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Marine Intelligent Transportation Systems: Data Mining and Control Optimization
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Marine Intelligent Transportation Systems: Data Mining and Control Optimization

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312).

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 13105

Special Issue Editors

Dr. Dongfang Ma

E-Mail Website
Guest Editor
Institute of Intelligent Transportation System, Zhejiang University, Hangzhou 310058, China
Interests: maritime data mining; intelligent control theory and method; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Huarong Zheng

E-Mail Website
Guest Editor
Ocean College, Zhejiang University, Hangzhou, China
Interests: model predictive control; ocean robotics; distributed control and coordination with applications to waterborne networked systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weibin Zhang

E-Mail Website
Guest Editor
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China
Interests: intelligent navigation situation awareness; maritime operations research; smart ship
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optimizing the maritime shipping scheduling strategy is an important method for shipping companies to save operating costs without any modification to ships. A large number of existing studies have focused on multiple aspects of ship scheduling, including route and speed decisions, ports-of-call sequence design, bunkering strategy design, and fleet deployment. However, affected by several recent shipping emission reduction regulations and measures, such as sulfur emission regulations (0.1% sulfur limit within emission control areas and 0.5% global sulfur cap), existing maritime shipping scheduling strategies should be redesigned to achieve operating cost savings under new policy scenarios.

Intelligent management is also the future development trend of shipping companies. A rapid development in data mining and artificial intelligence technology has been witnessed in recent years; these new technologies have great potential in solving traditional shipping scheduling problems, such as empty container repositioning, shipping network design, and analysis of ship emissions based on AIS data. A rapid development in maritime shipping management and technologies has been witnessed in the past decade, while the realization of green and intelligent shipping is still on the way.

This Special Issue aims to promote research on green and intelligent shipping. The beneficiaries of this issue include academic scholars, shipping companies, port operators, and policy makers. The guest editors call for high-quality research papers on the green shipping scheduling strategy, intelligent shipping management, and technologies. We welcome both research and review papers.

Dr. Dongfang Ma
Dr. Huarong Zheng
Prof. Dr. Weibin Zhang
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

  • green shipping scheduling optimization
  • modal shifts under sulfur limit rules
  • marine intelligent management technology
  • ship emission analysis based on AIS data mining
  • ship trajectory prediction based on AIS data mining

Published Papers (4 papers)

