Integrating query data for enhanced traffic forecasting: A Spatio-Temporal Graph Attention Convolution Network approach with delay modeling

Accurate prediction of road traffic conditions is essential for the effectiveness of Intelligent Transportation Systems (ITS) and the advancement of smart cities. While existing methodologies mainly focus on the complex structures of road networks and consider factors like weather, holidays, and accidents, the impact of social events is often overlooked. Queries, representing user requests in navigation applications, offer valuable insights into the popularity of social events and their effects on the road network. In this paper, we introduce the Query-aware Spatio-Temporal Graph Attention Convolution Network (QA-STGACN), which utilizes query data to model the impacts of events and forecast traffic conditions. Initially, spatial and temporal blocks are developed to extract spatial dependencies among road segments and capture traffic dynamics. A dynamic query-aware graph constructor then adaptively learns and retains periodic interactions between road segments and queries. Furthermore, three novel query-aware blocks – convolution, attention, and pattern transformation – are designed to uncover hidden dependencies between traffic conditions and query counts during events, as well as to model the delayed impacts of these counts on traffic. Ultimately, a gated fusion mechanism integrates the spatio-temporal correlations in road networks with the representations derived from queries. Comprehensive experiments on six datasets validate the superiority of QA-STGACN over robust baselines.