Linear-quadratic model predictive control for urban traffic networks

Le, Tung, Vu, Hai L., Nazarathy, Yoni, Vo, Quoc Bao and Hoogendoorn, Serge (2013) Linear-quadratic model predictive control for urban traffic networks. Transportation Research Part C: Emerging Technologies, 36 498-512. doi:10.1016/j.trc.2013.06.021

Author Le, Tung
Vu, Hai L.
Nazarathy, Yoni
Vo, Quoc Bao
Hoogendoorn, Serge
Title Linear-quadratic model predictive control for urban traffic networks
Journal name Transportation Research Part C: Emerging Technologies   Check publisher's open access policy
ISSN 0968-090X
Publication date 2013-11-01
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.trc.2013.06.021
Volume 36
Start page 498
End page 512
Total pages 15
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon
Language eng
Formatted abstract
Advancements in the efficiency, quality and manufacturability of sensing and communication systems are driving the field of intelligent transport systems (ITS) into the twenty first century. One key aspect of ITS is the need for efficient and robust integrated network management of urban traffic networks. This paper presents a general model predictive control framework for both centralized traffic signal and route guidance systems aiming to minimize network congestion. Our novel model explicitly captures both non-zero travel time and spill-back constraints while remaining linear and thus generally tractable with quadratic costs. The end result is a central control scheme that may be realized for large urban networks containing thousands of sensors and actuators.

We demonstrate the essence of our model and controller through a detailed mathematical description coupled with simulation results of specific scenarios. We show that using a central scheme such as ours may reduce the congestion inside the network by up to half while still achieving better throughput compared to that of other conventional control schemes.
Keyword Congestion control
Intelligent transport system
Model predictive control
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2014 Collection
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Citation counts: TR Web of Science Citation Count  Cited 7 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 11 times in Scopus Article | Citations
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