Skip to main content
SpringerLink
Log in

A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of Lyon region (France)

  • Published:
Transportation Aims and scope Submit manuscript

Abstract

Spatiotemporal data, and more specifically origin–destination matrices, are critical inputs to mobility studies for transportation planning and urban management purposes. Traditionally, high-cost and hard-to-update household travel surveys are used to produce large-scale origin–destination flow information of individuals’ whereabouts. In this paper, we propose a methodology to estimate origin–destination (O–D) matrices based on passively-collected cellular network signalling data of millions of anonymous mobile phone users in the Rhône-Alpes region, France. Unlike Call Detail Record (CDR) data which rely only on phone usage, signalling data include all network-based records providing higher spatiotemporal granularity. The explored dataset, which consists of time-stamped traces from 2G and 3G cellular networks with users’ unique identifier and cell tower locations, is used to first analyse the cell phone activity degree indicators of each user in order to qualify the mobility information involved in these records. These indicators serve as filtering criteria to identify users whose device transactions are sufficiently distributed over the analysed period to allow studying their mobility. Trips are then extracted from the spatiotemporal traces of users for whom the home location could be detected. Trips have been derived based on a minimum stationary time assumption that enables to determine activity (stop) zones for each user. As a large, but still partial, fraction of the population is observed, scaling is required to obtain an O–D matrix for the full population. We propose a method to perform this scaling and we show that signalling data-based O–D matrix carries similar estimations as those that can be obtained via travel surveys.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. Global System for Mobile communications.

  2. Universal Mobile Telecommunication System.

  3. A “Location Area” is a set of cells (antennas) that are grouped together to optimise signalling.

References

  • Alexander, L., Jiang, S., Murga, M., González, M.C.: Origin–destination trips by purpose and time of day inferred from mobile phone data. Transp. Res. Part C Emerg. Technol. 58, 240–250 (2015). https://doi.org/10.1016/j.trc.2015.02.018

    Article  Google Scholar 

  • ARCEP: Observatoire des marchés des communications électroniques en France, 4eme trimestre 2017. (2018)

  • Arentze, T., Timmermans, H.: Data needs, data collection, and data quality requirements of activity-based transport demand models. In: Transport Surveys: Raising the Standard, pp. 1–30 (2000)

  • Asgari, F., Gauthier, V., Becker, M.: A survey on human mobility and its applications (2013). arXiv:1307.0814

  • Blondel, V.D., Decuyper, A., Krings, G.: A survey of results on mobile phone datasets analysis. EPJ Data Sci. 4, 55 (2015). https://doi.org/10.1140/epjds/s13688-015-0046-0

    Article  Google Scholar 

  • Bohte, W., Maat, K.: Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: a large-scale application in the Netherlands. Transp. Res. Part C Emerg. Technol. 17, 285–297 (2009). https://doi.org/10.1016/j.trc.2008.11.004

    Article  Google Scholar 

  • Bonnel, P.: Postal, telephone, and face-to-face surveys: how comparable are they? In: Jones, P., Stopher, P.R. (eds.) Transport Survey Quality and Innovation, pp. 215–237. Emerald Group Publishing Limited, Bingley (2003)

    Chapter  Google Scholar 

  • Bonnel, P.: Prévoir la demande de transport. Presses de l’Ecole Nationale des Ponts et Chaussées, Paris (2004)

    Google Scholar 

  • Bonnel, P., Fekih, M., Smoreda, Z.: Origin–destination estimation using mobile network probe data. Transp. Res. Procedia 32, 69–81 (2018). https://doi.org/10.1016/j.trpro.2018.10.013

    Article  Google Scholar 

  • Bonnel, P., Hombourger, E., Olteanu-Raimond, A.-M., Smoreda, Z.: Passive mobile phone dataset to construct origin-destination matrix: potentials and limitations. Transp. Res. Procedia 11, 381–398 (2015). https://doi.org/10.1016/j.trpro.2015.12.032

    Article  Google Scholar 

  • Caceres, N., Wideberg, J.P., Benitez, F.G.: Deriving origin–destination data from a mobile phone network. IET Intel. Transp. Syst. 1, 15–26 (2007). https://doi.org/10.1049/iet-its:20060020

