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Maritime Transport and Port Management
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Maritime Transport and Port Management

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: 25 June 2025 | Viewed by 10991

Special Issue Editors

Dr. Livia Maglić

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Guest Editor
Faculty of Maritime Studies, University of Rijeka, Rijeka, Croatia
Interests: maritime transport; smart ports; port management; marine litter; maritime transport optimization
Dr. Lovro Maglić

E-Mail Website
Guest Editor
Center for Marine Technologies, Faculty of Maritime Studies, University of Rijeka, Rijeka, Croatia
Interests: maritime transport; maritime safety; navigation; marine pollution; remotely operated vehicle; port management; risk assessment
Prof. Dr. Ana Peric Hadzic

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Guest Editor
Faculty of Maritime Studies, University of Rijeka, Rijeka, Croatia
Interests: maritime economy; port management; decision-making management; transport logistics; econometrics
Dr. Marina Zanne

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Guest Editor
Faculty of Maritime Studies and Transport, University of Ljubljana, Portorož, Slovenia
Interests: maritime economics; port management; green ports; smart ports; port digitalization; port procedures; port safety
Prof. Dr. Nam Kyu Park

E-Mail Website
Guest Editor
Logi AI Innovation Lab, T2L, Gyung-gi do, Republic of Korea
Interests: port management; logistics; maritime transportation; transport modeling; port automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Maritime transport and port management are critical components of the global supply chain, facilitating the movement of goods and services across the world’s oceans. As a critical component of the global supply chain, maritime transport faces numerous challenges while continuously adapting to new technologies to ensure a sustainable and resilient future.

Recent research in maritime transport and port management includes advancements in smart port technology, automation, green shipping practices, and strategies to enhance the resilience of supply chains against interruptions.

This Special Issue aims to publish a wide range of papers focusing on these innovations, sustainability, and operational effectiveness within maritime transport and port management.

We seek forward-thinking research articles, comprehensive reviews, and innovative case studies that push the boundaries of maritime transport and port management, providing fresh perspectives, cutting-edge solutions, and strategic insights for the future.

We invite the submission of high-quality contributions directly related to the aspects mentioned below. Novel research techniques are encouraged.

Possible subjects of interest include, but are not limited to, the following:

- Technological innovations in maritime transportation and port activities;

- Environmentally friendly shipping technologies;

- Digitalization and automation in port operations;

- Intelligent port infrastructures;

- Supply chain strategies and emerging innovative practice;

- Smart port management strategies for enhancing port efficiency and productivity;

- Maritime safety and risk management.

Dr. Livia Maglić
Dr. Lovro Maglić
Prof. Dr. Ana Peric Hadzic
Dr. Marina Zanne
Prof. Dr. Nam Kyu 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

  • maritime transport
  • port management
  • smart port
  • port efficiency
  • port productivity
  • digitalization
  • automation
  • green shipping technologies
  • maritime safety
  • risk management

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

Published Papers (9 papers)

