@article{chen2026trajectory,title={Trajectory Data Management and Mining: A Survey From Deep Learning to the {LLM} Era},author={Chen, Wei and Zhu, Yuanshao and Chang, Yanchuan and Luo, Kang and Wen, Haomin and Li, Lei and Wen, Qingsong and Yu, Yanwei and Chen, Chao and Zheng, Kai and Gao, Yunjun and Zheng, Yu and Zhou, Xiaofang and Liang, Yuxuan},journal={IEEE Transactions on Knowledge and Data Engineering},year={2026},volume={38},number={11},pages={7795--7814},}
arXiv
MobiWave: Dispatch-Oriented Graph Wavelets and Drift-Guided Selective Optimization for Autonomous Fleet Rebalancing
Xiao Han, Pinbo Wang, Yuanshao Zhu, Guojiang Shen, and Xiangjie Kong
@article{han2026mobiwave,title={{MobiWave}: Dispatch-Oriented Graph Wavelets and Drift-Guided Selective Optimization for Autonomous Fleet Rebalancing},author={Han, Xiao and Wang, Pinbo and Zhu, Yuanshao and Shen, Guojiang and Kong, Xiangjie},journal={arXiv preprint arXiv:2607.24365},year={2026},}
arXiv
GeoRouter: Dynamic Paradigm Routing for Worldwide Image Geolocalization
Pengyue Jia, Derong Xu, Yingyi Zhang, Xiaopeng Li, Wenlin Zhang, Yi Wen, Yuanshao Zhu, and Xiangyu Zhao
@article{jia2026georouter,title={{GeoRouter}: Dynamic Paradigm Routing for Worldwide Image Geolocalization},author={Jia, Pengyue and Xu, Derong and Zhang, Yingyi and Li, Xiaopeng and Zhang, Wenlin and Wen, Yi and Zhu, Yuanshao and Zhao, Xiangyu},journal={arXiv preprint arXiv:2603.24376},year={2026},}
AAAI
Boosting Fine-Grained Urban Flow Inference via Lightweight Architecture and Focalized Optimization
Yuanshao Zhu, Xiangyu Zhao, Zijian Zhang, Xuetao Wei, and James Jianqiao Yu
In Proceedings of the AAAI conference on artificial intelligence, 2026
@inproceedings{zhu2026boosting,title={Boosting Fine-Grained Urban Flow Inference via Lightweight Architecture and Focalized Optimization},author={Zhu, Yuanshao and Zhao, Xiangyu and Zhang, Zijian and Wei, Xuetao and Yu, James Jianqiao},booktitle={Proceedings of the AAAI conference on artificial intelligence},volume={40},year={2026},}
arXiv
InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories
Yuanshao Zhu, Yuxuan Liang, Xiangyu Zhao, Liang Han, Xinwei Fang, Xun Zhou, Xuetao Wei, and James Jianqiao Yu
@article{zhu2026instraj,title={{InsTraj}: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories},author={Zhu, Yuanshao and Liang, Yuxuan and Zhao, Xiangyu and Han, Liang and Fang, Xinwei and Zhou, Xun and Wei, Xuetao and Yu, James Jianqiao},journal={arXiv preprint arXiv:2604.04106},year={2026},}
2025
NeurIPS
UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces
Yuanshao Zhu, James Jianqiao Yu, Xiangyu Zhao, Xun Zhou, Liang Han, Xuetao Wei, and Yuxuan Liang
In Proceedings of the 39th Annual Conference on Neural Information Processing Systems, 2025
@inproceedings{zhu2025unitraj,title={UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces},author={Zhu, Yuanshao and Yu, James Jianqiao and Zhao, Xiangyu and Zhou, Xun and Han, Liang and Wei, Xuetao and Liang, Yuxuan},booktitle={Proceedings of the 39th Annual Conference on Neural Information Processing Systems},year={2025},}
NeurIPS
TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting
Mingyuan Xia, Chunxu Zhang, Zijian Zhang, Hao Miao, Qidong Liu, Yuanshao Zhu, and Bin Yang
In Proceedings of the 39th Annual Conference on Neural Information Processing Systems, 2025
@inproceedings{xia2025timeemb,title={TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting},author={Xia, Mingyuan and Zhang, Chunxu and Zhang, Zijian and Miao, Hao and Liu, Qidong and Zhu, Yuanshao and Yang, Bin},booktitle={Proceedings of the 39th Annual Conference on Neural Information Processing Systems},year={2025},}
KDD
Building a Global-Scale Trajectory Dataset from OpenStreetMap
