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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.