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Research

21 pages, 11368 KiB  
Article
Development and Application of Evaluation Procedure for Performance Testing of Vacuum Suction Pad in Automatic Mooring System in a Lab Test
by Jeongnam Kim, Jaehyeon An, Youngki Kim and Youhee Cho
J. Mar. Sci. Eng. 2025, 13(3), 502; https://doi.org/10.3390/jmse13030502 - 4 Mar 2025
Viewed by 652
Abstract
The fourth Industrial Revolution is driving the maritime and port logistics industry toward greater operational efficiency and reduced human intervention through automatic mooring systems. This study developed and applied a lab-based evaluation procedure to assess the performance and stability of vacuum suction pads [...] Read more.
The fourth Industrial Revolution is driving the maritime and port logistics industry toward greater operational efficiency and reduced human intervention through automatic mooring systems. This study developed and applied a lab-based evaluation procedure to assess the performance and stability of vacuum suction pads in such systems. Test conditions were based on Cavotec’s specifications, with evaluation criteria tailored to the pads’ required performance. Relevant standards were reviewed to establish a vacuum pressure leakage criterion of 5% or less. Results showed that the pads maintained pressure reductions below this threshold, confirming their suitability for automatic mooring, though dynamic factors like heave and tides remain untested. Full article
(This article belongs to the Special Issue Maritime Transport and Port Management)
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26 pages, 4993 KiB  
Article
Actual Truck Arrival Prediction at a Container Terminal with the Truck Appointment System Based on the Long Short-Term Memory and Transformer Model
by Mengzhi Ma, Xianglong Li, Houming Fan, Li Qin and Liming Wei
J. Mar. Sci. Eng. 2025, 13(3), 405; https://doi.org/10.3390/jmse13030405 - 21 Feb 2025
Viewed by 536
Abstract
The implementation of the truck appointment system (TAS) in various ports shows that it can effectively reduce congestion and enhance resource utilization. However, uncertain factors such as traffic and weather conditions usually prevent the external trucks from arriving at the port on time [...] Read more.
The implementation of the truck appointment system (TAS) in various ports shows that it can effectively reduce congestion and enhance resource utilization. However, uncertain factors such as traffic and weather conditions usually prevent the external trucks from arriving at the port on time according to the appointed period for container pickup and delivery operations. Comprehensively considering the significant factors associated with truck appointment no-shows, this paper proposes a deep learning model that integrates the long short-term memory (LSTM) network with the transformer architecture based on the cascade structure, namely the LSTM-Transformer model, for actual truck arrival predictions at the container terminal using TAS. The LSTM-Transformer model combines the advantages of LSTM in processing time dependencies and the high efficiency of the transformer in parsing complex data contexts, innovatively addressing the limitations of traditional models when faced with complex data. The experiments executed on two datasets from a container terminal in Tianjin Port, China, demonstrate superior performance for the LSTM-Transformer model over various popular machine learning models such as random forest, XGBoost, LSTM, transformer, and GRU-Transformer. The root mean square error (RMSE) values for the LSTM-Transformer model on two datasets are 0.0352 and 0.0379, and the average improvements are 23.40% and 18.43%, respectively. The results of sensitivity analysis show that possessing advanced knowledge of truck appointments, weather, traffic, and truck no-shows will improve the accuracy of model predictions. Accurate forecasting of actual truck arrivals with the LSTM-Transformer model can significantly enhance the efficiency of container terminal operational planning. Full article
(This article belongs to the Special Issue Maritime Transport and Port Management)
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28 pages, 43394 KiB  
Article
A Hybrid Meta-Heuristic Approach for Solving Single-Vessel Quay Crane Scheduling with Double-Cycling
by Fahrettin Eldemir and Mustafa Egemen Taner
J. Mar. Sci. Eng. 2025, 13(2), 371; https://doi.org/10.3390/jmse13020371 - 17 Feb 2025
Viewed by 526
Abstract
The escalating global demand for containerized cargo has intensified pressure on container terminals, which serve as vital nodes in maritime logistics. This study aims to enhance operational efficiency in non-automated container terminals by examining two meta-heuristic approaches—Ant Colony Optimization (ACO) and a hybrid [...] Read more.
The escalating global demand for containerized cargo has intensified pressure on container terminals, which serve as vital nodes in maritime logistics. This study aims to enhance operational efficiency in non-automated container terminals by examining two meta-heuristic approaches—Ant Colony Optimization (ACO) and a hybrid Greedy Randomized Adaptive Search Procedure (GRASP)—Genetic Algorithm (GA)—for quay crane scheduling. Their performance is benchmarked across various problem scales, with process completion time serving as the primary metric. Based on these findings, the most effective approach is integrated into a newly developed Decision Support System (DSS) to streamline practical implementation. Statistical analyses confirm the robustness of both methods, underscoring how meta-heuristics combined with a DSS can optimize quay crane utilization, bolster maritime logistics, and ultimately boost terminal productivity. Full article
(This article belongs to the Special Issue Maritime Transport and Port Management)
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34 pages, 11137 KiB  
Article
Enhancement Strategy for Port Resilience: Shipping Route Optimization Methods Based on Network Characteristics of Ports
by Xiang Yuan and Xinhao He
J. Mar. Sci. Eng. 2025, 13(2), 325; https://doi.org/10.3390/jmse13020325 - 10 Feb 2025
