In urban rail transit, the existing methods used for arrival time prediction have low accuracy. A high-precision predictive method for train arrival times can reduce the workload of train drivers by providing the predictive terminal arrival time and satisfy the requirement of headway control model. This paper proposes the predictive algorithms for train arrival times using back-propagation (BP) neural network, wavelet neural network and genetic algorithm. These algorithms include two parts: running time prediction for sections between two stations and dwell time prediction for stations. The real data on train operation are used for training and testing the predictive algorithms. The feasibility of these algorithms is validated. A comparison between these algorithms is given. The results prove that these prediction algorithms for train arrival times achieve high accuracy.


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    Title :

    Prediction algorithms for train arrival time in urban rail transit


    Contributors:
    Liu, Yafei (author) / Tang, Tao (author) / Xun, Jing (author)


    Publication date :

    2017-10-01


    Size :

    506968 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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