Investigating Driver Compliance with Pedestrian Features at Signalized Intersections: SHRP 2 Naturalistic Driving Study Data Analysis
Vehicle-pedestrian crashes induce significant pedestrian fatalities and injuries, particularly at intersections given the complex traffic environment. Pedestrian safety-related features are effective in regulating driver behavior and increasing pedestrian safety, and it is of practical importance to investigate driver interactions with pedestrian features at signalized intersections in terms of compliant behavior. Based on SHRP2 Naturalistic Driving Study (NDS) data, this study examined driver compliance with selected pedestrian features at signalized intersections, including “Stop Here on Red,” “No Turn on Red,” “Turning Vehicles Yield to Pedestrians,” and “Right on Red Arrow after Stop” signs. Statistical analysis and a generalized linear mixed model (GLMM) were used to identify significant factors and their influence on driver compliant behavior regarding traffic environment conditions, driver demographic features, and risk and distraction behavior characteristics. It is found that driver compliance could be improved with all four features in the presence of pedestrians and that young drivers, older drivers, female drivers, and risky drivers tend to have lower probabilities of compliant behavior. The results provide beneficial reference to the understanding of driver behavior and implementable countermeasures to enhance pedestrian safety from the engineering, education, and enforcement perspectives. Future research directions regarding model structure improvement and countermeasure evaluation are also discussed.
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Citation / Publisher Attribution
Presented at the Transportation Research Board 97th Annual Meeting, in January 2018, in Washington, DC
Scholar Commons Citation
Lin, Pei-Sung; Chen, Cong; Kourtellis, Achilleas; Wang, Zhenyu; Guo, Rui; and Zhang, Zhao, "Investigating Driver Compliance with Pedestrian Features at Signalized Intersections: SHRP 2 Naturalistic Driving Study Data Analysis" (2018). CUTR Faculty Journal Publications. 149.