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)

Traffic Detection Using Artificial Intelligence

Author : Ashwini Kumar 1 Baibaswata Mohapatra 2

Date of Publication :20th February 2018

Abstract: The remote systems develop towards high portability and giving better help to associated vehicles, various new difficulties emerge because of the subsequent high elements in vehicular situations and therefore rationale reconsidering of conventional remote structure approaches. Future savvy vehicles, which are at the core of high versatility systems, are progressively furnished with different progressed installed sensors and continue producing huge volumes of information. AI, as a powerful way to deal with the man-made vehicular system, can give the best arrangement of instruments to endeavor such information to assist the systems. In this paper, the author initially recognizes the unmistakable attributes of high versatility vehicular systems and inspire the utilization of AI to address the subsequent difficulties. After a short presentation of the significant ideas of AI, the author talk about its applications to get familiar with the elements of the vehicular system. Specifically, the author examines in more prominent detail the use of reinforcement learning in managing system assets as an option in contrast to the common optimization approach.

Reference :

    1. A THEORETICAL RESEARCH ON ROUTING PROTOCOLS FOR VEHICULAR AD HOC NETWORKS (VANETS) 723.”
    2. J. Nzouonta, N. Rajgure, G. Wang, and C. Borcea, “VANET Routing on City Roads using Real-Time Vehicular Traffic Information,” 2008.
    3. H. El-Sayed et al., “Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier,” Sensors, vol. 18, no. 6, p. 1696, May 2018, doi: 10.3390/s18061696.
    4. A. H. Abdul Hanan, M. Yazid Idris, O. Kaiwartya, M. Prasad, and R. Ratn Shah, “Real traffic-data based evaluation of vehicular traffic environment and stateof-the-art with future issues in location-centric data dissemination for VANETs,” Digit. Commun. Networks, vol. 3, no. 3, pp. 195–210, Aug. 2017, doi: 10.1016/j.dcan.2017.04.002.
    5. B. PÅ‚aczek, “Selective data collection in vehicular networks for traffic control applications.”
    6. L. Liang, H. Ye, and G. Y. Li, “Towards Intelligent Vehicular Networks: A Machine Learning Framework,” 2019.
    7. K. Ota, M. Dong, H. Zhu, S. Chang, and X. Shen, “Traffic information prediction in Urban Vehicular Networks: A correlation based approach,” in 2011 IEEE Wireless Communications and Networking Conference, WCNC 2011, 2011, pp. 1021–1025, doi: 10.1109/WCNC.2011.5779275.
    8. O. Wolfson, B. Xu, and H. J. Cho, “Multimedia traffic information in vehicular networks,” in GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 2009, pp. 480–483, doi: 10.1145/1653771.1653849.
    9. H. El-Sayed et al., “Accurate traffic flow prediction in heterogeneous vehicular networks in an intelligent transport system using a supervised non-parametric classifier,” Sensors (Switzerland), vol. 18, no. 6, Jun. 2018, doi: 10.3390/s18061696.
    10. D. Pescaru, “Urban traffic congestion prediction based on routes information,” in SACI 2013 - 8th IEEE International Symposium on Applied Computational Intelligence and Informatics, Proceedings, 2013, pp. 121–126, doi: 10.1109/SACI.2013.6608951.
    11. R. Du, C. Chen, B. Yang, N. Lu, X. Guan, and X. Shen, “Effective urban traffic monitoring by vehicular sensor networks,” IEEE Trans. Veh. Technol., vol. 64, no. 1, pp. 273–286, 2015, doi: 10.1109/TVT.2014.2321010.
    12. Gagandeep Singh Narula, Usha Yadav, Neelam Duhan and Vishal Jain, “Lexical, Ontological & Conceptual Framework of Semantic Search Engine (LOC-SSE)”, BIJIT - BVICAM’s International Journal of Information Technology, Issue 16, Vol.8 No.2, July - December, 2016 having ISSN No. 0973-5658.
    13. Gagandeep Singh, Vishal Jain, “Information Retrieval through Semantic Web: An Overview”, Confluence 2012, held on 27th and 28th September, 2012 page no.114-118, at Amity School of Engineering & Technology, Amity University, Noida.
    14. Gagandeep Singh, Vishal Jain, Dr. Mayank Singh, “ An Approach For Information Extraction using Jade: A Case Study”, Journal of Global Research in Computer Science (JGRCS), Vol.4 No. 4 April, 2013, page no. 186-191, having ISSN No. 2229-371X .
    15. S Balamurugan, N Divyabharathi, K Jayashruthi, M Bowiya, RP Shermy, R Shanker, "Internet of agriculture: Applying IoT to improve food and farming technology," International Research Journal of Engineering and Technology (IRJET), Volume 3 issue 10, pp.713-719,e-ISSN: 2395 -0056, p-ISSN: 2395- 0072, 2016
    16. S.Balamurugan ,R.Madhukanth , V.M.Prabhakaran and Dr.R.GokulKruba Shanker, “Internet of Health: Applying IoT and Big Data to Manage Healthcare Systems,” International Research Journal of Engineering and Technology (IRJET), Volume 3 issue 10, pp.732-735,e-ISSN: 2395 -0056, p-ISSN: 2395- 0072, 2016

Recent Article