Iterative learning control based phase splits strategy for oversaturated urban traffic network

2016 ◽  
Author(s):  
Shangtai Jin ◽  
Zhongsheng Hou ◽  
Ronghu Chi ◽  
Danwei Wang ◽  
Xuhui Bu
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Urban Traffic ◽  
Traffic Network ◽  
Urban Traffic Network

scholarly journals Iterative Learning Control Approach for Signaling Split in Urban Traffic Networks with Macroscopic Fundamental Diagrams

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Fei Yan ◽  
Fu-li Tian ◽  
Zhong-ke Shi
Keyword(s):  
Spatial Distribution ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Urban Traffic ◽  
Recent Analysis ◽  
Traffic Networks ◽  
Control Approach ◽  
Congested Networks ◽  
Urban Traffic Networks

Recent analysis of field experiments in cities revealed that a macroscopic fundamental diagram (MFD) relating network outflow and network vehicle accumulation exists in the urban traffic networks. It has been further confirmed that an MFD is well defined if the network has regular network topology and homogeneous spatial distribution of vehicle accumulation. However, many real urban networks have different levels of heterogeneity in the spatial distribution of vehicle accumulation. In order to improve the mobility in heterogeneously congested networks, we propose an iterative learning control approach for signaling split, which aims at distributing the accumulation in the networks as homogeneously as possible and ensuring the networks have a larger outflow. The asymptotic convergence of the proposed approach is proved by rigorous analysis and the effectiveness is further demonstrated by extensive simulations.

Iterative Learning Control for a Class of Linear Discrete-time Switched Systems

2014 ◽  
Vol 39 (9) ◽  
pp. 1564-1569 ◽  
Author(s):  
Xu-Hui BU ◽  
Fa-Shan YU ◽  
Zhong-Sheng HOU ◽  
Fu-Zhong WANG
Keyword(s):  
Discrete Time ◽  
Switched Systems ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning
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