• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 56 Issue 5
Oct.  2021
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Article Contents
YANG Fei, JIANG Haihang, YAO Zhenxing, LIU Haode. Evaluation of Activity Location Recognition Using Cellular Signaling Data[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 928-936. doi: 10.3969/j.issn.0258-2724.20200086
Citation: YANG Fei, JIANG Haihang, YAO Zhenxing, LIU Haode. Evaluation of Activity Location Recognition Using Cellular Signaling Data[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 928-936. doi: 10.3969/j.issn.0258-2724.20200086

Evaluation of Activity Location Recognition Using Cellular Signaling Data

doi: 10.3969/j.issn.0258-2724.20200086
  • Received Date: 11 Mar 2020
  • Rev Recd Date: 08 Jun 2020
  • Available Online: 23 Mar 2021
  • Publish Date: 15 Oct 2021
  • In order to investigate the recognition results of individuals’ activity locations using cellular signaling data, the field experiment for collecting cellular signaling data was carried out. The GPS trajectory data and the travel logs were collected synchronously as the real data for reference. A three-step method for recognizing activity locations is proposed. Firstly, an equal time interval interpolation method is used to balance the time weight of each trace. Secondly, an agglomerative hierarchical clustering algorithm is applied to merge the traces into different clusters. Finally, a new method of correcting location oscillation is proposed, to solve the problem that clusters in the same activity location oscillate. Results show that the proposed method performs well in term of the accuracy of identifying activity locations, distance error and time error. The average recognition accuracy and distance error is over 84% and within 220 m, respectively. The average errors of departure and arrival time are 7.7 min and 5.3 min, respectively. In the comparison of different travel purposes, the activity locations for work receive the best recognition results, and the results of the locations for shopping is relatively inferior.

     

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  • AHAS R, SILM S, JÄRV O, et al. Using mobile positioning data to model locations meaningful to users of mobile phones[J]. Journal of Urban Technology, 2010, 17(1): 3-27. doi: 10.1080/10630731003597306
    ISAACMAN S, BECKER R, CÁCERES R, et al. Identifying important places in people’s lives from cellular network data[C]//International Conference on Pervasive Computing. Berlin, Heidelberg: Springer, 2011: 133-151.
    WANG P, HUNTER T, BAYEN A M, et al. Understanding road usage patterns in urban areas[J]. Scientific Reports, 2012, 2: 1-6.
    JÄRV O, AHAS R, WITLOX F. Understanding monthly variability in human activity spaces:a twelve-month study using mobile phone call detail records[J]. Transportation Research Part C:Emerging Technologies, 2014, 38: 122-135. doi: 10.1016/j.trc.2013.11.003
    XU Y, SHAW S L, ZHAO Z, et al. Another tale of two cities:understanding human activity space using actively tracked cellphone location data[J]. Annals of the American Association of Geographers, 2016, 106(2): 489-502.
    WANG Z, HE S Y, LEUNG Y. Applying mobile phone data to travel behaviour research:a literature review[J]. Travel Behaviour and Society, 2018, 11: 141-155. doi: 10.1016/j.tbs.2017.02.005
    CALABRESE F, COLONNA M, LOVISOLO P, et al. Real-time urban monitoring using cell phones:a case study in Rome[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(1): 141-151. doi: 10.1109/TITS.2010.2074196
    CALABRESE F, DIAO M, DI LORENZO G, et al. Understanding individual mobility patterns from urban sensing data:a mobile phone trace example[J]. Transportation Research Part C:Emerging Technologies, 2013, 26: 301-313. doi: 10.1016/j.trc.2012.09.009
    JIANG S, FIORE G A, YANG Y, et al. A review of urban computing for mobile phone traces: Current methods, challenges and opportunities[C]//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery, 2013: 1-9.
    WANG F, CHEN C. On data processing required to derive mobility patterns from passively-generated mobile phone data[J]. Transportation Research Part C:Emerging Technologies, 2018, 87: 58-74. doi: 10.1016/j.trc.2017.12.003
    HARIHARAN R, TOYAMA K. Project lachesis: parsing and modeling location histories[C]//Interna- tional Conference on Geographic Information Science. Berlin, Heidelberg: Springer, 2004: 106-124.
    ALEXANDER L, JIANG S, MURGA M, et al. Origin-destination trips by purpose and time of day inferred from mobile phone data[J]. Transportation Research Part C:Emerging Technologies, 2015, 58: 240-250. doi: 10.1016/j.trc.2015.02.018
    WIDHALM P, YANG Y, ULM M, et al. Discovering urban activity patterns in cell phone data[J]. Transportation, 2015, 42(4): 597-623. doi: 10.1007/s11116-015-9598-x
    WANG M, CHEN C, MA J. Time-of-day dependence of location variability: application of passively-generated mobile phone dataset[C]//The 94th Annual Meeting of Transportation Research Board. Washington D. C.: [s.n.], 2015: 11-15.
    LEE J K, HOU J C. Modeling steady-state and transient behaviors of user mobility: formulation, analysis, and application[C]//Proceedings of the 7th ACM International Symposium on Mobile ad Hoc Networking and Computing (MobiHoc). New York: Association for Computing Machinery, 2006: 85-96.
    BAYIR M A, DEMIRBAS M, EAGLE N. Mobility profiler:a framework for discovering mobility profiles of cell phone users[J]. Pervasive and Mobile Computing, 2010, 6(4): 435-454. doi: 10.1016/j.pmcj.2010.01.003
    SHAD S A, CHEN E. Cell oscillation resolution in mobility profile building[J]. International Journal of Computer Science Issues (IJCSI), 2012, 9(3): 205-213.
    QI L, QIAO Y, ABDESSLEM F B, et al. Oscillation resolution for massive cell phone traffic data[C]// Proceedings of the 1st Workshop on Mobile Data. New York: Association for Computing Machinery, 2016: 25-30.
    IOVAN C, OLTEANU-RAIMOND A M, COURONNÉ T, et al. Moving and calling: mobile phone data quality measurements and spatiotemporal uncertainty in human mobility studies[J]. Lecture Notes in Geoinformation and Cartography, 2013: 247-265.
    DEMISSIE M G, DE ALMEIDA CORREIA G H, BENTO C. Intelligent road traffic status detection system through cellular networks handover information:an exploratory study[J]. Transportation Research Part C:Emerging Technologies, 2013, 32: 76-88. doi: 10.1016/j.trc.2013.03.010
    WU W, WANG Y, GOMES J B, et al. Oscillation resolution for mobile phone cellular tower data to enable mobility modelling[C]// Proceedings of the 2014 IEEE 15th International Conference on Mobile Data Management. Washington D. C.: IEEE Computer Society, 2014, 1: 317-324.
    CHEN C, MA J, SUSILO Y, et al. The promises of big data and small data for travel behavior (aka human mobility) analysis[J]. Transportation Research Part C:Emerging Technologies, 2016, 68: 285-299. doi: 10.1016/j.trc.2016.04.005
    CHEN C, BIAN L, MA J. From traces to trajectories:How well can we guess activity locations from mobile phone traces?[J]. Transportation Research Part C:Emerging Technologies, 2014, 46: 326-337. doi: 10.1016/j.trc.2014.07.001
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