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JMSE
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Management and Control of Ship Traffic Behaviours
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Management and Control of Ship Traffic Behaviours

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: 10 January 2025 | Viewed by 3848

Special Issue Editors

Prof. Dr. Xiaoyuan Wang

E-Mail Website
Guest Editor
College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China
Interests: intelligent transportation systems; intelligent and connected driving; traffic behavior and safety; traffic flow theory; unmanned vehicles and water vehicles; human–machine-environment collaborative intelligence and control
Prof. Dr. Václav Píštěk

E-Mail Website
Guest Editor
Institute of Automotive Engineering, Brno University of Technology, Technická 2896/2, 616-69 Brno, Czech Republic
Interests: mechanical engineering; applied mechanics; computational methods; FEM; multi-body systems; vibration and noise reduction; mechatronics; CFD; optimization algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Longjin Wang

E-Mail Website
Guest Editor
College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China
Interests: system identification and motion control of marine crafts

Special Issue Information

Dear Colleagues,

With the increasing volume of global maritime trade, the scale of the shipping industry is expanding and the number of ships is also increasing, all of which has led to persistently high maritime accident rates. Consequently, effective management and control measures are needed to ensure the safe and efficient operation of ships in waterways. On the other hand, cutting-edge technologies, such as big data, the Internet of Things, and artificial intelligence provide new opportunities to develop improved marine transportation systems. This Special Issue focuses on the critical aspects of managing and controlling ship traffic to ensure the safety, efficiency, energy conservation, and sustainability of maritime transportation. Therefore, the scope of research for this Special Issue includes but is not limited to traffic flow optimization, collision avoidance systems, ship motion control, intelligent traffic management systems, and human factors influencing ship traffic behavior and emerging intelligent ship technologies.

Prof. Dr. Xiaoyuan Wang
Prof. Dr. Václav Píštěk
Dr. Longjin 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

  • marine traffic flow optimization
  • collision avoidance
  • motion control
  • intelligent traffic management systems
  • human factors
  • intelligent ship technologies

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Published Papers (4 papers)

