Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.719446
Title: Human factors of train driving with in-cab control and automation technology
Author: Naghiyev, Arzoo
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2017
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Abstract:
The European Train Control System (ETCS) as part of the ERTMS (European Rail Traffic Management System) is a train control and automation system, which has been introduced into the UK rail network. The major change with the introduction of the ERTMS has been the shift of the movement authority from signals outside on the tracks to inside the cab, and the introduction of speed profiles that the drivers must adhere to. The introduction of this new system triggered the need to understand its impact on the train driving task and train driver behaviour. In particular, the effect the ERTMS has on drivers’ cognitive strategies and demands. The overall aim of the thesis was to understand the effects of new train control and automation technology on train drivers’ behaviours. The research was conducted, using a mixed methods approach, in the rail environment with train drivers and rail experts. Literature reviews of existing train driving models, train driving research and associated issues of increasing control and automation on human behaviour; were used to provide the theoretical context for the thesis. The literature review highlighted that there was a potential shift in cognitive strategies and demands with the introduction of train automation and control technology. Due to the limited amount of research in train driving on the whole, but also train driving with automation and control technology, the majority of the literature hypothesised the possible impact of the introduction of automation and control technology. An exploratory study of some of the different forms of train driving in the UK, was used to generate insight about train driving with different forms of train technologies and provided the theoretical foundations for the following studies. The emerging cognitive themes also addressed the gap in knowledge about train driving with different forms of technologies. The emerging cognitive themes from this study included route knowledge and memory, monitoring, allocation of attention, anticipation, prioritisation and decision making. A semi-structured interview study with ERTMS drivers, addressed some of the questions raised in the previous study using ERTMS drivers’ subjective experiences. Since the exploratory study, the results demonstrated an adaptation and shift towards acceptance of the system and it also identified some of the driving strategies that had emerged. This chapter investigated drivers’ subjective experience, highlighting high-level strategy changes. A real world exploratory eye-tracking study with both conventional and ERTMS drivers on their normal timetabled routes, provided a wealth of data. The first level of the quantitative eye-tracking analysis, aimed to address the industry question of ‘heads up’ vs. ‘heads up, heads down driving’. It demonstrated a shift of typical visual attentional strategy from monitoring outside on the tracks to speed information inside the cab. Analysis of verbal protocol data collected in the eye-tracking study also provided some rich qualitative data about train driver strategies and demands. Further analyses of the eye-tracking data, identified events where there is a difference in visual behaviour between ERTMS driving and conventional driving, but also between each type of driving and its own baseline data. An expert elicitation workshop with ERTMS human factors experts, was used to generate requirements for a future train driving model. The main findings highlighted that several models are needed to help address some of the issues raised, as they could provide different uses, acting as ‘building blocks’ to the overall picture. Qualitative models can be used to provide the framework and language as a communication tool, whilst more quantitative models can be used to compute error and workload. Models need to be informed by cognitive theory but also focus on the train driving tasks and information used by train drivers. Finally, the studies presented in this thesis were used to develop an integrated human factors model of the influence of train automation and control technology on train driving and guidance was generated for future train driving models for both conventional and ERTMS train driving. The current research has contributed critical knowledge to both the academic literature but also informed human factors practitioners in the rail industry. The thesis has contributed novel understanding about train driving with a control and automation technology, which have already been utilised by the rail industry.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.719446  DOI: Not available
Keywords: TF Railroad engineering and operation
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