Self-Sustainable Intelligent Transportation System

  • Authors

    • Mohammed Morad Anad
    • Mohammed Ahmed Subhi
    • Mohammed Abdulameer Mohammed
  • Self-Sustainable, Intelligent, Transportation System
  • Intelligent Transportation Systems (ITS) today have a significant impact on community's well-being and satisfaction. It coordinates traffic movement and manages the capacity of highways and freeways by ultimately minimizing congestions and travel times. The amount of traffic data generated from these systems is increasing dramatically. This creates new challenges for data transmission, storage, and retrieval. Existing big-data solutions addresses such issues and provides real-time services of processing, storing and retrieving the data. Many technologies have emerged to make the best use of big-data solutions in combination with cloud computing technologies. Integrating these technologies within the ITS is a key objective of this research in addition to other objectives including maintaining secure transmission to preserve data integrity and to guarantee self-sustainability for autonomous error and failure recovery. The framework of the proposed module includes a multi-stage approach. The first stage is data acquirement from real-time sensors or monitoring devices such as traffic cameras. The second stage is to develop a pre-processing algorithm that process the acquired data and convert it to a proper format for cloud storage and transmission. The final stage is represented by cloud operations and services which include big-data analytics that ultimately delivers valuable information to the system which can manage or predict traffic congestions and queues. Inevitably, the system is put into testing stage to evaluate the results and how it conforms to the objectives of this research.


  • References

    1. [1] Amini, Sasan, Ilias Gerostathopoulos, and Christian Prehofer. "Big data analytics architecture for real-time traffic control." Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2017 5th IEEE International Conference on. IEEE, 2017.

      [2] Lv, Yisheng, et al. "Traffic flow prediction with big data: a deep learning approach." IEEE Transactions on Intelligent Transportation Systems 16.2 (2015): 865-873.

      [3] Kitchin, Rob. "The real-time city? Big data and smart urbanism." GeoJournal 79.1 (2014): 1-14.

      [4] Shakeel PM, Baskar S, Dhulipala VS, Mishra S, Jaber MM., “Maintaining security and privacy in health care system using learning based Deep-Q-Networksâ€, Journal of medical systems, 2018 Oct 1;42(10):186.

      [5] Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86-94.

      [6] Dobre, C., & Xhafa, F. (2014). Intelligent services for big data science. Future Generation Computer Systems, 37, 267-281.

      [7] Sridhar KP, Baskar S, Shakeel PM, Dhulipala VS., “Developing brain abnormality recognize system using multi-objective pattern producing neural networkâ€, Journal of Ambient Intelligence and Humanized Computing, 2018:1-9.

      [8] Amazon, 2013. Amazon Web Services. [Online], Available at [Accessed 25 August 2017].

      [9] Belson, K., 2010. The New York Times. [Online] Available at: [Accessed 15 August 2017].

      [10] Crowbar, D. C., 2013. Dell. [Online] Available at: [Accessed 25 August 2017].

      [11] Google, 2014. Google BigQuery. [Online] Available at: [Accessed 24 August 2017].

      [12] Shakeel PM. Neural Networks Based Prediction Of Wind Energy Using Pitch Angle Control. International Journal of Innovations in Scientific and Engineering Research (IJISER). 2014;1(1):33-7.

      [13] IBM, 2013. A Smarter Planet. [Online] Available at: [Accessed 24 August 2017].

      [14] IBM, 2013. IBM Traffic Prediction Tool. [Online] Available at: [Accessed 24 August 2017].

      [15] Ji, C. et al., 2012 . Big Data Processing in Cloud Computing Environments. International Symposium on Pervasive Systems, Algorithms and Networks.

      [16] Jorgensen, A. et al., 2014. Microsoft Big Data Solutions. s.l.:John Wiley & Sons, Inc.

      [17] Kher, S., Tokekar, S. & Chande, P., 2002. Self Sustaining Traffic Management System and its Compartmental Modeling. Singapore, The IEEE Fifth International Conference on Intelligent Transportation Systems.

      [18] P. Mohamed Shakeel; Tarek E. El. Tobely; Haytham Al-Feel; Gunasekaran Manogaran; S. Baskar., “Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensorâ€, IEEE Access, 2019, Page(s): 1

      [19] Whirr, A., 2013. Apache Whirr. [Online] Available at: [Accessed 20 August 2017].

      [20] Zhang, X. et al., 2007. A Novel Real-time Traffic Information System Based on Wireless Mesh. Seatle, IEEE Intelligent Transportation Systems Conference.

      [21] Wang, C., Li, X., Zhou, X., Wang, A., & Nedjah, N. (2016). Soft computing in big data intelligent transportation systems. Applied Soft Computing, 38, 1099-1108.

      [22] Hsu, C. Y., Yang, C. S., Yu, L. C., Lin, C. F., Yao, H. H., Chen, D. Y., ... & Chang, P. C. (2015). Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system. International Journal of Production Economics, 164, 454-461.

      [23] Shakeel PM, Baskar S, Dhulipala VS, Jaber MM., “Cloud based framework for diagnosis of diabetes mellitus using K-means clusteringâ€, Health information science and systems, 2018 Dec 1;6(1):16.

      [24] Hurwitz, J., Nugent, A. & Halper, D. F., 2013. Big Data for Dummies. s.l.:John Wiley & Sons.

      [25] Turner, S., 2001. Guidelines for Developing ITS Data Archiving Systems. Texas: Texas Transportation Institute.

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  • How to Cite

    Morad Anad, M., Ahmed Subhi, M., & Abdulameer Mohammed, M. (2018). Self-Sustainable Intelligent Transportation System. International Journal of Engineering & Technology, 7(3.20), 759-763.