Ship trajectory prediction based on machine learning and deep learning: A systematic review and methods analysis

Li, Huanhuan; Jiao, Hang; Yang, Zaili

doi:10.1016/j.engappai.2023.107062

Cited by 42 publications

(6 citation statements)

References 97 publications

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“…Future research: In the context of the rapid development of the shipping industry, the research on ship trajectory prediction is still necessary and urgent to ensure the safety of ship trajectories and reduce the occurrence of maritime accidents. In future research, the traditional machine learning-based method for ship trajectory prediction will be subject to more and more restrictions, and the deep learning-based ship trajectory prediction method should receive more and more attention from researchers [67]. In addition, future research on ship trajectories can also try to classify ship trajectories in order to further determine the behavior behind the trajectories and provide assistance to the work of maritime regulatory authorities [68].…”

Section: Ship Trajectorymentioning

confidence: 99%

The Application of Artificial Intelligence Technology in Shipping: A Bibliometric Review

Xiao,

Yang,

et al. 2024

JMSE

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Artificial intelligence (AI) technologies are increasingly being applied to the shipping industry to advance its development. In this study, 476 articles published in the Science Citation Index Expanded (SCI-EXPANDED) and the Social Sciences Citation Index (SSCI) of the Web of Science Core Collection from 2001 to 2022 were collected, and bibliometric methods were applied to conduct a systematic literature of the field of AI technology applications in the shipping industry. The review commences with an annual publication trend analysis, which shows that research in the field has been growing rapidly in recent years. This is followed by a statistical analysis of journals and a collaborative network analysis to identify the most productive journals, countries, institutions, and authors. The keyword “co-occurrence analysis” is then utilized to identify major research clusters, as well as hot research directions in the field, providing directions for future research in the field. Finally, based on the results of the keyword co-occurrence analysis and the content analysis of the papers published in recent years, the research gaps in AIS data applications, ship trajectory, and anomaly detection, as well as the possible future research directions, are discussed. The findings indicate that AIS data in the future research direction are mainly reflected in the analysis of ship behavior and AIS data repair. Ship trajectory in the future research direction is mainly reflected in the deep learning-based method research and the discussion of ship trajectory classification. Anomaly detection in the future research direction is mainly reflected in the application of deep learning technology in ship anomaly detection and improving the efficiency of ship anomaly detection. These insights offer guidance for researchers’ future investigations in this area. In addition, we discuss the implications of research in the field of shipping AI from both theoretical and practical perspectives. Overall, this review can help researchers understand the status and development trend of the application field of AI technology in shipping, correctly grasp the research direction and methodology, and promote the further development of the field.

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Section: Ship Trajectorymentioning

confidence: 99%

The Application of Artificial Intelligence Technology in Shipping: A Bibliometric Review

Xiao,

Yang,

et al. 2024

JMSE

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“…In addition, the application of machine learning techniques assists in optimizing collision avoidance strategies for autonomous aircraft. Machine learning algorithms can foresee likely collision situations by analyzing data on vessel trajectories, traffic patterns, and collision risk variables [96], [97]. These algorithms may prescribe evasive tactics to avoid accidents and ensure safe passage.…”

Section: ) Autonomous Navigation and Shippingmentioning

confidence: 99%

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Vu,

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.

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“…Struktura sustava upravljanja -Standardized time-serial data sets with maritime traffi c data collection systems facilitate the development of MTSP models; -The LSTM algorithm has a 3-gate structure that enables the processing of multi-layered data feedback. This allows the algorithm to extract deeper data features than the normal RNN algorithm [26].…”

Section: Maritime Traffi C State Prediction Model Based On Lstm Netwo...mentioning

confidence: 99%

Maritime Data Mining for Marine Safety Based on Deep Learning: Southern Vietnam Case Study

Pham,

Dang,

Koboević

et al. 2024

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High-speed passenger vessels, integrated river and sea vessels, container vessels, oil tankers, and other underwater vehicles operating in maritime traffic are among the types of vessels that must be equipped with AIS and VHF. The safety of navigation is one of the major problems in the maritime sector, particularly in Vietnam. Furthermore, marine traffic in the seaport zone is a common and difficult issue to manage in areas with a high volume of vessel traffic, mostly in places where the infrastructure supporting navigation is inadequately developed to meet the rapidly growing demands of the contemporary world. Therefore, it is necessary to create an integrated maritime management system to improve the efficiency of data exploitation and support maritime safety. To address this challenge, this study suggests a Maritime Traffic State Prediction (MTSP) model to predict traffic conditions in the channels where real-time data collection is insufficient in some specific locations. We recommend a deep learning method using Long Short-Term Memory (LSTM) networks to predict the safe path of the vessel in case of missing data segments. The findings have shown that the proposed approach encourages the mining of historical vessel data for maritime traffic, is ready to be applied, and can easily be implemented in a computer program or a web-based app.

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Ship trajectory prediction based on machine learning and deep learning: A systematic review and methods analysis

Cited by 42 publications

References 97 publications

The Application of Artificial Intelligence Technology in Shipping: A Bibliometric Review

The Application of Artificial Intelligence Technology in Shipping: A Bibliometric Review

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Maritime Data Mining for Marine Safety Based on Deep Learning: Southern Vietnam Case Study

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