A Novel Framework of Real-Time Regional Collision Risk Prediction Based on the RNN Approach

Liu, Dapei; Wang, Xin; Cai, Yao; Liu, Zihao; Liu, Zhengjiang

doi:10.3390/jmse8030224

Cited by 22 publications

(7 citation statements)

References 39 publications

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“…It's beneficial for the maritime traffic management stakeholders, such as Maritime Safety Administration (MSA), to obtain insights on the operation status and characteristics of the regional traffic. In the meantime, ship trajectory clustering is one of the fundamental methods for trajectory prediction, anomaly detection, and avoiding ship collision [8,9], which draws much attention from academia.…”

Section: Introductionmentioning

confidence: 99%

Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

Wang

Chen

et al. 2021

JMSE

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The Automatic Identification System (AIS) of ships provides massive data for maritime transportation management and related researches. Trajectory clustering has been widely used in recent years as a fundamental method of maritime traffic analysis to provide insightful knowledge for traffic management and operation optimization, etc. This paper proposes a ship AIS trajectory clustering method based on Hausdorff distance and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), which can adaptively cluster ship trajectories with their shape characteristics and has good clustering scalability. On this basis, a re-clustering method is proposed and comprehensive clustering performance metrics are introduced to optimize the clustering results. The AIS data of the estuary waters of the Yangtze River in China has been utilized to conduct a case study and compare the results with three popular clustering methods. Experimental results prove that this method has good clustering results on ship trajectories in complex waters.

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Section: Introductionmentioning

confidence: 99%

Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

Wang

Chen

et al. 2021

JMSE

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“…Therefore, the data-based navigation pattern analysis and the collision risk assessment of ships are considered to be crucial for control tasks to ensure an accurate situation assessment during the decisionmaking process by a VTSO. There have been numerous studies on various strategies to 2 of 14 enable a VTSO to make optimal decisions within the monitoring area, as presented in Table A1 [4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Existing studies largely classify ship prediction models as regional ship trajectory prediction models [6,9,11,12] and regional ship collision risk index prediction models [4,5,7,8,10], which are based on automatic identification system (AIS) maritime traffic data. Regional ship trajectory prediction models determine ship trajectories from ship-dense hotspots and learn these trajectories using neural networks; the learned models are then applied to ships operating in the region to predict navigation routes.…”

Section: Introductionmentioning

confidence: 99%

“…The aforementioned methods have significantly contributed to optimal decision making by VTSOs; however, the following challenges remain. Despite identifying collisionprone locations for ships [12], previous studies have simply developed a predictive model for high ship density hotspots without simultaneously considering the near-collision and actual-collision locations in surveillance sea areas [4][5][6][7][8][9][10][11][12]. Furthermore, although a VTSO should continuously manage collision risk assessments from the point when the risk of collision between ships begins until a collision occurs, previous studies have only used a combined collision risk index within the surveillance region.…”

Section: Introductionmentioning

confidence: 99%

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Regional Collision Risk Prediction System at a Collision Area Considering Spatial Pattern

Namgung

Kim

2021

JMSE

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To reduce the risk of collision in territorial sea areas, including trade ports and entry waterways, and to enhance the safety and efficiency of ship passage, the International Maritime Organization requires the governing body of every country to establish and operate a vessel traffic service (VTS). However, previous studies on risk prediction models did not consider the locations of near collisions and actual collisions and only employed a combined collision risk index in surveillance sea areas. In this study, we propose a regional collision risk prediction system for a collision area considering spatial patterns using a density-based spatial clustering of applications with noise (DBSCAN). Furthermore, a fuzzy inference system based on a near collision (FIS-NC) and long short-term memory (LSTM) is adopted to help a vessel traffic service operator (VTSO) make timely optimal decisions. In the local spatial pattern stage, the ship trajectory was determined by identifying the actual-collision and near-collision locations simultaneously. Finally, the system was developed by learning a sequence dataset from the extracted trajectory of the ship when a collision occurred. The proposed system can recommend an action faster than the fuzzy inference system based on the near-collision location. Therefore, using the developed system, a VTSO can quickly predict ship collision risk situations and make timely optimal decisions at dangerous surveillance sea areas.

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“…The indicator can be used to identify the collision risk of encountering ships, and it is the basis of the proposed multi-vessel encountering collision risk model. In [20], a novel framework of real-time collision risk prediction is proposed based on the Recurrent Neural Network (RNN) approach. The collision risk is firstly identified by the regional collision risk identification approach in [21], which is based on the encountering of ships.…”

Section: Introductionmentioning

confidence: 99%

An Improved Danger Sector Model for Identifying the Collision Risk of Encountering Ships

Liu

Zheng

2020

JMSE

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In order to avoid collision, ships usually take collision avoidance manoeuvres, such as course alteration. However, the effect of such a manoeuvre is influenced by ship manoeuvrability. This paper proposed an improved danger sector model to identify the collision risk between two encountering ships. The proposed model is improved from the danger sector model, which is based on a course alteration manoeuvre by taking ship manoeuvrability into consideration. To involve ship manoeuvrability in the proposed model, the turning circle of a ship is determined when it calculates the size of the danger sector. For validating the proposed model, the Automatic Identification System (AIS) data in the northern Yellow Sea of China were used to carry out some experiments of different encountering scenarios. The results show that the proposed improved danger sector model can identify the collision risk between two encountering ships effectively and has advantages compared with the original danger sector model. The proposed model can help mariners and maritime surveillance operators have a more accurate and effective assessment of ship collision risk in the water area, which can contribute to the improvement of navigational safety.

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A Novel Framework of Real-Time Regional Collision Risk Prediction Based on the RNN Approach

Cited by 22 publications

References 39 publications

Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

Regional Collision Risk Prediction System at a Collision Area Considering Spatial Pattern

An Improved Danger Sector Model for Identifying the Collision Risk of Encountering Ships

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