Mobility Predictability of College Students via Full Lifecycle Campus Consuming Logs

Human mobility is an interesting topic attracting researchers from various fields. In this paper, we investigate mobility of college students in campus during their full life cycle and find the demographic differences on the predictability of human mobility. We explore an extensive consuming record data set collected from a college campus. Specifically, our data set includes over 15 million consuming logs gathered from 4,741 students (with demographic information) from 2013 to 2016. We present and analyze the evolving patterns of mobility entropy by ages, academic majors, and genders. We find that entropy decreases with age increasing and entropy grouped by genders follows different distributions, which is opposite to previous studies. Our findings will supply novel insights in human mobility research and provide possible support for wireless networking design in campus.

[1]  Cun-lu Zhang,et al.  A student oriented campus layout planning method , 2013, 2013 10th International Conference on Service Systems and Service Management.

[2]  Marco Fiore,et al.  Large-Scale Mobile Traffic Analysis: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[3]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[4]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[5]  Zoran Zdravev,et al.  Big data for education data mining, data analytics and web dashboards , 2015 .

[6]  Anthony G. Picciano.  The Evolution of Big Data and Learning Analytics in American Higher Education , 2012 .

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

[8]  Mikko Alava,et al.  Patterns, Entropy, and Predictability of Human Mobility and Life , 2012, PloS one.

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

[10]  Dino Pedreschi,et al.  Mobility, Data Mining and Privacy - Geographic Knowledge Discovery , 2008, Mobility, Data Mining and Privacy.

[11]  Razvan Stanica,et al.  Mobile Traffic Analysis: a Survey , 2015 .

[12]  David Kotz,et al.  Analysis of a Campus-Wide Wireless Network , 2002, MobiCom '02.

[13]  Mingyan Liu,et al.  Building realistic mobility models from coarse-grained traces , 2006, MobiSys '06.

[14]  Wuyang Zhou,et al.  Performance comparison of full-batch BP and mini-batch BP algorithm on Spark framework , 2016, 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP).

[15]  Alessandro Vespignani,et al.  Multiscale mobility networks and the spatial spreading of infectious diseases , 2009, Proceedings of the National Academy of Sciences.

[16]  Tracy Camp,et al.  Trace-based mobility modeling for multi-hop wireless networks , 2011, Comput. Commun..

[17]  Kyunghan Lee,et al.  On the Levy-Walk Nature of Human Mobility , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[18]  Dong Xuan,et al.  On human mobility predictability via WLAN logs , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

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