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Effects of expertise for automatic train operations

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Abstract

The aim was to investigate the effects of automatic speed control and expertise on train driver performance in unexpected, degraded operations in the railway domain. Research from other domains suggested increasing levels of automation to exert detrimental effects on human performance in degraded operations. In addition, research about the effect of expertise on performance in different levels of railway automation was scarce. Reaction times of 26 train drivers to critical events in a high-fidelity railway simulator featuring manual and automatic speed control were assessed. Participants showed longer reaction times in degraded operations under automatic speed control. Results on the influence of expertise were insignificant and methodologically discussed to enable further research. These results highlight performance consequences of automatic speed control in railway operations and offer first insights on how existing expertise needs to be taken into account when introducing automatic speed control into the railway domain.

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References

  • Anderson JR (2000) Cognitive psychology and its implications. Worth Publishers, New York

    Google Scholar 

  • Bailey NR, Scerbo MW, Freeman FG, Mikulka PJ, Scott LA (2006) Comparison of a brain-based adaptive system and a manual adaptable system for invoking automation. Hum Factors. doi:10.1518/001872006779166280

    Google Scholar 

  • Borowsky A, Oron-Gilad T (2016) The effects of automation failure and secondary task on drivers’ ability to mitigate hazards in highly or semi-automated vehicles. Adv Transp Stud 1:59–70

    Google Scholar 

  • 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. doi:10.1177/0954409716676509

    Google Scholar 

  • Brandenburger N, Stamer M, Naumann A (2017) Comparing different types of the track side view in high speed train driving. In: de Waard D, Toffetti A,Wiczorek R, Sonderegger A, Röttger S, Bouchner P, Franke T, Fairclough S, Noordzij M, Brookhuis K (eds) Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2016 Annual Conference, Prague, pp 57–68

  • Buck L, Lamonde F (1993) Critical incidents and fatigue among locomotive engineers. Saf Sci 161:1–18. doi:10.1016/0925-7535(93)90003-V

    Article  Google Scholar 

  • Bullough R, Baughman K (1995) Changing context and expertise in teaching: first year teacher after seven years. Teach Teach Educ 2:461–477

    Article  Google Scholar 

  • Cai H, Lin Y (2012) Coordinating cognitive assistance with cognitive engagement control approaches in human machine collaboration. IEEE Trans Syst Man Cybern Part A Syst Hum 42:286–294

    Article  Google Scholar 

  • Chase WG, Simon HA (1973) Perception in chess. Cogn Psychol 4:55–81

    Article  Google Scholar 

  • Chavaillaz A, Wastell D, Sauer J (2015) System reliability, performance and trust in adaptable automation. Appl Ergon 52:333–342. doi:10.1016/j.apergo.2015.07.012

    Article  Google Scholar 

  • Crandall B, Getchell-Reiter K (1993) Critical decision method: a technique for eliciting concrete assessment indicators from the intuition of NICU nurses. Adv Nurs Sci 16:42–51

    Article  Google Scholar 

  • de Groot AD (1966) Perception and memory versus thought: some old ideas and recent findings. In: Kleinmuntz M (ed) Problem solving: research, method and theory. Wiley, New York, pp 19–50

    Google Scholar 

  • Dorrian J, Roach GD, Fletcher A, Dawson D (2007) Simulated train driving: fatigue, self-awareness and cognitive disengagement. Appl Ergon 382:155–166. doi:10.1016/j.apergo.2006.03.006

    Article  Google Scholar 

  • Dunn N, Williamson A (2011) Monotony in the rail industry: the role of task demand in mitigating monotony related effects on performance. In: Ergonomics Australia HFESA 2011 conference, pp 11–28

  • Dzindolet M, Peterson S, Pomranky R, Pierce L, Beck H (2003) The role of trust in automation reliance. Int J Hum Comput Stud 586:697–718

    Article  Google Scholar 

  • Edkins GD, Pollock CM (1997) The influence of sustained attention on Railway accidents. Accid Anal Prev 294:533–539. doi:10.1016/S0001-4575(97)00033-X

    Article  Google Scholar 

  • Endsley M, Kiris E (1995) The out-of-the-loop performance problem and level of control in automation. Hum Factors 372:381–394

    Article  Google Scholar 

  • European Commission (2011) Transport 2050 roadmap. www.europa.eu/rapid/press-release_IP-11-372_en.html Accessed 18 Jan 2017

  • European Railway Agency (2007) ERTMS/ETCS: functional requirements specification FRS

  • European Railway Agency (2016a) List of class B systems. www.era.europa.eu/Document-Register/Documents/ERA_TD_2011-11%20v30.pdf

  • European Railway Agency (2016b) ATO operational requirements. http://www.era.europa.eu/Document-Register/Documents/ATO_Ops_Requirements_v1_7.pdf

  • Farrington-Darby T, Wilson JR (2006) The nature of expertise: a review. Appl Ergon 37:17–32. doi:10.1016/j.apergo.2005.09.001

