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Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
地理科学与资源研究所 [3]
成都山地灾害与环境研... [2]
长春光学精密机械与物... [1]
数学与系统科学研究院 [1]
计算机网络信息中心 [1]
采集方式
OAI收割 [7]
iSwitch采集 [1]
内容类型
期刊论文 [7]
会议论文 [1]
发表日期
2019 [2]
2018 [3]
2016 [2]
2011 [1]
学科主题
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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
road emergency stations (RESs)
traffic police
geographic information system (GIS)
fuzzy analytical hierarchy process (FAHP)
road safety
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
Urban road system
Spatial heterogeneity
Traffic correlation
Traffic-enhanced dual graph
Community detection
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
Urban road system
Spatial heterogeneity
Traffic correlation
Traffic-enhanced dual graph
Community detection
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
Urban road system
Spatial heterogeneity
Traffic correlation
Traffic-enhanced dual graph
Community detection
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
Road traffic system
Vehicle emission
Fuel efficiency
Rebound effect
Induced effect
Policies
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
Backpropagation neural network
Cellular phone signaling data
Intelligent traffic system (its)
K-medoids
Kalman filter
Road traffic condition
Traffic speed estimation
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.