Advertisement

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
Chapter
Part of the Service Science: Research and Innovations in the Service Economy book series (SSRI)

Abstract

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 Twitter.com 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.

Keywords

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

Notes

Acknowledgment

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).

References

  1. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.CrossRefGoogle Scholar
  2. Atzori, L., Iera, A., & Morabito, G. (2014). From “smart objects” to “social objects”: The next evolutionary step of the internet of things. IEEE Communications Magazine, 52(1), 97–105.CrossRefGoogle Scholar
  3. Burke, J. A., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., & Srivastava, M. B. (2006). Participatory sensing. Center for Embedded Network Sensing.Google Scholar
  4. CitiBike, (2017). Citibike Services, Mar. 5, 2017 retrieved at https://www.citibikenyc.com/Google Scholar
  5. De Jong, M., Joss, S., Schraven, D., Zhan, C., & Weijnen, M. (2015). Sustainable–smart–resilient-low carbon-eco-knowledge cities; making sense of a multitude of concepts promoting sustainable urbanization. Journal of Cleaner Production, 109, 25–38.CrossRefGoogle Scholar
  6. Deloitte. (2015). The Internet of Things Really is Things, not People, Mar. 5, 2017 retrieved at https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Technology-Media-Telecommunications/gx-tmt-pred15-iot-is-things.pdf.Google Scholar
  7. Garschagen, M., & Romero-Lankao, P. (2015). Exploring the relationships between urbanization trends and climate change vulnerability. Climatic Change, 133(1), 37–52.CrossRefGoogle Scholar
  8. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 29(7), 1645–1660.CrossRefGoogle Scholar
  9. IBM, (2017), IBM SPSS Modeler, Mar. 5, 2017 retrieved at https://www.ibm.com/us-en/marketplace/spss-modelerGoogle Scholar
  10. Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M. (2014). An information framework for creating a smart city through internet of things. IEEE Internet of Things Journal, 1(2), 112–121.CrossRefGoogle Scholar
  11. Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of things: Vision, applications and research challenges. Ad Hoc Networks, 10(7), 1497–1516.CrossRefGoogle Scholar
  12. NYCDE. (2017). New York City daily events, Mar. 5, 2017 retrieved at http://www.nycdailyevents.com/Google Scholar
  13. Optimod’Lyon, (2017). Optimizing Urban Sustainable Mobility, Mar. 5, 2017 retrieved at http://www.optimodlyon.com/.Google Scholar
  14. Paradiso, J., Abler, C., Hsiao, K. Y., & Reynolds, M. (1997). The magic carpet: physical sensing for immersive environments. Extended Abstracts on Human Factors in Computing Systems (pp. 277–278). ACM.Google Scholar
  15. Press, G. (2015). 9 New Predictions and Market Assessments for the Internet of Things (IoT), Mar. 5, 2017 retrieved at http://www.forbes.com/sites/gilpress/2015/07/30/9-new-predictions-and-market-assessments-for-the-internet-of-things-iot.Google Scholar
  16. Qiu, R. G. (2009). Computational thinking of service systems: Dynamics and adaptiveness modeling. Service Science, 1(1), 42–55.CrossRefGoogle Scholar
  17. Qiu, R. G. (2014). Service science: The foundations of service engineering and management. John Wiley & Sons.Google Scholar
  18. Qiu, R.G., Wang, K., Li, S., Dong, J. and Xie, M., 2014, June. Big data technologies in support of real time capturing and understanding of electric vehicle customers dynamics. In Software Engineering and Service Science (ICSESS), 2014 5th IEEE International Conference on (pp. 263-267). IEEE.Google Scholar
  19. Qiu, R. G., Qiu, L., & Badr, Y. (2016). Integrating physical and social sensing to enable smart city mobility services. In the 14th IEEE International Conference on Industrial Informatics (pp. 909–915). IEEE.Google Scholar
  20. Qiu, R., Badr, Y., Wang, J., & Li, S. (2017). Developing a Smart Service System to Enrich Bike Riders' Experience. In the 2ndInternational Conference on Software, Multimedia and Communication Engineering (SMCE), 455–459.Google Scholar
  21. Randall, T., (2015). The Smartest Building in the World, Mar. 5, 2017 retrieved at http://www.bloomberg.com/features/2015-the-edge-the-worlds-greenest-building/.Google Scholar
  22. Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P. (2012). Analytics: The real-world use of big data. IBM Global Business Services, 1–20.Google Scholar
  23. UN-HABITAT. (2017). Habitat III Issue Paper: 21 - Smart Cities. UN-Habitat, Mar. 5, 2017 retrieved at http://unhabitat.org/issue-papers-and-policy-units/.

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

Personalised recommendations