Adaptive Traffic Control System using Reinforcement Learning

Опубликовано: 09 Ноябрь 2024
на канале: IJERT

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IJERTV9IS020159
Adaptive Traffic Control System using Reinforcement Learning

Kranti Shingate , Komal Jagdale , Yohann Dias

The advent of the automobile revolution has led to various traffic congestion problems. People can't arrive at their destination on time because of gigantic traffic. The framework utilized for coordinating traffic isn't reliant on the ongoing situation of an intersection. Traffic Light Control System with pre-set clocks are broadly used to invigilate and control the traffic generated at the intersections of numerous streets. However, the synchronization of multiple traffic light systems at adjacent intersections is a complicated problem given the various parameters involved. To handle such traffic either expansion of road networks or adaptive traffic control system which handles such traffic intelligently. This paper presents a system which handles traffic using Artificial Intelligence technique for adapting signal according to the density of traffic thereby automatically increasing or decreasing traffic signal time using Experience Replay mechanism. In this system, the Reinforcement Learning algorithm was used to determine optimal traffic light configuration and using deep Neural Networks the obtained results were used to extract the features required to make a decision.