Artificial intelligence (AI) technologies are increasingly being applied to the shipping industry to advance its development. In this study, 476 articles published in the Science Citation Index Expanded (SCI-EXPANDED) and the Social Sciences Citation Index (SSCI) of the Web of Science Core Collection from 2001 to 2022 were collected, and bibliometric methods were applied to conduct a systematic literature of the field of AI technology applications in the shipping industry. The review commences with an annual publication trend analysis, which shows that research in the field has been growing rapidly in recent years. This is followed by a statistical analysis of journals and a collaborative network analysis to identify the most productive journals, countries, institutions, and authors. The keyword “co-occurrence analysis” is then utilized to identify major research clusters, as well as hot research directions in the field, providing directions for future research in the field. Finally, based on the results of the keyword co-occurrence analysis and the content analysis of the papers published in recent years, the research gaps in AIS data applications, ship trajectory, and anomaly detection, as well as the possible future research directions, are discussed. The findings indicate that AIS data in the future research direction are mainly reflected in the analysis of ship behavior and AIS data repair. Ship trajectory in the future research direction is mainly reflected in the deep learning-based method research and the discussion of ship trajectory classification. Anomaly detection in the future research direction is mainly reflected in the application of deep learning technology in ship anomaly detection and improving the efficiency of ship anomaly detection. These insights offer guidance for researchers’ future investigations in this area. In addition, we discuss the implications of research in the field of shipping AI from both theoretical and practical perspectives. Overall, this review can help researchers understand the status and development trend of the application field of AI technology in shipping, correctly grasp the research direction and methodology, and promote the further development of the field.

The global outbreak of COVID-19 has cast a protracted shadow over the international economic landscape, with maritime transport emerging as a cornerstone of resilience and adaptation

The information exchange between the forwarder and vessel operating common carrier (VOCC) directly leads to a shortage or surplus of capacity and influences operational efficiency. Meanwhile, the development of information technology has inspired VOCCs to establish online booking platforms, which has changed the traditional booking behavior of shippers. Thus, this paper investigates the equilibriums of platform encroachment and information sharing with two symmetric VOCCs and a forwarder. Using a game-theoretic model, it can be characterized as Stackelberg-Nash game with Bertrand competition to determine whether to encroach existing freight forwarding market and share information or not. We observe whether freight forwarder shared information is affected by the encroachment strategy of VOCCs and the market demand state. Further, the encroachment strategy can prompt freight forwarder to share information with VOCCs if the magnitude of high demand is lower. In addition, for VOCCs, the best chance of establishing online platforms is when the magnitude of high demand is relatively low, not when the demand state has increased.