Familiar versus Unfamiliar Drivers on Curves: Naturalistic Data Study

Abstract

Human factors studies have shown that route familiarity affects driver behavior in various ways. Specifically, when drivers become more familiar with a roadway, they pay less attention to signs, adopt higher speeds, cut curves more noticeably, and exhibit slower reaction times to stimuli in their peripheral vision. Numerous curve speed models have been developed for purposes such as predicting driver behavior, evaluating roadway design consistency, and setting curve advisory speeds. These models are typically calibrated using field data, which gives information about driver behavior in relation to speed and sometimes lane placement, but does not provide insights into the drivers themselves. The objective of this paper is to examine the differences between the speeds of familiar and unfamiliar drivers as they traverse curves. The authors identified four two-lane rural highway sections in the State of Indiana which include multiple horizontal curves, and queried the Second Strategic Highway Research Program (SHRP2) database to obtain roadway inventory and naturalistic driving data for traversals through these curves. The authors applied a curve speed prediction model from the literature to predict the speed at the curve midpoints and compared the predicted speeds with observed speeds. The results of the analysis confirm earlier findings that familiar drivers choose higher speeds through curves. The successful use of the SHRP2 database for this analysis of route familiarity shows that the database can facilitate similar efforts for a wider range of driver behavior and human factors issues.