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dc.contributor.authorHøye, Alena
dc.contributor.authorHesjevoll, Ingeborg Storesund
dc.date.accessioned2021-07-13T11:28:10Z
dc.date.available2021-07-13T11:28:10Z
dc.date.created2020-09-07T10:24:30Z
dc.date.issued2020-08-07
dc.identifier.citationAccident Analysis and Prevention. 2020, 145 (September), 1-21.en_US
dc.identifier.issn0001-4575
dc.identifier.urihttps://hdl.handle.net/11250/2764275
dc.description.abstractThe present study has investigated the relationship between traffic volume and crash numbers by means of meta-analysis, based on 521 crash prediction models from 118 studies. The weighted pooled volume coefficient for all crashes and all levels of crash severity (excluding fatal crashes) is 0.875. The most important moderator variable is crash type. Pooled volume coefficients are systematically greater for multi vehicle crashes (1.210) than for single vehicle crashes (0.552). Regarding crash severity, the results indicate that volume coefficients are smaller for more fatal crashes (0.777 for all fatal crashes) than for injury crashes but no systematic differences were found between volume coefficients for injury and property-damage-only crashes. At higher levels of volume and on divided roads, volume coefficients tend to be greater than at lower levels of volume and on undivided roads. This is consistent with the finding that freeways on average have greater volume coefficients than other types of road and that two-lane roads are the road type with the smallest average volume coefficients. The results indicate that results from crash prediction models are likely to be more precise when crashes are disaggregated by crash type, crash severity, and road type. Disaggregating models by volume level and distinguishing between divided and undivided roads may also improve the precision of the results. The results indicate further that crash prediction models may be misleading if they are used to predict crash numbers on roads that differ from those that were used for model development with respect to composition of crash types, share of fatal or serious injury crashes, road types, and volume levels.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleTraffic volume and crashes and how crash and road characteristics affect their relationship – A meta-analysisen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2020 Elsevier Ltd. All rights reserved.en_US
dc.source.articlenumber105668en_US
dc.description.versionpublishedVersionen_US
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1
dc.identifier.doi10.1016/j.aap.2020.105668
dc.identifier.cristin1827641
dc.source.journalAccident Analysis and Preventionen_US
dc.source.volume145en_US
dc.source.issueSeptemberen_US
dc.source.pagenumber1-21en_US


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