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This research proposes a neural Fully Connected Reconciler (FCR) to enforce structural consistency in hierarchical passenger demand forecasts for urban rail transit, addressing the common issue where independent predictions violate conservation constraints. By mapping incoherent base forecasts into coherent outputs, the method significantly reduces error metrics even under operational disruptions like train delays, outperforming classical statistical approaches and standard machine learning baselines. The study highlights that reconciliation gains are most pronounced when cross-level disagreement is high, offering a robust framework for optimizing transit operations through better information sharing across forecast levels.
Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptions
arXiv: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 year of Rejsekort smart-card and Banedanmark operational data from a 12-station Copenhagen S-train subnetwork, covering one-step, multi-step, and disruption forecasting. We also compare against a matched-parameter multi-task station--OD baseline with a soft consistency penalty. Reconciliation improves aggregate OD accuracy while enforcing exact coherence. Under standard conditions, FCR is competitive with statistical methods, while an oracle experiment using observed station demand reduces OD MSE by about 34\%. Under train delays and cancellations, reconciliation continues to improve OD accuracy, with FCR achieving larger reductions than MinT-Sample in disruption-affected subsets. Greater cross-level disagreement is associated with larger reconciliation gains, highlighting the value of hierarchical reconciliation under both regular and disrupted operations.
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 year of Rejsekort smart-card and Banedanmark operational data from a 12-station Copenhagen S-train subnetwork, covering one-step, multi-step, and disruption forecasting. We also compare against a matched-parameter multi-task station--OD baseline with a soft consistency penalty. Reconciliation improves aggregate OD accuracy while enforcing exact coherence. Under standard conditions, FCR is competitive with statistical methods, while an oracle experiment using observed station demand reduces OD MSE by about 34\%. Under train delays and cancellations, reconciliation continues to improve OD accuracy, with FCR achieving larger reductions than MinT-Sample in disruption-affected subsets. Greater cross-level disagreement is associated with larger reconciliation gains, highlighting the value of hierarchical reconciliation under both regular and disrupted operations.
Machine Learning (cs.LG)
arXiv:2606.07044 [cs.LG]
(or arXiv:2606.07044v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.07044 Focus to learn more arXiv-issued DOI via DataCite
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