Abstract
Da Wang, Luo Chen
Abstract
Authors
Institutions
No ROR-resolved institution is linked to this work yet.
Provenance
crossref
Confidence 100%
pubmed
Confidence 98%
europepmc
Confidence 96%
unpaywall
Confidence 95%
doaj
Confidence 92%
datacite
Confidence 0%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Unresolved referenced work
2024
Traffic pattern mining and forecasting technologies in maritime traffic service networks: A comprehensive survey
10.1109/tits.2019.2908191 · 2020
Semantic modelling of ship behavior in harbor based on ontology and dynamic Bayesian network
10.3390/ijgi8030107 · 2019
Exploring the factors of major road traffic accidents: A case study of China
10.1007/s42524-024-4059-x · 2025
Efficient predictive control strategy for mitigating the overlap of EV charging demand and residential load based on distributed renewable energy
10.1016/j.renene.2024.122154 · 2025
Microscopic aggregated traffic parameter extraction against complex camera motion interference
10.1093/tse/tdaf056 · 2025
MKG-GNN: Maritime Knowledge Graph and GNN Framework for ship speed forecasting in port
10.1016/j.oceaneng.2026.125179 · 2026
Spatiotemporal ship carbon emission exploitation and low-carbon shipping pathway evaluation
10.1016/j.apenergy.2026.128334 · 2026
Exploiting AIS data for intelligent maritime navigation: A comprehensive survey from data to methodology
10.1109/tits.2017.2724551 · 2018
How big data enriches maritime research—A critical review of Automatic Identification System (AIS) data applications
10.1080/01441647.2019.1649315 · 2019
Harnessing the power of machine learning for AIS data-driven maritime research: A comprehensive review
10.1016/j.tre.2024.103426 · 2024
Maritime anomaly detection: A review
10.1002/widm.1266 · 2018
Anomaly detection in maritime AIS tracks: A review of recent approaches
10.3390/jmse10010112 · 2022
AIS-based maritime anomaly traffic detection: A review
10.1016/j.eswa.2023.120561 · 2023
A comprehensive survey of machine learning-based techniques for anomaly detection in ship behavior within maritime transportation systems
10.1016/j.engappai.2025.112596 · 2026
Vessel trajectory prediction in maritime transportation: Current approaches and beyond
10.1109/tits.2022.3192574 · 2022
Ship trajectory prediction based on machine learning and deep learning: A systematic review and methods analysis
10.1016/j.engappai.2023.107062 · 2023
A review of vessel trajectory prediction research: Methodological evolution, current challenges, and future directions
10.1016/j.ijnaoe.2026.100754 · 2026
Exploring recent maritime research on AIS-based ship behavior analysis and modeling
10.3390/jmse14080712 · 2026
Vessel detection and classification from spaceborne optical images: A literature survey
10.1016/j.rse.2017.12.033 · 2018
Unresolved referenced work
2014
Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian hidden Markov models
10.1016/j.ecolmodel.2010.04.005 · 2010
Unresolved referenced work
2026
Correlating real-world incidents with vessel traffic off the coast of Hawaii, 2017–2020
10.1007/s44289-023-00001-6 · 2024
A contextually supported abnormality detector for maritime trajectories
10.3390/jmse11112085 · 2023
Unresolved referenced work
2025
Tracking the global footprint of fisheries
10.1126/science.aao5646 · 2018
Improving fishing pattern detection from satellite AIS using data mining and machine learning
2016
NCL-DASB: GEO-located maritime surveillance labeled dataset and annotation API
2024
Unresolved referenced work
2026
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels
10.3390/s22166063 · 2022
Semantic segmentation of AIS trajectories for detecting complete fishing activities
2022
Semantic modeling of ship behavior in cognitive space
10.3390/jmse10101347 · 2022
Ontological ship behavior modeling based on COLREGs for knowledge reasoning
10.3390/jmse10020203 · 2022
Review of maritime traffic models from vessel behavior modeling perspective
10.1016/j.trc.2019.06.004 · 2019
Ship-handling behavior pattern recognition using AIS sub-trajectory clustering analysis based on the T-SNE and spectral clustering algorithms
10.1016/j.oceaneng.2020.106919 · 2020
Online event recognition from moving vessel trajectories
10.1007/s10707-016-0266-x · 2017
Composite event recognition for maritime monitoring
2019
A review of knowledge graph and its application in maritime transportation
2024
Construction of Ship Activity Knowledge Graph Using Trajectory Semantics
2023
An interpretable knowledge-based decision support method for ship collision avoidance using AIS data
10.1016/j.ress.2022.108919 · doi-reference
Interactive testing for evaluating the performance of collision avoidance algorithms in multi-ship encounter scenarios
10.1016/j.atres.2025.11.001 · doi-reference
Ontology-based method for identifying abnormal ship behavior: A navigation rule perspective
