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

本项目重点预测BIXI共享单车系统每个站点每小时的需求和回报。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（例如往返每个车站的平均和方差）。该模型首先提取自行车站点的主要交通行为。然后对这些简化的行为进行预测，并用于基于机器学习和统计推断技术执行站级预测。最后，该模型确定库存间隔，这通常被共享单车公司用于在线再平衡操作。

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.