Emerging approaches applied to maritime transport research: Past and future
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 the authors 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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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/27724247
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Supplemental Notes:
- © 2021 Ran Yan et al. Published by Elsevier Ltd on behalf of Tsinghua University Press. Abstract reprinted with permission of Elsevier.
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Authors:
- Yan, Ran
- Wang, Shuaian
- Zhen, Lu
- Laporte, Gilbert
- Publication Date: 2021-12
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 100011
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Serial:
- Communications in Transportation Research
- Volume: 1
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 2772-4247
- Serial URL: https://www.sciencedirect.com/journal/communications-in-transportation-research
Subject/Index Terms
- TRT Terms: Data; Digitization; Machine learning; Ports; Research; Shipping; Water transportation
- Subject Areas: Data and Information Technology; Marine Transportation; Research;
Filing Info
- Accession Number: 01836083
- Record Type: Publication
- Files: TRIS
- Created Date: Feb 17 2022 3:16PM