@inproceedings{zhu2025learning,title={Learning Generalized and Flexible Trajectory Models from Omni-Semantic Supervision},author={Zhu, Yuanshao and Yu, James Jianqiao and Zhao, Xiangyu and Han, Xiao and Liu, Qidong and Wei, Xuetao and Liang, Yuxuan},booktitle={Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining},year={2025},}
arXiv
Select, then Balance: Exploring Exogenous Variable Modeling of Spatio-Temporal Forecasting
@article{chen2025select,title={Select, then Balance: Exploring Exogenous Variable Modeling of Spatio-Temporal Forecasting},author={Chen, Wei and Wu, Yuqian and Zhu, Yuanshao and Hao, Xixuan and Wang, Shiyu and Zhou, Xiaofang and Liang, Yuxuan},journal={arXiv preprint arXiv:2509.05779},year={2025},}
AAAI
LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation
@inproceedings{liu2025llmemb,title={LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation},author={Liu, Qidong and Wu, Xian and Wang, Wanyu and Wang, Yejing and Zhu, Yuanshao and Zhao, Xiangyu and Tian, Feng and Zheng, Yefeng},booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},year={2025},}
AAAI
POI-Enhancer: An LLM-based Semantic Enhancement Framework for POI Representation Learning
Jiawei Cheng, Jingyuan Wang, Yichuan Zhang, Jiahao Ji, Yuanshao Zhu, Zhibo Zhang, and Xiangyu Zhao
In Proceedings of the AAAI Conference on Artificial Intelligence, 2025
@inproceedings{cheng2025poi,title={POI-Enhancer: An LLM-based Semantic Enhancement Framework for POI Representation Learning},author={Cheng, Jiawei and Wang, Jingyuan and Zhang, Yichuan and Ji, Jiahao and Zhu, Yuanshao and Zhang, Zhibo and Zhao, Xiangyu},booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},year={2025},}
AAAI
UniTR: A Unified Framework for Joint Representation Learning of Trajectories and Road Networks
Jie Zhao, Chao Chen, Yuanshao Zhu, Mingyu Deng, and Yuxuan Liang
In Proceedings of the AAAI Conference on Artificial Intelligence, 2025
@inproceedings{zhao2025unitr,title={UniTR: A Unified Framework for Joint Representation Learning of Trajectories and Road Networks},author={Zhao, Jie and Chen, Chao and Zhu, Yuanshao and Deng, Mingyu and Liang, Yuxuan},booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},year={2025},}
AAAI
GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching
Xiao Han, Zijian Zhang, Xiangyu Zhao, Yuanshao Zhu, Guojiang Shen, Xiangjie Kong, Xuetao Wei, Liqiang Nie, and Jieping Ye
In Proceedings of the AAAI Conference on Artificial Intelligence, 2025
@inproceedings{han2025garlic,title={GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching},author={Han, Xiao and Zhang, Zijian and Zhao, Xiangyu and Zhu, Yuanshao and Shen, Guojiang and Kong, Xiangjie and Wei, Xuetao and Nie, Liqiang and Ye, Jieping},booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},year={2025},}
2024
KDD
ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model
Yuanshao Zhu, James Jianqiao Yu, Xiangyu Zhao, Qidong Liu, Yongchao Ye, Wei Chen, Zijian Zhang, Xuetao Wei, and Yuxuan Liang
In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024
@inproceedings{zhu2024controltraj,title={ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model},author={Zhu, Yuanshao and Yu, James Jianqiao and Zhao, Xiangyu and Liu, Qidong and Ye, Yongchao and Chen, Wei and Zhang, Zijian and Wei, Xuetao and Liang, Yuxuan},booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},year={2024},}
SIGIR
When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications
@inproceedings{liu2024moelora,title={When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications},author={Liu, Qidong and Wu, Xian and Zhao, Xiangyu and Zhu, Yuanshao and Xu, Derong and Tian, Feng and Zheng, Yefeng},booktitle={Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval},year={2024},pages={1104–1114},numpages={11},}
