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Electrical, Control and Communication Engineering

The Journal of Riga Technical University

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Call Duration Characteristics based on Customers Location

Karolis Žvinys / Darius Guršnys
Published Online: 2014-05-17 | DOI: https://doi.org/10.2478/ecce-2014-0010


Nowadays a lot of different researches are performed based on call duration distributions (CDD) analysis. However, the majority of studies are linked with social relationships between the people. Therefore the scarcity of information, how the call duration is associated with a user's location, is appreciable. The goal of this paper is to reveal the ties between user's voice call duration and the location of call. For this reason we analyzed more than 5 million calls from real mobile network, which were made over the base stations located in rural areas, roads, small towns, business and entertainment centers, residential districts. According to these site types CDD’s and characteristic features for call durations are given and discussed. Submitted analysis presents the users habits and behavior as a group (not an individual). The research showed that CDD’s of customers being them in different locations are not equal. It has been found that users at entertainment, business centers are tend to talk much shortly, than people being at home. Even more CDD can be distorted strongly, when machinery calls are evaluated. Hence to apply a common CDD for a whole network it is not recommended. The study also deals with specific parameters of call duration for distinguished user groups, the influence of network technology for call duration is considered.

Keywords: Communications technology; Cellular networks; Communication system traffic; Telecommunication services


  • [1] ERICSSON, “HD Voice - It Speaks for Itself (ERICSSON White Paper),” 2011. [Online]. Available: http://www.ericsson.com/res/docs/whitepapers/WP-HD-voice.pdf. [Accessed: 10-Apr-2014].Google Scholar

  • [2] T. Durga Laxmi, R. Baby Akila, K. S. Ravichandran, and B. Santhi, “Study of User Behavior Pattern in Mobile Environment,” Research Journal of Applied Sciences, Engineering and Technology, vol. 4, no. 23, pp. 5021-5026, 2012.Google Scholar

  • [3] V. B. Iversen, Teletraffic Engineering (Handbook), Draft. Lyngby: Technical University of Denmark, COM Center, 2001, p. 321.Google Scholar

  • [4] R. B. Cooper and D. P. Heyman, “Teletraffic Theory and Engineering,” in in Encyclopedia of Telecommunications: Volume 16 - Subscriber Loop Signaling to Teletraffic Theory and Engineering, F. E. Froehlich and A. Kent, Eds. CRC Press, 1998, p. 500.Google Scholar

  • [5] A. Kivi, “Measuring Mobile User Behavior and Service Usage: Methods, Measurement Points, and Future Outlook,” Helsinki University of Technology, Finland, 2007. [Online]. Available: http://classic.marshall.usc.edu/assets/006/5570.pdf. [Accessed: 11-Oct-2013].Google Scholar

  • [6] G.-H. Tu, C. Peng, H. Wang, C.-Y. Li, and S. Lu, “How voice calls affect data in operational LTE networks,” in Proceedings of the 19th annual international conference on Mobile computing & networking - MobiCom ’13, 2013, p. 87.Google Scholar

  • [7] V. B. Iversen, Teletraffic Engineering and Network Planning. Lyngby: Technical University of Denmark, 2010, p. 639.Google Scholar

  • [8] P. O. S. Vaz de Melo, L. Akoglu, C. Faloutsos, and A. A. F. Loureiro, “Surprising Patterns for the Call Duration Distribution of Mobile Phone Users,” in in Machine Learning and Knowledge Discovery in Databases (European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part III), vol. 6323, Springer, 2010, pp. 354-369.Google Scholar

  • [9] Y. Dong, J. Tang, T. Lou, B. Wu, and N. V. Chawla, “How Long Will She Call Me? Distribution, Social Theory and Duration Prediction,” in in Machine Learning and Knowledge Discovery in Databases (European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II), vol. 8189, Springer, 2013, pp. 16-31.Google Scholar

  • [10] J. Guo, F. Liu, and Z. Zhu, “Estimate the Call Duration Distribution Parameters in GSM System Based on K-L Divergence Method,” in 2007 International Conference on Wireless Communications, Networking and Mobile Computing, 2007, pp. 2988-2991.Google Scholar

  • [11] C. Kang, Y. Liu, X. Ma, and L. Wu, “Towards Estimating Urban Population Distributions from Mobile Call Data,” Journal of Urban Technology, vol. 19, no. 4, pp. 3-21, Oct. 2012.Google Scholar

  • [12] S. Gao, “Human mobility, urban structure analysis, and spatial community detection from mobile phone data,” University of California, USA, 2013. [Online]. Available: http://www.slideshare.net/songgaogeo/ song-gao-ucsbmobilephonedataanalysis. [Accessed: 11-Oct-2013].Google Scholar

  • [13] Center for Spatial Information Science (The University of Tokyo), “A Study on Urban Mobility and Dynamic Population Estimation by Using Aggregate Mobile Phone Sources,” CSIS Discussion Paper No. 115. [Online]. Available: http://www.csis.u-tokyo.ac.jp/dp/115.pdf. [Accessed: 11-Oct-2013].Google Scholar

  • [14] W. Loibl and J. Peters-Anders, “Mobile Phone Data as Source to Discover Spatial Activity and Motion Patterns,” in in GI Forum 2012: Geovizualisation, Society and Learning, Berlin: VDE VERLAG GMBH, 2012, pp. 524-533.Google Scholar

  • [15] C. Iovan, A.-M. Olteanu-Raimond, T. Couronné, and Z. Smoreda, “Moving and Calling: Mobile Phone Data Quality Measurements and Spatiotemporal Uncertainty in Human Mobility Studies,” in in Geographic Information Science at the Heart of Europe, Springer, 2013, pp. 247-265. Google Scholar

About the article

Published Online: 2014-05-17

Published in Print: 2014-05-01

Citation Information: Electrical, Control and Communication Engineering, Volume 5, Issue 1, Pages 67–73, ISSN (Online) 2255-9159, DOI: https://doi.org/10.2478/ecce-2014-0010.

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© Riga Technical University. This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. BY-NC-ND 3.0

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