期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 11, 页码 20021-20035出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2022.3179893
关键词
Deep learning; reinforcement learning; adaptive traffic signal control
This study proposes an adaptive traffic signal controller that utilizes deep learning techniques to process high-dimensional sensory inputs, aiming to optimize traffic flow and reduce delay time.
Despite the constant rise in global urban populations and subsequent rise in transportation demand, significant expansion of infrastructure has been hampered by the constraints of space, cost, and environmental concerns. Therefore, optimizing the efficiency of existing infrastructure is becoming increasingly important. Adaptive traffic signal controllers aim to provide demand-responsive strategies to minimize motorists' delay and achieve higher throughput at signalized intersections. With the advent of new sensory technologies and more intelligent control methods, the contribution of this paper is an adaptive traffic signal controller able to receive un-preprocessed high-dimensional sensory information such as GPS traces from connected vehicles and self-learn to minimize intersection delays. We use deep neural networks to operate directly on detailed sensory inputs and feed them into a reinforcement learning-based optimal control agent. The integration of these two components is known as deep learning. Using deep learning, we achieve two goals: (1) We eliminate the need for handcrafting a feature extraction process such as determining queue lengths, which is challenging and location-specific, and (2) we achieve better performance and faster training times compared to conventional tabular reinforcement learning approaches. We test our proposed controller against a tabular reinforcement learning agent, a reinforcement learning agent with a fully-connected Neural Network as a function approximator, and a state-of-practice, actuated traffic signal controller.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据