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Special Issues
Sustainable Maritime Transport and Port Intelligence
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Sustainable Maritime Transport and Port Intelligence

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: 30 April 2025 | Viewed by 2150

Special Issue Editors

Prof. Dr. Chao Mi

E-Mail Website
Guest Editor
College of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: port equipment automation; intelligent traffic equipment
Prof. Dr. Guangnian Xiao

E-Mail Website
Guest Editor
School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
Interests: intelligent transportation; green shipping; sustainable transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, global maritime trade is navigating a complex and dynamic macroenvironment. Geopolitical tensions, conflicts, and climate change increasingly impact vital shipping lanes, such as the Suez Canal, Panama Canal, and Red Sea. These factors lead to maritime challenges, including extended routes and rising costs. Furthermore, over 80% of global trade relies on maritime transport, with the volume of seaborne trade being on the rise. The United Nations Conference on Trade and Development's Maritime Transport Review indicates that global seaborne trade is projected to grow by 2.4% to 12.3 billion tons in 2023, with an expected growth of 2% in 2024. In this context, sustainable maritime transport and port intelligence have become essential for promoting the green transformation of the marine economy.

This Special Issue aims to explore how technological innovation can enhance the efficiency of maritime operations, reduce environmental impacts, and foster smarter and more sustainable port management. We invite scholars, policymakers, and industry experts worldwide to share their research findings and contribute valuable insights for the sustainable development of the global shipping and port industries.

This research topic will encompass comprehensive review articles and original research on the theme of “Sustainable Maritime Transport and Port Intelligence.” We will focus on critical issues such as smart operations management in maritime and port sectors, intelligent equipment in maritime and port operations, sustainable maritime transport strategies, and innovative practices in port management. Our goal is to provide useful ideas and solutions for the sustainable development of the global shipping industry and port sector.

Sample topics of interest include, but are not limited to, the following:

  1. Climate change and carbon emission reduction path of the shipping industry: Study of the impact of climate change on the shipping industry, and how the shipping industry can achieve carbon emission reduction targets through technological innovation and policy guidance.
  2. Port digital transformation and intelligent development: Analysis of the application of digital and intelligent technology in port management, and a discussion of how to improve port operation efficiency and service quality.
  3. Green port development and environmental protection measures: Study of the practical cases of ports in green and low-carbon transformation, a discussion on how to reduce the impact of port operation on the environment, and the promotion of the construction of green ports.
  4. Collaborative innovation between shipping industry and port industry: Analysis of the collaborative cooperation between shipping industry and port industry in terms of technological innovation and service optimization, and a discussion on how to promote the collaborative innovation and development between them.
  5. Future development of sustainable maritime transport and port Management: Based on current research and practice, a discussion on the future development direction and goals of sustainable maritime transport and port management.
  6. Smart Operations Management in Maritime and Port Sectors: Exploration of the integration of digital technologies and artificial intelligence to enhance operational efficiency and decision making in maritime and port management.
  7. Intelligent Equipment in Maritime and Port Operations: Investigation of the advancements in intelligent marine and port equipment, including automated vessels and smart cargo handling systems, and their roles in improving safety and reducing operational costs.

Prof. Dr. Chao Mi
Prof. Dr. Guangnian Xiao
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

  • sustainable maritime transport
  • port management
  • digital technology
  • artificial intelligence
  • smart operations management
  • intelligent equipment
  • maritime logistics
  • automation in ports
  • environmental sustainability
  • maritime industry innovations