arXiv
Large Language Model Distilling Medication Recommendation Model
@article{liu2024llmdist,title={Large Language Model Distilling Medication Recommendation Model},author={Liu, Qidong and Wu, Xian and Zhao, Xiangyu and Zhu, Yuanshao and Zhang, Zijian and Tian, Feng and Zheng, Yefeng},journal={arXiv preprint arXiv:2402.02803},year={2024},}
IJCAI
Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning
Kang Luo, Yuanshao Zhu, Wei Cheng, Kun Wang, Zhengyang Zhou, Sijie Ruan, and Yuxuan Liang
In Proceedings of the 33rd international joint conference on artificial intelligence, 2024
@inproceedings{Luo2024TrajCL,title={Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning},author={Luo, Kang and Zhu, Yuanshao and Cheng, Wei and Wang, Kun and Zhou, Zhengyang and Ruan, Sijie and Liang, Yuxuan},booktitle={Proceedings of the 33rd international joint conference on artificial intelligence},year={2024},}
2023
NeurIPS
DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model
Yuanshao Zhu, Yongchao Ye, Shiyao Zhang, Xiangyu Zhao, and James J.Q. Yu
In Proceedings of the 37th Annual Conference on Neural Information Processing Systems, 2023
@inproceedings{zhu2023difftraj,title={DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model},author={Zhu, Yuanshao and Ye, Yongchao and Zhang, Shiyao and Zhao, Xiangyu and Yu, James J.Q.},booktitle={Proceedings of the 37th Annual Conference on Neural Information Processing Systems},year={2023},}
@inproceedings{zhu2023synmob,title={SynMob: Creating High-Fidelity Synthetic GPS Trajectory Dataset for Urban Mobility Analysis},author={Zhu', Yuanshao and Ye', Yongchao and Wu, Ying and Zhao, Xiangyu and Yu, James J.Q.},booktitle={Proceedings of the 37th Annual Conference on Neural Information Processing Systems},year={2023},}
ITSC
Data-Driven Methods for Travel Time Estimation: A Survey
Zhipeng Zheng, Yongchao Ye, Yuanshao Zhu, Shiyao Zhang, and James J.Q. Yu
In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023
@inproceedings{ETASurvey,author={Zheng, Zhipeng and Ye, Yongchao and Zhu, Yuanshao and Zhang, Shiyao and Yu, James J.Q.},booktitle={2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)},title={Data-Driven Methods for Travel Time Estimation: A Survey},year={2023},volume={},number={},pages={1292-1299},}
WWW
AutoDenoise: Automatic Data Instance Denoising for Recommendations
Weilin Lin, Xiangyu Zhao, Yejing Wang, Yuanshao Zhu, and Wanyu Wang
@inproceedings{AutoDenoise,title={AutoDenoise: Automatic Data Instance Denoising for Recommendations},author={Lin, Weilin and Zhao, Xiangyu and Wang, Yejing and Zhu, Yuanshao and Wang, Wanyu},booktitle={ACM Web Conference 2023 (WWW)},year={2023},}
TITS
Traffic Prediction With Transfer Learning: A Mutual Information-Based Approach
Yunjie Huang, Xiaozhuang Song, Yuanshao Zhu, Shiyao Zhang, and James J.Q. Yu
IEEE Transactions on Intelligent Transportation Systems, 2023
@article{huang2023traffic,author={Huang, Yunjie and Song, Xiaozhuang and Zhu, Yuanshao and Zhang, Shiyao and Yu, James J.Q.},journal={IEEE Transactions on Intelligent Transportation Systems},title={Traffic Prediction With Transfer Learning: A Mutual Information-Based Approach},year={2023},}
arXiv
Enhancing Traffic Prediction with Learnable Filter Module
Yuanshao Zhu, Yongchao Ye, Xiangyu Zhao, and James J.Q. Yu
@article{zhu2023filter,title={Enhancing Traffic Prediction with Learnable Filter Module},author={Zhu, Yuanshao and Ye, Yongchao and Zhao, Xiangyu and Yu, James J.Q.},journal={arXiv preprint arXiv:2310.16063},year={2023},}
2022
TITS
CatETA: A Categorical Approximate Approach for Estimating Time of Arrival
Yongchao Ye’, Yuanshao Zhu’, Christos Markos, and James J.Q. Yu
