I am Dr. Yi Yu (Eve), a Young Researcher at Shanghai AI laboratory specializing in the intersection of Artificial Intelligence and Intelligent Transportation Systems. My passion lies at leveraging cutting-edge AI technologies to solve complex urban mobility challenges and ensure the safe, efficient development of smart cities.

My research interests spans Multimodal Large Lanugage Models(MLLMs), Intelligent Transportation Systems, MLLM Safety, and AI agents. I am particularly passionate about:

• Exploring the inner safety alignment of MLLMs and their societal impacts of AI in intelligent systems
• Optimizing intelligent systems via advanced AI techniques
• Developing AI-driven simulation platforms

I received my PhD from Zhejiang University, where I worked under the supervision of Prof. Dianhai Wang. My doctoral research focused on developing comprehensive urban traffic state evaluation systems, which have been successfully applied in real-world projects. From 2020 to 2022, I was working at Imperial College London as a visiting scholar, where I collaborated closely Prof. Washington Ochieng, FREng. I obtained my bachelor’s degree major in Civil Engineering and minor in Law from Zhejiang University in 2017. My academic journey has been enriched by international experiences at institutions including University of Tokyo, Waseda University, University of Toronto, University of Ottawa, Western University, Queens Unversity, York University.

My interdisciplinary background allows me to approach transportation challenges from multiple perspectives, fostering innovative solutions that consider technological, legal, and societal implications. I have authored over 20 peer-reviewed publications in journals and conferences, as well as actively contributed to national and municipal research projects. Additionally, I am passionate about contributing to the academic community and fostering the next generation of researchers, actively serve as a reviewer for journals and conferences such as IEEE Transactions on Systems, Man and Cybernetics.

As a researcher, I am committed to developing AI technologies that not only advance the field of Intelligent Transportation Systems but also contribute positively to society. I strive to create solutions that enhance urban mobility, promote sustainability, and ensure equitable access to the benefits of smart city innovations. My life motto: “It is better to light a candle than to curse the darkness.”

Education

Research Interests

• AI Safety
• Traffic State Estimation
• Traffic Flow Modeling
• Intelligent Traffic System

Selected Publications

• Zeng, J, Yu, Y, Chen, Y, Yang, D, Zhang, L, and Wang, D (2023). Trajectory-as-a-Sequence: A novel travel mode identification framework. Transportation Research Part C: Emerging Technologies 146, 103957. 10.1016/j.trc.2022.103957.

• Qi H, Yu Y, Tang Q, Hu X (2022) Investigation of intersection traffic deadlock formation and the probability with a petri net-based modeling approach. IET Intelligent Transport Systems, accept.

• Yu, Y., Cui, Y., Zeng, J., He, C., Wang, D., 2022. Identifying traffic clusters in urban networks based on graph theory using license plate recognition data. Physica A: Statistical Mechanics and its Applications 591, 126750. https://doi.org/10.1016/j.physa.2021.126750

• Cui, Y., Yu, Y., Cai, Z., Wang, D., 2022. Optimizing Road Network Density Considering Automobile Traffic Efficiency: Theoretical Approach. J. Urban Plann. Dev. 148, 04021062. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000780

• Yu Y, Zeng J, Wang D (2021) Free-flow travel time estimation in urban roads based on a data sampling method. Journal of Zhejiang University(Engineering Science), accept.

• Zeng, J., Wang, D., Zhang, G., Yu, Y., Cai, Z., 2021. Passenger-to-Car Assignment Optimization Model for High-Speed Railway with Risk of COVID-19 Transmission Consideration. Mathematical Problems in Engineering 2021, e7121010. https://doi.org/10.1155/2021/7121010

• Cui, Y., Yu, Y., Wang, D., 2021. Impact of the Link Length on the Delay in Two-Way Signal Coordination. IEEE Access 9, 130823–130833. https://doi.org/10.1109/ACCESS.2021.3113695

• Yu, Y., Chen, M., Qi, H., Wang, D., 2020. Copula-Based Travel Time Distribution Estimation Considering Channelization Section Spillover. IEEE Access 8, 32850–32861.