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With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona\u2019s bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system\u2019s predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage patterns of mechanical bikes and electric bikes, with a growing user preference for the latter despite their extra costs. These differences in mobility were found to have a considerable impact on the maintenance needs within the bike-sharing system. Moreover, the predictive maintenance models proved effective in forecasting these maintenance needs, capable of operating across an entire bike fleet. Despite challenges such as approximated bike usage metrics and data imbalances, the study successfully showcases the feasibility of an accurate predictive maintenance system capable of improving operational costs, bike availability, and security.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-024-00486-x","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T14:22:19Z","timestamp":1720707739000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Cycling into the workshop: e-bike and m-bike mobility patterns for predictive maintenance in Barcelona\u2019s bike-sharing system"],"prefix":"10.1140","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9755-3095","authenticated-orcid":false,"given":"Jordi","family":"Grau-Escolano","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5588-2117","authenticated-orcid":false,"given":"Aleix","family":"Bassolas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0643-0469","authenticated-orcid":false,"given":"Julian","family":"Vicens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"key":"486_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.120846","volume":"259","author":"X Ma","year":"2020","unstructured":"Ma X, Yuan Y, Van Oort N, Hoogendoorn S (2020) Bike-sharing systems\u2019 impact on modal shift: a case study in delft, the Netherlands. 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