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Perception of Safety and Liking Associated to the Colour Intervention of Bike Lanes: Contribution from the Behavioural Sciences to Urban Design and Wellbeing

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  • Pablo Vera-Villarroel
  • Daniela Contreras
  • Sebastián Lillo
  • Christian Beyle
  • Ariel Segovia
  • Natalia Rojo
  • Sandra Moreno
  • Francisco Oyarzo

Abstract

The perception of colour and its subjective effects are key issues to designing safe and enjoyable bike lanes. This paper addresses the relationship between the colours of bike lane interventions—in particular pavement painting and intersection design—and the subjective evaluation of liking, visual saliency, and perceived safety related to such an intervention. Utilising images of three real bike lane intersections modified by software to change their colour (five in total), this study recruited 538 participants to assess their perception of all fifteen colour-design combinations. A multivariate analysis of covariance (MANCOVA) with the Bonferroni post hoc test was performed to assess the effect of the main conditions (colour and design) on the dependent variables (liking towards the intervention, level of visual saliency of the intersection, and perceived safety of the bike lane). The results showed that the colour red was more positively associated to the outcome variables, followed by yellow and blue. Additionally, it was observed that the effect of colour widely outweighs the effect of design, suggesting that the right choice and use of colour would increase the effectiveness on bike-lanes pavement interventions. Limitations and future directions are discussed.

Suggested Citation

  • Pablo Vera-Villarroel & Daniela Contreras & Sebastián Lillo & Christian Beyle & Ariel Segovia & Natalia Rojo & Sandra Moreno & Francisco Oyarzo, 2016. "Perception of Safety and Liking Associated to the Colour Intervention of Bike Lanes: Contribution from the Behavioural Sciences to Urban Design and Wellbeing," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-15, August.
  • Handle: RePEc:plo:pone00:0160399
    DOI: 10.1371/journal.pone.0160399
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    Cited by:

    1. Lucas Magnana & Herve Rivano & Nicolas Chiabaut, 2022. "Implicit GPS-based bicycle route choice model using clustering methods and a LSTM network," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-20, March.

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