(Created page with " == 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...") |
m (Scipediacontent moved page Draft Content 640585714 to Creighton et al 2015a) |
(No difference)
| |
Latest revision as of 16:49, 3 February 2021
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 values for green phase. Results show the higher performance of ANFIS controller.
Original document
The different versions of the original document can be found in:
Document information
Published on 01/01/2015
Volume 2015, 2015
DOI: 10.5220/0005135001750180
Licence: CC BY-NC-SA license
Share this document
Keywords
claim authorship
Are you one of the authors of this document?