Dr.Bo Yu, is an Associate Professor with College of Transportation Engineering, Tongji University. His research interests are the theory and method of digital intelligent planning and design of road facilities. He was selected into Shanghai Overseas High-level Talent Introduction Program, served as a member of the Youth Expert Committee of Shanghai Highway Society, presided over several projects, such as National Natural Science Foundation project, National Key Research and Development Plan sub-projects, and Shanghai Natural Science Foundation project, and published more than 40 peer-reviewed papers. He won the second prize of National Teaching Achievement Award and the first prize of Science and Technology Award of China Highway Society.
Education
2017-2019 University of Michigan Transportation Research Institute, Joint Ph.D.
2014-2019 Transportation Engineering, Tongji University Ph.D.
2010-2014 Civil Engineering, Tongji University Bachelor
Experience
2024- present College of Transportation Engineering, Tongji University, Associate Professor
2021-2023 College of Transportation Engineering, Tongji University, Assistant Professor
2020-2021 University of Michigan Transportation Research Institute, Postdoctoral Researcher
Research interestsIntelligent optimization design of road facilities environment，Autonomous driving and human factors
Honors & Awards
The second prize of National Teaching Achievement Award
The first prize of Science and Technology Award of China Highway Society
Publications
Yu, B., Bao, S., Zhang, Y., Sullivan, J., & Flannagan, M. (2021). Measurement and prediction of driver trust in automated vehicle technologies: an application of hand position transition probability matrix. Transportation Research Part C: Emerging Technologies, 124, 102957.
Yu, B., Bao, S., Feng, F., & Sayer, J. (2019). Examination and prediction of drivers’ reaction when provided with V2I communication-based intersection maneuver strategies. Transportation Research Part C: Emerging Technologies, 106, 17-28.
Gao, J., Yu, B., Chen, Y., Bao, S., Gao, K., & Zhang, L. (2024). An ADAS with better driver satisfaction under rear-end near-crash scenarios: A spatio-temporal graph transformer-based prediction framework of evasive behavior and collision risk. Transportation research part C: emerging technologies, 159, 104491.
Yu, B., Feng, X., Kong, Y., Chen, Y., Cheng, Z., & Bao, S. (2024). Using meta-learning to establish a highly transferable driving speed prediction model from the visual road environment. Engineering Applications of Artificial Intelligence, 130, 107727.
Yu, B., Bao, S., Chen, Y., & LeBlanc, D. J. (2021). Effects of an integrated collision warning system on risk compensation behavior: An examination under naturalistic driving conditions. Accident Analysis & Prevention, 163, 106450.
Yu, B., Chen, Y., & Bao, S. (2019). Quantifying visual road environment to establish a speeding prediction model: an examination using naturalistic driving data. Accident Analysis & Prevention, 129, 289-298.
Yu, B., Chen, Y., Bao, S., & Xu, D. (2018). Quantifying drivers’ visual perception to analyze accident-prone locations on two-lane mountain highways. Accident Analysis & Prevention, 119, 122-130.
He, L., Yu, B., Chen, Y., Bao, S., Gao, K., & Kong, Y. (2023). An interpretable prediction model of illegal running into the opposite lane on curve sections of two-lane rural roads from drivers’ visual perceptions. Accident Analysis & Prevention, 186, 107066.
Li, Z., Yu, B., Wang, Y., Chen, Y., Kong, Y., & Xu, Y. (2023). A novel collision warning system based on the visual road environment schema: An examination from vehicle and driver characteristics. Accident Analysis & Prevention, 190, 107154.