Analysis of spatiotemporal trajectories for stops along taxi paths

Liang Huang, Yuanqiao Wen, Xinyue Ye, Chunhui Zhou, Faming Zhang, Jay Lee

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Stops along taxi trajectories, such as picking up and dropping off passengers, are spatially clustered and related to certain attributes of places where stops are made. To detect the hidden knowledge regarding these places, this article examines the semantics of massive taxi stops in a large city. Each taxi trajectory is modeled as a series of sequential semantic stops labeled by street names. All the trajectories can be examined as a document corpus, from which the hidden themes of the stops are identified through Latent Dirichlet Allocation model. Conventional GIS tools are coupled with topic modeling toolkit to visualize and analyze potential information of stop topics for understanding intra-city dynamics. The effectiveness of this approach is illustrated by a case study using a large dataset of taxi trajectories including approximately 4,000 taxis in Wuhan, China.

Original languageEnglish (US)
Pages (from-to)194-216
Number of pages23
JournalSpatial Cognition and Computation
Volume18
Issue number3
DOIs
StatePublished - Jul 3 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Experimental and Cognitive Psychology
  • Computer Vision and Pattern Recognition
  • Earth-Surface Processes
  • Computer Graphics and Computer-Aided Design

Keywords

  • China
  • human dynamics
  • semantic trajectory analysis
  • taxi
  • topic modeling

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