Deep bi-directional information-empowered ship trajectory prediction for maritime autonomous surface ships

Transportation Research Part E Logistics and Transportation Review, Journal Year: 2023, Volume and Issue: 181, P. 103367 - 103367

Published: Dec. 6, 2023

It is critical to have accurate ship trajectory prediction for collision avoidance and intelligent traffic management of manned ships emerging Maritime Autonomous Surface Ships (MASS). Deep learning methods based on AIS data emerged as a contemporary maritime transportation research focus. However, concerns about its accuracy computational efficiency widely exist across both academic industrial sectors, necessitating the discovery new solutions. This paper aims develop approach called Bi-Directional Information-Empowered (DBDIE) by utilising integrated multiple networks an attention mechanism address above issues. The DBDIE model extracts valuable features fusing Bi-directional Long Short-Term Memory (Bi-LSTM) Gated Recurrent Unit (Bi-GRU) neural networks. Additionally, weights two bi-directional units are optimised using mechanism, final results obtained through weight self-adjustment mechanism. effectiveness proposed verified comprehensive comparisons with state-of-the-art deep methods, including Neural Network (RNN), (LSTM), (GRU), Bi-LSTM, Bi-GRU, Sequence (Seq2Seq), Transformer experimental demonstrate that achieves most satisfactory outcomes than all other classical providing solution improving predicting trajectories, which becomes increasingly important in era safe navigation mixed MASS. As result, findings can aid development implementation proactive preventive measures avoid collisions, enhance efficiency, ensure safety.

Language: Английский

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A hybrid deep learning method for the prediction of ship time headway using automatic identification system data

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108172 - 108172

Published: March 8, 2024

Ship Time Headway (STH) is used in maritime navigation to describe the time interval between arrivals of two consecutive ships same water area. This measurement may offer a straightforward way gauge frequency ship traffic and likelihood congestion particular STH an important factor understanding managing dynamics movements busy waterways. paper introduces hybrid deep learning method for predicting domain. The integrates Seasonal-Trend Decomposition using Loess (STL), Multi-head Self-Attention (MSA) mechanism into Long Short-Term Memory (LSTM) neural network. dataset was extracted from Automatic Identification System (AIS) through trajectory spatial motion, seasonal, trend residual components decomposition were then determined STL algorithms. MSA-LSTM adopted comprehensively capture evolving patterns sequence. Comparison studies with existing methods demonstrate accuracy robustness predictions provided by this method, indicating that proposed outperforms other models terms prediction performance capabilities. By STH, offers potential assist managers navigators assessing flow, thereby enabling them make informed decisions on safety efficiency.

Language: Английский

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Spatial-temporal analysis of carbon emissions from ships in ports based on AIS data

Ocean Engineering, Journal Year: 2024, Volume and Issue: 308, P. 118394 - 118394

Published: June 7, 2024

Language: Английский

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A spatial-temporal attention method for the prediction of multi ship time headways using AIS data

Ocean Engineering, Journal Year: 2024, Volume and Issue: 311, P. 118927 - 118927

Published: Aug. 19, 2024

Ship Time Headway (STH) is the time interval between two consecutive ships arriving in same water area. It serves as a crucial indicator for visually measuring probability of ship congestion and frequency passage busy waterways. Accurately predicting STH effective maritime traffic management. In this paper, we propose deep learning method aimed at simultaneously multiple areas (multi-STH). This integrates Variational Mode Decomposition (VMD) algorithm with Spatial-Temporal Attention Graph Convolution Network (STAGCN) to deeply capture complex spatial-temporal features STHs each areas. sequences were obtained from Automatic Identification System (AIS) reach, ensuring that these remained numerically continuous on timeline. The VMD was employed decompose into multi-feature inputs STAGCN, training model conjunction inland waterway network patterns variation Extensive experiments demonstrate proposed prediction surpasses accuracy robustness other existing methods, exhibiting excellent performance various multi-STH study accounts inherent correlation waterways, substantially improving efficiency compared single-waterway prediction. may have potential provide useful support be practical significance enhancing safety waterways navigation.

Language: Английский

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

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 120, P. 109611 - 109611

Published: Sept. 19, 2024

Language: Английский

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Spatio-temporal Graph Neural Network Fused with Maritime Knowledge for Predicting Traffic Flows in Ports

Regional Studies in Marine Science, Journal Year: 2025, Volume and Issue: unknown, P. 104106 - 104106

Published: March 1, 2025

Language: Английский

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Temporal-TimesNet: a novel hybrid model for vessel traffic flow multi-step prediction

Ships and Offshore Structures, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: March 13, 2025

Language: Английский

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Deep multi-view information-powered vessel traffic flow prediction for intelligent transportation management

Transportation Research Part E Logistics and Transportation Review, Journal Year: 2025, Volume and Issue: 197, P. 104072 - 104072

Published: March 21, 2025

Language: Английский

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Model selection for predicting marine traffic flow in coastal waterways using deep learning methods

Ocean Engineering, Journal Year: 2025, Volume and Issue: 329, P. 121151 - 121151

Published: April 12, 2025

Language: Английский

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Incorporating graph theory and time series analysis for fine-grained traffic flow prediction in port areas

Ocean Engineering, Journal Year: 2025, Volume and Issue: 335, P. 121693 - 121693

Published: June 4, 2025

Language: Английский

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