Prediction of Public Bus Passenger Flow Using Spatial–Temporal Hybrid Model of Deep Learning

Abstract

Passenger flow predictions are of great significance to bus scheduling and route optimization. In this paper, a novel algorithm, namely the Spatial–Temporal Graph Sequence with Attention Network (STGSAN), was proposed to predict transit passenger flow. The algorithm mainly focused on the following three aspects: (1) a graph attention network (GAT) was used to capture the spatial correlation of various bus stops; (2) to make full use of the historical and real-time data, a bidirectional long short-term memory and attention mechanism was conducted to extract the temporal correlation of historical ridership at bus stations; and (3) external factors that affect passenger choices were taken into account. We conducted an experiment using field data collected in Urumqi, China. After comparison with five other models, the proposed model was proven to have excellent performance prediction.