MathSciDoc: An Archive for Mathematician ∫

 
Log In
Sign Up
Help
Go
 

GPS trajectory data segmentation based on probabilistic logic

Sini Guo Xiang Li Wai-Ki Ching Ralescu Dan Wai-Keung Li Zhiwen Zhang

Numerical Analysis and Scientific Computing mathscidoc:1912.431044

International Journal of Approximate Reasoning, 103, 227-247, 2018.12
With the rapid development of internet economy, transparent logistics is stepping into a prosperity period with massive transportation data generated and collected every day. In this paper, we focus on the segmentation of GPS trajectory data generated in logistics transportation to analyze the vehicle behaviors and extract business affair information according to the vehicle behavior characteristics, which is challenging due to the complexity of trajectory data and unavailability of road information. We extract the stopping points from the trajectory data sequence based on the duration of nonmovement, and construct business time window and electronic fence by analyzing the driving habits of vehicles. Furthermore, we propose a probabilistic logic based data segmentation method (PLDSM) which not only helps finding all the business points but also assists in inferring the business affair categories. An efficient
No keywords uploaded!
[ Download ] [ 2019-12-24 21:14:52 uploaded by Zhiwen_Zhang ] [ 915 downloads ] [ 0 comments ] 
@inproceedings{sini2018gps,
  title={GPS trajectory data segmentation based on probabilistic logic},
  author={Sini Guo, Xiang Li, Wai-Ki Ching, Ralescu Dan, Wai-Keung Li, and Zhiwen Zhang},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224211452982968608},
  booktitle={International Journal of Approximate Reasoning},
  volume={103},
  pages={227-247},
  year={2018},
}
Sini Guo, Xiang Li, Wai-Ki Ching, Ralescu Dan, Wai-Keung Li, and Zhiwen Zhang. GPS trajectory data segmentation based on probabilistic logic. 2018. Vol. 103. In International Journal of Approximate Reasoning. pp.227-247. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224211452982968608.
Please log in for comment!
 
 
Contact us: office-iccm@tsinghua.edu.cn | Copyright Reserved