Browse > Article
http://dx.doi.org/10.3745/KTSDE.2017.6.3.141

A Method for Group Mobility Model Construction and Model Representation from Positioning Data Set Using GPGPU  

Song, Ha Yoon (홍익대학교 컴퓨터공학과)
Kim, Dong Yup (홍익대학교 컴퓨터공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.6, no.3, 2017 , pp. 141-148 More about this Journal
Abstract
The current advancement of mobile devices enables users to collect a sequence of user positions by use of the positioning technology and thus the related research regarding positioning or location information are quite arising. An individual mobility model based on positioning data and time data are already established while group mobility model is not done yet. In this research, group mobility model, an extension of individual mobility model, and the process of establishment of group mobility model will be studied. Based on the previous research of group mobility model from two individual mobility model, a group mobility model with more than two individual model has been established and the transition pattern of the model is represented by Markov chain. In consideration of real application, the computing time to establish group mobility mode from huge positioning data has been drastically improved by use of GPGPU comparing to the use of traditional multicore systems.
Keywords
Group Mobility Model; Clustering; Parallel Computing; Markov Model; R; GPGPU;
Citations & Related Records
연도 인용수 순위
  • Reference
1 H. Y. Song, "Probabilistic space-time analysis of human mobility patterns," WSEAS TRANSACTIONS on COMPUTERS, vol.15, pp.222-238, 2016.
2 Wolf, P. S. A. and W. J. Jacobs, "GPS technology and human psychological research: a methodological proposal," Journal of Methods and Measurement in the Social Sciences, Vol.1, No.1, pp.1-15, 2010.   DOI
3 C. Song, Q. Zehui, B. Nicholas, and B. Albert-laszio, "Limits of predictability in human mobility," Science 19, Vol.327, No.5968, pp.1018-1021, 2010.   DOI
4 L. Liao, D. Fox, and H. Kautz, "Extracting places and activities from GPS traces using hierarchical conditional random fields," International Journal of Robotics Research (IJRR), Vol.26, No.1, pp.119-134, 2007.   DOI
5 A. Noulas, S. Scellato, N. Lathia, and C. Mascolo, "Mining user mobility features for next place prediction in location-based services," in Proc. of 12th IEEE ICDM, pp.1038-1043, 2012.
6 A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via EM algorithm," Journal of the Royal Statistical Society, Series B (Methodological), Vol.39, No.1, pp.1-38, 1977.
7 D. Y. Choi and H. Y. Song, "Defining Measures for Location Visiting Preference," Procedia Computer Science, Vol.63, pp. 142-147, 2015.   DOI
8 J. S. Lee and H. Y. Song, "Efficient Detection of Positioning Data Error," Advanced Science Letters, Vol.21, No.3, pp. 328-331, 2015.   DOI
9 J. H. Baik and H. Y. Song, "Mobility State Classification with Particle Filter," New Developments in Computational Intelligence and Computer Science, Vol.28, pp.75-82, 2015.
10 S. Y. Kim and H. Y. Song, "Predicting Human Locations with Big Five Personality and Neural Network," Journal of Economics, Business and Management, Vol.2, No.4, pp.273-280, 2014.
11 E. B. Lee and H. Y. Song, "An Analysis of the Relationship between Human Personality and Favored Location," AFIN 2015, pp.6-10, 2015.
12 D. Y. Kim, D. Y. Choi, and H. Y. Song, "Modeling Group Mobility from Individual Mobility Model," KIPS, Vol.21, No. 2, pp.376-379, 2014.
13 Cyril Zeller, "CUDA C/C++ Basics," Supercomputing 2011 Tutorial.
14 Paul Baines, "RCUDA: General programming facilities for GPUs in R," Journal of Statistical Software.
15 Roy D. Yates and David J. Goodman, "Probability and Stochastic Processes, Second edition," pp.445-500, 2005.