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One of the recurring questions in designing dynamic control environments is whether providing more information leads to better operational decisions. The idea of having every piece of information is increasingly tempting (and in safety critical domains often mandatory) but has become a potential obstacle for designers and operators. The present research study examined this challenge of appropriate information design and usability within a railway control setting. A laboratory study was conducted to investigate the presentation of different levels of information (taken from data processing framework, Dadashi et al., 2014) and the association with, and potential prediction of, the performance of a human operator when completing a cognitively demanding problem solving scenario within railways. Results indicated that presenting users only with information corresponding to their cognitive task, and in the absence of other, non task-relevant information, improves the performance of their problem solving/alarm handling. Knowing the key features of interest to various agents (machine or human) and using the data processing framework to guide the optimal level of information required by each of these agents could potentially lead to safer and more usable designs.

Rail disruption management is central to operational continuity and customer satisfaction. Disruption is not a unitary phenomenon - it varies by time, cause, location and complexity of coordination. Effective, user-centred technology for rail disruption must reflect this variety. A repertory grid study was conducted to elicit disruption characteristics. Construct elicitation with a group of experts (n=7) captured 26 characteristics relevant to rail disruption. A larger group of operational staff (n=28) rated 10 types of rail incident against the 26 characteristics. The results revealed distinctions such as business impact and public perception, and the importance of management of the disruption over initial detection. There were clear differences between those events that stop the traffic, as opposed to those that only slow the traffic. The results also demonstrate the utility of repertory grid for capturing the characteristics of complex work domains.

Alarm management is a key component of the successful operation of a prognostic or health-monitoring technology. Although alarms can alert the operator to critical information, false alarms and alarm flooding can cause major difficulties for successfully diagnosing and acting upon infrastructure faults. Human factors approaches seek to design more-effective alarm systems through a deep understanding of the contextual factors that influence alarm response, including strategies and heuristics used by operators. This paper presents an extensive analysis of alarm-handling activity in the setting of an Electrical Control Room on the rail network. The analysis is based on contextual observation, and the application of a time-stamped observation checklist. Functions, performance requirements, and general operating conditions that influence alarm handling are presented, delineating the typical operational constraints that need to be considered in the design and deployment of asset-based alarm systems. The analysis of specific alarm-handling incidents reveals the use of specific strategies that may bias operator performance. Implications for the design of health-monitoring systems are discussed.

One of the recurring questions in designing dynamic control environments is whether providing more information leads to better operational decisions. The idea of having every piece of information and increasing situation awareness is so tempting (and in safety critical domains often mandatory) that has become an obstacle for designers and operators. This research examined this challenge within a railway control setting. A laboratory study was conducted to investigate the presentation of different levels of information (taken from data processing framework, Dadashi et al., 2014) and the association with, and potential prediction of, the performance of a human operator when completing a cognitively demanding problem solving scenario within railways. Results indicated that presenting users with information corresponding to their cognitive task (and no more) improves the performance of their problem solving/alarm handling. Knowing the key features of interest to various agents (machine or human) and using the data processing framework to guide the optimal level of information required by each of these agents could potentially lead to safer and more usable designs.

While automation of signal and route setting is routine, the use of automation or decision support in disruption management processes is far less common. Such support offers significant advantages in optimising re-planning of both timetable and resources (crew and rolling stock), and has value in offering a 'shared view' of re-planning across the many actors manage disruption. If this vision is to be realised, however, disruption management decision support and automation must adhere to proven principles for effective human-agent cooperation. This paper synthesises data from a programme of work to understand user requirements for automated disruption support tools. It then compares these outputs with two frameworks for human-centred automation - one general (Klein et al's [2004] ten challenges for automation) and one transport specific (Balfe et al’s [2012] principles for transport automation). Emergent design requirements include the need for iterative modification of rescheduling parameters throughout a disruption, visibility of the reasoning behind options, accountability remaining in the hands of disruption controllers, and the need for the automated disruption support tools to take a multi-dimensional view of disruption that varies depending on the event encountered. The paper reflects on the practical utility of high-level design principles for automated disruption support tools.