Sensing Bluetooth Mobility Data: Potentials and Applications

  • João Filgueiras
  • Rosaldo J. F. Rossetti
  • Zafeiris Kokkinogenis
  • Michel Ferreira
  • Cristina Olaverri-Monreal
  • Marco Paiva
  • João Manuel R. S. Tavares
  • Joaquim Gabriel
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 262)


Information related to mobility dynamics constitutes an important factor to be considered in traffic management to improve the efficiency of existing systems. We present a proof-of-concept deployment of sensors using the Bluetooth technology to detect traffic flow conditions. Besides traditional method consisting of a network of stationary sensors, we present a novel approach that uses sensors deployed in moving vehicles that allows new type studies and captures new insights of mobility. Both approaches complement the most common methods of traffic sensing while being more cost-effective and easily available. Early experimental results show the variety of information available through both approaches spanning from Origin/Destination matrices and travel times to insights into emerging mobile neighborhoods. These metrics are important to improve traffic management increasing the efficiency of urban mobility networks.


Urban mobility data Bluetooth sensing Traffic monitoring 



This project has been partially supported by FCT (Fundação para a Ciência e a Tecnologia), the Portuguese Agency for R&D, under the Bluetooth Sensing Technology project IT/LA/01081/2011 and the grant SFRH/BD/67202/2009.