Cited by 1 | Viewed by 980
Abstract
Ports and their affiliated shipping routes are fundamental to the maritime logistics system, crucial for global trade. However, they face risks from natural disasters and human-induced crises. Enhancing port resilience, the ability to quickly recover and maintain operations during disruptions is vital for [...] Read more.
Ports and their affiliated shipping routes are fundamental to the maritime logistics system, crucial for global trade. However, they face risks from natural disasters and human-induced crises. Enhancing port resilience, the ability to quickly recover and maintain operations during disruptions is vital for a robust maritime network. This study focuses on enhancing port resilience by improving the shipping route network, using an innovative link-prediction-based approach. Initially, a multi-dimensional resilience analysis is conducted to identify key low-resilience and bottleneck ports, guiding targeted network optimizations. Then, a novel link prediction algorithm is applied to find potential new shipping connections, significantly enhancing network efficiency, robustness, and port resilience. The optimized network effectively improves the connectivity of critical low-resilience ports with central hub ports and bottleneck ports with surrounding ones. Route diversification mitigates risks and strengthens overall resilience. Key low-resilience ports and bottleneck ports are reduced by an average of 20% and 25%. Finally, practical strategies are proposed. Low-resilience ports should establish direct connections with major hubs, and regional sub-networks can offer support. For bottleneck ports, additional secondary and short distance links should be added to transform them into more integrated hubs, enhancing the network’s robustness. These strategies improve the network’s operational capacity during crises, ensuring efficient cargo flow. Full article
(This article belongs to the Special Issue Maritime Transport and Port Management)
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24 pages, 3893 KiB  
Article
Deep Reinforcement Learning Based Optimal Operation of Low-Carbon Island Microgrid with High Renewables and Hybrid Hydrogen–Energy Storage System
by Wangwang Zhu, Shuli Wen, Qiang Zhao, Bing Zhang, Yuqing Huang and Miao Zhu
J. Mar. Sci. Eng. 2025, 13(2), 225; https://doi.org/10.3390/jmse13020225 - 25 Jan 2025
Cited by 1 | Viewed by 980
Abstract
Hybrid hydrogen–energy storage systems play a significant role in the operation of islands microgrid with high renewable energy penetration: maintaining balance between the power supply and load demand. However, improper operation leads to undesirable costs and increases risks to voltage stability. Here, multi-time-scale [...] Read more.
Hybrid hydrogen–energy storage systems play a significant role in the operation of islands microgrid with high renewable energy penetration: maintaining balance between the power supply and load demand. However, improper operation leads to undesirable costs and increases risks to voltage stability. Here, multi-time-scale scheduling is developed to reduce power costs and improve the operation performance of an island microgrid by integrating deep reinforcement learning with discrete wavelet transform to decompose and mitigate power fluctuations. Specifically, in the day-ahead stage, hydrogen production and the hydrogen blending ratio in gas turbines are optimized to minimize operational costs while satisfying the load demands of the island. In the first intraday stage, rolling adjustments are implemented to smooth renewable energy fluctuations and increase system stability by adjusting lithium battery and hydrogen production equipment operations. In the second intraday stage, real-time adjustments are applied to refine the first-stage plan and to compensate for real-time power imbalances. To verify the proposed multi-stage scheduling framework, real-world island data from Shanghai, China, are utilized in the case studies. The numerical simulation results demonstrate that the proposed innovative optimal operation strategy can simultaneously reduce both the costs and emissions of island microgrids. Full article
(This article belongs to the Special Issue Maritime Transport and Port Management)
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15 pages, 6762 KiB  
Article
Frequency-Based Analysis of Matching Accuracy Between Satellite Radio Frequency and AIS Data for Ship Identification
by Chan-Su Yang and Sree Juwel Kumar Chowdhury
J. Mar. Sci. Eng. 2025, 13(2), 191; https://doi.org/10.3390/jmse13020191 - 21 Jan 2025
Viewed by 929
Abstract
Vessels can deactivate their Automatic Identification System (AIS) to operate undetected and potentially engage in illegal activities. To address this, satellite-based radio frequency (RF) data are increasingly being used for identifying such vessels. This study evaluates the matching accuracy among RF and AIS [...] Read more.
Vessels can deactivate their Automatic Identification System (AIS) to operate undetected and potentially engage in illegal activities. To address this, satellite-based radio frequency (RF) data are increasingly being used for identifying such vessels. This study evaluates the matching accuracy among RF and AIS data based on the frequency and distance. RF data were acquired on 22 September, 25 September, and 7 December 2023. According to the frequency range, the dataset was separated into frequency-1 (3.024–3.077 GHz) and frequency-2 (9.3734–9.4249 GHz). Six distance thresholds (2 km, 3 km, 6 km, 8 km, 13 km, and 18 km) were employed for the matching process. The results depicted that the average matching rates were 95%, 92%, and 92% for the RF dataset on 22 September, 25 September, and 7 December, respectively. Additionally, the results revealed that the matching rates decreased with distance, e.g., for the RF dataset on 22 September, the average highest matching rate (47%) was found at a 2 km distance and the minimum matching rate (0.9%) was observed at an 18 km distance. Furthermore, the analysis delineated that frequency-2 consistently exceeded frequency-1, particularly at longer distances, showing a more stable trend in matching accuracy. Full article
