In this paper, we model the intensity of cross-border railway traffic on the Alpine-Western Balkan Rail Freight Corridor (AWB RFC). For each of the four border crossing points: Dimitrovgrad, Presevo, Sid, and Subotica, time series composed of 102 monthly export and import railway freight traffic observations are used for training and testing of alternative forecasting models. Traditional ARIMA, Long-Short-Term Memory (LSTM) neural network, hybrid ARIMA-LSTM and Singular Spectrum Analysis (SSA) models, are fitted to each of the time series. For all the considered time series, the best model was chosen based on the lowest values of commonly used metrics for measuring the performance of forecasting models. LSTM models outperformed all other models with the highest prediction accuracy while SSA models exhibited the lowest accuracy. By utilizing advanced forecasting models, this research contributes to finding effective solutions for addressing the issue of inadequate planning of border crossing procedures in railway traffic.