DocumentCode
1867616
Title
Model and Optimal Control for Urban Traffic Network under Incident Condition
Author
Yang, Xiaoguang ; Wu, Zhen ; Li, Ying ; Shen, Feng
Author_Institution
Tongji Univ., Shanghai
fYear
2007
fDate
Sept. 30 2007-Oct. 3 2007
Firstpage
1120
Lastpage
1125
Abstract
Incident-induced traffic congestion is a critical issue in formulation urban traffic congestion problems. This paper presents a method which serves to alleviate incident-induced traffic congestion for urban traffic network. The proposed approach includes two major parts: models and optimal control algorithm. Based on the typical phase types in Chinese cities, calculation models for controlled flow at intersection exit lanes, for movement offsets, for delay and queue of each movement, and for delay and queue of links adjacent to incident location were formulated respectively. Movement was the basic unit when delay and queue were calculated. Then a real-code genetic algorithm was presented to implement the optimal control under incident condition, in which we used a penalty function to ensure constraints. In the preliminary test, eight optimization strategies were chosen for the network control. The result showed that the total delay could decrease approximately 20% even if no vehicle diversion was considered under incident condition.
Keywords
genetic algorithms; optimal control; road traffic; Incident-induced traffic congestion; optimal control; optimization; real-code genetic algorithm; urban traffic network; Cities and towns; Communication system traffic control; Delay; Genetic algorithms; Intelligent networks; Intelligent transportation systems; Laboratories; Optimal control; Traffic control; USA Councils; genetic algorithm; incident; movement offsets; optimal control; optimization strategy;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Transportation Systems Conference, 2007. ITSC 2007. IEEE
Conference_Location
Seattle, WA
Print_ISBN
978-1-4244-1396-6
Electronic_ISBN
978-1-4244-1396-6
Type
conf
DOI
10.1109/ITSC.2007.4357663
Filename
4357663
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