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Predicting Ship Trajectory Based on Neural Networks Using AIS Data

https://doi.org/10.3390/jmse9030254
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-57421-9_20openalex-snapshot
An Automatic Identification System (AIS) Database for Maritime Trajectory Prediction and Data Mining
inferredcites →https://doi.org/10.1007/s10707-016-0266-xopenalex-snapshot
Online event recognition from moving vessel trajectories
inferredcites →https://doi.org/10.1016/j.apor.2012.05.008openalex-snapshot
A study on the collision avoidance of a ship using neural networks and fuzzy logic
inferredcites →https://doi.org/10.1016/j.eswa.2012.05.060openalex-snapshot
Machine learning for vessel trajectories using compression, alignments and domain knowledge
inferredcites →https://doi.org/10.1016/j.oceaneng.2019.106635openalex-snapshot
Use of AIS data for guidance and control of path-following autonomous vessels
inferredcites →https://doi.org/10.1016/j.oceaneng.2020.106919openalex-snapshot
Ship-handling behavior pattern recognition using AIS sub-trajectory clustering analysis based on the T-SNE and spectral clustering algorithms
inferredcites →https://doi.org/10.1016/j.oceaneng.2020.106936openalex-snapshot
Data mining approach to shipping route characterization and anomaly detection based on AIS data
inferredcites →https://doi.org/10.1016/j.oceaneng.2020.107478openalex-snapshot
A dual linear autoencoder approach for vessel trajectory prediction using historical AIS data
inferredcites →https://doi.org/10.1016/j.oceaneng.2020.108182openalex-snapshot
A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network
inferredcites →https://doi.org/10.1017/s0373463317000066openalex-snapshot
Study of Automatic Anomalous Behaviour Detection Techniques for Maritime Vessels
inferredcites →https://doi.org/10.1017/s0373463320000442openalex-snapshot
Vessel Trajectory Prediction Using Historical Automatic Identification System Data
inferredcites →https://doi.org/10.1080/19475683.2020.1840434openalex-snapshot
Prediction of vessels locations and maritime traffic using similarity measurement of trajectory
inferredcites →https://doi.org/10.1080/20464177.2019.1665258openalex-snapshot
A model for vessel trajectory prediction based on long short-term memory neural network
inferredcites →https://doi.org/10.1109/maes.2021.3070884openalex-snapshot
Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques
inferredcites →https://doi.org/10.1109/tits.2019.2908191openalex-snapshot
Traffic Pattern Mining and Forecasting Technologies in Maritime Traffic Service Networks: A Comprehensive Survey
inferredcites →https://doi.org/10.1109/tits.2020.3040268openalex-snapshot
Big Data Driven Vessel Trajectory and Navigating State Prediction With Adaptive Learning, Motion Modeling and Particle Filtering Techniques
inferredcites →https://doi.org/10.1145/2809563.2809573openalex-snapshot
Using machine learning for unsupervised maritime waypoint discovery from streaming AIS data
inferredcites →https://doi.org/10.3182/20140824-6-za-1003.00767openalex-snapshot
Trajectory tracking of autonomous vessels using model predictive control
inferredcites →https://doi.org/10.3390/s17061432openalex-snapshot
The Ship Movement Trajectory Prediction Algorithm Using Navigational Data Fusion
inferredcites →https://doi.org/10.48550/arxiv.2001.01592openalex-snapshot
Modeling Historical AIS Data For Vessel Path Prediction: A Comprehensive Treatment

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Self-Organizing Data-Driven Online Prediction Model of Ship Maneuvering Fast-Dynamics
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Trajectory Prediction in Ship Movement Using Geohash Embedding
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Machine learning for naval architecture, ocean and marine engineering
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Ship trajectory prediction based on machine learning and deep learning: A systematic review and methods analysis
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A data mining-then-predict method for proactive maritime traffic management by machine learning
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Exploration of safe navigation zones for large cruise ships entering Keelung port by fast time simulations
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A review of vessel trajectory prediction research: Methodological evolution, current challenges, and future directions
inferred← citeshttps://doi.org/10.1016/j.oceaneng.2022.110836openalex-snapshot
Hybrid-driven vessel trajectory prediction based on uncertainty fusion
