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Page 12 of 14Majalah Ilmiah UNIKOM
Vol.8, No. 2
214
H a l a m a n
Muhammad Aria
6 show the comparison of T2FL, T1FL and
fixed-time computation time. So Interval
Type 2 TSK Fuzzy system takes 3.6 times
slower than Fixed Timer and 1.8 slower than
Type 1 TSK Fuzzy algorithm. In application,
Interval Type 2 TSK Fuzzy algorithm needs
17888 units of memory, while Fixed Timer
only needs 4840 unit memory and Type 1
TSK Fuzzy algorithm just needs 13032 units
memory.
CONCLUSION
In this paper, we have proposed the traffic
controller for complex intersection group
based on interval type 2 TSK fuzzy systems
and implement the simulator for perform-
ance evaluations. To control a set of inter-
section, we distribute controls to each con-
troller. Each controller takes charge of con-
trolling its traffic signal and cooperating with
its neighborhood. Our approach can be eas-
ily extended to any situation. According to
the simulation studies, results of the fuzzy
logic controller is the same as the fixed-time
controller in normal traffic flow. But the
simulation shows promising results in the
cases of heavy traffic and time-varying traf-
fic with large variance. In the cases of heavy
traffic and time-varying traffic, T2FL algo-
rithm reduced average vehicle delay 13,2 %
better than Fixed Timer and 1,6 % than T1FL
algorithm. But T2FL computation time is
more complex than both Fixed Timer and
T1FL algorithm, so T2FL system takes 3.6
times slower than Fixed Timer and 1.8
slower than T1FL algorithm. In application,
T2FL needs 17888 units of memory, while
Fixed Timer only needs 4840 unit memory
and T1FL just needs 13032 units memory.
REFERENCES
Mohammad Hossein Fazel Zarandi, Shab-
A Fuzzy Signal Controller
for Isolated Intersections
Uncertain System, vol 3, No. 3, pp 174
– 182, 2009
Lin Zhang, Honglong Li, Panos D. Preve-
Signal Control for Oversaturated
Intersections Using Fuzzy Logic
portation Research Record, Hawaii,
2004
Jee-Hyong Lee, Keon-Myung Lee, KyoungA
Seoing, Chang Bum Kim and Hyung Lee-
Traffic Control of Intersection
Group Based on Fuzzy Logic
Marzuki Khalid, See Chin Liang and Rubiyah
Control of a Complex Traffic Jun-
cion using Fuzzy Inference
Tan Kok Khiang, Marzuki Khalid and Rubi-
Intelligent Traffic Lights Con-
trol by Fuzzy Logic
A New
Approach for Fuzzy Traffic Signal Con-
trol
Type-2 Fuzzy Sets
and Systems : An Overview
putational Intelligence Magazine, vol 2,
no.1, pp. 20-29
Ching-Hung Lee, Yu-Ching Lin, and Wei-Yu
Systems Identification Using Type-2
Fuzzy Neural Network (Type-2 FNN) Sys-
tems
Symposium on Computational Intelli-
gence in Robotics and Automation,
2003
Adaptive
Noise Cancellation Using Type-2 Fuzzy
Logic and Neural Networks
Fuzzy 2004, IEEE press, 2004
Case
Fixed-Time
T1FL
T2FL
2.a
57.0
47.8
48.3
2.b
61.1
56.9
53.0
2.c
110.9
72.6
74.7
Table 5. Average delay time for Case 2
Average of time
computation
(microsecond)
Fixed-Time
9375
T1FL
18750
T2FL
33854
Table 6. Computation time