High-fidelity data supported ship trajectory prediction via an ensemble machine learning framework

Zhao, Jiansen; Lu, Jinquan; Chen, Xinqiang; Yan, Zhongwei; Yan, Ying; Sun, Yang

doi:10.1016/j.physa.2021.126470

Cited by 27 publications

(8 citation statements)

References 29 publications

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“…Valsamis et al [50] implemented a ship trajectory online predictive service tha dles data streams using conventional, multi-scan, pre-trained models. According research, a well-trained model that produces predictions with a high degree of ac may very well be able to satisfy a predictive service's need for real-time reaction w requiring retraining or compromising accuracy.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…In order to interpret large amounts of AIS data and utilize it to develop ship r Wen et al [51] suggested a research framework. They combined DBSCAN with an cial neural network that could automatically create a ship's path between ports u Valsamis et al [50] implemented a ship trajectory online predictive service that handles data streams using conventional, multi-scan, pre-trained models. According to the research, a well-trained model that produces predictions with a high degree of accuracy may very well be able to satisfy a predictive service's need for real-time reaction without requiring retraining or compromising accuracy.…”

Section: Hybrid Of Neural Network and Other Algorithmsmentioning

confidence: 99%

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Artificial Intelligence in Ship Trajectory Prediction

Bi,

Cheng,

Zhang

et al. 2024

JMSE

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Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techniques, to uncover their respective merits and drawbacks. In this work, a variety of studies were investigated that applied different algorithms in ship trajectory prediction, including regression models (RMs), artificial neural networks (ANNs), Kalman filtering (KF), and random forests (RFs) in machine learning, along with deep learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gate recurrent unit (GRU) networks, and sequence-to-sequence (Seq2seq) networks. The performance of predictive models based on different algorithms in trajectory prediction tasks was graded and analyzed. Among the existing studies, deep learning methods exhibit significant performance and considerable potential application value for maritime traffic systems, which can be assessed by future work on ship trajectory prediction research.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Hybrid Of Neural Network and Other Algorithmsmentioning

confidence: 99%

Artificial Intelligence in Ship Trajectory Prediction

Bi,

Cheng,

Zhang

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…Concerning activity data preprocessing we differentiate between the common tasks of data smoothing and data transformation. Data smoothing is considered as an essential step for the data preparation, to address missing data and suppress outliers by data denoising schemes, such as the Empirical Mode Decomposition (Chen et al 2021;Zhao et al 2022). This has been taken into account with the preparation of the provided preliminary EI; thus, data smoothing is not further implemented here.…”

Section: Data Preprocessingmentioning

confidence: 99%

Tiered prediction models for port vessel emissions inventories

Cammin

Voß

2022

Flex Serv Manuf J

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Albeit its importance, a large number of port authorities do not provide continuous or publicly available air emissions inventories (EIs) and thereby obscure the emissions contribution of ports. This is caused by, e.g., the economic effort generated by obtaining data. Therefore, the performance of abatement measures is not monitored and projected, which is specifically disadvantageous concerning top contributors such as container ships. To mitigate this issue, in this paper we propose port vessel EI prediction models by exploring the combination of different machine-learning algorithms, data from the one-off application of an activity-based bottom-up methodology and vessel-characteristics data. The results for this specific case show that prediction models enable acceptable trade-offs between the prediction performance and data requirements, promoting the creation of EIs.

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“…In the above studies, the UAV remote sensing videos generally have the advantages of high pixel and large monitoring range [18], and are suitable for estimating ship speed in the field of waterway traffic. However, due to the slow speed of the ship, the motion trends are fuzzy and the navigation trajectories are complex [19,20]. There are few studies on ship speed estimation based on UAV remote sensing images, and the real-time and continuity need to be improved.…”

Section: Introductionmentioning

confidence: 99%

Extracting Vessel Speed Based on Machine Learning and Drone Images during Ship Traffic Flow Prediction

Zhao

Chen

Zhou

et al. 2022

Journal of Advanced Transportation

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In the water transportation, ship speed estimation has become a key subject of intelligent shipping research. Traditionally, Automatic Identification System (AIS) is used to extract the ship speed information. However, transportation environment is gradually becoming complex, especially in the busy water, leading to the loss of some AIS data and resulting in a variety of maritime accidents. To make up for this deficiency, this paper proposes a vessel speed extraction framework, based on Unmanned Aerial Vehicle (UAV) airborne video. Firstly, YOLO v4 is employed to detect the ship targets in UAV image precisely. Secondly, a simple online and real time tracking method with a Deep association metric (Deep SORT) is applied to track ship targets with high quality. Finally, the ship motion pixel is computed based on the bounding box information of the ship trajectories, at the same time, the ship speed is estimated according to the mapping relationship between image space and the real space. Exhaustive experiments are conducted on the various scenarios. Results verify that the proposed framework has an excellent performance with average speed measurement accuracy is above 93% in complex waters. This paper also paves a way to further predict ship traffic flow in water transportation.

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High-fidelity data supported ship trajectory prediction via an ensemble machine learning framework

Cited by 27 publications

References 29 publications

Artificial Intelligence in Ship Trajectory Prediction

Artificial Intelligence in Ship Trajectory Prediction

Tiered prediction models for port vessel emissions inventories

Extracting Vessel Speed Based on Machine Learning and Drone Images during Ship Traffic Flow Prediction

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