Using Big Data Analytics and Visualization to Create IoT-enabled Science Park Smart Governance Platform

  • Hsiao-Fang Yang
  • Chia-Hou Kay Chen
  • Kuei-Ling Belinda ChenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11589)


Science parks are important industrial clusters in the development of Taiwan’s technology industry. Nearly 280,000 employees commute to the science parks on a daily basis. Thus, traffic congestion not only wastes the time of and creates extra fuel costs for the road users, but also leads the vehicles to release more pollutants in the environment. With the rise of Internet of Things technology, the science park administration has established multiple IoT-enabled systems since 2017, in order to collect data and monitor traffic flow and air quality in a more accurate manner.

However, it is still a question that how emerging technology should be applied to provide accurate and timely information to assist administration to observe the historical trends and current status of traffic congestion and air quality, so as to formulate traffic control and air pollution prevention strategies. To that end, there are two purposes in this paper: (1) to establish a Science Park Smart Governance Platform to collect data collected from the IoT devices, and (2) to design and develop data visualization functions for the smart management of traffic and air quality.

The research garners three results from the smart traffic monitoring service: (1) helping administration check the traffic status in real time, in order to facilitate traffic control; (2) presenting the historical trends of traffic flow on a typical day, month, and year, and allowing administration to understand in what intersections and at what periods traffic congestion is more prone to take place; (3) creating a predictive model of how traffic flow and weather can influence the traffic volume interactively to predict traffic flow for every intersection in the following 10 min, so that administration can operate the traffic lights in order to reduce traffic congestion.

Besides the aforementioned results, three other results from the smart air quality monitoring service are presented in the study: (1) allowing administration to monitor real-time air quality status in various areas of the science parks; (2) presenting a historical trend of air quality, and allowing administration to understand in what month/time air pollution is occurring; (3) when the concentration of certain air pollutant exceeds a particular threshold, the smart environmental monitoring Chabot service will push warning messages to the managers.


Taiwan Science Park IoT Traffic congestion Air quality Science Park Smart Governance Platform Map-based dashboard 



This paper is supported by the Ministry of Science and Technology, the Hsinchu Science Park, the Central Taiwan Science Park, and the Southern Taiwan Science Park.


