Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
8,
No.
1,
February
2018,
pp.
246
–
253
ISSN:
2088-8708
246
I
ns
t
it
u
t
e
o
f
A
d
v
a
nce
d
Eng
ine
e
r
i
ng
a
nd
S
cie
nce
w
w
w
.
i
a
e
s
j
o
u
r
n
a
l
.
c
o
m
T
raffic
Light
Signal
P
arameters
Optimization
Using
Modification
of
Multielement
Genetic
Algorithm
I
Gede
P
asek
Suta
W
ijaya
1
,
K
eeichi
Uchimura
2
,
and
Gou
K
outaki
3
1
Informatics
Engineering
Dept.,
Engineering
F
aculty
,
Mataram
Uni
v
ersity
,
Indonesia
2,3
Electrical
Engineering
and
Computer
Science
Dept.,
K
umamoto
Uni
v
ersity
,
Japan
Article
Inf
o
Article
history:
Recei
v
ed:
Jun
3,
2017
Re
vised:
No
v
29,
2017
Accepted:
Dec
14,
2017
K
eyw
ord:
Artificial
intelligence
GA
optimization
signal
parameters
transportation
system
ABSTRA
CT
A
strate
gy
to
optimize
traf
fic
light
signal
parameters
is
presented
for
solving
traf
fic
con-
gestion
problem
using
modification
of
the
Multielement
Genetic
Algorithm
(MEGA).
The
aim
of
this
method
is
to
impro
v
e
the
lack
of
v
ehicle
throughput
(
F
F
)
of
the
w
orks
called
as
traf
fic
light
signal
parameters
optimization
using
the
MEGA
and
P
ar
ticle
Sw
arm
Opti-
mization
(PSO).
In
this
case,
the
modification
of
MEGA
is
done
by
adding
Hash-T
able
for
sa
ving
some
best
populations
for
accelerating
the
recombi
nation
process
of
MEGA
which
is
shortly
called
as
H-MEGA.
The
e
xperimental
results
sho
w
that
the
H-MEGA
based
opti-
mization
pro
vides
better
performance
than
MEGA
and
PSO
based
methods
(impro
ving
the
F
F
of
both
MEGA
and
PSO
based
optimization
methods
by
about
10.01%
(from
82,63%
to
92.64%)
and
6.88%
(from
85.76%
to
92.64%),
respecti
v
ely).
In
addition,
the
H-MEGA
impro
v
e
significantly
the
real
F
F
of
Ooe
T
oroku
road
netw
ork
of
K
umamoto
City
,
Japan
about
21.62%.
Copyright
c
2018
Institute
of
Advanced
Engineering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
Name
I
Gede
P
asek
Suta
W
ijaya
Af
filiation
Informatics
Engineering
Dept.,
Engineering
F
aculty
,
Mataram
Uni
v
ersity
Address
Jl.
Majapahit
62
Mataram,
Lombok,
INDONESIA
Phone
+62-37-636126
Email
gpsuta
wijaya@unram.ac.id
1.
INTR
ODUCTION
The
traf
fic
congestion
is
big
problems
which
causes
man
y
ne
g
ati
v
e
ef
fects
not
only
to
road
users
ph
ysiological
b
ut
also
t
o
economic
and
en
vironmental
[
1].
Ph
ysiologically
,
the
traf
fic
congestion
mak
es
the
pedestrians
and
dri
v
ers
ha
v
e
to
pay
a
lot
of
attentions
during
on
the
roads.
Economically
,
the
traf
fic
jam
increases
the
fuel
consumption,
which
implies
to
transportation
cost.
En
v
i
ronmentally
,
the
traf
fic
jam
increases
the
pollution
of
v
ehicle
disposa
l
g
as
such
as
C
O
2
raising
the
greenhouse
ef
fect
on
the
en
vironment.
There
are
three
cate
gories
of
strate
gy
to
opt
imize
traf
fic
signals
which
are
w
ork
ed
based
on
the
le
v
el
of
v
ehicle
in
v
olv
ement
[2].
