Daily Mobility Practices Through Mobile Phone Data: An Application in Lombardy Region

  • Paola PucciEmail author
  • Fabio Manfredini
  • Paolo Tagliolato
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


Beginning with the results of a research carried out in the Italian region of Lombardy utilising mobile phone data provided by Telecom Italia, this chapter will demonstrate how new maps , based on mobile phone data and better tailored to the dynamic processes taking place, can represent spatialized urban practices and origin-destination flows of daily movements. Three different types of mobile phone data were employed in the analysis of complex temporal and spatial patterns. The first data type concerns the mobile phone traffic registered by the network over the entire Lombardy Region (Northern Italy). Data are expressed in Erlang, a measure of the density of calls. The second typology of data consists in localized and aggregated tracks of anonymized mobile phone users . It is an origin-destination datum derived from the Call Detail Record database. The third type of data refers to the mobile switching centre (MSC), which is the primary service delivery node for GSM, responsible for routing voice calls and text messages. With the maps based on the processing of the three types of mobile phone data, it was possible to offer information on temporary populations and city usage patterns (daily/nightly practices, non-systematic mobility).


Erlang Data Origin-Destination matrix Mobile Switching Center Treelet decomposition Geographic analysis Big Event 


  1. Bekhor S, Cohen Y, Solomon C (2011) Evaluating long-distance travel patterns in Israel by tracking cellular phone positions. J Adv Transp n/a–n/a. doi: 10.1002/atr.170
  2. Bernareggi GM (2013) L’istituzionalizzazione della città metropolitana di Milano—Aspetti economici. In: Ceriani A (ed) Aggiornamento della ricerca “Gli enti locali nella transizione verso il federalismo-Effetti ordinamentali della Spending review in Lombardia. Éupolis Lombardi, pp 75–84Google Scholar
  3. Bolla R, Davoli F (2000) Road traffic estimation from location tracking data in the mobile cellular network. In: 2000 IEEE wireless communications and networking conference. Conference record (Cat. No.00TH8540), vol 3. doi: 10.1109/WCNC.2000.904783
  4. Caceres N, Romero LM, Benitez FG, Del Castillo JM (2012) Traffic flow estimation models using cellular phone data. IEEE Trans Intell Transp Syst 13:1430–1441. doi: 10.1109/TITS.2012.2189006 CrossRefGoogle Scholar
  5. Caceres N, Wideberg JP, Benitez FG (2008) Review of traffic data estimations extracted from cellular networks. Intell Transp Syst IET 2(3):179–192. doi: 10.1049/iet-its:20080003 CrossRefGoogle Scholar
  6. Cayford R, Johnson T (2003) Operational parameters affecting use of anonymous cell phone tracking for generating traffic information. In: Institute of transportation studies for the 82th TRB annual meeting, vol 1Google Scholar
  7. Calabrese F, Lorenzo G, Di Liu L, Ratti C (2011) Estimating origin-destination flows using mobile phone location data. IEEE Pervasive Comput 10(4):36–44CrossRefGoogle Scholar
  8. Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall. Englewood Cliffn, NYGoogle Scholar
  9. Lee AB, Nadler B, Wasserman L (2008) Treelets—an adaptive multi-scale basis for sparse unordered data. Ann Appl Stat 2(2):435–471CrossRefGoogle Scholar
  10. Manfredini F, Tagliolato P, Di Rosa C (2011) Monitoring temporary populations through cellular core network data. In: 11th international conference on computational science and its applications, Santander, Spain, June 2011, Proceedings, Part II. Springer, pp 151–161. doi: 10.1007/978-3-642-21887-3; Print ISBN:978-3-642-21886-6; Online ISBN:978-3-642-21887-6
  11. Manfredini F, Pucci P, Secchi P, Tagliolato P, Vantini S, Vitelli V (2012a) Treelet decomposition of mobile phone data for deriving city usage and mobility pattern in the Milan urban region. MOX Report 25. 25 June 2012
  12. Manfredini F, Pucci P, Tagliolato P (2012b) Mobile phone network data: new sources for urban studies? In: Borruso G et al (eds) Geographic information analysis for sustainable development and economic planning: new technologies. IGI Global, Hershey, pp 115–128Google Scholar
  13. Manfredini F, Pucci P, Tagliolato P (2013) Deriving mobility practices and patterns from mobile phone data. In: 13th International conference on computational science and its applications, Ho Chi Minh City, Vietnam, June 24–27, 2013, Proceedings, Part III. Springer, pp 438–451. doi: 10.1007/978-3-642-39646-5_32, Print ISBN:978-3-642-39645-8, Online ISBN:978-3-642-39646-5
  14. Manfredini F, Pucci P, Tagliolato P (2014) Toward a systemic usage of manifold cell phone network data for urban analysis and planning. J Urban Technol 21(2):39–59. doi: 10.1080/10630732.2014.888217 CrossRefGoogle Scholar
  15. Manfredini F, Pucci P, Secchi P, Tagliolato P, Vantini S, Vitelli V (2015) Treelet decomposition of mobile phone data for deriving city usage and mobility pattern in the Milan urban region. In: Paganoni, Secchi P (eds) Advances in complex data modeling and computational methods in statistics. Springer ISBN:978-3-319-11148-3Google Scholar
  16. Pucci P (2014) Identifying communities of practice through mobile phone data. Urbe. Revista Brasileira de Gestão Urbana (Braz J Urban Manage) 6(1):75–97Google Scholar
  17. Pucci P, Manfredini F, Tagliolato P (2013) Mobile phone data for mapping urban dynamics., ISSN: 2281-6283
  18. Pucci P, Manfredini F, Tagliolato P (2014) A new map of the Milan urban region through mobile phone data. In: Contin A, Paolini P, Salerno R (eds) Innovative technologies in urban mapping. Built Space and Mental Space. Springer, Cham, pp 83–92CrossRefGoogle Scholar
  19. Tagliolato P, Manfredini F, Pucci P (2013) Aggregated OD tracks of mobile phone data for the recognition of daily mobility spaces: an application to Lombardia region. In: International conference on the analysis of mobile phone datasets, vol 3, Cambridge, MA. Proceedings. MIT, Cambridge, pp 42–44Google Scholar
  20. Tagliolato P, Manfredini F, Pucci P (2014) Discovering regularity patterns of mobility practices through mobile phone data. Int J Agric Environ Inf Syst (IJAEIS) 5(3)Google Scholar
  21. Vantini S, Vitelli V, Zanini P (2012) Treelet analysis and independent component analysis of milan mobile-network data: investigating population mobility and behaviour. In: Analysis and modeling of complex data in behavioural and social sciences—joint meeting of the Italian and the Japanese Statistical SocietiesGoogle Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Paola Pucci
    • 1
    Email author
  • Fabio Manfredini
    • 1
  • Paolo Tagliolato
    • 2
    • 3
  1. 1.Architecture and Urban StudiesPolitecnico di MilanoMilanItaly
  2. 2.Istituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA)CNRMilanItaly
  3. 3.Istituto di Scienze Marine (ISMAR)CNRVeniceItaly

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