Multi-Graph Inductive Representation Learning for Large-Scale Urban Rail Demand Prediction under Disruptions

With the expansion of cities over time, URT (Urban Rail Transit) networks have also grown significantly. Demand prediction plays an important role in supporting planning, scheduling, fleet management, and other operational decisions. In this study, we propose an Origin-Destination (OD) demand prediction model called Multi-Graph Inductive Representation Learning (mGraphSAGE) for large-scale URT networks under operational uncertainties. Our main contributions are twofold. First, we enhance prediction results while ensuring scalability for large networks: we use multiple graphs simultaneously, where each OD pair is represented as a node on a graph, and distinct OD relationships, such as temporal and spatial correlations, are considered. As a second contribution, we demonstrate the importance of incorporating operational uncertainties, such as train delays and cancellations, as inputs in demand prediction for daily operations. The model is validated on three different scales of the URT network in Copenhagen, Denmark. Experimental results show that by leveraging information from neighboring ODs and learning node representations via sampling and aggregation, mGraphSAGE is particularly suitable for OD demand prediction in large-scale URT networks, outperforming reference machine learning methods. Furthermore, during periods with train cancellations and delays, the performance gap between mGraphSAGE and other methods improves, demonstrating its ability to leverage system reliability information for predicting OD demand under system disruptions.