A Survey on Traffic Flow Prediction with Deep Learning Algorithms on Big Data
J. Swami Naik1, N. Kasiviswanath2, K. Ishthaq Ahamed3, S. Raghunath Reddy4
1J. Swami Naik, Research Scholar, JNTUA, Ananthapuramu (Andhra Pradesh), India.
2Dr. N. Kasiviswanath, Professor & Head, Department of CSE, G. Pulla Reddy Engineering College Autonomous, Kurnool (Andhra Pradesh), India.
3K. Ishthaq Ahamed, Associate Professor, Department of CSE, G. Pulla Reddy Engineering College Autonomous, Kurnool (Andhra Pradesh), India.
4S. Raghunath Reddy, Assistant Professor, Department of CSE, G. Pulla Reddy Engineering College Autonomous, Kurnool (Andhra Pradesh), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 27 March 2019 | Manuscript Published on 28 April 2019 | PP: 28-33 | Volume-7 Issue-5C February 2019 | Retrieval Number: E10080275C19/19©BEIESP
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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: Correct and very much planned activity stream data is vital for the fruitful arrangement of astute transportation frameworks. In the course of the most recent couple of years, activity information have been report, and we have truly entered the period of huge information for transportation. Existing movement stream expectation strategies for the most part utilize shallow activity forecast models and square measure as yet frustrating for a few genuine world applications. The objective of the smart transportation framework (ITS) is utilizing the correspondence framework to entirely consolidate the vehicle arrangement of individuals, vehicles and street. Propelled movement control framework and dynamic activity the executives framework are required to give real time activity stream data. The conventional movement stream show is named the activity stream state variables(velocity, thickness and stream) with the correction of your time and area. Traffic stream examination is essential research idea in the transportation framework. Deep Learning is a type of machine learning used to anticipate movement flow. This circumstance moves us to take the activity stream expectation issue dependent on profound design models with enormous activity information.
Keywords: Traffic Flow Prediction, Deep Learning, Learning Algorithms, Intelligent Transportaion System, Artificial Neural Network.
Scope of the Article: Deep Learning