Analyzing urban human mobility patterns through a thematic model at a finer scale

Faming Zhang, Xinyan Zhu, Wei Guo, Xinyue Ye, Tao Hu, Liang Huang

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Taxi trajectories reflect human mobility over a road network. Pick-up and drop-off locations in different time periods represent origins and destinations of trips, respectively, demonstrating the spatiotemporal characteristics of human behavior. Each trip can be viewed as a displacement in the random walk model, and the distribution of extracted trips shows a distance decay effect. To identify the spatial similarity of trips at a finer scale, this paper investigates the distribution of trips through topic modeling techniques. Firstly, trip origins and trip destinations were identified from raw GPS data. Then, different trips were given semantic information, i.e., link identification numbers with a semantic enrichment process. Each taxi trajectory was composed of a series of trip destinations corresponding to the same taxi. Subsequently, each taxi trajectory was analogous to a document consisting of different words, and all taxi's trajectories could be regarded as document corpora, enabling a semantic analysis of massive trip destinations. Finally, we obtained different trip destination topics reflecting the spatial similarity and regional property of human mobility through LDA topic model training. The effectiveness of this approach was illustrated by a case study using a large dataset of taxi trajectories collected from 2 to 8 June 2014 in Wuhan, China.

Original languageEnglish (US)
Article number78
JournalISPRS International Journal of Geo-Information
Issue number6
StatePublished - Jun 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Computers in Earth Sciences
  • Earth and Planetary Sciences (miscellaneous)


  • China
  • Finer scale
  • Lda topic model
  • Mobility
  • Semantic enrichment


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