The
first
cate
gory
utilizes
le
g
ac
y
de
vices
with
no
v
ehicular
in
v
olv
ement,
which
can
be
to
redefine
the
signal
timing
of
the
junction
using
certain
technique.
The
second
cate
gory
utilizes
v
ehicles
on
the
road
to
wirelessly
transmit
data
about
themselv
es
(e.g.
location,
v
elocity).
It
means
the
signal
timing
is
optimized
by
considering
the
reports
of
v
ehicles
on
the
roads.
The
last
cate
gory
seems
costly
because
it
requires
sophisticat
ed
de
vices
and
softw
are
to
performing
automatically
the
optimization
on-board.
In
this
research,
the
first
cate
gory
of
traf
fic
light
signal
parameters
optimization
is
proposed
by
modifying
the
Multielement
Genetic
Algorithm
using
Hash-T
able
which
is
shortly
called
as
H-MEGA.
The
H-MEGA
is
an
impro
v
e-
ment
of
pre
vious
w
orks
called
as
traf
fic
light
signal
parameters
optimization
using
the
Multielement
Genetic
Algorithm
(MEGA)
and
P
article
Sw
arm
Optimization
(PSO)
[1,
3].
2.
RELA
TED
W
ORKS
Some
w
orks
for
traf
fic
light
signal
parameters
optimizations
ha
v
e
been
proposed
which
can
be
classified
to
three
approaches:
firstly
,
using
artificial
intelligence
(GA,
Fuzzy
,
Neural
Netw
orks)
and
their
v
ariations;
secondly
,
using
statistical
such
as
stochastic[4];
and
finally
using
v
ehicle
in
v
olv
ement
[2].
Among
them,
the
approaches
using
J
ournal
Homepage:
http://iaescor
e
.com/journals/inde
x.php/IJECE
I
ns
t
it
u
t
e
o
f
A
d
v
a
nce
d
Eng
ine
e
r
i
ng
a
nd
S
cie
nce
w
w
w
.
i
a
e
s
j
o
u
r
n
a
l
.
c
o
m
,
DOI:
10.11591/ijece.v8i1.pp246-253
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
247
artificial
intel
ligence
play
important
roles
for
traf
fic
light
signal
parameters
optimizations
such
as
approaches
based
on
PSO
[1],
GA
[5,
6,
7,
8,
9],
fuzzy
logic
which
determine
the
best
signal
parameters
using
fuzzy
rule
[10,
11].
Ho
we
v
er
,
some
of
them
were
not
implemented
on
real
road
netw
ork
and
had
lack
of
performances.
T
raf
fic
light
optimization
also
can
be
performed
by
considering
v
ehicular
in
v
olv
ement
via
communication
de
vices.
The
Ref.
[12]
also
de
v
eloped
a
signal
control
algorithm
that
allo
ws
for
v
ehicle
paths
and
si
gnal
control
to
be
jointly
optimized
based
on
adv
anced
communication
technology
between
approaching
v
ehicles
and
signal
controller
.
Ho
we
v
er
,
the
algori
thm
assumed
that
v
ehicle
trajectories
could
be
fully
optimized
and
it
w
as
de
v
eloped
assuming
a
simple
intersection
with
tw
o
single-lane.
The
Ref.
[13]
proposed
signal
setting
optimization
on
urban
road
transport
netw
orks
which
w
ork
ed
based
on
tr
a
v
el
demand
to
congested
road
transport
netw
ork.
In
this
case,
tw
o
interacting
pro-
cedures
are
de
v
eloped
to
solv
e
the
system
of
models:
(i)
an
optimization
procedure
to
obtain
an
optimal
configuration
of
signal
setting
parameters
and
(ii)
an
assignment
procedure,
incorporating
a
path
choice
model
with
e
xplicit
path
enumeration
and
a
flo
w
propag
ation
model,
to
capture
the
ef
fects
of
signal
setting
configuration
on
user
path
choice
be-
ha
vior
.
The
Ref.
