We’re pleased to share that our recent journal paper “TSFusion: Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting,” has just been published in April 2026, in the IET Communications Journal.
- Zhenan Lin, Yuni Lai, Wai Lun Lo, Tai-Chiu Hsung, Harris Sik-Ho Tsang, Xiaoyu Xue, Kai Zhou, and Yulin Zhu, “TSFusion: Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting,” IET Communications, 2026.
Paper Link: https://doi.org/10.1049/cmu2.70150
Abstract
Time-evolving traffic flow forecasting is playing a vital role in intelligent transportation systems and smart cities. However, the dynamic traffic flow forecasting is a highly nonlinear problem with complex temporal-spatial dependencies. Although the existing methods have provided great contributions to mine the temporal-spatial patterns in the complex traffic networks, they fail to encode the globally temporal-spatial patterns and are prone to overfitting on the pre-defined geographical correlations, and thus hinder the model’s robustness in the complex traffic environment. To tackle this issue, in this work, we proposed TSFusion, a multi-grained temporal-spatial graph learning framework to adaptively augment the globally temporal-spatial patterns obtained from a crafted graph transformer encoder with the local patterns from the graph convolution by a crafted gated fusion unit with residual connection techniques. Under these circumstances, our proposed model can mine the hidden global temporal-spatial relations between each monitor station and balance the relative importance of local and global temporal-spatial patterns. Experiment results demonstrate the strong representation capability of our proposed method, and our model consistently outperforms (more than 11.5% for MAE) other strong baselines on various real-world traffic networks.
The Research Team Members
Hong Kong Chu Hai College
- Prof. Wai Lun Lo, Professor and Head of the Department of Computer Science
- Dr. Richard Tai Chiu Hsung, Associate Professor in the Department of Computer Science
- Dr. Harris Sik-Ho Tsang, Assistant Professor in the Department of Computer Science
- Dr. Tony Yulin Zhu, Assistant Professor in the Department of Computer Science
- Zhenan Lin, Department of Computer Science
The Hong Kong Polytechnic University
- Kai Zhou, Assistant Professor in the Department of Computing
- Yuni Lai, Postdoctoral Fellow in the Department of Computing
- Xiaoyu Xue, Postdoctoral Fellow in the Department of Computing
Some photos regarding the academic paper
Figure 1: Regions with different functions. Vanilla spatial-temporal localized message passing mechanism fails to capture the potential geographical connections (dotted line) between long-range similar nodes.
Figure 2: Spatial heterogeneity in the traffic graph data. Nodes with different functions in the traffic graph tend to have distinct traffic flow patterns.
