Big Data Analytics for Maintaining Transportation Systems

  • Ravdeep KourEmail author
  • Adithya Thaduri
  • Sarbjeet Singh
  • Alberto Martinetti
Part of the Asset Analytics book series (ASAN)


Big Data Analytics (BDA) is becoming a research focus in transportation systems, which can be seen from many projects within the world. By using sensor and Internet of Things (IoT) technology in transportation system, huge amount of data is been generated from different sources. This data can be integrated, analyzed and visualized for efficient and effective decision-making for maintaining transportation systems. The key challenges that exist in managing Big Data are the designing of the systems, which would be able to handle huge amount of data efficiently and effectively and to filter the most significant information from all the collected data. This chapter will draw attention towards the present scenario and future projections of big data in transportation systems. It also presents big data tools and techniques and then presents one brief case study of BDA in each type of transportation system. In this chapter, a broad overview of Big Data definitions, its history, present, and future prospects are briefed. Several tools and technologies especially for transportation are pointed out for maintaining transportation systems. At the end of the chapter, a definitive case studies on each transportation area is demonstrated.


Big data analytics Transportation system Maintenance Railway Road Aviation Shipping 


  1. Bearfield, G., Holloway, A., & Marsh, W. (2013). Change and safety: Decision-making from data. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 227(6), 704–714.Google Scholar
  2. Chong, K., & Sung, H. (2015, October). Prediction of road safety using road/traffic big data. In The International Conference on Semantic Web Business and Innovation (SWBI2015) (p. 23).Google Scholar
  3. Davenport, T. (2014). Big data at work: Dispelling the myths, uncovering the opportunities. Harvard Business Review Press.Google Scholar
  4. Dongmei, H., & Du Yanling, H. Q. (2014). Migration algorithm for big marine data in hybrid cloud storage. Journal of Computer Research and Development, 1(1), 199–205.Google Scholar
  5. Ghofrani, F., He, Q., Rob Goverde, M. P., & Liu, X. (2018). Recent applications of big data analytics in railway transportation systems: A survey. Transportation Research Part C, 90, 226–246.Google Scholar
  6. Ghomi, H., Bagheri, M., Fu, L., & Miranda-Moreno, L. F. (2016). Analysing injury severity factors at highway railway grade crossing accidents involving vulnerable road users: A comparative study. Traffic Injury Prevention.Google Scholar
  7. Giben, X., Patel, V. M., & Chellappa, R. (2015, June). Material classification and semantic segmentation of railway track images with deep convolutional neural networks. In Proceedings—International Conference on Image Processing, ICIP (pp. 621–625).Google Scholar
  8. Hu, C., & Liu, X. (2016). Modeling track geometry degradation using support vector machine technique. In 2016 Joint Rail Conference (p. V001T01A011). American Society of Mechanical Engineers.Google Scholar
  9. Huang, D., Zhao, D., Wei, L., Wang, Z., & Du, Y. (2015). Modeling and analysis in marine big data: Advances and challenges. Mathematical Problems in Engineering.Google Scholar
  10. Hughes, P., Van Gulijk, C., & Figueres-Esteban, M. (2015, June). Learning from text-based close call data. In Proceedings of the 25th European Safety and Reliability Conference, ESREL 2015, 7353 (p. 8).Google Scholar
  11. Jamshidi, A., Faghih-Roohi, S., Hajizadeh, S., Núñez, A., Babuska, R., Dollevoet, R., & De Schutter, B. (2017). A big data analysis approach for rail failure risk assessment. Risk Analysis, 37(8).Google Scholar
  12. Katal, A., Wazid, M., & Goudar, R. H. (2013, August). Big data: Issues, challenges, tools and good practices. In 2013 Sixth International Conference on Contemporary Computing (IC3) (pp. 404–409). IEEE.Google Scholar
  13. Li, S., Yang, Y., Yang, L., Su, H., Zhang, G., & Wang, J. (2017). Civil aircraft big data platform. In 2017 IEEE 11th International Conference on Semantic Computing (ICSC) (pp. 328–333). IEEE Computer Society.Google Scholar
