中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services 期刊论文  OAI收割
SUSTAINABLE DEVELOPMENT, 2019, 卷号: 8, 期号: 9, 页码: 371
作者:  
Naboureh, Amin;  Feizizadeh, Bakhtiar;  Naboureh, Abbas;  Bian, Jinhu;  Blaschke, Thomas
  |  收藏  |  浏览/下载:50/0  |  提交时间:2020/04/01
Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach 期刊论文  OAI收割
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2018, 卷号: 501, 页码: 227-237
作者:  
Lu, Feng;  Liu, Kang;  Duan, Yingying;  Cheng, Shifen;  Du, Fei
  |  收藏  |  浏览/下载:74/0  |  提交时间:2019/05/30
Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach 期刊论文  OAI收割
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2018, 卷号: 501, 页码: 227-237
作者:  
Lu, Feng;  Liu, Kang;  Duan, Yingying;  Cheng, Shifen;  Du, Fei
  |  收藏  |  浏览/下载:25/0  |  提交时间:2019/05/30
Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach 期刊论文  OAI收割
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2018, 卷号: 501, 页码: 227-237
作者:  
Lu, Feng;  Liu, Kang;  Duan, Yingying;  Cheng, Shifen;  Du, Fei
  |  收藏  |  浏览/下载:23/0  |  提交时间:2019/05/30
Fuel efficiency and emission in China's road transport sector: Induced effect and rebound effect 期刊论文  OAI收割
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2016, 卷号: 112, 页码: 188-197
作者:  
Chai, Jian;  Yang, Ying;  Wang, Shouyang;  Lai, Kin Keung
  |  收藏  |  浏览/下载:32/0  |  提交时间:2018/07/30
A novel freeway traffic speed estimation model with massive cellular signaling data 期刊论文  iSwitch采集
International journal of web services research, 2016, 卷号: 13, 期号: 1, 页码: 69-87
作者:  
Zhu, Tongyu;  Song, Zhixin
收藏  |  浏览/下载:44/0  |  提交时间:2019/05/09
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:44/0  |  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.