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Discovering Trip Hot Routes Using Large Scale Taxi Trajectory Data

  • Linjiang ZhengEmail author
  • Qisen Feng
  • Weining Liu
  • Xin Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10086)

Abstract

Discovering trip hot routes is very meaningful for drivers to pick up a passenger, as well as for managers to plan urban public transport. Riding by taxis is one of the important means of transportation. Large scale taxi trajectory data from taxi GPS device implicates residents’ trip behavior. In this paper, we present a method to discover trip hot routes using large scale taxi trajectory data. Firstly, we measure taxi trajectory similarity with longest common subsequence (LCS). LCS-based DBSCAN trajectory clustering algorithm was proposed. Then hot routes were extracted using large scale taxi trajectory data. Our experiment shows that the trajectory clustering algorithm and hot route extraction method are effective.

Keywords

Taxi trajectory LCS Trajectory clustering Hot routes 

Notes

Acknowledgments

This work was supported by the National High-tech R&D Program of China (2015AA015308), China Post-doctoral Science Foundation (2014T70852), Fundamental Research Funds for the Central Universities (106112014CDJZR188801), Chongqing Postdoctoral Science Foundation Project (Xm201305), and Key Projects of Chongqing Application Development (cstc2014yykfB30003).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Linjiang Zheng
    • 1
    • 2
    Email author
  • Qisen Feng
    • 1
    • 2
  • Weining Liu
    • 1
    • 2
  • Xin Zhao
    • 1
    • 2
  1. 1.Key Laboratory of Dependable Service Computing in Cyber Physical SocietyChongqing University, Ministry of EducationChongqingChina
  2. 2.College of Computer ScienceChongqing UniversityChongqingChina

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