The 12th International Conference on Fuzzy Systems and Data Mining (FSDM 2026)

Abstract: Accurate traffic forecasting requires effective modeling of temporal dynamics, spatial interactions, and heterogeneous data sources. This invited talk presents our recent studies on multimodal fusion and efficient spatiotemporal modeling for traffic forecasting. We first investigate multimodal metro flow prediction under distributed and privacy-sensitive settings, where cross-modal alignment, efficient long-range sequence modeling, and collaborative learning are jointly considered to improve predictive robustness. We then explore adaptive traffic flow forecasting by combining multi-scale temporal representations, multi-granularity spatial graph construction, and cross-domain feature interaction to better capture complex spatiotemporal dependencies. Moving toward finer-grained traffic analysis, we further study lane-level forecasting with dynamic spatial memory and bidirectional knowledge transfer, aiming to improve both modeling efficiency and adaptability across traffic scenarios. Finally, we broaden the discussion to vision-spatiotemporal fusion, examining how visual observations can be aligned with structured traffic dynamics at different representation levels, and highlighting the semantic and geometric gaps that remain in current forecasting systems. Together, these studies illustrate a progression from multimodal representation and adaptive spatiotemporal learning toward more robust, generalizable, and spatially aware traffic forecasting.

Biography: Chao Zhang is a Professor at the Institute of Intelligent Information Processing, Shanxi University, China. His research interests include data mining, time-series analysis, and intelligent decision-making. He has published more than 100 papers in leading conferences and journals, including The ACM Web Conference, IJCAI, CIKM, and several IEEE Transactions, and has authored two academic monographs and holds several national invention patents. He has led more than ten research projects supported by the National Natural Science Foundation of China and other funding programs. His research has received the Second Prize of the Natural Science Award of Shanxi Province. He currently serves as an Associate Editor of Computational Intelligence and an Editorial Board Member of Information Processing & Management, and has been listed among the World’s Top 2% Most-Cited Scientists.