A framework for discovering maritime traffic conflict from AIS network

Lei, Po-Ruey; Tsai, Tzu-Hao; Wen, Yu‐Ting; Peng, Wen-Chih

doi:10.1109/apnoms.2017.8094169

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…The evolution of maritime transportation networks provides ample data and communication resources, fostering the development of smart navigation systems. This contributes to heightened safety, efficiency, and sustainability while bolstering the operation and management of the entire maritime transportation industry [2][3][4][5]. For instance, Po-Ruey et al conducted analyses of ship collision accidents using actual AIS data to inform encounter posture judgment and collision avoidance decision-making, thereby enhancing maritime traffic safety control [5].…”

Section: Introductionmentioning

confidence: 99%

A Ship State Recognition Method Based on Graph Convolutional Neural Network

Ma,

Cao,

Shi

et al. 2024

Preprint

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To address the challenge of recognizing ship states accurately amidst the complexities of marine environments, this study proposes a novel ship state recognition approach leveraging a graph convolutional neural network (GCNN). Initially, the method extracts canonical and efficient ship motion trajectories from AIS data. Subsequently, a state recognition network tailored for ship motion trajectories is devised and implemented employing graph convolution. Notably, the accuracy of this model is enhanced through the introduction of novel weights and optimization of the Adj parameter. Experimental evaluations conducted on a ship state dataset demonstrate significant performance improvements. Specifically, the proposed recognition network achieves a recognition accuracy of 98.3% for regulated ship trajectories, marking an impressive 8.4% enhancement over traditional convolutional neural networks. This advancement holds promise for enhancing ship state recognition accuracy across diverse maritime applications including maritime supervision, navigation safety, and ship management.

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Section: Introductionmentioning

confidence: 99%

A Ship State Recognition Method Based on Graph Convolutional Neural Network

Ma,

Cao,

Shi

et al. 2024

Preprint

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“…According to the previous definition (Du et al, 2020a), the NACE can be broadly understood as a realistic scene of navigation in which a ship continues to sail in its current state and may have an accident with other objects in time and space. In this study, the narrow definition of the NACE is a situation of near-collision, which has great potential to be a collision (Lei et al, 2017). In case of a ship encounter, two ships will face the consequences of collision if they do not take measures.…”

Section: Introductionmentioning

confidence: 99%

Temporal features of non-accident critical events impact from tides around the Yangtze Estuary and adjacent coastal waters

et al. 2022

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Applying non-accident critical events (NACEs) as an alternative measuring method to assess ship collision risk has become popular in recent years. NACEs has shown temporal features in different waters. This study uses the quaternion ship domain method to identify NACEs around the Yangtze Estuary and adjacent coastal waters from the Automatic Identification System (AIS) data in October 2019. The results indicate that NACEs show different temporal features in estuaries and coastal waters. The relationship between tides, channel types and ships is discussed. In addition, we established a statistical method for the occurrence time of NACEs and the state of tides according to the half-tide level and the nearest time. The outcomes of this study provide a direction for exploring the relationship between NACEs and environmental conditions, which is also instructive for the study of the causes of ship accidents.

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“…In addition to the term known as collision candidate, several other terminologies can be found in the literature, some of which differ in definition but follow the same general concept [20]. Some of the related terminologies are near miss [21,22], traffic conflict [23], dangerous encounters [24], etc. In research [20], all of the above terminologies are placed under the common framework known as non-accident critical events.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Ship Collision Frequency Estimation Models: A Review

Čorić

Mandžuka

Gudelj

et al. 2021

JMSE

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Ship collisions are one of the most common types of maritime accidents. Assessing the frequency and probability of ship collisions is of great importance as it provides a cost-effective and practical way to mitigate risk. In this paper, we present a review of quantitative ship collision frequency estimation models for waterway risk assessment, accompanied by a classification of the models and a description of their main modelling characteristics. Models addressing the macroscopic perspective in the estimation of ship collision frequency on waterways are reviewed in this paper with a total of 29 models. We extend the existing classification methodology and group the collected models accordingly. Special attention is given to the criteria used to detect potential ship collision candidates, as well as to causation probability and the correlation of models with real ship collision statistics. Limitations of the existing models and future improvement possibilities are discussed. The paper can be used as a guide to understanding current achievements in this field.

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A framework for discovering maritime traffic conflict from AIS network

Cited by 9 publications

References 13 publications

A Ship State Recognition Method Based on Graph Convolutional Neural Network

A Ship State Recognition Method Based on Graph Convolutional Neural Network

Temporal features of non-accident critical events impact from tides around the Yangtze Estuary and adjacent coastal waters

Quantitative Ship Collision Frequency Estimation Models: A Review

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