Abstract
Big Data originating from the digital breadcrumbs of human activities, sensed as by-product of the technologies that we use for our daily activities, allows us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data “proxies”, such as the mobile calls data for mobility. In this paper we investigate to what extent data coming from mobile operators could be a support in producing reliable and timely estimates of intra-city mobility flows. The idea is to define an estimation method based on calling data to characterize the mobility habits of visitors at the level of a single municipality.
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Notes
- 1.
These data consist of location estimations which are generated each time when a mobile device is connected to the cellular network for calls, messages and Internet connections.
References
Andrienko, G., Andrienko, N., Bak, P., Bremm, S., Keim, D., von Landesberger, T., Poelitz, C., Schreck, T.: A framework for using self-organising maps to analyse spatio-temporal patterns, exemplified by analysis of mobile phone usage. J. Locat. Based Serv. 4, 3–4 (2010)
Ahas, R., Silm, S., Järv, S., Saluveer, E.: Using mobile positioning data to model locations meaningful to users of mobile phones. J. Urban Technol. 17, 1 (2010)
Calabrese, F., Colonna, M., Lovisolo, P., Parata, D., Ratti, C.: Real-time urban monitoring using cell phones: a case study in rome. IEEE Trans. Intell. Transp. Syst. 12, 141–151 (2011)
Ratti, C., Sevtsuk, A., Huang, S., Pailer, R.: Mobile Landscapes: Graz in Real Time. MIT Senseable City Lab, Massachusetts (2005)
Furletti, B., Gabrielli, L., Monreale, A., Nanni, M., Pratesi, F., Rinzivillo, S., Giannotti, F., Pedreschi, D.: Assessing the privacy risk in the process of building call habit models that underlie the sociometer. Technical report. http://puma.isti.cnr.it/dfdownload.php?ident=/cnr.isti/2014-TR-011&langver=it&scelta=Metadata
Furletti, B., Gabrielli, L., Renso, C., Rinzivillo, S.: Identifying users profiles from mobile calls habits. In: The Proceedings of UrbComp (2012)
Furletti, B., Gabrielli, L., Renso, C., Rinzivillo, S.: Turism fluxes observatory: deriving mobility indicators from GSM calls habits. In: The Book of Abstracts of NetMob (2013)
Furletti, B., Gabrielli, L., Renso, C., Rinzivillo, S.: Analysis of GSM calls data for understanding user mobility behavior. In: The Proceedings of Big Data (2013)
Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F., Renso, C., Rinzivillo, S., Trasarti, R.: Unveiling the complexity of human mobility by querying and mining massive trajectory data. VLDB J. 20, 695–719 (2011)
Nanni, M., Trasarti, R., Furletti, B., Gabrielli, L., Mede, P.V.D., Bruijn, J.D., Romph, E.D., Bruil, G.: MP4-A project: mobility planning for Africa. In: D4D Challenge @ 3rd Conference on the Analysis of Mobile Phone datasets (NetMob 2013)
Oltenau, A.-M., Trasarti, R., Couronne, T., Giannotti, F., Nanni, M., Smoreda, Z., Ziemlicki, C.: GSM data analysis for tourism application. In: Proceedings of 7th International Symposium on Spatial Data Quality (ISSDQ) (2011)
Pereira, F.C., Liu, L., Calabrese, F.: Profiling transport demand for planned special events: prediction of public home distributions (2010). www.scienceDirect.com
Quercia, D., Lathia, N., Calabrese, F., Di Lorenzo, G., Crowcroft, J.: Recommending social events from mobile phone location data. In: International Conference on Data Mining, ICDM (2010)
Schlaich, J., Otterst\(\ddot{a}\)tter, T., Friedrich, M.: Generating trajectories from mobile phone data. In: The Proceedings of the 89th Annual Meeting Compendium of Papers, Transportation Research Board of the National Academies (2010)
Wikipedia. Tourism. http://en.wikipedia.org/wiki/Tourism
Wang, D., Pedreschi, D., Song, C., Giannotti, F., Barabasi, A.-L.: Human mobility, social ties, and link prediction. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 11. ACM, New York (2011)
Acknowledgments
This work has been partially funded by the European Union under the FP7-ICT Program: Project DataSim n. FP7-ICT-270833, and Project Petra n. 609042; and by the MIUR and MISE under the Industria 2015 program: Project MOTUS grating degree n.0000089 - application code MS01_00015.
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Gabrielli, L., Furletti, B., Giannotti, F., Nanni, M., Rinzivillo, S. (2015). Use of Mobile Phone Data to Estimate Visitors Mobility Flows. In: Canal, C., Idani, A. (eds) Software Engineering and Formal Methods. SEFM 2014. Lecture Notes in Computer Science(), vol 8938. Springer, Cham. https://doi.org/10.1007/978-3-319-15201-1_14
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DOI: https://doi.org/10.1007/978-3-319-15201-1_14
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