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Data Analytics in Maritime Research
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Data Analytics in Maritime Research

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (25 June 2023) | Viewed by 6129

Special Issue Editors

Dr. Ran Yan

E-Mail Website
Guest Editor
Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China
Interests: data analytics in maritime transport; green shipping management; port and shipping management
Special Issues, Collections and Topics in MDPI journals
Dr. Lingxiao Wu

E-Mail Website
Guest Editor
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: maritime studies; data-driven decision-making; machine learning; large-scale optimization; optimization under uncertainty; production and logistics operations
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuaian Wang

E-Mail Website
Guest Editor
Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: shipping network design; port operations planning; urban transport network modeling; sustainable transportation and green logistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Background: Maritime transportation is the backbone of global trade. As a traditional and relatively conservative industry, it is going through the digital transformation, where data analytics models act as the foundation.

Aim and scope: The aim and scope of this special issue are to consider submissions in general themes on the application of data analytics models to improve the efficiency of shipping and port management, where the data analytics models can be descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics, while the specific models can be statistical learning, empirical analysis, machine learning, deep learning, pattern recognition, data mining, etc. The research topics can be any contemporary issues related to shipping management and port operation.

History: In the era of big data, techniques of digitalization, the internet of things, and automation are of increasing relevance to maritime transport than ever before and generate massive data with higher granularity from various maritime activities. Although they have already helped to optimize existing decision processes and create new business opportunities in the conservative maritime industry, the progress in the implementation of data analytics to improve the efficiency of the maritime system is slow.

Cutting-edge researches: There have been a certain number of papers on developing data-driven models to improve the maritime transport system, but there are still many obstacles, including the drawbacks brought by the limited quantity and low quality of data generated and collected, the difficulties in developing effective and efficient prediction models, the reluctant adoption of emerging models by industry practitioners, cybersecurity issues, and the risk caused by artificial intelligence systemic errors, among others.

What kind of paper we are looking for: Original research, editorial, comment, and review papers are welcome.

This special issue aims to improve the efficiency of maritime transport system by addressing classic and contemporary issues in shipping and port management from various aspects using data analytics models. Submissions can be original research, editorial or comment papers, and review papers. Themes of the submissions can be the development of tailored data analytics decision support system for maritime transport, solving classic or emerging issues in shipping and port management using data analytics methods, collecting and analyzing big data from maritime activities to generate managerial or political insights, analyzing the potentials and difficulties of the adoption of data analytics models in the maritime industry and the solution approach, and the review of recent progress in the application of data analytics models in the entire maritime industry or in specific practical problems.

Dr. Ran Yan
Dr. Lingxiao Wu
Prof. Dr. Shuaian Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • big data analytics
  • data mining
  • maritime transport
  • shipping and port management
  • maritime digitalization

Published Papers (3 papers)

