Застосування новітніх технологій аналізу великих даних для підвищення надійності транспортної системи міста

Problem. Rapid urbanization, increasing traffic flows, and a high rate of road accidents create critical challenges for Ukraine’s transport systems. Existing approaches to traffic analysis are unable to process large volumes of data in real time, which reduces prediction accuracy, complicates decision-making, and hinders progress toward sustainable development goals. Goal. The study aims to develop approaches for improving traffic management efficiency through the application of cloud computing and Big Traffic Data analytics, enabling the formation of intelligent systems for adaptive transport control. Methodology. A structural-functional architecture for cloud-based transport analytics is proposed, comprising three interrelated layers: data collection from heterogeneous sources, cloud-based processing using parallel computing technologies and machine-learning algorithms, and visual representation of analytical results through monitoring dashboards to support decision-making. The methodology is further enhanced by incorporating fractal analysis to detect critical states of the transport system and improve predictive modelling accuracy. Results. The research substantiates the potential to reduce data-processing time by 10–20 times and increase the accuracy of traffic flow forecasting by 25–30%. The proposed system can perform adaptive traffic management, automatic accident detection, route optimization, and environmental monitoring. Originality. The study integrates cloud technologies, machine learning, and fractal analysis into a multidisciplinary framework for digital transport analytics. A method for evaluating transport infrastructure performance is introduced, incorporating technical, economic, social, and environmental criteria. Practical value. The findings are valuable for shaping the national digital mobility strategy, developing Smart City infrastructure, enhancing road-safety management, and advancing Sustainable Development Goals. The implementation of the proposed system contributes to increasing the reliability and adaptability of urban transport governance