Self-regulation of driving by older adults: Comparison of self-report and objective driving data

Until recently, objective data have been lacking on the extent to which older adults modify their driving by driving less or avoiding situations considered challenging; a process commonly referred to as self-regulation. Advances in technology now make it possible to examine driving exposure, patterns, and habits using low-cost global positioning system (GPS) technology to record a vehicle’s location on a continuous basis along with the date and time. The purpose of this exploratory study was to better understand the process of self-regulation among older adults by examining their trip-specific driving patterns using objectively-derived GPS measures of driving and comparing these patterns with drivers’ self-reports. The study used a sample of 156 adults age 75 or older, recruited from the greater Melbourne area of Australia as part of the Ozcandrive project, a partnership between Monash University Accident Research Centre and the Canadian Driving Research Initiative for Vehicular Safety in the Elderly (Candrive), a prospective cohort study of older drivers. Objective driving data were collected through equipment installed in participants’ personal vehicles. Participants were asked to drive as they normally would with the equipment installed in their vehicle. After approximately the first 4 months of driving with the device, data were downloaded and participants completed a computer-based questionnaire on self-regulation of driving. Results suggest that there was correspondence, albeit not perfect, between some objective driving measures and their comparable self-reported measures, but a lack of correspondence for others. For avoidance of various driving situations, comparisons were statistically significant for driving at night, driving in unfamiliar areas, and on high speed roads. For each driving situation, participants’ actual driving predicted the likelihood of reporting trying to avoid that situation, although perfect one-to-one correspondence between the self-reported and objective data on self-regulatory driving patterns was lacking. For measures of driving exposure, self-reported and objective driving exposure measures were correlated, but participants tended to underreport their average number of days per week and kilometers per week driven. This discrepancy between self-reported and objective measures is of concern as the ability to measure driving exposure not only contributes to a better understanding of the complex process of self-regulation, but is also a critical element in understanding crash risk.     

Attitudes,norms and driving behaviour: A comparison of young drivers in South Africa and Sweden

Culture is increasingly recognised among traffic psychologists to be a factor influencing driving behaviour. This study examines whether a cultural background characterised by rapid social change and high levels of violence and aggression, as in the South African context, has any discernible influences on driving standards or the behaviour of individual drivers. The experiences and attitudes of young drivers in South Africa are compared with a group of young drivers from Sweden, a country whose society has exhibited high levels of stability and where road user behaviour is renowned for its restraint and compliance with regulations.The two cohorts provide information about their exposure to traffic injuries, their attitudes to other drivers and to a range of traffic offences, and to the types of behaviour they personally engage in. Among the South African respondents the notion of a declining standard of driving emerges very clearly, and specific new norms of driving are identified. Such norms are explained to be a consequence of new social values or challenges inherent within contemporary South African society.     

Cognitive and psychomotor tests as predictors of on-road driving ability in older primary care patients

This study examined the accuracy with which different cognitive and psychomotor assessment tools were able to predict driving ability among older primary care patients. A cross-sectional study of 50 older drivers (with an average age of 73.1 ± 7.0 years) was conducted. Participants who had been referred by their physicians for psychological assessment following a fitness-to-drive examination underwent both an on-road driving test and a cognitive assessment protocol that included the Senior Drivers Battery (SDB) that is currently administered at the Mobility and Land Transports Institute (MLTI) in Portugal, the Useful Field of View (UFOV) test, the Stroke Drivers Screening Assessment (SDSA), Addenbrooke’s Cognitive Examination Revised (ACE-R), the Trail Making Test, the Key Search test, and the Wechsler Adult Intelligence Scale (WAIS-III) Vocabulary and Block Design tests. Logistic regression analysis revealed that the performances of the participants on the SDSA, ACE-R, UFOV and SDB were the best predictors of on-road driving. Specific measures of processing speed and divided attention, visuospatial abilities, executive functions, psychomotor speed and global cognitive functioning may be useful for predicting unsafe driving. The practical implications of these findings are discussed with a view to developing new assessment models for determining driving fitness in older adults.     

Modeling duration of overtaking in two lane highways

The paper attempts to reveal which factors may influence the duration of overtaking in two lane highways. Questions such as what is the duration of young male and female drivers’ overtaking activities and, given that a driver conducts an overtaking maneuver, how long will it take, are addressed using classical survival analysis. Data are collected using a driving simulator. Different models are developed for describing the total overtaking duration, as well as the duration of the acceleration and back-to-lane phases. Results show that the duration of each of the phases of overtaking considered, as well as the total overtaking duration may be best described by a Log–logistic distribution. Analyses point out that, apart from the acceleration phase, the gender is a critical factor to the duration modeling. Other influential factors are the speed difference from the lead vehicle, the speed of opposing traffic, the spacing from the lead and opposing traffic, as well as whether the driver is engaged in multiple overtakes. Finally, the modeling implications to driving assistance systems are discussed.