# Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods
###### Abstract
In the work, the author established an influence pattern of the area's distance from the city center on the specific correspondence generation, which is described by a linear equation. In this case, it is necessary to take into account the seasonal unevenness of transport demand in relation to gardening partnerships in comparison with the areas of private residential development. At the same time, the number of residents in the city affects the volume of passenger transportation by public transport. Transport behavior and mobility structure depend on the level of development of public transport and the road network [8]. At the same time, traffic routes and stopping points are connected to transport areas of the city and affect the level of development of public transport infrastructure in the city areas. The share of trips by private and public transport (PT) is a formal assessment of the transport behavior of the population. A number of works note the need to consider the concept of transport behavior from the point of view of psychology, sociology, and urban studies. An important element of urbanism is spatial settlement and the level of transport services for the population in city districts. Research in the field of sustainable mobility has become actively widespread since the digitalization of the transport complex began and there emerged technical means that record the parameters of the urban transport system in real time or with a short time delay. The values of these parameters are necessary for planning the parameters of the city's transport infrastructure. For example, in [9], the parameters of transport infrastructure were used when planning mixed land use. Moreover, the number of studies of this kind aimed at socially sustainable transport is only growing [10]. To determine the parameters of the urban transport system, simulation modeling can be used on specialized software that requires the macromodel itself, data, and a lot of time and computational resources. The appearance on the city streets of an increasing number of detectors [11], video cameras, electronic methods of paying for travel on PT, parking, and rental vehicles (car sharing cars, bicycles, electric scooters) made it possible to generate big data and, through its processing, reduce the error and increase the adequacy of the patterns and models established by researchers, as well as increase the accuracy of forecasting when carrying out simulation modeling of the operation of transport systems. To process big data on the operation of urban transport systems when studying sustainable active mobility such as Mobility-on-Demand systems [12; 13], bike sharing [14], and others [15], machine learning methods, including neural network technologies, are used. The work [16] describes how deep learning models and neural networks are created to predict the structure of population mobility, i.e., the share of trips by private and public transport. The authors considered seven different models with varying accuracy. The model with an artificial neural network (ANN) turned out to be the most accurate. The work [17] describes the use of ANNs for processing data on the actual transport mobility of the population by processing the GPS signal to further determine the mode of transport. The model determines the transportation mode by analyzing the trajectories and speeds obtained from the tracker. The authors study the effectiveness of different deep learning models and note the greatest efficiency of the ANN-based model. Deep learning and ANNs are used to predict trips by taxi and bicycles using GPS, taking into account holidays, weather conditions, ambient temperature, and travel speed [18]. Computer vision using an ANN is used to determine the flow density from a video camera [19] with the purpose of further taking into account data on the traffic flow rate for operational traffic management, as well as assessing pollutant emissions from vehicle exhaust gases [20; 21].