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Smart Shipping and Maritime Transportation
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Smart Shipping and Maritime Transportation

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 (5 January 2024) | Viewed by 4252

Special Issue Editors

Prof. Dr. Jung Sik Jeong

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Guest Editor
Department of Maritime Transportation Science, Mokpo National Maritime University, Mokpo, Republic of Korea
Interests: autonomous navigation; ship collision avoidance; ship route optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Volkan Aydogdu

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Guest Editor
Independent Researcher, Hamburg, Germany
Interests: maritime safety; navigational safety; maritime traffic management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Youngsoo Park

E-Mail Website
Guest Editor
Division of Maritime Transportation Sciences, Korea Maritime and Ocean University, Busan, Republic of Korea
Interests: ship collision avoidance; marine traffic safety
Special Issues, Collections and Topics in MDPI journals
Dr. Dae Won Park

E-Mail Website
Guest Editor
College of Maritime Sciences, Korea Maritime & Ocean University, Busan 49112, Republic of Korea
Interests: ship data analysis; marine traffic safe

Special Issue Information

Dear Colleagues,

The Fourth Industrial Revolution,4IR, or Industry 4.0, describes the recent breathtaking development in automation through machine-to-machine communication, the Internet of Things (IoT), digital twins, and artificial intelligence. We are experiencing these changes in our social lives as interconnectivity and smart automation increase. “Maritime 4.0” conceptualizes the adoption of Industry 4.0 technology to the maritime industry for safer operations. These developments are not made only for autonomous ships but also for all aspects of the maritime operation. Cutting-edge autonomous navigation technologies boost smart shipping. It covers both onboard technologies and the digitalization of ports and waterways by using data collected from sensors and communications networks with hyper-connectivity. The recent developments in artificial intelligent technology accelerated autonomous navigation and smart ports concept based on data-driven learning algorithms, which also attract the attention of researchers in this field. 

This Special Issue presents the cutting-edge technologies of autonomous navigation, including smart ports and intelligent waterways, toward smart shipping and maritime transportation. The topic includes data-driven learning algorithms related to autonomous navigation, which includes situational awareness and collision avoidance, route optimization, and smart ports and waterways. However, relevant topics are not limited to autonomous navigation, including smart ports and intelligent waterways. This issue calls for research articles covering novel approaches to maritime data mining, informatics, machine learning, digital twin, data management, and cybersecurity related to marine science and technologies. Papers on the following topics are welcome: data science algorithms, digitalized systems, autonomous navigation technologies, smart ports, and waterways for smart shipping and maritime transportation. 

Prof. Dr. Jung Sik Jeong
Dr. Volkan Aydogdu
Prof. Dr. Youngsoo Park
Dr. Dae Won Park
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

  • autonomous operation
  • autonomous navigation
  • smart port
  • smart shipping
  • cybersecurity
  • intelligent waterways
  • digital twin

Published Papers (3 papers)

