Predictive Traffic Management of Inland Waterways Using Artificial Intelligence and Simulation Models

Abstract

Inland waterway transport is an important part of sustainable freight transportation systems due to its low environmental impact and high transport efficiency. However, increasing traffic density, infrastructure limitations, and operational complexity create significant challenges for navigation management. Modern digital technologies such as Artificial Intelligence (AI), predictive analytics, simulation models, and digital twins provide new opportunities for improving inland waterway traffic management. The aim of this article is to analyse the application of AI and predictive simulation models in inland waterway transport and to evaluate their potential for improving operational efficiency, safety, and sustainability. The article is based on a comparative analysis of scientific literature, simulation approaches, and intelligent transport management systems used in inland navigation. The study focuses on the use of Automatic Identification System (AIS) data, machine learning algorithms, predictive analytics, and simulation techniques for traffic optimization. The results indicate that AI-supported predictive systems can significantly improve traffic flow management, reduce delays, optimize lock scheduling, and minimize fuel consumption. Furthermore, the article proposes a conceptual framework for smart inland waterway traffic management integrating simulation models and real-time operational data. The findings contribute to the development of intelligent transport systems and support the digital transformation of inland navigation.