Modelling driver decision-making at railway level crossings using the abstraction decomposition space

Abstract

The objective of this paper is to cast users of railway level crossings as flexible and adaptive decision-makers, and to apply a cognitive systems engineering approach to discover new behaviour-based insights for improving safety. Collisions between trains and road vehicles at railway level crossings/grade crossings remain a global issue. It is still far from apparent why drivers undertake some of the behaviours that lead to collisions, and there remains considerable justification for continuing to explore this issue with novel methods and approaches. In this study, 220 level crossing encounters by 22 car drivers were subject to analysis. Concurrent verbal protocols provided by drivers as they drove an instrumented vehicle around a pre-defined route were subject to content analysis and mapped onto Rasmussen’s Abstraction Decomposition Space. Three key results emerged. First, when they realise they are in a crossing environment, drivers’ natural tendencies are to look for trains (even if not required), slow down (again, even if not required), and for their behaviour to be shaped by a wide variety of constraints and affordances (some, but not all, put there for that purpose by railway authorities). The second result is that expert decision-making in these situations does not describe a trajectory from high-level system purposes to low-level physical objects. Instead, drivers remain at intermediate and lower levels of system abstraction, with many loops and iterations. The final finding is that current level crossing systems are inadvertently constraining some desirable behaviours, affording undesirable ones, and that unexpected system elements are driving behaviour in ways not previously considered. Railway level crossings need to be designed to reveal their functional purpose much more effectively than at present.

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Source: Hoffman and Lintern (2006) p. 212

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Acknowledgements

This research was supported by an Australian Research Council (ARC) Linkage Grant (LP100200387) to the University of the Sunshine Coast, Monash University, and the University of Southampton, along with the following partner organisations: the Victorian Rail Track Corporation, Transport Safety Victoria, Public Transport Victoria, Transport Accident Commission, Roads Corporation (VicRoads) and V/Line Passenger Pty Ltd. Vanessa Beanland is supported by an ARC Discovery Early Career Researcher Award (DE150100083). Paul Salmon is supported by an ARC Future Fellowship (FT140100681). We gratefully acknowledge the support of the project partners, community participants and taff of the VicRoads Northern Region Office. We also thank Nebojsa Tomasevic for technical assistance.

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Correspondence to Guy Walker.

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Walker, G., Mendes, L.M.N., Lenne, M. et al. Modelling driver decision-making at railway level crossings using the abstraction decomposition space. Cogn Tech Work (2021). https://doi.org/10.1007/s10111-020-00659-4

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Keywords

  • Decision-making
  • Problem-solving
  • Cognitive task analysis
  • Cognitive work analysis
  • Driver behaviour
  • Railway
  • Work domain analysis