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Research

24 pages, 11552 KiB  
Article
Global Path Planning for Autonomous Ship Navigation Considering the Practical Characteristics of the Port of Ulsan
by Sang-Woong Yun, Dong-Ham Kim, Se-Won Kim, Dong-Jin Kim and Hye-Jin Kim
J. Mar. Sci. Eng. 2024, 12(1), 160; https://doi.org/10.3390/jmse12010160 - 13 Jan 2024
Cited by 1 | Viewed by 927
Abstract
This study introduces global path planning for autonomous ships in port environments, with a focus on the Port of Ulsan, where various environmental factors are modeled for analysis. Global path planning is considered to take place from departure to berth, specifically accounting for [...] Read more.
This study introduces global path planning for autonomous ships in port environments, with a focus on the Port of Ulsan, where various environmental factors are modeled for analysis. Global path planning is considered to take place from departure to berth, specifically accounting for scenarios involving a need to navigate via anchorage areas as waypoints due to unexpected increases in port traffic or when direct access to the berth is obstructed. In this study, a navigable grid for autonomous ships was constructed using land, breakwater, and water depth data. The modeling of the Port of Ulsan’s traffic lanes and anchorage areas reflects the port’s essential maritime characteristics for global path planning. In this study, an improved A* algorithm, along with grid-based path planning, was utilized to determine a global path plan. We used smoothing algorithms to refine the global paths for practical navigation, and the validation of these paths was achieved through conducting ship maneuvering simulations from model tests, which approximate real-world navigation in navigational simulation. This approach lays the groundwork for enhanced route generation studies in complex port environments. Full article
(This article belongs to the Special Issue Smart Shipping and Maritime Transportation)
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17 pages, 6344 KiB  
Article
Inland Vessel Travel Time Prediction via a Context-Aware Deep Learning Model
by Tengze Fan, Deshan Chen, Chen Huang, Chi Tian and Xinping Yan
J. Mar. Sci. Eng. 2023, 11(6), 1146; https://doi.org/10.3390/jmse11061146 - 30 May 2023
Cited by 1 | Viewed by 1178
Abstract
Accurate vessel travel time estimation is crucial for optimizing port operations and ensuring port safety. Existing vessel travel time prediction models primarily rely on path-finding algorithms and corresponding distance/speed relationships to calculate travel time. However, these models overlook the complex nature of vessel [...] Read more.
Accurate vessel travel time estimation is crucial for optimizing port operations and ensuring port safety. Existing vessel travel time prediction models primarily rely on path-finding algorithms and corresponding distance/speed relationships to calculate travel time. However, these models overlook the complex nature of vessel travel time, which is influenced by multiple traffic-related factors such as collision avoidance, shortest path selection, and vessel personnel performance. The lack of consideration for these specific aspects limits the accuracy and applicability of current models. We propose a novel context-aware deep learning approach for inland vessel travel time prediction. Firstly, we introduce a complex network that captures vessel–vessel interaction contexts, providing valuable traffic environment information as an input for the deep learning model. Additionally, we employ a convolutional neural network to extract spatial trajectory information, which is then integrated with interaction contexts and indirect context information. In the vessel travel time prediction procedure, we utilize a long short-term memory network to capture the temporal dependence within consecutive channel sections’ fused multiple context feature sets. Extensive experiments incorporating historical data from the Wuhan section of the Yangtze River in China demonstrate the superiority of our proposed model over classical models in predicting vessel travel time. Importantly, our model accounts for the specific traffic contexts that had previously been overlooked, leading to improved accuracy and applicability in inland vessel travel time prediction. Full article
(This article belongs to the Special Issue Smart Shipping and Maritime Transportation)
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19 pages, 3667 KiB  
Article
Deriving Optimal Capacity for Typhoon Shelters—An Analysis of the Jinhae Bay Typhoon Shelter in South Korea
by Sangwon Park, Wonsik Kang, Young-Soo Park and Daewon Kim
J. Mar. Sci. Eng. 2023, 11(5), 1031; https://doi.org/10.3390/jmse11051031 - 12 May 2023
Viewed by 1152
Abstract
Jinhae Bay in South Korea is a common typhoon shelter, but there are no established criteria for the area or vessel capacity. The aim of this study was to determine the optimal capacity and arrangement of typhoon shelters for vessels in the sea [...] Read more.
Jinhae Bay in South Korea is a common typhoon shelter, but there are no established criteria for the area or vessel capacity. The aim of this study was to determine the optimal capacity and arrangement of typhoon shelters for vessels in the sea area surrounding Jinhae Bay. The study identified several areas that could serve as typhoon shelters and conducted a survey with experienced VTS operators and ship operators to identify the best typhoon avoidance areas. The study found that the Japanese and Spanish design criteria for anchoring in strong winds were useful in computing the optimal capacity of typhoon shelters. A nesting algorithm based on the genetic algorithm and the No-Fit-Polygon theory was used to optimize the arrangement of shelters. The study found that the Jinhae Bay typhoon shelter can be effectively managed by arranging shelter-seeking vessels based on the nesting algorithm. The study contributes to supporting quantitative methodology-based decision-making and has practical significance for managing typhoon shelters in the Jinhae Bay area. Further research is needed to evaluate the proposed arrangement plan for typhoon shelters and confirm the validity of the results through simulation and practical implementation. Additionally, the time complexity for vessels to approach the anchorage should be considered in future studies. Full article
(This article belongs to the Special Issue Smart Shipping and Maritime Transportation)
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