  1. 1.
  2. 2.
    IDC’s Smart Cities Spending Guide Expands Its Coverage to More Than 100 Cities. Accessed 14 Mar 2019
  3. 3.
    Chen, K.L.B.: Smart park ICT re-engineering initiative. In: 2017 Energy Smart Communities Initiative (ESCI). Accessed 22 Feb 2019
  4. 4.
    Hsinchu Science Park. Accessed 12 Feb 2019
  5. 5.
    Southern Taiwan Science Park. Accessed 12 Feb 2019
  6. 6.
    Central Taiwan Science Park. Accessed 12 Feb 2019
  7. 7.
    Zhang, K., Batterman, S.: Air pollution and health risks due to vehicle traffic. Sci. Total Environ. 450–451, 307–316 (2013)CrossRefGoogle Scholar
  8. 8.
    Electronic Toll Collection (Taiwan). Accessed 16 Feb 2019
  9. 9.
    Nuaimi, E.A., Neyadi, H.A., Mohamed, N., Al-Jaroodi, J.: Applications of big data to smart cities. J. Internet Serv. Appl. 6(1), 1–15 (2015)CrossRefGoogle Scholar
  10. 10.
  11. 11.
    Graves, A., Hendler, J.: Visualization tools for open government data. In: Proceedings of the 14th Annual International Conference on Digital Government Research, pp. 136–145 (2013)Google Scholar
  12. 12.
    Burkhardt, D., Nazemi, K., Parisay, M., Kohlhammer, J.: Visual correlation analysis to explain open government data based on linked-open data for decision making. Int. J. Digit. Soc. 5(3), 947–955 (2014)CrossRefGoogle Scholar
  13. 13.
    Bera, P., Sirois, L.P.: Displaying background maps in business intelligence dashboards. IT Prof. 18(5), 58–65 (2016)CrossRefGoogle Scholar
  14. 14.
    Krishna, C.N., Suneetha, M.: Business intelligence solutions for processing hugedata to the business user’s using dashboards. In: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), Paralakhemundi, India, pp. 1672–1677 (2016)Google Scholar
  15. 15.
    Pokorný, P., Stokláska, K.: Graphics visualization of specific dashboards in transport technologies. In: 2016 Third International Conference on Mathematics and Computers in Sciences and in Industry (MCSI), Chania, Greece, pp. 203–206 (2016)Google Scholar
  16. 16.
    More, R., Goudar, R.H.: DataViz model: a novel approach towards big data analytics and visualization. Int. J. Eng. Manuf. 6(7), 43–49 (2017)Google Scholar
  17. 17.
    Gledson, A., Dhafari, T.B., Paton, N., Keane, J.: A smart city dashboard for combining and analysing multi-source data streams. In: IEEE 16th International Conference on Smart City, Exeter, United Kingdom, pp. 1366–1373 (2018)Google Scholar
  18. 18.
    Science Park Smart Governance Platform. Accessed 13 Feb 2019
  19. 19.
    Mehmood, Y., Ahmad, F., Yaqoob, I., Adnane, A., Imran, M., Guizani, S.: Internet-of-things-based smart cities: recent advances and challenges. IEEE Commun. Mag. 55(9), 16–24 (2017)CrossRefGoogle Scholar
  20. 20.
    Sun, G.D., Wu, Y.C., Liang, R.H., Liu, S.X.: A survey of visual analytics techniques and applications: State-of-the-art research and future challenges. J. Comput. Sci. Technol. 28(5), 852–867 (2013)CrossRefGoogle Scholar
  21. 21.
    Suakanto, S., Supangkat, S.H., Suhardi, Saragih, R.: Smart city dashboard for integrating various data of sensor networks. In: International Conference on ICT for Smart Society, Jakarta, Indonesia, pp. 1–5. (2013)Google Scholar
  22. 22.
    Kitchin, R., Lauriault, T.P., McArdle, G.: Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards. Reg. Stud. Reg. Sci. 2(1), 6–28 (2015)Google Scholar
  23. 23.
    McArdle, G., Kitchin, R.: The dublin dashboard: design and development of a real-time analytical urban dashboard. In: International Society for Photogrammetry and Remote Sensing IV-4/W1, pp. 19–25 (2016)CrossRefGoogle Scholar
  24. 24.
    City Dashboard: London. Accessed 8 Mar 2019
  25. 25.
    Boston Smart City dashboard. Accessed 8 Mar 2019
  26. 26.
    Bandung City Dashboard. Accessed 8 Mar 2019
  27. 27.
    Smart CEI Moncola Dashboard. Accessed 8 Mar 2019
  28. 28.
    DublinDashboard. Accessed 8 Mar 2019
  29. 29.
    Keim, D., Kohlhammer, J., Ellis, G., Mansmann, F.: Mastering the Information Age - Solving Problems with Visual Analytics. Eurographics Association (2010)Google Scholar
  30. 30.
    Wang, X.M., Zhang, T.Y., Ma, Y.X., Xia, J., Chen, W.: A survey of visual analytic pipelines. J. Comput. Sci. Technol. 31(4), 787–804 (2016)CrossRefGoogle Scholar
  31. 31.
    Open Weather Data. Accessed 12 Feb 2019
  32. 32.
    McDonald, J.H.: Handbook of Biological Statistics, 3rd edn. Sparky House Publishing, Baltimore (2014)Google Scholar
  33. 33.
    Science Park Mobile Wizard 2.0 APP, Android. Accessed 14 Feb 2019

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hsiao-Fang Yang
    • 1
  • Chia-Hou Kay Chen
    • 1
  • Kuei-Ling Belinda Chen
    • 1
    Email author
  1. 1.Digital Service Innovation InstituteInstitute for Information IndustryTaipeiTaiwan

Personalised recommendations