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An Approach Towards Intelligent Traffic Environment Using Machine Learning Algorithms

An Approach Towards Intelligent Traffic Environment Using Machine Learning Algorithms

Kavita Pandey, Akshansh Narula, Dhiraj Pandey, Ram Shringar Raw
Copyright: © 2021 |Pages: 22
ISBN13: 9781799827641|ISBN10: 179982764X|ISBN13 Softcover: 9781799827658|EISBN13: 9781799827665
DOI: 10.4018/978-1-7998-2764-1.ch001
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MLA

Pandey, Kavita, et al. "An Approach Towards Intelligent Traffic Environment Using Machine Learning Algorithms." Cloud-Based Big Data Analytics in Vehicular Ad-Hoc Networks, edited by Ram Shringar Rao, et al., IGI Global, 2021, pp. 1-22. https://doi.org/10.4018/978-1-7998-2764-1.ch001

APA

Pandey, K., Narula, A., Pandey, D., & Raw, R. S. (2021). An Approach Towards Intelligent Traffic Environment Using Machine Learning Algorithms. In R. Rao, N. Singh, O. Kaiwartya, & S. Das (Eds.), Cloud-Based Big Data Analytics in Vehicular Ad-Hoc Networks (pp. 1-22). IGI Global. https://doi.org/10.4018/978-1-7998-2764-1.ch001

Chicago

Pandey, Kavita, et al. "An Approach Towards Intelligent Traffic Environment Using Machine Learning Algorithms." In Cloud-Based Big Data Analytics in Vehicular Ad-Hoc Networks, edited by Ram Shringar Rao, et al., 1-22. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-2764-1.ch001

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Abstract

To make an optimal movement of vehicles and to reduce the accident rate, the government has installed traffic lights at almost every intersection. Traffic lights are intended to decrease congestion. However, the dynamic nature of traffic movement causes congestion always. This congestion leads to increased waiting times for every vehicle. In this chapter, two machine learning-based approaches used to improve in the congested traffic environment. The first part of the work is Deep-Learning based traffic signaling, which identifies the congestion on all sides of the intersection with the help of image processing techniques. By analyzing the congestion, the algorithm proposes dynamic green-light times rather than the traditional fixed lighting system. In the second part, a Q-learning-based approach has been suggested in which an agent decides the state of the traffic light based on a cumulative reward. Further, these algorithms have been tested on different traffic simulated environments using SUMO, and detailed analysis has been carried out.

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