In the previous work of the authors [1], an intelligent system for the automation of dispatching activities in organizing the movement of railway transport was presented. This concerned adjusting schedules in multiagent systems in the event of unexpected changes in situations. This article specifies the main approach and describes in detail the model, solution method, and possible modernization of the system.

The results of studying the architectures of deep neural networks designed to solve classification problems are presented. As a result, attributes are formed for effective decision-making automation. Multidimensional time series of financial markets are used as data. The problems of binary and multiple classification are considered. Fully connected, recurrent (long short-term memory (LSTM)) and hybrid combined architectures of neural networks are analyzed. The studied multivariate time series is obtained by combining a one-dimensional time series of asset value, trading volume, technical indicators, and other parameters.

The application of artificial intelligence in the development of а decision support system for the implementation of transport traffic is presented. Such systems are designed to adjust the schedule of objects in cases of unforeseen situations. A fully connected artificial neural network with several hidden layers, trained using a genetic algorithm, is used. During training, the functionality that characterizes the deviation from the specified schedule is minimized. Railway traffic is one of the most important types of transport in Russia. Every year it becomes more and more intense, the density and volume of both cargo and passenger traffic increases. As a result, the requirements for the exact execution of the planned traffic schedule increase, since any deviation leads to significant penalties due, for example, to an increase in train delays, their cancellation, etc. The work of a dispatcher, a person who controls railway traffic, is quite time-consuming and becomes more difficult every day, so the development of dispatcher assistance systems is one of the most relevant areas in control automation in this area. At the same time, the existing high requirements for traffic safety, which impose additional restrictions, finally lead to the fact that in this kind of system, the final decision is left to the person, and computer development has a recommendatory character. This article describes the artificial intelligence apparatus in the form of training neural networks using a genetic algorithm to build an automated dispatcher that corrects movement.

A description of the use of artificial intelligence in the development of decision support systems, which are used for various types of transport, is given. These systems are aimed at restructuring the schedule of movement of objects due to unforeseen deviations from the preplanned schedules. Machine learning of a neural network using a genetic algorithm is used. This minimizes the functionality that characterizes the deviation from the given schedule.