The bicycle is a very important mode for travel in various countries, particularly in the Netherlands. However, it is in practice often modelled with less detail than other urban modes, such as the car and public transport. Moreover, the increasing use of e-bikes and the differences with conventional bikes show that more research into this transport mode is needed. E-bikes require less physical effort and allow higher speeds, making the e-bike suitable for longer distances. The goals of this research are to (1) create a mode choice model that predicts an accurate modal split for urban areas in the Netherlands and this model is used to (2) find significant factors that influence the modal split, in order to support municipalities of Dutch urban areas to stimulate the use of the (e-)bike. Within both goals, potential differences between conventional bikes and e-bikes are considered. A conceptual model, following from the literature, describes the assumed modal choice including factors relevant to cycling. Data was used mainly from the Dutch National Travel Survey (ODiN). Discrete choice models, a multinomial logit and a nested logit, are estimated to identify significant influencing factors. Results show that a nested logit model is the most explanatory one compared to the other models, with a rho-square-bar of 0.469. The model includes 15 main variables, 3 quadratic components and 4 interaction effects. The nested structure is formed by a correlation between the bike and the e-bike. The factors that show to be generally highly influential for the bike and the e-bike are the travel distance, owning a driver’s license and street density. The model is practically applicable for municipalities to form expectations in the modal shift for changes in their networks or policies. However, modelling these changes has not been validated and thus needs further research.


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    Title :

    The role of the (e-)bike: a mode choice model for short distances


    Contributors:
    Chantal Huurman (author) / Adam Pel (author) / Winnie Daamen (author) / Kees Maat (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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