[14]
presented
traf
fic
bot
tleneck
identification
and
optimization.
T
w
o
main
f
actor
traf
fic
bottlenecks
are
signal
timing
at
intersections
together
with
static
properties
of
left-turn
and
straight-through
lanes
of
roads[14].
The
ant
colon
y
algorithm
w
as
proposed
to
find
out
optimal
coordinated
signal
timing
for
a
re
gional
netw
ork.
The
Ref.
[15]
had
proposed
an
optimization
of
pedest
rian
phase
patterns
and
signal
timings
for
isolated
intersection
which
establishes
quantitati
v
e
criteria
for
selecting
pedestrian
phase
pat
terns
between
the
e
xclusi
v
e
pedestrian
phase
(EPP)
and
the
normal
tw
o-w
ay
crossing
(TWC)
with
both
safety
and
ef
ficienc
y
f
actors
traded-of
f
in
an
economic
e
v
aluation
frame
w
ork.
The
proposed
method
is
able
to
assist
transportation
professionals
in
properly
selecting
pedestrian
phase
patterns
at
signalized
intersections.
The
Ref.
[9]
also
proposed
intersection
signal
control
multi-objecti
v
e
optimization
using
GA,
which
w
orks
to
obtain
a
signal
control
multi-object
optimization
method
to
reduce
v
ehicle
emissions,
fuel
consumption
and
v
ehicle
delay
simultaneously
at
an
intersection.
Moreo
v
er
,
the
v
ehicle
anti-collision
alert
system
in
FPGA
has
been
de
v
eloped
to
decrease
the
number
of
road
accidents[16]
which
not
only
cause
injurie
s,
deaths
b
ut
also
traf
fic
jam.
It
means
the
alert
system
is
an
de
vice
that
can
be
used
to
drop-of
f
traf
fic
congestion.
In
addition,
a
v
ariation
of
GA
such
as
optimization
using
MEGA
has
been
proposed
for
finding
traf
fic
light
signal
parameters[3,
7,
17].
That
method
has
been
pro
v
ed
to
solv
e
traf
fic
congestion
in
real
Ooe
T
oroku
road
netw
ork,
K
umamoto
Shi,
Japan.
Ho
we
v
er
,
it
is
lack
of
netw
ork
throughput
(percentage
of
v
ehicle
flo
w)
and
time
consuming
on
obtaining
the
optimal
traf
fic
light
signal
parameters
on
the
Aimsun
6.1
for
simple
road
netw
ork
(see
Fig.
3(a)).
T
o
impro
v
e
MEGA
’
s
performance,
particle
sw
arm
optimization
(PSO)
w
as
emplo
yed
instead
of
MEGA[1].
Ho
we
v
er
,
it
just
impro
v
ed
3.13%
of
MEGA
’
s
achie
v
ement.
In
addition,
it
also
needed
almost
the
same
computational
time.
3.
THE
OPTIMIZA
TION
ALGORITHM
The
optimization
algorithm
is
based
on
H-MEGA
that
is
emplo
yed
to
search
the
optimum
of
fset,
c
ycles,
splits
time
of
four
nodes/junctions
of
Ooe
T
oroku
road
netw
ork.
The
Ooe
T
oroku
road
netw
ork
(Fig.
3(b))
is
located
in
K
umamoto
City
Japan,
at
latitude
and
longitude
point
32.81
and
130.72
or
in
url:
https://www.google.com/
maps/@32.8054628,
130.7218806,
17z
.
It
is
one
of
road
netw
ork
ha
ving
most
traf
fic
congestion
in
K
umamoto
city
.
The
properties
of
Ooe
T
oroku
road
netw
ork
including
the
node/j
unction,
signal
model,
and
signal
timing
has
been
clearly
presented
by
Ref.
[1].
3.1.
T
raffic
Light
Signal
P
arameters
Each
node/junction
has
traf
fic
light
equipped
with
signal
parameters:
of
fset,
c
ycle,
Y
ello
w
,
all
Red,
and
split
[3,
7].
