Bike sharing system rebalancing by reinforcement learning algorithms

This internship project focuses on a specific component of a broader initiative to improve the dynamic rebalancing of bike-sharing systems. 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+1. 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. 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. 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. The advantage of this approach lies in its ability to adapt to contextual disturbances and to resolve issues on a large scale. However, this performance comes at a cost and is detrimental to ensuring the most optimised solution is achieved. This internship will focus on the first stage of the project, which concerns the prediction and modeling of bicycle availability and demand dynamics. The objective will be to design and evaluate predictive models capable of capturing both spatial and temporal dependencies in the bikeshare system. The intern will explore and compare different machine learning approaches, such as time series forecasting, graph neural networks, or spatio-temporal convolutional architectures, to estimate short-term variations in bicycle flows at the station and network levels by using clustering, for example. The performance of the models will be evaluated against real operational data, and the results will serve as input for the reinforcement learning framework used in the second phase of the project. Depending on the progress and interests of the intern, additional exploration may include studying the integration of uncertainty quantification in predictions or the use of online learning methods to adapt models in real time as new data become available. The internship will provide the opportunity to gain hands-on experience in data science, spatio-temporal modeling, and urban mobility systems, while contributing to an innovative research topic with potential real-world applications.