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| Tipologia | Anno | Titolo | Titolo inglese | Autore | File |
|---|---|---|---|---|---|
| Lauree magistrali | 2023 | Predicting the Level of Sustainability in Bike-Sharing Systems Using Machine Learning Techniques: A Study on RideMovies in Vicenza | This study applies machine learning to predict the sustainability level of bike-sharing systems, using data from the RideMovies system in Vicenza, Italy, collected between 2022 and 2023. Key performance indicators (KPIs) were defined to assess various dimensions of sustainability. Based on these KPIs, sustainability scores were calculated and categorized into five levels: Critical (0% - 20%), Unsatisfactory (21% - 40%), Acceptable (41% - 60%), Satisfactory (61% - 80%), and Exceptional (81% - 100%). Machine learning techniques were then employed to classify users according to their sustainability levels based on their usage data. The study demonstrates the potential of data-driven methods in improving the sustainability of urban mobility systems, providing valuable insights for policymakers and city planners to promote sustainable transportation. | JOKAR, ZAHRA |
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