  1. 1.
    Asmundsdottir, R.: Dynamic od matrix estimation using foating car data. thesis, Delft University of Technology (2008)Google Scholar
  2. 2.
    Barcelo, J., Montero, L., Marques, L., Carmona, C.: Travel time forecasting and dynamic od estimation in freeways based on bluetooth traffic monitoring. Transp. Res. Rec. J. Transp. Res. Board. 2175, 19–27 (2010)CrossRefGoogle Scholar
  3. 3.
    Bullock, D., Haseman, R., Wasson, J., Spitler, R.: Anonymous bluetooth probes for measuring airport security screening passage time: the indianapolis pilot deployment. In: Transportation Research Board 89th Annual Meeting. CDROM. Transportation Research Board, Washington DC (2010)Google Scholar
  4. 4.
    González, M.C., Hidalgo, C.A., Barabási, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRefGoogle Scholar
  5. 5.
    Haghani, A., Hamedi, M., Sadabadi, K., Young, S., Tarnoff, P.: Data collection of freeway travel time ground truth with bluetooth sensors. Transp. Res. Rec. J. Transp. Res. Board 2160, 60–68 (2010)CrossRefGoogle Scholar
  6. 6.
    Haseman, R., Wasson, J., Bullock, D.: Real-time measurement of travel time delay in work zones and evaluation metrics using bluetooth probe tracking. Transp. Res. Rec. J. Transp. Res. Board 2169(1), 40–53 (2010)CrossRefGoogle Scholar
  7. 7.
    Karlsson, N.: Floating car data deployment and traffic advisory services. Bridging the European ITS Business Cooperation with China, 40 (2003)Google Scholar
  8. 8.
    Kostakos, V., Camacho, T., Mantero, C.: Towards proximity-based passenger sensing on public transport buses. Pers. Ubiquit. Comput. 17, 1807–1816 (2013)Google Scholar
  9. 9.
    Kostakos, V., ONeill, E.: Cityware: urban computing to bridge online and real-world social networks. In: Foth, M. (ed.) Handbook of Research on Urban Informatics: The Practice and Promise of the Real-Time City, pp. 195–204. IGI Global (2008)Google Scholar
  10. 10.
    Kostakos, V., O’Neill, E., Penn, A., Roussos, G., Papadongonas, D.: Brief encounters: sensing, modelling and visualizing urban mobility and copresence networks. ACM Trans. Comp. Hum. Interact. 17(1), 1–38 (2010)CrossRefGoogle Scholar
  11. 11.
    Leduc, G.: Road traffic data: collection methods and applications. In: Working Papers on Energy, Transport and Climate Change. pp. 47–67 (2008)Google Scholar
  12. 12.
    Liebig, T., Wagoum, A.U.K.: Modelling microscopic pedestrian mobility using bluetooth. In: 4th International Conference on Agents and Artificial Intelligence. 2, 270–275 (2012)Google Scholar
  13. 13.
    Malinovskiy Y., Wu Y., Wang Y., Lee U.: Field experiments on bluetooth-based travel time data collection. In: Transportation Research Board 89th Annual Meeting. CD-ROM. Transportation Research Board, Washington DC (2010)Google Scholar
  14. 14.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Ann. Rev. Sociol. 27, 415–444 (2001)CrossRefGoogle Scholar
  15. 15.
    ONeill, E., Kostakos, V., Kindberg, T., Schiek, A., Penn, A., Fraser, D., Jones, T.: Instrumenting the city: Developing methods for observing and understanding the digital cityscape. In: Dourish, P., Friday, A. (eds.) 8th International Conference of Ubiquitous Computing (UbiComp 2006). Lecture Notes in Computer Science, vol. 4206, pp. 315–332. Springer, Heidelberg (2006)Google Scholar
  16. 16.
    Pels, M., Barhorst, J., Michels, M., Hobo, R., Barendse, J.: Tracking people using bluetooth: implications of enabling bluetooth discoverable mode. Final report, University of Amsterdam. (2005). Accessed 16 Jan 2014
  17. 17.
    Sharifi, E., Hamedi, M., Haghani, A.:Vehicle detection rate for bluetooth travel time sensors: a case study in Maryland and Delaware. Paper presented at the 91st annual transportation research board meeting, Washington DC, US (2010)Google Scholar
  18. 18.
    Tarnoff, P.J., Bullock, D.M., Young, S.E., Wasson, J., Ganig, N., Sturdevant, J.R.: Continuing evolution of travel time data information collection and processing. In: Transportation Research Board 88th Annual Meeting (2009)Google Scholar
  19. 19.
    Tsubota, T., Bhaskar, A., Chung, E., Billot, R.: Arterial traffic congestion analysis using Bluetooth duration data. In: Australasian Transport Research Forum (ATRF), 34 (2011)Google Scholar
  20. 20.
    Valerio, D., D’Alconzo, A., Ricciato, F., Wiedermann, W.: Exploiting cellular networks for road traffic estimation: A survey and a research roadmap. In: IEEE 69th Vehicular Technology Conference. pp. 1–5. IEEE (2009)Google Scholar
  21. 21.
    Waadt, A., Wang, S., Bruck, G., Jung, P.: Traffic congestion estimation service exploiting mobile assisted positioning schemes in gsm networks. Procedia Earth Planet. Sci. 1(1), 1385–1392 (2009)CrossRefGoogle Scholar
  22. 22.
    Wasson, J.S., Sturdevant, J.R., Bullock, D.M.: Real-time travel time estimates using media access control address matching. ITE J. 78(6), 20–23 (2008)Google Scholar
  23. 23.
    Young, S.: Bluetooth traffic monitoring technology: concepts of operation and deployment guidelines. Accessed Jan 2014
  24. 24.
    Young, S.E.: Bluetooth traffic detectors for use as permanently installed travel time instruments. Technical Report, Maryland State Highway Administration, University of Maryland, College Park (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • João Filgueiras
    • 1
  • Rosaldo J. F. Rossetti
    • 2
  • Zafeiris Kokkinogenis
    • 2
  • Michel Ferreira
    • 3
  • Cristina Olaverri-Monreal
    • 3
  • Marco Paiva
    • 3
  • João Manuel R. S. Tavares
    • 4
  • Joaquim Gabriel
    • 4
  1. 1.Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (INESC-ID)LisbonPortugal
  2. 2.LIACC, DEI, Faculty of EngineeringUniversity of PortoPortoPortugal
  3. 3.Instituto de TelecomunicaçõesUniversity of PortoPortoPortugal
  4. 4.DEMec, Faculty of EngineeringUniversity of PortoPortoPortugal

Personalised recommendations