Bike sharing systems: a new incentive rebalancing method based on spatial outliers detection

Since its launch, Velib' (the Bike Sharing System – BSS – in Paris) has emerged in the Parisian landscape and has been a model for similar systems in many cities. A major problem with BSS is the stations' heterogeneity caused by the attractivity of some stations located in particular areas. In this paper, we focus on spatial outliers defined as stations having a behaviour significantly different from their neighbouring stations. First, we propose an improved version of Moran scatterplot to exploit the similarity between neighbours, and we test it on a real dataset issued from Velib' system to identify outliers. Then, we design a new method that globally improves the resources' availability in bike stations by adapting the users' trips to the resources' availability. Results show that with a partial collaboration of the users or a limitation to the rush hours, the proposed method enhances significantly the resources' availability in Velib' system.