A Research of Traffic Prediction using Deep Learning Techniques
B. Karthika1, N. Uma Maheswari2, R. Venkatesh3
1Mrs. B. Karthika, Assistant professor, Department of Information Technology, PSNA college of engineering and Technology, Dindigul (TamilNadu), India.
2Dr. N. Uma Maheswari, Professor, Department of Computer science and Engineering, PSNA college of engineering and Technology, Dindigul (TamilNadu), India.
3Dr. R.Venkatesh, Professor, Department of Information Technology, PSNA college of engineering and Technology, Dindigul (TamilNadu), India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 31 August 2019 | PP: 725-728 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I11510789S219/19©BEIESP DOI: 10.35940/ijitee.I1151.0789S219
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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 data is very important in designing a smart city. Now –a day’smany intelligent transport systems use modern technologies to predict traffic flow, to minimize accidents on road, to predict speed of a vehicle and etc. The traffic flow prediction is an appealing study field. Many techniques of data mining are employed to forecast traffic. Deep learning techniques can be used with technological progress to prevent information from real time. Deep algorithms are discussed to forecast real-world traffic data. When traffic data becomes big data, some techniques to improve the accuracy of trafficprediction are also discussed.
Keywords: Deep Learning, Neural Network, Traffic flow Prediction, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Stacked Autoencoder (SAE).
Scope of the Article: Neural Information Processing