Cycling in Urban and Tourism Areas in the COVID-19 Era: Weather Sensitivity and Sustainable Management Response

This study investigates how cycling behaviour in urban and tourism areas of Slovenia responded to the COVID-19 pandemic and its aftermath, with implications for forecasting and sustainable mobility planning. Using high-frequency daily data from January 2020 to August 2024 across Ljubljana (urban) and Rateče (tourism), we model the interdependence between weather conditions, cycling volume, and reported COVID-19 cases. The results reveal contrasting dynamics: in Ljubljana, higher cycling activity correlates with fewer infections, supporting cycling as a low-risk commuting mode, whereas in Rateče, tourism-driven cycling coincides with higher variability in infections. Regression and vector autoregressive (VAR(2)) models highlight the significant roles of precipitation and sunlight in shaping these patterns and enable short-term forecasts of COVID-19 cases up to January 2025. Machine learning methods complemented the VAR model, improving forecasting accuracy and revealing nonlinear interactions. These findings demonstrate the value of integrating behavioural and environmental indicators into public health forecasting and support region-specific strategies for resilient, sustainable mobility during future health or climate disruptions.