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Detecting Risk Factors of Road Work Zone Crashes from the Information Provided in Police Crash Reports: The Case Study of Portugal

2021-02-04Journal article Open access
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dc.contributor.authorSantos, Bertha
dc.contributor.authorTrindade, Valdemiro
dc.contributor.authorPolónia, Cláudia
dc.contributor.authorSantos, Luis Picado
dc.date.accessioned2021-02-11T12:34:32Z
dc.date.available2021-02-11T12:34:32Z
dc.date.issued2021-02-04
dc.description.abstractSeveral studies have shown that European police crash reports provide different detail degrees of work zone crash-related data. In this sense, the present study aims to verify the possibility of identifying significant risk factors involved in the occurrence of road work zone crashes with casualties, based on the official data usually available, through a descriptive, binary logistic, and probit regression statistical analysis. To accomplish the analysis, a total of 2597 police-reports related to 1767 Portuguese work zone crashes that occurred during the 2013–2015 period were considered and binary logistic and probit regression models were estimated by the main type of crash, contributing factor, and driver age group. Fifteen explanatory variables, selected based on the literature review and crash data provided in police crash reports, were considered in the analysis. The results obtained for the estimated coefficients and goodness-of-fit test values were found very similar for both link functions (logit and probit) and it was possible to identify risk factors. The modeling results pointed to excessive speed, disregard for vertical signs, luminosity, intersections, and motorcycle and heavy vehicle involvement as the most significant risk factors. Given the results, it is possible to conclude that binary logistic regression can be used in the statistical analysis of the available police official work zone crash data to identify and get some insight into the risk factors involved in work zone crashes. Data analysis also revealed the need to promote adequate and complete crash report filling by police officers. While police crash reports are not revised and standardized to incorporate more detailed work zone crash information, this approach can be used to support a more efficient road operation decision making and the review of some aspects related to work zone layout design.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSantos, B.; Trindade, V.; Polónia, C.; Picado-Santos, L. Detecting Risk Factors of Road Work Zone Crashes from the Information Provided in Police Crash Reports: The Case Study of Portugal. Safety 2021, 7, 12. https://doi.org/ 10.3390/safety7010012pt_PT
dc.identifier.doi10.3390/safety7010012pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/11105
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relation.publisherversionhttps://www.mdpi.com/2313-576X/7/1/12pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectRoad safetypt_PT
dc.subjectWork zonespt_PT
dc.subjectPolice crash reportspt_PT
dc.subjectCrash datapt_PT
dc.subjectLogit and binary logistic regressionpt_PT
dc.titleDetecting Risk Factors of Road Work Zone Crashes from the Information Provided in Police Crash Reports: The Case Study of Portugalpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue1pt_PT
oaire.citation.startPage12pt_PT
oaire.citation.titleSafetypt_PT
oaire.citation.volume7pt_PT
person.familyNameSantos
person.familyNamePicado Santos
person.givenNameBertha
person.givenNameLuis
person.identifier.ciencia-idBE1A-879F-8282
person.identifier.ciencia-idC314-1475-D16A
person.identifier.orcid0000-0002-5545-892X
person.identifier.orcid0000-0003-2072-3188
person.identifier.scopus-author-id54880406000
person.identifier.scopus-author-id55909533400
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication444d1a96-ae0d-4778-93d1-8fcbf30ec96a
relation.isAuthorOfPublication854ba4b1-97bc-4a19-bcf2-ec4a2bbd8a47
relation.isAuthorOfPublication.latestForDiscovery444d1a96-ae0d-4778-93d1-8fcbf30ec96a

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