This paper describes an experiment that was undertaken to compare three levels of automation in rail signalling; a high level in which an automated agent set routes for trains using timetable information, a medium level in which trains were routed along pre-defined paths, and a low level where the operator (signaller) was responsible for the movement of all trains. These levels are described in terms of a rail automation model based on previous automation theory (Parasuraman, Sheridan, & Wickens, 2000). Performance, subjective workload, and signaller activity were measured for each level of automation running under both normal operating conditions and abnormal, or disrupted, conditions. The results indicate that perceived workload, during both normal and disrupted phases of the experiment, decreased as the level of automation increased and performance was most consistent (i.e. showed the least variation between participants) with the highest level of automation. The results give a strong case in favour of automation, particularly in terms of demonstrating the potential for automation to reduce workload, but also suggest much benefit can achieved from a mid-level of automation potentially at a lower cost and complexity.
Impact StatementResearch in the area of automation, and in particular in the examination of human interaction with different levels of automation, has normally been undertaken in Balfe et al, Impact of Automation: Measurement of Performance, Workload and Behaviour in a Complex Control Environment 2 laboratory settings using simple tasks and naïve participants where the level of automation can be easily manipulated. This research was undertaken with expert participants using complex simulation of three ecologically valid levels of automation and provides empirical field validation of some of the results found in laboratory studies.
Objective:This paper aims to explore the role of factors pertaining to trust in real-world automation systems through the application of observational methods in a case study from the railway sector.Background:Trust in automation is widely acknowledged as an important mediator of automation use, but the majority of the research on automation trust is based on laboratory work. In contrast, this work explored trust in a real-world setting.Method:Experienced rail operators in four signaling centers were observed for 90 min, and their activities were coded into five mutually exclusive categories. Their observed activities were analyzed in relation to their reported trust levels, collected via a questionnaire.Results:The results showed clear differences in activity, even when circumstances on the workstations were very similar, and significant differences in some trust dimensions were found between groups exhibiting different levels of intervention and time not involved with signaling.Conclusion:Although the empirical, lab-based studies in the literature have consistently found that reliability and competence of the automation are the most important aspects of trust development, understanding of the automation emerged as the strongest dimension in this study. The implications are that development and maintenance of trust in real-world, safety-critical automation systems may be distinct from artificial laboratory automation.Application:The findings have important implications for emerging automation concepts in diverse industries including highly automated vehicles and Internet of things.
The work reported in this article was completed with the active involvement of operational rail staff who regularly use automated systems in rail signalling. The outcomes are currently being used to inform decisions on the extent and type of automation and user interfaces in future generations of rail control systems.
Rail signalling in the UK has seen a move from mechanical lever frame boxes to entry and exit signalling, and on through to situating signallers within a visual display unit-based workstation environment. These developments have taken place in tandem with changes such as making the signallers more remote from their area of control and the introduction of automation. These changes have implications not only at a cognitive level for factors such as workload and situation awareness , but also at an organizational level, such as the shift away from traditional career progression, and the resulting implications for training and the development of expertise. Understanding the implications of the signalling interface and the design implementation of automation is critical in facilitating more effective performance, safety, and signaller well-being, as well as informing the design of future rail control systems. Bainbridge articulated a set of ironies of automation -unintended consequences of introducing automation that may not be beneficial to the overall system effectiveness. The work presented in this article uses a structured observation approach to examine behavioural indicators of the impact of automation, either as a successful tool to support signalling or as a source of some or all of the ironies noted by Bainbridge. The work was conducted over a period of 2 years, to investigate the effect of levels of automation on rail signallers' activity and workload as part of the EPSRC Rail Research UK B6 programme.
HighlightsDevelopment of an integrated company-wide risk register.End user engagement and feedback on the requirements for the tool. implementation of a company-wide risk register, based on a clear set of data structures. A case study from an electricity generation company is presented and the process followed is (6) linking with day-to-day operational practice. The paper concludes with a framework for placing risk registers at the heart of Process Safety.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.