Resumen
A method has been proposed to predict the expected departure time for a cargo dispatch at the marshaling yard in a railroad system without complying with a freight trains departure schedule. The impact of various factors on the time over which a wagon dispatch stays within a marshaling system has been studied using a correlation analysis. The macro parameters of a transportation process that affect most the time over which a wagon dispatch stays within a marshaling system have been determined. To improve the input data informativeness, it has been proposed to use a data partitioning method that makes it possible to properly take into consideration the impact of different factors on the duration of downtime of dispatches at a station. A method has been developed to forecast the expected cargo dispatch time at a marshaling yard, which is based on the random forest machine learning method; the prediction accuracy has been tested. A mathematical forecasting model is represented in the form of solving the problem of multiclassification employing the processing of data with a large number of attributes and classes. A classification method with a trainer has been used. The random forest optimization was performed by selecting hyperparameters for the mathematical prediction model based on a random search. The undertaken experimental study involved data on the operation of an out-of-class marshaling yard in the railroad network of Ukraine. The forecasting accuracy of classification for dispatching from the wagon flow "transit without processing" is 86 % of the correct answers; for dispatching from the wagon flow "transit with processing" is 54 %.The approach applied to predict the expected time of a cargo dispatch makes it possible to considerably improve the accuracy of obtained forecasts taking into consideration the actual operational situation at a marshaling yard. That would provide for a reasonable approach to the development of an automated system to predict the duration of operations involving cargo dispatches in a railroad system