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Article: Identifying crash-prone locations with quantile regression

TitleIdentifying crash-prone locations with quantile regression
Authors
KeywordsConfidence interval
Heterogeneity
Poisson-gamma
Quantile regression
Issue Date2010
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/336/description#description
Citation
Accident Analysis And Prevention, 2010, v. 42 n. 6, p. 1531-1537 How to Cite?
Abstract
Identifying locations that exhibit the greatest potential for safety improvements is becoming more and more important because of competing needs and a tightening safety improvement budget. Current crash modeling practices mainly target changes at the mean level. However, crash data often have skewed distributions and exhibit substantial heterogeneity. Changes at mean level do not adequately represent patterns present in the data. This study employs a regression technique known as the quantile regression. Quantile regression offers the flexibility of estimating trends at different quantiles. It is particularly useful for summarizing data with heterogeneity. Here, we consider its application for identifying intersections with severe safety issues. Several classic approaches for determining risk-prone intersections are also compared. Our findings suggest that relative to other methods, quantile regression yields a sensible and much more refined subset of risk-prone locations.
Persistent Identifierhttp://hdl.handle.net/10722/132664
ISSN
2013 Impact Factor: 2.571
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorQin, Xen_HK
dc.contributor.authorNg, Men_HK
dc.contributor.authorReyes, PEen_HK
dc.date.accessioned2011-03-28T09:27:38Z-
dc.date.available2011-03-28T09:27:38Z-
dc.date.issued2010en_HK
dc.identifier.citationAccident Analysis And Prevention, 2010, v. 42 n. 6, p. 1531-1537en_HK
dc.identifier.issn0001-4575en_HK
dc.identifier.urihttp://hdl.handle.net/10722/132664-
dc.description.abstractIdentifying locations that exhibit the greatest potential for safety improvements is becoming more and more important because of competing needs and a tightening safety improvement budget. Current crash modeling practices mainly target changes at the mean level. However, crash data often have skewed distributions and exhibit substantial heterogeneity. Changes at mean level do not adequately represent patterns present in the data. This study employs a regression technique known as the quantile regression. Quantile regression offers the flexibility of estimating trends at different quantiles. It is particularly useful for summarizing data with heterogeneity. Here, we consider its application for identifying intersections with severe safety issues. Several classic approaches for determining risk-prone intersections are also compared. Our findings suggest that relative to other methods, quantile regression yields a sensible and much more refined subset of risk-prone locations.en_HK
dc.languageengen_US
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/336/description#descriptionen_HK
dc.relation.ispartofAccident Analysis and Preventionen_HK
dc.subjectConfidence intervalen_HK
dc.subjectHeterogeneityen_HK
dc.subjectPoisson-gammaen_HK
dc.subjectQuantile regressionen_HK
dc.subject.meshAccidents, Traffic - prevention and control - statistics and numerical data - trends-
dc.subject.meshCity Planning - organization and administration - trends-
dc.subject.meshEnvironment Design - trends-
dc.subject.meshForecasting-
dc.subject.meshSafety Management - organization and administration - trends-
dc.titleIdentifying crash-prone locations with quantile regressionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0001-4575&volume=42&issue=6&spage=1531&epage=1537&date=2010&atitle=Identifying+crash-prone+locations+with+quantile+regression-
dc.identifier.emailNg, M: marieng@hku.hken_HK
dc.identifier.authorityNg, M=rp01451en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.aap.2010.03.009en_HK
dc.identifier.pmid20728599-
dc.identifier.scopuseid_2-s2.0-77955983203en_HK
dc.identifier.hkuros186132-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77955983203&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume42en_HK
dc.identifier.issue6en_HK
dc.identifier.spage1531en_HK
dc.identifier.epage1537en_HK
dc.identifier.isiWOS:000282240500002-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridQin, X=14632561100en_HK
dc.identifier.scopusauthoridNg, M=36155754200en_HK
dc.identifier.scopusauthoridReyes, PE=35800440400en_HK
dc.identifier.citeulike7109686-

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