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Self-organizing data-driven prediction model of ship maneuvering fast-dynamics
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SNIINet: Trajectory prediction using ship navigation information interaction-aware neural network
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Comprehensive analytics for predicting traffic in maritime navigation
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Mix&Fix-Net: A dual-stage trajectory prediction model for AIS and vision-derived vessel data
inferred← citeshttps://doi.org/10.1016/j.oceaneng.2026.128617openalex-snapshot
Zero-shot ship berthing trajectory prediction: A Target-Driven Residual Bi-LSTM with ego-centric spatial awareness based on real berthing data and port geometry
inferred← citeshttps://doi.org/10.1016/j.physa.2023.129275openalex-snapshot
Interval prediction of vessel trajectory based on lower and upper bound estimation and attention-modified LSTM with bayesian optimization
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Risk influencing factors on the consequence of waterborne transportation accidents in China (2013–2023) based on data-driven machine learning
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Enhancing risk perception by integrating ship interactions in multi-ship encounters: A Graph-based Learning method
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Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder
inferred← citeshttps://doi.org/10.1016/j.tre.2023.103152openalex-snapshot
AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods
inferred← citeshttps://doi.org/10.1016/j.tre.2023.103367openalex-snapshot
Deep bi-directional information-empowered ship trajectory prediction for maritime autonomous surface ships
inferred← citeshttps://doi.org/10.1016/j.tre.2024.103426openalex-snapshot
Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review
inferred← citeshttps://doi.org/10.1016/j.tre.2024.103770openalex-snapshot
Bi-directional information fusion-driven deep network for ship trajectory prediction in intelligent transportation systems
inferred← citeshttps://doi.org/10.1049/tje2.70151openalex-snapshot
TG‐GPT: A Generative Pre‐Trained Transformer With Gated Recurrent Units for AIS‐Based Ship Trajectory Prediction
inferred← citeshttps://doi.org/10.1080/17445302.2026.2655449openalex-snapshot
Sea-KSformer: a spatiotemporal transformer-based dynamic clustering model for robust ship trajectory prediction in maritime and offshore engineering
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A Hybrid Algorithm of Ship Trajiectories based on Random Forest and K-Nearest Neighbor
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Compressing AIS Trajectory Data Based on the Multi-Objective Peak Douglas–Peucker Algorithm
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A Transformer Network With Sparse Augmented Data Representation and Cross Entropy Loss for AIS-Based Vessel Trajectory Prediction
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Mobility Management With AI
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Vessel trajectory prediction with a Gated Spatio-Temporal Graph Aggregation Network
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Improved Context-Sensitive Transformer Model for Inland Vessel Trajectory Prediction
inferred← citeshttps://doi.org/10.1109/oceanslimerick52467.2023.10244698openalex-snapshot
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Ship trajectory anomaly detection based on multi-feature fusion
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Grid-Based Bayesian Bootstrap Approach for Real-Time Detection of Abnormal Vessel Behaviors From AIS Data in Maritime Logistics
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Vessel Trajectory Prediction in Maritime Transportation: Current Approaches and Beyond
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A Dynamic Context-Aware Approach for Vessel Trajectory Prediction Based on Multi-Stage Deep Learning
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AISFuser: Encoding Maritime Graphical Representations With Temporal Attribute Modeling for Vessel Trajectory Prediction
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Centerline Keeping Control and Multi-Object Collision Avoidance of Riverine Autonomous Surface Vehicle
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Improvement Interpolation Method for Vessel Trajectory Prediction based on AIS Data
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Vietnam's Maritime Shipping Ports: Using Data-Driven Model Analysis to Determine Connectivity and Networking
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