10.3390/jmse12060881 · doi-reference
A dual-driven method for ship navigation intention recognition: Integrating trajectory prediction and knowledge reasoning
10.1016/j.oceaneng.2026.124595 · doi-reference
An environment-kinetic compound space-time prism-based approach for assessing multi-ship collision risk in confined water
10.1017/s0373463325101045 · doi-reference
A direction-constrained space-time prism-based approach for quantifying possible multi-ship collision risks
10.1109/tits.2019.2955048 · doi-reference
Real-time intelligent maritime accident prediction and prevention system for narrow waterways
10.1016/j.atres.2025.09.002 · doi-reference
Mapping global shipping density from AIS data
10.1017/s0373463316000345 · doi-reference
Recognizing instantaneous group patterns in vessel trajectory data: A snapshot perspective
10.3390/jmse11122246 · doi-reference
Exploring AIS data for intelligent maritime routes extraction
10.1016/j.apor.2020.102271 · doi-reference
AIS-LLM: A unified framework for maritime trajectory prediction, anomaly detection, and collision risk assessment with explainable forecasting
10.1109/access.2026.3705215 · doi-reference
Toolformer: Language models can teach themselves to use tools
10.52202/075280-2997 · doi-reference
LLM4STP: A large language model-driven multi-feature fusion method for ship trajectory prediction
10.1016/j.tre.2025.104599 · doi-reference
Regional ship behavior and trajectory prediction for maritime traffic management: A social generative adversarial network approach
10.1016/j.oceaneng.2024.117186 · doi-reference
A prediction model of vessel trajectory based on generative adversarial network
10.1017/s0373463321000382 · doi-reference
Ship trajectory prediction in complex waterways based on transformer and social variational autoencoder (SocialVAE)
10.3390/jmse12122233 · doi-reference
STGVAD: Spatio-temporal graph-based vessel behavior anomaly detection
10.1109/access.2025.3609783 · doi-reference
Trajectories prediction in multi-ship encounters: Utilizing graph convolutional neural networks with GRU and self-attention mechanism
10.1016/j.compeleceng.2024.109679 · doi-reference
A ship trajectory prediction method based on GAT and LSTM
10.1016/j.oceaneng.2023.116159 · doi-reference
G-Trans: A hierarchical approach to vessel trajectory prediction with GRU-based transformer
10.1016/j.oceaneng.2024.117431 · doi-reference
Ship trajectory prediction: An integrated approach using ConvLSTM-based sequence-to-sequence model
10.3390/jmse11081484 · doi-reference
Research on ship trajectory classification based on a deep convolutional neural network
10.3390/jmse10050568 · doi-reference
An AIS-based deep learning framework for regional ship behavior prediction
10.1016/j.ress.2021.107819 · doi-reference
A novel MP-LSTM method for ship trajectory prediction based on AIS data
10.1016/j.oceaneng.2021.108956 · doi-reference
Deep learning methods for vessel trajectory prediction based on recurrent neural networks
10.1109/taes.2021.3096873 · doi-reference
Estimating the support of a high-dimensional distribution
10.1162/089976601750264965 · doi-reference
AIS data driven general vessel destination prediction: A random forest based approach
10.1016/j.trc.2020.102729 · doi-reference
Ship classification based on AIS data and machine learning methods
10.3390/electronics13010098 · doi-reference
Analysis of the characteristics of ship collision-avoidance behavior based on Apriori and complex network
10.3390/jmse13010035 · doi-reference
A dimensionality reduction-based multi-step clustering method for robust vessel trajectory analysis
10.3390/s17081792 · doi-reference
Ship AIS trajectory clustering: An HDBSCAN-based approach
10.3390/jmse9060566 · doi-reference
Modeling vessel behaviours by clustering AIS data using optimized DBSCAN
10.3390/su13158162 · doi-reference
Mobility pattern analysis of ship trajectories based on semantic transformation and topic model
10.1016/j.oceaneng.2020.107092 · doi-reference
Anomaly detection in vessel tracks using Bayesian networks
10.1016/j.ijar.2013.03.012 · doi-reference
Data mining approach to shipping route characterization and anomaly detection based on AIS data
10.1016/j.oceaneng.2020.106936 · doi-reference
Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction
10.3390/e15062218 · doi-reference
Fullest COLREGs evaluation using fuzzy logic for collaborative decision-making analysis of autonomous ships in complex situations
10.1109/tits.2022.3151826 · doi-reference
Validation of risk analysis for ship collision in narrow waters by using fuzzy Bayesian networks approach
10.1016/j.oceaneng.2021.108973 · doi-reference
Bayesian networks for maritime traffic accident prevention: Benefits and challenges
10.1016/j.aap.2014.09.017 · doi-reference
Fuzzy logic based approach for ship-bridge collision alert system
10.1016/j.oceaneng.2019.106152 · doi-reference