Publications (5)
Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning
Chengkai Han, Jingyuan Wang, Yongyao Wang +4
Effective urban traffic management is vital for sustainable city development, relying on intelligent systems with machine learning tasks such as traffic flow prediction and travel…
HieraMix: A Hierarchical MLP-Mixer for Large-Scale Traffic Forecasting
Yongyao Wang, Xie Yu, Jingyuan Wang +2
Traffic forecasting task is significant to modern urban management. Recently, there is growing attention on large-scale forecasting, as it better reflects the complexity of real-wo…
HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning
Yu Feng, Zhen Tian, Haoran Luo +8
Domain Incremental Learning is a critical scenario that requires models to continuously adapt to new data domains without retraining. However, domain shifts often cause severe perf…
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning
Yu Feng, Yangli-ao Geng, Yifan Zhu +7
Federated learning (FL) has gained widespread attention for its privacy-preserving and collaborative learning capabilities. Due to significant statistical heterogeneity, traditiona…
BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis
Xie Yu, Jingyuan Wang, Yifan Yang +2
Typical dynamic ST data includes trajectory data (representing individual-level mobility) and traffic state data (representing population-level mobility). Traditional studies often…