Intelligent Train Operation Models Based on Ensemble Regression Trees - DeWang Chen; XiangYu Zeng; GuiWen Jia (Institute of Advanced Engineering and Science, 2014)
Traditional control algorithms in Automatic Train Operation (ATO) system have some drawbacks, such as high energy consumption and low riding comfort. Combined with data mining methods and driving experience, two Intelligent Train Operation (ITO) models for the subway train control are proposed. Firstly the training data set was sorted out and sieved out from the real train operation data set by drivers in Beijing subway line Yizhuang to establish the standard database. By using Classification and Regression Trees (CART) algorithm and Bagging ensemble learning method which base on CART algorithm, two ITO models are dug out to represent the output of controller with limited speed, running time and gradient. In the train control simulation platform, ITO models were compared with the traditional PID (Proportional Integral Derivative) control algorithm of ATO systems. The simulation results indicate the proposed ITO models are better than PID control in energy consumption, riding comfort and switching times of controller’s output. Furthermore, the ITO model with bagging ensemble learning method is better especially in energy consumption and riding comfort.