Forecasting the Number of Freight Trains by Categories Using Time Series Regression Analysis

A new hybrid forecasting model of the number of freight trains is proposed in this paper. This forecasting is performed by combining classification analysis and the use of Histogram-based Gradient Boosting Regressor (HGBR). The freight trains were classified into categories according to the transportation parameters and the train’s structure. A total of 100,441 freight trains with a constant number of wagons have been studied. The trains were ran over a 3-year basis period. The forecasting was performed using train categories for a forecast period lasting 1 year. The results obtained in this study have been validated by comparing them against real data from the trains that ran during the forecast period. The adequacy of the forecasting model was assessed using standard indicators, with the value of the Coefficient of Determination equal of 0.805. The results show the high level of accuracy of the model, despite discrepancies in the data of the base period concerning the train parameters for 34% of the trains.