A context-aware approach for vessels’ trajectory prediction

Mehri, Saeed; Alesheikh, Ali Asghar; Basiri, Anahid

doi:10.1016/j.oceaneng.2023.114916

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…Guo et al [33] proposed a multimodal-data-based approach for predicting ship trajectories by incorporating additional hidden states to define complex modes independently. A context-driven, data-driven framework for predicting ship trajectories was presented by Mehri et al [34]. This framework first enriched the contextual information of a trajectory, including the evaluation methods for annotating a trajectory, and then applied feature selection techniques to solve high-dimensional problems.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data

Xiao,

Hu,

Liu

et al. 2024

JMSE

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Ship trajectory prediction is essential for ensuring safe route planning and to have advanced warning of the dangers at sea. With the development of deep learning, most of the current research has explored advanced prediction methods based on historical spatio-temporal Automatic Identification System (AIS) data. However, environmental factors such as sea wind and visibility also affect ship navigation in real-world maritime shipping. Therefore, developing reliable models utilizing multimodal data, such as AIS and environmental data, is challenging. In this research, we design an adaptive multimodal vessel trajectory data prediction model (termed AMD) based on satellite AIS and environmental data. The AMD model mainly consists of an AIS-based extraction network, an environmental-based extraction network, and a fusion block. In particular, this work considers multimodal data such as historical spatio-temporal information and environmental factors. Time stamps and distances are correlated with AIS and environmental data, and a multilayer perceptron and gated recurrent unit networks are used to design multimodal feature extraction networks. Finally, the fusion block realizes the fusion output of multimodal features to improve the reliability of the AMD model. Several quantitative and qualitative experiments are conducted using real-world AIS and multimodal environmental datasets. Numerous experimental results prove that prediction performance using multimodal data can ensure satisfactory accuracy and reliability while exhibiting a positive impact on improving maritime transport services.

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Section: Deep Learning Methodsmentioning

confidence: 99%

An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data

Xiao,

Hu,

Liu

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

Deep learning framework for vessel trajectory prediction using auxiliary tasks and convolutional networks

Shin,

Kim,

Lee

et al. 2024

Engineering Applications of Artificial Intelligence

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Spatio-Temporal Transformer Networks for Inland Ship Trajectory Prediction with Practical Deficient Automatic Identification System Data

Xiao,

Luo,

Wang

et al. 2024

Applied Sciences

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Inland waterways, characterized by their complex, narrow paths, see significantly higher traffic volumes compared to maritime routes, increasing the regulatory demands on traffic management. Predictive modeling of ship traffic flows, utilizing real AIS historical data, enhances route and docking planning for ships and port managers. This approach boosts transportation efficiency and safety in inland waterway navigation. Nevertheless, AIS data are flawed, marred by noise, disjointed paths, anomalies, and inconsistent timing between points. This study introduces a data processing technique to refine AIS data, encompassing segmentation, outlier elimination, missing point interpolation, and uniform interval resampling, aiming to enhance trajectory analysis reliability. Utilizing this refined data processing approach on ship trajectory data yields independent, complete motion profiles with uniform timing. Leveraging the Transformer model, denoted TRFM, this research integrates processed AIS data from the Yangtze River to create a predictive dataset, validating the efficacy of our prediction methodology. A comparative analysis with advanced models such as LSTM and its variants demonstrates TRFM’s superior accuracy, showcasing lower errors in multiple metrics. TRFM’s alignment with actual trajectories underscores its potential for enhancing navigational planning. This validation not only underscores the method’s precision in forecasting ship movements but also its utility in risk management and decision-making, contributing significantly to the advancement in maritime traffic safety and efficiency.

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A context-aware approach for vessels’ trajectory prediction

Cited by 3 publications

References 47 publications

An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data

An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data

Deep learning framework for vessel trajectory prediction using auxiliary tasks and convolutional networks

Spatio-Temporal Transformer Networks for Inland Ship Trajectory Prediction with Practical Deficient Automatic Identification System Data

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