    Article  Google Scholar 

  • 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 (2011a). https://doi.org/10.1109/TITS.2010.2074196

    Article  Google Scholar 

  • Calabrese, F., Di Lorenzo, G., Liu, L., Ratti, C.: Estimating origin–destination flows using mobile phone location data. IEEE Pervasive Comput. 10, 36–44 (2011b). https://doi.org/10.1109/MPRV.2011.41

    Article  Google Scholar 

  • Calabrese, F., Diao, M., Di Lorenzo, G., Ferreira, J., Ratti, C.: Understanding individual mobility patterns from urban sensing data: a mobile phone trace example. Transp. Res. Part C Emerg. Technol. 26, 301–313 (2013)

    Article  Google Scholar 

  • Calabrese, F., Ferrari, L., Blondel, V.D.: Urban sensing using mobile phone network data: a survey of research. ACM Comput. Surv. 47, 1–20 (2014). https://doi.org/10.1145/2655691

    Article  Google Scholar 

  • CERTU: L’enquête ménages déplacements standard CERTU, éditions du CERTU (2008)

  • Chen, C., Bian, L., Mac, J.: From traces to trajectories: how well can we guess activity locations from mobile phone traces? Transp. Res. Part C Emerg. Technol. 46, 326–337 (2014). https://doi.org/10.1016/j.trc.2014.07.001

    Article  Google Scholar 

  • Chen, C., Gong, H., Lawson, C., Bialostozky, E.: Evaluating the feasibility of a passive travel survey collection in a complex urban environment: lessons learned from the New York City case study. Transp. Res. Part A Policy Pract. 44, 830–840 (2010). https://doi.org/10.1016/j.tra.2010.08.004

    Article  Google Scholar 

  • Chen, C., Ma, J., Susilo, Y., Liu, Y., Wang, M.: The promises of big data and small data for travel behavior (aka human mobility) analysis. Transp. Res. Part C Emerg. Technol. 68, 285–299 (2016). https://doi.org/10.1016/j.trc.2016.04.005

    Article  Google Scholar 

  • Choujaa, D., Dulay, N.: Activity recognition from mobile phone data: state of the art, prospects and open problems. In: Imperial College London, pp. 1–32 (2009)

  • Deutsch, K., McKenzie, G., Janowicz, K., Li, W., Hu, Y., Goulias, K.: Examining the use of smartphones for travel behavior data collection. In: Presented at the Conference of the International Association for Travel Behavior Research, Torento, Canada (2012)

  • Feng, T., Timmermans, H.J.P.: Extracting activity-travel diaries from gps data: towards integrated semi-automatic imputation. Procedia Environ. Sci. 22, 178–185 (2014). https://doi.org/10.1016/j.proenv.2014.11.018

    Article  Google Scholar 

  • Fiadino, P., Ponce-Lopez, V., Antonio, J., Torrent-Moreno, M., D’Alconzo, A.: Call detail records for human mobility studies: taking stock of the situation in the “always connected era.” In: Big-DAMA, pp. 43–48. ACM Press, New York (2017)

  • Fiadino, P., Valerio, D., Ricciato, F., Hummel, K.A.: Steps towards the extraction of vehicular mobility patterns from 3G signaling data. In: Pescapè, A., Salgarelli, L., Dimitropoulos, X. (eds.) Traffic monitoring and analysis, pp. 66–80. Springer, Berlin (2012)

    Chapter  Google Scholar 

  • Frias-Martinez, V., Virseda, J., Rubio, A., Frias-Martinez, E.: Towards large scale technology impact analyses: Automatic residential localization from mobile phone-call data. In: Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development, p. 11. ACM (2010)

  • Giannotti, F., Pedreschi, D. (eds.): Mobility, data mining and privacy: geographic knowledge discovery. Springer, Berlin (2008)

    Google Scholar 

  • González, M.C., Hidalgo, C.A., Barabási, A.-L.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008). https://doi.org/10.1038/nature06958

    Article  Google Scholar 

  • Gonzalez, P.A., Weinstein, J.S., Barbeau, S.J., Labrador, M.A., Winters, P.L., Georggi, N.L., Perez, R.: Automating mode detection for travel behaviour analysis by using global positioning systems-enabled mobile phones and neural networks. IET Intel. Transp. Syst. 4, 37–49 (2010)

