Advancing container shipping with machine learning: a bibliometric analysis and systematic review of seaport–hinterland studies

This study explores how Machine Learning (ML) is being applied across seaport–hinterland networks to improve operational decision-making in container shipping. It draws on a comprehensive systematic review of research published from 2008 to 2025, complemented by a bibliometric analysis of publication trends. The reviewed literature is organised by ML methods, the operational challenges they target, and the data required for model development. Among the various applications identified, container throughput forecasting is the most common. Frequently used datasets include equipment counts, container throughput records, quay crane performance data, truck traffic volumes, and weather information. Findings reveal a clear shift toward combining ML with hybrid frameworks, operations research techniques, and simulation-based models, resulting in stronger prediction accuracy and more robust decision support. The analysis also illustrates the ways ML contributes to decision-making across seaport-hinterland operations, outlining emerging research avenues, stakeholder-driven trends, key implementation issues, and practical recommendations. Overall, the study provides a structured synthesis of current knowledge, mapping major themes and developments while offering methodological insights for future ML research in this domain.