Learning spatial–temporal pairwise and high-order relationships for short-term passenger flow prediction in urban rail transit

Abstract

Short-term passenger flow prediction (STPFP) helps ease traffic congestion and optimize urban rail transit (URT) system resource allocation. Although graph-based models have been widely used in STPFP because of their ability to model complex spatial–temporal dependencies, accurate STPFP still faces challenges. The graph-based methods exploit pairwise stations to model spatial dependencies without considering the non-pairwise high-order relationships between multiple stations. Besides, the event’s occurrence will make the passenger flow at a particular moment have a more profound impact on the passenger flow at a future moment. To solve these issues, a spatial–temporal hypergraph attention recurrent network (STHGARN) method is proposed for STPFP. Specifically, STHGARN first models passenger flow data’s recent, daily, and weekly characteristics through three parallel components. Then, each parallel component uses the hypergraph attention recurrent network (HGARN) cell to jointly learn the pairwise and high-order relationships of multiple stations. Finally, a temporal Hawkes attention mechanism is constructed and concatenated after each parallel HGARN cell to simulate the influence of events and capture the historical key moments that affect the future passenger flow. Numerous experiments on three metro ridership datasets show that STHGARN is superior to the most advanced methods, and good prediction results can lay the foundation for the operation and management of URT.