Advertisement

Task-induced fatigue when implementing high grades of railway automation

  • Niels Brandenburger
  • Anja NaumannEmail author
  • Meike Jipp
Original Article
  • 21 Downloads

Abstract

The study was focused on the effects of different grades of railway automation on task-induced fatigue and workload in train drivers and, when considering high grades of automation, operational staff in a control centre, so-called train operators. Train operators remotely monitor and manually drive automated trains upon system request during disruptions. As the task environment substantially differs depending on the grade of automation, effects on task- induced fatigue and workload levels were expected. To quantify and compare these effects, a simulator study with professional train drivers (N = 32) was conducted and the grade of automation was manipulated experimentally between subjects according to the railway specific automation taxonomy. Fatigue was assessed by the Karolinska Sleepiness Scale prior to and after a simulated working period of 2 h. Workload was assessed using the NASA-TLX. The results showed (a) significantly increasing fatigue levels over time (b) significantly higher fatigue ratings as a result of working with an intermediate grade of automation in comparison to working with a high grade of automation. Workload scores were (c) significantly higher in the high grade of automation group. Consequently, the introduction of high grades of automation may be used to tackle longstanding fatigue issues associated with train driving. The role of workload and limitations of the current research are discussed and recommendations for future research and operating companies are provided.

Keywords

Train driver Train operator Grades of automation Remote operation Fatigue Rail automation Rail human factors Levels of automation 

