PDF(Pubmed)
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 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.
摘要：
准确的地理交通预测在城市交通规划中起着至关重要的作用,交通管理,和地理空间人工智能(GeoAI)。尽管深度学习模型在地理交通预测方面取得了重大进展，它们在有效捕获长期时间依赖性和建模异构动态空间依赖性方面仍然面临挑战。为了解决这些问题,我们提出了一种新颖的基于深度变压器的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转换器，可以捕获交通数据中的长期时间模式，而无需简单的数据融合。此外，我们在不同的图层中引入了自适应归一化图结构，使模型能够捕获动态空间依赖关系并适应不同的交通场景，特别是对于异质关系。我们对四个主要的公共数据集进行了全面的实验和可视化，并证明与现有方法相比，我们的模型实现了最先进的结果。
