Features of application of progressive methods of predictive modeling for solving problems on transport
The paper analyzes the potential of forecasting approaches based on progressive methods of predictive modeling in relation to the transport sector. In the context of peculiarities of Russian and foreign business practices, the possibilities of modern technologies for working with data are considered, schemes and conceptual models formalizing their specifics are formed. Characteristic features of common algorithms and methods are described, the risks and disadvantages of unjustified use of traditional predictive models on the example of simple and multiple regressions are shown. Special attention is paid to the potential of machine learning in solving applied transport problems. The author’s definition of machine learning methods is presented for consideration, characterizing their position in relation to other classes of rational approaches and methods of decision support. The prospects, advantages and challenges of using modern algorithmic implementation of the machine learning concept for forecasting are outlined by the example of analogy using Monte Carlo methods. The results of evaluation of promising models on the example of recurrent neural networks of the architecture of long-term short-term memory such as LSTM for time series forecasting, as well as the problems of introducing such models into the production practice of transport companies, are presented for consideration.