[STAGE MASTER 2] - Bike sharing system rebalancing by reinforcement learning algorithms

Description

This internship project focuses on a specific component of a broader initiative to improve the dynamic rebalancing of bike-sharing systems [1,2,3]. The problem is addressed in two stages. Based on data at the station and travel needs at a given moment t, the number of bicycles available and needed will be predicted at time t. Points of origin and destination can be grouped together to improve the performance of spatio-temporal calculations of flow gradients from the micro scale at the station to the city scale [3,4].

This approach will thus make it possible to predict more quickly the number of bicycles used on the network and at stations in order to obtain a quasi-dynamic description of the system [6,7]. In a second stage, using these new estimated input data, real-time rebalancing is deployed. A reinforcement learning algorithm is then used and trained to propose and refine the dynamic redistribution strategy for bicycles [8,9]. The advan...