Рубрика: Технические науки

Аннотация: (English) The article is devoted to the search for algorithms that allow drivers and fleet operators to reduce road risks based on telematics data, behavioral analysis and risk scoring. The relevance of the topic is due to the fact that in the conditions of digitalization of the short-term car rental market, driving safety can no longer be ensured only by traditional control of violations or technical monitoring of the vehicle. Dynamic assessment of driver behavior is becoming more important, which allows identifying risk patterns directly during the trip. The purpose of the article is to substantiate the features of dynamic assessment of driving risk based on telematics data in short-term car rental fleets on the US market. The study used general scientific methods of analysis, generalization, comparison and systematization of scientific literature. The author's methodology for comprehensive risk assessment is also presented, which can be adapted to the conditions of the US market and integrated into modern telematics, insurance and fleet-management systems. The article also shows that approaches to assessing driving risk have different construction logics. Some models are focused on the driver's perception of danger, while others use sensory signals, a smartphone, GPS or OBD-II data to recognize driving style. This allowed us to show that it is advisable to assess the risk in rental fleets not as a single event, but as a sequence of driver behavioral reactions within a specific trip. It is this approach that creates the basis for earlier detection of unstable or aggressive driving behavior. The practical significance of the study is that the proposed methodology can be used by international fleets, short-term car rental companies, insurance companies that use usage-based insurance models, as well as state and municipal transport systems. Its strengths include the ability to prevent fleet accidents through early detection of risky behavior, reduce insurance and repair costs, create an AI-model of behavioral risk scoring of the driver, scale to various telematics and fleet-management platforms, and transform fleets into data-driven safety and risk management systems.

Аннотация: (Українська) У роботі розглянуто інтеграцію підсистеми прогнозування попиту в розподілені інформаційні системи торговельних мереж. Запропоновано комбіновану архітектуру, що поєднує локальну обробку даних, федеративне узгодження параметрів і мікросервісну взаємодію. Визначено ключові компоненти та технології для забезпечення масштабованості, автономності локальних вузлів і зменшення передавання сирих даних.

Аннотация: (English) The progressive retirement of legacy cellular technologies represents one of the most intricate operational challenges faced by contemporary mobile network operators. While the migration toward LTE and fifth-generation infrastructures has been widely documented, the methodological foundations governing the controlled sunset of earlier radio access technologies remain comparatively underexplored. In particular, the decommissioning of UMTS networks introduces a complex interplay between spectrum refarming, traffic redistribution, service continuity, and operational risk. If performed without analytical guidance, the shutdown of legacy radio layers may provoke severe congestion phenomena, localized capacity collapse, and degradation of user-perceived service quality. This study proposes a structured analytical framework for strategic UMTS sunset implementation within national-scale mobile networks. The research develops a mathematical model that conceptualizes technology retirement not as an isolated operational procedure but as a multi-stage optimization problem embedded within the broader dynamics of traffic migration and spectrum reallocation. The model integrates probabilistic risk estimation, predictive traffic forecasting, and performance-oriented optimization in order to determine the most balanced trajectory of legacy infrastructure decommissioning. To validate the analytical model, a dedicated experimental software environment was implemented in Python, enabling simulation of progressive UMTS shutdown scenarios and comparative evaluation of alternative decision strategies. The system incorporates modules for dataset preprocessing, predictive traffic modeling, iterative sunset simulation, and analytical visualization of network behavior. An openly available dataset describing cellular network performance indicators was used to construct a synthetic yet statistically plausible representation of sector-level radio conditions. Simulation results demonstrate that risk-aware sunset strategies significantly reduce the probability of network overload events while preserving higher LTE throughput levels during the transition process. In contrast, load-driven or stochastic shutdown strategies exhibit increased susceptibility to blackout conditions under heavy traffic migration scenarios. The obtained findings indicate that integrating predictive modeling and probabilistic risk assessment into sunset planning procedures enables a more stable and resource-efficient transformation of cellular infrastructures. The proposed methodological framework contributes to the emerging body of research on technological transitions in mobile networks by providing a coherent analytical approach to legacy technology retirement. Beyond its theoretical implications, the model may serve as a practical decision-support instrument for mobile operators planning large-scale spectrum refarming and infrastructure modernization initiatives.

Аннотация: (English) Artificial intelligence has grown capable enough to reopen a basic question: how should work be divided between humans and machines? Fully autonomous AI performs well on structured, data-rich tasks, yet collaboration between humans and AI consistently produces stronger results in complex, context-dependent, and creative settings. This article reviews how human-AI interaction models have evolved and proposes a three-level classification of collaboration: automation, augmentation, and identity-level partnership. Drawing on established complementarity frameworks and hands-on experience in AI-driven software development, the author introduces the Scalable Human Model (SHM) – a framework in which AI operates as an extension of one individual’s cognitive style, communication patterns, and decision-making logic. A case study of OPV Systems validates the model: one developer, supported by AI agents, reached productivity levels normally associated with a team of five to eight. The findings suggest that this mode of partnership differs qualitatively from augmentation – it lets professionals scale their expertise without sacrificing authenticity or strategic control. Implications for software engineering, digital legacy systems, and human-centric AI design are discussed.