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Communications in Transportation Research 2021, 1(1): 100011 https://doi.org/10.1016/j.commtr.2021.100011
Research Article | Open Access | Issue | Published: 10 November 2021

Emerging approaches applied to maritime transport research: Past and future

Show Author's Information Ran Yana,  Shuaian Wanga,  Lu Zhenb( ),  Gilbert Laportec,d
Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, 999077, Hong Kong, China
School of Management, Shanghai University, Shanghai, 200444, China
Department of Decision Sciences, HEC Montréal, Montréal, Québec, H3T 2B1, Canada
School of Management, University of Bath, Bath, BA2 7AY, UK
Keywords:
Data-driven modeling, Maritime transport, Shipping, Port, Digitalization in the maritime industry
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Yan R, Wang S, Zhen L, et al. Emerging approaches applied to maritime transport research: Past and future. Communications in Transportation Research, 2021, 1(1): 100011. https://doi.org/10.1016/j.commtr.2021.100011
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Abstract Full text About this article

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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About this article

Emerging approaches applied to maritime transport research: Past and future

Show Author's information Ran Yana, Shuaian Wanga, Lu Zhenb( ), Gilbert Laportec,d
Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, 999077, Hong Kong, China
School of Management, Shanghai University, Shanghai, 200444, China
Department of Decision Sciences, HEC Montréal, Montréal, Québec, H3T 2B1, Canada
School of Management, University of Bath, Bath, BA2 7AY, UK

Abstract

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.

Keywords: Data-driven modeling, Maritime transport, Shipping, Port, Digitalization in the maritime industry

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Publication history

Received: 18 August 2021
Revised: 29 October 2021
Accepted: 30 October 2021
Published: 10 November 2021
Issue date: December 2021

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© 2021 The Authors. Published by Elsevier Ltd on behalf of Tsinghua University Press.

Acknowledgements

Acknowledgements

The authors thank the editor and the reviewer for their constructive comments on improving this manuscript. This research is supported by the National Natural Science Foundation of China (Grant numbers 72025103, 71831008, 72071173).

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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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