Analysis of behavioral differentiation in smart cities based on mobile users’ usage detail record data

The expansion of big data has played an important role in the feasibility of the smart city initiative. The massive amounts of data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources. Usage detail records not only include plentiful spatial–temporal information, but also describe users’ activities in content space and time. They have three dimensions of information, which makes them favorable for the research of human behavior dynamics. To support smart cities, we collected usage detail records containing three dimensions of information from individuals and analyzed the relationship between them to get modes of users’ behavior. In this article, we propose a method to discover the needed content for users and a way to provide these data to them. The result shows that two of these three dimensions have an invisible association. New behavioral patterns that we discovered from usage detail records can be derived for configuring resources reasonably and supporting creation of smart cities.

[1]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Chirag Shah,et al.  User Activity Patterns During Information Search , 2015, ACM Trans. Inf. Syst..

[3]  Xin Lu,et al.  Approaching the Limit of Predictability in Human Mobility , 2013, Scientific Reports.

[4]  Adilson E. Motter,et al.  A Poissonian explanation for heavy tails in e-mail communication , 2008, Proceedings of the National Academy of Sciences.

[5]  Balázs Csanád Csáji,et al.  Exploring the Mobility of Mobile Phone Users , 2012, ArXiv.

[6]  Carlo Combi,et al.  Data mining with Temporal Abstractions: learning rules from time series , 2007, Data Mining and Knowledge Discovery.

[7]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[8]  M. Newman.  Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[10]  Hadi Seyedarabi,et al.  A non-reference image fusion metric based on mutual information of image features , 2011, Comput. Electr. Eng..

[11]  Qi Wang,et al.  Exploring the sequential usage patterns of mobile Internet services based on Markov models , 2016, Electron. Commer. Res. Appl..

[12]  Yi-Wei Lin,et al.  Mining mobile application sequential patterns for usage prediction , 2014, 2014 IEEE International Conference on Granular Computing (GrC).

[13]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[14]  Analía Amandi,et al.  Modeling user interests by conceptual clustering , 2006, Inf. Syst..

[15]  Juyong Park,et al.  The eigenmode analysis of human motion , 2010, 1603.04810.

[16]  Ming Tang,et al.  Crowdsourced mobility prediction based on spatio-temporal contexts , 2016, 2016 IEEE International Conference on Communications (ICC).

[17]  Yu-Ru Lin,et al.  Mesoscopic Structure and Social Aspects of Human Mobility , 2012, PloS one.

[18]  Chaoming Song,et al.  Modelling the scaling properties of human mobility , 2010, 1010.0436.

[19]  Lei Hou,et al.  Long-term memories in online users' selecting activities , 2014 .

[20]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Daqiang Zhang,et al.  NextCell: Predicting Location Using Social Interplay from Cell Phone Traces , 2015, IEEE Transactions on Computers.

[22]  Margaret Martonosi,et al.  Identifying Important Places in People's Lives from Cellular Network Data , 2011, Pervasive.

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

[24]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[25]  Lusheng Ji,et al.  Characterizing geospatial dynamics of application usage in a 3G cellular data network , 2012, 2012 Proceedings IEEE INFOCOM.

[26]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Guofeng Zhao,et al.  A novel model for user clicks identification based on hidden semi-Markov , 2013, J. Netw. Comput. Appl..

[28]  Prithwish Basu,et al.  Discovering Latent Semantic Structure in Human Mobility Traces , 2015, EWSN.

[29]  An Zeng,et al.  Temporal Effects in Trend Prediction: Identifying the Most Popular Nodes in the Future , 2014, PloS one.

[30]  Chao Wu,et al.  Mobile Web User Behavior Modeling , 2014, WISE.