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Research

29 pages, 6113 KiB  
Article
Enhancing Container Vessel Arrival Time Prediction through Past Voyage Route Modeling: A Case Study of Busan New Port
by Jeong-Hyun Yoon, Dong-Ham Kim, Sang-Woong Yun, Hye-Jin Kim and Sewon Kim
J. Mar. Sci. Eng. 2023, 11(6), 1234; https://doi.org/10.3390/jmse11061234 - 15 Jun 2023
Cited by 5 | Viewed by 2067
Abstract
Container terminals are at the center of global logistics, and are highly dependent on the schedule of vessels arriving. Conventional ETA records from ships, utilized for terminal berth planning, lack sufficient accuracy for effective plan implementation. Thus, there is a pressing need for [...] Read more.
Container terminals are at the center of global logistics, and are highly dependent on the schedule of vessels arriving. Conventional ETA records from ships, utilized for terminal berth planning, lack sufficient accuracy for effective plan implementation. Thus, there is a pressing need for improved ETA prediction methods. In this research, we propose a novel approach that leverages past voyage route patterns to predict the ETA of container vessels arriving at a container terminal at Busan New Port, South Korea. By modeling representative paths based on previous ports of call, the method employs real-time position and speed data from the Automatic Identification System (AIS) to predict vessel arrival times. By inputting AIS data into segmented representative routes, optimal parameters yielding minimal ETA errors for each vessel are determined. The algorithm’s performance evaluation during the modeling period demonstrates its effectiveness, achieving an average Mean Absolute Error (MAE) of approximately 3 h and 14 min. These results surpass the accuracy of existing ETA data, such as ETA in the Terminal Operating System and ETA in the AIS of a vessel, indicating the algorithm’s superiority in ETA estimation. Furthermore, the algorithm consistently outperforms the existing ETA benchmarks during the evaluation period, confirming its enhanced accuracy. Full article
(This article belongs to the Special Issue Data Analytics in Maritime Research)
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34 pages, 4551 KiB  
Article
An Interpretable Gray Box Model for Ship Fuel Consumption Prediction Based on the SHAP Framework
by Yiji Ma, Yuzhe Zhao, Jiahao Yu, Jingmiao Zhou and Haibo Kuang
J. Mar. Sci. Eng. 2023, 11(5), 1059; https://doi.org/10.3390/jmse11051059 - 16 May 2023
Cited by 4 | Viewed by 1428
Abstract
Shipping companies and maritime organizations want to improve the energy efficiency of ships and reduce fuel costs through optimization measures; however, the accurate fuel consumption prediction of fuel consumption is a prerequisite for conducting optimization measures. In this study, the white box models [...] Read more.
Shipping companies and maritime organizations want to improve the energy efficiency of ships and reduce fuel costs through optimization measures; however, the accurate fuel consumption prediction of fuel consumption is a prerequisite for conducting optimization measures. In this study, the white box models (WBMs), black box models (BBMs), and gray box models (GBMs) are developed based on sensor data. GBMs have great potential for the prediction of ship fuel consumption, but the lack of interpretability makes it difficult to determine the degree of influence of different influencing factors on ship fuel consumption, making it limited in practical engineering applications. To overcome this difficulty, this study obtains the importance of GBM input characteristics for ship fuel consumption by introducing the SHAP (SHAPley Additive exPlanations) framework. The experimental results show that the prediction performance of the WBM is much lower than that of the BBM and GBM, while the GBM has better prediction performance by applying the a priori knowledge of WBMs to BBMs. Combining with SHAP, a reliable importance analysis of the influencing factors is obtained, which provides a reference for the optimization of ship energy efficiency, and the best input features for fuel consumption prediction are obtained with the help of importance ranking results. Full article
(This article belongs to the Special Issue Data Analytics in Maritime Research)
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15 pages, 2055 KiB  
Article
A Comparative Study to Estimate Fuel Consumption: A Simplified Physical Approach against a Data-Driven Model
by Alessandro La Ferlita, Yan Qi, Emanuel Di Nardo, Ould el Moctar, Thomas E. Schellin and Angelo Ciaramella
J. Mar. Sci. Eng. 2023, 11(4), 850; https://doi.org/10.3390/jmse11040850 - 17 Apr 2023
Cited by 3 | Viewed by 1929
Abstract
Two methods were compared to predict a ship’s fuel consumption: the simplified naval architecture method (SNAM) and the deep neural network (DNN) method. The SNAM relied on limited operational data and employed a simplified technique to estimate a ship’s required power by determining [...] Read more.
Two methods were compared to predict a ship’s fuel consumption: the simplified naval architecture method (SNAM) and the deep neural network (DNN) method. The SNAM relied on limited operational data and employed a simplified technique to estimate a ship’s required power by determining its resistance in calm water. Here, the Holtrop–Mennen technique obtained hydrostatic information for each selected voyage, the added resistance in the encountered natural seaways, and the brake power required for each scenario. Additional characteristics, such as efficiency factors, were derived from literature surveys and from assumed working hypotheses. The DNN method comprised multiple fully connected layers with the nonlinear activation function rectified linear unit (ReLU). This machine-learning-based method was trained on more than 12,000 sample voyages, and the tested data were validated against realistic operational data. Our results demonstrated that, for some ship topologies (general cargo and containerships), the physical models predicted more accurately the realistic data than the machine learning approach despite the lack of relevant operational parameters. Nevertheless, the DNN method was generally capable of yielding reasonably accurate predictions of fuel consumption for oil tankers, bulk carriers, and RoRo ships. Full article
(This article belongs to the Special Issue Data Analytics in Maritime Research)
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