This paper examines the impact of transport infrastructure on the economic development of regions, with an emphasis on sustainable development. Econometric models were used for the analysis, including generalized models and models with fixed and random effects. An assessment of factors such as the number of cars and the inflow of foreign direct investment showed their significant positive impact on the GRP per capita. At the same time, the consumer price index and the number of buses showed a negative impact. The best results in considering the specifics of the regions were obtained when using models with fixed effects. The results of the study highlight the key role of transport infrastructure for economic growth and highlight the importance of integrating sustainable development principles into the planning and development of transport systems. Sustainable development requires increasing economic activity and minimizing negative environmental impacts and ensuring social justice. Embedding sustainability principles in transport planning is expected to contribute to the creation of balanced and environmentally friendly infrastructure solutions that will ensure long-term prosperity. The models developed in the study can become the basis for the development of recommendations for improving transport systems aimed at maintaining sustainable economic growth and environmental stability.

Introduction. Road safety issues occupy scientists around the world. This study is aimed at creating a comprehensive digital model that will become a tool for developing recommendations for improving road safety in the regions of the Russian Federation. Methods. The assessment of the current state of road safety in the regions of Russia was carried out by means of rating. The object of the study was studied using econometric models, machine learning models, and system dynamics; sensitivity analysis and a balanced scorecard were used. Results. The regions of Russia were divided into three groups according to the level of safety. The econometric model and machine learning model made it possible to assess the influence of independent variables on dependent variables. The identified interrelations formed the basis of a system dynamics model. It was concluded that it is possible to extrapolate the results to groups of regions. For each group of regions, recommendations are given on the formation of a strategy for improving road safety. Conclusions. The practical significance of the study lies in the creation of a tool for the formation of recommendations for the creation of a strategy for improving road safety in the regions of the Russian Federation.

The article explores the impact of digital transformation on the processes associated with industrial facilities in the oil and gas sector, with the aim of enhancing their efficiency. The object of the study is the economic entities involved in the operation of an oil and gas field, while the focus is on the integration of advanced digital technologies — such as the Internet of Things, building information modeling (BIM), additive manufacturing, robotics, and artificial intelligence —across the entire life cycle of industrial facilities. The application of digital tools is examined at various life cycle stages, from conceptual design through facility commissioning to decommissioning. The article outlines the stages of the field development life cycle — "Design," "Field Development," and "Decommissioning"— and provides a detailed breakdown of the "Design" and "Field Development" phases. A matrix linking digital technologies to specific life cycle stages of oil and gas field development has been formed, serving as the foundation for constructing an ontology of facility development. Based on this framework, the authors propose recommendations to support the digital transformation of economic entities involved in oil and gas field development, presented in the form of a strategic roadmap.