(This article belongs to the Special Issue Maritime Transport and Port Management)
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25 pages, 3916 KiB  
Article
Process Optimization in Sea Ports: Integrating Sustainability and Efficiency Through a Novel Mathematical Model
by Maria Teresa Pereira, Nuno Rocha, Francisco Gomes Silva, Miguel Ângelo Lellis Moreira, Yusuf Ozden Altinkaya and Marisa João Pereira
J. Mar. Sci. Eng. 2025, 13(1), 119; https://doi.org/10.3390/jmse13010119 - 10 Jan 2025
Viewed by 1503
Abstract
Ports are essential nodes in global trade, linking maritime and land transport. As maritime logistics increasingly drive global supply chains, optimizing port operations has become vital for enhancing economic efficiency and environmental sustainability. This study presents a Mixed Integer Linear Programming (MILP) model [...] Read more.
Ports are essential nodes in global trade, linking maritime and land transport. As maritime logistics increasingly drive global supply chains, optimizing port operations has become vital for enhancing economic efficiency and environmental sustainability. This study presents a Mixed Integer Linear Programming (MILP) model to address inefficiencies in berth allocation and stevedoring processes at the Port of Leixões, Portugal. By integrating real operational data, the model reduces vessel waiting times by 47.56% (from 8.1 to 4.2 h) and operational delays by 37.39% (from 11.5 to 7.2 h). These optimizations also result in a 41.85% reduction in greenhouse gas emissions per ship, aligning with global emissions regulations and promoting sustainable port management. The model’s innovations include real-time data integration and a holistic resource allocation approach to mitigate congestion and inefficiencies. Key findings demonstrate the model’s potential to streamline operations and minimize environmental impacts. These advancements align economic efficiency with environmental sustainability, addressing global emissions regulations. However, the study acknowledges limitations, such as excluding unpredictable factors like weather conditions and equipment failures. Future research should explore dynamic variables, such as weather conditions and mechanical failures, and expand the model’s applicability to other seaports worldwide. Full article
(This article belongs to the Special Issue Maritime Transport and Port Management)
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16 pages, 2346 KiB  
Article
Accident Data-Driven Consequence Analysis in Maritime Industries
by Jiahui Shi and Zhengjiang Liu
J. Mar. Sci. Eng. 2025, 13(1), 117; https://doi.org/10.3390/jmse13010117 - 10 Jan 2025
Cited by 2 | Viewed by 721
Abstract
Maritime accidents are significant obstacles to the development of shipping industries. Their consequences are another important issue because they often involve significant economic losses and human casualties. Accident consequences do not occur randomly, but are triggered by a series of influential factors. To [...] Read more.
Maritime accidents are significant obstacles to the development of shipping industries. Their consequences are another important issue because they often involve significant economic losses and human casualties. Accident consequences do not occur randomly, but are triggered by a series of influential factors. To determine the critical factors contributing to accident consequences, a data-driven research framework is proposed. Firstly, 198 maritime accident investigation reports from the Marine Accident Investigation Branch (MAIB) and Australian Transport Safety Bureau (ATSB) are collected to build a database. Secondly, relevant influential factors are identified based on a literature review. Thirdly, a TAN (Tree Augmented Network)-based BN (Bayesian network) model is developed. Fourthly, a model validation process, including a comparative analysis, Kappa test, and scenario analysis are performed. The five critical factors are determined as accident type, ship type, ship age, ship length and gross tonnage. Valuable implications are generated through this research framework and can be a valuable reference for the safety management of concerned parties. In addition, the TAN model can be a predictor for developing mitigation measures to minimize accident consequences. Full article
(This article belongs to the Special Issue Maritime Transport and Port Management)
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14 pages, 1700 KiB  
Article
A Study on the Limitations of Green Alternative Fuels in Global Shipping in the Foreseeable Future
by Jan Emblemsvåg
J. Mar. Sci. Eng. 2025, 13(1), 79; https://doi.org/10.3390/jmse13010079 - 5 Jan 2025
Cited by 1 | Viewed by 2566
Abstract
Shipping carries over 80% of global trade volumes and emits 3% of global greenhouse gas emissions, but it is hard to abate due to the simple fact that ships require a lot of energy and move around. Therefore, a large amount of research [...] Read more.
Shipping carries over 80% of global trade volumes and emits 3% of global greenhouse gas emissions, but it is hard to abate due to the simple fact that ships require a lot of energy and move around. Therefore, a large amount of research and development is poured into understanding the choices of alternative fuels and developing new technologies. Unfortunately, much of the work and policies derived, therefore, seem to rest on a hidden assumption that a relevant amount of green alternative fuel will be available, but that assumption does not stand up to scrutiny on a global level. For example, the results show that decarbonizing global shipping using green ammonia produced from renewable energy sources will require 3.7 times the total EU-27 power production in 2022. The purpose and novelty of this paper are to offer a clear rationale for the correct contextualization of research and development on curbing greenhouse gas emissions from global shipping and individual shipping segments to avoid overpromising and underdelivering. Full article
(This article belongs to the Special Issue Maritime Transport and Port Management)
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