Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptions
arXiv:2606.07044v1 Announce Type: new Abstract: Accurate and coherent passenger demand forecasting is essential for Urban Rail Transit (URT) operations. Passenger demand has a hierarchical structure in which origin-destination (OD) flows aggregate to station-level inflows and outflows through conservation constraints. In practice, station-level and OD-level forecasts are often generated independently, producing incoherent predictions that violate these constraints and introduce inconsistencies into operational decision-making. Such issues become more severe during disruptions, when forecasting
This research paper is published now as part of the ongoing academic advancement in AI applications for infrastructure optimization.
While relevant for urban planning and transit, this specific paper does not present information that broadly impacts high-level strategic readers outside of specific domains.
This paper refines a mathematical method for forecasting urban rail transit, offering an incremental improvement in a niche application of AI.
- · Urban Rail Transit Operators
- · Smart City Planners
- · Academic Researchers (AI/Logistics)
More accurate urban rail transit scheduling in specific cities using this forecasting method.
Reduced operational costs and improved passenger experience for transit systems adopting such models.
Potential for broader integration of similar hierarchical forecasting methods into other complex urban systems.
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