For the heavy weight, long marshalling, complex line conditions and difficult driving of heavy-haul trains, the train control mode needs to be developed towards automatic driving urgently. At present, the automatic driving control algorithm applied to urban rail transit cannot meet the needs of heavy-haul trains. In this paper, we collect the operation data of heavy-haul trains in Shuohuang railway, and design an algorithm to calculate the air brake decompression to complement the original data. Besides, we extract the data by random forest algorithm, and introduce the AdaBoost algorithm and CART classifier to build the model of automatic air brake. In order to heighten the precision of the model, we optimize the AdaBoost algorithm by improving the generation of training subsets and the weight of voting. The simulation results demonstrate the effectiveness of our proposed method.


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    Title :

    An AdaBoost-based Intelligent Driving Algorithm for Heavy-haul Trains


    Contributors:
    Wei, Siyu (author) / Zhu, Li (author) / Wang, Hao (author) / Lin, Qingqing (author)


    Publication date :

    2021-09-19


    Size :

    788680 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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