I am currently a Ph.D. candidate at the School of Computer Science, Fudan University (2023–present), under the supervision of Prof. Weidong Yang. I am also a joint Ph.D. student at Nanyang Technological University (2025–present), advised by Prof. Siqiang Luo. Prior to this, I received my M.S. degree from Guangzhou University in June 2023, advised by Prof. Xiaofang Zhou.

🔍 Research Interests

My research interests focus on spatio-temporal data mining and time-series modeling, with applications in intelligent transportation systems (ITS) and beyond. My current work spans regional-, road-, and lane-level traffic prediction, aiming to achieve high-accuracy forecasting to support downstream tasks such as routing, signal control, and tidal lane management. I also explore the integration of large-scale pre-trained models and large language models (LLMs) to improve the scalability, generalization, and multimodal capabilities of traffic forecasting. Specifically, my work covers:

• Lane-level traffic prediction: Fine-grained modeling and inference of lane-level traffic states for real-world applications.
• Spatio-temporal fine-grained forecasting: Multi-granularity and cross-scale sequence modeling for accurate traffic prediction.
• Lightweight traffic pre-training models: Designing efficient and transferable spatio-temporal pre-training frameworks.
• LLMs for intelligent transportation: Investigating the potential of LLMs in traffic forecasting, decision-making, and multimodal applications.

I warmly welcome collaborations with researchers interested in spatio-temporal data mining, intelligent transportation, time-series modeling, and large models.

For more information, please visit my academic profiles: Google Scholar · DBLP.

💥 News

• [May 2026] Our paper MiniTraffic on fine-grained traffic prediction has been accepted to ICML 2026.
• [Jan 2026] Our paper ST-LEGO on LLM-based spatio-temporal model design has been accepted to The Web Conference 2026.
• [May 2025] Our paper on fine-grained traffic inference has been accepted to KDD 2025.
• [Feb 2025] Our work on the lane-level traffic prediction benchmark and baselines has been accepted to TKDE.
• [Jul 2024] Our paper McgVAE on lane-level traffic prediction has been accepted to CIKM 2024.
• [Mar 2024] Our paper ST-ABC on lane-level traffic prediction has been accepted to ICDE 2024.

📑 Selected Publications

2026

ICML

Being More Lightweight and Practical: Mini-sized Contrastive Learning Pre-trained Models for Fine-grained Traffic Task

Shuhao Li, Weidong Yang, Ben Fei, Yue Cui, Lipeng Ma, Fan Zhang

International Conference on Machine Learning (ICML), 2026CCF A

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WWW

ST-LEGO: Large Language Models as Modular Architects for Traffic Prediction

Shuhao Li, Weidong Yang, Yue Cui, Lipeng Ma, Yixuan Li, Chaoteng Wu, Lu Qin, Fan Zhang

Proceedings of the ACM Web Conference (WWW), 2026CCF A

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2025

KDD

Fine-Grained Traffic Inference from Road to Lane via Spatio-Temporal Graph Node Generation

Shuhao Li, Weidong Yang, Yue Cui, Xiaoxing Liu, Lingkai Meng, Lipeng Ma, Fan Zhang

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025CCF A

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TKDE

Unifying Lane-Level Traffic Prediction From a Graph Structural Perspective: Benchmark and Baseline

Shuhao Li, Yue Cui, Jingyi Xu, Libin Li, Lingkai Meng, Weidong Yang, Fan Zhang, Xiaofang Zhou

IEEE Transactions on Knowledge and Data Engineering (TKDE), 37(9): 5699–5718, 2025CCF ASCI Q1

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2024

CIKM

Seeing the Forest for the Trees: Road-Level Insights Assisted Lane-Level Traffic Prediction

Shuhao Li, Yue Cui, Jingyi Xu, Jing Zhao, Fan Zhang, Weidong Yang, Xiaofang Zhou

ACM International Conference on Information and Knowledge Management (CIKM), 2024CCF B

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ICDE

ST-ABC: Spatio-Temporal Attention-Based Convolutional Network for Multi-Scale Lane-Level Traffic Prediction

Shuhao Li, Yue Cui, Libin Li, Weidong Yang, Fan Zhang, Xiaofang Zhou

IEEE International Conference on Data Engineering (ICDE), 2024CCF A

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2023

CIKM

ST-MoE: Spatio-Temporal Mixture-of-Experts for Debiasing in Traffic Prediction

Shuhao Li, Yue Cui, Yan Zhao, Weidong Yang, Ruiyuan Zhang, Xiaofang Zhou

ACM International Conference on Information and Knowledge Management (CIKM), 2023CCF B

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ICDE

ROI-demand Traffic Prediction: A Pre-train, Query and Fine-tune Framework

Yue Cui, Shuhao Li, Wenjin Deng, Zhaokun Zhang, Jing Zhao, Kai Zheng, Xiaofang Zhou

IEEE International Conference on Data Engineering (ICDE), 2023CCF A

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📖 Academic Services

Conference PC Member

• 2026: ICML, KDD, AAAI, SIGIR, ICLR, MM
• 2025: NeurIPS, KDD, WWW, ICML, ICLR
• 2024: NeurIPS

Invited Journal Reviewer

• 2026: DKE, KBS, Neural Networks
• 2025: TKDE, TEENG
• 2024: TKDE, Neural Networks, TCCN

External Reviewer

• 2024: ICDE, CIKM, DASFAA, ICASSP
• 2023: ICDM

🏆 Honors & Awards

• CAST Youth Talent Support Project for Doctoral Students (2025)
• CSC National Scholarship (2025)
• Fudan–Huawei Scholarship (2024)
• First-Class Academic Scholarship, Fudan University (2023)
• GZU–Jinhang Technology Scholarship (2022)

👀 Visitors