# Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods
###### Abstract  
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. transport mobility; private and public transport; population mobility structure; artificial neural network; machine learning; road network +
Footnote †: journal: Medical  
transport models are also used to forecast transport demand in new areas and determine plans for the development of road transport infrastructure in transport planning documents. In recent years, the topic of simulated transport modeling has become increasingly more interesting in the global scientific community [4]. When creating new built-up areas in a transport model, it is important to predict the number of trips for various purposes and the structure of residents' mobility in this area. After receiving a forecast assessment of transport demand, it is possible to determine an effective traffic management scheme for sections of the road network located inside and on the border of a new transport area [5]. When carrying out renovation or implementing integrated development projects for territories, the category of land plots in the city master plan changes and the development areas with private or mid-rise residential buildings are replaced with multi-story ones [6]. At the same time, the population density and transport mobility in the development area significantly increase. At the border of transport areas, in places where local driveways adjoin the supporting road network, traffic congestion occurs, and public transport operates with overload; i.e., the quality of transport services for the population declines. For an informed decision on infrastructure development, it is necessary to determine the correspondence matrix in the transport macromodel and the structure of population mobility. At the same time, due to the lack of residents in the new area, it is impossible to conduct sociological surveys, and there is a need to use other methods for forecasting and assessing the parameters of population mobility. In the work [7], [PERSON] notes that transport demand in suburban areas, which in the case study of Irkutsk include gardening partnerships and cottage villages, is influenced by the area of the territory. At the same time, the share of trips by private cars varies from 0.74 to 0.94. When clustering, all the main transport areas of Irkutsk are divided into six classes: residential; industrial; central part and business center; and areas with a high, moderate, and low share of housing area.