Horizon-Aware Forecasting of Passenger Assistance Demand for Rail Station Workforce Planning

TL;DR

This paper introduces a horizon-aware forecasting and planning framework for passenger assistance demand at rail stations, improving staffing accuracy and reducing service failures.

Contribution

It presents a novel integrated system combining horizon-aware Prophet forecasting with a risk-based staffing model for rail station assistance.

Findings

Forecast accuracy improved with up to 76.9% error reduction.

Staffing based on forecasts reduced failed assistance deliveries by approximately 50%.

The system supports routine planning across multiple stations.

Abstract

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.