Ports are pivotal nodes in supply chain and transportation networks in which most of the existing data remain underutilized. Machine learning methods are versatile tools to utilize and harness the hidden power of the data. Considering ever-growing adoption of machine learning as a data-driven decision-making tool the port industry is far behind other modes of transportation in this transition. To fill the gap we aimed to provide a comprehensive systematic literature review on this topic to analyze the previous research from different perspectives such as area of the application type of application machine learning method data and location of the study. Results showed that the number of articles in the field has been increasing annually and the most prevalent use case of machine learning methods is to predict different port characteristics. However there are emerging prescriptive and autonomous use cases of machine learning methods in the literature. Furthermore research gaps and challenges are identified and future research directions have been discussed from method-centric and application-centric points of view.
Seaport ; Port ; Machine learning ; Data analytics ; Systematic literature review ; Container terminals (search for similar items in EconPapers)
Transportation Research Part E: Logistics and Transportation Review 2022 vol. 161 issue C