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Research

12 pages, 1819 KiB  
Article
Automatic Identification System (AIS) Data Supported Ship Trajectory Prediction and Analysis via a Deep Learning Model
by Xinqiang Chen, Chenxin Wei, Guiliang Zhou, Huafeng Wu, Zhongyu Wang and Salvatore Antonio Biancardo
J. Mar. Sci. Eng. 2022, 10(9), 1314; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10091314 - 16 Sep 2022
Cited by 11 | Viewed by 2909
Abstract
Automatic Identification System (AIS) data-supported ship trajectory analysis consistently helps maritime regulations and practitioners make reasonable traffic controlling and management decisions. Significant attentions are paid to obtain an accurate ship trajectory by learning data feature patterns in a feedforward manner. A ship may [...] Read more.
Automatic Identification System (AIS) data-supported ship trajectory analysis consistently helps maritime regulations and practitioners make reasonable traffic controlling and management decisions. Significant attentions are paid to obtain an accurate ship trajectory by learning data feature patterns in a feedforward manner. A ship may change her moving status to avoid potential traffic accident in inland waterways, and thus, the ship trajectory variation pattern may differ from previous data samples. The study proposes a novel ship trajectory exploitation and prediction framework with the help of the bidirectional long short-term memory (LSTM) (Bi-LSTM) model, which extracts intrinsic ship trajectory features with feedforward and backward manners. We have evaluated the proposed ship trajectory performance with single and multiple ship scenarios. The indicators of mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE) suggest that the proposed Bi-LSTM model can obtained satisfied ship trajectory prediction performance. Full article
(This article belongs to the Special Issue Marine Intelligent Transportation Systems: Data Mining and Control Optimization)
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24 pages, 5370 KiB  
Article
Evaluation of Ship Pollutant Emissions in the Ports of Los Angeles and Long Beach
by Guangnian Xiao, Tian Wang, Xinqiang Chen and Lizhen Zhou
J. Mar. Sci. Eng. 2022, 10(9), 1206; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10091206 - 28 Aug 2022
Cited by 44 | Viewed by 4291
Abstract
The role of the shipping industry in international logistics has been highlighted with the development of the global economy and the increase in international trade. Simultaneously, some of the environmental problems caused by shipping activities have gradually surfaced. The development of modern communication [...] Read more.
The role of the shipping industry in international logistics has been highlighted with the development of the global economy and the increase in international trade. Simultaneously, some of the environmental problems caused by shipping activities have gradually surfaced. The development of modern communication technology and marine communication equipment increased the feasibility of real-time ship dynamic data, as an information source for monitoring ship sailing states, and provided a data basis for the control of ship pollutant emissions. Based on the Automatic Identification System (AIS) data and ship-related data obtained from the waters of the ports of Los Angeles and Long Beach in 2020, the dynamic method is combined with the ship traffic emissions model STEAM2 to calculate the ship pollutant emissions in the two ports, and the relevant analysis work is conducted to evaluate the control effect of the Emission Control Area (ECA) policies on pollutant emissions. Results show that the ship pollutant emissions for CO, CXHX, NOX, SO2, PM10, and PM2.5 were 1230, 510, 11,700, 6670, 248, and 232 tons, respectively. These results also indicate the possible presence of a large gap in the distribution trend of ship pollutant emissions, according to different ship types and sailing states. Moreover, the control effect of various ECA policies on pollutant emissions is not the same, that is, the impact of ECA policies on SO2 and particulate matter is the largest, and that on NOX is minimal. Full article
(This article belongs to the Special Issue Marine Intelligent Transportation Systems: Data Mining and Control Optimization)
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18 pages, 1913 KiB  
Article
Integrated Carbon Emission Estimation Method and Energy Conservation Analysis: The Port of Los Angles Case Study
by Yao Yu, Ruikai Sun, Yindong Sun and Yaqing Shu
J. Mar. Sci. Eng. 2022, 10(6), 717; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10060717 - 24 May 2022
Cited by 11 | Viewed by 2448
Abstract
Port environmental problems have gradually become the primary concern of port authorities. The future trend of port carbon emissions is crucial to port authorities and managers in formulating regulations and optimizing operation schedules. Owing to the limitations of current prediction methods and the [...] Read more.
Port environmental problems have gradually become the primary concern of port authorities. The future trend of port carbon emissions is crucial to port authorities and managers in formulating regulations and optimizing operation schedules. Owing to the limitations of current prediction methods and the complex social–environmental impact, the estimation results of port carbon emissions have insufficient accuracy to support port development in the future. In this work, the stochastic impacts by regression on population, affluence, and technology (STIRPAT)–long short-term memory (LSTM)–autoregressive integrated moving average with explanatory variable (ARIMAX) integrated model is proposed for the estimation of the carbon emission of Port of Los Angeles to improve the reliability of emission prediction. Macroeconomic indicators that affect port throughput are selected using the principal component analysis—multiple linear regression model. The chosen indicators are then combined with long-term historical port throughput data as the input of the multivariate autoregressive integrated moving average (ARIMAX) model to predict port throughput. Indicators related to port carbon emissions are verified by the STIRPAT model. The LSTM–ARIMAX integrated model is then applied to estimate the emission tendency, which can be useful in developing corresponding carbon reduction strategies and further understanding port emissions. Results show that the proposed method can significantly improve the estimation accuracy for port emission by 11% compared with existing techniques. Energy conservation strategies are also put forward to assist port authorities in achieving the peak clipping of port carbon emission. Full article
(This article belongs to the Special Issue Marine Intelligent Transportation Systems: Data Mining and Control Optimization)
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16 pages, 3572 KiB  
Article
Real-Time Ship Tracking under Challenges of Scale Variation and Different Visibility Weather Conditions
by Hu Liu, Xueqian Xu, Xinqiang Chen, Chaofeng Li and Meilin Wang
J. Mar. Sci. Eng. 2022, 10(3), 444; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10030444 - 20 Mar 2022
Cited by 6 | Viewed by 2339
Abstract
Visual ship tracking provides crucial kinematic traffic information to maritime traffic participants, which helps to accurately predict ship traveling behaviors in the near future. Traditional ship tracking models obtain a satisfactory performance by exploiting distinct features from maritime images, which may fail when [...] Read more.
Visual ship tracking provides crucial kinematic traffic information to maritime traffic participants, which helps to accurately predict ship traveling behaviors in the near future. Traditional ship tracking models obtain a satisfactory performance by exploiting distinct features from maritime images, which may fail when the ship scale varies in image sequences. Moreover, previous frameworks have not paid much attention to weather condition interferences (e.g., visibility). To address this challenge, we propose a scale-adaptive ship tracking framework with the help of a kernelized correlation filter (KCF) and a log-polar transformation operation. First, the proposed ship tracker employs a conventional KCF model to obtain the raw ship position in the current maritime image. Second, both the previous step output and ship training sample are transformed into a log-polar coordinate system, which are further processed with the correlation filter to determine ship scale factor and to suppress the negative influence of the weather conditions. We verify the proposed ship tracker performance on three typical maritime scenarios under typical navigational weather conditions (i.e., sunny, fog). The findings of the study can help traffic participants efficiently obtain maritime situation awareness information from maritime videos, in real time, under different visibility weather conditions. Full article
(This article belongs to the Special Issue Marine Intelligent Transportation Systems: Data Mining and Control Optimization)
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