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Insights into factors affecting the combined bicycle-transit mode

Author

Listed:
  • Joeri F. P. Mil

    (AT Osborne)

  • Tessa S. Leferink

    (Witteveen+Bos)

  • Jan Anne Annema

    (Delft University of Technology)

  • Niels Oort

    (Delft University of Technology)

Abstract

This paper considers an increasingly popular, sustainable multimodality: the combination of bicycle and transit. The flexibility of the bicycle combined with the speed and comfort of good transit can be a highly competitive alternative to the car. This study shows that many factors influence the uptake and attractiveness of the bicycle-transit combination. An in-depth literature review resulted in over thirty unique factors: six transit-related factors, twenty-first-last mile factors and fifteen context-related factors. All these factors might influence the demand for this ‘new’ mode positively or negatively. An exploratory choice modelling study showed the weights of some factors that Dutch bicycle-train users consider when choosing to cycle to a railway station. The weights showed that people are especially willing to cycle to a station with longer bicycle time (or bicycle parking time) when by doing so they can avoid a transfer in their train trip thereafter. The willingness to pay found were €0.11 for 1 min less bicycle time, €0.08 for a minute less train time, €0.11 for a minute of less time to park and €0.60 per avoided transfer. These kinds of insights give the bicycle and transit sector valuable information to be used in modelling multimodality and cost–benefit analyses, thereby supporting improved decision making and integrated design of bicycle and transit networks.

Suggested Citation

  • Joeri F. P. Mil & Tessa S. Leferink & Jan Anne Annema & Niels Oort, 2021. "Insights into factors affecting the combined bicycle-transit mode," Public Transport, Springer, vol. 13(3), pages 649-673, October.
  • Handle: RePEc:spr:pubtra:v:13:y:2021:i:3:d:10.1007_s12469-020-00240-2
    DOI: 10.1007/s12469-020-00240-2
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    References listed on IDEAS

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    Cited by:

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    3. Zhang, Yingheng & Li, Haojie & Ren, Gang, 2022. "Quantifying the social impacts of the London Night Tube with a double/debiased machine learning based difference-in-differences approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 288-303.

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    Keywords

    Transit; Cycling; Behaviour;
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