A novel infrastructure design known as the J-turn intersection reduces the risk of serious and fatal crashes at thru-STOP intersections through decreasing points of conflict at an intersection by restricting crossing movements from the minor road. Despite their demonstrated safety efficacy, J-turns have not been met with uniformly positive support. In this research, we first examine novice driver baseline attitudes and driving behaviors on J-turns using a driving simulator study. Results demonstrate that critical errors are decreased with driving exposure to the J-turn; however, attitudes toward J-turns are not improved by exposure alone. A series of studies then evaluates the efficacy of various messaging strategies and educational materials on improving attitudes toward J-turns. The findings from these studies identify that the use of both educational materials and persuasive and customized messaging strategies is an effective method for increasing acceptance of J-turns across diverse resident populations (i.e., rural, suburban, and urban) and among stakeholders in Minnesota. This work demonstrates the importance of the role of proactive educational programs and community initiatives in promoting the acceptance and buy-in toward novel roadway treatments, such as J-turns, among diverse drivers, communities, and stakeholder groups.
Snowplow operators are often tasked with clearing snow from roadways under challenging conditions. One such situation is low visibility due to falling or blowing snow that makes it difficult to navigate, stay centered in the lane, and identify upcoming hazards. To support snowplow operators working in these conditions, University of Minnesota researchers developed a snowplow driver-assist system that provides the operator with visual and auditory information that is suitable for low-visibility situations. A lane-guidance system uses high-accuracy Global Navigation Satellite System (GNSS) and maps of the roadway to provide information to drivers about their lateral positions. A forward-obstacle-detection system uses forward-facing radar to detect potential hazards in the roadway. The design of the system, and in particular its interface, is guided by extensive user testing to ensure the system is easy to understand, easy to use, and well liked among its users.
The system was deployed in two phases over the 2020-2021 and 2021-2022 winter seasons. In total, nine systems were deployed on snowplows across Minnesota, four in the first winter season and an additional five in the second. Participating truck stations represented all eight MnDOT districts as well as Dakota County. Over the course of the deployment, additional user feedback was collected to identify system strengths and areas for improvement. The system was found to be a cost-effective addition to snowplows that increase driver safety, reduce plow downtime, and increase driver efficacy for plowing operations, thus providing support to operators working in demanding, low-visibility conditions.