Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptions

תקציר מקורי באנגליתarXiv:2606.07044v2 Announce Type: replace Abstract: Accurate and coherent passenger demand forecasting is essential for Urban Rail Transit (URT) operations. Passenger demand is hierarchical: origin--destination (OD) flows aggregate to station-level inflows and outflows through conservation constraints. However, independently generated station- and OD-level forecasts may violate these constraints and limit information sharing across levels. This paper develops a hierarchical forecast reconciliation framework for joint station- and OD-level demand prediction. A neural Fully Connected Reconciler (FCR) maps incoherent base forecasts to coherent predictions with exact structural consistency by construction. We benchmark FCR against classical and machine-learning reconciliation methods using one