AUTOMATED TRAFFIC CLEARANCE ON HOLDING VEHICLE COUNT USING ARTIFICIAL NEURAL NETWORK
DOI:
https://doi.org/10.29121/shodhkosh.v5.i5.2024.2700Keywords:
Efficient Traffic Management, AI Based Monitoring, Dcxata2Vector Algorithm, Congestion Avoidance, Real-Time Notifications, Traffic Flow OptimizationAbstract [English]
The difficult issue of traffic presents critical obstacles to the two individuals and traffic specialists. Resolving this issue requires creative fixes, and our recommended framework endeavors to give powerful traffic signal utilizing AI-based monitoring. Joining standard CCTV checking with strong examination fueled by the Data2Vector calculation. Our procedure centers around object recognizable proof and casing change. Utilizing the Data2Vector procedure, we can exactly perceive vehicles inside reconnaissance outlines, permitting us to examine traffic volume dependably. Moreover, outline transformation strategies permit us to distinguish vehicle speeds, which supports thorough traffic examination. At the point when our innovation recognizes areas with high gridlock and occurrences of speeding vehicles, it immediately sends constant warnings to traffic specialists. With this quick data, specialists may rapidly answer assuage blockage and keep up with nonstop traffic stream. The better accuracy of our methodology when contrasted with conventional PC vision investigation strategies shows its convenience. Our Programmed Traffic The board Framework gives an improved on way to deal with traffic signal by utilizing the capacities of the Data2Vector calculation, enabling proactive moves toward decrease clog and streamline by and large traffic effectiveness.
References
Vlahogianni, E. I., M. G. Karlaftis, and J. C. Golias. Optimized and Meta-Optimized Neural Networks for Short Term Traffic Flow Prediction: A Genetic Approach. Transportation Research Part C: Emerging Technologies, Vol. 13, No. 3, 2022, pp. 211–234. DOI: https://doi.org/10.1016/j.trc.2005.04.007
Bao G, Zeng Z, Shen Y. Region stability analysis and tracking control of memristive recurrent neural network. Neural Netw. 2020;5(1):74-89.
Jiang, X., and H. Adeli.Dynamic Wavelet Neural Network Model for Traffic Flow Forecasting. Journal of Transportation Engineering, Vol. 131, No. 10, 2016, pp. 771– 779 DOI: https://doi.org/10.1061/(ASCE)0733-947X(2005)131:10(771)
S. H. Shah and I. Yaqoob, “A survey: internet of things (IOT) technologies, applications and challenges,” in Proceedings of the 2016 IEEE Smart Energy Grid Engineering (SEGE), pp. 381–385, IEEE, Oshawa, Canada, August 2016. DOI: https://doi.org/10.1109/SEGE.2016.7589556
A. Sumalee and H. W. Ho, “Smarter and more connected: future intelligent transportation system,” Iatss Research, vol. 42, no. 2, pp. 67– 71, 2018. DOI: https://doi.org/10.1016/j.iatssr.2018.05.005
M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, “Deep learning for IoT big data and streaming analytics: a survey,” IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2923–2960, 2018. DOI: https://doi.org/10.1109/COMST.2018.2844341
M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, “Deep learning applications and challenges in big data analytics,” Journal of Big Data, vol. 2, no. 1, pp. 1–21, 2015. DOI: https://doi.org/10.1186/s40537-014-0007-7
Y. Ai, M. Peng, and K. Zhang, “Edge computing technologies for internet of things: a primer,” Digital Communications and Networks, vol. 4, no. 2, pp. 77–86, 2018. DOI: https://doi.org/10.1016/j.dcan.2017.07.001
N. Kumar, V. Poonia, B. B. Gupta, and M. K. Goyal, “A novel framework for risk assessment and resilience of critical infrastructure towards climate change,” Technological Forecasting and Social Change, vol. 165, p. 120532, 2021. DOI: https://doi.org/10.1016/j.techfore.2020.120532
D. Li, L. Deng, B. Bhooshan Gupta, H. Wang, and C. Choi, “A novel CNN based security guaranteed image watermarking generation scenario for smart city applications,” Information Sciences, vol. 479, pp. 432–447, 2019. DOI: https://doi.org/10.1016/j.ins.2018.02.060
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Copyright (c) 2024 T. Nandhakumar, Abburi Rahul, DronadulaSiva Reddy, Kodela Venkata Sudhir
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