Application of artificial neural networks for shortterm prediction of container train flows in direction of China – Europe via Kazakhstan

International container transport plays an important role in the exchange of goods between China and Europe, and accordingly, the efficiency of the transportation increases with the organization of special container lines (land and sea). Owing to its geographical location, the territory of Kazakhstan has become one of the main international landlines for passage of container cargo in recent years. Priority is given to solution of such problems as reduction of cargo delivery time, simplification of customs operations, setting attractive and competitive tariffs, ensuring a high degree of cargo safety, development of transport infrastructure, assessment of the transit potential of railway network of the country, and predicting future cargo flows. This article shows the use of artificial neural networks (ANN) for predicting container train flows in the direction of China – Europe. For this purpose, a three-layer perceptron with a learning algorithm, based on the back-propagation of the error signal, was used. A concreto example shows how the ANN training process is conducted and how the adjustable parameters are selected.