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Dynamic Traffic Management Using Reinforcement Learning.
- Source :
- International Journal of Computing & Digital Systems; Feb2024, Vol. 15 Issue 1, p1007-1017, 11p
- Publication Year :
- 2024
-
Abstract
- Traffic congestion has become a major problem in this rapidly growing world. Everyone operating a vehicle, as well as the traffic police in charge of managing the traffic, finds it difficult to become stuck in heavy traffic. For this a set, predetermined timing for traffic flow for each direction at the junction is utilized by traditional traffic light controllers. However, the concept of a fixed time traffic signal controller does not work well in places with uneven traffic. A dynamic traffic control system is therefore required, which regulates the traffic signals in accordance with the volume of traffic. This paper proposes a model that uses reinforcement learning (RL) along with deep neural networks (DNN) to manage discretions (signal status) for an environment with the help of Simulation of Urban MObility (SUMO). A simulation of real-world environment consisting a network of Four-way crossroad junction that contains 4 arriving lanes and 4 exiting lanes is used to train the agent. The main objective of this research study is to construct a model that can independently determine the best course of action and aims to provide better traffic management that will decrease the average waiting time, cause lower congestion, and provide a smooth flow of traffic. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 25359886
- Volume :
- 15
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- International Journal of Computing & Digital Systems
- Publication Type :
- Academic Journal
- Accession number :
- 176160172
- Full Text :
- https://doi.org/10.12785/ijcds/150171