  14. Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F. Y. (2015). Traffic flow prediction with big data: A deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2), 865–873.Google Scholar
  15. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.Google Scholar
  16. Mirabadi, A., & Sharifian, S. (2010). Application of association rules in Iranian Railways (RAI) accident data analysis. Safety Science, 48(10), 1427–1435.CrossRefGoogle Scholar
  17. NIST 1500-1. (2015). NIST big data interoperability framework: Volume 1, definitions. Available at
  18. OCMIF. (2018). Available at
  19. Parkinson, H. J., & Bamford, G. (2016, April). The potential for using big data analytics to predict safety risks by analyzing rail accidents. In 3rd International Conference on Railway Technology: Research, Development and Maintenance (pp. 5–8). Cagliari, Sardinia, Italy.Google Scholar
  20. Sammouri, W., Come, E., Oukhellou, L., Aknin, P., & Fonlladosa, C.-E. (2013, October). Floating train data systems for preventive maintenance: A data mining approach. In Proceedings of 2013 International Conference on Industrial Engineering and Systems Management (IESM) (pp. 1–7).Google Scholar
  21. Shao, F., Li, K., & Xu, X. (2016). Railway accidents analysis based on the improved algorithm of the maximal information coefficient. Intelligent Data Analysis, 20(3), 597–613.CrossRefGoogle Scholar
  22. Sharma, S., Cui, Y., He, Q., Mohammadi, R., & Li, Z. (2018). Data-driven optimization of railway maintenance for track geometry. Transportation Research Part C, 90, 34–58.CrossRefGoogle Scholar
  23. Stratman, B., Liu, Y., & Mahadevan, S. (2007). Structural health monitoring of railroad wheels using wheel impact load detectors. Journal of Failure Analysis and Prevention, 7(3), 218–225.CrossRefGoogle Scholar
  24. Su, Z., Nunez, A., Baldi, S., & De Schutter, B. (2016). Model predictive control for rail condition-based maintenance: A multilevel approach. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) (Vol. 19, pp. 354–359).Google Scholar
  25. Takikawa, M. (2016). Innovation in railway maintenance utilizing information and communication technology (Smart Maintenance Initiative). Communication Technology, 22–35.Google Scholar
  26. Thaduri, A., Galar, D., & Kumar, U. (2015). Railway assets: A potential domain for big data analytics. Procedia Computer Science, 53, 457–467.CrossRefGoogle Scholar
  27. Xiong, G., Zhu, F., Fan, H., Dong, X., Kang, W., & Teng, T. (2014, October). Novel ITS based on space-air-ground collected big-data. In 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC) (pp. 1509–1514). IEEE.Google Scholar
  28. Years, A. F. F. (2013). Years 2013–2033 (p. 1). Federal Aviation Administration.Google Scholar
  29. Yilboga, H., Eker, Ö. F., Güçlü, A., & Camci, F. (2010). Failure prediction on railway turnouts using time delay neural networks. In CIMSA 2010—IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Proceedings (pp. 134–137).Google Scholar
  30. Yin, J., & Zhao, W. (2016). Fault diagnosis network design for vehicle on-board equipments of high-speed railway: A deep learning approach. Engineering Applications of Artificial Intelligence, 56(October), 250–259.CrossRefGoogle Scholar
  31. Yu, X., Starke, M. R., Tolbert, L. M., & Ozpineci, B. (2007). Fuel cell power conditioning for electric power applications: A summary. IET Electric Power Applications, 1(5), 643–656.CrossRefGoogle Scholar
  32. Zaman, I., Pazouki, K., Norman, R., Younessi, S., & Coleman, S. (2017). Challenges and opportunities of big data analytics for upcoming regulations and future transformation of the shipping industry. Procedia Engineering, 194, 537–544.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ravdeep Kour
    • 1
    Email author
  • Adithya Thaduri
    • 1
  • Sarbjeet Singh
    • 1
    • 2
  • Alberto Martinetti
    • 3
  1. 1.Division of Operation and MaintenanceLuleå University of TechnologyLuleåSweden
  2. 2.Mechanical Engineering DepartmentGovernment College of Engineering and Technology, JammuJammuIndia
  3. 3.Maintenance Engineering Group, Design, Production and Management DepartmentUniversity of TwenteEnschedeThe Netherlands

Personalised recommendations