Analyse Near Collision Situations of Ships Using Automatic Identification System Dataset

Watawana, Thisara; Caldera, Amitha

doi:10.1109/iscmi.2018.8703228

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…Many non-accident critical events-related terminologies and the corresponding methods have been proposed to analyze maritime traffic risk (Lei, 2019;Zhang et al, 2015). Among the literature, one can find that non-accident critical events are often related but not limited to traffic conflict (Debnath, 2009;Lei, 2019), near-miss (Szłapczyński and Niksa-Rynkiewicz, 2018), near-collision (Watawana and Caldera, 2018;Zhang et al, 2015), collision candidate (Chen et al, 2018(Chen et al, , 2019a(Chen et al, , 2019b and critical encounter (Hassel et al, 2019). These key events can indicate the potential for collision.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-time collision risk based safety management for vessel traffic in busy ports and waterways

Mou

Chen

et al. 2023

Ocean & Coastal Management

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Section: Literature Reviewmentioning

confidence: 99%

Real-time collision risk based safety management for vessel traffic in busy ports and waterways

Mou

Chen

et al. 2023

Ocean & Coastal Management

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“…AIS data) for vessel collision risk detection and analysis [7]. Various measurable definitions and indices, such as traffic conflict [9], [34], collision candidate [35], [36], near miss [12], [13], and near collision [37] are applied to detect and characterize collision risks. One of the widely used collision risk detection methods is the ship domain-based methods.…”

Section: Ship Collision Risk Analysismentioning

confidence: 99%

A probabilistic risk approach for the collision detection of multi-ships under spatiotemporal movement uncertainty

Xin

Liu

Yang

et al. 2021

Reliability Engineering & System Safety

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“…Rong et al [21] presented a spatial correlation analysis based on near-collision hotspots. Watawana and Caldera [22] developed a machine learning-based classification of potential near-miss collisions. Li et al [23] have combined clustering and ship-to-ship encounter risk to model the macro complexity of the subject area to identify encounter risk.…”

Section: Introductionmentioning

confidence: 99%

Risky Maritime Encounter Patterns via Clustering

Oruc

Altan

2023

JMSE

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The volume of maritime traffic is increasing with the growing global trade demand. The effect of volume growth is especially observed in narrow and congested waterways as an increase in the ship-ship encounters, which can have severe consequences such as collision. This study aims to analyze and validate the patterns of risky encounters and provide a framework for the visualization of model variables to explore patterns. Ship–ship interaction database is developed from the AIS messages, and interactions are analyzed via unsupervised learning algorithms to determine risky encounters using ship domain violation. K-means clustering-based novel methodology is developed to explore patterns among encounters. The methodology is applied to a long-term dataset from the Strait of Istanbul. Findings of the study support that ship length and ship speed can be used as indicators to understand the patterns in risky encounters. Furthermore, results show that site-specific risk thresholds for ship–ship encounters can be determined with additional expert judgment. The mid-clusters indicate that the ship domain violation is a grey zone, which should be treated carefully rather than a bold line. The developed approach can be integrated to narrow and congested waterways as an additional safety measure for maritime authorities to use as a decision support tool.

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Analyse Near Collision Situations of Ships Using Automatic Identification System Dataset

Cited by 5 publications

References 8 publications

Real-time collision risk based safety management for vessel traffic in busy ports and waterways

Real-time collision risk based safety management for vessel traffic in busy ports and waterways

A probabilistic risk approach for the collision detection of multi-ships under spatiotemporal movement uncertainty

Risky Maritime Encounter Patterns via Clustering

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