Section: New Results

Ressource allocation in bike sharing systems

Vehicle sharing systems are becoming an urban mode of transportation, and launched in many cities, as Velib' and Autolib' in Paris. Managing such systems is quite difficult. One of the major issues is the availability of the resources: vehicles or free slots. These systems became a hot topic in Operation Research and the importance of stochasticity on the system behavior leads us to propose mathematical stochastic models. The aim is to understand the system behavior and how to manage these systems in order to improve the allocation of both resources to users.

To improve BSS (bike-sharing systems), two types of policies can be deployed: incentives to the users to choose a better station, called natural or green regulation, or redistribution by trucks, called active regulation. In a simple mathematical model, we proved the efficiency of the 2-choice incentive policy for BSS (bike-sharing systems). The drawback of the model is that it ignores the geometry of the system, where the choice is only local. The purpose of this first work is to deal with this policy in real systems.

We use data trip data obtained from JCDecaux and reports on station status collected as open data, to test local choice policy. Indeed we designed and tested a new policy relying on a local small change in user behaviors, by adapting their trips to resource availability around their departure and arrival stations, based on 2-choice policy. Results show that, even with a small user collaboration, the proposed method increases significantly the global balance of the bike sharing system and therefore the user satisfaction. This is done using trip data sets and detecting spatial outliers, stations having a behavior significantly different from their spatial neighbors, in a context where neighbors are heavily correlated. For that we proposed an improved version of the well-known Moran scatterplot method, using a robust distance metric called Gower similarity. Using this new version of Moran scatterplot, we show that, for the occupancy data set obtained by modifiying trips, the number of spatial outliers drastically decreases. We generalize this study with W. Ghanem and L. Massoulié testing incentive and redistribution policies on a simulator, where the tradeoff between the number of frustrated trips and the penalty for the users can be measured. We propose new versions of these policies including prediction.