Appropriate automation of rail signalling systems: a human factors study

Balfe, Nora (2010) Appropriate automation of rail signalling systems: a human factors study. PhD thesis, University of Nottingham.

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Abstract

This thesis examines the effect of automation in the rail signalling environment. The level of automation in a system can be described as ranging along a continuum from manual control to fully autonomous automation and development of appropriate automation for a system is likely to enhance overall system performance. Network Rail, the company which owns, operates, and maintains the rail infrastructure in the UK, envisions increasing levels of automation in future rail systems, but prior to this research, little structured evaluation of current automation had been undertaken.

The research performed for this thesis set out to examine the impact of automation on rail signalling. A rail automation model was developed to illustrate the levels of automation present in different generations of signalling system. The research focussed on one system in particular, the Automatic Routing System (ARS). The ARS has been present in modern signalling centres since the late 1980s. It uses timetable information to set routes for trains arriving on its area of control and incorporates complex algorithms to resolve conflicts between trains.

Multiple methods were used to investigate current signalling automation. An understanding of the signalling domain underpinned the research, and a model was developed to illustrate the type and level of automation present in different generations of current signalling systems. Structured observations were employed to investigate differences in activity between individual signallers. As a part of this study, a relationship was found between observed intervention levels and some of the trust dimensions identified from the literature. A video archive analysis gave initial insight into some of the issues signallers had with automation, and semi-structured interviews carried out with signallers at their workstations built on these themes. The interviews investigated four areas; signallers’ opinions of ARS, system performance issues, knowledge of ARS, and interaction with ARS. Data were gathered on a wide variety of individual issues, for example on different monitoring strategies employed, interaction preferences, signallers’ understanding of the system and their ability to predict it. Data on specific issues with ARS also emerged from the interviews, for example the impact of poor programming and planning data, and the poor competence of the system, particularly during disruption. An experiment was performed to investigate the differences between different levels of automation under both normal and disrupted running. The experiment gathered quantitative data on the effect of different levels of automation on workload and performance in addition to eye tracking data which were used to gain insight into signaller monitoring strategies. The results indicate that ARS does reduce workload and increase performance, and it does so in spite of deficiencies in terms of feedback to the signaller. This lack of feedback makes it difficult for the signaller to understand and predict the automation and, hence, creates difficulties for the operator. In addition, the methods for controlling ARS are limited and it can be difficult for the signallers to work cooperatively with the system.

Principles of good automation were identified from the literature and recommendations based on these and the findings of the research were developed for future signalling automation systems. These highlighted the importance of improving feedback from ARS and the ability of the signaller to direct the system. It is anticipated that these improvements would allow the signaller and the automation to work more closely together in order to maximise overall system performance. The principles of automation are intended as a generic guidance tool and their application is not confined to rail signalling. There may also be wider implications from the research such as the influence of operators’ ability to understand and predict automation in automation use, and the existence of different types of monitoring behaviour.

Item Type:Thesis (PhD)
Supervisors:Wilson, J.R.
Sharples, S.C.
Faculties/Schools:UK Campuses > Faculty of Engineering > Department of Mechanical, Materials and Manufacturing Engineering
ID Code:1031
Deposited By:Dr Nora Balfe
Deposited On:21 Sep 2010 15:31
Last Modified:21 Sep 2010 15:31

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