Trajectory Compression with Spatio-Temporal Semantic Constraints

被引:0
|
作者
Zhou, Yan [1 ,2 ]
Zhang, Yunhan [2 ]
Zhang, Fangfang [1 ,3 ]
Zhang, Yeting [4 ]
Wang, Xiaodi [2 ]
机构
[1] Minist Nat Resources, Key Lab Urban Land Resources Monitoring & Simulat, Shenzhen 518063, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 611731, Peoples R China
[3] Shenzhen Data Management Ctr Planning & Nat Resour, Shenzhen 518040, Peoples R China
[4] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430072, Peoples R China
基金
中国国家自然科学基金;
关键词
spatio-temporal trajectory; trajectory compression; geometric similarity; semantic similarity; information entropy; ALGORITHM;
D O I
10.3390/ijgi13060212
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Most trajectory compression methods primarily focus on geometric similarity between compressed and original trajectories, lacking explainability of compression results due to ignoring semantic information. This paper proposes a spatio-temporal semantic constrained trajectory compression method. It constructs a new trajectory distance measurement model integrating both semantic and spatio-temporal features. This model quantifies semantic features using information entropy and measures spatio-temporal features with synchronous Euclidean distance. The compression principle is to retain feature points with maximum spatio-temporal semantic distance from the original trajectory until the compression rate is satisfied. Experimental results show these methods closely resemble each other in maintaining geometric similarity of trajectories, but our method significantly outperforms DP, TD-TR, and CascadeSync methods in preserving semantic similarity of trajectories. This indicates that our method considers both geometric and semantic features during compression, resulting in the compressed trajectory becoming more interpretable.
引用
收藏
页数:18
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