Perception of risk, feelings of vulnerability and effects on driving behaviour in women drivers across the lifespan.

Sustained driving in older age has implications for quality of life and mental health. Studies have shown that despite the recognised importance of driving in maintaining health and social engagement, many women give up driving prematurely or adopt self-imposed restrictive driving practices. Emotional responses to driving have been implicated in these decisions. This research examined the effect of risk perception and feelings of vulnerability on women’s driving behaviour across the lifespan. It also developed and tested a modified theory of planned behaviour intervention to positively affect driving habits. The first two studies (N=395) used quantitative analysis to model driving behaviours affected by risk perception and feelings of vulnerability, and established that feelings of vulnerability do indeed affect women’s driving behaviour, specifically resulting in increases in driving avoidance and the adoption of maladaptive driving styles. Further, that self-regulation, conceptualised as avoidance, is used by drivers across the lifespan. Qualitative analysis of focus group data (N=48) in the third study provided a deeper understanding of the variations in coping behaviours adopted by sub-groups of drivers and extended the definition of self-regulation to incorporate adaptive coping strategies. The next study (N=64) reported the construction and preliminary validation of the novel self-regulation index (SRI) to measure wider self-regulation behaviours using an objective measure of driving behaviour, a simulated driving task. The understanding gained from the formative research was used in the final study, an extended theory of planned behaviour intervention to promote wider self-regulation behaviour, measured using the previously validated self-regulation index. The intervention achieved moderate success with changes in affective attitude and normative beliefs as well as self-reported behaviour. The results offer promise for self-regulation, incorporating a spectrum of planning and coping behaviours, to be used as a mechanism to assist drivers in achieving their personal mobility goals whilst promoting safe driving.

Driving licensing renewal policy using neural network-based probabilistic decision support system

This paper investigates neural network-based probabilistic decision support system to assess drivers' knowledge for the objective of developing a renewal policy of driving licences. The probabilistic model correlates drivers' demographic data to their results in a simulated written driving exam (SWDE). The probabilistic decision support system classifies drivers' into two groups of passing and failing a SWDE. Knowledge assessment of drivers within a probabilistic framework allows quantifying and incorporating uncertainty information into the decision-making system. The results obtained in a Jordanian case study indicate that the performance of the probabilistic decision support systems is more reliable than conventional deterministic decision support systems. Implications of the proposed probabilistic decision support systems on the renewing of the driving licences decision and the possibility of including extra assessment methods are discussed.