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Urban Mobility: Mobile Crowdsensing Applications

  • João Simões
  • Rui Gomes
  • Ana AlvesEmail author
  • Jorge Bernardino
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 806)

Abstract

Mobility has become one of the most difficult challenges that cities must face. More than half of world’s population resides in urban areas and with the continuously growing population it is imperative that cities use their resources more efficiently. Obtaining and gathering data from different sources can be extremely important to support new solutions that will help building a better mobility for the citizens. Crowdsensing has become a popular way to share data collected by sensing devices with the goal to achieve a common interest. Data collected by crowdsensing applications can be a promising way to obtain valuable mobility information from each citizen. In this paper, we study the current work on the integrated mobility services exploring the crowdsensing applications that were used to extract and provide valuable mobility data. Also, we analyze the main current techniques used to characterize urban mobility.

Keywords

Urban mobility Ubiquitous systems Mobile crowdsensing 

Notes

Acknowledgements

URBY.Sense is co-financed by COMPETE 2020, Portugal 2020 - Programa Operacional Competitividade e Internacionalização (POCI), FEDER and FCT.

References

  1. 1.
    UN-Habitat, Urbanization and Development: Emerging Futures (2016)Google Scholar
  2. 2.
    Dargay, J., et al.: Vehicle ownership and income growth, worldwide: 1960-2030. Energy J. 28(4), 143–170 (2007)CrossRefGoogle Scholar
  3. 3.
    World Health Organization, World Health statistics 2014 (2014)Google Scholar
  4. 4.
    Becker, R.A., et al.: COMMUNICATIONS human mobility characterization from cellular network data. Commun. ACM 56(1), 74–82 (2013)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Rodrigues, J.G.P., et al.: Opportunistic mobile crowdsensing for gathering mobility information: Lessons learned. In: Proceedings of the IEEE Conference ITSC, no. 978, pp. 1654–1660 (2016)Google Scholar
  7. 7.
    Faye, S., et al.: Characterizing user mobility using mobile sensing systems. Int. J. Distrib. Sens. Networks 13(8), 1–13 (2017)CrossRefGoogle Scholar
  8. 8.
    Stojanovic, D., et al.: Mobile crowd sensing for smart urban mobility. In: Capineri, C., Haklay, M., Huang, H., Antoniou, V., Kettunen, J., Ostermann, F., Purves, R. (eds.) European Handbook of Crowdsourced Geographic Information, pp. 371–382. Ubiquity Press, London (2016)Google Scholar
  9. 9.
    Pereira, F., et al.: The Future Mobility Survey: overview and preliminary evaluation. In: Proceedings of the Eastern Asia Society for Transportation Studies, vol. 9 (2013)Google Scholar
  10. 10.
    Shafique, M.A., Hato, E.: Travel mode detection with varying smartphone data collection frequencies. Sensors (Switzerland) 16(5), 716 (2016)CrossRefGoogle Scholar
  11. 11.
    Zhang, J., et al.: Public sense: refined urban sensing and public facility management with crowdsourced data. In: Proceedings of UIC-ATC-ScalCom, Beijing, pp. 1407–1441(2015)Google Scholar
  12. 12.
    Kimijima, S., Nagai, M.: Human mobility analysis for extracting local interactions under rapid socio-economic transformation in Dawei. Sustain. 9(9), 1598 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • João Simões
    • 1
  • Rui Gomes
    • 1
    • 2
  • Ana Alves
    • 1
    • 2
    Email author
  • Jorge Bernardino
    • 1
    • 2
  1. 1.Polytechnic Institute of Coimbra – ISECCoimbraPortugal
  2. 2.Centre for Informatics and Systems of the University of Coimbra – CISUCCoimbraPortugal

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