# Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods
## 2 Materials and Methods
### Description of Initial Datasets
It is more efficient to eliminate data from large, systemic solutions, primarily for large areas of the city, than from separate, private solutions on sections of the road network. After the third stage, the determination of measures for ensuring sustainable mobility requires simulation modeling and an assessment of the mobility structure and operating parameters of the transport system. If the level of these indicators does not match the target value, it is necessary to proceed to the fourth stage. This stage consists of clustering correspondence on separate transport systems (private and PT) and identifying the most problematic public transport routes and sections of the road network. The first dataset is called \"Traffic\" and reflects the results of modeling the parameters of traffic between different areas of the city, taking into account the characteristics of these areas. As a dependent variable (y), the variable \(\mathfrak{t}_{\mathfrak{a}}\) was considered--the travel time in a loaded network, s. Factors in the amount of 32 were considered as independent variables (X), among which we can single out the distance of the source area from the city center, the average number of lanes on the road segments, the number of traffic lights on the way, the total number of maneuvers performed, the total delay time at intersections, and the like. The choice of 32 parameters from the city transport macromodel related to the base parameters is due to their total influence on the attribute of the PTV Visum program resistance, which ultimately determines the travel time. The dataset contains 139,341 rows (139,340 observations) and 33 columns corresponding to the number of dependent and independent variables. As part of the second set of data, \"Structure of mobility\", the influence of factors on the structure of the mobility of the city population was considered. Figure 1: Macromodel of traffic flows in the city of Tyumen, Russia. To plan the development of the urban transport network, among others, the indicator of the mobility structure is used, which expresses the percentage of types of transport used by the inhabitants of the area. As a rule, to determine the structure of mobility, simulation is performed using specialized software that requires significant time and computational resources. However, the model of the influence of factors on the structure of the mobility of the population of the city area has not yet been determined. The resulting model will make it possible to predict the structure of the mobility of the urban population depending on a number of initial factors, among which we can distinguish the travel time, taking into account the load of the road network, the distance of the area of correspondence formation to the city center, the number of traffic light objects along the route of road transport, etc. In total, 50 factors act as dependent variables that do not have linear relationships with each other and with the independent variable, which significantly complicates the use of other regression models. As the desired value (dependent variable y), the mobility structure of the city population is determined, which is expressed as a percentage of the following values: the share of trips by private transport (car_%); share of trips by public transport bus_%; share of trips by bike_%; share of pedestrian movements ped_%. The transposed dataset (first three rows) is presented in an abbreviated form in Table 1.  
The dataset contains 139,342 rows (139,341 observations) and 57 columns corresponding to the number of dependent and independent variables. Within the framework of the third dataset \"Regions\", the influence of factors characterizing the parameters of the city area on the share of population trips from and to the area, as well as on the structure of these trips, was considered.