Zhidan Liu (刘志丹) is currently an Assistant Professor at Intelligent Transportation Thrust, System Hub, Hong Kong University of Science and Technology (Guangzhou). Before joining HKUST (GZ), he was a faculty member with College of Computer Science and Software Engineering, Shenzhen University (2017-2024), and a postdoctoral research fellow at School of Computer Science and Engineering, Nanyang Technological University (2015-2017), working closely with Prof. Mo Li. He received the B.E. degree and Ph.D. degree, both in computer science and technology, from Northeastern University in June 2009 and Zhejiang University in September 2014, respectively. He is now heading the MobiX research group at HKUST (GZ). He is a senior member of CCF, a member of IEEE and ACM.

Please find my CV here.

Research Interests:

• Urban Computing and Smart Mobility;
• Internet of Things (IoT) and AIoT;
• Crowdsensing and Mobile Computing;
• Spatio-Temporal Data Mining and Analytics

Recruiting: I'm actively seeking self-motivated Ph.D. students (Fall 2025, and Spring/Fall 2026), Research Assistants, and Postdoc. If interested, please feel free to send an email with your information (e.g., resume, transcripts, research proposal, and publications if any). Perspective students are highly encouraged to apply to my Research Assistant positions first. You can have a close look at my research group, and we can also try to find common research interests before you start your research career.
Please read this file for more details (in Chinese). 招聘详情请参考此文件以及公众号文章.

Recent Highlights:

• 2024.11 - Our paper “Joint Order Dispatching and Vehicle Repositioning for Dynamic Ridesharing” has been accepted by IEEE Transactions on Mobile Computing. Congrats to Guofeng and Bolin. 🎉
• 2024.10 - Our paper “Learning Road Network Index Structure for Efficient Map Matching” has been accepted by IEEE Transactions on Knowledge and Data Engineering. Congrats to Yingqian and Xiaosi. Welcome to explore our advanced map matching method, LiMM, now publicly available at github. 🎉
• 2024.09 - Our paper “Towards Efficient Ridesharing via Order-Vehicle Pre-Matching Using Attention Mechanism” has been accepted as a regular paper by IEEE ICDM 2024. Congrats to Jinye and Zhiyu. 🎉
• 2024.08 - Challenge accepted! Joining HKUST (GZ) to start a new journey. 🚀
• 2024.07 - Invited to serve on the TPC of IEEE MSN 2024. Please consider submitting.
• 2024.05 - Our paper “Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition” has been accepted by ACM KDD 2024. Congrats to Junru. 🎉
• 2023.11 - Our paper “Greta: Towards A General Roadside Unit Deployment Framework” has been accepted by IEEE Transactions on Mobile Computing. Congrats to Xianjing. 🎉
• 2023.11 - Our paper “An Optimized Lossless Graph Summarization for Large-Scale Graphs” has been accepted by IEEE ICPADS 2023. Congrats to Meiquan. 🎉
• 2023.11 - Our paper “Towards Hierarchical Clustered Federated Learning with Model Stability on Mobile Devices” has been accepted by IEEE Transactions on Mobile Computing. Congrats to Biyao. 🎉
• 2023.10 - Selected to be the executive member of CCF Intelligent Transportation Division. 🤪
• 2023.10 - Selected to be the executive member of CCF TCIoT. 🤪
• 2023.07 - Invited to serve on the TPC of IEEE ICPADS 2023. Please consider submitting.
• 2023.06 - Selected to be CCF Senior Member! 🤪
• 2023.04 - Selected to be the executive member of ACM SIGSpatial China. 🤪

Selected Publications:

• Zhidan Liu, Yingqian Zhou, Xiaosi Liu, Haodi Zhang, Yabo Dong, Dongming Lu, Kaishun Wu. Learning Road Network Index Structure for Efficient Map Matching, IEEE Transactions on Knowledge and Data Engineering, accepted to appear. (codes)

  • Enhancing HMM-Based map matching through learned Indexing and adaptive search range for precise candidate refinement

• Zhidan Liu, Jiancong Liu, Xiaowen Xu, Kaishun Wu. DeepGPS: Deep Learning Enhanced GPS Positioning in Urban Canyons, IEEE Transactions on Mobile Computing, Vol. 23, Issue 1, Pages 376-392, January 2024. (codes)

  • Exploiting deep learning to decode the relationship between positioning contexts and GPS estimations in urban canyons

• Zhidan Liu, Jiangzhou Li, Kaishun Wu. Context-Aware Taxi Dispatching at City-Scale Using Deep Reinforcement Learning, IEEE Transactions on Intelligent Transportation Systems, Vol. 23, Issue 3, Pages 1996-2009, March 2022. (codes)

  • An ESI highly cited paper, which innovates the application of deep reinforcement learning in large-scale vehicle dispatching

• Zhidan Liu, Zengyang Gong, Jiangzhou Li, Kaishun Wu. Mobility-Aware Dynamic Taxi Ridesharing, in IEEE ICDE, Dallas, Texas, USA, April 2020.

  • Exploiting mobility information to efficiently serve both online and offline riders in dynamic ridesharing

• Zhidan Liu, Pengfei Zhou, Zhenjiang Li, Mo Li. Think Like A Graph: Real-Time Traffic Estimation at City-Scale, IEEE Transactions on Mobile Computing, Vol. 18, Issue 10, Pages 2446-2459, October 2019.

  • Leveraging graph-parallel computing for precise, timely, and scalable traffic estimations through Apache Spark implementation

• Zhidan Liu, Zhenjiang Li, Kaishun Wu, Mo Li. Urban Traffic Prediction from Mobility Data Using Deep Learning, IEEE Network, Vol. 32, Issue 4, Pages 40-46, August 2018.

  • Unveiling the promise and broad-ranging applications of deep learning in forecasting diverse traffic metrics