Although shared micromobility systems in cities have been extensively studied, their potential for non-urban settings such as university campuses and rural communities has not been explored much yet. This study aims to fill this gap by examining a multimodal shared micromobility service that offers various options through a single app, such as city bikes, e-bikes, e-cargo bikes, e-mopeds, and e-scooters. The study analyzed this campus-based system's first four months, considering factors like weather, time, user demographics, pre-reservation duration, and vehicle types. Machine learning models like Negative Binomial Regression, Random Forests, Gradient Boosted Regression Trees, and Neural Networks were used to analyze the data. The study found that e-scooters were the most popular, followed by e-bikes. E-mopeds were used less but were reserved for more extended periods. Most trips were taken on weekdays, especially between 8 AM and 6 PM. Reservation numbers peaked in the first month, and subsequent months showed longer reservation durations and distances. Rain decreased trip numbers and distances but increased reservation durations. Reservations on Fridays, weekends, and holidays were shorter but covered more distance. Female users tended to travel longer distances. These findings can benefit similar non-urban environments, broadening the application of shared micromobility systems.    «

Although shared micromobility systems in cities have been extensively studied, their potential for non-urban settings such as university campuses and rural communities has not been explored much yet. This study aims to fill this gap by examining a multimodal shared micromobility service that offers various options through a single app, such as city bikes, e-bikes, e-cargo bikes, e-mopeds, and e-scooters. The study analyzed this campus-based system's first four months, considering factors like we...    »