Traffic Flow Prediction using Combination of Support Vector Machine and Rough Set
Minal Deshpande1, Preeti Bajaj2
1Minal Deshpande, Research Scholar, G H Raisoni College of Engineering , Nagpur, India.
2Preeti Bajaj, Electronics Engineering, G H Raisoni College of Engineering , Nagpur, India.
Manuscript received on 23 August 2019. | Revised Manuscript received on 03 September 2019. | Manuscript published on 30 September 2019. | PP: 3334-3338 | Volume-8 Issue-11, September 2019. | Retrieval Number: K24550981119/2019©BEIESP | DOI: 10.35940/ijitee.K2455.0981119
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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: Congestion is the primary issue related to traffic flow. Avoiding congestion after getting into is not possible. So the only way is to make the informed decision by knowing the traffic situation in advance. This can be achieved with the help of traffic flow prediction. In the proposed work, short term traffic flow prediction is performed using support vector machine in combination with rough set. Traffic data used for analysis is collected from three adjacent intersections of Nagpur city and traffic flow is predicted at downstream junction. The work has attempted to study the effect of aggregation intervals and past samples on the prediction performance using MSE threshold variation. Rough set is used as a post processor to validate the prediction result. Accurate and timely prediction can provide reliability for optimized traffic control and guidance.
Keywords: Intelligent Transportation Systems (ITS), Short term traffic Flow Prediction, Support Vector Machine (SVM), Rough Set Theory (RST).
Scope of the Article: Artificial Intelligent Methods, Models, Techniques