    Article  Google Scholar 

  • Graells-Garrido, E., Saez-Trumper, D.: A day of your days: estimating individual daily journeys using mobile data to understand urban flow. In: 2nd International Conference on IoT in Urban Space, pp. 1–7. ACM Press (2016)

  • Gundlegård, D., Rydergren, C., Breyer, N., Rajna, B.: Travel demand estimation and network assignment based on cellular network data. Comput. Commun. 95, 29–42 (2016). https://doi.org/10.1016/j.comcom.2016.04.015

    Article  Google Scholar 

  • Hoteit, S., Chen, G., Viana, A.C., Fiore, M.: Filling the gaps: on the completion of sparse call detail records for mobility analysis. In: Eleventh ACM Workshop on Challenged Networks (2016)

  • Hoteit, S., Secci, S., Sobolevsky, S., Ratti, C., Pujolle, G.: Estimating human trajectories and hotspots through mobile phone data. Comput. Netw. 64, 296–307 (2014)

    Article  Google Scholar 

  • Huang, L., Li, Q., Yue, Y.: Activity identification from GPS trajectories using spatial temporal POIs’ attractiveness. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks—LBSN’10, p. 27. ACM Press, San Jose, California (2010)

  • Huang, Z., Ling, X., Wang, P., Zhang, F., Mao, Y., Lin, T., Wang, F.-Y.: Modeling real-time human mobility based on mobile phone and transportation data fusion. Transp. Res. Part C Emerg. Technol. 96, 251–269 (2018). https://doi.org/10.1016/j.trc.2018.09.016

    Article  Google Scholar 

  • Iqbal, MdS, Choudhury, C.F., Wang, P., González, M.C.: Development of origin–destination matrices using mobile phone call data. Transp. Res. Part C Emerg. Technol. 40, 63–74 (2014). https://doi.org/10.1016/j.trc.2014.01.002

    Article  Google Scholar 

  • Janzen, M., Vanhoof, M., Smoreda, Z., Axhausen, K.W.: Closer to the total? Long-distance travel of French mobile phone users. Travel Behav. Soc. 11, 31–42 (2018). https://doi.org/10.1016/j.tbs.2017.12.001

    Article  Google Scholar 

  • Jiang, S., Ferreira, J., González, M.C.: Activity-based human mobility patterns inferred from mobile phone data: a case study of Singapore. IEEE Trans. Big Data 3, 208–219 (2017)

    Article  Google Scholar 

  • Jiang, S., Fiore, G.A., Yang, Y., Ferreira Jr, J., Frazzoli, E., González, M.C.: A review of urban computing for mobile phone traces: current methods, challenges and opportunities. In: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, p. 9. ACM (2013)

  • Jiang, S., Yang, Y., Gupta, S., Veneziano, D., Athavale, S., González, M.C.: The TimeGeo modeling framework for urban mobility without travel surveys. Proc. Natl. Acad. Sci. 113, E5370–E5378 (2016). https://doi.org/10.1073/pnas.1524261113

    Article  Google Scholar 

  • Mellegard, E., Moritz, S., Zahoor, M.: Origin/destination-estimation using cellular network data. In: 11th International Conference on Data Mining Workshops (ICDMW), pp. 891–896. IEEE (2011)

  • Munizaga, M.A., Palma, C.: Estimation of a disaggregate multimodal public transport origin–destination matrix from passive smartcard data from Santiago, Chile. Transp. Res. Part C Emerg. Technol. 24, 9–18 (2012). https://doi.org/10.1016/j.trc.2012.01.007

    Article  Google Scholar 

  • Naboulsi, D., Fiore, M., Ribot, S., Stanica, R.: Large-scale mobile traffic analysis: a survey. IEEE Commun. Surv. Tutor. 18, 124–161 (2016). https://doi.org/10.1109/COMST.2015.2491361

    Article  Google Scholar 

  • Ni, L., Wang, X. (Cara), Chen, X. (Michael): A spatial econometric model for travel flow analysis and real-world applications with massive mobile phone data. Transp. Res. Part C Emerg. Technol. 86, 510–526 (2018). https://doi.org/10.1016/j.trc.2017.12.002

