Explainable real-time predictive analytics on employee workload in digital railway control rooms

Abstract

Both workload peaks and lows contribute to lower employee well-being. Predictive employee workload analytics can empower management to undertake proactive prevention. For this purpose, we develop a real-time machine learning framework to predict and explain future workload in a challenging environment with variable and imbalanced workload: the digital control rooms for railway traffic management of Infrabel, Belgium’s railway infrastructure company. The proposed two-stage methodology leverages granular data of workload categories that are very different in nature and separates the effects of workload presence and magnitude. In this way, the set-up addresses the changing workload mix over 15-minute intervals. We extensively benchmark machine learning and deep learning models within this context, leading to LightGBM (Light Gradient Boosting Machine) as the best-performing model. SHAP (SHapley Additive exPlanations) values highlight the benefits of disentangling presence and magnitude and reveal associations with human-machine interaction and team exposure. As a proof of concept, our implemented predictive model offers tailored decision support to the traffic supervisor in an explainable way. In particular, the tool depicts overloaded and/or underloaded workstations and provides in-depth insights through local SHAP values.