Impact of Waiting Times on Risky Driver Behaviour at Railway Level Crossings

  • Grégoire S. LarueEmail author
  • Ross Blackman
  • James Freeman
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 823)


Increased road and rail traffic in Australia results in actively protected crossings being closed for extended periods of time during peak hours. This results in road congestion. It is known that extended periods of warning/waiting times at level crossings have impacts on drivers’ decision making in regards to violating crossing rules. Excessive waiting times could lead to non-compliant behaviour by motorists, resulting in incidents, including injuries and fatalities. However, the correlation between waiting time and rule violation is not well documented, although it is known that a range of personal and environmental factors influence rule non-compliance. This leads to the question of whether longer waiting times affect motorists’ assessment of risk and how long motorists are prepared to wait at level crossings before undertaking risky behaviour. A driving simulator study was used to obtain objective measures of railway level crossing (RLX) rule violations. Sixty participants completed six driving tasks each, with the tasks varying in terms of waiting times. Compliance with road rules at the level crossing during the simulated drives was examined. Main results include that increased waiting times result in increased likelihood of risky driving behaviour, particularly for waiting times longer than three minutes. Risky driving behaviours included entering the activated crossing before boom gates are down; entering the crossing after the train passage but before signals are deactivated; and stopping/reversing on the crossing. The results suggest that, where possible, waiting times should be standardized at values lower than three minutes in order to reduce the likelihood of risky road user behaviour.



The authors gratefully acknowledge the Australasian Centre for Rail Innovation (ACRI) for funding this research (Project LC/7-8).


  1. 1.
    Australian Transport Safety Bureau, Australian Rail Safety Occurrence Data 1 July 2002 to 30 June 2012 (2012) Australian Transport Safety Bureau, CanberraGoogle Scholar
  2. 2.
    Searle A, Di Milia L, Dawson D (2012) An investigation of risk-takers at railway level crossings. CRC for Rail Innov, Rep 2:79Google Scholar
  3. 3.
    McCollister GM, Pflaum CC (2007) A model to predict the probability of highway rail crossing accidents. Proc Inst Mech Eng, Part F: J Rail Rapid Transit 221(3):321–329CrossRefGoogle Scholar
  4. 4.
    Tey L-S, Ferreira L, Wallace A (2011) Measuring driver responses at railway level crossings. Accid Anal Prev 43(6):2134–2141CrossRefGoogle Scholar
  5. 5.
    Abraham J, Datta TK, Datta S (1998) Driver Behaviour at Rail-Highway Crossings. Transp Res Rec 1648:28–34CrossRefGoogle Scholar
  6. 6.
    Freeman J, McMaster M, Rakotonirainy A (2015) An exploration into younger and older pedestrians’ risky behaviours at train level crossings. Safety 1(1):16–27CrossRefGoogle Scholar
  7. 7.
    Mulvihil CM et al (2016) Using the decision ladder to understand road user decision making at actively controlled rail level crossings. Appl Ergon 56:1–10CrossRefGoogle Scholar
  8. 8.
    Naweed A et al (2016) Level with me: Human factors in pedestrian and road-user violations at a notorious Victorian railway level crossing. Road Transp Res 25(2):40–47Google Scholar
  9. 9.
    Lenné MG et al (2011) Driver Behaviour at Rail Level Crossings: Responses to Flashing Lights, Traffic signals and Stop Signs in Simulated Rural Driving. Appl Ergon 42:548–554CrossRefGoogle Scholar
  10. 10.
    Liu J et al (2015) What are the differences in driver injury outcomes at highway-rail grade crossings? Untangling the role of pre-crash behaviors. Accid Anal Prev 85:157–169CrossRefGoogle Scholar
  11. 11.
    Rudin-Brown CM et al (2012) Effectiveness of traffic light vs. boom barrier controls at road-rail level crossings: A simulator study. Accid Anal Prev 45:187–194CrossRefGoogle Scholar
  12. 12.
    Raub R (2009) Examination of highway-rail grade crossing collisions nationally from 1998 to 2007. Transp Res Rec J Transp Res Board 2122(1):63–71CrossRefGoogle Scholar
  13. 13.
    Hao W, Daniel J (2013) Severity of Injuries to Motor Vehicle Drivers at Highway-Rail Grade Crossings in the United States. Transp Res Rec J Transp Res Board 2384:102–108CrossRefGoogle Scholar
  14. 14.
    Oh J, Washington SP, Nam D (2006) Accident prediction model for railway-highway interfaces. Accid Anal Prev 38(2):346–356CrossRefGoogle Scholar
  15. 15.
    Larue GS, Naweed A, Rodwell D (2018) The road user, the pedestrian, and me: Investigating the interactions, errors and escalating risks of users of fully protected level crossings. Saf SciGoogle Scholar
  16. 16.
    Fairclough SH, Spiridon E (2012) Cardiovascular and electrocortical markers of anger and motivation during a simulated driving task. Int J Psychophysiol 84(2):188–193CrossRefGoogle Scholar
  17. 17.
    Lee Y-C (2010) Measuring Drivers’ Frustration in a Driving Simulator. Proc Hum Factors Ergonom Soc Ann Meet 54(19):1531–1535CrossRefGoogle Scholar
  18. 18.
    Bureau of Infrastructure Transport and Regional Economics (2013) Road deaths Australia - 2012 statistical summary. Department of Infrastructure and Transport, Editor. Australian Government, Canberra, AustraliaGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Grégoire S. Larue
    • 1
    • 2
    Email author
  • Ross Blackman
    • 1
  • James Freeman
    • 1
  1. 1.Centre for Accident Research and Road Safety – QueenslandQueensland University of Technology (QUT)BrisbaneAustralia
  2. 2.Australasian Centre for Rail Innovation (ACRI)CanberraAustralia

Personalised recommendations