Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports

Bye, Rolf Johan; Aalberg, Asbjørn Lein

doi:10.1016/j.ress.2018.03.033

Cited by 163 publications

(64 citation statements)

References 46 publications

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“…The studies about relative space mainly appear in the fields of vessels in maritime and airplane in aviation. In maritime, the moving object data model also can be applied for assessing collision risk (Bye and Aalberg, 2018;Fang et al, 2018), predicting vessel behavior (Zissis et al, 2016;Xiao et al, 2017) and planning path (Cummings et al, 2010;Hornauer et al, 2015). In aviation field, motion guidance and control (Yu et al, 2016;Sun et al, 2017;Li and Zhu, 2018;Zhu et al, 2018) for spacecraft rendezvous, position and attitude estimation (Philip and Ananthasayanam, 2003;Qiao et al, 2013) between the chaser and the target satellites were also referred to the applications of moving object data model.…”

Section: Related Workmentioning

confidence: 99%

Relative space-based GIS data model to analyze the group dynamics of moving objects

Feng

Shaw

Fang

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

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The relative motion of moving objects is an essential research topic in geographical information science (GIScience), which supports the innovation of geodatabases, spatial indexing, and geospatial services. This analysis is very popular in the domains of urban governance, transportation engineering, logistics and geospatial information services for individuals or industrials. Importantly, data models of moving objects are one of the most crucial approaches to support the analysis for dynamic relative motion between moving objects, even in the age of big data and cloud computing. Traditional geographic information systems (GIS) usually organize moving objects as point objects in absolute coordinated space. The derivation of relative motions among moving objects is not efficient because of the additional geo-computation of transformation between absolute space and relative space. Therefore, current GISs require an innovative approach to directly store, analyze and interpret the relative relationships of moving objects to support their efficient analysis. This paper proposes a relative space-based GIS data model of moving objects (RSMO) to construct, operate and analyze moving objects' relationships and introduces two algorithms (relationship querying and relative relationship dynamic pattern matching) to derive and analyze the dynamic relationships of moving objects. Three scenarios (epidemic spreading, tracker finding, and motion-trend derivation of nearby crowds) are implemented to demonstrate the feasibility of the proposed model. The experimental results indicates the execution times of the proposed model are approximately 5-50% those of the absolute GIS method for the same function of these three scenarios. It's better computational performance of the proposed model when analyzing the relative relationships of moving objects than the absolute methods in a famous commercial GIS software based on this experimental results. The proposed approach fills the gap of traditional GIS and shows promise for relative space-based geo-computation, analysis and service.

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Section: Related Workmentioning

confidence: 99%

Relative space-based GIS data model to analyze the group dynamics of moving objects

Feng

Shaw

Fang

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

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“…Bye et al analyzed maritime accidents by mining the inshore ship AIS data considering varied maritime static and kinematic information, such as sailed nautical miles, accumulated engine working hours, port call number, ship type, flag state, gross tonnage, etc. [7]. Integrating the AIS data with other maritime sources (synthetic aperture radar (SAR), radar, etc.)…”

Section: Introductionmentioning

confidence: 99%

Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis

Chen

Yang

et al. 2020

Journal of Advanced Transportation

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Video-based detection infrastructure is crucial for promoting connected and autonomous shipping (CAS) development, which provides critical on-site traffic data for maritime participants. Ship behavior analysis, one of the fundamental tasks for fulfilling smart video-based detection infrastructure, has become an active topic in the CAS community. Previous studies focused on ship behavior analysis by exploring spatial-temporal information from automatic identification system (AIS) data, and less attention was paid to maritime surveillance videos. To bridge the gap, we proposed an ensemble you only look once (YOLO) framework for ship behavior analysis. First, we employed the convolutional neural network in the YOLO model to extract multi-scaled ship features from the input ship images. Second, the proposed framework generated many bounding boxes (i.e., potential ship positions) based on the object confidence level. Third, we suppressed the background bounding box interferences, and determined ship detection results with intersection over union (IOU) criterion, and thus obtained ship positions in each ship image. Fourth, we analyzed spatial-temporal ship behavior in consecutive maritime images based on kinematic ship information. The experimental results have shown that ships are accurately detected (i.e., both of the average recall and precision rate were higher than 90%) and the historical ship behaviors are successfully recognized. The proposed framework can be adaptively deployed in the connected and autonomous vehicle detection system in the automated terminal for the purpose of exploring the coupled interactions between traffic flow variation and heterogeneous detection infrastructures, and thus enhance terminal traffic network capacity and safety.

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“…A full literature review on research in maritime accidents is provided in Reference [7] which lists different research methods and data sources in this context. To help reduce the occurrence rates of such accidents, qualitative research methods were deployed to understand the root causes such as maritime navigation risk indicators [8] with explanatory variables such as vessel type as well as flag of convenience and visibility conditions, human and organizational factors [9] and including decision errors due to conditions of operators and personnel factors and socio-technical factors [10] such as the interactions between ship operators. The qualitative approach is however not very precise as Figure 2 shows since most accidents occurred during good conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The qualitative approach is however not very precise as Figure 2 shows since most accidents occurred during good conditions. Moreover, taking into account the traffic densities during each condition and its frequencies, the correlations between sea/wind/visibility conditions and the numbers of accidents are very weak as found in Reference [8]. Alternatively, the authors of Reference [11] demonstrated that the general maritime safety approach is reactive and that accidents cannot be predicted.…”

Section: Introductionmentioning

confidence: 99%

AIS-Based Multiple Vessel Collision and Grounding Risk Identification based on Adaptive Safety Domain

Bakdi

Glad

Vanem

et al. 2019

JMSE

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The continuous growth in maritime traffic and recent developments towards autonomous navigation have directed increasing attention to navigational safety in which new tools are required to identify real-time risk and complex navigation situations. These tools are of paramount importance to avoid potentially disastrous consequences of accidents and promote safe navigation at sea. In this study, an adaptive ship-safety-domain is proposed with spatial risk functions to identify both collision and grounding risk based on motion and maneuverability conditions for all vessels. The algorithm is designed and validated through extensive amounts of Automatic Identification System (AIS) data for decision support over a large area, while the integration of the algorithm with other navigational systems will increase effectiveness and ensure reliability. Since a successful evacuation of a potential vessel-to-vessel collision, or a vessel grounding situation, is highly dependent on the nearby maneuvering limitations and other possible accident situations, multi-vessel collision and grounding risk is considered in this work to identify real-time risk. The presented algorithm utilizes and exploits dynamic AIS information, vessel registry and high-resolution maps and it is robust to inaccuracies of position, course and speed over ground records. The computation-efficient algorithm allows for real-time situation risk identification at a large-scale monitored map up to country level and up to several years of operation with a very high accuracy.

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Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports

Cited by 163 publications

References 46 publications

Relative space-based GIS data model to analyze the group dynamics of moving objects

Relative space-based GIS data model to analyze the group dynamics of moving objects

Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis

AIS-Based Multiple Vessel Collision and Grounding Risk Identification based on Adaptive Safety Domain

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