Cognitive Attention and Its Application in Countermeasures on a Curve Section

US Transportation Collection2021PDFTech ReportShen, JunanGao, WeinanAhad, MohammadChen, YunfengIroghama, OsahonWu, HongyueGeorgia Southern UniversityFlorida Institute of TechnologyPurdue UniversityGeorgia Southern University Research and Service Foundation, IncGeorgia. Department of Transportation. Office of Performance-Based Management & ResearchUnited States. Department of Transportation. Federal Highway AdministrationGeorgia. Department of Transportation. Office of Performance-Based Management & ResearchUnited StatesCase studiesCognitionCountermeasuresDriversHighway curvesHighway safetyMethodologyTraffic safetyEye movementsCOVID-19Driving PerformanceFHWA-GA-21-1912PI#0016865Final Report; September 2019 \u2013 July 2021To reduce crashes and improve traffic safety on roads, this project explored a methodology of evaluating the safety countermeasures based on cognitive attention and driving performance with eye-tracking and driving simulation technologies and comprehensive analysis of eye movements, driving performance, and short-term memory. An experiment for data collection of cognitive response and driving performance to 11 countermeasures was designed with two weather conditions (clear and foggy) and two traffic flows (light and heavy) in a rural road curve section with a right and a left turn. Four combinations of the weather conditions and traffic flow were formed. Four sequence groups of the combinations were followed to eliminate the bias in data collection. Data of 60 participants were collected. Analysis of variance (ANOVA) tests indicated that countermeasures, weather conditions, and traffic flow impacted the drivers\u2019 cognitive attention, driving behavior, and short-term memory. Dividing participants into groups with different sequences of simulation combinations was useful to improve the bias for a limited sample size, while different starting time points of the combinations did not cause significant differences in the data collected. Finally, regression analyses using machine learning technology indicated that edge line pavement marking, shoulder rumble strips, flexible delineator posts, curve warning sign, and increased shoulder width are effective countermeasures that can attract drivers\u2019 attention and maintain the proper level of cognitive workload and visual information to reduce traffic crashes and improve the traffic safety. The effectiveness of the countermeasures from the regressions that considered the cognitive properties was much closer to what is expected compared to those that did not consider the cognitive properties. The proposed methodology using both eye tracker and driving simulator was found to be a useful way to evaluate the effectiveness of countermeasures to improve traffic safety.120