期刊
COMMUNICATIONS IN TRANSPORTATION RESEARCH
卷 1, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.commtr.2021.100011
关键词
Maritime transport; Shipping; Port; Data -driven modeling; Digitalization in the maritime industry
资金
- National Natural Science Foundation of China [71831008, 72071173]
- [72025103]
Maritime transport research is mainly divided into shipping and port aspects, with most classic methods based on long-term observations and expert knowledge, and few based on historical data. Emerging approaches such as machine learning and deep learning have gained attention in recent years for solving maritime transport problems.
Maritime transport is the backbone of international trade and globalization. Maritime transport research can be roughly divided into two categories, namely the shipping side and the port side. Most of the classic approaches adopted to address practical problems in these research topics are based on long-term observations and expert knowledge, while few of them are based on historical data accumulated from practice. In recent years, emerging approaches, which we refer to as machine learning and deep learning techniques in this essay, have been receiving a wider attention to solve practical problems. As a relatively conservative industry, there are some initial trials of applying the emerging approaches to solve practical problems in the maritime sector. The objective of this essay is to review the application of emerging approaches to maritime transport research. The main research topics in maritime transport and classic methods developed to solve them are first presented. The introduction of emerging approaches and their suitability to be applied in maritime transport research is then discussed. Related existing studies are then reviewed according to problem settings, main data sources, and emerging approaches adopted. Challenges and solutions in the process are also discussed from the perspectives of data, model, users, and targets. Finally, promising future research directions are identified. This essay is the first to give a comprehensive review of existing studies on developing machine learning and deep learning models together with popular data sources used to address practical problems in maritime transport.
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