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.

The article presents the initial experience (spring-summer 2023) of using artificial neural networks (ANN) to improve traffic management in the large Russian city of Tyumen. Using the example of one of the intersections of the city's road network, it is shown how much transport delays are reduced when the duration of the traffic light cycle phases is quickly adjusted to the actual traffic intensity when compared with the usual previously used traffic light predictive mode. For the specific intersection of Odesskaya and Kotovskogo streets in Tyumen, considered in this article, the traffic light control mode using an ANN can significantly (by 20.6 ... 22.4%) reduce the average delay time of vehicles. It is also important that the reduction in traffic delays, which is possible with the regulation of traffic using ANN, helps to reduce stress for road users and improve road safety. The article presents historical data illustrating the dynamics of changes in the field of traffic management and road safety in Tyumen. This information confirms the thesis about the dialectic of systemic development and the need for a gradual increase in the intellectual component of traffic management in large cities. The Applications (Appendix A and Appendix B) present the code of the auxiliary procedures and functions module and the code of the main data collection module used to optimize the traffic light control mode at the experimental intersection of the Tyumen road network. The main conclusion of the study is that the use of an ANN allows for taking into account a much larger number of factors and optimizing the control of the entire object, consisting of several intersections, which is not achievable using predictive modes and local adaptive control.

This study investigates the influence of spatial heterogeneity on urban transport demand forecasting. A two-stage framework combining cluster analysis and machine learning was applied to origin-destination trip data generated by a calibrated transport model of a large city. Origin-destination pairs were grouped according to travel conditions using K-Means clustering based on private transport trip length and public transport travel time. The identified clusters were subsequently analyzed with respect to travel costs, modal split, spatial distribution, and transport demand characteristics. Machine learning models were then developed to predict transport demand for public transport, private transport, and walking. Two forecasting strategies were compared: a unified model trained on the entire dataset and cluster-specific models trained separately for each identified cluster. The results revealed significant spatial heterogeneity in travel conditions and transport demand structure. Cluster-specific machine-learning models reduced prediction errors for public and private transport demand, while the magnitude of improvement varied across travel modes and evaluation metrics. The findings demonstrate that accounting for spatial heterogeneity influences transport demand prediction performance, with the greatest improvements observed for public and private transport demand, whereas the effect was less pronounced for walking demand.

This article examines the spatial and temporal unevenness of the transport complex operation in a large city with a population of about 0.9 million people and without off-street transport. The patterns of changes in the number of passengers transported in the city are described by a harmonic model, and seasonal unevenness with different numbers of peak values is noted. All routes can be divided into three groups based on the trend in passenger volume. The largest number of routes exhibited a downward trend in passenger volume. A downward trend in passenger volume is observed in the total number of passengers on all routes, despite an increase in the city’s population. Parking occupancy rates also show seasonal fluctuations. A downward trend in paid parking occupancy rates is emerging in the city’s central administrative and business district. The results of the study are relevant for choosing methods for managing the transport behavior model. Analysis of uneven passenger numbers on individual routes is necessary for improving the route network and determining the optimal number and passenger capacity of buses. Analyzing uneven occupancy rates in paid parking lots allows for the development of differentiated rates. The methods used in this article can be integrated into a city’s digital twin to improve forecasting accuracy.

The application of computer vision and traffic flow modeling with an adaptive traffic light control algorithm for prioritizing the movement of cargo transport is described. The study objective is to increase the efficiency of traffic management in the cities of the oil and gas complex by developing a traffic light control algorithm that gives priority to cargo vehicles. Python, Ultralytics framework (YOLO), OpenCV, and libraries for working with data (Pandas, NumPy) are used to solve computer vision problems. For micromodeling of traffic flows, PTV Vissim is used together with VisVap or LISA+ module for adaptive traffic light control. With the introduction of priority passage for cargo vehicles at a controlled intersection, the traffic parameters for the general traffic flow change slightly, while for cargo vehicles the changes are significant. With an increase in the traffic intensity of cargo vehicles, priority travel has a positive effect on the traffic parameters of the entire traffic flow. With the use of neural network technologies and the integration of data into technical means of traffic management, it is possible to increase the average speed of traffic flows.