Call Duration Characteristics based on Customers Location
DOI:
https://doi.org/10.2478/ecce-2014-0010Keywords:
Communications technology, Cellular networks, Communication system traffic, Telecommunication servicesAbstract
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.References
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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.
V. B. Iversen, Teletraffic Engineering (Handbook), Draft. Lyngby: Technical University of Denmark, COM Center, 2001, p. 321.
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.
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].
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.
V. B. Iversen, Teletraffic Engineering and Network Planning. Lyngby: Technical University of Denmark, 2010, p. 639.
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.
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.
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.
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.
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].
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].
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.
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.
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2014-05-01
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Copyright (c) 2014 Karolis Žvinys, Darius Guršnys (Author)
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Žvinys, K., & Guršnys, D. (2014). Call Duration Characteristics based on Customers Location. Electrical, Control and Communication Engineering, 5(1), 67-73. https://doi.org/10.2478/ecce-2014-0010
