Welcome to the InfoSci Platform
Significant Enhancement of Classification Efficiency for Automated Traffic Management System

Significant Enhancement of Classification Efficiency for Automated Traffic Management System

Upendra Kumar, Pawan Kumar Tiwari, Tejasvi Mishra, Lalita Jaiswar, Safiya Ali
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 16
ISSN: 2637-7888|EISSN: 2637-7896|EISBN13: 9781683183471|DOI: 10.4018/IJDAI.291086
Cite Article Cite Article

MLA

Kumar, Upendra, et al. "Significant Enhancement of Classification Efficiency for Automated Traffic Management System." IJDAI vol.14, no.1 2022: pp.1-16. http://doi.org/10.4018/IJDAI.291086

APA

Kumar, U., Tiwari, P. K., Mishra, T., Jaiswar, L., & Ali, S. (2022). Significant Enhancement of Classification Efficiency for Automated Traffic Management System. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-16. http://doi.org/10.4018/IJDAI.291086

Chicago

Kumar, Upendra, et al. "Significant Enhancement of Classification Efficiency for Automated Traffic Management System," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-16. http://doi.org/10.4018/IJDAI.291086

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.