  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.