IEEE Transactions on Intelligent Transportation Systems, 2022
@article{CatETA,author={Ye', Yongchao and Zhu', Yuanshao and Markos, Christos and Yu, James J.Q.},journal={ IEEE Transactions on Intelligent Transportation Systems},title={CatETA: A Categorical Approximate Approach for Estimating Time of Arrival},year={2022},}
TITS
Cross-Area Travel Time Uncertainty Estimation From Trajectory Data: A Federated Learning Approach
Yuanshao Zhu’, Yongchao Ye’, Yi Liu, and James J.Q. Yu
IEEE Transactions on Intelligent Transportation Systems, 2022
@article{FedETA,author={Zhu', Yuanshao and Ye', Yongchao and Liu, Yi and Yu, James J.Q.},journal={IEEE Transactions on Intelligent Transportation Systems},title={Cross-Area Travel Time Uncertainty Estimation From Trajectory Data: A Federated Learning Approach},year={2022},}
2021
ICTAI
Improving Transportation Mode Identification with Limited GPS Trajectories
Yuanshao Zhu, Christos Markos, and James J.Q. Yu
In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), 2021
@inproceedings{zhuIctai,author={Zhu, Yuanshao and Markos, Christos and Yu, James J.Q.},booktitle={2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)},title={Improving Transportation Mode Identification with Limited GPS Trajectories},year={2021},pages={655-660},}
IJCNN
FedOVA: One-vs-All Training Method for Federated Learning with Non-IID Data
Yuanshao Zhu, Christos Markos, Ruihui Zhao, Yefeng Zheng, and James J.Q. Yu
In 2021 International Joint Conference on Neural Networks (IJCNN), 2021
@inproceedings{Zhufedova,author={Zhu, Yuanshao and Markos, Christos and Zhao, Ruihui and Zheng, Yefeng and Yu, James J.Q.},booktitle={2021 International Joint Conference on Neural Networks (IJCNN)},title={FedOVA: One-vs-All Training Method for Federated Learning with Non-IID Data},year={2021},}
TNSE
Resource-Constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis
Yi Liu, Yuanshao Zhu, and James J.Q. Yu
IEEE Transactions on Network Science and Engineering, 2021
@article{zhuTnse,author={Liu, Yi and Zhu, Yuanshao and Yu, James J.Q.},journal={IEEE Transactions on Network Science and Engineering},title={Resource-Constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis},year={2021},equal={true},}
IoTJ
Toward Crowdsourced Transportation Mode Identification: A Semisupervised Federated Learning Approach
Chaoyun Zhang, Yuanshao Zhu, Christos Markos, Shui Yu, and James J.Q. Yu
@article{zhang2021crowdsourced,author={Zhang, Chaoyun and Zhu, Yuanshao and Markos, Christos and Yu, Shui and Yu, James J.Q.},journal={IEEE Internet of Things Journal},title={Toward Crowdsourced Transportation Mode Identification: A Semisupervised Federated Learning Approach},year={2021},}
TITS
Semi-Supervised Federated Learning for Travel Mode Identification From GPS Trajectories
Yuanshao Zhu, Yi Liu, James J.Q. Yu, and Xingliang Yuan
IEEE Transactions on Intelligent Transportation Systems, 2021
@article{zhusemi,author={Zhu, Yuanshao and Liu, Yi and Yu, James J.Q. and Yuan, Xingliang},journal={IEEE Transactions on Intelligent Transportation Systems},title={Semi-Supervised Federated Learning for Travel Mode Identification From {GPS} Trajectories},year={2021},pages={1-12},}
2020
ICPADS
Robust Federated Learning Approach for Travel Mode Identification from Non-IID GPS Trajectories
Yuanshao Zhu, Shuyu Zhang, Yi Liu, Dusit Niyato, and James J.Q. Yu
In 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), 2020
@inproceedings{zhu2020robust,title={Robust Federated Learning Approach for Travel Mode Identification from Non-IID GPS Trajectories},author={Zhu, Yuanshao and Zhang, Shuyu and Liu, Yi and Niyato, Dusit and Yu, James J.Q.},booktitle={2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)},year={2020},}
