Transportation Systems pp 73-91 | Cite as
Big Data Analytics for Maintaining Transportation Systems
- 1 Citations
- 397 Downloads
Abstract
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.
Keywords
Big data analytics Transportation system Maintenance Railway Road Aviation ShippingReferences
- 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
- 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
- Davenport, T. (2014). Big data at work: Dispelling the myths, uncovering the opportunities. Harvard Business Review Press.Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Mirabadi, A., & Sharifian, S. (2010). Application of association rules in Iranian Railways (RAI) accident data analysis. Safety Science, 48(10), 1427–1435.CrossRefGoogle Scholar
- NIST 1500-1. (2015). NIST big data interoperability framework: Volume 1, definitions. Available at https://dx.doi.org/10.6028/NIST.SP.1500-1.
- OCMIF. (2018). Available at https://www.ocimf.org/sire/about-sire/.
- 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
- RightShip. (2018). Available at https://site.rightship.com/about-rightship/insights/#how-accurate-is-our-predictive-rating.
- 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
- 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
- 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
- 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
- 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
- Takikawa, M. (2016). Innovation in railway maintenance utilizing information and communication technology (Smart Maintenance Initiative). Communication Technology, 22–35.Google Scholar
- Thaduri, A., Galar, D., & Kumar, U. (2015). Railway assets: A potential domain for big data analytics. Procedia Computer Science, 53, 457–467.CrossRefGoogle Scholar
- 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
- Years, A. F. F. (2013). Years 2013–2033 (p. 1). Federal Aviation Administration.Google Scholar
- 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
- 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
- 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
- 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