Authors:
Sahar Araghi
;
Abbas Khosravi
and
Douglas Creighton
Affiliation:
Deakin University, Australia
Keyword(s):
Traffic Signal Controlling, Fuzzy Logic Systems, ANFIS, Isolated Intersection.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Fuzzy Control
;
Fuzzy Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Learning and Adaptive Fuzzy Systems
;
Neuro-Fuzzy Systems
;
Real-Time Learning of Fuzzy and Neuro-Fuzzy Systems
;
Soft Computing
Abstract:
Traffic signal controlling is one of the solutions to reduce the traffic congestion in cities. To set appropriate
green times for traffic signal lights, we have applied Adaptive Neuro-Fuzzy Inference System (ANFIS) method
in traffic signal controllers. ANFIS traffic signal controller is used for controlling traffic congestion of a single
intersection with the purpose of minimizing travel delay time. The ANFIS traffic controller is an intelligent
controller that learns to set an appropriate green time for each phase of traffic signal lights at the start of the
phase and based on the traffic information. The controller uses genetic algorithm to tune ANFIS parameters
during learning time. The results of the experiments show higher performance of the ANFIS traffic signal
controller compared to three other traffic controllers that are developed as benchmarks. One of the benchmarks
is GA-FLC (Araghi et al., 2014), next one is a fixed-FLC, and a fixed-time controller with three different va
lues
for green phase. Results show the higher performance of ANFIS controller.
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