The
of
fset
parameter
is
the
time
coordination
between
traf
fic
light
(node)
representing
the
starting
of
green
signal
timing.
F
or
instance,
the
Node
1
and
2
ha
ving
0
and
3
seconds
of
fset
parameters
means
that
the
Node
2
starts
the
c
ycle
signal
timing
at
3
seconds
after
started
the
c
ycle
of
Node
1.
The
c
ycle
parameter
represents
the
total
time
of
traf
fic
light
starting
from
Green
and
returning
to
Green.
The
Y
ello
w
and
all
Red
are
usually
defined
constantly
representing
the
duration
of
yello
w
and
all
red
signal
of
the
traf
fic
light.
The
split
that
consists
of
main
and
sub
split
means
the
Green
time
percentage
of
main
road
and
sub
road,
respecti
v
ely
.
In
this
paper
,
the
optimization
algorithm
searches
the
optimum
of
fset,
c
ycles,
and
split
to
get
maximum
v
ehicle
gone
out,
minimum
v
ehicle
in
and
w
ait
out,
less
v
ehicle
stop,
and
short
delay
time
of
considered
real
road
netw
ork.
3.2.
Modification
of
MEGA
There
are
some
v
ariations
of
genetic
algorithm
(GA)
which
were
de
v
eloped
to
solv
e
specific
problems[3,
18,
19].
F
or
instance,
the
multielement
GA
(MEGA[3])
w
as
de
v
eloped
to
optimize
traf
fic
light
signal
parameters,
the
parallel
GA[18]
w
as
de
v
eloped
for
solving
the
uni
v
ersity
scheduling
problem,
and
the
augmented
GA[19]
w
as
formed
to
utilize
feature
reduction
on
data
mining.
The
algorithm
of
MEGA
for
finding
the
best
traf
fic
light
signal
parameters
T
r
af
fic
Light
Signal
P
ar
ameter
s
Optimization
Using
Modification
...
(I
Gede
P
asek
Suta
W
ijaya)
Evaluation Warning : The document was created with Spire.PDF for Python.
248
ISSN:
2088-8708
is
gi
v
en
in
Fig.
1(a).
In
MEGA,
the
populations
consist
of
man
y
chromosomes
e
xtracted
from
the
road
netw
ork
traf
fic
lights.
The
MEGA,
which
is
also
included
by
elitism,
has
been
pro
v
ed
that
it
could
be
used
to
find
good
traf
fic
light
signal
parameters
as
presented
in
Refs.
[7,
8,
17].
In
this
paper
,
the
MEGA
and
PSO
based
opt
imization
are
impro
v
ed
by
modifying
the
MEGA
using
Hash-
T
able
(H-MEGA).
This
idea
comes
from
the
PSO
algorithm
which
w
as
inspired
by
social
beha
vior
of
bird
flocking
or
fish
schooling[20].
It
means
the
solution
is
searched
in
around
current
optimum
solution.
Therefore,
a
Hash-T
able
ha
ving
k
e
y
for
inde
xing
the
n
-best
populations
is
added
to
MEGA.
Lik
e
PSO,
the
best
solution
of
MEGA
is
just
searched
in
around
the
Hash-T
able
by
performing
the
recombination
such
as
selection,
crosso
v
er
and
mutation.
The
dif
ferent
between
MEGA
and
H-MEGA
is
presented
in
Fig.
1.
There
are
some
addition
processes
to
impro
v
e
the
MEGA
(Fig.
1(a))
which
are
sho
wn
by
light
green
block
(see
Fig.
1(b))
as
follo
ws:
1.
H-MEGA
initialization
which
gi
v
es
initial
v
alue
Hash-T
able
size,
number
of
populations
and
chromosomes.
2.
Putting
first
n
-best
fitness
to
Hash-T
able
means
first
n
-best
populations
corresponding
to
first
n
best
fitness
are
appended
to
Hash-T
able
for
ne
xt
recombination
process.
The
recombination
process
in
v
olving
selection,
crosso
v
er
,
and
mutation
are
performed
based
on
the
populations
e
xisted
in
Hash-T
able.
