A Graph Convolutional Network with Attention Mechanism for Bus Arrival Time Prediction

Abstract

Accurate prediction of bus arrival time is critical to improving transit service reliability and scheduling efficiency. The estimated time of arrival (ETA) is jointly determined by two processes, namely inter-station travel and station dwell, which are dominated by different traffic mechanisms and thus follow essentially different operating patterns. Most existing methods model the entire route in a unified way, making it difficult to account for both processes simultaneously. This paper therefore proposes a feature-separation prediction method. For inter-station travel time, which is driven by the continuous evolution of traffic flow, we design a Dual-Branch Spatio-Temporal Graph Convolutional Network (DSTGCN); its spectral graph convolution and spatial self-attention branches work together to capture road-network topology and the spatial propagation of traffic flow. For station dwell time, which follows a long-tailed distribution, we design a Spatio-Temporal Graph Convolutional Network with Transformer (STGCN-Trans) and introduce a robust loss function to suppress the effect of extreme outliers. Comparative and ablation experiments show that both models achieve the lowest RMSE, reducing it by 21.7% and 6.7% over the second-best baselines, respectively. They also remain stable under disturbances such as peak hours and extreme weather.