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

Léon Sobrie, Marijn Verschelde and Bart Roets

European Journal of Operational Research, 2024, vol. 317, issue 2, 437-448

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.

Keywords: Analytics; Explainable prediction; Employee workload; Real-time; Control room management (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:317:y:2024:i:2:p:437-448

DOI: 10.1016/j.ejor.2023.09.016

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