3.
Deleting
the
same
populations:
populations
result
of
recombination
that
are
same
as
e
xisted
populations
in
Hash-
T
able
are
deleted
for
decreasing
the
computation
time
because
the
y
pre
viously
ha
v
e
been
e
v
aluated.
(
a
)
M
E
G
A
(
b
)
H
-
M
E
G
A
S
t
a
r
t
I
n
i
t
i
a
l
i
z
e
(
M
E
-
G
A
)
F
o
r
G
e
n
=
0
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o
N
-
1
d
o
C
a
l
c
u
l
a
t
e
F
i
t
n
e
ss
(
P
o
p
[
i
]
)
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r
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r
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e
e
l
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o
r
se
l
e
c
t
i
o
n
M
u
t
a
t
i
o
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•
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e
f
i
n
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t
h
e
g
e
n
f
o
r
m
u
t
a
t
i
o
n
•
D
o
i
n
g
m
u
t
a
t
i
o
n
G
e
n
=
M
a
x
?
G
e
n
+
+
T
h
e
b
e
st
P
o
p
E
n
d
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o
i
n
g
S
i
mu
l
a
t
i
o
n
o
n
A
P
I
+
A
I
M
S
U
N
6
.
1
N
o
Y
e
s
G
e
n
=
M
a
x
?
T
h
e
b
e
st
P
o
p
E
n
d
N
o
Y
e
s
D
D
e
e
l
l
e
e
t
t
e
e
Upda
t
e
d
P
o
p
I
n
i
t
i
a
l
i
z
e
H
a
sh
a
n
d
M
E
G
A
D
o
i
n
g
S
i
mu
l
a
t
i
o
n
o
n
A
P
I
+
A
I
M
S
U
N
6
.
1
F
o
r
G
e
n
=
0
t
o
N
-
1
d
o
C
a
l
c
u
l
a
t
e
F
i
t
n
e
ss
(
P
o
p
[
i
]
)
S
S
o
o
r
r
t
t
i
i
n
n
g
g
F
F
i
i
t
t
n
n
e
e
s
s
s
s
P
P
u
u
t
t
t
t
i
i
n
n
g
g
f
f
i
i
r
r
s
s
t
t
-
-
n
n
b
b
e
e
s
s
t
t
F
i
t
n
e
ss
t
t
o
o
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H
a
a
s
s
h
h
-
-
T
T
a
a
b
b
l
l
e
e
C
C
r
r
o
o
s
s
s
s
o
o
v
v
e
e
r
r
a
a
n
n
d
d
M
M
u
u
t
t
a
a
t
t
i
i
o
o
n
n
U
U
p
p
d
d
a
a
t
t
e
e
d
d
P
P
o
o
p
p
i
i
s
s
e
e
x
x
i
i
s
s
t
t
i
i
n
n
H
H
a
a
s
s
h
h
-
-
T
T
a
a
b
b
l
l
e
e
?
?
M
M
a
a
x
x
?
?
Y
Y
e
e
s
s
N
N
o
o
G
G
e
e
n
n
+
+
+
+
S
S
t
t
a
a
r
r
t
t
R
R
o
o
u
u
l
l
e
e
t
t
t
t
e
e
W
W
h
h
e
e
e
e
l
l
Figure
1.
Flo
w
Chart
of
MEGA[3]
and
H-MEGA.
In
this
case,
the
fitness
formula
for
performing
populations
e
v
aluation
is
gi
v
en
by
F
p
=
exp
V
w
o
C
w
o
+
exp
V
in
C
in
+
exp
t
0
D
C
tD
.
Where
the
t
0
D
is
defined
as
t
0
D
=
t
D
tot
D
T
r
.
The
constant
v
alues
(
C
w
o
,
C
in
,
and
C
tD
)
are
gi
v
en
as
follo
ws:
C
w
o
=
100,
C
in
=500,
and
C
tD
=500.
