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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: 5 October 2025 | Viewed by 3673

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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Published Papers (8 papers)

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Research

30 pages, 5553 KiB  
Article
Data-Driven Multi-Scale Channel-Aligned Transformer for Low-Carbon Autonomous Vessel Operations: Enhancing CO2 Emission Prediction and Green Autonomous Shipping Efficiency
by Jiahao Ni, Hongjun Tian, Kaijie Zhang, Yihong Xue and Yang Xiong
J. Mar. Sci. Eng. 2025, 13(6), 1143; https://doi.org/10.3390/jmse13061143 - 9 Jun 2025
Abstract
The accurate prediction of autonomous vessel CO2 emissions is critical for achieving IMO 2050 carbon neutrality and optimizing low-carbon maritime operations. Traditional models face limitations in real-time multi-source data analysis and dynamic cross-variable dependency modeling, hindering data-driven decision-making for sustainable autonomous shipping. [...] Read more.
The accurate prediction of autonomous vessel CO2 emissions is critical for achieving IMO 2050 carbon neutrality and optimizing low-carbon maritime operations. Traditional models face limitations in real-time multi-source data analysis and dynamic cross-variable dependency modeling, hindering data-driven decision-making for sustainable autonomous shipping. This study proposes a Multi-scale Channel-aligned Transformer (MCAT) model, integrated with a 5G–satellite–IoT communication architecture, to address these challenges. The MCAT model employs multi-scale token reconstruction and a dual-level attention mechanism, effectively capturing spatiotemporal dependencies in heterogeneous data streams (AIS, sensors, weather) while suppressing high-frequency noise. To enable seamless data collaboration, a hybrid transmission framework combining satellite (Inmarsat/Iridium), 5G URLLC slicing, and industrial Ethernet is designed, achieving ultra-low latency (10 ms) and nanosecond-level synchronization via IEEE 1588v2. Validated on a 22-dimensional real autonomous vessel dataset, MCAT reduces prediction errors by 12.5% MAE and 24% MSE compared to state-of-the-art methods, demonstrating superior robustness under noisy scenarios. Furthermore, the proposed architecture supports smart autonomous shipping solutions by providing demonstrably interpretable emission insights through its dual-level attention mechanism (visualized via attention maps) for route optimization, fuel efficiency enhancement, and compliance with CII regulations. This research bridges AI-driven predictive analytics with green autonomous shipping technologies, offering a scalable framework for digitalized and sustainable maritime operations. Full article
(This article belongs to the Special Issue Sustainable Maritime Transport and Port Intelligence)
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30 pages, 3436 KiB  
Article
Collaborative Scheduling of Yard Cranes, External Trucks, and Rail-Mounted Gantry Cranes for Sea–Rail Intermodal Containers Under Port–Railway Separation Mode
by Xuhui Yu and Cong He
J. Mar. Sci. Eng. 2025, 13(6), 1109; https://doi.org/10.3390/jmse13061109 - 2 Jun 2025
Viewed by 164
Abstract
The spatial separation of port yards and railway hubs, which relies on external truck drayage as a necessary link, hampers the seamless transshipment of sea–rail intermodal containers between ports and railway hubs. This creates challenges in synchronizing yard cranes (YCs) at the port [...] Read more.
The spatial separation of port yards and railway hubs, which relies on external truck drayage as a necessary link, hampers the seamless transshipment of sea–rail intermodal containers between ports and railway hubs. This creates challenges in synchronizing yard cranes (YCs) at the port terminal, external trucks (ETs) on the road, and rail-mounted gantry cranes (RMGs) at the railway hub. However, most existing studies focus on equipment scheduling or container transshipment organization under the port–railway integration mode, often overlooking critical time window constraints, such as train schedules and export container delivery deadlines. Therefore, this study investigates the collaborative scheduling of YCs, ETs, and RMGs for synchronized loading and unloading under the port–railway separation mode. A mixed-integer programming (MIP) model is developed to minimize the maximum makespan of all tasks and the empty-load time of ETs, considering practical time window constraints. Given the NP-hard complexity of this problem, an improved genetic algorithm (GA) integrated with a “First Accessible Machinery” rule is designed. Extensive numerical experiments are conducted to validate the correctness of the proposed model and the performance of the solution algorithm. The improved GA demonstrates a 6.08% better solution quality and a 97.94% reduction in computation time compared to Gurobi for small-scale instances. For medium to large-scale instances, it outperforms the adaptive large neighborhood search (ALNS) algorithm by 1.51% in solution quality and reduces computation time by 45.71%. Furthermore, the impacts of objective weights, equipment configuration schemes, port–railway distance, and time window width are analyzed to provide valuable managerial insights for decision-making to improve the overall efficiency of sea–rail intermodal systems. Full article
(This article belongs to the Special Issue Sustainable Maritime Transport and Port Intelligence)
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28 pages, 3908 KiB  
Article
Enhancing Port Shipping Synergy Through Bayesian Network: A Case of Major Chinese Ports
by Siqian Cheng, Jiankun Hu, Youfang Huang and Zhihua Hu
J. Mar. Sci. Eng. 2025, 13(6), 1093; https://doi.org/10.3390/jmse13061093 - 30 May 2025
Viewed by 186
Abstract
Port shipping collaboration is vital to greener, more resilient trade, yet decisions remain siloed and uncertain. This study develops a Bayesian network model grounded in empirical data from major Chinese ports, aiming to systematically analyze and enhance port shipping collaborative capacity. The methodology [...] Read more.
Port shipping collaboration is vital to greener, more resilient trade, yet decisions remain siloed and uncertain. This study develops a Bayesian network model grounded in empirical data from major Chinese ports, aiming to systematically analyze and enhance port shipping collaborative capacity. The methodology integrates expert knowledge and structural learning algorithms to construct a Directed Acyclic Graph (DAG), representing complex multi-stakeholder interactions among port enterprises, shipping companies, customers, and governmental bodies. Through forward and backward probabilistic inference, the study quantifies how coordinated improvements yield substantial synergistic benefits. Five leverage points stand out: customer engagement in green supply chains, perceived service quality, port digital information integration, multilateral trading maturity, and strict policy enforcement. A newly revealed feedback loop between digital integration and enforcement extends Emerson et al.’s collaborative governance framework, highlighting “digital-era connectivity” as a critical governance dimension and offering managers a focused, evidence-based action agenda. Full article
(This article belongs to the Special Issue Sustainable Maritime Transport and Port Intelligence)
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Graphical abstract

