In today’s world, many processes and events depend on forecasting. With development of mathematical models, an increasing number of factors influencing the final result of the forecast are taken into account, which in turn leads to the use of neural networks. But for training a neural network, source data sets are required, which are often not always sufficient or may not exist at all. The article describes a method of obtaining information as close to reality as possible. The proposed approach is to generate input data using simulation models of an object. The solution of a problem of generation of data sets and of training of a neural network is shown at the example of a typical marshalling railway station, and of a simulation of operations of a shunting hump. The considered examples confirmed the validity of the proposed methodological approach to generation of source data for neural networks using simulation models of a real object, based on a digital mathematical model, which makes it possible to obtain a simulation model of movement of transport objects, which is reliable in forecasting transport processes and creating relevant control algorithms.