Weather-Driven Cycling: Developing a Predictive Model for Urban Bicycle Usage Based on Five Key Weather Factors

Abstract

Weather conditions significantly influence urban cycling, shaping both its frequency and intensity. This study develops a predictive model to evaluate the impact of five key meteorological factors, namely temperature, humidity, precipitation, wind speed, and daylight duration, on urban cycling trends. Using non-linear regression analysis, the research examines cycling data from 2017 to 2019 in Hamburg, Germany, comparing predicted values for 2019 with actual data to assess model accuracy. The statistical analyses reveal strong correlations between weather parameters and cycling activity, highlighting each factor’s unique influence. The model achieved high accuracy, with R2 values of 0.942 and 0.924 for 2017 and 2019, respectively. To further validate its robustness, the model is applied to data from 2021 and 2023—years not included in its initial development—yielding R2 values of 0.893 and 0.919. These results underscore the model’s reliability and adaptability across different timeframes. This study not only confirms the critical influence of weather on urban cycling patterns, but also provides a scalable framework for broader urban planning applications. Beyond the immediate findings, this research proposes expanding the model to incorporate urban factors, such as land use, population density, and socioeconomic conditions, offering a comprehensive tool for urban planners and policymakers to enhance sustainable transportation systems.

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