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Bike sharing systems offer a convenient, ecologic, and economic transport mode that has been increasingly adopted. However, the distribution of bikes is often unbalanced, which decreases user satisfaction and potential revenues. Moreover, bike sharing literature is mostly focused on the prediction of demand on large scale systems and uses simulations for the assessment of relocation operations to increase the number of utilizations. We propose prediction models based on machine learning approaches to improve the bike sharing re-balancing in a small city of Portugal. The algorithm aims to improve three metrics, namely (1) increase the number of utilizations, (2) reduce the number of stations without bikes, (3) reduce the time without available bikes in the stations. The relocation operations are validated using real data. Our findings show that (a) the estimated number of utilizations created by this system is substantially higher than the current system by 223%, (b) our model allows the correct identification of more 70%, 165%, 249% empty stations with the same or substantially higher precision than the existing approach, (c) the total time of bike unavailability reduced by the predictive model is 283% higher than the time reduced by current approach (1,394,454 vs 363,971 minutes).

Prediction of Bike Mobility in Cascais’s Sharing System