5 accepted papers
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 performance degradation. To address this, we propose Hybrid Energy-Distance Prompt, a domain-incremental framework inspired …
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-world traffic networks. However, existing models often exhibit quadratic computational complexity, making them impractical
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 time estimation. Traditional approaches usually focus on static road network and trajectory representation learning, and…
Traffic flow data are of great value in smart city applications. However, limited by data collection costs and privacy sensitivity, it is rather difficult to obtain large-scale traffic flow data. Therefore, various data generation methods have been proposed in the literature. Nevertheless, these met…
Maps are crucial for various smart city applications as a core component of city geographic information systems (GIS). Developing effective Map Entity Representation Learning methods can extract semantic information for downstream tasks like crime rate prediction and land use classification, with si