Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data

  • Yihong Yuan
  • Martin Raubal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7478)


The rapid development of information and communication technologies (ICTs) has provided rich resources for spatio-temporal data mining and knowledge discovery in modern societies. Previous research has focused on understanding aggregated urban mobility patterns based on mobile phone datasets, such as extracting activity hotspots and clusters. In this paper, we aim to go one step further from identifying aggregated mobility patterns. Using hourly time series we extract and represent the dynamic mobility patterns in different urban areas. A Dynamic Time Warping (DTW) algorithm is applied to measure the similarity between these time series, which also provides input for classifying different urban areas based on their mobility patterns. In addition, we investigate the outlier urban areas identified through abnormal mobility patterns. The results can be utilized by researchers and policy makers to understand the dynamic nature of different urban areas, as well as updating environmental and transportation policies.


Mobile phone datasets Urban mobility patterns Dynamic Time Warping Time series 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yihong Yuan
    • 1
    • 2
  • Martin Raubal
    • 1
  1. 1.Institute of Cartography and GeoinformationETH ZurichZurichSwitzerland
  2. 2.Department of GeographyUniversity of CaliforniaSanta BarbaraUSA

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