Deep transformer-based heterogeneous spatiotemporal graph learning for geographical traffic forecasting.

ABSTRACT: Accurate geographical traffic forecasting plays a critical role in urban transportation planning, traffic management, and geospatial artificial intelligence (GeoAI). Although deep learning models have made significant progress in geographical traffic forecasting, they still face challenges in effectively capturing long-term temporal dependencies and modeling heterogeneous dynamic spatial dependencies. To address these issues, we propose a novel deep transformer-based heterogeneous spatiotemporal graph learning model for geographical traffic forecasting. Our model incorporates a temporal transformer that captures long-term temporal patterns in traffic data without simple data fusion. Furthermore, we introduce adaptive normalized graph structures within different graph layers, enabling the m

SUBMITTER: Shi G 

PROVIDER: S-EPMC11302005 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature