用可解释模型预测车站乘客援助需求，帮铁路公司更准配人
Horizon-Aware Forecasting of Passenger Assistance Demand for Rail Station Workforce Planning
- 基于多源数据的时序预测，考虑不同时间范围的影响
- 预测误差比旧方法低76.9%，人员不足导致的服务失败减少50%
- 适合铁路运营与人力规划团队使用
乘客援助服务对无障碍铁路出行至关重要，但各车站和时段的需求差异大，给人员排班带来挑战。本文提出一个数据驱动的决策支持框架，用于预测车站级援助需求，并将预测结果转化为可行的人员计划。预测部分采用考虑时间跨度影响的Prophet模型，融合多源运营数据；规划部分通过可解释的红-黄-绿风险框架，在服务与运营约束下将需求映射为人力要求。该系统已在英国铁路运营商LNER的生产环境中部署，用于日常调度与人员安排。结果显示，相比年度基准方法，预测绝对误差降低最高达76.9%；基于预测排班后，因人员不足导致的援助服务失败率约下降50%。这表明将可解释预测与实际运营结合具有显著价值。
原文摘要 · Abstract (English)
Passenger assistance services are essential for accessible rail travel, yet demand varies substantially across stations and over time, creating challenges for workforce planning and staff rostering. This paper presents a data-driven decision support framework for forecasting station-level passenger assistance demand and translating forecasts into workforce plans. The forecasting component applies a horizon-aware Prophet modelling approach using multi-source operational data, while the planning component maps demand forecasts to staffing requirements under service and operational constraints through an interpretable red-amber-green risk framework. The approach has been implemented within a production-grade system to support routine planning and staffing decisions across LNER-managed stations. Results demonstrate improved forecast accuracy relative to year-on-year baseline methods, with absolute error reduced by up to 76.9%, and show that forecast-informed staffing is associated with an approximate 50% reduction in failed passenger assistance deliveries attributable to staff availability. These findings highlight the value of integrating interpretable forecasting with operational work.
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