2003•Unpublished venueRequires access

Fuzzy logic control and simulation of urban traffic intersections group

Xiaoping Fan

Open publisher page 2 citations

Abstract

This paper presented a fuzzy logic controller and made a comparison of its performance with other controllers. The fuzzy rules was established by gathering the traffic flow information from the detectors placed in different intersections, and the green phase and the phase sequences at an intersection were obtained to control its traffic signals and cooperated with its neighbors. Simulation results show that the presented controller can adapt to the conditions of traffic flow well, and decrease the average delay time at multi-intersections.3 tabs,3 figs,7 refs.

About this research paper

What this paper is about

This paper presented a fuzzy logic controller and made a comparison of its performance with other controllers. The fuzzy rules was established by gathering the traffic flow information from the detectors placed in different intersections, and the green phase and the phase sequences at an intersection were obtained to control its traffic signals and cooperated with its neighbors. Simulation results show that the presented controller can adapt to the conditions of traffic flow well, and decrease the average delay time at multi-intersections.3 tabs,3 figs,7 refs.

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OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper presented a fuzzy logic controller and made a comparison of its performance with other controllers. The fuzzy rules was established by gathering the traffic flow information from the detectors placed in different intersections, and the green phase and the phase sequences at an intersection were obtained to control its traffic signals and cooperated with its neighbors. Simulation results show that the presented controller can adapt to the conditions of traffic flow well, and decrease the average delay time at multi-intersections.3 tabs,3 figs,7 refs.

Key concepts: Intersection (aeronautics), Fuzzy logic, Controller (irrigation), Traffic flow (computer networking), Fuzzy control system, Control theory (sociology), Group (periodic table), Computer science

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