These
constant
v
alues
were
chosen
to
minimize
the
ef
fect
of
each
v
ariables
to
the
fitness
v
alue.
These
v
alues
ha
v
e
been
utilized
to
e
v
aluate
the
PSO[1]
and
MEGA[8],
and
the
y
could
obtain
good
solution.
The
parameters
(v
ehicle
w
ait
out
(
V
w
o
),
v
ehicle
in
(
V
in
),
tra
v
el
distance
(
tot
D
T
r
),
and
time
delay
(
t
D
))
are
tak
en
from
simulation
outputs.
The
V
w
o
means
the
total
v
ehicles
which
are
w
aiting
to
enter
into
the
road
netw
ork,
V
in
means
the
total
v
ehicles
that
still
e
xist
in
the
road
netw
ork,
the
tot
D
T
r
is
total
tra
v
el
distance
of
the
v
ehicles
in
simulation,
and
the
t
D
is
defined
as
the
delay
time
of
v
ehicles
in
simulation.
3.3.
Optimization
Pr
ocess
Optimization
process
in
v
olv
es
Aimsun
6.1
simulator
,
application
interf
ace
(API)
which
is
a
DLL
modul
that
is
pro
vided
by
Aimsun
6.1
written
in
C++,
and
H-MEGA
modules.
Aimsun
6.1
simulator
is
transport
modeling
soft-
IJECE
V
ol.
8,
No.
1,
February
2018:
246
–
253
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
249
w
are
which
is
used
to
perform
the
traf
fic
simulati
on
of
Ooe
T
oroku
road
netw
ork.
Aimsun
6.1
simulator
is
de
v
eloped
and
mark
eted
by
TSS-T
ransport
Simulation
Systems
and
is
widely
used
by
uni
v
ersities,
consultants,
and
go
v
ernment
agencies
w
orldwide
for
transportation
planning,
traf
fic
simulation,
and
emer
genc
y
e
v
acuation
studies[1].
It
is
em-
plo
yed
to
i
mpro
v
e
road
infrastructure,
reduce
emissions,
cut
congestion
and
design
urban
en
vironments
for
v
ehicles
and
pedestrians.
The
coordination
and
communication
of
t
hree
modules
of
optimization
process
w
orks
based
on
Diagram
block
are
gi
v
en
in
the
Fig.
2.
The
optimization
process
can
be
described
as
follo
ws:
1.
The
Aimsun
6.1
gi
v
es
the
API
initial
data
for
n
populations,
m
-generations,
yello
w
time,
all
red
time,
and
the
range
v
alue
of
of
fset,
c
ycles,
and
split,
2.
The
API
passes
the
initial
data
to
H-MEGA
and
orders
the
H-MEGA
performing
initialization
n
-populations
of
traf
fic
light
signal
parameters.
3.
Through
the
API,
the
n
-populations
of
traf
fic
light
signal
parameters
are
sa
v
ed
as
output
by
H-MEGA
.
4.
The
API
orders
the
Aimsun
6.1
performing
traf
fic
simulation
on
road
netw
ork
for
all
n
-populations
of
traf
fic
light
signal
parameters
and
sa
v
e
the
simulation
results
on
the
database.
5.
After
finishing
traf
fic
simulation,
the
API
passes
results
to
H-MEGA
for
performing
e
v
al
uation
and
recombina-
tion
of
all
traf
fic
light
signal
parameters,
and
finally
sa
ving
the
results
as
ne
w
traf
fic
light
signal
parameters.
6.
Repeat
the
point
3
to
5
until
reaching
m
-generations.
AP
I
H
-
MEGA
A
I
MS
UN
6
.
1
S
i
m
u
l
a
t
i
o
n
Out
pu
t
V
go
,
V
in
,
V
wo
,
t
D
i
s
t
,
&
t
D
e
l
ay
O
u
t
p
u
t
H
-
M
EG
A
:
S
i
g
n
a
l
P
a
r
a
me
t
e
r
s
Figure
2.
Diagram
block
of
coordination
and
communication
among
Aimsun
6.1,
API,
and
H-MEGA[1].
