提出首个轨道交通需求预测的分层修正框架，解决客流预测不一致问题。
Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptions
- 用神经网络学习非线性映射，将不一致的基线预测修正为满足层级约束的合理结果。
- 在哥本哈根S-train数据上，多步预测下可降低17.45%的终点站延误预测误差。
- 尤其在严重运营中断时表现最优，适合需高可靠性的城市轨道交通调度场景。
精准且一致的乘客需求预测对城市轨道交通（URT）运营至关重要。乘客需求具有层次结构：起讫点（OD）流量通过守恒约束聚合为车站级进出站量。实际中，车站级与OD级预测常独立生成，导致预测不一致，违反物理约束，影响决策。这一问题在运营中断时尤为严重。本文首次提出联合车站级与OD级预测的分层预测修正框架。采用神经全连接修正器（FCR），学习从不一致基线预测到一致层级预测的非线性映射，天然保证结构一致性。在哥本哈根S-train网络的Rejsekort刷卡数据上，对比OLS、WLS和最小迹（MinT）方法，在单步、多步及中断预测场景下验证。结果表明，修正显著提升OD预测精度并确保层级一致性。正常情况下，FCR性能媲美基于MinT的方法；原假设分析显示，理想车站预测可使OD误差降低34%。在严重中断下，FCR优于经典方法，多步目的地延迟场景下误差降低最多达17.45%。研究确立分层修正是提升预测鲁棒性的有效机制，其优势在最严峻工况下最为显著。
原文摘要 · Abstract (English)
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 reliability is most critical. This paper presents the first hierarchical forecast reconciliation framework for joint station-level and OD-level URT demand prediction. A neural Fully Connected Reconciler (FCR) learns a non-linear mapping from incoherent base forecasts to coherent hierarchical predictions while guaranteeing exact structural consistency by construction. The method is benchmarked against OLS, WLS, and Minimum Trace (MinT) variants using Rejsekort smart-card data from the Copenhagen S-train network under one-step, multi-step, and disruption forecasting scenarios. Results show that reconciliation consistently improves OD forecasting accuracy while ensuring hierarchical coherence. Under normal conditions, FCR performs competitively with MinT-based methods. An oracle analysis indicates that perfect station-level forecasts could reduce OD prediction error by up to 34 percent, highlighting the value of improved base forecasts. Under severe disruptions, FCR outperforms classical methods, reducing OD forecasting error by up to 17.45 percent in multi-step destination-side delay scenarios. These findings establish hierarchical reconciliation as an effective mechanism for improving forecast robustness, with the largest benefits occurring under the most challenging operating conditions.
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