Notes

Funding

This work was part of the projects Next Generation Train and Next Generation Railway Systems of the German Aerospace Center (DLR). These projects are funded by the Helmholtz Association.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Akerstedt T, Gillberg M (1990) Subjective and objective sleepiness in the active individual. Int J Neurosci 52(1–2):29–37.  https://doi.org/10.3109/00207459008994241 CrossRefGoogle Scholar
  2. Brandenburger N, Jipp M (2017) Effects of expertise for automatic train operations. Cogni Technol Work 19(4):699–709.  https://doi.org/10.1007/s10111-017-0434-2 CrossRefGoogle Scholar
  3. Brandenburger N, Naumann A (2018a) Enabling automatic train operation through human problem solving. Signal Draht 3:6–13Google Scholar
  4. Brandenburger N, Naumann A (2018b) Towards remote supervision and recovery of automated railway systems: the staff’s changing contribution to system resilience. In: Proceedings of the International Conference on Intelligent Rail Transportation. IEEE,  Singapore, pp 1–5Google Scholar
  5. Brandenburger N, Naumann A (2018c) From in—cabin driving to remote interventions—train driver tasks change with railway automation. In de Waard D, Brookhuis K, Coelho D, Fairclough S, Manzey D, Naumann A, Onnasch L, Röttger S, Toffetti,Wiczorek R (eds) Human Factors and Ergonomics Society Europe Chapter 2018 Annual Conference. Downloaded from https://www.hfes-europe.org/wp-content/uploads/2018/10/Brandenburger2018Poster.pdf. Accessed 23 Aug 2019  
  6. Brandenburger N, Hörmann HJ, Stelling D, Naumann A (2016) Tasks, skills, and competencies of future high-speed train drivers. Proc Inst Mech Eng Part F J Rail Rapid Transit.  https://doi.org/10.1177/0954409716676509 CrossRefGoogle Scholar
  7. Brandenburger N, Wittkowski M, Naumann A (2017) Countering train driver fatigue in automatic train operation. In: Golightly D, Fowler A, Ryan B, Kurup S, Mills A (eds) Proceedings of the sixth international human factors rail conference, rail safety and standards board, London, pp 57–65Google Scholar
  8. Brandenburger N, Thomas-Friedrich B, Naumann A, Grippenkoven J (2018) Automation in railway operations: effects on signaller and train driver workload. In: Milius B, Naumann A (eds) Proceedings of the 3rd. German workshop on rail human factors. ITS mobility nord, Brunswick, pp 51–60Google Scholar
  9. Buck L, Lamonde F (1993) Critical incidents and fatigue among locomotive engineers. Safety Sci 16(1):1–18.  https://doi.org/10.1016/0925-7535(93)90003-V CrossRefGoogle Scholar
  10. Caldwell JA, Mallis MM, Caldwell JL, Paul MA, Miller JC, Neri DF (2009) Fatigue countermeasures in aviation. Aviation Space Environ Med 80(1):29–59.  https://doi.org/10.3357/ASEM.2435.2009 CrossRefGoogle Scholar
  11. DaCoTA (2012) Fatigue: deliverable 4.8 h of the EC FP7 project DaCoTA. Retrieved from www.dacota-project.eu. Accessed 23 Aug 2019
  12. de Waard D (1996) The measurement of drivers’ mental workload. University of Groningen, GroningenGoogle Scholar
  13. Desmond P, Hancock PA (2001) Active and passive fatigue states. In: Hancock PA, Desmond P (eds) Stress, workload, and fatigue. Lawrence Erlbaum Associates, New Jersey, pp 455–465Google Scholar
  14. Desmond P, Hancock PA, Monette J (1998) Fatigue and automation-induced impairments in simulated driving performance. Transp Res Rec 1628:8–14CrossRefGoogle Scholar
  15. Di Milla L, Smolensky MH, Costa G, Howarth HD, Ohayon MM, Philip P (2011) Demographic factors, fatigue, and driving accidents: an examination of the published literature. Accid Anal Prevent 43(2):516–532.  https://doi.org/10.1016/j.aap.2009.12.018 CrossRefGoogle Scholar
  16. Dinges DF (1995) An overview of sleepiness and accidents. J Sleep Res 4:4–14CrossRefGoogle Scholar
  17. Dinges DF, Kribbs NB (1991) Performing while sleepy: effects of experimentally-induced sleepiness. In: Monk TH (ed) Human performance and cognition. Sleep, sleepiness and performance. Wiley, Oxford, pp 97–128Google Scholar
  18. Dorrian J, Roach GD, Fletcher A, Dawson D (2006) The effects of fatigue on train handling during speed restrictions. Transp Res Part F Traff Psychol Behav 9(4):243–257.  https://doi.org/10.1016/j.trf.2006.01.003 CrossRefGoogle Scholar
  19. Dorrian J, Roach GD, Fletcher A, Dawson D (2007) Simulated train driving: fatigue, self-awareness and cognitive disengagement. Appl Ergon 38(2):155–166.  https://doi.org/10.1016/j.apergo.2006.03.006 CrossRefGoogle Scholar
  20. Dunn N, Williamson A (2012) Driving monotonous routes in a train simulator: the effect of task demand on driving performance and subjective experience. Ergonomics 55(9):997–1008CrossRefGoogle Scholar
  21. Endsley M, Kiris E (1995) The out-of-the-loop performance problem and level of control in automation. Human Fact 37(2):381–394CrossRefGoogle Scholar
  22. European Commission (2011) White paper on transport: roadmap to a single European transport area—towards a competitive and resource-efficient transport system. European Union, Brussels. https://ec.europa.eu/transport/sites/transport/files/themes/strategies/doc/2011_white_paper/white-paper-illustrated-brochure_en.pdf. Accessed 23 Aug 2019
  23. European Railway Agency (2016) ATO operational requirements. Retrieved from http://www.era.europa.eu/Document-Register/Documents/ATO_Ops_Requirements_v1_7.pdf. Accessed 23 Aug 2019
  24. Filtness AJ, Naweed A (2017) Causes, consequences and countermeasures to driver fatigue in the rail industry: the train driver perspective. Appl Ergon 60:12–21.  https://doi.org/10.1016/j.apergo.2016.10.009 CrossRefGoogle Scholar
  25. Friswell R, Williamson A (2008) Exploratory study of fatigue in light and short haul transport drivers in NSW, Australia. Accid Anal Prevent 40(1):410–417.  https://doi.org/10.1016/j.aap.2007.07.009 CrossRefGoogle Scholar
