INTELLIGENT TRAFFIC LIGHT CYCLE CONTROL USING MACHINE LEARNING - Mr.S.Santhoshkumar, A.Gayathri, R.Hemalatha, A.Indhumathi , C.Kavishree (International Journal of Advanced Science and Technology, 2020)
When there is a red signal for a longer duration vehicles takes longer time to pass the road whichmay cause traffic jam and also increases the queue length. To address this issue, we have to operateon the existing infrastructure by improving the systems that have a significant impact on the trafficflow, such as traffic signal controllers. This approach is desirable since it has a low cost ofimplementation. Therefore, the recent advancements in the field of Machine learning allows us topropose a Deep Reinforcement Learning algorithm in which the learning agent is able to control atraffic light system, which aims to increase the efficiency of road transportation. The agent makes useof a deep neural network to choose which light phase activate and it is trained using the Q-learning.The traffic interaction is handled by the traffic micro-simulator SUMO that is used to let the agentexperience a multitude of situations and learn by mistake which effects have the light phase activatedduring the simulations.