The impact of machine learning forecasting on strategic decision-making for Bike Sharing Systems
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911518808866816 |
|---|---|
| author | Angelelli, Enrico Mor, Andrea Orsenigo, Carlotta Speranza, M. Grazia Vercellis, Carlo |
| author_facet | Angelelli, Enrico Mor, Andrea Orsenigo, Carlotta Speranza, M. Grazia Vercellis, Carlo |
| contents | In this paper, machine learning techniques are used to forecast the difference between bike returns and withdrawals at each station of a bike sharing system. The forecasts are integrated into a simulation framework that is used to support long-term decisions and model the daily dynamics, including the relocation of bikes. We assess the quality of the machine learning-based forecasts in two ways. Firstly, we compare the forecasts with alternative prediction methods. Secondly, we analyze the impact of the forecasts on the quality of the output of the simulation framework. The evaluation is based on real-world data of the bike sharing system currently operating in Brescia, Italy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14901 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The impact of machine learning forecasting on strategic decision-making for Bike Sharing Systems Angelelli, Enrico Mor, Andrea Orsenigo, Carlotta Speranza, M. Grazia Vercellis, Carlo Optimization and Control Machine Learning In this paper, machine learning techniques are used to forecast the difference between bike returns and withdrawals at each station of a bike sharing system. The forecasts are integrated into a simulation framework that is used to support long-term decisions and model the daily dynamics, including the relocation of bikes. We assess the quality of the machine learning-based forecasts in two ways. Firstly, we compare the forecasts with alternative prediction methods. Secondly, we analyze the impact of the forecasts on the quality of the output of the simulation framework. The evaluation is based on real-world data of the bike sharing system currently operating in Brescia, Italy. |
| title | The impact of machine learning forecasting on strategic decision-making for Bike Sharing Systems |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2603.14901 |