Applying Reinforcement Learning to Optimize Traffic Light Cycles

arXiv:2402.14886v1 Announce Type: new
Abstract: Manual optimization of traffic light cycles is a complex and time-consuming task, necessitating the development of automated solutions. In this paper, we propose the application of reinforcement learning to optimize traffic light cycles in real-time. We present a case study using the Simulation Urban Mobility simulator to train a Deep Q-Network algorithm. The experimental results showed 44.16% decrease in the average number of Emergency stops, showing the potential of our approach to reduce traffic congestion …

abstract application arxiv automated case case study cs.ai cs.lg development light mobility optimization paper real-time reinforcement reinforcement learning simulation solutions study the simulation traffic type urban