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Research

18 pages, 7824 KiB  
Article
Vessel Traffic Flow Prediction in Port Waterways Based on POA-CNN-BiGRU Model
by Yumiao Chang, Jianwen Ma, Long Sun, Zeqiu Ma and Yue Zhou
J. Mar. Sci. Eng. 2024, 12(11), 2091; https://doi.org/10.3390/jmse12112091 - 19 Nov 2024
Viewed by 368
Abstract
Vessel traffic flow forecasting in port waterways is critical to improving safety and efficiency of port navigation. Aiming at the stage characteristics of vessel traffic in port waterways in time sequence, which leads to complexity of data in the prediction process and difficulty [...] Read more.
Vessel traffic flow forecasting in port waterways is critical to improving safety and efficiency of port navigation. Aiming at the stage characteristics of vessel traffic in port waterways in time sequence, which leads to complexity of data in the prediction process and difficulty in adjusting the model parameters, a convolutional neural network (CNN) based on the optimization of the pelican algorithm (POA) and the combination of bi-directional gated recurrent units (BiGRUs) is proposed as a prediction model, and the POA algorithm is used to search for optimized hyper-parameters, and then the iterative optimization of the optimal parameter combinations is input into the best combination of iteratively found parameters, which is input into the CNN-BiGRU model structure for training and prediction. The results indicate that the POA algorithm has better global search capability and faster convergence than other optimization algorithms in the experiment. Meanwhile, the BiGRU model is introduced and compared with the CNN-BiGRU model prediction; the POA-CNN-BiGRU combined model has higher prediction accuracy and stability; the prediction effect is significantly improved; and it can provide more accurate prediction information and cycle characteristics, which can serve as a reference for the planning of ships’ routes in and out of ports and optimizing the management of ships’ organizations. Full article
(This article belongs to the Special Issue Management and Control of Ship Traffic Behaviours)
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25 pages, 15710 KiB  
Article
TG-PGAT: An AIS Data-Driven Dynamic Spatiotemporal Prediction Model for Ship Traffic Flow in the Port
by Jianwen Ma, Yue Zhou, Yumiao Chang, Zhaoxin Zhu, Guoxin Liu and Zhaojun Chen
J. Mar. Sci. Eng. 2024, 12(10), 1875; https://doi.org/10.3390/jmse12101875 - 18 Oct 2024
Viewed by 689
Abstract
Accurate prediction of ship traffic flow is essential for developing intelligent maritime transportation systems. To address the complexity of ship traffic flow data in the port and the challenges of capturing its dynamic spatiotemporal dependencies, a dynamic spatiotemporal model called Temporal convolutional network-bidirectional [...] Read more.
Accurate prediction of ship traffic flow is essential for developing intelligent maritime transportation systems. To address the complexity of ship traffic flow data in the port and the challenges of capturing its dynamic spatiotemporal dependencies, a dynamic spatiotemporal model called Temporal convolutional network-bidirectional Gated recurrent unit-Pearson correlation coefficient-Graph Attention Network (TG-PGAT) is proposed for predicting traffic flow in port waters. This model extracts spatial features of traffic flow by combining the adjacency matrix and spatial dynamic coefficient correlation matrix within the Graph Attention Network (GAT) and captures temporal features through the concatenation of the Temporal Convolutional Network (TCN) and Bidirectional Gated Recurrent Unit (BiGRU). The proposed TG-PGAT model demonstrates higher prediction accuracy and stability than other classic traffic flow prediction methods. The experimental results from multiple angles, such as ablation experiments and robustness tests, further validate the critical role and strong noise resistance of different modules in the TG-PGAT model. The experimental results of visualization demonstrate that this model not only exhibits significant predictive advantages in densely trafficked areas of the port but also outperforms other models in surrounding areas with sparse traffic flow data. Full article
(This article belongs to the Special Issue Management and Control of Ship Traffic Behaviours)
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16 pages, 4998 KiB  
Article
A YOLOv7-Based Method for Ship Detection in Videos of Drones
by Quanzheng Wang, Jingheng Wang, Xiaoyuan Wang, Luyao Wu, Kai Feng and Gang Wang
J. Mar. Sci. Eng. 2024, 12(7), 1180; https://doi.org/10.3390/jmse12071180 - 14 Jul 2024
Cited by 2 | Viewed by 1099
Abstract
With the rapid development of the shipping industry, the number of ships is continuously increasing, and maritime accidents happen frequently. In recent years, computer vision and drone flight control technology have continuously developed, making drones widely used in related fields such as maritime [...] Read more.
With the rapid development of the shipping industry, the number of ships is continuously increasing, and maritime accidents happen frequently. In recent years, computer vision and drone flight control technology have continuously developed, making drones widely used in related fields such as maritime target detection. Compared to the cameras fixed on ships, a greater flexibility and a wider field of view is provided by cameras equipped on drones. However, there are still some challenges in high-altitude detection with drones. Firstly, from a top-down view, the shapes of ships are very different from ordinary views. Secondly, it is difficult to achieve faster detection speeds because of limited computing resources. To solve these problems, we propose YOLOv7-DyGSConv, a deep learning-based model for detecting ships in real-time videos captured by drones. The model is built on YOLOv7 with an attention mechanism, which enhances the ability to capture targets. Furthermore, the Conv in the Neck of the YOLOv7 model is replaced with the GSConv, which reduces the complexity of the model and improves the detection speed and detection accuracy. In addition, to compensate for the scarcity of ship datasets in top-down views, a ship detection dataset containing 2842 images taken by drones or with a top-down view is constructed in the research. We conducted experiments on our dataset, and the results showed that the proposed model reduced the parameters by 16.2%, the detection accuracy increased by 3.4%, and the detection speed increased by 13.3% compared with YOLOv7. Full article
(This article belongs to the Special Issue Management and Control of Ship Traffic Behaviours)
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29 pages, 2805 KiB  
Article
Port Accessibility Depends on Cascading Interactions between Fleets, Policies, Infrastructure, and Hydrodynamics
by Floor P. Bakker, Solange van der Werff, Fedor Baart, Alex Kirichek, Sander de Jong and Mark van Koningsveld
J. Mar. Sci. Eng. 2024, 12(6), 1006; https://doi.org/10.3390/jmse12061006 - 17 Jun 2024
Cited by 2 | Viewed by 1048
Abstract
Reducing waiting times is crucial for ports to be efficient and competitive. Important causes of waiting times are cascading interactions between realistic hydrodynamics, accessibility policies, vessel-priority rules, and detailed berth availability. The main challenges are determining the cause of waiting and finding rational [...] Read more.
Reducing waiting times is crucial for ports to be efficient and competitive. Important causes of waiting times are cascading interactions between realistic hydrodynamics, accessibility policies, vessel-priority rules, and detailed berth availability. The main challenges are determining the cause of waiting and finding rational solutions to reduce waiting time. In this study, we focus on the role of the design depth of a channel on the waiting times. We quantify the performance of channel depth for a representative fleet rather than the common approach of a single normative design vessel. The study relies on a mesoscale agent-based discrete-event model that can take processed Automatic Identification System and hydrodynamic data as its main input. The presented method’s validity is assessed by hindcasting one year of observed anchorage area laytimes for a liquid bulk terminal in the Port of Rotterdam. The hindcast demonstrates that the method predicts the causes of 73.4% of the non-excessive laytimes of vessels, thereby correctly modelling 60.7% of the vessels-of-call. Following a recent deepening of the access channel, cascading waiting times due to tidal restrictions were found to be limited. Nonetheless, the importance of our approach is demonstrated by testing alternative maintained bed level designs, revealing the method’s potential to support rational decision-making in coastal zones. Full article
(This article belongs to the Special Issue Management and Control of Ship Traffic Behaviours)
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