CAREER: Improving Bicycling Safety by Developing a Research Framework for Studying Driver-Bicyclist Interactions

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This Faculty Early Career Development (CAREER) award supports research to advance the safety of environmentally sustainable, active, and equitable mobility modes by exploring ways to improve bicycling safety. Bicycling has long been an important mobility mode for its environmental, health, and economic benefits. Nonetheless, bicycling is still largely underutilized in the U.S. The perceived danger of bicycling in motorized traffic has deterred many from considering it as a viable mobility option. This NSF grant will fill the knowledge gap to better understand how drivers and bicyclists interact with each other in the context of real-world roadway designs, and the key factors that shape their behaviors. The outcome of this research will support engineers and practitioners, city planners, and policymakers and legislators by providing data-driven insights to design safer road infrastructures, bicycle facilities, traffic laws and regulations, training and education programs, and safety technologies. The research activities are integrated with educational activities to foster local K-12 students' interests in STEM fields and train the next generation of scientists and engineers to work towards the urban future in which sustainable and active mobility will play a key role. The outreach to the local communities and dissemination of the online free educational materials on bicycling safety will promote active mobility modes to the general public nationwide.The main research questions of this project are (a) how do drivers and bicyclists interact with each other in the context of roadways designs, (b) what are the key factors and underlying mechanisms for driver-bicyclist crashes and conflicts, and (c) how can researchers systematically generate data-driven insights in active mobility safety research? To this end, this grant will develop a bicycling safety research framework that incorporates a variety of complementary methodologies and technologies, including (1) observational studies in real-world natural settings from complementary perspectives using naturalistic driving, cycling, and drone data; (2) laboratory experiments in a safe, controlled, and replicable environment using high-fidelity virtual reality cycling and driving simulators; (3) crash data analysis of bicyclist fatalities and injuries; and (4) computational modeling and simulation. A number of common and dangerous driver-bicyclist interaction types will be examined, including overtaking bicyclists and intersection-related conflicts. A wide range of factors involving driver and vehicle, bicyclist, and road infrastructure will be examined for their effects on bicycling safety measured by surrogate safety measures. This research also integrates many tools including big data, causal inference, human factors, virtual reality, lidar sensing, and computational human modeling. In addition, an open-source data repository of driver-bicyclist interactions will be developed to support the broader transportation research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.