Alternative intersection (AI) designs, such as the Median U-Turn (MUT), Reduced Conflict Intersection (RCI), Continuous Flow Intersection (CFI), and Quadrant Roadway Intersection (QRI), introduce innovative geometric and control features compared to a conventional intersection (CI), which offer the potential for substantial safety and operational improvements. Nevertheless, most AI designs present unconventional ways of maneuvering traffic through an intersection, such as restriction of movements, crossover of traffic to the opposite side of the road, separating left turning movements, etc. As corridor construction or improvement projects continue to utilize AI designs, understanding their impacts on driver behavior, especially when implemented successively along a corridor, is essential for effective deployment. This research developed a comprehensive driving simulator experiment to evaluate driver performance when navigating AI corridors, focusing on four key metrics: number of failure movements (FMs), approach speed (AS), hard-braking events (HBEs), and approach lane changes (ALCs). Three background corridor treatments were investigated: a CI corridor, an RCI corridor, and a corridor with varied AI designs. A total of 12 intersection pairs were created to represent typical and practical combinations of AIs, with each pair consisting of a test intersection and a preceding intersection. Based on data collected from 48 participants, this research found that gender, age, and background corridor treatment did not significantly influence driver behavior. In contrast, trial number, preceding intersection configuration, test intersection movement, and intersection pair were all found to have significant effects. Among the test movements, the MUT side street left-turn presented the highest risk of FMs. Approach speed was lowest at the MUT side street left-turn and highest at the RCI side street through movement, with speeds generally increasing over successive trials. HBEs occurred most frequently at QRI, MUT, and RCI configurations. ALCs were more common when the test intersection was preceded by a CI, with the highest ALCs observed during CI main street left-turns, followed by MUT and CFI configurations. Post-experiment interviews highlighted the importance of clear and reasonably placed traffic signs and pavement markings to inform drivers of unconventional traffic patterns at AIs.
Yang et al. (Mon,) studied this question.