Literature Review on the Application of Machine Learning in the Dry port-seaport System

The exponential growth in world trade and the complexity of logistics chains have put increasing pressure on seaports. In this context, dry ports are emerging as complementary solutions to seaports for improving logistics efficiency and reducing congestion. To fill this gap, we sought to provide a literature review on this topic to explore the application of Machine Learning (ML) in the integration of dry port-seaport systems, highlighting its potential to improve logistics efficiency and reduce congestion in ports. We highlight the theoretical underpinnings of ML, including supervised and unsupervised approaches, and discuss the significant benefits ML can bring to port operations, such as cost reduction and improved efficiency. In addition, the challenges faced in adopting ML technologies, noting that the sector lags behind other areas of transportation. Finally, the article highlights the need for ongoing research to adapt ML solutions to the complexity of dry port-seaport operations.