ICTACT Journal on Soft Computing

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Neuro-Fuzzy and Rough Set Based Traffic Flow Prediction


Affiliations
1 Department of Electronics Engineering, G H Raisoni College of Engineering, India
     

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With the rapid growth in urban population and vehicle ownership, traffic congestion has become a severe problem everywhere in the world and is only expected to rise. This problem can be avoided by knowing the traffic situation in advance which is achieved with the help of traffic flow prediction. In the proposed work, traffic flow is predicted on short term basis using neuro-fuzzy hybrid system in combination with rough set. The neuro-fuzzy hybrid system combines the complementary capabilities of both neural networks and fuzzy logic. 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 processing tool. The objective is to improve prediction accuracy. Data from highway of Chennai, India is used for the analysis. It is found that use of rough set results in considerable improvement in the prediction performance as indicated by performance measures like MSE, RMSE etc.

Keywords

Intelligent Transportation Systems (ITS), Rough Set Theory (RST), Short Term Traffic Flow Prediction, Neuro-Fuzzy Hybrid System.
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  • Neuro-Fuzzy and Rough Set Based Traffic Flow Prediction

Abstract Views: 261  |  PDF Views: 0

Authors

Minal Deshpande
Department of Electronics Engineering, G H Raisoni College of Engineering, India

Abstract


With the rapid growth in urban population and vehicle ownership, traffic congestion has become a severe problem everywhere in the world and is only expected to rise. This problem can be avoided by knowing the traffic situation in advance which is achieved with the help of traffic flow prediction. In the proposed work, traffic flow is predicted on short term basis using neuro-fuzzy hybrid system in combination with rough set. The neuro-fuzzy hybrid system combines the complementary capabilities of both neural networks and fuzzy logic. 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 processing tool. The objective is to improve prediction accuracy. Data from highway of Chennai, India is used for the analysis. It is found that use of rough set results in considerable improvement in the prediction performance as indicated by performance measures like MSE, RMSE etc.

Keywords


Intelligent Transportation Systems (ITS), Rough Set Theory (RST), Short Term Traffic Flow Prediction, Neuro-Fuzzy Hybrid System.

References