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	Autonomous Vehicle Platoons In Urban Road Networks: A Joint Distributed Reinforcement Learning and Model Predictive Control Approach 期刊论文 OAI收割 IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 1, 页码: 141-156 作者: Luigi D’Alfonso; Francesco Giannini; Giuseppe Franzè; Giuseppe Fedele; Francesco Pupo \| 收藏 \| 浏览/下载：16/0 \| 提交时间：2024/01/02 Distributed model predictive control distributed reinforcement learning routing decisions urban road networks
	Evaluating road selectivity of urban-trip based on dynamic betweenness centrality 会议论文 OAI收割 Proceedings - 2012 20th International Conference on Geoinformatics, Geoinformatics 2012, Hong Kong, China, 2012 Zhou Liang(周亮); Lu Feng; Zhang Hengcai 收藏 \| 浏览/下载：43/0 \| 提交时间：2012/12/01 Roads and streets Efficiency Graph theory Information services Betweenness centrality Different time windows Evaluation Method Evaluation results Hierarchical criteria Hierarchical spatial reasoning ON dynamics Path finding Path queries road selectivity evaluation Travel efficiency Urban road Urban road networks
	Neural network based online traffic signal controller design with reinforcement training (EI CONFERENCE) 会议论文 OAI收割 14th IEEE International Intelligent Transportation Systems Conference, ITSC 2011, October 5, 2011 - October 7, 2011, Washington, DC, United states Dai Y.; Hu J.; Zhao D.; Zhu F. 收藏 \| 浏览/下载：29/0 \| 提交时间：2013/03/25 Traffic congestion leads to problems like delays decreasing flow rate and higher fuel consumption. Consequently keeping traffic moving as efficiently as possible is not only important to economy but also important to environment. Traffic system is a large complex nonlinear stochastic system. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus computational intelligence (CI) technologies gain more and more attentions. Neural Networks (NNs) is a well developed CI technology with lots of promising applications in traffic signal control (TSC). In this paper a neural network (NN) based signal controller is designed to control the traffic lights in an urban traffic road network. Scenarios of simulation are conducted under a microscopic traffic simulation software. Several criterions are collected. Results demonstrate that through online reinforcement training the controllers obtain better control effects than the widely used pre-time and actuated methods under various traffic conditions. 2011 IEEE.