A SYSTEMATIC REVIEW AND BIBLIOMETRIC ANALYSIS OF RECENT STUDIES ON CONTAINER THROUGHPUT FORECASTING - GOHARI, ADEL; MOKHTAR, KASYPI; ISIACIK COLAK, ARIFE TUGSAN; MD HANAFIAH, RUDIAH; OLORUNTOBI, OLAKUNLE; ABD MANAN, TEH SABARIAH; AB LATIP, AMIR SHARIFUDDIN; TEH, HASPINOR; MOHAMMED GISMALLA, MOHAMMED SALIH (Journal of Maritime Logistics, 2025)
This study investigates container throughput forecasting by examining recent scholarly contributions and the forecasting methods employed. To ensure a comprehensive and systematic review of the academic literature, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. A total of 57 documents, published since 2014 and retrieved from the Scopus database, were selected from an initial pool of 156 records. Bibliometric analysis of these documents revealed that the most commonly applied methods fall into categories such as neural networks and machine learning approaches, traditional statistical models, optimisation algorithms, decomposition techniques, grey models, and support vector machines. Among these, neural networks and machine learning approaches, along with traditional statistical models, emerge as the most prominent. Elsevier is identified as the leading publisher of journal articles, while IOP Publishing is noted for contributing the most conference papers. Asia is recognised as the dominant region for container throughput forecasting research, with China recognised as the foremost global contributor. Additionally, Dalian Maritime University is highlighted as the most active institution between corresponding authors.