@article{Chen_van Gelder_Mou_2019, author = {Chen, Pengfei and van Gelder, Pieter H.A.J.M. and Mou, Junmin}, title = {Integration of Elliptical Ship Domains and Velocity Obstacles for Ship Collision Candidate Detection}, journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation}, volume = {13}, number = {4}, pages = {751-758}, year = {2019}, url = {./Article_Integration_of_Elliptical_Ship_Chen,52,952.html}, abstract = {The maritime shipping industry has been making significant contributions to the development of the regional and global economy. However, maritime accidents and their severe consequences have been posing an incrementing risk to the individuals and societies. It is therefore important to conduct risk analysis on such accidents to support maritime safety management. In this paper, a modified ship collision candidate detection method is proposed as a tool for collision risk analysis in ports and waterways. Time-Discrete Velocity Obstacle algorithm (TD-NLVO) is utilized to detect collision candidates based on the encounter process extracted from AIS data. Ship domain model was further integrated into the algorithm as the criteria for determination. A case study is conducted to illustrate the efficacy of the improved model, and a comparison between the existing method and actual ship trajectories are also performed. The results indicate that with the integration of ship domain, the new method can effectively detect the encounters with significant collision avoidance behaviours. The choice of criteria can have a significant influence on the results of collision candidate detection.}, doi = {10.12716/1001.13.04.07}, issn = {2083-6473}, publisher = {Gdynia Maritime University, Faculty of Navigation}, keywords = {Collision Avoidance, Elliptical Ship Domain, Ship Collision Candidate Detection, Time-Discrete Velocity Obstacle Algorithm (TD-NLVO), AIS Data, Ship Trajectory, Collision Candidate Detection, Collision Candidate} }