, Volume 40, Issue 1, pp 1-22

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Exploring temporal fluctuations of daily cycling demand on Dutch cycle paths: the influence of weather on cycling

  • Tom ThomasAffiliated withCentre for Transport Studies, University of Twente Email author 
  • , Rinus JaarsmaAffiliated withLand Use Planning Group, Wageningen University
  • , Bas TutertAffiliated withCentre for Transport Studies, University of Twente


In the pursuit of sustainable mobility policy makers are giving more attention to cycling. The potential of cycling is shown in countries like the Netherlands, where cycling covers 25 % of all person trips. However, the effect of policy interventions on cycling demand is difficult to measure, not least caused by difficulties to control for changing context variables like weather conditions. According to several authors weather has a strong influence on cycling demand, but quantitative studies about the relationship are scarce. We therefore further explored this relationship, with the aim of contributing to the development of a generic demand model with which trend and coincidence in bicycle flows might be unraveled. The study is based on time-series between 1987 and 2003 of daily bicycle flows, collected on 16 cycle paths near two cities in the Netherlands. The regression analyses show that, not surprisingly, recreational demand is much more sensitive to weather than utilitarian demand. Most daily fluctuations (80 %) are described by weather conditions, and no less than 70 % of the remaining variation is locally constrained. The regression can therefore mainly be improved by incorporating path specific, as yet unknown, variables. We used the regression results to calculate weather-inclusive bicycle flow predictions and found indications of a downward trend in recreational demand. This trend has been off-set in the observed flows by more favorable weather conditions over the years considered.


Bicycle flows Recreation Sustainable mobility Time-series Regression Residuals