Driver performance on horizontal curves

Thus, it is important to understand how drivers can more safely transverse these sections of roadways, such as slowing to a safe speed. This perception is linked to the curve radius and deflection angles of horizontal curves. Previous research found that on the tangent section before a curve began, drivers cruised at their highest speeds before slowing most significantly right before the start of the curve Point of Curvature. Following this, drivers increased their speed slightly until the midpoint of the curve, before exiting the curve at approximately the same speed as the midpoint.

However, to date, research has not considered a continuous study of driver speed throughout the entire length of a curve in terms of the impact of the curve deflection angle alone. Thus, it is not known how the intensity of a curve impacts driver performance throughout the time a driver spends traversing the length of a curve, and what the relationship between speed, lane position, curve radii, and deflection angle is throughout each portion of a horizontal curve, from the tangent section prior to the curve, through the tangent section following the curve.

These scenarios do not require the physical presence of the participants, but instead can be completed remotely. The scenarios will record the actions of the participants as if they are playing a video game or playback a video of the horizontal curve.

Pages: Abstract The goal of this study was to examine driver performance under different combinations of horizontal and vertical alignment in rural two-lane roads. The SHRP2 roadway information database RID was used to identify study sites on rural two lane roads from New York and Pennsylvania that included tangent sections, straight grades, horizontal curves, vertical curves, and combinations. The study included the development of an algorithm that used the RID to identify vertical curves.

The time series data included kinematics variables such as speed, acceleration, lane position, distance from left and right lane markings, and steering wheel position. The eye glance data provided information on where a driver was looking every fraction of a second.

The analysis of the NDS data showed that the alignment categories that include a horizontal curve have the worst performance in terms of the lane deviation measures.