Machine learning methods for station-level traffic prediction in bike-sharing systems **************

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This project focus on predicting the hourly demand for demand rentals and returns at each station of the BIXI's bike sharing system. Our proposed model uses temporal and weather features to predict the main characteristics of the traffic demand (e.g. mean and variance of trips from/to each station). The model first extracts the main traffic behaviors from the bike stations. These simplified behaviors are then predicted and used to perform station-level predictions based on machine learning and statistical inference techniques. Finally, the model determines inventory intervals, which are often used by bike sharing companies for their online rebalancing operations.