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Big Data Role in Improving Intelligent Transportation Systems Safety: A Survey

  • Mohammed Arif AminEmail author
  • Samah Hadouej
  • Tasneem S. J. Darwish
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 29)

Abstract

Achieving smart and intelligent transportation requires the use of millions of devices which generate a huge volume of data, termed as Big Data. With the flourishing of Big Data analytics, intelligent transportation management and control is now becoming more data driven. Big data can provide ample information obtained from vehicles, traffic infrastructure, smart phones and weather stations. Such data has promising applications in intelligent transportation systems, especially in the road safety sector. In particular, utilizing Big Data analytics assesses accident prevention and detection, thereby reducing causalities, loses and damage. This survey paper explores the role of big data in shaping the intelligent transportation systems with a focus on the road safety sector. In addition, the limitations of existing studies are discussed and future research directions are suggested.

References

  1. 1.
    Beyer, M.A., Laney, D.: The Importance of ‘Big Data’: A Definition, pp. 2014–2018. Gartner, Stamford (2012)Google Scholar
  2. 2.
    Perkins, S.R., Harris, J.I.: Traffic conflict characteristics: accident potential at intersections. Research Laboratories, General Motors Corporation, Warren (1967)Google Scholar
  3. 3.
    Savolainen, P.T., Mannering, F.L., Lord, D., Quddus, M.A.: Accid. Anal. Prev. 43(5), 1666 (2011)CrossRefGoogle Scholar
  4. 4.
    Akagi, Y., Raksincharoensak, P.: IEEE Intelligent Vehicles Symposium (IV), pp. 368–373 (2015)Google Scholar
  5. 5.
    Al Najada, H., Mahgoub, I.: IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2016)Google Scholar
  6. 6.
    Al Najada, H., Mahgoub, I.: IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 1–6. IEEE (2016)Google Scholar
  7. 7.
    Amadeo, M., Campolo, C., Molinaro, A.: IEEE Commun. Mag. 54(2), 98 (2016)CrossRefGoogle Scholar
  8. 8.
    Turner, S.: United States. Federal Highway Administration. Office of Highway Information Management, Texas Transportation Institute: Travel Time Data Collection Handbook. Office of Highway Information Management, Federal Highway Administration, U.S. Department of Transportation (1998). https://books.google.ae/books?id=XIDbAQAACAAJ
  9. 9.
    Grant, M., Bowen, B., Day, M., Winick, R., Bauer, J., Chavis, A., Trainor, S.: Congestion management process: a guidebook. Technical report, Transportation Research Board (2011)Google Scholar
  10. 10.
    Abdel-Aty, M., Pande, A.: 83rd Annual Meeting of the Transportation Research Board, Washington, DC (2004)Google Scholar
  11. 11.
    Lee, C., Saccomanno, F., Hellinga, B.: Transp. Res. Rec. J. Transp. Res. Board 1784(1), 1 (2002)CrossRefGoogle Scholar
  12. 12.
    Lee, C., Hellinga, B., Saccomanno, F.: Real-time crash prediction model for application to crash prevention in freeway traffic. Transp. Res. Rec. J. Transp. Res. Board 1840, 67–77 (2003)CrossRefGoogle Scholar
  13. 13.
    Raksincharoensak, P., Akamatsu, Y., Moro, K., Nagai, M.: IFAC Proc. 46(21), 335 (2013)CrossRefGoogle Scholar
  14. 14.
    Wolf, M.T., Burdick, J.W.: IEEE International Conference on Robotics and Automation, pp. 3731–3736 (2008)Google Scholar
  15. 15.
    Bauer, E., Lotz, F., Pfromm, M., Schreier, M., Abendroth, B., Cieler, S., Eckert, A., Hohm, A., Lüke, S., Rieth, P., Willert, V., Adamy, J.: PRORETA 3: an integrated approach to collision avoidance and vehicle automation, vol. 60 (2012). https://www.degruyter.com/view/j/auto.2012.60.issue-12/auto.2012.1046/auto.2012.1046.xmlCrossRefGoogle Scholar
  16. 16.
    Aoude, G.S., Desaraju, V.R., Stephens, L.H., How, J.P.: IEEE Trans. Intell. Transp. Syst. 13(2), 724 (2012)CrossRefGoogle Scholar
  17. 17.
    Broggi, A., Cattani, S., Patander, M., Sabbatelli, M., Zani, P.: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC (2013)Google Scholar
  18. 18.
    Shi, Q., Abdel-Aty, M.: Transp. Res. Part C Emerg. Technol. 58, 380 (2015)CrossRefGoogle Scholar
  19. 19.
    Freiberger, A., Izhaky, D., Shamir, A., Steinberg, O., Tamir, A.: System and method for classifying and identifying a driver using driving performance data. US Patent 14/049,837 (2013)Google Scholar
  20. 20.
    Musicant, O., Bar-Gera, H., Schechtman, E.: Accid. Anal. Prev. 70, 55 (2014)CrossRefGoogle Scholar
  21. 21.
    Kumar, S., Toshniwal, D.: J. Big Data 2(1), 26 (2015)CrossRefGoogle Scholar
  22. 22.
    Yokoyama, D., Toyoda, M.: IEEE International Conference on Big Data (Big Data), pp. 2877–2879. IEEE (2015)Google Scholar
  23. 23.
    Ozbayoglu, M., Kucukayan, G., Dogdu, E.: IEEE International Conference on Big Data (Big Data), pp. 1807–1813. IEEE (2016)Google Scholar
  24. 24.
    Park, S.h., Kim, S.m., Ha, Y.g.: J. Supercomp. 72(7), 2815 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohammed Arif Amin
    • 1
    Email author
  • Samah Hadouej
    • 1
  • Tasneem S. J. Darwish
    • 2
  1. 1.Higher Colleges of TechnologyAbu DhabiUAE
  2. 2.Universiti Teknologi Malaysia (UTM)Johor BahruMalaysia

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