Fuzzy logic to optimize traffic signal control in a congested area / Nor Suhaili Abdul Aziz and Noraini Noordin

Traffic congestion is a critical problem to road users worldwide. The present traffic signal control system in Malaysia has not produced optimal solutions for existing traffic congestions. This case study was carried out to test if applying fuzzy logic to traffic signal controllers can optimally sol...

Full description

Saved in:
Bibliographic Details
Main Authors: Abdul Aziz, Nor Suhaili Abdul Aziz (Author), Noordin, Noraini (Author)
Format: Book
Published: Universiti Teknologi MARA, Perlis, 2019-06.
Subjects:
Online Access:Link Metadata
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000 am a22000003u 4500
001 repouitm_58707
042 |a dc 
100 1 0 |a Abdul Aziz, Nor Suhaili Abdul Aziz  |e author 
700 1 0 |a Noordin, Noraini  |e author 
245 0 0 |a Fuzzy logic to optimize traffic signal control in a congested area / Nor Suhaili Abdul Aziz and Noraini Noordin 
260 |b Universiti Teknologi MARA, Perlis,   |c 2019-06. 
500 |a https://ir.uitm.edu.my/id/eprint/58707/1/58707.pdf 
500 |a  Fuzzy logic to optimize traffic signal control in a congested area / Nor Suhaili Abdul Aziz and Noraini Noordin. (2019) Jurnal Intelek <https://ir.uitm.edu.my/view/publication/Jurnal_Intelek/>, 14 (1): 2. pp. 9-18. ISSN 2231-7716  
520 |a Traffic congestion is a critical problem to road users worldwide. The present traffic signal control system in Malaysia has not produced optimal solutions for existing traffic congestions. This case study was carried out to test if applying fuzzy logic to traffic signal controllers can optimally solve traffic congestion at the four junctions in a developed urban area on Jalan Kerinchi, Kuala Lumpur. Two input variables (average number of vehicles queueing and arriving at a junction) and one input variable (green time duration) were considered in the study. Five linguistics were associated to each of the input variables thus MATLAB generated 25 fuzzy inference base rules. Results showed large differences between current green time duration (90.81 seconds, 92.72 seconds, 31.58 seconds, 13.88 seconds) and fuzzy logic green time duration (15.30 seconds, 55.00 seconds, 20.00 seconds, 10.00 seconds). Consequently, green time signal durations were minimized. Effectiveness of fuzzy logic applications can be further researched on and tested by adding more input variables or varying the conditions of the junctions. 
546 |a en 
690 |a Signals and signaling 
690 |a Fuzzy logic 
655 7 |a Article  |2 local 
655 7 |a PeerReviewed  |2 local 
787 0 |n https://ir.uitm.edu.my/id/eprint/58707/ 
787 0 |n https://myjms.mohe.gov.my/index.php/intelek 
787 0 |n 10.24191/ji.v14i1.8546 
856 4 1 |u https://ir.uitm.edu.my/id/eprint/58707/  |z Link Metadata