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Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
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Reading cyclist intentions: Can a lead cyclist's behaviour be predicted?

Frank Westerhuis1, Dick De Waard1

  • 1Faculty of Behavioural and Social Sciences, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, The Netherlands.

Accident; Analysis and Prevention
|July 18, 2016
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Summary

Predicting cyclist intentions is difficult. Visual cues like head movements and speed can help anticipate a cyclist

Keywords:
BicyclingIntentionObservationPredictionSurveyTurning

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Area of Science:

  • Human-Computer Interaction
  • Traffic Safety
  • Behavioral Psychology

Background:

  • Cyclists rely on anticipating other road users' intentions for safe navigation.
  • Official communication methods (e.g., arm signals) are often not used by cyclists in everyday traffic.
  • Non-verbal visual cues become critical for predicting cyclist behavior, especially before maneuvers.

Purpose of the Study:

  • To investigate the predictability of a lead cyclist's turning intentions based on visual cues.
  • To identify which visual behaviors influence observers' predictions of a cyclist's direction.

Main Methods:

  • 108 participants completed an online video survey predicting a lead cyclist's turning direction.
  • Videos were paused before the cyclist initiated turning, requiring predictions based on preceding visual cues.
  • Participants reported prediction certainty and the specific cues used for their judgments.

Main Results:

  • Predicting a turning cyclist's direction based solely on pre-maneuver visual cues is challenging.
  • Observable behaviors, including head movements and cycling speed, were found to correlate with prediction accuracy.
  • Observer certainty and the specific cues utilized varied among participants.

Conclusions:

  • Current visual cues are insufficient for reliably predicting a cyclist's turning intentions before initiation.
  • Head movements and cycling speed are key visual indicators that may aid in anticipating cyclist behavior.
  • Findings can inform strategies to improve cyclist-observer interactions and traffic safety.