Toward Multimodal Vessel Trajectory Prediction by modeling the distribution of modes

Guo, Shilin; Zhang, Hui; Guo, Yaming

doi:10.1016/j.oceaneng.2023.115020

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…Zhao et al [26] designed a model for predicting ship trajectories using a graph attention network (GAT) and the LSTM model. 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].…”

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

“…Multimodal approaches represent a prominent method for mining ship behavior by extracting features from various sensing modes and integrating these features to gain a more thorough understanding and enhanced data mining capabilities. Guo et al (2023) developed a multimodal ship trajectory prediction approach through pattern distribution modeling. This approach utilizes a vector, randomly sampled from a multivariate Gaussian distribution, as the representation of trajectory patterns to generate multiple predicted trajectories.…”

Section: Overview Of Related Work On Ship Behavior Miningmentioning

confidence: 99%

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

Li,

Liu,

Wang

et al. 2024

Front. Mar. Sci.

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In maritime logistics optimization, considerable research efforts are focused on the extraction of deep behavioral characteristics from comprehensive shipping data to discern patterns in maritime vessel behavior. The effective linkage of these characteristics with maritime infrastructure, such as berths, is critical for the enhancement of ship navigation systems. This endeavor is paramount not only as a research focus within maritime information science but also for the progression of intelligent maritime systems. Traditional methodologies have primarily emphasized the analysis of navigational paths of vessels without an extensive consideration of the geographical dynamics between ships and port infrastructure. However, the introduction of knowledge graphs has enabled the integration of disparate data sources, facilitating new insights that propel the development of intelligent maritime systems. This manuscript presents a novel framework using knowledge graph technology for profound analysis of maritime data. Utilizing automatic identification system (AIS) data alongside spatial information from port facilities, the framework forms semantic triplet connections among ships, anchorages, berths, and waterways. This enables the semantic modeling of maritime behaviors, offering precise identification of ships through their diverse semantic information. Moreover, by exploiting the semantic relations between ships and berths, a reverse semantic knowledge graph for berths is constructed, which is specifically tailored to ship type, size, and category. The manuscript critically evaluates a range of graph embedding techniques, dimensionality reduction methods, and classification strategies through experimental frameworks to determine the most efficacious methodologies. The findings reveal that the maritime knowledge graph significantly enhances the semantic understanding of unmanned maritime equipment, thereby improving decision-making capabilities. Additionally, it establishes a semantic foundation for the development of expansive maritime models, illustrating the potential of knowledge graph technology in advancing intelligent maritime systems.

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Construction of a Large-Scale Maritime Elements Semantic Schema Based on Heterogeneous Graph Models

Liu,

Li,

Wang

et al. 2024

Spatial Data and Intelligence

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Toward Multimodal Vessel Trajectory Prediction by modeling the distribution of modes

Cited by 7 publications

References 25 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

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

Construction of a Large-Scale Maritime Elements Semantic Schema Based on Heterogeneous Graph Models

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