Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering Algorithm

• 作者: Huihui Jiao Jianjiang Li Jie Wang Jie Wu Zhiguo Liu
• 来源期刊: 大数据挖掘与分析(英文)
• 学科: 工学
• 关键词: STORM trajectory clustering ADAPTIVE data mining density grid
• 年，卷（期）: 2020,（2）
• 页码范围: 131-142
• 页数: 12页
• 分类号: TP311.13

摘要

With the development of Chinese international trade,real-time processing systems based on ship trajectory have been used to cluster trajectory in real-time,so that the hot zone information of a sea ship can be discovered in real-time.This technology has great research value for the future planning of maritime traffic.However,ship navigation characteristics cannot be found in real-time with a ship Automatic Identification System(AIS)positioning system,and the clustering effect based on the density grid fixed-time-interval algorithm cannot resolve the shortcomings of real-time clustering.This study proposes an adaptive time interval clustering algorithm based on density grid(called DAC-Stream).This algorithm can perform adaptive time-interval clustering according to the size of the real-time ship trajectory data stream,so that a ship’s hot zone information can be found efficiently and in real-time.Experimental results show that the DAC-Stream algorithm improves the clustering effect and accelerates data processing compared with the fixed-time-interval clustering algorithm based on density grid(called DC-Stream).