# Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods
## 4 Discussion  
A scientific problem has been solved to determine models and predict the influence of factors characterizing the transport infrastructure of a city area on the structure of the mobility of the population of this area and on traffic parameters, as well as on the share of population trips from and to the area and on the structure of these trips. Neural network architectures have been developed to solve the regression problem for predicting the indicated parameters. The method of artificial neural networks with one (for predicting traffic parameters and mobility structure) or several hidden layers (for predicting the share of population trips from and to the area, as well as the patterns of these trips) was used. Neural networks are trained on a large sample of initial data obtained from the results of simulation macromodeling of urban transport network parameters and divided into three datasets: \"Traffic\", \"Mobility\", \"Areas\". Based on the results of determining the quality metrics of neural network models, their accuracy was determined, which is 99%, 92%, and 68%, respectively, for the datasets \"Traffic\", \"Mobility\", and \"Areas\", from which we can conclude that they are adequate and have a high predictive value capability. Figure 11: Average public transport travel time and travel distance influence on the share of travel by private and PT (target areas): (**a**) travel time influence; (**b**) travel distance influence. Figure 10: Average car travel time and travel distance influence on the share of travel by private and PT (target areas): (**a**) travel distance influence; (**b**) travel time influence. The above models of artificial neural networks were saved as separate files that store the architectures of the models and the weight coefficients of neurons. Pre-trained models can be used to predict the parameters of urban mobility without the need to form a macromodel of the city and conduct repeated simulations. Based on the obtained artificial neural network, a program was developed to assess the structure of the mobility of the city population. The program is intended for the Administration of the city of Tyumen, subordinate industry institutions, and design organizations involved in the development and adjustment of transport planning documents, as well as the organization of transport services for the population of the city. Transport areas belonging to the same class have different ratios of the share of trips by private and PT. Transport behavior is determined by a group of factors. Examples include the level of transport services for the population, taking into account the number of public transport routes, the coverage of the territory of a populated area by public transport routes in each area, and the number of multimodal routes that require a transfer to another mode of transport or to another route with re-payment of travel fares. For example, there are differences in the model of transport behavior in residential areas located at different distances from the city center. Thus, in the cottage village of Komarovo, located on the border with the bypass of the city of Tyumen, the share of trips by public transport is 0.55. For the village Derbyshi, which is located further from the bypass road, this figure is 0.4. The ways of further research are determined and consist of the following:  
Clustering of correspondence on separate transport systems (private and PT) and determination of the most problematic PT routes and road network sections;  
Use of artificial neural networks in the transport planning of urban infrastructure, taking into account the economic factors of its organization. The implementation of measures to ensure sustainable urban mobility requires financial resources from municipal or regional budgets. At the same time, the effectiveness of individual measures may differ significantly for transport areas, taking into account their location, and requires detailed elaboration in transport planning documents. Conceptualization, D.Z. ; formal analysis, E.K. and A.P.