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Pattern Knowledge Discovery of Ship Collision Avoidance based on AIS Data Analysis

Volume 14, Number 10, October 2018, pp. 2449-2457
DOI: 10.23940/ijpe.18.10.p22.24492457

Peng Chena,b, Guoyou Shia, Shuang Liuc, and Miao Gaoa

aNavigation College, Dalian Maritime University, Dalian, 116026, China
bDepartment of Software Engineering, Dalian Neusoft University of Information, Dalian, 116030, China
cSchool of Computer Science and Engineering, Dalian Minzu University, Dalian, 116605, China

(Submitted on July 20, 2018; Revised on August 15, 2018; Accepted on September 18, 2018)


Maritime traffic pattern is very important for intelligent ship collision avoidance applications, as it can help provide decision support to avoid ship collision accidents and reduce casualties. There has been a large amount of Automatic Identification System (AIS) data saved by ports or management departments. If these data can be processed and analyzed scientifically to provide an early warning with appropriate collision avoidance measures, injuries or more serious results from maritime traffic may be reduced or eliminated. Our focus is to synthesize ship behaviors of interest in a clear and effective way based on automatic preprocessing and analyzing original static AIS data. One improved DBSCAN algorithm is first called to reduce the data scale and discover important data points. Then, from the perspective of Own ship, seven patterns including course change and speed change are defined to be discovered. For each special pattern, the space collision risk DCPA (distance to closest point of approach) and time collision risk TCPA (time to closest point of approach) at the beginning time and ending time are computed to confirm its situation as heading on, crossing, or overtaking with other ships in sight of one another. This unsupervised learning approach will help discover traffic pattern knowledge in current trajectories and provide decision support for future route design or anomaly analysis.


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