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Significant Enhancement of Classification Efficiency for Automated Traffic Management System
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Author(s): Upendra Kumar (Institute of Engineering and Technology, Lucknow, India), Pawan Kumar Tiwari (Institute of Engineering and Technology, Lucknow, India), Tejasvi Mishra (Institute of Engineering and Technology, Lucknow, India), Lalita Jaiswar (Institute of Engineering and Technology, Lucknow, India)and Safiya Ali (Institute of Engineering and Technology, Lucknow, India)
Copyright: 2022
Volume: 14
Issue: 1
Pages: 16
Source title:
International Journal of Distributed Artificial Intelligence (IJDAI)
Editor(s)-in-Chief: Firas Abdulrazzaq Raheem (University of Technology - Iraq, Iraq)and Israa AbdulAmeer AbdulJabbar (University of Technology - Iraq, Iraq)
DOI: 10.4018/IJDAI.291086
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Abstract
India as a country has 17.7% of the world’s population with the limited availability of land resource which is about only 2.4% of the world’s land. Being a developing nation and such huge population to accommodate, a number of problems can be seen on a daily basis such as high traffic congestion and unmanaged traffic on the roads. Irritating rush, wastage of time and fuel, are being severe hindrance to make the transportation comfortable. As a country, due to availability of limited lands, the only option is to manage the traffic smartly. Hitherto, a number of attempts have been made in this regard, still the statically managed traffic lights can be seen at the junction of roads. So in this work, it was tried to give an easy, but implementable method to manage traffic lights effectively. A hybrid approach based enhanced Convolution Neural Network model was used for the classification and have given the comparison with other model based technique i.e. Support Vector Machine. Our proposed enhanced model produced 91.01% accuracy and it is able to outperform the existing model.
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