Evaluation of driving behavior on rural 2-lane curves using the SHRP 2 naturalistic driving study data

Abstract

IntroductionOver half of motor vehicle fatalities are roadway departures, with rural horizontal curves being of particular interest because they make up only a small share of the system mileage but have a crash rate that is significantly higher than tangent sections. However the interaction between the driver and roadway environment is not well understood, and, as a result, it is difficult to select appropriate countermeasures. MethodIn order to address this knowledge gap, data from the SHRP 2 naturalistic driving study were used to develop relationships between driver, roadway, and environmental characteristics and risk of a road departure on rural curves. The SHRP 2 NDS collected data from over 3,000 male and female volunteer passenger vehicle drivers, ages 16–98, during a three year period, with most drivers participating between one to two years. A Roadway Information Database was collected in parallel and contains detailed roadway data collected on more than 12,500 centerline miles of highways in and around the study sites. ResultsRoadway data were reduced for rural 2-lane curves and included factors such as geometry, shoulder type, presence of rumble strips, etc. Environmental and traffic characteristics, such as time of day, ambient conditions, or whether the subject vehicle was following another vehicle, were reduced from the forward roadway video view. Driver characteristics, such as glance location and distraction were reduced from the driver and over the shoulder videos. ConclusionsLogistic regression models were developed to assess the probability (odds) of a given type of encroachment based on driver, roadway, and environmental characteristics. At the point this study was undertaken, crashes and near crashes were not yet available and only around 1/3 of the full SHRP NDS dataset could be queried. As a result, the likelihood of crossing the right or left lane line (encroachments) and speeding were used as dependent variables.