Horizon CDT Research Highlights

Research Highlights

The Impact of New Data and Technology on Human and System Performance in Rail

  Abi Fowler (2016 cohort)   www.nottingham.ac.uk/~psxacf


The aim of this PhD is to improve understanding of human personal performance in rail. In Britain, the capacity of the rail network needs to increase to meet growing demand. Passenger journeys are up 80% since 2000 [1]. The safety of passengers and staff remains crucial. New technology is being implemented to meet this increased demand whilst retaining safety and optimising costs. Examples include new technology in cabs for drivers and software based decision support in signalling systems. Responsibility for safe and resilient operations, and the decisions that go with it, remain with human staff in rail. It is important therefore to understand the impact these technologies have on human, and therefore broader operational, performance. This PhD amalgamates human performance and personal data. The PhD considers the question ‘what does good look like?’ in terms of personal performance data of staff in rail?

The PhD is sponsored by a rail consortium of the Railway Safety and Standards Board (RSSB), Network Rail (NR) and Rail Delivery Group (RDG) (formerly Association of Train Operating Companies (ATOC)). The rail industry is both the context for the research and the recipient of the research output. It is important therefore that the research is relevant to a need within the rail industry.


There is a drive within the rail industry to gain more value from data [2]. Whilst this includes all areas of rail engineering, an opportunity exists with this PhD to consider what value from human performance data can be gained. The focus of this PhD in terms of specific technology, human role, industry project, or type of personal data is yet to be confirmed and will be decided through consultation with industry over the next few months.

The Office of Road and Rail (ORR) wish to increase the prominence of occupational health and well-being across all workstreams in rail [3]. Whilst the specific area of focus of this research is yet to be determined, an example of a human performance issue in rail is staff fatigue. Fatigue is known to be a contributing factor to accidents [4]. Procedures and policies are in place to mitigate these factors e.g. shift patterns, shift length, rest period provision. Fatigue is a good example of an issue with overlapping boundaries of responsibility between the individual staff’s self-assessment of their fitness to work, and their employer’s provision of suitable conditions to ensure staff can achieve sufficient rest. It is envisaged that this boundary of responsibility will be pertinent to many topics that could be covered by this PhD.


The first stage of research will involve engaging with industry experts to identify existing data in rail of human performance, examples of the impact of new technology including the benefits, and current issues in rail that relate to human performance. Semi-structured interviews will be conducted with rail experts. The findings from these interviews will be used to build a rich picture [5] or ‘map’ of the current landscape of human performance data in rail. The initial findings will also guide which specific staff roles in rail, data, or technology the remaining PhD focuses on. Data currently gathered in rail will be considered to determine if it can indicate personal performance. This includes data collected currently but not analysed. If insufficient data is available, new data will be collected. Should the latter apply, physiological measures offer a potential new way to collect data on human performance.

An initial literature review was conducted of physiological measures in rail and other safety critical industries. The review of studies found physiological measures can contribute to our understanding of human performance. The studies reviewed were conducted in controlled conditions, simulators and live environments. In a live train driving study Heart Rate Variability (HRA) was found to reduce at tunnels, and before and after stops [6]. Skin Electrodermal Activity (EDA) distinguished different task demand levels in a train driving simulator [7]. Brain activity measured by Electroencephalography (EEG) detected periods of reduced alertness in train driving [8] and drowsiness in car driving [9]. Physiological measures offer continuous, objective data, which can be particularly rich data if measures are combined. Different measures detect different elements of cognitive performance. One feature of physiological data that could be useful is the detection of cumulative effects, showing changes over time without a return to the original baseline. On a practical note however, the reliability and validity of physiological data is still being developed. Also, physical movement can alter readings, so measures best suit sedentary tasks. Research is ongoing to determine the suitability of these methods for this PhD and which performance issues they could be used to assess. Identifying feasible methods is key to this PhD, including gaining access to staff and data.


This PhD aim is to expand the available personal performance data of staff in rail, with a focus on capturing good performance. The benefit would be to increase the understanding in rail of human performance, and progress the theoretical understanding of performance. The precise output format will be determined based on which role or specific technology the research data relates to. If the outcome is aimed at addressing fatigue management or staff wellbeing issues, the output will be a framework that offers guidance to managers responsible for the safety and offers guidance to staff regarding self-awareness of personal performance. If the outcome is aimed at addressing the impact of new technology on staff, then the output would be a framework to inform those involved in the procurement of future technologies designed to support staff decision making. Both outcomes will apply data to improve the decision making of staff, but they differ in which staff the guidance would be aimed at. In addition, it is intended that the research will highlight perceptions and attitudes of staff surrounding the use of their personal performance data. The knowledge gained from this, with consideration of the ethical and legal issues surrounding using personal data, will have implications for industries beyond rail.



  1. ORR (2017) Rail usage, infrastructure and performance (RAI01). Available at: www.gov.uk/government/statistical-data-sets [Accessed 09.10.17]
  2. RSSB (2017) Rail technical strategy: Capability Delivery Plan. Available at: www.rssb.co.uk/rts/Documents/2017-01-27-rail-technical-strategy-capability-delivery-plan-brochure.pdf [Assessed 12.03.18]
  3. ORR (2016) ORR’s Independent Review of RSSB. Available at orr.gov.uk
  4. Young, M. S. and Steel, T. (2017) Rail worker fatigue: Identification, management and countermeasures. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. SAGE publications, Sage UK: London, England, 231 (10), pp. 1098–1106.
  5. Lewis, P. J. (1992) Rich picture building in the soft systems methodology. European Journal of Information Systems. Palgrave Macmillan UK, 1(5), pp. 351–360.
  6. Song, Y.S., Baek, J.H., Hwang, D. and Lee, J. (2014) Physiological Status Assessment of Locomotive Engineer During Train Operation. Journal of Electrical Engineering and Technology, 9(1), pp.324-333.
  7. Collet, C., Salvia, E. and Petit-Boulanger, C. (2014) Measuring workload with electrodermal activity during common braking actions. Ergonomics, 57(6), pp.886–96.
  8. Zawiah, N. and Dawal, M. (2016) The mental workload and alertness levels of train drivers under simulated conditions based on electroencephalogram signals. Malaysian Journal of Public Health Medicine 16(1), pp.115-123.
  9. Borghini, G., Vecchiato, G., Topp, J, Astolfi, L., Maglione, A., Isabella, R., Caltagirone, C., Kong, W., Wei, D., Zhou, Z., Polidori, L., Vitiello, S. and Babiloni, F. (2012) Assessment of mental fatigue during car driving by using high resolution EEG activity and neurophysiologic indices, in Proceedings of Annual International Conference of IEEE Engineering in Medicine and Biology Society, EMBS. IEEE, pp.6442–6445.

This author is supported by the Horizon Centre for Doctoral Training at the University of Nottingham (RCUK Grant No. EP/L015463/1) and RSSB, Network Rail and RDG.