This study analyzes user engagement with a micromobility sharing system tailored for students and staff offering a monthly mobility allowance. The system comprises a variety of vehicles, including city bikes, e-bikes, e-cargo bikes, e-mopeds, and e-scooters. Using K-Means clustering based on monthly trip frequency, we categorized users into five segments — non-users, one-time users, infrequent users, frequent users, and super users. Our results indicate a high inactivity rate among registered users, with low conversion from initial to regular use. The trend shows that many users do not progress to more frequent usage levels, with the majority being one-time or infrequent users. While infrequent users tend to favor e-bikes and e-scooters, the most active users—super users—are more likely to utilize a wider variety of vehicles. Key demographic data and trip patterns were significant in determining user engagement levels and predicting potential churn. These findings emphasize the importance of understanding user behavior to effectively tailor service offerings. The insights gained from this study can inform service enhancements aimed at stimulating user activity and reducing churn, providing actionable guidance for micromobility service providers to improve customer retention and service utilization.    «

This study analyzes user engagement with a micromobility sharing system tailored for students and staff offering a monthly mobility allowance. The system comprises a variety of vehicles, including city bikes, e-bikes, e-cargo bikes, e-mopeds, and e-scooters. Using K-Means clustering based on monthly trip frequency, we categorized users into five segments — non-users, one-time users, infrequent users, frequent users, and super users. Our results indicate a high inactivity rate among registered us...    »