Caroline Ariën

A simulator study on the impact of traffic calming measures in urban areas on driving behavior and workload.

This study examined the impact of traffic calming measures (TCM) on major roads in rural and urban areas. More specifically we investigated the effect of gate constructions located at the entrance of the urban area and horizontal curves within the urban area on driving behavior and workload. Forty-six participants completed a 34km test-drive on a driving simulator with eight thoroughfare configurations, i.e., 2 (curves: present, absent)×2 (gates: present, absent)×2 (peripheral detection task (PDT): present, absent) in a within-subject design. PDT performance (mean response time (RT) and hit rate) indicated that drivers experienced the road outside the urban area as cognitively less demanding relative to the more complex road environment inside the urban area. Whereas curves induced a speed reduction that was sustained throughout the entire urban area, variability of acceleration/deceleration and lateral position were increased. In addition, PDT performance indicated higher workload when curves were present (versus absent). Gate constructions locally reduced speed (i.e., shortly before and after the entrance) and slightly increased variability of acceleration/deceleration and lateral position nearby the entrance. However, the effects on SDL-A/D and SDLP are too small to expect traffic safety problems. It can be concluded that both curves and gate constructions can improve traffic safety. Notwithstanding, the decision to implement these measures will depend on contextual factors such as whether the road serves a traffic-, rather than a residential function.

Combining Driving Performance Information in an Index Score: A Simulated Curve-Taking Experiment

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 drivers 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).