Aggregate Human Mobility Modeling Using Principal Component Analysis

Accurate modeling of aggregate human mobility benefits many aspects of cellular mobile networks. Compared with traditional approaches, the cellular networks provide information for aggregate human mobility in urban space with large spatial extent and continuous temporal coverage, due to the high penetration of cell phones. In this paper, a model by utilizing Principal Component Analysis (PCA) is proposed to explore the space-time structure of aggregate human mobility. The original data were collected by cellular networks in a southern city of China, recording population distribution by dividing the city into thousands of pixels. By applying PCA to original data, the low intrinsic dimensionality is revealed. The structure of all the pixel population variations could be well captured by a small set of eigen pixel population variations, an introduced notion capturing significant temporal patterns across all the pixel population variations. According to their temporal features, eigen pixel population variations can be divided into three categories, and each pixel population variation can be decomposed into three corresponding constitutions: deterministic trends, short-lived spikes, and noise. Furthermore, there is also a relation between the variance of a pixel population variation and its dominated constitution. The most significant eigen pixel population variations are utilized in the applications of forecasting and anomaly detection.

[1]  Carlo Ratti,et al.  Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis , 2006 .

[2]  Carlo Ratti,et al.  Cellular Census: Explorations in Urban Data Collection , 2007, IEEE Pervasive Computing.

[3]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[4]  Gene H. Golub,et al.  Singular value decomposition and least squares solutions , 1970, Milestones in Matrix Computation.

[5]  Jennifer Widom,et al.  Teletraffic modeling for personal communications services , 1997 .

[6]  Carlo Ratti,et al.  Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network , 2009 .

[7]  Konstantina Papagiannaki,et al.  Structural analysis of network traffic flows , 2004, SIGMETRICS '04/Performance '04.

[8]  Christian Bettstetter,et al.  Mobility modeling in wireless networks: categorization, smooth movement, and border effects , 2001, MOCO.

[9]  R. Ahas,et al.  Location based services—new challenges for planning and public administration? , 2005 .

[10]  Xiuming Shan,et al.  A PCA-based approach for exploring space-time structure of urban mobility dynamics , 2010, IWCMC.

[11]  Vassilis Kostakos,et al.  Instrumenting the City: Developing Methods for Observing and Understanding the Digital Cityscape , 2006, UbiComp.

[12]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[13]  Carlo Ratti,et al.  Real time Rome , 2006 .

[14]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[15]  Konstantina Papagiannaki,et al.  Long-term forecasting of Internet backbone traffic: observations and initial models , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).