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The relation between bicycle commuting and non-work cycling: results from a mobility panel

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  • Maarten Kroesen
  • Susan Handy

Abstract

This study aims to establish whether or not bicycle commuting and cycling for other purposes (e.g. shopping, visiting friends) are related over time. Using previously gathered panel data (the Dutch mobility panel) these relationships are revealed by (1) a series of conditional change models and (2) a latent transition model. The conditional change models indicate that, with a lag of 1 year and controlling for a range of background characteristics, bicycle commuting and non-work cycling (in number of weekly trips) have a positive reciprocal influence on each other. The models show that work-related factors, such as the distance to work or whether a person receives a travel allowance, affect not only bicycle commuting but also non-work cycling. The latent transition model indicates that people can be clustered into four groups: non-cyclists, non-work cyclists, all-around cyclists and commuter cyclists. This model shows that people with a consistent propensity to not cycle at all (non-cyclists) or to cycle for both work and non-work purposes (all-around cyclists) are most stable in their travel behavior. Non-work cyclists and commuter cyclists are less stable in travel behavior. The model also shows that all-around cyclists are not (significantly) affected by a change in the distance to work. The article concludes with several directions for future research. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Maarten Kroesen & Susan Handy, 2014. "The relation between bicycle commuting and non-work cycling: results from a mobility panel," Transportation, Springer, vol. 41(3), pages 507-527, May.
    • Handle: RePEc:kap:transp:v:41:y:2014:i:3:p:507-527
    DOI: 10.1007/s11116-013-9491-4
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    References listed on IDEAS

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    3. Hudde, Ansgar, 2023. "Have Cycling-Friendly Cities Achieved Cycling Equity? Analyses of the Educational Gradient in Cycling in Dutch and German Cities," SocArXiv 7c6d2, Center for Open Science.
    4. Seungil Yum, 2020. "The association between minority racial/ethnic groups and travel mode choices," Growth and Change, Wiley Blackwell, vol. 51(3), pages 1017-1044, September.
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    6. Cabral, Laura & Kim, Amy M., 2020. "An empirical reappraisal of the four types of cyclists," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 206-221.
    7. Hudde, Ansgar, 2022. "The unequal cycling boom in Germany," Journal of Transport Geography, Elsevier, vol. 98(C).
    8. Piras, Francesco & Sottile, Eleonora & Tuveri, Giovanni & Meloni, Italo, 2021. "Could there be spillover effects between recreational and utilitarian cycling? A multivariate model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 297-311.
    9. Piras, Francesco & Sottile, Eleonora & Tuveri, Giovanni & Meloni, Italo, 2021. "Could psychosocial variables help assess pro-cycling policies?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 108-128.
    10. Ansgar Hudde, 2024. "Have cycling-friendly cities achieved cycling equity? Analyses of the educational gradient in cycling in Dutch and German cities," Urban Studies, Urban Studies Journal Limited, vol. 61(1), pages 78-94, January.
    11. Shin, Eun Jin, 2020. "Commuter benefits programs: Impacts on mode choice, VMT, and spillover effects," Transport Policy, Elsevier, vol. 94(C), pages 11-22.
    12. Bhat, Chandra R. & Mondal, Aupal, 2022. "A New Flexible Generalized Heterogeneous Data Model (GHDM) with an Application to Examine the Effect of High Density Neighborhood Living on Bicycling Frequency," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 244-266.

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