-07-16T09:45:03Z-
dc.date.available2012-07-16T09:45:03Z-
dc.date.issued2012en_US
dc.identifier.citationIEEE Transactions on Intelligent Transportation Systems, 2012, v. 13 n. 4, p. 1727-1737en_US
dc.identifier.urihttp://hdl.handle.net/10722/152648-
dc.description.abstractRecent growth in demand for proactive real-time transportation management systems has led to major advances in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, and genetic algorithms to short-term 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, which restricts the range of data collection tools to presence-type detectors or other equipment that generates regular data. The study reported here is an attempt to extend several existing time series forecasting methods to accommodate data recorded at irregular time intervals, which would allow transportation management systems to obtain predicted traffic speeds from intermittent data sources such as Global Positioning System (GPS). To improve forecasting performance, acceleration information was introduced, and information from segments adjacent to the current forecasting segment was adopted. The study tested several methods using GPS data from 480 Hong Kong taxis. The results show that the best performance in terms of mean absolute relative error is obtained by using a neural network model that aggregates speed information and acceleration information from the current forecasting segment and adjacent segments.-
dc.languageengen_US
dc.relation.ispartofIEEE Transactions on Intelligent Transportation Systemsen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2012 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_US
dc.typeArticleen_US
dc.identifier.emailSzeto, WY: ceszeto@hku.hken_US
dc.identifier.emailWong, SC: hhecwsc@hku.hken_US
dc.identifier.authoritySzeto, WY=rp01377en_US
dc.identifier.authorityWong, SC=rp00191en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TITS.2012.2203122-
dc.identifier.scopuseid_2-s2.0-84870549790-
dc.identifier.hkuros201975en_US
dc.identifier.volume13-
dc.identifier.issue4-
dc.identifier.spage1727-
dc.identifier.epage1737-
dc.identifier.isiWOS:000314291400023-

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