Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. 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's 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's 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.

翻译：共享单车系统已成为城市交通的重要组成部分，提供了一种环保的出行替代方案。随着电动自行车与机械自行车的日益融合，揭示不同的使用模式及其对维护的影响至关重要。因此，本研究旨在全面理解出行动态，区分不同出行模式，并引入一种专为共享单车量身定制的创新预测性维护系统。通过利用巴塞罗那共享单车系统Bicing的行程信息与维护数据，本研究对出行模式及其与自行车部件故障的关系进行了深入分析。为准确预测关键自行车部件的维护需求，本研究深入探究了多种出行指标，并应用了包括深度学习模型在内的统计及机器学习生存模型。鉴于模型复杂性，并为了增强对系统预测结果的信心，本研究采用可解释性技术来阐释维护需求的主要预测因素。分析显示，机械自行车与电动自行车的使用模式存在显著差异，尽管后者成本更高，但用户对其偏好日益增长。研究发现，这些出行差异对共享单车系统的维护需求产生了显著影响。此外，预测性维护模型在预测这些维护需求方面表现出色，能够对整个车队进行有效运作。尽管面临诸如近似化自行车使用指标和数据集不平衡等挑战，本研究仍成功展示了一种能够优化运营成本、提高车辆可用性与安全性的精准预测性维护系统的可行性。