Advancing container shipping with machine learning: a bibliometric analysis and systematic review of seaport–hinterland studies
系统梳理2008至2025年机器学习在港口-腹地网络中的应用，发现集装箱吞吐量预测最常见，常用数据包括设备计数、吞吐量记录等，并指出向混合框架和仿真模型融合的趋势。
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.