With the global economy’s relentless growth and heightened environmental consciousness, sustainable maritime transport emerges as a pivotal development trajectory for the shipping sector. This study systematically analyzes 478 publications searched in the Web of Science Core Collection, from 2000 to 2023, utilizing bibliometric methods to investigate the application areas in sustainable development within the shipping industry. This study begins with an analysis of annual publication trends, which reveals a substantial expansion in research endeavors within this discipline over recent years. Subsequently, a comprehensive statistical evaluation of scholarly journals and a collaborative network assessment are conducted to pinpoint the foremost productive journals, nations, organizations, and individual researchers. Furthermore, a keyword co-occurrence methodology is applied to delineate the core research themes and emerging focal points within this domain, thereby outlining potential research directions for future research. In addition, drawing on the keyword co-occurrence analysis, the advancements in intelligent shipping technologies and green port construction applications within sustainable maritime transport are discussed. Finally, the review discusses the existing challenges and opportunities of sustainable maritime transport from a theoretical and practical perspective. The research shows that, in terms of intelligent shipping technology, data security and multi-source data are the focus that people need to pay attention to in the future; a trajectory prediction for different climates and different ship types is also an area for future research. In terms of green ports, Cold Ironing (CI) is one of the key points of the green port strategy, and how to drive stakeholders to build sustainable green ports efficiently and economically is the future developmental direction. This review serves to enhance researchers’ comprehension of the current landscape and progression trajectory of intelligent shipping technologies, thereby fostering the continued advancement and exploration in this vital domain.

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.

This paper presents a super-efficiency network data envelopment analysis (SE-NDEA) model for 11 cities in China. The model focuses on measuring the performance of public transit system by integrating multiple stakeholders involved in the public transit system with the exogenous environment in which they operated. Thus, local authority, bus operators, passengers, uncontrollable environmental factors, and the externality of the public transit are all taken into account in the measurement framework and are both interrelated inputs and outputs. The measurement framework can simultaneously capture each public transit system’s production efficiency, service effectiveness, and operational effectiveness. Meanwhile, undesirable outputs, uncontrollable factors, and boundary-valued variables are considered. The paper evaluates the performance of public transit system of 11 Chinese cities from 2009 to 2016. The results reveal that the exogenous environment has a marked impact on the performance measurement of the public transit system. Super cities tended to perform better than mega cities, and mega cities tended to perform better than large cities. Furthermore, service effectiveness has a significantly positive correlation with production efficiency, and transit rail tends to perform better than the conventional bus. These findings have an important implication for China’s bus priority implementation and more general managerial insights for public transit development.