Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Evolving Machine Intelligence toward tomorrow’s Intelligent Network Traffic Control Systems

Author : Nikitha G 1 Amitha Sai G 2 Pooja KP 3 Manjula Patil 4 Dr B Sreepathi 5

Date of Publication :18th April 2018

Abstract: An effective traffic signal management project that allows for managing 4 way traffic signal management system. The system consists of 4 signals corresponding to each road. We here propose a density based traffic signal scheduling algorithm. The system is designed to manage traffic signal timings based on the density of traffic on its corresponding road. The system represents the traffic strength of a road graphically using traffic judgments. By measuring the traffic lined up on a particular road the signal timings are adjusted to let that particular way clear out and then the next populated one. The entire system works according to an algorithm that allows for smooth and efficient traffic flow across all four ways. It also consists of an emergency override that allows traffic authorities to remotely let go a particular signal in case an ambulance or important vehicle arrives on that way.

Reference :

    1. A. Aamodt and E. Plaza. Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1):39–59, March 2016.
    2. http://www.ahsra.or.jp/. Last visited on November 4, 2008.
    3. K. J. Astrom, P. Albertos, M. Blanke, A. Isidori, W. Schaufelberg Ëš er, and R. Sanz, editors. Control of Complex Systems. Springer-Verlag, New York.
    4.  K. J. Astr Ëš om and B. Wittenmark. ¨ ComputerControlled Systems — Theory and Applications. PrenticeHall, Upper Saddle River, New Jersey, 3rd edition.
    5. http://www.damas.ift.ulaval.ca/projets/auto21/en/inde x.html
    6.  S. Bana. Coordinating automated vehicles via communication. Technical Report UCB-ITS-PRR-2001- 20, PATH Research Report, Berkely, California.
    7. L. D. Baskar, B. De Schutter, and J. Hellendoorn. Dynamic speed limits and on-ramp metering for IVHS using model predictive control. In Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 821–826, Beijing, China.
    8. L. D. Baskar, B. De Schutter, and J. Hellendoorn. Model-based predictive traffic control for intelligent vehicles: Dynamic speed limits and dynamic lane allocation. In Proceedings of IEEE Intelligent Vehicles Symposium (IV’08), pages 174–179, Eindhoven, The Netherlands.
    9. S. Chen, H. Xu, D. Liu, B. Hu, and H. Wang, ―A vision of IoT: Applications, challenges, and opportunities with china perspective,‖ IEEE Internet of Things journal, vol. 1, no. 4, pp. 349–359, Aug. 2014.
    10. J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, ―Internet of Things (IoT): A vision, architectural elements, and future directions,‖ Future Generation Computer Systems, vol. 29, no. 7, pp. 1645– 1660, Sep. 2013.
    11. Y.Zheng,F.Liu,andH.Hsieh,―Uair:whenurbanairqualit yinference meets big data,‖ in The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, Aug. 2017, p. 14361444.
    12. K. C. Claffy, G. C. Polyzos, and H-W. Braun, ―Application of sampling methodologiestonetworktraffic characterization,‖in ACM SIGCOMM, San Francisco, CA, USA.
    13. J. S. Marcus, ―The Economic Impact of Internet Traffic Growth on Network Operators,‖ 2014, wIKConsult (for Google, Inc.), Germany. [Online]. Available: http://www.wik.org/uploads/media/Google TwoSided Mkts.pdf
    14. M. Barabas, G. Boanea, and V. Dobrota, ―Multipath routing management using neural networks-based traffic prediction,‖ in The 3rd International Conference on Emerging Network Intelligence, Lisbon, Portugal, Nov. 2017.
    15. Zhang and Thomopoulos, ―Neural network imp5ementation of the shortest path algorithm for traffic routing in communication networks,‖ inInternational1989JointConferenceonNeuralNetworks,S anDiego, CA, USA.
    16. J. Barbancho, C. Le´on, F. J. Molina, and A. Barbancho, ―A new QoS routing algorithm based on selforganizing maps for wireless sensor networks,‖ Telecommunication Systems, vol. 36, no. 1, pp. 73–83, Nov. 2017.
    17. M. K. M. Ali and F. Kamoun, ―Neural networks for shortest path computation and routing in computer networks,‖ IEEE Transactions on Neural Networks, vol. 4, no. 6, pp. 941–954.

Recent Article