Intelligent driving methods based on sparse LSSVM and ensemble CART algorithms for high-speed trains

Abstract

• Intelligent driving methods (IDMs) are proposed to solve the multi-objective control problem of HST. • We integrate the expert knowledge system, sparse algorithm and ensemble CART algorithm to realize IDMs. • We propose dynamic allocated strategies of operation time to reduce the operation time error. • L0-norm minimization and ensemble CART algorithms can greatly enhance the generalization ability of proposed IDMs. • The proposed IDMs are better than traditional ATO controller and manual driving method. Currently, high-speed train (HST) is mainly controlled by manual driving and automatic train protection system, which may reduce the comfort of passengers and impair the intelligence of train operation. In recent years, some intelligent driving methods have been proposed for subway line. However, because of the continuous rise of HST’s operation speed and mileage, the driving data collected from HST is more than that of subway and the intelligent driving model will be complex if the source driving data is directly trained by data mining algorithms. So, the source driving data sets are classified into several classes in terms of the features of the driving data. In addition, iterative sparse L 0 -norm minimization is applied to sparsify the classified driving data and thus the redundant data will be deleted, which can speed up the computation speed of learning process. Furthermore, ensemble CART, including B-CART and A-CART are used to find the driving rules of both experienced drivers and ATO controller. Finally, the field data of Beijing-Shanghai high-speed railway and ATO simulation data are used to test the performance of the proposed intelligent driving methods. Compared with A-CART, the energy consumption, and the redundancy of the training data set of S-A-CART algorithm can be respectively decreased by 0.27% and 40% and the passengers’ riding comfort can be increased by 17.71%.