    Article  Google Scholar 

  • Grube R (2016) Bahn plant Züge ohne Lokführer. www.faz.net/aktuell/wirtschaft/deutsche-bahn-chef-ruediger-grube-plant-zuege-ohne-lokfuehrer-14278928.html. Accessed 02 Nov 2016

  • Hamilton W, Clarke T (2005) Driver performance modelling and its practical application to railway safety. Appl Ergon 36:661–670. doi:10.1016/j.apergo.2005.07.005

    Article  Google Scholar 

  • 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. www.uitp.org/metro-automation-facts-figures-and-trends. Accessed 18 Jan 2017

  • Jipp M (2016) Expertise development with different types of automation: a function of different cognitive abilities. Hum Factors J Hum Factors Ergon Soc 581:92–106. doi:10.1177/0018720815604441

    Article  Google Scholar 

  • 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 Cognit Eng Decis Mak 102:1–29. doi:10.1177/1555343416637517

    Google Scholar 

  • Klein G, Baxter HC (2009) Cognitive transformation theory: contrasting cognitive and behavioral learning. In: The PSI handbook of virtual environments for training and education: developments for the military and beyond Santa Barbara, CA, pp 50–65

  • Klein G, Moon B, Hoffman RR (2006a) Making sense of sensemaking 1: alternative perspectives. IEEE Intell Syst 214:22–26. doi:10.1109/MIS.2006.75

    Google Scholar 

  • Klein G, Moon B, Hoffman RR (2006b) Making sense of sensemaking 2: a macrocognitive model. IEEE Intell Syst 215:88–92. doi:10.1109/MIS.2006.100

    Article  Google Scholar 

  • Kluwe RH (1997) Acquisition of knowledge in the control of a simulated technical system. Le Travail Humain 60:61–85

    Google Scholar 

  • Lau N, Jamieson GA, Skraaning G (2013) Distinguishing three accounts of situation awareness based on their domains of origin. Proc Hum Factors Ergon Soc Annu Meet 571:220–224. doi:10.1177/1541931213571049

    Article  Google Scholar 

  • Lee JD, See K (2004) Trust in automation: designing for appropriate reliance. Hum Factors 461:50–80

    Article  Google Scholar 

  • Lorenz B, Di Nocera F, Röttger S, Parasuraman R (2001) The effect of level of automation on the out-of-the-loop unfamiliarity in a complex fault management task during simulated spaceflight operations. Proc Hum Factors Ergon Soc Annu Meet 45:44–48

    Article  Google Scholar 

  • Luke T, Brook-Carter N, Parkes A, Grimes E, Mills A (2006) An investigation of train driver visual strategies. Cogn Technol Work 8:15–29. doi:10.1007/s10111-005-0015-7

    Article  Google Scholar 

  • Ma R, Kaber D (2007) Effects of in-vehicle navigation assistance and performance on driver trust and vehicle control. Int J Ind Ergon 37:665–673

    Article  Google Scholar 

  • McLeod RW, Walker GH, Moray N (2005) Analysing and modelling train driver performance. Appl Ergon 36(6):671–680. doi:10.1016/j.apergo.2005.05.006

    Article  Google Scholar 

  • Merlo JL, Wickens C, Yeh M (2000) Effect of reliability on cue effectiveness and display signaling. In: Proceedings of the 4th annual army federated laboratory symposium, College Park, MD, pp 27–31

  • Merritt S, Heimbaugh H, LaChapell J, Lee D (2012) I trust it, but I don’t know why: effects of implicit attitudes toward automation on trust in an automated system. Hum Factors 55:520–534. doi:10.1177/0018720812465081

    Article  Google Scholar 

  • Moray N, Inagaki T, Itoh M (2000) Adaptive automation, trust, and self-confidence in fault management of time-critical tasks. J Exp Psychol Appl 61:44–58

    Article  Google Scholar 

  • Naghiyev A, Sharples S, Carey M, Coplestone A, Ryan B (2014a) ERTMS train driving-in cab vs. outside: an explorative eye-tracking field study. In: Sharples S, Shorrock S (eds) Contemporary Ergonomics and Human Factors, Taylor & Francis, Boca Raton, FL, pp 343–350

    Google Scholar 

  • Naghiyev A, Sharples S, Ryan B, Coplestone A, Carey M (2014b) Alerts and alarms in conventional and ERTMS train driving. Proc Hum Factors Ergon Soc Annu Meet 58:2083–2087. doi:10.1177/1541931214581438

    Article  Google Scholar 

  • Naumann A, Wörle J, Dietsch S (2016) The effect of train protection systems on train drivers’ visual attention. In: HFES Europe chapter annual meeting. http://www.hfes-europe.org/posters-2016/

  • Naweed A (2013) Investigations into the skills of modern and traditional train driving. Appl Ergon 453:462–470. doi:10.1016/j.apergo.2013.06.006

