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This study used data from a driving simulator to identify the best car drivers in a sample and gain insight about the most problematic behavior of each driver. To this end, 38 participants varying in age and gender were enrolled to take part in a particular simulator scenario, curve taking. Based on a review of the literature, a driver's speed, acceleration, and lateral position are the three most important driving performance indicators. In the simulations, the three indicators were monitored at points before, during, and after a curve. As a widely accepted tool for performance monitoring, benchmarking, and policy analysis, the concept of composite indicators, which combines single indicators into one index score, was employed. The technique of data envelopment analysis, which is an optimization model for measuring the relative performance of a set of decision-making units, or drivers in this study, was used for the index construction. On the basis of the results, best performers were distinguished from underperforming drivers. Moreover, by analyzing the weights allocated to each indicator from the model, the most problematic parameter (such as lateral position) and point along the curve (such as at curve end) were identified for each driver; this process led to specific driver improvement recommendations (such as training programs).

Among different road user types, drivers represent the largest share of road fatalities. As a result, more attention should be paid to the behavior of drivers, especially their behavior over time. By using driving simulator data, this study aims to investigate the relative performance of individual drivers over time. To this end, 20 participants (14 in the end) completed a particular simulator scenario over five days, and their driving performance at various points along the driving scenario was recorded. By taking all this information into account, the technique of data envelopment analysis was applied to assess the relative performance of each driver, and the window analysis was used to measure the variations in performance over time.

This study investigates the relative performance of older drivers at the individual level, based on specific measures of functional and driving abilities. To do so, 55 participants aged 70 years and above completed tests of an assessment battery of psychological and physical aspects as well as knowledge of road signs; moreover, they took a driving simulator test in which specific driving situations that are known to cause difficulties for older drivers were included. To evaluate the overall performance of each driver, all the above information was combined by using the concept of composite indicators, and the technique of data envelopment analysis, which is an optimization model for measuring the relative performance of a set of units, or drivers in this study, was employed. The model output distinguishes the best-performers from those underperforming drivers, and helps in guiding future development of training interventions tailored to each individual by specifically targeting those functions that are (mostly) impaired.

The main purpose of the present study is to investigate individual driver's behavior by using the data from a driving simulator, in order to distinguish the best drivers and identify the problematic behavior of 'underperforming' drivers. To this end, 129 participants with different age and gender were enrolled to take part in a particular simulator scenario (i.e., curve taking) and their speed, acceleration and lateral position, the three most important driving performance indicators based on literature review, were monitored at various points (before, during and after the curve) while driving a STISIM simulator. As a widely accepted tool for performance monitoring, benchmarking and policy analysis, the concept of composite indicators (CIs), i.e., combining single indicators into one index score, was employed, and the technique of data envelopment analysis -an optimization model for measuring the relative performance of a set of decision making units, or drivers in this studywas used for the index construction. Based on the results from the model, all drivers were ranked, and valuable insight were gained by comparing the relative performance of each driver. Finally, the sensitivity of the results was examined.