This paper presents a cognitive task analysis to derive models of decision-making for rail maintenance processes. Maintenance processes are vital for safe and continuous availability of rail assets and services. These processes are increasingly embracing the ‘Intelligent Infrastructure’ paradigm, which uses automated analysis to predict asset state and potential failure. Understanding the cognitive processes of maintenance operators is critical to underpin design and acceptance of Intelligent Infrastructure. A combination of methods, including observation, interview and an adaptation of critical decision method, was employed to elicit the decision-making strategies of operators in three different types of maintenance control centre, with three configurations of pre-existing technology. The output is a model of decision-making, based on Rasmussen’s decision ladder, that reflects the varying role of automation depending on technology configurations. The analysis also identifies which types of fault were most challenging for operators and identifies the strategies used by operators to manage the concurrent challenges of information deficiencies (both underload and overload). Implications for design are discussed.

中文翻译：

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铁路维修控制室内的建模决策

本文提出了一种认知任务分析，以推导出铁路维护过程的决策模型。维护流程对于铁路资产和服务的安全和持续可用性至关重要。这些流程越来越多地采用“智能基础设施”范式，该范式使用自动化分析来预测资产状态和潜在故障。了解维护操作员的认知过程对于支持智能基础设施的设计和接受至关重要。在三种不同类型的维修控制中心，采用三种不同的现有技术配置，结合观察、访谈和关键决策方法的调整，得出操作员的决策策略。输出是一个决策模型，基于 Rasmussen 的决策阶梯，这反映了自动化的不同作用取决于技术配置。该分析还确定了哪些类型的故障对运营商最具挑战性，并确定了运营商用来管理信息不足（欠载和过载）并发挑战的策略。讨论了对设计的影响。