Revealing the impact of weather on changes in individual-level habitual cycling frequencies using a Bayesian framework

The impact of weather on cycling demand has usually been investigated for day-to-day variations, often neglecting the impacts on individual-level habitual cycling behavioral changes over time. This study addresses this gap by developing a framework based on Bayesian online changepoint detection (BOCD). We first use the BOCD method to detect changes in habitual weekly cycling frequencies for each individual. Multinomial logit (MNL) models are then used to examine the effects of weather conditions and sociodemographics on the occurrence of habitual behavioral changes. To model the nonlinear effects of weather attributes, we propose an automatic procedure for ’optimal’ piecewise linear specifications instead of using predefined categories. We tested our approach on GPS datasets from Switzerland comprising 698 cyclists with observation periods from 31 to 79 weeks, combined with public weather data. Results show relatively stable cycling frequencies in our sample, with an average of 1.95 changepoints detected per individual. MNL models indicate that snowfall and precipitation significantly reduce habitual cycling frequency. Temperature shows a nonlinear relationship, with moderate temperatures triggering an increase in habitual cycling frequency, and more extreme conditions leading to a decline. Moreover, heterogeneity in weather response is observed, particularly with respect to e-bike ownership and cycling experience. This framework allows for consistent comparison of weather effects across regions and provides a basis for examining behavioral adaptation to a changing climate.