An intelligent traffic responsive contraflow lane control system

Author

Zhou, W.W. ; Livolsi, P. ; Miska, E. ; Zhang, H. ; Wu, J. ; Yang, D.

Author_Institution

Minist. of Transp. & Highways, Victoria, BC, Canada

fYear

1993

fDate

12-15 Oct. 1993

Firstpage

174

Lastpage

181

Abstract

An intelligent-self learning dynamic optimal contraflow lane control system developed for the George Massey Tunnel in southern Greater Vancouver is introduced. A program was developed to permit the accurate estimation of realtime traffic demands. Online traffic data are sorted by a fuzzy modeling algorithm to identify the best matching pattern. A self learning mechanism is utilized to modify the predicted demand incrementally. An optimization algorithm is developed for online calculation of the optimal contraflow schedule based on the predicted demand. The total delay of both traffic approaches is minimized.

Keywords

road traffic; fuzzy modeling algorithm; intelligent traffic responsive contraflow lane control system; intelligent-self learning dynamic optimal contraflow lane control system; matching pattern; online calculation; online traffic data; optimal contraflow schedule; optimization algorithm; predicted demand; realtime traffic demands; self learning mechanism; Computational efficiency; Control systems; Delay; Niobium; Optimal scheduling; Pattern matching; Processor scheduling; Road transportation; Scheduling algorithm; Traffic control;

fLanguage

English

Publisher

ieee

Conference_Titel

Vehicle Navigation and Information Systems Conference, 1993., Proceedings of the IEEE-IEE

Conference_Location

Ottawa, Ontario, Canada

Print_ISBN

0-7803-1235-X

Type

conf

DOI

10.1109/VNIS.1993.585610

Filename

585610