Leveraging Big Data Platform Technologies and Analytics to Enhance Smart City Mobility Services

  • Robin G. QiuEmail author
  • Tianhai Zu
  • Ying Qian
  • Lawrence Qiu
  • Youakim Badr
Part of the Service Science: Research and Innovations in the Service Economy book series (SSRI)


The Internet of Things (IoT) allows objects to be sensed and managed over networks, creating opportunities for beneficial interactions and integration between the physical world, computer-based systems, and human beings. The recently enabled people-centric sensing or social sensing transforms how we sense and interact with the world. For instance, social sensing via mobile apps complements physical sensing (e.g., IoT) by substantially extending the horizon we know about our living communities and environments in real time. This chapter presents how we can integrate physical and social sensing to enable better and smarter services in great detail. With the support of big data technologies, we use city mobility services to demonstrate the great potential of the proposed data integration and aggregation. Specifically, real time data from Citi Bike and are collected, processed, and modelled. The developed prototype in support of city mobility management and operations shows numerous potential benefits of the proposed digital ecosystem platform.


Smart service systems Smart service modeling Smart city Smart mobility service Internet of Things (IoT) Data analytics Machine learning Bike sharing 



This work was done with great support and help from the Big Data Lab at Penn State. The project of Big Data Platform (Massive Data) for Proactive Analyses of Behaviors of Users in Urban Worlds is financially supported by the Rhône-Alpes Region, France (CMIRA2015/15.005426). This project was also partially supported by IBM Faculty Awards (RDP-Qiu2016: IBM784769020—Data Analytics in support of City’s Smart and Green Mobility Services, RDP-Qiu2017: IBM2305939850—Temporospatial Analytics to Enable Smarter City Mobility Services).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Robin G. Qiu
    • 1
    Email author
  • Tianhai Zu
    • 1
  • Ying Qian
    • 1
  • Lawrence Qiu
    • 2
  • Youakim Badr
    • 1
    • 3
  1. 1.Engineering Division, Big Data LabPenn State UniversityMalvernUSA
  2. 2.School of EE & CSPenn State UniversityUniversity ParkUSA
  3. 3.University of Lyon, CNRS, INSA-Lyon, LIRIS, UMR5205LyonFrance

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