Modeling of older adults’ driving exposure and avoidance using objective driving data in a naturalistic driving study

Simple item page
dc.contributor.authorLiang, Danen
dc.contributor.authorLau, Nathanen
dc.contributor.authorAntin, Jonathan F.en
dc.date.accessioned2022-06-27T17:26:37Zen
dc.date.available2022-06-27T17:26:37Zen
dc.date.issued2022-09-01en
dc.date.updated2022-06-27T13:48:07Zen
dc.description.abstractOlder adults in the United States rely heavily on driving their own vehicles to commute to work, shop for groceries, and access public services. To effectively help older adults maintain mobility and independence, we need to better understand how the cognitive, visual functioning, and health declines influence their tendency to self-restrict their driving. The objective of this study is to develop a causal model to examine the effects of age, gender, household status (specifically living alone), physical, cognitive, visual abilities, and health status on older adults’ driving mobility in terms of driving exposure and avoidance. Driving exposure was measured by actual driving data, whereas driving avoidance was assessed by both self-report data and actual driving exposure to challenging situations. Structural equation modeling was used to analyze data collected in the Second Strategic Highway Research Program Naturalistic Driving Study for establishing relationships between the selected factors and mobility. The structural equation model included a total of 794 participants aged 65 and over (367 or 46.22% females and 427 or 53.78% males). Results indicate that poorer health is associated with less driving exposure; deteriorating cognitive and physical capabilities are associated with more self-reported driving avoidance and less actual driving in challenging situations; visual function is associated with self-reported avoidance; living alone is associated with higher driving exposure in general as well as in challenging situations; self-reported driving avoidance of challenging situations has a negative association with actual driving in those same situations. The final model could be applied to predict older adults’ mobility changes according to their age, gender, household status, as well as their visual, physical, cognitive and health status.en
dc.description.versionAccepted versionen
dc.format.extentPages 106728en
dc.format.mimetypeapplication/pdfen
dc.identifier106728 (Article number)en
dc.identifier.doihttps://doi.org/10.1016/j.aap.2022.106728en
dc.identifier.eissn1879-2057en
dc.identifier.issn0001-4575en
dc.identifier.orcidLau, Nathan [0000-0003-2235-9527]en
dc.identifier.otherS0001-4575(22)00164-6 (PII)en
dc.identifier.pmid35689967en
dc.identifier.urihttp://hdl.handle.net/10919/110943en
dc.identifier.volumeAAAP-S-21-01823en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urihttps://doi.org/10.1016/j.aap.2022.106728en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectDriving avoidanceen
dc.subjectDriving exposureen
dc.subjectNaturalistic driving studyen
dc.subjectOlder driversen
dc.subjectStructural equation modelingen
dc.titleModeling of older adults’ driving exposure and avoidance using objective driving data in a naturalistic driving studyen
dc.title.serialAccident Analysis and Preventionen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dcterms.dateAccepted2022-05-31en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Virginia Tech Transportation Instituteen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
AAAP-D-21-01823_accepted.pdf
Size:
2.12 MB
Format:
Adobe Portable Document Format
Description:
Accepted version
Download

Collections

All Faculty Deposits
Scholarly Works, Industrial and Systems Engineering
Scholarly Works, Virginia Tech Transportation Institute