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dc.contributor.authorSmit, Robinen_US
dc.contributor.authorBrown, Lexen_US
dc.contributor.authorChan, Andrew Yiu-chungen_US
dc.contributor.editorProf A. J. Jakemanen_US
dc.date.accessioned2017-04-24T10:05:11Z
dc.date.available2017-04-24T10:05:11Z
dc.date.issued2008en_US
dc.date.modified2009-02-26T07:58:30Z
dc.identifier.issn13648152en_US
dc.identifier.doi10.1016/j.envsoft.2008.03.001en_AU
dc.identifier.urihttp://hdl.handle.net/10072/21474
dc.description.abstractRoad transport emission and fuel consumption models are currently used extensively to predict levels of air pollution along roadway links and networks. This paper examines how, and to what extent, models which are currently used to predict emissions and fuel consumption from road traffic include the effects of congestion. A classification framework is presented in which a key factor, driving pattern, connects emissions to congestion. Prediction of the effects of different driving patterns in emission models is generally restricted to certain aspects of modelling, i.e. hot-running emissions of regulated pollutants. As a consequence, the effects of congestion are only partially incorporated in the predictions. The majority of emission models explicitly incorporate congestion in the modelling process, but for one important family of emission models, namely average speed models, this could not be determined directly. Re-examination of the (light-duty) driving patterns on which three average speed models (COPERT, MOBILE, EMFAC) are based, shows that it is likely that congestion is represented in these patterns. Since (hot-running) emission factors are based on these patterns, this implies that the emission factors used in these emission models also reflect different levels of congestion. Congestion is thus indirectly incorporated in these models. It is recommended, that, in order to get more accurate (local) emission predictions and to achieve correct application in particular situations, it is important to improve current average speed models by including a congestion algorithm, or alternatively, at least provide information on the level of congestion in the driving patterns on which these models are based and recommendations on what applications the models are suitable for.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherElsevieren_US
dc.publisher.placeUnited Kingdomen_US
dc.publisher.urihttp://www.elsevier.com/locate/envsoften_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom1262en_US
dc.relation.ispartofpageto1270en_US
dc.relation.ispartofjournalEnviornmnetal Modelling & Softwareen_US
dc.relation.ispartofvolume23en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode300804en_US
dc.titleDo air pollution emissions and fuel consumption models for roadways include the effects of congestion in the roadway traffic flow?en_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Environmenten_US
gro.rights.copyrightCopyright 2008 Elsevier. Please refer to the journal's website for access to the definitive, published version.en_AU
gro.date.issued2008
gro.hasfulltextNo Full Text


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