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Volume 7 Issue 3
Apr.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(3): 627-637
Mohammadhossein Ghahramani, MengChu Zhou and Gang Wang, "Urban Sensing Based on Mobile Phone Data: Approaches, Applications, and Challenges," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 627-637, May 2020. doi: 10.1109/JAS.2020.1003120
Citation: Mohammadhossein Ghahramani, MengChu Zhou and Gang Wang, "Urban Sensing Based on Mobile Phone Data: Approaches, Applications, and Challenges," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 627-637, May 2020. doi: 10.1109/JAS.2020.1003120
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Urban Sensing Based on Mobile Phone Data: Approaches, Applications, and Challenges

doi: 10.1109/JAS.2020.1003120
Funds:  This work was supported by Fundo para o Desenvolvimento das Ciencias e da Tecnologia (FDCT) (119/2014/A3)
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    Abstract
  • Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications. The ability to accurately and extensively monitor and analyze these data is necessary. Much concern in cellular data analysis is related to human beings and their behaviours. Due to the potential value that lies behind these massive data, there have been different proposed approaches for understanding corresponding patterns. To that end, analyzing people’s activities, e.g., counting them at fixed locations and tracking them by generating origin-destination matrices is crucial. The former can be used to determine the utilization of assets like roads and city attractions. The latter is valuable when planning transport infrastructure. Such insights allow a government to predict the adoption of new roads, new public transport routes, modification of existing infrastructure, and detection of congestion zones, resulting in more efficient designs and improvement. Smartphone data exploration can help research in various fields, e.g., urban planning, transportation, health care, and business marketing. It can also help organizations in decision making, policy implementation, monitoring, and evaluation at all levels. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data. We classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.

     

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