Non-Parametric Association Rules Mining and Parametric Ordinal Logistic Regression for an In-Depth Investigation of Driver Speed Selection Behavior in Adverse Weather using SHRP2 Naturalistic Driving Study Data

Abstract

Human error is considered to be one of the major causes of crashes, especially in inclement weather. Although many studies have investigated the effect of adverse weather on traffic safety and operations, there is a lack of research into the differences in driving behavior and performance during adverse weather, particularly at a trajectory level. With this research gap in mind, this study presents a novel approach for an in-depth investigation of driver speed selection behavior in adverse weather utilizing trajectory-level data acquired from the SHRP2 Naturalistic Driving Study using a promising association rules data mining technique. The preliminary analysis revealed that drivers reduced their speeds by 3.9% in the presence of light rain, by 10.2% in heavy rain, 15.2% in light snow, 29.8% in heavy snow, 1.8% with distant fog, and 7.4% with near fog. The findings from the association rules mining approach indicated that driving more than 5 mph above the speed limit was closely associated with clear weather as well as young and inexperienced drivers; whereas a reduction in speed to more than 5 mph below the speed limit was closely associated with snowy road surfaces combined with affected visibility. These findings are also in line with the results from the ordered logistic regression, which revealed that drivers were 1.4 times more likely to reduce their speeds in light rain, 1.7 times in heavy rain, 4.3 in light snow, 12.2 in heavy snow, 1.7 with distant fog, and 2.0 with near fog. The findings from this study provide an unprecedented opportunity to develop a Human-in-the-Loop Variable Speed Limit algorithm.