  • Nitsche, P., Widhalm, P., Breuss, S., Brändle, N., Maurer, P.: Supporting large-scale travel surveys with smartphones—a practical approach. Transp. Res. Part C Emerg. Technol. 43, 212–221 (2014). https://doi.org/10.1016/j.trc.2013.11.005

    Article  Google Scholar 

  • Nour, A., Hellinga, B., Casello, J.: Classification of automobile and transit trips from Smartphone data: enhancing accuracy using spatial statistics and GIS. J. Transp. Geogr. 51, 36–44 (2016). https://doi.org/10.1016/j.jtrangeo.2015.11.005

    Article  Google Scholar 

  • Ortúzar, JdD, Willumsen, L.G.: Modelling Transport. Wiley, New York (2011)

    Book  Google Scholar 

  • Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., Srivastava, M.: Using mobile phones to determine transportation modes. ACM Trans. Sen. Netw. 6, 13:1–13:27 (2010). https://doi.org/10.1145/1689239.1689243

    Article  Google Scholar 

  • Ricciato, F., Widhalm, P., Pantisano, F., Craglia, M.: Beyond the “single-operator, CDR-only” paradigm: an interoperable framework for mobile phone network data analyses and population density estimation. Pervasive Mob. Comput. 35, 65–82 (2016). https://doi.org/10.1016/j.pmcj.2016.04.009

    Article  Google Scholar 

  • Schlaich, J., Otterstätter, T., Friedrich, M.: Generating trajectories from mobile phone data. In: TRB 89th Annual Meeting Compendium of Papers, p. 18. Washington, D.C., USA (2010)

  • Shen, L., Stopher, P.R.: Review of GPS travel survey and GPS data-processing methods. Transp. Rev. 34, 316–334 (2014). https://doi.org/10.1080/01441647.2014.903530

    Article  Google Scholar 

  • Smoreda, Z., Olteanu-Raimond, A.-M., Couronné, T.: Spatiotemporal data from mobile phones for personal mobility assessment. In: Zmud, J., Lee-Gosselin, M., Carrasco, J.A., Munizaga, M.A. (eds.) Transport Survey Methods: Best Practice for Decision Making, pp. 745–768. Emerald Group Publishing Limited, Bingley (2013)

    Chapter  Google Scholar 

  • Stopher, P., FitzGerald, C., Zhang, J.: Search for a global positioning system device to measure person travel. Transp. Res. Part C Emerg. Technol. 16, 350–369 (2008). https://doi.org/10.1016/j.trc.2007.10.002

    Article  Google Scholar 

  • Stopher, P.R., Greaves, S.P.: Household travel surveys: where are we going? Transp. Res. Part A Policy Pract. 41, 367–381 (2007). https://doi.org/10.1016/j.tra.2006.09.005

    Article  Google Scholar 

  • Tettamanti, T., Demeter, H., Varga, I.: Route choice estimation based on cellular signaling data. Acta Polytech. Hung. 9, 207–220 (2012)

    Google Scholar 

  • Tettamanti, T., Istvan, V.: Mobile phone location area based traffic flow estimation in urban road traffic. Adv. Civ. Environ. Eng. 1, 1–15 (2014)

    Google Scholar 

  • Toole, J.L., Colak, S., Sturt, B., Alexander, L.P., Evsukoff, A., González, M.C.: The path most traveled: travel demand estimation using big data resources. Transp. Res. Part C Emerg. Technol. 58, 162–177 (2015). https://doi.org/10.1016/j.trc.2015.04.022

    Article  Google Scholar 

  • Wang, F., Chen, C.: On data processing required to derive mobility patterns from passively-generated mobile phone data. Transp. Res. Part C Emerg. Technol. 87, 58–74 (2018). https://doi.org/10.1016/j.trc.2017.12.003

    Article  Google Scholar 

  • Wang, P., Hunter, T., Bayen, A.M., Schechtner, K., González, M.C.: Understanding road usage patterns in urban areas. Sci. Rep. 2, 1–6 (2012). https://doi.org/10.1038/srep01001

    Article  Google Scholar 

  • Wang, Z., He, S.Y., Leung, Y.: Applying mobile phone data to travel behaviour research: a literature review. Travel Behav. Soc. (2017). https://doi.org/10.1016/j.tbs.2017.02.005

