TY  - JOUR
PY  - 2024//
TI  - Deep transformer-based heterogeneous spatiotemporal graph learning for geographical traffic forecasting
JO  - iScience
A1  - Shi, Guangsi
A1  - Luo, Linhao
A1  - Song, Yongze
A1  - Li, Jing
A1  - Pan, Shirui
SP  - e110175
EP  - e110175
VL  - 27
IS  - 7
N2  - 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 model to capture dynamic spatial dependencies and adapt to diverse traffic scenarios, especially for the heterogeneous relationship. We conduct comprehensive experiments and visualization on four primary public datasets and demonstrate that our model achieves state-of-the-art results in comparison to existing methods.<p />  <p>Language: en</p>
LA  - en
SN  - 2589-0042
UR  - http://dx.doi.org/10.1016/j.isci.2024.110175
ID  - ref1
ER  -