4.
EV
ALU
A
TION
AND
DISCUSSION
In
order
to
kno
w
the
performance
of
H-MEGA
for
obtaining
the
optimum
traf
fic
light
signal
parameters,
some
e
xperiments
were
carried
out
using
tw
o
road
netw
orks:
simple
road
netw
ork
(Fig.
3(a))
and
real
road
netw
ork
(Fig.
3(b)).
The
e
xperiments
in
the
simple
road
netw
ork
w
as
to
find
out
whether
the
H-MEGA
can
deli
v
er
optimum
traf
fic
light
signal
parameters.
While
the
e
xperiments
in
the
real
road
netw
ork
w
as
to
confirm
that
the
H-MEGA
could
be
used
to
find
out
the
best
traf
fic
light
signal
parameters.
All
e
xperiments
used
5
minutes
w
arming
up
and
signal
parameters
constraints
as
follo
ws:
firstly
,
0
O
f
f
set
120
and
O
f
f
set
=
1
;
secondly
,
60
C
y
cl
e
180
and
C
y
cl
e
=
5
;
and
thirdly
,
10
S
pl
it
90
and
S
p
l
it
=
5
.
Red
Nu
m
b
e
r
:
Ro
a
d
I
D
288
(a)
Simple
(b)
Ooe
T
oroku
Figure
3.
T
w
o
road
netw
orks
for
e
xperiments[3,
7,
8,
17]
T
r
af
fic
Light
Signal
P
ar
ameter
s
Optimization
Using
Modification
...
(I
Gede
P
asek
Suta
W
ijaya)
Evaluation Warning : The document was created with Spire.PDF for Python.
250
ISSN:
2088-8708
4.1.
Experiment
on
Simple
Road
Netw
ork
Experiments
on
simple
road
netw
ork
were
carried
out
using
tw
o
netw
ork
states
ha
ving
v
ehicle
flo
w
(VF)
4800
per
hour
which
its
distrib
ution
is
sho
wn
in
T
able
1.
The
first
netw
ork
state
had
straightw
ay
and
turn
left,
while
the
second
netw
ork
state
had
straightw
ay
and
turn
right
signals[3].
The
v
ehicle
turning
percentage
of
each
junction
for
simple
netw
ork
is
50%.
The
first
e
xperimental
results
sho
w
that
the
proposed
method
can
find
the
best
traf
fic
T
able
1.
V
ehicle
flo
w
distrib
ution
in
simple
road
netw
ork
model[3,
8].
Road
ID*
2
86
29
2
29
8
3
02
3
1
0
31
2
3
2
0
32
4
T
otal
VF
8
00
40
0
40
0
8
00
8
0
0
40
0
4
0
0
80
0
48
0
0
*
:
Ro
ad
ID
o
f
F
i
g
.
3
(
a)
T
able
2.
Throughput
of
simple
road
netw
ork
model.
No
P
a
t
t
er
n
VF
M
et
ho
d
V
go
V
in
V
wo
=V
F
-
V
go
Dela
y
T
im
e
F
F
(
%)
1
T
h
e
First
Netw
o
r
k
S
tate
4800
ME
GA
4431
231
299
369
1
8
3
.
8
0
8
9
.
3
2
P
SO
4308
242
427
492
1
9
9
.
1
5
8
6
.
5
6
H
-
M
E
G
A
4449
266
256
351
1
9
9
.
9
4
8
9
.
5
0
2
T
h
e
Seco
n
d
Netw
o
r
k
State
4800
ME
GA
3269
347
1422
1531
4
7
0
.
9
0
6
4
.
8
9
P
SO
3408
421
1183
1392
4
6
0
.
9
7
6
8
.
0
0
H
-
M
E
G
A
3509
368
1
139
1291
4
7
4
.
4
5
6
9
.
9
6
T
able
3.
The
ef
fect
of
nElites
to
H-MEGA
on
the
second
netw
ork
states
of
simple
road
netw
ork.
