Dynamic graph learning for bus passenger profiling in urban transportation networks

Abstract

Bus passenger profiling is a critical task for optimizing urban transportation, but it is hindered by three key challenges: the heterogeneity of passenger behaviors, complex station-level interactions, and the prevalence of sparse, noisy transit data. Conventional end-to-end models that operate on aggregated traffic flow often fail to address these issues systematically. To overcome these limitations, this paper proposes GRASP, a novel two-stage paradigm for passenger profiling and flow prediction. In the first stage, GRASP acts as a disentangling module, constructing a passenger-centric graph to cluster individuals into distinct behavioral profiles based on their co-occurrence patterns. In the second stage, it performs profile-aware forecasting by learning group-specific, dynamic spatio-temporal dependencies using an adaptive station graph. This station-level model is further enhanced by a contrastive learning objective to ensure robustness against data imperfections. Extensive experiments on three real-world datasets demonstrate that GRASP not only achieves significantly superior flow prediction accuracy but also uncovers actionable passenger profiles. By structurally decoupling passenger behavior from station-level dynamics, GRASP offers a more interpretable and effective solution for data-driven public transportation management.