This article examines the potential of Big Data and artificial intelligence technologies for traffic flow management. It analyzes the theoretical foundations, international experience, and practical models of adaptive traffic control. Calculation formulas for predicting traffic volume and optimizing traffic light phase durations are presented, along with a comparative table of indicators "before" and "after" the implementation of an intelligent traffic management system. The results demonstrate increased throughput, reduced delay times, an increase in average traffic speed, and a reduction in the conditional CO₂ emissions index. Recommendations for the implementation of adaptive systems are provided, including a phased implementation, the creation of an integrated data platform, the use of machine learning algorithms, information security, and performance monitoring. It is noted that the use of Big Data and AI technologies creates the foundation for sustainable digitalization of transport infrastructure, improving urban mobility, and reducing environmental impacts.

Big Data, artificial intelligence, intelligent transport systems, traffic flow management, adaptive traffic control, traffic forecasting, traffic light optimization, throughput, digitalization of transport infrastructure, urban mobility, transport ecology.

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