An AdaBoost-Based Intelligent Driving Algorithm for Heavy-Haul Trains

Abstract

Heavy-haul trains have the characteristics of large volume, long formation, and complex line conditions, which increase the driving difficulty of drivers and can easily cause safety problems. In order to improve the safety and efficiency of heavy-haul railways, the train control mode urgently needs to be developed towards the direction of automatic driving. In this paper, we take the Shuohuang Railway as the research background and analyze the train operation data of SS4G locomotives. We find that the proportion of operation data under different working conditions is seriously out of balance. Aiming at this unbalanced characteristic, we introduce the classification method in the field of machine learning and design an intelligent driving algorithm for heavy-haul trains. Specifically, we extract the data by random forest algorithm and compare the classification performance of C4.5 and CART algorithms. We then select the CART algorithm as the base classifier of the AdaBoost algorithm to build the model of the automatic air brake. For the purpose of heightening the precision of the model, we optimize the AdaBoost algorithm by improving the generation of training subsets and the weight of voting. The numerical results certify the effectiveness of our proposed approach.