Short Term Road Traffic Flow Forecasting using Artificial Neural Network
V. Sumalatha1, Manohar Dingari2, C.Jayalakshmi3
1V.Sumalatha, Research Scholor, Department of Statistics, OSMANIA University, Hyderabad, India.
2Manohar Dingari, Research Scholor, Department of mathematics, GITAM University, Hyderabad,502329 India.
3Prof. C. Jayalakshmi, Professor, Department of Statistics, OSMANIA University, Hyderabad, India.
Manuscript received on 07 August 2019. | Revised Manuscript received on 14 August 2019. | Manuscript published on 30 September 2019. | PP: 7998-8000 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6407098319/2019©BEIESP | DOI: 10.35940/ijrte.C6407.098319
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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: In recent days, road traffic management and congestion control has become major problems in any busy junction in Hyderabad city. Hence short term traffic flow forecasting has gained greater importance in Intelligent Transport System(ITS). Artificial Neural Network(ANN) models have been fruitfully applied for classification and prediction of time series. In this paper, an attempt has been made to model and forecast short-term traffic flow at 6.no. junction in Amberpet, Hyderabad, Telangana state, India applying Neural Network models. The traffic data has been considered for peak hours in the morning for 8A.M to 12 Noon, for 5 days. Multilayer Perceptron (MLP) network model is used in this study. These results can be considered to monitor traffic signals and explore methods to avoid congestion at that junction.
Keywords: Traffic volume, Multilayer Perceptron, Artificial Neural Network, Intelligent Transport System, Forecasting.
Scope of the Article: Network Traffic Characterization and Measurements