# Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods
## 2 Materials and Methods  
The city transport macromodel consists of transport demand and transport supply models. The main element of the transport supply model is the transport area. It is the area in which the number of residents living in this territory is recorded, as well as the number of jobs in various sectors of the economy. Based on these data, the most important element for modeling, the correspondence matrix, is formed. Transport areas are elementary units of the spatial structure of the modeling area. The share of trips of city residents by various modes of transport is a parameter for assessing the transport behavior model. Increasing the share of trips by public transport and reducing the number of trips by private cars is a target function in municipal programs and projects to ensure sustainable mobility with a defined numerical value or mandatory conditions for achieving the goals of the project. For example, in Tyumen, in the transport planning document for 2040, the share of trips by PT should be increased from 0.41 to 0.55. To achieve the specified values, it is necessary to know what the share of trips by a mode of transport depends on and what factors have the greatest impact on this indicator. The level of development of the road infrastructure depends on the number of exits from subdistricts to the supporting road network, traffic density, coefficient of non-straightness of traffic routes, etc. The level of development of the transport infrastructure depends on the density of the route network, the carrying capacity of sections of the road network, the connectivity of planned areas of the city with private and PT routes, and the number of top routes at stopping points, and it differs for the central and peripheral areas of the city. Factors influencing the model of transport behavior are determined by the spatial characteristics of the city, the level of development of the road and route network, and the quality of the PT operation and include the area and shape of the city, the distance of the source area from the target area, the length of the private cars and PT traffic routes, trip time during peak hours and inter-peak times, and the number of transfers when traveling by PT. This study examines the impact of travel time on the total number and share of trips taken by private or public transport in a large city without off-street transport. The paper considers a hypothesis that the model of transport behavior and the structure of population mobility depend on the location of transport areas relative to the city center and that as travel time increases, the share of trips by personal transport increases, and the share of trips by public transport decreases. The influence of the average travel time from the PT source area on the share of trips by public transport is determined by Equation (1):  
\[\Delta_{PT}=\frac{1}{1+e^{-(a+bT_{j}PT)}}, \tag{1}\]  
where \(\Delta_{PT}\) is the share of trips by public transport; \(\Delta_{0}\) is the maximum value of the share of PT trips at the minimum value of \(T_{j}PT\); \(T_{j}PT\) is the average travel time of the PT from the \(j\)-th transport area source (target) of travel, min; \(a\), \(b\) are model parameters. The form of Equation (1) is determined by taking into account the use of a multinomial logit model to assess the probability of a user choosing a transportation mode. It is assumed that with minimal average values of PT travel time for the j-th transport area source of travel, the change in the share of PT trips is small. As the PT travel time value leaves the \"comfort zone\" for passengers, their desire to reduce time loss by using a personal car increases. With a significant increase in the travel time of the PT, the share of its use changes slightly and is determined by the group of people who do not have a personal car or the ability to use it.