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Schedule-driven intersection control

delete2012-10-01
delete91
PRE
AI
X
Xiaofeng Xie *
S
Stephen F. Smith
L
Liang Lu
G
Gregory J. Barlow
DOI:10.1016/j.trc.2012.03.004delete
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摘要

摘要

En 中文
Model-based intersection optimization strategies have been widely investigated for distributed traffic signal control in road networks. Due to the form of black-box optimization that is typically assumed, a basic challenge faced by these strategies is the combinatorial nature of the problem that must be solved. The underlying state space is exponential in the number of time steps in the look-ahead optimization horizon at a given time resolution. In this paper, we present a schedule-driven intersection control strategy, called SchIC, which addresses this challenge by exploiting the structural information in non-uniformly distributed traffic flow. Central to our method is an alternative formulation of intersection control optimization as a scheduling problem, which effectively reduces the state space through use of an aggregate representation on traffic flow data in the prediction horizon. A forward recursive algorithm is proposed for solving the scheduling problem, which makes use of a dominance condition to efficiently eliminate most states at early stages. SchIC thus achieves near optimal solutions with a polynomial complexity in the prediction horizon, and is insensitive to the granularity of time resolution that is assumed. The performance of SchIC with respect to both intersection control and implicit coordination between intersections is evaluated empirically on two ideal scenarios and a real-world urban traffic network. Some characteristics and possible real-world extensions of SchIC are also discussed. (C) 2012 Elsevier Ltd. All rights reserved.
Keyword:
Adaptive traffic signal control
Model-based intersection optimization
Real-time systems
Urban traffic control
Intelligent transportation systems
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期刊

Transportation Research Part C-Emerging Technologies 封面图
Transportation Research Part C-Emerging Technologies
IF:
7.9
论文数:
4.7K
被引数:
3.2W

机构

C
Carnegie Mellon University
学者数:
1.4W
论文数: 1.4W
被引数: 2.7W
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