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The Contribution of Open Big Data Sources and Analytics Tools to Sustainable Urban Mobility

  • Samaras-Kamilarakis Stavros
  • Vogiatzakis Petros-AngelosEmail author
  • Eftihia Nathanail
  • Lambros Mitropoulos
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 879)

Abstract

Sustainable urban mobility is one of the top priorities in European Union and worldwide, as there is an intense tendency of population density increase in urban areas, which results in traffic, economic, environmental and societal impacts. To allocate smart solutions and address successfully urban mobility, communities need to build awareness and knowledge on the demand for people’s mobility and goods transportation, as well as to develop appropriate tools to manage and assess transportation system performance. The above, raise the necessity of data availability. In the era of rapid technological development and endless production of data, electronic devices, including smartphones, personal computers, autonomous vehicles, GPS (Global Positioning System), SDR (Software-defined radio) devices and Bluetooth, have become sources of big data. Urban mobility is a sector that could benefit from using big data by understanding, analyzing and processing data to manage traffic, predict demand, affect travelers’ choices and assess level of service.

The purpose of this paper is to identify and review available open big data sources, big data tools and transport related applications in European and international transport platforms. Collected information is used to formulate a roadmap of available and open big data sources, open big data processing tools and applications which aim at improving urban mobility.

Keywords

Open big data sources Sustainable urban mobility Data processing Prediction Analytics tools 

References

  1. 1.
    de Mauro, A., Grimaldi, M., Greco, M.: A formal definition of big data based on its essential features. Libr. Rev. 65(3), 122–135 (2016)CrossRefGoogle Scholar
  2. 2.
    Ejaz, A., Ibrar, Y., Ibrahim Abaker, T., Imran, K., Abdelmuttlib Ibrahim, A., Muhammad, I., Vasilakos, A.V.: The role of big data analytics in Internet of Things. Comput. Netw. 129, 459–471 (2017). part 2CrossRefGoogle Scholar
  3. 3.
    European Commission Homepage (n.d.). European Commission Homepage: https://ec.europa.eu
  4. 4.
    Scott, J.: A Matter of Record. University of Cambridge Press, Cambridge (1990)Google Scholar
  5. 5.
    De Gennaro, M., Paffumi, E., Martini, G.: big data for supporting low-carbon road transport policies in Europe: applications, challenges and opportunities. Big Data Res. 6, 11–25 (2016)CrossRefGoogle Scholar
  6. 6.
    Babar, M., Arif, F.: Smart urban planning using Big Data analytics to contend with the interoperability in Internet of Things. Futur. Gener. Comput. Syst. 77, 65–76 (2017)CrossRefGoogle Scholar
  7. 7.
    Rathore, M.M., Paul, A., Hong, W.-H., Seo, H., Awan, I., Saeed, S.: Exploiting IoT and big data analytics: Defining Smart Digital City using real-time urban data. Sustain. Cities Soc. (2017)Google Scholar
  8. 8.
    Nathali Sylva, B., Khan, M., Han, K.: Integration of Big Data analytics embedded smart city architecture with RESTful web of things for efficient service provision and energy management. Futur. Gener. Comput. Syst. (2017)Google Scholar
  9. 9.
    Paul, A., Rathore, M.M., Ahmad, A., Chilamkurthi, N., Hong, W.-H., Seo, H.: Real-time secure communication for Smart City in high-speed Big Data environment. Futur. Gener. Comput. Syst. (2017)Google Scholar
  10. 10.
    Suma, S., Mehmood, R., Albugami, N., Katib, I., Albeshri, A.: Enabling next generation logistics and planning for smarter societies. Procedia Comput. Sci. 109C, 1122–1127 (2017)CrossRefGoogle Scholar
  11. 11.
    Mehmood, R., Graham, G.: Big data logistics: a health-care transport capacity sharing model. Procedia Comput. Sci. 64, 1107–1114 (2015)CrossRefGoogle Scholar
  12. 12.
    Zhong, R.Y., Huang, G.Q., Lan, S., Dai, Q.Y., Xu, C., Zhang, T.: A big data approach for logistics trajectory discovery from RFID-enabled production data. Int. J. Prod. Econ. 165, 260–272 (2015)CrossRefGoogle Scholar
  13. 13.
    Kaur, H., Prakash Singh, S.: Heuristic modeling for sustainable procurement and logistics in a supply chain using big data. Comput. Oper. Res., 1–21 (2017)Google Scholar
  14. 14.
    Zuojian, Z., Wanchun, D., Guochao, J., Chunhua, H., Xiaolong, X., Xiaotong, W., Jingui, P.: A method for real-time trajectory monitoring to improve taxi service using GPS big data. Inf. Manag. 53, 964–977 (2016)CrossRefGoogle Scholar
  15. 15.
    Li, W., Shuo, G., Chen, W., Ying, J., Mingrui, M., Lei, Y.: Big data and urban system model - Substitutes or complements A case study of modelling commuting patterns in Beijing. Comput. Environ. Urban Syst. 68, 64–77 (2018)CrossRefGoogle Scholar
  16. 16.
    Ankit, S., Deepak, J., Ishant, M., Jishnu, M., Saurabh, A.: Application of big data in supply chain management. Mater. Today Proc. 4, 1106–1115 (2017)CrossRefGoogle Scholar
  17. 17.
    Adithya, T., Diego, G., Uday, K.: Railway assets: a potential domain for big data analytics. Procedia Comput. Sci. 53, 457–467 (2015)CrossRefGoogle Scholar
  18. 18.
    Bao Rong, C., Hsiu-Fen, T., Po-Hao, L.: Applying intelligent data traffic adaptation to high-performance multiple big data analytics platforms. Comput. Electr. Eng., 1–21 (2017)Google Scholar
  19. 19.
    Liye, Z., Qiang, M., Tien Fang, F.: Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters. Transp. Res. Part E (2017)Google Scholar
  20. 20.
    Qi, S., Mohamed, A.-A.: Big data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Transp. Res. Part C 58, 380–394 (2015)CrossRefGoogle Scholar
  21. 21.
    Yingjie, X., Jinlong, C., Xindai, L., Chunhui, W., Chao, X.: Big traffic dataprocessing framework for intelligent monitoring and recording systems. Neurocomputing 181, 139–146 (2016)CrossRefGoogle Scholar
  22. 22.
    Jameson, T.L., Serdar, C., Bradley, S., Lauren, A.P., Alexandre, E., Marta, G.C.: The path most traveled: travel demand estimation using big data resources. Transp. Res. Part C 58, 162–177 (2015)CrossRefGoogle Scholar
  23. 23.
    Chao, W., Xi, L., Xuehai, Z., Aili, W., Nadia, N.: Soft computing in big data intelligent transportation systems. Appl. Soft Comput. 38, 1099–1108 (2016)CrossRefGoogle Scholar
  24. 24.
    Jiang, Z., Yu, S., Zhou, M., Chen, Y., Liu, Y.: Model study for intelligent transportation system with big data. Procedia Comput. Sci. 107, 418–426 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Samaras-Kamilarakis Stavros
    • 1
  • Vogiatzakis Petros-Angelos
    • 1
    Email author
  • Eftihia Nathanail
    • 1
  • Lambros Mitropoulos
    • 1
  1. 1.Department of Civil EngineeringUniversity of ThessalyVolosGreece

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