  26. Grant JS (1971) Concepts of fatigue and vigilance in relation to railway operation. Ergonomics 14(1):111–118.  https://doi.org/10.1080/00140137108931229 CrossRefGoogle Scholar
  27. Grippenkoven J, Rodd J, Brandenburger N (2018) DLR-WAT: Ein Instrument zur Untersuchung des optimalen Beanspruchungsniveaus in hochautomatisierten Mensch-Maschine-Systemen. In: AAET- Automatisiertes und vernetztes Fahren. ITS automotive nord, Brunswick Germany, pp 199–213 [publication in German]Google Scholar
  28. Harris WC, Hancock PA, Arthur EJ, Caird JK (1995) Performance, workload, and fatigue changes associated with automation. Int J Aviat Psychol 5(2):169–185.  https://doi.org/10.1207/s15327108ijap0502_3 CrossRefGoogle Scholar
  29. International Association of public Transport (2012) Metro automation facts, figures and trends: a global bid for automation: UITP observatory of automated metros confirms sustained growth rates for the coming years. Retrieved from www.uitp.org/metro-automation-facts-figures-and-trends. Accessed 23 Aug 2019
  30. Jap B, Fischer P, Bekiaris E (2007) Using spectral analysis to extract frequency components from electroencephalography: application for fatigue countermeasure in train drivers. In: The 2nd international conference on wireless broadband and ultra wideband communication. IEEE, Sydney, pp 13–13Google Scholar
  31. Jap B, Lal S, Fischer P (2011) Comparing combinations of EEG activity in train drivers during monotonous driving. Expert Systems with Applications 38(1):996–1003CrossRefGoogle Scholar
  32. Jipp M, Ackerman PL (2016) The impact of higher levels of automation on performance and situation awareness: a function of information-processing ability and working-memory capacity. J Cogni Eng Decis Mak.  https://doi.org/10.1177/1555343416637517 CrossRefGoogle Scholar
  33. Johns MW (2000) A sleep physiologist’s view of drowsy driving. Transp Res Part F Traff Psychol Behav 3:241–249CrossRefGoogle Scholar
  34. Kaida K, Takahashi M, Akerstedt T, Nakata A, Otsuka Y, Haratani T, Fukasawa K (2006) Validation of the Karolinska sleepiness scale against performance and EEG variables. Clin Neurophysiol 117(7):1574–1581.  https://doi.org/10.1016/j.clinph.2006.03.011 CrossRefGoogle Scholar
  35. Lal SK, Craig A (2002) Driver fatigue: electroencephalography and psychological assessment. Psychophysiology 39(3):313–321.  https://doi.org/10.1017/S0048577201393095 CrossRefGoogle Scholar
  36. Mackworth NH (1948) The breakdown of vigilance during prolonged visual search. Q J Exp Psychol 1(1):6–21.  https://doi.org/10.1080/17470214808416738 CrossRefGoogle Scholar
  37. Naweed A (2013) Investigations into the skills of modern and traditional train driving. Appl Ergon 45(3):462–470.  https://doi.org/10.1016/j.apergo.2013.06.006 CrossRefGoogle Scholar
  38. Onnasch L, Wickens CD, Li H, Manzey D (2014) Human performance consequences of stages and levels of automation: an integrated meta-analysis. Human Fact 56(3):476–488.  https://doi.org/10.1177/0018720813501549 CrossRefGoogle Scholar
  39. Parasuraman R, Sheridan T, Wickens C (2000) A model for types and levels of human interaction with automation. In: IEEE Transactions on systems, man and cybernetics-Part A: systems and humans, vol 30, issue 3. IEEE, pp 286–297Google Scholar
  40. Parasuraman R, Sheridan TB, Wickens CD (2008) Situation awareness, mental workload, and trust in automation: viable, empirically supported cognitive engineering constructs. J Cognit Eng Dec Mak 2(2):140–160.  https://doi.org/10.1518/155534308X284417 CrossRefGoogle Scholar
  41. Persson R, Garde AH, Hansen ÅM, Ørbaek P, Ohlsson K (2003) The influence of production systems on self-reported arousal, sleepiness, physical exertion and fatigue-consequences of increasing mechanization. Stress Health 19(3):163–171.  https://doi.org/10.1002/smi.967 CrossRefGoogle Scholar
  42. Saxby DJ, Matthews G, Hitchcock EM, Warm JS (2007) Development of active and passive fatigue manipulations using a driving simulator. In: Proceedings of the human factors and ergoniomics society 51st annual meeting. Human factors and ergonomics society, Baltimore, pp 1237–1241CrossRefGoogle Scholar
  43. Saxby DJ, Matthews G, Hitchcock EM, Warm J S, Funke G, Gantzer T (2008) Effect of active and passive fatigue on performance using a driving simulator. In: Proceedings of the human factors and ergoniomics society 52nd annual meeting. Human Factors and Ergonomics Society, New York City, pp 1751–1755CrossRefGoogle Scholar
  44. Spring P, McIntosh A, Caponecchia C, Baysari M (2008) Level of automation: effects on train driver vigilance. 44th annual human factors and ergonomics society of Australia Conference, pp 264–271Google Scholar
  45. Spring P, McIntosh A, Baysari M (2009) Counteracting the negative effects of high levels of train automation on driver vigilance. HFESA Conf Proc 45:93–101Google Scholar
  46. Staveland L, Hart S (1988) Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Adv Psychol 52:139–183CrossRefGoogle Scholar
  47. Stein J, Naumann A (2016) Monotony, fatigue and microsleeps—train drivers daily routine: a simulator study. In: Milius B, Naumann A (eds) Proceedings of the 2nd workshop on human factors. ITS automotive nord, Braunschweig, pp 96–102Google Scholar
  48. Vogelpohl T, Kühn M, Hummel T, Vollrath M (2019) Asleep at the automated wheel—sleepiness and fatigue during highly automated driving. Acc Anal Prevent 126:70–84.  https://doi.org/10.1016/j.aap.2018.03.013 CrossRefGoogle Scholar
  49. Wickens CD, Li H, Santamaria A, Sebok A, Sarter NB (2010) Stages and levels of automation: an integrated meta-analysis. Proc Human Fact Ergon Soc Ann Meet 54(4):389–393.  https://doi.org/10.1177/154193121005400425 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.German Aerospace Center (DLR)Institute of Transportation SystemsBerlinGermany

Personalised recommendations