Heterogeneous graph traffic prediction considering spatial information around roads

Abstract

Precise traffic prediction is crucial in the domain of intelligent transportation. However, the task of accurately predicting traffic has struggled to keep pace with escalating application demands. One of the main reasons for this difficulty is the neglect of the dependence of surrounding spatial data on traffic information. In this study, we introduce a novel framework that incorporates the surrounding spatial data from the road network into the analysis of existing sensor graphs. We delve into an innovative method for traffic prediction by employing a heterogeneous graph that integrates the surrounding spatial information from the road network. The method is based on the common observation that traffic conditions are closely associated with surrounding spatial information, which can be analyzed utilizing heterogeneous graphs. Consequently, we propose a new framework, the heterogeneous attentive spatial–temporal network (HASTN). This framework constructs a heterogeneous graph that merges road networks with surrounding geographic features and employs attention mechanisms to learn traffic patterns. Our method achieves promising results on public datasets as well as a dataset we proposed. Additionally, we employ spatial information to analyze the impact of road traffic patterns on attention. This research offers a fresh perspective on addressing traffic prediction problems by integrating spatial information.

Keywords

• Traffic prediction
• Spatial information
• Heterogeneous graph
• Time series