    Google Scholar 

  • Onnasch L, Ruff S, Manzey D (2012) Operators’ adaption to unreliability of alarm systems: a performance and eye-tracking analysis. Proc Hum Factors Ergon Soc Annu Meet 561:248–252. doi:10.1177/1071181312561059

    Article  Google Scholar 

  • Onnasch L, Wickens CD, Li H, Manzey D (2014) Human performance consequences of stages and levels of automation: an integrated meta-analysis. Hum Factors 563:476–488. doi:10.1177/0018720813501549

    Article  Google Scholar 

  • Parasuraman R, Riley V (1997) Humans and automation: use, misuse, disuse, abuse. Hum Factors 392:230–253

    Article  Google Scholar 

  • Parasuraman R, Sheridan TB, Wickens CD (2000) A model for types and levels of human interaction with automation. IEEE Trans Syst Man Cybern Part A Syst Hum 30:286–297

    Article  Google Scholar 

  • Rajaonah B, Anceaux F, Vienne F (2006) Study of driver trust during cooperation with adaptive cruise control. Le Travail Humain 692:99–127. doi:10.3917/th.692.0099

    Article  Google Scholar 

  • Rose J, Bearman C (2012) Making effective use of task analysis to identify human factors issues in new rail technology. Appl Ergon 43:614–624. doi:10.1016/j.apergo.2011.09.005

    Article  Google Scholar 

  • Rovira E, McGarry K, Parasuraman R (2007) Effects of imperfect automation on decision making in a simulated command and control task. Hum Factors 49:76–87

    Article  Google Scholar 

  • Royston P, Altman D, Sauerbrei W (2006) Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 25:127–141

    Article  MathSciNet  Google Scholar 

  • Sarter N, Woods D, Billings C (1997) Automation surprises. In: Salvendy G (ed) Handbook of human factors and ergonomics. Wiley, New York, pp 1–25

    Google Scholar 

  • Sauer J, Nickel P, Wastell D (2013) Designing automation for complex work environments under different levels of stress. Appl Ergon 441:119–127. doi:10.1016/j.apergo.2012.05.008

    Article  Google Scholar 

  • Schaefer KE, Chen JYC, Szalma JL, Hancock PA (2016) A meta-analysis of factors influencing the development of trust in automation: implications for understanding autonomy in future systems. Hum Factors 583:377–400. doi:10.1177/0018720816634228

    Article  Google Scholar 

  • Sheridan T, Parasuraman R (2006) Human–automation interaction. Rev Hum Factors Ergon 11:89–129. doi:10.1518/155723405783703082

    Google Scholar 

  • Sheridan T, Verplank W (1978) Human and computer control of undersea teleoperators. Massachusetts Institute of Technology, Man-machine Systems Lab, Cambridge, MA

  • Spring P, McIntosh A, Baysari M (2009) Counteracting the negative effects of high levels of train automation on driver vigilance. In: Proceedings of the 45th annual human factors and ergonomics society of Australia conference 45:93–101

  • 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, Brunswick, Germany, pp 96–102

  • Taylor J, O’Hara R, Mumenthaler MS, Rosen AC, Yesavage JA (2005) Cognitive ability, expertise, and age differences in following air-traffic control instructions. Psychol Aging 20:117–133. doi:10.1037/0882-7974.20.1.117

    Article  Google Scholar 

  • Tichon J (2007) The use of expert knowledge in the development of simulations for train driver training. Cogn Technol Work 9:177–187

    Article  Google Scholar 

  • Wickens CD, Dixon S (2007) The benefits of imperfect diagnostic automation: a synthesis of the literature. Theor Issues Ergon Sci 8:201–212

    Article  Google Scholar 

  • Wickens CD, Kessel C (1979) The effects of participatory mode and task workload on the detection of dynamic system failures. IEEE Trans Syst Man Cybern 91:24–34

    Article  Google Scholar 

  • Wickens CD, Li H, Santamaria A, Sebok A, Sarter NB (2010) Stages and levels of automation: an integrated meta-analysis. Proc Hum Factors Ergon Soc Annu Meet 544:389–393. doi:10.1177/154193121005400425

    Article  Google Scholar 

  • Wiegmann D, Rich A, Zhang H (2001) Automated diagnostic aids: the effects of aid reliability on users’ trust and reliance. Theor Issues Ergon Sci 2:352–367

    Article  Google Scholar 

  • Woods D (1996) Decomposing Automation: Apparent Simplicity, Real Complexity. In: Parasuraman R, Mouloua M (eds) Automation and human performance: Theory and applications. Lawrence Erlbaum associates, Mahwah, NJ, pp 3–17

    Google Scholar 

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Brandenburger, N., Jipp, M. Effects of expertise for automatic train operations. Cogn Tech Work 19, 699–709 (2017). https://doi.org/10.1007/s10111-017-0434-2

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