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

该项目的重点是预测比锡自行车共享系统每个站点的每小时需求、租赁和回报需求。我们提出的模型使用时间和天气特征来预测交通需求的主要特征(例如，从每个站点到每个站点的出行的平均值和方差)。该模型首先提取单车站点的主要交通行为。这些简化的行为然后被预测，并用于基于机器学习和统计推理技术的站级预测。最后，该模型确定库存间隔，这是共享单车公司在进行在线再平衡操作时经常使用的。

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.