Control and Coordination of Self-Adaptive Traffic Signal Using Deep Reinforcement Learning
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Abstract
The most observable obstacle to sustainable mobility is traffic congestions. These congestions cannot effectively be fixed by traditional control of traffic signals. Safe and smooth movement of traffic is ensured by a self-controlled traffic signal. As such, to coordinate the traffic flow it is necessary to implement dynamic traffic signal subsequences. Primarily, Traffic Signal Controllers (TSC) provides sophisticated control and coordination of vehicles. The control and coordination of traffic signal control systems can be effectively achieved by implementing the Deep Reinforcement Learning (DRL) approaches. The decision-making capabilities at intersections are improved by having variations of traffic signal timing using an adaptive TSC. Alternatively, the actual traffic demand is nothing but managing the traffic systems. It analyses the incoming number and type of vehicles and gives a realtime response at intersection geometrics and controls the traffic signals accordingly. The proposed DRL algorithm observes traffic data and operates optimum management plans for the regulation of the traffic flow. Furthermore, an existing traffic simulator is used to help provide a realistic environment to support the proposed algorithm.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
pallavi mandhare, Dr. Jyoti Yadav, Prof. Vilas Kharat, & Prof. C. Y. Patil. (2021). Control and Coordination of Self-Adaptive Traffic Signal Using Deep Reinforcement Learning. International Journal of Next-Generation Computing, 12(2), 190–199. https://doi.org/10.47164/ijngc.v12i2.207
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