Discovering Urban Spatio-temporal Structure from Time-Evolving Traffic Networks

  • Jingyuan Wang
  • Fei Gao
  • Peng Cui
  • Chao Li
  • Zhang Xiong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8709)


The traffic networks reflect the pulse and structure of a city and shows some dynamic characteristic. Previous research in mining structure from networks mostly focus on static networks and fail to exploit the temporal patterns. In this paper, we aim to solve the problem of discovering the urban spatio-temporal structure from time-evolving traffic networks. We model the time-evolving traffic networks into a 3-order tensor, each element of which indicates the volume of traffic from i-th origin area to j-th destination area in k-th time domain. Considering traffic data and urban contextual knowledge together, we propose a regularized Non-negative Tucker Decomposition (rNTD) method, which discovers the spatial clusters, temporal patterns and relations among them simultaneously. Abundant experiments are conducted in a large dataset collected from Beijing. Results show that our method outperforms the baseline method.


urban computing pattern discovery 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jingyuan Wang
    • 1
  • Fei Gao
    • 1
  • Peng Cui
    • 2
  • Chao Li
    • 1
    • 3
  • Zhang Xiong
    • 1
  1. 1.School of Computer Science and EngineeringBeihang UniversityBeijingChina
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  3. 3.Research Institute of Beihang UniversityShenZhenChina

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