Shi Guangsi, Luo Linhao, Song Yongze, Li Jing, Pan Shirui
Department of Chemical & Biological, Faculty of Engineering, Monash University, Clayton, VIC 38000, Australia.
Department of Data Science & AI, Faculty of IT, Monash University, Clayton VIC 38000, Australia.
iScience. 2024 Jun 25;27(7):110175. doi: 10.1016/j.isci.2024.110175. eCollection 2024 Jul 19.
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.
准确的地理交通预测在城市交通规划、交通管理和地理空间人工智能（GeoAI）中起着关键作用。尽管深度学习模型在地理交通预测方面取得了显著进展，但它们在有效捕捉长期时间依赖性和对异构动态空间依赖性进行建模方面仍面临挑战。为了解决这些问题，我们提出了一种基于深度Transformer的新型异构时空图学习模型用于地理交通预测。我们的模型包含一个时间Transformer，它无需简单的数据融合就能捕捉交通数据中的长期时间模式。此外，我们在不同的图层中引入了自适应归一化图结构，使模型能够捕捉动态空间依赖性并适应各种交通场景，特别是对于异构关系。我们在四个主要公共数据集上进行了全面的实验和可视化，并证明我们的模型与现有方法相比取得了最优结果。
