Yunwen Xu | Intelligent Transportation Control | Best Researcher Award

Dr. Yunwen Xu | Intelligent Transportation Control | Best Researcher Award

Associate researcher at Shanghai Jiao Tong University | China

Dr. Yunwen Xu is an accomplished Associate Researcher and Doctoral Supervisor at Shanghai Jiao Tong University, recognized for her impactful contributions to intelligent transportation systems, autonomous driving control, and predictive control for complex and embedded systems. Her research bridges advanced control theory with practical applications in intelligent mobility and energy management. She has authored over 50 peer-reviewed publications, including 15 in top-tier journals, and her work is highly cited, with a total of 499 citations and 473 since 2020. Dr. Xu’s research impact is reflected in an h-index of 9 and an i10-index of 9, highlighting the influence and relevance of her work in the field. She has developed innovative predictive control models that optimize large-scale, stochastic traffic systems, advancing traffic flow regulation under vehicle-to-everything (V2X) and autonomous vehicle frameworks. Her contributions have led to six authorized patents and multiple successful technology transfers, demonstrating her ability to translate research into practical industrial solutions. She has led multiple national and provincial projects and collaborated with industry partners to implement intelligent control systems for microgrids and advanced temperature regulation technologies. Her achievements have earned her academic and technological awards, including recognition for excellence in process control and autonomous driving algorithm innovation. As an active member of technical committees under the Chinese Association of Automation, Dr. Xu continues to drive advancements in predictive control and intelligent decision-making systems, fostering the development of smarter, more efficient, and sustainable urban mobility ecosystems.

Profile: Google Scholar

Featured Publications

Sheng, Z., Xu, Y., Xue, S., & Li, D. (2022). Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 23(10), 17654–17665.

He, S., Chen, W., Li, D., Xi, Y., Xu, Y., & Zheng, P. (2021). Iterative learning control with data-driven-based compensation. IEEE Transactions on Cybernetics, 52(8), 7492–7503.

Xu, Y., Li, D., Xi, Y., Lan, J., & Jiang, T. (2018). An improved predictive controller on the FPGA by hardware matrix inversion. IEEE Transactions on Industrial Electronics, 65(9), 7395–7405.

Yang, L., Lu, J., Xu, Y., Li, D., & Xi, Y. (2020). Constrained robust model predictive control embedded with a new data‐driven technique. IET Control Theory & Applications, 14(16), 2395–2405.

Xu, Y., Xi, Y., Li, D., & Zhou, Z. (2016). Traffic signal control based on Markov decision process. IFAC-PapersOnLine, 49(3), 67–72.

Jianfeng Qiao | Accident Analysis | Best Researcher Award

Mr. Jianfeng Qiao | Accident Analysis | Best Researcher Award

Associate Professor at Capital University of Economics and Business, China

Dr. Jianfeng Qiao is an Associate Professor at the School of Management and Engineering, Capital University of Economics and Business (CUEB), Beijing. His research focuses on accident analysis and safety risk early warning using advanced machine learning and artificial intelligence technologies. He has authored influential publications in top-tier international journals and has collaborated globally, including serving as a visiting scholar at Rutgers University, USA. Dr. Qiao’s work bridges engineering management, data science, and safety technology, contributing significantly to the fields of construction safety, risk optimization, and intelligent decision support systems.

Publication Profile 

Scopus

Educational Background 🎓

  • Doctor of Engineering
    Beijing University of Posts and Telecommunications, Beijing, China
    September 2011 – July 2015

  • Visiting Scholar
    Rutgers, The State University of New Jersey, USA
    March 2019 – May 2022
    (Focus: Machine learning applications in accident analysis and safety systems)

Professional Experience 💼

  • Associate Professor
    School of Management and Engineering, Capital University of Economics and Business, Beijing, China
    Dates: Ongoing

    • Teaching courses related to engineering management and safety systems

    • Conducting research in machine learning-driven safety risk assessment and early warning models

    • Supervising graduate students in safety engineering and AI applications

  • Research Collaborator
    Rutgers University, USA (as Visiting Scholar)

    • Conducted collaborative research projects focused on AI in construction accident prevention

    • Developed methods for classifying narrative accident data using deep learning techniques

Research Interests 🔬

  • Accident analysis and prediction using machine learning

  • Early warning systems for safety risks

  • Text mining and natural language processing in safety narratives

  • Entropy-based methods in project management

  • AI applications in construction and industrial safety

  • Resource optimization and scheduling

Conclusion🌟

Dr. Jianfeng Qiao is a committed academic and researcher whose work advances the intersection of machine learning and safety engineering. Through his academic publications and international collaborations, particularly in the area of construction accident prevention, he continues to impact both scholarly communities and practical safety management systems. His expertise and contributions make him a valued expert in AI-driven safety solutions.

Publications 📚

  • 🏗️ Qiao, J., Wang, C., Guan, S., Lv, S. (2022).
    Construction-Accident Narrative Classification Using Shallow and Deep Learning.
    Journal of Construction Engineering and Management, 148(9), 04022088.
    🔗 https://doi.org/10.1061/(ASCE)CO.1943-7862.0002354
    Used machine learning techniques to classify construction accident narratives.


  • ⚙️ Qiao, J., Li, Y. (2018).
    Resource Leveling Using Normalized Entropy and Relative Entropy.
    Automation in Construction, 87, 263–272.
    🔗 https://doi.org/10.1016/j.autcon.2017.12.022
    📊 Introduced entropy-based methods for efficient resource scheduling in construction.