关键词: 列车精确停车, 高斯过程, Boosting, 回归

Abstract: Precise train stopping is one of the key technologies of automatic train control system.Traditional technologies of precise train stopping depend on complicated physical model and expensive sensor equipment，and it is hard to achieve high precision.The data themselves are utilized，applying Gaussian process regression and Boosting regression in the filed of machine learning，to study the problem of precise train stopping.The above methods are compared with linear regression.It is shown in the experiment that，the methods of machine learning are effective to the problem of precise train stopping.Gaussian process regression attains the best performance compared with the other methods.Gradient-based Boosting regression，with its performance approximating to that of Gaussian process regression in the lack of prior knowledge，demonstrates its flexibility and adaptability in practical applications.

Key words: precise train stopping, Gaussian process, Boosting, regression