Вирішення проблеми інформаційної асиметрії в міській логістиці на основі розподілених реєстрів

English

Addressing information asymmetry in urban logistics using distributed ledgers

Abstract. Summary. Introduction. Modern urbanization trends and the rapid growth of e-commerce volumes lead to a significant increase in the load on the transport infrastructure of cities. Traditional approaches to urban freight transport management, based on centralized decision-making and static planning, prove inefficient in highly dynamic urban environments. This causes negative consequences: increased congestion, environmental degradation, and decreased social comfort. Solving these problems requires transitioning to sustainable development. The integration of advanced IT, such as AI and distributed ledgers, opens new opportunities for adaptive flow management. Purpose. The purpose of this study is to develop a comprehensive method for forming a strategy for the sustainable development of urban freight transport based on the synergy of graph neural networks, deep reinforcement learning, and blockchain smart contracts. Materials and methods. The theoretical and methodological basis of the study is a systems approach, graph theory, Markov decision processes (DRL), multi-criteria optimization methods (TOPSIS), and the entropy method. Blockchain technology is applied for data verification and access control. Traffic modeling is performed using graph neural networks (GNN). Results. The conceptual architecture of urban freight flow management was formalized as a complex multi-criteria problem. The system of criteria for evaluating strategy efficiency was improved. A mathematical model for determining the parameters of two-tier city logistics was developed, taking into account the dynamic capacity limits of environmental zones. A comprehensive fourstage strategy formation method is proposed, including the use of a Digital Twin for simulation stress testing. Prospects. Further research will be directed towards the practical approbation of the developed method in real conditions of large transport hubs and the integration of the obtained models with municipal Smart City systems.

Keywords: urban freight transport; sustainable development; multi-objective optimization; blockchain; artificial intelligence; smart contracts