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Revealed Preference Methods for Studying Bicycle Route Choice—A Systematic Review

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  • Ray Pritchard

    (Department of Architecture and Planning, Faculty of Architecture and Design, NTNU—Norwegian University of Science and Technology, 7491 Trondheim, Norway)

Abstract

One fundamental aspect of promoting utilitarian bicycle use involves making modifications to the built environment to improve the safety, efficiency and enjoyability of cycling. Revealed preference data on bicycle route choice can assist greatly in understanding the actual behaviour of a highly heterogeneous group of users, which in turn assists the prioritisation of infrastructure or other built environment initiatives. This systematic review seeks to compare the relative strengths and weaknesses of the empirical approaches for evaluating whole journey route choices of bicyclists. Two electronic databases were systematically searched for a selection of keywords pertaining to bicycle and route choice. In total seven families of methods are identified: GPS devices, smartphone applications, crowdsourcing, participant-recalled routes, accompanied journeys, egocentric cameras and virtual reality. The study illustrates a trade-off in the quality of data obtainable and the average number of participants. Future additional methods could include dockless bikeshare, multiple camera solutions using computer vision and immersive bicycle simulator environments.

Suggested Citation

  • Ray Pritchard, 2018. "Revealed Preference Methods for Studying Bicycle Route Choice—A Systematic Review," IJERPH, MDPI, vol. 15(3), pages 1-30, March.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:3:p:470-:d:135218
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    References listed on IDEAS

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    1. Proulx, Frank, 2014. "Frank Proulx, NCHRP 07-19: Methods and Technologies for Pedestrian and Bicycle Volume Data Collection," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0bj1w7w2, Institute of Transportation Studies, UC Berkeley.
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    6. Proulx, Frank, 2014. "Methods and Technologies for Pedestrian and Bicycle Volume Data Collection," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt06z8t1h3, Institute of Transportation Studies, UC Berkeley.
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    Cited by:

    1. Tineke de Jong & Lars Böcker & Christian Weber, 2023. "Road infrastructures, spatial surroundings, and the demand and route choices for cycling: Evidence from a GPS-based mode detection study from Oslo, Norway," Environment and Planning B, , vol. 50(8), pages 2133-2150, October.
    2. Xinwei Ma & Ruiming Cao & Jianbiao Wang, 2019. "Effects of Psychological Factors on Modal Shift from Car to Dockless Bike Sharing: A Case Study of Nanjing, China," IJERPH, MDPI, vol. 16(18), pages 1-16, September.
    3. Paulsen, Mads & Rich, Jeppe, 2023. "Societally optimal expansion of bicycle networks," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    4. Łukawska, Mirosława & Paulsen, Mads & Rasmussen, Thomas Kjær & Jensen, Anders Fjendbo & Nielsen, Otto Anker, 2023. "A joint bicycle route choice model for various cycling frequencies and trip distances based on a large crowdsourced GPS dataset," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    5. Ali Al-Ramini & Mohammad A Takallou & Daniel P Piatkowski & Fadi Alsaleem, 2022. "Quantifying changes in bicycle volumes using crowdsourced data," Environment and Planning B, , vol. 49(6), pages 1612-1630, July.
    6. Chiara Ricchetti & Lucia Rotaris, 2024. "The role of linear green infrastructure for cycling: A literature review," ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT, FrancoAngeli Editore, vol. 2024(1), pages 219-256.

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