Liangzhe Han
韩良喆
研究兴趣
Dynamic Graph Representation Learning, Data Mining
Liangzhe Han is a Postdoctoral Researcher whose research lies at the intersection of Artificial Intelligence and Urban Computing. He received his Ph.D. degree in Computer Science from Beihang University in June 2025. He also obtained his B.Eng. degree from the School of Software, Beihang University. His research focuses on spatio-temporal data mining, human mobility modeling, intelligent transportation systems, graph representation learning, and foundation models for urban intelligence. His goal is to understand large-scale collective human behaviors from urban data and develop intelligent analytical frameworks for smart city governance and decision-making. He participated in several major national and industrial research projects, including the National Natural Science Foundation of China (NSFC) Regional Joint Fund Program, the Guangxi Innovation-Driven Development Program, and industry-sponsored collaborative projects. His representative contributions include dynamic graph learning for traffic speed forecasting, origin-destination demand prediction, and generic crowd flow modeling. Beyond academic research, he has been actively involved in developing large-scale mobility foundation models based on real-world mobile signaling data. He contributed to the development of the Jiutian Chuanliu Mobility Foundation Model, which supports a wide range of urban intelligence applications, including urban governance, transportation planning, and business intelligence. Related achievements have received multiple recognitions from academia and industry, including the ITU AI Award and the Top Ten Leading Scientific and Technological Achievements Award at the China International Big Data Industry Expo. To date, he has published 31 papers, including 7 CCF-A level journals/conference papers, with more than 1171 citations on Google Scholar. His work has received several research awards, including the Special Award of the China Highway Society Science and Technology Award.
学术论文
Influence-aware Dynamic Graph Learning for Popularity Prediction
Yumeng Zhou, Mingzhe Liu, Leilei Sun, Xuejie Xu, Liangzhe Han, Tongyu Zhu
Large-scale Human Mobility Data Regeneration for Open Urban Research
Ruixing Zhang, Yunqi Liu, Liangzhe Han, Leilei Sun, Charles Liu, Jibin Wang, Weifeng Lv
MobLLM: Bridging Human Mobility Patterns and Large Language Models for Enhanced Urban Geospatial Intelligence in Transportation Systems
Yi Xu, Ziqi Miao, Tongyu Zhu, Liangzhe Han, Jibin Wang, and Leilei Sun
Position-Aware Neighbor Aggregation for Dynamic Link Prediction
Yumeng Zhou, Mingzhe Liu, Leilei Sun, Yifei Huang, Liangzhe Han, Charles Liu, Tongyu Zhu
Urban In-context Learning: A New Paradigm for Urban Indicator Prediction
Zerong Deng, Liangzhe Han, Tongyu Zhu, Ziqi Miao, Yi Xu, Leilei Sun
SimPRL: A Simple Contrastive Learning for Path Representation Learning by Joint GPS Trajectories and Road Paths
Tianxi Liao, Xuxiang Ta, Yi Xu, Liangzhe Han, Leilei Sun, Weifeng Lv
JiuTian·Chuanliu: A Large Spatiotemporal Model for General-purpose Dynamic Urban Sensing
Liangzhe Han, Leilei Sun, Tongyu Zhu, Tao Tao, Jibin Wang, Weifeng Lv
Generating Evolving Region Embedding with Memory-based Graph for Dynamic Urban Sensing
Yi Xu, Zerong Deng, Tongyu Zhu, Liangzhe Han, Leilei Sun, Zhuo Chen, Hao Sheng
Multi-Faceted Route Representation Learning for Travel Time Estimation
Tianxi Liao, Liangzhe Han, Yi Xu, Tongyu Zhu, Leilei Sun, Bowen Du
MFGCN: Multi-faceted spatial and temporal specific graph convolutional network for traffic-flow forecasting
Jingwen Tian, Liangzhe Han, Mao Chen, Yi Xu, Zhuo Chen, Tongyu Zhu, Leilei Sun, Weifeng Lv
Temporal-aware structure-semantic-coupled graph network for traffic forecasting
Mao Chen, Liangzhe Han, Yi Xu, Tongyu Zhu, Jibin Wang, Leilei Sun
Generic and Dynamic Graph Representation Learning for Crowd Flow Modeling
Liangzhe Han, Ruixing Zhang, Leilei Sun, Bowen Du, Yanjie Fu, Tongyu Zhu
Generic Dynamic Graph Convolutional Network for traffic flow forecasting
Yi Xu, Liangzhe Han, Tongyu Zhu, Leilei Sun, Bowen Du, Weifeng Lv
Multivariate Long-Term Traffic Forecasting with Graph Convolutional Network and Historical Attention Mechanism
Zhaohuan Wang, Yi Xu, Liangzhe Han, Tongyu Zhu, Leilei Sun
Sampling Spatial-Temporal Attention Network for Traffic Forecasting
Mao Chen, Yi Xu, Liangzhe Han, Leilei Sun
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction
Liangzhe Han, Xiaojian Ma, Leilei Sun, Bowen Du, Yanjie Fu, Weifeng Lv, Hui Xiong
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction
Ruixing Zhang, Liangzhe Han, Boyi Liu, Jiayuan Zeng, Leilei Sun
Spatial Semantic Learning for Travel Time Estimation
Yi Xu, Leilei Sun, Bowen Du, Liangzhe Han
Deep spatio-temporal graph convolutional network for traffic accident prediction
Le Yu, Bowen Du, Xiao Hu, Leilei Sun, Liangzhe Han, Weifeng Lv
Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting
Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, Hui Xiong
Landslide susceptibility prediction based on image semantic segmentation
Bowen Du, Zirong Zhao, Xiao Hu, Guanghui Wu, Liangzhe Han, Leilei Sun, Qiang Gao
Multi-Semantic Path Representation Learning for Travel Time Estimation
Liangzhe Han, Bowen Du, Jingjing Lin, Leilei Sun, Xucheng Li, Yizhou Peng
Continuous-Time and Discrete-Time Representation Learning for Origin-Destination Demand Prediction
Yi Xu, Liangzhe Han, Tongyu Zhu, Leilei Sun, Bowen Du, Weifeng Lv
Positive mood-related gut microbiota in a long-term closed environment: A multiomics study based on the Lunar Palace 365 experiment
Zikai Hao, Chen Meng, Leyuan Li, Siyuan Feng, Yinzhen Zhu, Jianlou Yang, Liangzhe Han, Leilei Sun, Weifeng Lv, Daniel Figeys, Hong Liu