Organized by the IEEE Computational Intelligence Society Shenzhen Chapter.

Activity Aim & Talk

As urban transportation systems grow increasingly complex, the effective use of big data to improve traffic management efficiency and decision quality has become a critical issue. This talk addresses intelligent transportation systems from three perspectives: data-driven exploration, optimization, rule mining, and decision visualization. It investigates how visual analytics and human-computer collaboration technologies can promote optimization and transparency in intelligent transportation decision-making. The speaker will demonstrate how visual analysis can uncover patterns in urban travel and how interactive tools can help identify potential traffic modes. The talk also explores how large-scale collaborative optimization can enhance the layout and utilization efficiency of electric vehicle charging stations. Furthermore, it will present how multi-agent reinforcement learning can be applied in traffic signal control, with a focus on interpretability and transparency. This talk aims to showcase how data-driven visual analytics and human-computer collaboration techniques can enhance the quality of decision-making and management in intelligent transportation systems, driving the development of efficient, transparent, and sustainable smart city traffic.

Meet the Speaker

Dr. Haipeng Zeng is an Associate Professor at the School of Intelligent Engineering, Sun Yat-sen University, and a Master’s Supervisor. He is recognized as a High-Level Overseas Talent in Shenzhen. Dr. Zeng earned his Ph.D. in Computer Science and Engineering from the Hong Kong University of Science and Technology and has conducted research visits at Georgia Institute of Technology and Stanford University. His research interests include data visualization, machine learning, and intelligent transportation. He has published nearly 20 academic papers in top international journals and conferences such as TVCG and CHI, and has led multiple scientific research projects, including National Natural Science Foundation Youth Projects and sub-projects under the National Key R&D Program.