Forecasting short-term availability of bikes at sharing stations : a deep-learning approach

The growing popularity of bike-sharing systems represents a significant urban mobility trend, offering substantial economic, environmental, territorial and social benefits. The achievement of these advantages is contingent on the operational efficiency of the service. In this paper, the short-term bike availability forecasting at the station level is addressed using real-time open data from public bike station feed across many French cities. It is shown that deep learning methods are very good candidates with a mean forecasting error lower than 10% relative to the station capacity, even for limited training data volumes and number of features. The results are thus computed in a reduced time, opening the way for intelligent fleet management, particularly in near real-time.