The paper formulates the car-specific metro train crowding prediction problem based on real-time load data and evaluates the performance of several prediction methods (stepwise regression, lasso, and boosted tree ensembles). The problem is studied for multiple stations along a metro line in Stockholm, Sweden. Prediction accuracy is evaluated with respect to absolute passenger loads and predefined discrete crowding levels. When available, predictions with real-time load data significantly outperform historical averages, with accuracy improvements varying in magnitude across target stations and prediction horizons.


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    Title :

    Car-Specific Metro Train Crowding Prediction Based on Real-Time Load Data


    Contributors:


    Publication date :

    2018-11-01


    Size :

    737028 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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