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FuzzyNet-Based Modelling Smart Traffic System in Smart Cities Using Deep Learning Models

FuzzyNet-Based Modelling Smart Traffic System in Smart Cities Using Deep Learning Models

Pawan Kumar Mall, Vipul Narayan, Sabyasachi Pramanik, Swapnita Srivastava, Mohammad Faiz, Srinivasan Sriramulu, M. Naresh Kumar
Copyright: © 2023 |Pages: 20
ISBN13: 9781668464083|ISBN10: 166846408X|ISBN13 Softcover: 9781668464090|EISBN13: 9781668464106
DOI: 10.4018/978-1-6684-6408-3.ch005
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MLA

Mall, Pawan Kumar, et al. "FuzzyNet-Based Modelling Smart Traffic System in Smart Cities Using Deep Learning Models." Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities, edited by Sabyasachi Pramanik and K. Martin Sagayam, IGI Global, 2023, pp. 76-95. https://doi.org/10.4018/978-1-6684-6408-3.ch005

APA

Mall, P. K., Narayan, V., Pramanik, S., Srivastava, S., Faiz, M., Sriramulu, S., & Kumar, M. N. (2023). FuzzyNet-Based Modelling Smart Traffic System in Smart Cities Using Deep Learning Models. In S. Pramanik & K. Sagayam (Eds.), Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities (pp. 76-95). IGI Global. https://doi.org/10.4018/978-1-6684-6408-3.ch005

Chicago

Mall, Pawan Kumar, et al. "FuzzyNet-Based Modelling Smart Traffic System in Smart Cities Using Deep Learning Models." In Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities, edited by Sabyasachi Pramanik and K. Martin Sagayam, 76-95. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-6408-3.ch005

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Abstract

The current lockouts, climatic variations, population expansion, and constraints on convenience and natural resource access are some of the factors that are making the need for smart cities more critical than ever before. On the other hand, these difficulties may be conquered more effectively with the use of emerging technology. In smart cities, the number of cars on the road has skyrocketed over the years, resulting in severe problems such as gridlock, accidents, and a myriad of other issues. Increased travel time reliability, decreased congestion, more equitable distribution of green phase time, faster response to traffic conditions, timely assistance and support, and accurate prediction of traffic volumes, including timing adjustments for traffic signals; these are some of the benefits that can be achieved. It is possible that the current, conventional traffic management system isn't up to deal with the increased traffic congestion and traffic violations. Image processing is the foundation of the sophisticated traffic management system that is now in place.

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