Traffic Flow Calculation using Big Data
S.Kalaiarasi1, G. Leela2, K. Nikesh3, Ch. Prasad4
1G. Leela Krishna Reddy, Pursuing B.Tech, Computer Science Department, SRM Institute of Science and Technology Tamil Nadu, India.
2K.Nikesh Sai Veda, Pursuing B.Tech, Computer Science Department, SRM Institute of Science and Technology Tamil Nadu, India.
3Ch. Vara Prasad, Pursuing B.Tech, Computer Science Department, SRM Institute of Science and Technology Tamil Nadu, India.
4Mrs. S. Kalaiarasi, Assistant Professor, Department of Computer Science SRMIST, Ramapuram.
Manuscript received on November 25, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1777-1780 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2534129219/2019©BEIESP | DOI: 10.35940/ijeat.B2534.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Traffic is one of the primary issues in world. It makes numerous medical issues people on foot and bikers. It is additionally one of the practical setting of a nation. U.S.A. alone squandered almost $160 billion of fuel in year 2014 alone. Mumbai remains at no.1 position in the rundown of most exceedingly awful traffic stream while Delhi taking no.4 position. In this task we use BIGDATA for guaranteeing that the explorers doesn’t get struck in the rush hour gridlock. BIGDATA can enable clients to settle on better travel choices, lighten traffic blockage, diminish carbon outflows, and improve traffic activity proficiency. Our goal of traffic stream forecast is to give a superior traffic stream data. Traffic stream forecast has picked up its significance because of fast development in urban areas and increment in rush hour gridlock blockage. Traffic stream forecast intensely relies upon authentic and ongoing traffic information gathered from different sensor sources, including inductive circles, radars, cameras, portable Global Positioning System, publicly supporting, internet based life, and so on. In this paper, we propose a profound learning-based traffic stream forecast technique.
Keywords: Deep learning, real time information, traffic stream prediction.