    Article  Google Scholar 

  • White, J., Wells, I.: Extracting origin destination information from mobile phone data. In: Eleventh International Conference on Road Transport Information and Control, 2002. (Conf. Publ. No. 486), pp. 30–34 (2002)

  • Widhalm, P., Nitsche, P., Brändie, N.: Transport mode detection with realistic smartphone sensor data. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 573–576 (2012)

  • Widhalm, P., Yang, Y., Ulm, M., Athavale, S., González, M.C.: Discovering urban activity patterns in cell phone data. Transportation 42, 597–623 (2015). https://doi.org/10.1007/s11116-015-9598-x

    Article  Google Scholar 

  • Wismans, L.J.J., Friso, K., Rijsdijk, J., de Graaf, S.W., Keij, J.: Improving a priori demand estimates transport models using mobile phone data: a rotterdam-region case. J. Urban Technol. 25, 63–83 (2018). https://doi.org/10.1080/10630732.2018.1442075

    Article  Google Scholar 

  • Wolf, J., Oliveira, M., Thompson, M.: Impact of underreporting on mileage and travel time estimates: results from global positioning system-enhanced household travel survey. Transp. Res. Rec. J. Transp. Res. Board 1854, 189–198 (2003). https://doi.org/10.3141/1854-21

    Article  Google Scholar 

  • Wolf, J., Schönfelder, S., Samaga, U., Oliveira, M., Axhausen, K.W.: Eighty weeks of global positioning system traces: approaches to enriching trip information. Transp. Res. Rec. J. Transp. Res. Board 1870, 46–54 (2004). https://doi.org/10.3141/1870-06

    Article  Google Scholar 

  • Xu, Y., Shaw, S.-L., Zhao, Z., Yin, L., Fang, Z., Li, Q.: Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach. Transportation 42, 625–646 (2015). https://doi.org/10.1007/s11116-015-9597-y

    Article  Google Scholar 

  • Zhao, Z., Shaw, S.-L., Xu, Y., Lu, F., Chen, J., Yin, L.: Understanding the bias of call detail records in human mobility research. Int. J. Geogr. Inf. Sci. 30, 1738–1762 (2016). https://doi.org/10.1080/13658816.2015.1137298

    Article  Google Scholar 

Download references

Acknowledgements

MF’s Ph.D. is funded by Orange. The authors would like to thank Orange for giving them access to the mobile phone data collected in the Rhône-Alpes region and Region Auvergne Rhône-Alpes for making the EDR data available. This work has been also supported by the French ANR project, PROMENADE, grant number ANR-18-CE22-0008. The authors are grateful for the comments by the anonymous reviewers that greatly improved the paper. The authors alone are responsible for the contents of this paper.

Author information

Authors and Affiliations

  1. Transportation Research Institute (IMOB), Hasselt University, Wetenschapspark 5 bus 6, 3590, Diepenbeek, Belgium

    Mariem Fekih & Tom Bellemans

  2. SENSE, Orange Labs, 92326, Chatillon Cedex, France

    Mariem Fekih & Zbigniew Smoreda

  3. LAET, ENTPE, Univ. Lyon, 69120, Vaulx-en-Velin, France

    Patrick Bonnel

  4. LICIT, ENTPE, Univ. Lyon, Univ. Gustave Eiffel, 69675, Bron Cedex, France

    Angelo Furno

  5. CIAD, Univ. Bourgogne Franche-Comté, UTBM, 90010, Belfort, France

    Stéphane Galland

Authors
  1. Mariem Fekih
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tom Bellemans
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zbigniew Smoreda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Patrick Bonnel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Angelo Furno
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Stéphane Galland
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MF: Literature search and review, study conception and design, draft manuscript preparation. MF, TB, ZS, PB, AF and SG: Analysis and interpretation of results, manuscript editing and review.

Corresponding author

Correspondence to Mariem Fekih.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fekih, M., Bellemans, T., Smoreda, Z. et al. A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of Lyon region (France). Transportation 48, 1671–1702 (2021). https://doi.org/10.1007/s11116-020-10108-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11116-020-10108-w

Keywords

Search

Navigation