Transport Research Institute

This work focuses on the theoretical and experimental study of the dynamic behavior of the Vilnius electronic oscillator. The theoretical study involves modeling the system using a set of three first-order differential equations and performing its numerical analysis for different values of the circuit components. The experimental study is carried out using electronic components with a control parameter. It is demonstrated that, depending on the value of the resistance connected to the non-inverting input of the operational amplifier, the Vilnius oscillator can exhibit different behaviors and generate various types of dynamics, including harmonic oscillations and chaos. This result is supported by a bifurcation diagram and the Lyapunov exponent curve. The numerical and experimental results show good qualitative agreement.

The article is devoted to the urgent problem of the sustainable development of the industrial potential of the regions in Russia in the context of structural transformations and external challenges. Using the example of the Orenburg region, the patterns and imbalances of industrial development are studied, which are important for the formation of a scientifically sound regional economic policy. The relevance is due to the need to develop differentiated approaches to managing the industrial complex of the region, taking into account the specifics of individual types of economic activity. A comprehensive analysis of the dynamics of the volume of shipments of goods of our own production in the Orenburg region for the period 2020-2023 by main types of economic activity was carried out. The study revealed a steady trend towards strengthening the sectoral diversification of the region's industrial complex. The application of polynomial trends demonstrated the multidirectional dynamics of the development of individual sectors of the economy. The developed multifactorial regression models for each type of activity revealed the differentiated influence of key factors of production. Based on the analysis, practical recommendations are proposed for the region's governing bodies, including the development of a differentiated sectoral policy, stimulating investment in the most effective areas, developing special programs to support problem sectors and creating a monitoring system based on the developed models. The behavioral research provides a scientific basis for making managerial decisions and developing a medium-term strategy for the industrial development of the Orenburg region.

This article discusses the maintenance of railway bridges. It lists the factors that affect the frequency of inspections of railway bridges. It also describes the technologies used for the current maintenance of railway bridges and the consequences that may arise if the maintenance standards for railway bridges are not followed.

Climate change brings more challenges to agriculture, ecosystems, and human health, with increasing incidences of soil degradation, water scarcity, and contamination from heavy metals. Hydrophytes are water-loving plants which offer a sustainable, environment friendly solution through their natural capacity for phytoremediation. The study evaluates the role of hydrophytes in climate change mitigation, and Sustainable Development Goal 13 (SDG13), mainly focusing on the role of hydrophytes in the bioremediation of heavy metals. By utilizing hydrophytes in bioremediation practices, the study explores innovative pathways for developing resilient food systems and restoring aquatic ecosystems amid changing climatic conditions. Integration of hydrophyte-based bioremediation systems into climate change adaptation and mitigation strategies offers a low-cost, nature-based solution, particularly for developing regions. The chapter emphasizes the need for interdisciplinary approaches, supportive policies, and public awareness to harness the full potential of hydrophytes in achieving SDG13.

Electric Vehicle (EV) smart charging has the potential to alleviate strain on the electricity grid due to increasing EV charging demands, while also offering additional advantages such as improved integration of renewable energy sources and cheaper electricity. However, the success of this technology hinges on the willingness of EV drivers to participate in smart charging programs. This study utilises stated preference data (n = 1017) to examine consumer preferences towards smart charging program attributes. It is found that an increase in electricity prices associated with EV smart charging has a significant negative effect while higher daily guaranteed driving range, greater flexibility and financial incentives have a significant positive effect. This study contributes knowledge crucial for the design and successful implementation of EV smart charging initiatives. The findings will guide stakeholders in accelerating the EV fleet transition while mitigating potential conflicts associated with the impact of EV charging on the electricity grid.

Machine learning (ML) models are increasingly getting attention in predicting pavement maintenance methods to improve decision-making. This study investigates the use of ML at the municipal level to predict the street pavement condition index (PCI) rating over a 4-year span. Several supervised learning models, namely linear regression (LR), random forest (RF), and neural network (NN), were applied to the visually assessed pavement condition data of Skellefteå municipality, Sweden. Pavement distress, pavement age, and traffic data were used in several combinations to evaluate and compare the performance of the models. The RF model was based on paired variables of pavement age and pavement distress data. The results were comparatively accurate with R2=0.59 and Spearman’s coefficient=0.74 for residential streets in the model testing stage. Similarly, for main, collector, and industrial (MCI) streets, the RF model, based on pavement age and traffic variables, performed best with R2=0.79 and Spearman’s coefficient=0.88 during the model testing stage. The importance of input variables varies with the level of the model’s sophistication and pavement performance goal; however, pavement age is the dominant variable. The prediction models can be useful in effectively managing street networks among municipalities, even those with scarce resources.