Combining Driving Performance Information in an Index Score

Abstract

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).

Measuring Driver's Relative Performance Over Time Using Dea and Window Analysis

“…To obtain a clear understanding of driving behavior of different drivers, driving performance evaluation in the manner of constructing a composite indicator (CI) is often applied [1]. It has an advantage of taking the multi-dimension of driving performance into account.…”