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北京航空航天大学 [2]
长春光学精密机械与物... [1]
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期刊论文 [2]
会议论文 [1]
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2019 [1]
2017 [1]
2011 [1]
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Some features of car-following behaviour in the vicinity of signalised intersection and how to model them
期刊论文
IET INTELLIGENT TRANSPORT SYSTEMS, 2019, 卷号: 13, 页码: 1686-1693
作者:
Zhang, Jian
;
Tang, Tie-qiao
;
Wang, Tao
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  |  
浏览/下载:5/0
  |  
提交时间:2019/12/30
road traffic
generation simulation database
car-following data
driving data
macro-perspectives
statistic features
simulation testbed
traditional car
driving behaviour
signalised intersection
simulation results
traditional models
signalised road
data analyses
actual data
car-following behaviour
urban traffic increasing
signal lights
trajectory data
signal data
signal states
Modelling the driving behaviour at a signalised intersection with the information of remaining green time
期刊论文
IET INTELLIGENT TRANSPORT SYSTEMS, 2017, 卷号: 11, 页码: 596-603
作者:
Tang, Tie-Qiao
;
Yi, Zhi-Yan
;
Zhang, Jian
;
Zheng, Nan
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  |  
浏览/下载:2/0
  |  
提交时间:2019/12/30
road safety
numerical analysis
behavioural sciences
sensitivity analysis
road traffic
fuel economy
energy consumption
driving behaviour modelling
signalised intersection
green time information
signal lights
urban road network safety
urban road network efficiency
operational efficiency
transportation science
car-following model
fuel consumption
numerical analysis
traffic safety
sensitivity analysis
vehicle initial time headway
Neural network based online traffic signal controller design with reinforcement training (EI CONFERENCE)
会议论文
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.
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浏览/下载:26/0
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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.
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