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How do people perceive driving risks in small towns? A case study in Central Texas

dc.contributor.authorLi, X
dc.contributor.authorRybarczyk, G
dc.contributor.authorLi, W
dc.contributor.authorUsman, M
dc.contributor.authorBian, J
dc.contributor.authorChen, A
dc.contributor.authorYe, X
dc.coverage.spatialEngland
dc.date.accessioned2023-10-03T20:35:54Z
dc.date.available2023-10-03T20:35:54Z
dc.date.issued2023-12-01
dc.identifier.issn0001-4575
dc.identifier.issn1879-2057
dc.identifier.urihttps://www.ncbi.nlm.nih.gov/pubmed/37716196
dc.identifier.urihttps://hdl.handle.net/2027.42/178273en
dc.description.abstractThe number of studies investigating the relationship between perceived and objective traffic risk from drivers’ perspective is limited. This study aims to investigate this dynamic within an understudied transportation environment – small towns in Texas, USA, defined as incorporated places with a population of less than 50,000. A web-based survey was distributed to six small towns in central Texas to ascertain perceptual traffic risk factors and personal characteristics. A participatory GIS exercise was also conducted to collect where high-risk locations were perceived and to correlate them to high crash zones. This study spatially examined the relations between perceived and observed risk locations and statistically identified a set of contributing factors which could make crash-intensive areas more perceivable by road users. The results indicated that road users’ perceived risk locations are not always associated with high crash rates. The match rate between perceived and observed risk locations varied significantly across studied sites. We found that some personal and built environment factors significantly impacted people's sensitivity to perceiving crash-intensive locations. The binary logistic regression model was accurate (74.13%) in highlighting whether a perceived risk location matches observed risk locations. The results emphasize the importance of considering perceived and objective risk simultaneously to gain a better understanding of traffic risk mitigation, especially in underserved small towns.
dc.format.mediumPrint-Electronic
dc.languageeng
dc.publisherElsevier
dc.relation.haspart107285
dc.subjectGIS
dc.subjectRisk analysis
dc.subjectSpatial analysis
dc.subjectTraffic risk perception
dc.subjectTransportation safety
dc.titleHow do people perceive driving risks in small towns? A case study in Central Texas
dc.typeArticle
dc.identifier.pmid37716196
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/178273/2/Li_Rybarczyk_AAP_2023.pdf
dc.identifier.doi10.1016/j.aap.2023.107285
dc.identifier.doihttps://dx.doi.org/10.7302/8662
dc.identifier.sourceAccident Analysis and Prevention
dc.description.versionAccepted version
dc.date.updated2023-10-03T20:35:46Z
dc.identifier.orcid0000-0002-6762-2475
dc.identifier.orcid0000-0002-3920-2780
dc.description.filedescriptionDescription of Li_Rybarczyk_AAP_2023.pdf : Accepted version
dc.identifier.volume193
dc.identifier.startpage107285
dc.identifier.name-orcidLi, X; 0000-0002-6762-2475
dc.identifier.name-orcidRybarczyk, G; 0000-0002-3920-2780
dc.identifier.name-orcidLi, W
dc.identifier.name-orcidUsman, M
dc.identifier.name-orcidBian, J
dc.identifier.name-orcidChen, A
dc.identifier.name-orcidYe, X
dc.working.doi10.7302/8662en
dc.owningcollnameArts & Sciences, College of (UM-Flint)


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