Spatiotemporal variability and prediction of e-bike battery levels in bike-sharing systems
Abstract Bike Sharing Systems (BSSs) play a crucial role in promoting sustainable urban mobility by facilitating short-range trips and connecting with other transport modes. Traditionally, most BSS fleets have consisted of mechanical bikes (m-bikes), but electric bikes (e-bikes) are being progressiv...
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| Format: | Article |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88952-y |
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| author | Aleix Bassolas Jordi Grau-Escolano Julian Vicens |
| author_facet | Aleix Bassolas Jordi Grau-Escolano Julian Vicens |
| author_sort | Aleix Bassolas |
| collection | DOAJ |
| description | Abstract Bike Sharing Systems (BSSs) play a crucial role in promoting sustainable urban mobility by facilitating short-range trips and connecting with other transport modes. Traditionally, most BSS fleets have consisted of mechanical bikes (m-bikes), but electric bikes (e-bikes) are being progressively introduced due to their ability to cover longer distances and appeal to a wider range of users. However, the charging requirements of e-bikes often hinder their deployment and optimal functioning. This study examines the spatiotemporal variations in battery levels of Barcelona’s BSS, revealing that bikes stationed in the city’s expansion area (Eixample) tend to have shorter rest periods and lower average battery levels. Additionally, to improve the management of e-bike fleets, a Markov-chain approach is developed to predict both bike availability and battery levels. This research offers a unique perspective on the dynamics of e-bike battery levels and provides a practical tool to overcome the main operational challenges in their implementation. |
| format | Article |
| id | doaj-art-e5f8909aba204a31b1d2871cc26d981d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e5f8909aba204a31b1d2871cc26d981d2025-08-20T03:53:12ZengNature PortfolioScientific Reports2045-23222025-02-0115111210.1038/s41598-025-88952-ySpatiotemporal variability and prediction of e-bike battery levels in bike-sharing systemsAleix Bassolas0Jordi Grau-Escolano1Julian Vicens2Eurecat, Centre Tecnològic de CatalunyaEurecat, Centre Tecnològic de CatalunyaEurecat, Centre Tecnològic de CatalunyaAbstract Bike Sharing Systems (BSSs) play a crucial role in promoting sustainable urban mobility by facilitating short-range trips and connecting with other transport modes. Traditionally, most BSS fleets have consisted of mechanical bikes (m-bikes), but electric bikes (e-bikes) are being progressively introduced due to their ability to cover longer distances and appeal to a wider range of users. However, the charging requirements of e-bikes often hinder their deployment and optimal functioning. This study examines the spatiotemporal variations in battery levels of Barcelona’s BSS, revealing that bikes stationed in the city’s expansion area (Eixample) tend to have shorter rest periods and lower average battery levels. Additionally, to improve the management of e-bike fleets, a Markov-chain approach is developed to predict both bike availability and battery levels. This research offers a unique perspective on the dynamics of e-bike battery levels and provides a practical tool to overcome the main operational challenges in their implementation.https://doi.org/10.1038/s41598-025-88952-yBike-sharingHuman mobilitye-bikes |
| spellingShingle | Aleix Bassolas Jordi Grau-Escolano Julian Vicens Spatiotemporal variability and prediction of e-bike battery levels in bike-sharing systems Scientific Reports Bike-sharing Human mobility e-bikes |
| title | Spatiotemporal variability and prediction of e-bike battery levels in bike-sharing systems |
| title_full | Spatiotemporal variability and prediction of e-bike battery levels in bike-sharing systems |
| title_fullStr | Spatiotemporal variability and prediction of e-bike battery levels in bike-sharing systems |
| title_full_unstemmed | Spatiotemporal variability and prediction of e-bike battery levels in bike-sharing systems |
| title_short | Spatiotemporal variability and prediction of e-bike battery levels in bike-sharing systems |
| title_sort | spatiotemporal variability and prediction of e bike battery levels in bike sharing systems |
| topic | Bike-sharing Human mobility e-bikes |
| url | https://doi.org/10.1038/s41598-025-88952-y |
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