43 pages, 2191 KiB  
Article
Carbon Dioxide Storage Site Location and Transport Assignment Optimization for Sustainable Maritime Transport
by Yanmeng Tao, Ying Yang, Yuquan Du and Shuaian Wang
J. Mar. Sci. Eng. 2025, 13(6), 1055; https://doi.org/10.3390/jmse13061055 - 27 May 2025
Viewed by 287
Abstract
Maritime carbon dioxide (CO2) transport plays a pivotal role in facilitating carbon capture and storage (CCS) systems by connecting emission sources with appropriate storage sites. This process often incurs significant transportation costs, which must be carefully balanced against penalties for untransported [...] Read more.
Maritime carbon dioxide (CO2) transport plays a pivotal role in facilitating carbon capture and storage (CCS) systems by connecting emission sources with appropriate storage sites. This process often incurs significant transportation costs, which must be carefully balanced against penalties for untransported CO2 resulting from cost-driven decisions. This study addresses the CO2 storage site location and transport assignment (CSSL-TA) problem, aiming to minimize total tactical costs, including storage site construction, ship chartering, transportation, and penalties for direct CO2 emissions. We formulate the problem as a mixed-integer programming (MIP) model and demonstrate that the objective function exhibits submodularity, reflecting diminishing returns in facility investment and ship operations. A case study demonstrates the model’s effectiveness and practical value, revealing that optimal storage siting, strategic ship chartering, route allocation, and efficient transportation significantly reduce both transportation costs and emissions. To enhance practical applicability, a two-stage planning framework is proposed, where the first stage selects storage sites, and the second employs a genetic algorithm (GA) for transport assignment. The GA-based solution achieves a total cost only 2.4% higher than the exact MIP model while reducing computational time by 57.9%. This study provides a practical framework for maritime CO2 transport planning, contributing to cost-effective and sustainable CCS deployment. Full article
(This article belongs to the Special Issue Sustainable Maritime Transport and Port Intelligence)
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19 pages, 426 KiB  
Article
Digital Transformation and Business Model Innovation: Enhancing Productivity in the Croatian Maritime Transport Sector
by Marija Jović Mihanović, Saša Aksentijević, Edvard Tijan and Gregor Lenart
J. Mar. Sci. Eng. 2025, 13(5), 999; https://doi.org/10.3390/jmse13050999 - 21 May 2025
Viewed by 210
Abstract
This research investigates how business model changes induced by digital transformation impact productivity within the maritime transport sector in Croatia. Given the limited existing literature addressing digital transformation’s productivity implications, specifically in maritime contexts, this study aims to identify and analyze key mediating [...] Read more.
This research investigates how business model changes induced by digital transformation impact productivity within the maritime transport sector in Croatia. Given the limited existing literature addressing digital transformation’s productivity implications, specifically in maritime contexts, this study aims to identify and analyze key mediating factors. An online survey conducted among Croatian maritime transport stakeholders resulted in 82 valid responses, which were statistically analyzed using descriptive statistics, Spearman’s correlation, and principal component analysis (PCA). The study identifies two primary dimensions of business model changes—innovation and process digitalization—that significantly correlate with increased productivity. Key influencing factors include the digitalization of internal and external business processes, development of new digital revenue streams, introduction of innovative services, and novel pricing models. Results underscore the importance of targeted digital transformation initiatives and serve as a valuable reference for maritime transport stakeholders, aiming to enhance their productivity and competitiveness through digital innovation. Full article
(This article belongs to the Special Issue Sustainable Maritime Transport and Port Intelligence)
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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
Cited by 1 | Viewed by 447
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
Cited by 1 | Viewed by 772
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 833
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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