The article discusses the issues of spatial unevenness of transport demand in the city by various transport modes. It describes the creation of models using an artificial neural network to estimate the travel time and share by private and public transport in a large city that does not have off-street transport. The city transport macromodel in PTV Visum (V.18) was used as a data source, from which data were obtained on 50 basic parameters taken into account in the specialized software during the development of the transport model. In total, 50 factors act as independent variables that do not have linear relationships with each other and with the dependent variable, which significantly complicates the use of other models. These models allow assessing the influence degree of the most important factors. Further, the article shows the uneven spatial distribution of the shares of trips by private and public transport across the areas of a city. Using machine learning methods, the transport areas of Tyumen were clustered into nine classes belonging to the central sector, where the share of public transport is significantly higher than at the city border. The dependence of the trip share by cars and shuttle buses on the average travel time and distance by private and public transport for each class of transport areas has been established. The research results can be used when creating new transport areas in the city macromodel and when adjusting transport planning documents. The methods used for analyzing big data on the operation of the transport complex can be implemented in the digital twin of the city and the Intelligent Transport System.

The study describes the use of evolutionary methods or genetic algorithms for the technological design of newly constructed or modernized car service stations. Genetic algorithms are one of the types of machine learning models and are actively used to solve multifactor optimization problems. A task of this type is to search for the technical parameters of a car service enterprise under which the economic indicators of its activities will correspond to the profit or capital cost restrictions set by the user. The paper presents the parameters of the developed model, fitness functions, and also provides an assessment of the effectiveness of using the method of genetic algorithms relative to the method of simply enumerating different options for combinations of initial factors. Purpose. Increasing the efficiency of management of road transport enterprises by using the method of genetic algorithms for strategic planning tasks. Methodology. The research uses the method of genetic algorithms to solve a multi-criteria reverse optimization problem in the technological design of a car service station Results. The use of the method of genetic algorithms for the design of service stations and enterprises for the maintenance and repair of vehicles is justified, taking into account the restrictions or targets established at the beginning of the design. Practical implications. The results of the research can be used by the management of enterprises for the maintenance and repair of vehicles in their technological design, strategic planning of activities and modernization.