计算机科学
卷积神经网络
Boosting(机器学习)
人工智能
深度学习
强化学习
实时计算
梯度升压
智能交通系统
人工神经网络
机器学习
土木工程
随机森林
工程类
作者
Nada Faqir,Chakir Loqman,Jaouad Boumhidi
出处
期刊:International Journal of Advanced Computer Science and Applications
[The Science and Information Organization]
日期:2022-01-01
卷期号:13 (9)
标识
DOI:10.14569/ijacsa.2022.0130961
摘要
Traffic signal control is a way for reducing traffic jams in urban areas, and to optimize the flow of vehicles by minimizing the total waiting times.Several intelligent methods have been proposed to control the traffic signal.However, these methods use a less efficient road features vector, which can lead to suboptimal controls.The objective of this paper is to propose a deep reinforcement learning approach as the hybrid model that combines the convolutional neural network with eXtreme Gradient Boosting to traffic light optimization.We first introduce the deep convolutional neural network architecture for the best features extraction from all available traffic data and then integrated the extracted features into the eXtreme Gradient Boosting model to improve the prediction accuracy.In our approach; cross-validation grid search was used for the hyperparameters tuning process during the training of the eXtreme Gradient Boosting model, which will attempt to optimize the traffic signal control.Our system is coupled to a microscopic agent-based simulator (Simulation of Urban MObility).Simulation results show that the proposed approach improves significantly the average waiting time when compared to other well-known traffic signal control algorithms.
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