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

21 pages, 4906 KiB  
Article
Optimizing Stack-Yard Positioning in Full Shoreline Loading Operations
by Xueqiang Du, Bencheng Luo, Jing Wang, Jieting Zhao, Dahai Li, Qian Sun and Haobin Li
J. Mar. Sci. Eng. 2025, 13(3), 593; https://doi.org/10.3390/jmse13030593 - 17 Mar 2025
Viewed by 275
Abstract
Loading operations are a crucial part of container terminal activities and play a key role in influencing shoreline operation efficiency. To overcome the challenge of mismatched local ship decisions and global yard decisions during single-vessel operations, which often result in conflicts related to [...] Read more.
Loading operations are a crucial part of container terminal activities and play a key role in influencing shoreline operation efficiency. To overcome the challenge of mismatched local ship decisions and global yard decisions during single-vessel operations, which often result in conflicts related to container retrieval in the yard, a novel intelligent decision-making model for stack-yard positioning in full shoreline loading operations is proposed. This model seeks to optimize the balance between yard operation instructions and quay crane operation instructions. An enhanced Constrained Optimization Genetic Algorithms-Greedy Randomized Adaptive Search (COGA-GRASP) algorithm is introduced to tackle this decision-making issue, and it is applied to identify the most optimal bay configuration for full shoreline loading operations. The proposed model’s effectiveness is validated through testing and solution outcomes. Full article
(This article belongs to the Special Issue Sustainable Maritime Transport and Port Intelligence)
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17 pages, 3362 KiB  
Article
Truck Lifting Accident Detection Method Based on Improved PointNet++ for Container Terminals
by Yang Shen, Xintai Man, Jiaqi Wang, Yujie Zhang and Chao Mi
J. Mar. Sci. Eng. 2025, 13(2), 256; https://doi.org/10.3390/jmse13020256 - 30 Jan 2025
Viewed by 648
Abstract
In container terminal operations, truck lifting accidents pose a serious threat to the safety and efficiency of automated equipment. Traditional detection methods using visual cameras and single-line Light Detection and Ranging (LiDAR) are insufficient for capturing three-dimensional spatial features, leading to reduced detection [...] Read more.
In container terminal operations, truck lifting accidents pose a serious threat to the safety and efficiency of automated equipment. Traditional detection methods using visual cameras and single-line Light Detection and Ranging (LiDAR) are insufficient for capturing three-dimensional spatial features, leading to reduced detection accuracy. Moreover, the boundary features of key accident objects, such as containers, truck chassis, and wheels, are often blurred, resulting in frequent false and missed detections. To tackle these challenges, this paper proposes an accident detection method based on multi-line LiDAR and an improved PointNet++ model. This method uses multi-line LiDAR to collect point cloud data from operational lanes in real time and enhances the PointNet++ model by integrating a multi-layer perceptron (MLP) and a mixed attention mechanism (MAM), optimizing the model’s ability to extract local and global features. This results in high-precision semantic segmentation and accident detection of critical structural point clouds, such as containers, truck chassis, and wheels. Experiments confirm that the proposed method achieves superior performance compared to the current mainstream algorithms regarding point cloud segmentation accuracy and stability. In engineering tests across various real-world conditions, the model exhibits strong generalization capability. Full article
(This article belongs to the Special Issue Sustainable Maritime Transport and Port Intelligence)
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25 pages, 8032 KiB  
Article
A Decomposed-Ensemble Prediction Framework for Gate-In Operations at Container Terminals
by Yifan Shen, Beng Xuan, Hongtao Hu, Yansong Wu, Ning Zhao and Zhen Yang
J. Mar. Sci. Eng. 2025, 13(1), 45; https://doi.org/10.3390/jmse13010045 - 30 Dec 2024
Viewed by 716
Abstract
Container terminals play a crucial role in global logistics and trade, with gate-in operations significantly impacting overall terminal efficiency and cargo turnover speed. This paper analyzes a series of problems caused by the randomness of the arrival of export containers at the container [...] Read more.
Container terminals play a crucial role in global logistics and trade, with gate-in operations significantly impacting overall terminal efficiency and cargo turnover speed. This paper analyzes a series of problems caused by the randomness of the arrival of export containers at the container yard, including wastage of yard space, excessive waiting time for external trucks, and conflicts with other production operations. To address these issues, a method based on a decomposed ensemble framework is proposed to predict short-term container quantities for gate-in operations at container terminal gates. The experiment compares the autoregressive integrated moving average (ARIMA) algorithm, the prophet algorithm, and the Long Short-Term Memory (LSTM) algorithm, with results indicating the clear advantage of Long Short-Term Memory in decomposed time series modeling. The introduction of this method is expected to enhance the accuracy and flexibility of terminal production planning, optimizing resource utilization. Contributions of this paper include the proposal of predictive models, a shipping route-based decomposed-ensemble framework, and confirmation of the superiority of Long Short-Term Memory in prediction through comparative analysis. These contributions are expected to improve terminal operational efficiency, reduce resource wastage, and better adapt to the highly stochastic gate-in operation environment. Full article
(This article belongs to the Special Issue Sustainable Maritime Transport and Port Intelligence)
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