Framework for Ship Trajectory Forecasting Based on Linear Stationary Models Using Automatic Identification System

Srivastava, Smita; Kumar, Lovneet; Jeyanthi, R.; Deepa, K.; Aggrawal, Vinay

doi:10.1016/j.procs.2023.01.125

Cited by 3 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xiao et al [28] introduced a novel model that integrates motion modeling and filtering processes. Srivastava et al [14] presented a lightweight, short-term forecast model based on linear stationary models for ship trajectory prediction and real-time anomaly detection. They integrated the best-fitting auto-regressive integrated moving average (ARIMA) model with window generator models of different sizes and visualizations for recursive real-time predictions.…”

Section: Traditional Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

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.

show abstract

Section: Traditional Methodsmentioning

confidence: 99%

“…Existing methods for predicting vessel trajectory fall into three categories: traditional [13,14], machine learning [15,16], and deep learning [17][18][19]. Traditional approaches primarily rely on empirical and mathematical models following specific physical laws [20,21].…”

Section: Introductionmentioning

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

Geohash coding-powered deep learning network for vessel trajectory prediction using clustered AIS data in maritime Internet of Things industries

Li,

Chen,

Liu

et al. 2024

Computers and Electrical Engineering

View full text Add to dashboard Cite

Prediction of ship trajectory based on deep learning

Li,

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The rapid development of computer technology strongly promotes the study of maritime traffic safety. The application of artificial intelligence technology makes the ship’s trajectory prediction not limited to complex physical models, and improves the generality of ship’s track prediction. To address the problems of relatively poor prediction accuracy in existing ship trajectory prediction research and high coupling of algorithms during ship navigation while the prediction model needs to be improved, this paper combines grey forecast prediction model and long and short-term memory (LSTM) neural network to establish a neural network model for ship trajectory prediction. This model can effectively increase the number of messages in LSTM model, and reduce the loss value of the model and improve the accuracy of ship track prediction. The prediction method in this paper is verified by AIS data of a ship. The results show that compared with other time series prediction algorithms, this algorithm has higher prediction accuracy.

show abstract

Framework for Ship Trajectory Forecasting Based on Linear Stationary Models Using Automatic Identification System

Cited by 3 publications

References 7 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

Geohash coding-powered deep learning network for vessel trajectory prediction using clustered AIS data in maritime Internet of Things industries

Prediction of ship trajectory based on deep learning

Contact Info

Product

Resources

About