Evaluating individual risk proneness with vehicle dynamics and self-report data - toward the efficient detection of At-risk drivers.

Vehicle-dynamics data, now more readily available thanks to moderate-cost, embedded data logging solutions, have been used to study drivers' behavior (acceleration, braking, and yaw rate) through naturalistic driving research aimed at detecting critical safety events. In addition, self-reported measures have been developed to describe these events and to assess various individual risk factors such as sensation seeking, lack of experience, anger expression while driving, and sensitivity to distraction. In the present study, we apply both of these methods of gathering driving data in order to assess risk proneness as accurately as possible. Data were obtained from 131 drivers, who filled in an introductory questionnaire pertaining to their driving habits. Their vehicles were equipped with an external, automatic data-capture device for approximately two months. During that period, the participants reported critical safety events that occurred behind the wheel by (a) pressing a button connected to the device and (b) describing the events in logbooks. They also filled in weekly questionnaires, and at the end of the participation period, a final questionnaire with various self-reported measures pertaining to their driving activity. We processed the data by (a) performing a multiple correspondence analysis of the characteristics assessed via the automatic data capture and self-reports, and (b) categorizing the participants via hierarchical clustering of their coordinates on the dimensions obtained from the correspondence analysis. This allowed us to identify a group of drivers (n = 43) at risk, based on several self-reported measures, in particular, their recent crash involvement, and the frequency of critical acceleration/deceleration events as an objective measure. However, the at-risk drivers did not themselves report more critical safety events than the other two groups.

Causal attribution in explanations of near-crash events behind the wheel, and its relationship to comparative judgments.

INTRODUCTION: The development of skills essential for avoiding crashes depends, in particular, on how drivers explain the causes of dangerous driving behaviors that resulted in a near crash. Here, we analyze causes attributed to such behaviors by car drivers in a self-report study. We explore the relationships between the dimensions of causal attribution, attribution of responsibility for the near crash, and drivers' comparative judgments. METHOD: For approximately two months, drivers used logbooks to document the near crashes that occurred during their trips. The causes attributed in those reports to driving behaviors resulting in near crashes were then coded by two judges on the basis of several causal dimensions. Drivers also estimated their own and an average driver's skill levels, and their risk of being involved, as a driver, in a crash. RESULTS: We distinguished main types of causes of the near crashes reported. Drivers had a tendency to more often attribute external causes to their own behaviors resulting in near crashes than to those of others. The probability of attributing a controllable cause increased with overestimation of one's own skills and decreased with underestimation of one's own risk in comparison to other drivers. The probability of attributing a stable cause increased with underestimation of one's own risk. CONCLUSIONS: When they explained their own behaviors resulting in near crashes, drivers mentioned different causes than when they explained those of others. Overestimation of one's own skills as compared to other drivers could be beneficial for developing crash-avoiding skills, insofar as it seems to foster attribution of controllable causes. By contrast, underestimation of one's own risk could have the opposite effect. PRACTICAL APPLICATIONS: Vulnerability to road risks should be stressed in driver's training and risk communication campaigns. However, self-confidence with respect to one's skills should not always be targeted as a safety problem.

Applying an extended theory of planned behavior to predicting violations at automated railroad crossings.

Based on an extended Theory of Planned Behavior (TPB, Ajzen, 1985, 1991), we conducted surveys in order to explain and predict violations at a railroad crossing, among pedestrians (n=153) and car drivers (n=151). Measures were made with respect to three chronologically related railroad crossing situations that varied in risk level. The situations were described in scenarios and depicted on photographs. The participants were recruited in the suburbs of Paris, at two automated railroad crossings with four half-barriers. We found that the pedestrians had stronger crossing intentions than did car drivers, especially at the more congested crossing of the two under study. For both categories of road users, intentions and the amount of intention variance explained by the extended TPB factors decreased significantly with risk level. In the most dangerous situations, risk-taking was the most unlikely and the least predictable Self-reported past frequency of crossing against safety warning devices was the main predictor of the intention to commit this violation again, especially among males, followed by the attitude and the injunctive norm in favor the violation. Moreover, car drivers were influenced in their crossing intentions by the descriptive norm. The presence of another vehicle on the tracks when the safety warning devices were activated was perceived not as facilitating, but as an additional risk factor. The discussion addresses the importance of taking into account these determinants of violations in conceiving countermeasures. Our findings could be especially useful for conceiving risk-communication campaigns.