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

Abstract

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.

An Efficient Feature Augmentation and LSTM-Based Method to Predict Maritime Traffic Conditions

“…In order to prevent marine accidents, the possibility of maritime accidents can be predicted, and there is also a method of predicting the risk of collision, which is the main cause of marine accidents. Liu et al [22] and Namgung and Kim [23] proposed the system to predict regional collision risk using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique and a Recurrent Neural Network (RNN). To predict maritime accidents, Yang et al [24] used Machine learning (ML) technology such as the random forest (RF) model, Adaboost model, gradient boosting decision tree (GBDT) model, and Stacking combined model and compared the result.…”

Research on Ship Collision Probability Model Based on Monte Carlo Simulation and Bi-LSTM

“…However, more accurate results can be obtained with a more complex ship domain. Therefore, a ship domain presented in [2,60] will be used for further research. The calculated long (a 0 ) and short radii (b 0 ) of the ship domain can be determined for 0.1 kt each using Equations ( 2) and (3).…”

“…Parallel sorting means that ports and positions are determined at the same time, since there are vessels in the research area that pass the same route multiple times on the same day (same route and opposite course). Then, everything that does not belong to the research area is deleted and the routes are created using the Ramer Douglas Peucker algorithm (RDP) [60]. The proposed model of AIS data processing, MC, and collision probability estimation consists of four steps:…”

An online identification approach for ship domain model based on AIS data

“…Only by calculating the ship encounter parameters can the superposition of the target ships around the ship be dynamically grasped, the situation distribution of the ship encounter be obtained, the required data for solving the domain model equation be obtained, and then the model parameters can be identified. The parameters are calculated as follows [ 26 ]:…”