ABSTRACT
The exploitation of cellular network data for studying human mobility has been a popular research topic in the last decade. Indeed, mobile terminals could be considered ubiquitous sensors that allow the observation of human movements on large scale without the need of relying on non-scalable techniques, such as surveys, or dedicated and expensive monitoring infrastructures. In particular, Call Detail Records (CDRs), collected by operators for billing purposes, have been extensively employed due to their rather large availability, compared to other types of cellular data (e.g., signaling). Despite the interest aroused around this topic, the research community has generally agreed about the scarcity of information provided by CDRs: the position of mobile terminals is logged when some kind of activity (calls, SMS, data connections) occurs, which translates in a picture of mobility somehow biased by the activity degree of users. By studying two datasets collected by a Nation-wide operator in 2014 and 2016, we show that the situation has drastically changed in terms of data volume and quality. The increase of flat data plans and the higher penetration of "always connected" terminals have driven up the number of recorded CDRs, providing higher temporal accuracy for users' locations.
Supplemental Material
- 2015. Spring Survey. http://www.pewglobal.org/2015/06/23/spring-2015-survey. (2015). Accessed: 2017-03-17.Google Scholar
- 2017. Mobile phone internet user penetration worldwide 2014--2019. www.statista.com/statistics/284202/mobile-phone-internet-user-penetration-worldwide. (2017). Accessed: 2017-03-17.Google Scholar
- H. Bar-Gera. 2007. Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: A case study from Israel. Transportation Research Part C: Emerging Technologies 15, 6 (2007), 380--391.Google ScholarCross Ref
- N. Caceres, J. P. Wideberg, and F. G. Benitez. 2007. Deriving origin destination data from a mobile phone network. IET Intelligent Transport Systems 1, 1 (March 2007), 15--26.Google ScholarCross Ref
- F. Calabrese, M. Colonna, P. Lovisolo, D. Parata, and C. Ratti. 2011. Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome. IEEE Transactions on Intelligent Transportation Systems 12, 1 (March 2011), 141--151. Google ScholarDigital Library
- C. de Fabritiis, R. Ragona, and G. Valenti. 2008. Traffic Estimation And Prediction Based On Real Time Floating Car Data. In 2008 11th International IEEE Conference on Intelligent Transportation Systems.Google Scholar
- M. Ester, H. Kriegel, J. Sander, and X. Xu. 1996. A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise (KDD'96). AAAI Press. http://dl.acm.org/citation.cfm?id=3001460.3001507 Google ScholarDigital Library
- P. Fiadino, D. Valerio, F. Ricciato, and K. A. Hummel. 2012. Steps towards the Extraction of Vehicular Mobility Patterns from 3G Signaling Data. Springer Berlin Heidelberg, Berlin, Heidelberg, 66--80. Google ScholarDigital Library
- M. C. Gonzalez, C. Hidalgo, and A. Barabási. 2008. Understanding individual human mobility patterns. Nature 453 (June 2008), 779--782.Google Scholar
- Matthew Iji. 2017. GSMA Intelligence - Unique mobile subscribers to surpass 5 billion this year. https://www.gsmaintelligence.com/research/2017/02/unique-mobile-subscribers-to-surpass-5-billion-this-year/613. (2017). Accessed: 2017-05-24.Google Scholar
- A. Janecek, D. Valerio, K. Hummel, F. Ricciato, and H. Hlavacs. 2015. The Cellular Network as a Sensor: From Mobile Phone Data to Real-Time Road Traffic Monitoring. IEEE Transactions on Intelligent Transportation Systems (2015).Google Scholar
- S. Jiang, J. Ferreira, and M. C. Gonzalez. 2017. Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore. IEEE Trans. on BigData (2017).Google Scholar
- S. Jiang, G. A. Fiore, Y. Yang, J. Ferreira, Jr., E. Frazzoli, and M. C. González. 2013. A Review of Urban Computing for Mobile Phone Traces: Current Methods, Challenges and Opportunities (UrbComp '13). ACM, New York, USA. Google ScholarDigital Library
- G. Ranjan, H. Zang, Z. Zhang, and J. Bolot. 2012. Are Call Detail Records Biased for Sampling Human Mobility? SIGMOEILE Mob. Comput. Commun. Rev. 16, 3 (Dec. 2012). Google ScholarDigital Library
- F. Ricciato. 2006. Traffic monitoring and analysis for the optimization of a 3G network. IEEE Wireless Communications 13, 6 (Dec 2006), 42--49. Google ScholarDigital Library
- F. Ricciato, P. Widhalm, F. Pantisano, and M. Craglia. 2017. Beyond the "single-operator, CDR-only" paradigm: An interoperable framework for mobile phone network data analyses and population density estimation. Pervasive and Mobile Computing 35 (2017), 65--82.Google ScholarCross Ref
- C. Song, Z. Qu, N. Blumm, and A. L. Barabási. 2010. Limits of Predictability in Human Mobility. Science (2010). arXiv:http://science.sciencemag.org/content/327/5968/1018.full.pdfGoogle Scholar
- J. Yuan, Y. Zheng, X. Xie, and G. Sun. 2011. Driving with Knowledge from the Physical World. In Proceedings of the nth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '11). ACM, New York, NY, USA, 316--324. Google ScholarDigital Library
Index Terms
- Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the "Always Connected Era"
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