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Conference Paper: Negative Binomial Regression Model for Road Accident Analysis in Hong Kong

TitleNegative Binomial Regression Model for Road Accident Analysis in Hong Kong
Authors
Issue Date2010
PublisherTransportation Research Board (TRB).
Citation
The 89th Annual Meeting of Transportation Research Board (TRB), Washington, D.C. USA, 10-14 January 2010, p. abstract no. 10-1968 How to Cite?
AbstractThis study evaluates the influence of traffic volume on accident frequency and investigates other possible factors that contribute to accident risk, using vehicle kilometers (VKM) as a proxy of exposure. Information on traffic volume and road design factors was obtained from the Hong Kong Annual Traffic Census (ATC), and accident data were extracted from the Hong Kong Accident Database System (TRADS) respectively. These data were incorporated into a road network map of Hong Kong using the Geographical Information System (GIS). Count data models were employed to the analysis of accident frequency. As the data are subject to over-dispersion, a negative binomial regression method was deployed to measure the association between accident frequency and traffic volume and to control for the effects of factors such as temporal variation and road environment. The results indicate that greater traffic volume leads to a less than proportionate increase (a coefficient estimate of 0.62) in accident frequency and thus a decrease of accident risk. The interaction effects by traffic volume and other possible factors on accident occurrence are also revealed.
DescriptionSession 567: Road Safety Evaluation
Fulltext of the abstract in: http://pressamp.trb.org/conferenceinteractiveprogram/PresentationDetails.aspx?ID=33988&Email=
Persistent Identifierhttp://hdl.handle.net/10722/111612

 

DC FieldValueLanguage
dc.contributor.authorPei, Xen_HK
dc.contributor.authorWong, SCen_HK
dc.contributor.authorSze, NNen_HK
dc.date.accessioned2010-09-26T02:56:27Z-
dc.date.available2010-09-26T02:56:27Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 89th Annual Meeting of Transportation Research Board (TRB), Washington, D.C. USA, 10-14 January 2010, p. abstract no. 10-1968-
dc.identifier.urihttp://hdl.handle.net/10722/111612-
dc.descriptionSession 567: Road Safety Evaluation-
dc.descriptionFulltext of the abstract in: http://pressamp.trb.org/conferenceinteractiveprogram/PresentationDetails.aspx?ID=33988&Email=-
dc.description.abstractThis study evaluates the influence of traffic volume on accident frequency and investigates other possible factors that contribute to accident risk, using vehicle kilometers (VKM) as a proxy of exposure. Information on traffic volume and road design factors was obtained from the Hong Kong Annual Traffic Census (ATC), and accident data were extracted from the Hong Kong Accident Database System (TRADS) respectively. These data were incorporated into a road network map of Hong Kong using the Geographical Information System (GIS). Count data models were employed to the analysis of accident frequency. As the data are subject to over-dispersion, a negative binomial regression method was deployed to measure the association between accident frequency and traffic volume and to control for the effects of factors such as temporal variation and road environment. The results indicate that greater traffic volume leads to a less than proportionate increase (a coefficient estimate of 0.62) in accident frequency and thus a decrease of accident risk. The interaction effects by traffic volume and other possible factors on accident occurrence are also revealed.-
dc.languageengen_HK
dc.publisherTransportation Research Board (TRB).-
dc.relation.ispartofAnnual Meeting of Transportation Research Boarden_HK
dc.titleNegative Binomial Regression Model for Road Accident Analysis in Hong Kongen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailWong, SC: hhecwsc@hkucc.hku.hken_HK
dc.identifier.emailSze, NN: nnsze@graduate.hku.hken_HK
dc.identifier.authorityWong, SC=rp00191en_HK
dc.identifier.hkuros169074en_HK
dc.identifier.spageabstract no. 567-
dc.identifier.epageabstract no. 567-
dc.publisher.placeUnited States-

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