Theoretical Basis of Decision-Making in Intelligent Public Transport Systems

This article explores the development of a theoretical framework and conceptual model for decision-making in an intelligent public transportation system. It examines current challenges in managing urban passenger transport in a dynamic and uncertain environment. A comprehensive model is proposed that integrates the collection and analysis of big data and the generation of operational and strategic management decisions, taking into account the interests of passengers, carriers, and the city as a whole. A formalized objective function allows for assessing the effectiveness of management decisions in an intelligent public transportation system. Particular attention is paid to detailing the role of the intelligent decision support center (IDSC), machine learning, and the division into operational and strategic management loops. The technological, methodological, organizational, and regulatory challenges associated with the practical implementation of the model are outlined.