This study proposes a real-time Decision Support System (DSS) using machine learning to enhance proactive management of Human–Machine Interaction (HMI) in safety–critical digital control rooms. The DSS provides explainable predictions and recommendations regarding near-future automation usage, customized for the railway control room management, who supervise the operations of traffic controllers (TCs). In this setting, TCs decide on the spot whether to manually or automatically open signals to regulate railway traffic, a critical aspect of ensuring punctuality and safety. This time-setting specific HMI differs across TCs and is not yet supported by a data-driven tool. The proposed DSS includes agreement levels for predictions among different modeling paradigms: linear models, tree-based models, and deep neural networks. SHAP (SHapley Additive exPlanations) values are deployed to assess the agreement level in explainability between these different modeling paradigms. The prescriptions are based on the HMI of well-performing peers. We implement the DSS as proof of concept at the Belgian railway infrastructure company and report end-user feedback on the perception, the operational impact, and the inclusion of agreement levels.

中文翻译：

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数字铁路控制室人机交互的实时决策支持

本研究提出了一种使用机器学习的实时决策支持系统（DSS），以增强安全关键数字控制室中人机交互（HMI）的主动管理。 DSS 提供有关近期自动化使用的可解释的预测和建议，专为铁路控制室管理人员定制，负责监督交通管制员 (TC) 的操作。在这种情况下，TC 会当场决定是否手动或自动打开信号来调节铁路交通，这是确保准时和安全的一个关键方面。这种时间设置特定的 HMI 因 TC 的不同而不同，并且尚未得到数据驱动工具的支持。所提出的 DSS 包括不同建模范式之间预测的一致性级别：线性模型、基于树的模型和深度神经网络。 SHAP（SHapley Additive exPlanations）值用于评估这些不同建模范例之间可解释性的一致性水平。这些处方是基于表现良好的同事的人机界面。我们在比利时铁路基础设施公司实施 DSS 作为概念验证，并报告最终用户对看法、运营影响和协议级别的反馈。