A Graph-Based Transfer Learning Approach for Short-Term Bus Passenger Flow Prediction

Abstract

Bus passenger flow prediction is a crucial task in bus transportation management and optimization, characterized by strong spatiotemporal dependencies and influenced by external factors such as weather and holidays. However, existing methods face challenges in responding to sudden events, modeling dynamic changes in bus network topology, and improving model generalization. To address these issues, this paper proposes a graph transfer learning-based approach for bus passenger flow prediction. First, a dynamic graph neural network is used to construct the passenger flow network, and Framelet Transform along with Self-Expressiveness Regularization is applied for denoising, enhancing data quality. Second, an adaptive neighborhood-aware dynamic graph convolutional network is introduced, integrating random mask enhancement, hop count perception fusion, and multi-channel spatiotemporal feature extraction for accurate passenger flow modeling. Furthermore, a combination of source data and source free transfer learning is leveraged to optimize feature distribution alignment through pseudo-label generation, graph diffusion, and consistency loss. Finally, a GRU is used for passenger flow prediction, and extensive experiments on real-world datasets test the proposed method. Results demonstrate superior accuracy and generalization performance compared to existing state of the art (SOTA) models.