Next-Generation Vessel Traffic Services Systems—From “Passive” to “Proactive”

Xiao, Zhe; Fu, Xiuju; Zhao, Liangbin; Zhang, Liye; Teo, Tze Kern; Li, Ning; Zhang, Wanbing

doi:10.1109/mits.2022.3144411

Cited by 15 publications

(5 citation statements)

References 18 publications

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“…The ship's lateral position, speed, and heading data, provided by the AIS information, are recorded at each crossing line so that the temporal and spatial distribution of the training data is consistent. To assist the Ship Traffic Service (VTS) operation, tracking ship behavior, anomaly detection, and warnings prediction are crucial topics in developing modern intelligent waterway systems [18]. Nevertheless, a prerequisite for setting up this system is comprehending ship types, their distinct maneuvering capabilities, and cargo characteristics across various ship types.…”

Section: The Changhua Wind Farm Channelmentioning

confidence: 99%

Ship Classification Based on AIS Data and Machine Learning Methods

Huang,

Lee,

Nieh

et al. 2023

Electronics

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AIS ship-type code categorizes ships into broad classes, such as fishing, passenger, and cargo, yet struggles with finer distinctions among cargo ships, such as bulk carriers and containers. Different ship types significantly impact acceleration, steering performance, and stopping distance, thus making precise identification of unfamiliar ship types crucial for maritime monitoring. This study introduces an original classification study based on AIS data for cargo ships, presenting a classifier tailored for bulk carriers, containers, general cargo, and vehicle carriers. The model’s efficacy was tested within the Changhua Wind Farm Channel using eight classification algorithms across tree-structure-based, proximity-based, and regression-based categories and employing standard metrics (Accuracy, Precision, Recall, F1-score) to assess the performance. The results show that tree-structure-based algorithms, particularly XGBoost and Random Forest, demonstrated superior performance. This study also implemented a feature selection strategy with five methods, revealing that a model trained with only four features (three ship-geometric features and one trajectory behavior feature) can achieve high accuracy. Conclusively, the classifier effectively overcame the challenges of limited AIS data labels, achieving a classification accuracy of 97% for ships in the Changhua Wind Farm Channel. These results are pivotal in identifying abnormal ship behavior, highlighting the classifier’s potential for maritime monitoring applications.

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Section: The Changhua Wind Farm Channelmentioning

confidence: 99%

Ship Classification Based on AIS Data and Machine Learning Methods

Huang,

Lee,

Nieh

et al. 2023

Electronics

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“…While the range of coastal radar is restricted compared to satellite systems, it is a traditional component in vessel traffic service systems for monitoring non-cooperating vessels in highly trafficked areas [52]. There are many examples of coastal radar as a component of a common operating picture that considers multiple inputs (e.g., [53][54][55]). Persistent target tracking, facilitated by the Automatic Radar Plotting Aid (ARPA), provides a continuous record of a unique vessel's trajectory, which is difficult to reconstruct from infrequent satellite images [56].…”

Section: Related Workmentioning

confidence: 99%

Building a Practical Multi-Sensor Platform for Monitoring Vessel Activity near Marine Protected Areas: Case Studies from Urban and Remote Locations

Cope¹,

Tougher²,

Zetterlind³

et al. 2023

Remote Sensing

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Monitoring vessel activity is an important part of managing marine protected areas (MPAs), but small-scale fishing and recreational vessels that do not participate in cooperative vessel traffic systems require additional monitoring strategies. Marine Monitor (M2) is a shore-based, multi-sensor platform that integrates commercially available hardware, primarily X-band marine radar and optical cameras, with custom software to autonomously track and report on vessel activity regardless of participation in other tracking systems. By utilizing established commercial hardware, the radar system is appropriate for supporting the management of coastal, small-scale MPAs. Data collected in the field are transferred to the cloud to provide a continuous record of activity and identify prohibited activities in real-time using behavior characteristics. To support the needs of MPA managers, both hardware and software improvements have been made over time, including ruggedizing equipment for the marine environment and powering systems in remote locations. Case studies are presented comparing data collection by both radar and the Automatic Identification System (AIS) in urban and remote locations. At the South La Jolla State Marine Reserve near San Diego, CA, USA, 93% of vessel activity (defined as the cumulative time vessels spent in the MPA) was identified exclusively by radar from November 2022 through January 2023. At the Caye Bokel Conservation Area, within the Turneffe Atoll Marine Reserve offshore of Belize, 98% was identified exclusively by radar from April through October 2022. Spatial and temporal patterns of radar-detected and AIS activity also differed at both sites. These case study site results together demonstrate the common and persistent presence of small-scale vessel activity near coastal MPAs that is not documented by cooperative systems. Therefore, an integrated radar system can be a useful tool for independent monitoring, supporting a comprehensive understanding of vessel activity in a variety of areas.

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“…In this context, Internet of Things (IoT)-based maritime services leverage the huge amount of collected, stored, and processed data [1]. Hence, real-time monitoring and automated optimization procedures are expected to play a key role in future maritime transportation systems for ship path optimization or energyconsumption-dependent and speed optimization purposes [2][3][4]. To this end, traditional optimization approaches might be inefficient, as they might lead to non-convex problems, which are solved via iterative optimization approaches.…”

Section: Introductionmentioning

confidence: 99%

Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues

Giannopoulos,

Gkonis,

Bithas

et al. 2024

JMSE

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Maritime transportation is crucial for global trade and responsible for the majority of goods movement worldwide. The optimization of maritime operations is challenged by the complexity and heterogeneity of maritime nodes. This paper presents the emerging deployment of federated learning (FL) in maritime environments to address these challenges. FL enables decentralized machine learning model training, ensuring data privacy and security while overcoming issues associated with non-i.i.d. data. This paper explores various maritime use cases, including fuel consumption reduction, predictive maintenance, and just-in-time arrival. Experimental results using real datasets demonstrate the superiority of FL in predicting the fuel consumption of large cargo ships in terms of accuracy and spatiotemporal complexity over traditional collaborative machine learning approaches. The findings indicate that FL can significantly improve the performance of fuel consumption models in a collaborative way, while ensuring data privacy preservation and no data transmission during the learning process. Finally, this paper discusses open issues and future research directions necessary for the widespread adoption of FL in maritime transportation and settings.

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Next-Generation Vessel Traffic Services Systems—From “Passive” to “Proactive”

Cited by 15 publications

References 18 publications

Ship Classification Based on AIS Data and Machine Learning Methods

Ship Classification Based on AIS Data and Machine Learning Methods

Building a Practical Multi-Sensor Platform for Monitoring Vessel Activity near Marine Protected Areas: Case Studies from Urban and Remote Locations

Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues

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