Abstract Boosting the rail freight modal share is an ambitious target in Europe and North America. Yards, where freight trains are arranged, can be crucial in realizing this target by reliable dispatching to the network. This paper predicts freight train departures by developing a simulation‐assisted machine learning model with two concepts: general (adding all predictors at once) and step‐wise (adding predictors as they become available in sub‐yard operations) for hump yards with the conventional layout to provide a generalized model for European and North American contexts. The developed model is a decision tree algorithm, validated via 10‐fold cross‐validation. The model's performance on three data sets—a real‐world European yard, a baseline simulation, and an ultimate randomness simulation for a comparable North American yard—shows a respective R2 of 0.90, 0.87, and 0.70. Step‐wise inclusion of the predictors results differently for the real‐world and simulation data. The global feature importance highlights maximum planned length, departure weekday, the number of arriving trains, and minimum arrival deviation as key predictors for the real‐world data. For the simulation data, the most significant predictors are departure yard predictors, the number of arriving trains, and the maximum hump duration. Additionally, utilization rates—except for the receiving yard—enhance the predictions.


    Access

    Download


    Export, share and cite



    Title :

    Enhancing freight train delay prediction with simulation‐assisted machine learning


    Contributors:


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Enhancing freight train delay prediction with simulation‐assisted machine learning

    Minbashi, Niloofar / Zhao, Jiaxi / Dick, C. Tyler et al. | Wiley | 2024

    Free access

    Train Delay Prediction Using Machine Learning

    Dawale, Nilesh N. / Nandgave, Sunita | Springer Verlag | 2023


    FREIGHT TRAIN CHASSIS AND FREIGHT TRAIN

    LIU YINHUA / DUAN YUANYONG / ZHAI PENGJUN | European Patent Office | 2024

    Free access

    Freight train carriage and freight train with same

    ZHANG JUNLIN / ZHAO TIANJUN / YUE LINGHAN et al. | European Patent Office | 2020

    Free access

    MACHINE TO MACHINE COMMUNICATION SYSTEM FOR FREIGHT TRAIN

    WON JONG UN / KWON YONG JANG / SUK LEE et al. | European Patent Office | 2016

    Free access