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cs.LG2025
FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning
Zhihao Zeng, Ziquan Fang, Wei Shao +2
Trajectory data, which capture the movement patterns of people and vehicles over time and space, are crucial for applications like traffic optimization and urban planning. However,…
cs.LG2025
Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach
Yuting Huang, Ziquan Fang, Zhihao Zeng +2
Spatio-temporal prediction plays a crucial role in intelligent transportation, weather forecasting, and urban planning. While integrating multi-modal data has shown potential for e…
cs.LG2025
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective
Zhihao Zeng, Ziquan Fang, Yuting Huang +2
Traffic prediction aims to forecast future traffic conditions using historical traffic data, serving a crucial role in urban computing and transportation management. While transfer…