P
a
t
t
er
n
M
et
ho
d
nE
lite
s
V
go
V
in
V
wo
D
Dela
y
T
im
e
F
F
(
%)
T
h
e
First
Netw
o
r
k
State
H
-
ME
GA
2
4449
266
256
351
1
9
9
.
9
4
8
9
.
5
0
4
4447
256
258
353
1
9
3
.
5
3
8
9
.
6
4
6
4467
251
238
333
1
9
4
.
0
4
9
0
.
1
3
8
4437
254
272
363
2
0
0
.
7
1
8
9
.
4
0
10
4411
290
258
389
1
9
7
.
0
7
8
8
.
9
5
light
signal
parameters
for
both
teste
d
netw
orks
state.
In
addition,
the
proposed
method
pro
vided
almost
similar
performance
as
tw
o
most
related
methods
(MEGA[3,
7,
8]
and
PSO[1],
which
is
sho
wn
by
almost
similar
throughput
(
F
F
)
about
89.50%
for
the
first
netw
ork
state
and
69.96%
for
the
second
netw
ork
state
(See
T
able
2).
It
means
that
H-MEGA
method
is
pro
v
ed
that
it
can
be
emplo
yed
to
search
the
best
traf
fic
light
signal
parameters
for
solving
the
traf
fic
congestion
on
the
simple
road
netw
ork
The
second
simulation
w
as
carried
out
to
kno
w
the
ef
fect
of
number
of
best
populations
on
finding
the
best
traf
fic
light
signal
parameters
on
simple
road
netw
ork.
In
this
simulation,
the
number
of
best
populations
(nElites)
sa
ving
in
Hash-T
able
w
as
v
aried
from
2
10
.
The
simulation
results
sho
w
that
the
best
nElites
for
H-MEGA
to
search
the
best
traf
fic
light
signal
parameters
is
six
(6)
which
can
pro
vide
the
highest
F
F
among
the
others,
as
sho
wn
in
T
able
3.
In
addition,
this
simulation
result
also
sho
ws
that
H-MEGA
pro
vides
higher
F
F
compared
to
that
of
MEGA
and
PSO
of
pre
vious
e
xperiments
(see
T
able
2).
It
confirms
that
the
H-MEGA
can
be
empl
o
yed
to
obtain
the
best
traf
fic
light
signal
parameters
of
simple
road
netw
ork.
Re
g
arding
to
computational
time,
the
H-MEGA
needs
much
shorter
computational
time
(41.73
minutes)
than
that
of
MEGA
and
PSO
(62.54
and
50.85
minutes,
respecti
v
ely).
The
computational
time
is
defined
as
a
total
time
that
is
required
by
Aimsun
6.1,
API,
and
H-MEGA
to
accomplish
the
simulation
with
40
populations
and
50
generations.
Mostly
computational
ti
me
in
the
simulation
is
influenced
by
Aimsun
6.1
which
tak
es
the
almost
0.671
s
econds
to
simulate
replication
of
road
netw
ork
for
1
hour
v
ehicles
mo
v
ement
in
the
road
netw
ork.
It
means
that
the
H-MEGA
not
only
impro
v
e
the
traf
fic
light
signal
parameters
b
ut
also
the
com
pu
t
ational
time
of
the
e
xisting
methods.
It
can
be
achie
v
ed
because
the
H-MEGA
searches
optimum
t
raf
fic
light
signal
parameters
in
the
entire
some
best
populations
sa
v
ed
in
Hash-T
able
and
the
H-MEGA
also
does
not
performance
the
e
v
aluation
on
populations
which
are
the
same
as
those
of
in
the
Hash-T
able.
4.2.
Experiment
on
Real
Ooe
T
or
oku
Road
Netw
ork
Further
e
v
aluation
of
H-MEGA
w
as
carried
out
in
rea
l
Ooe
T
oroku
road
netw
ork
(see
Fig
3(b)).
The
Ooe
T
oroku
road
netw
ork
had
5510
v
ehicles
flo
w
(VF)
per
hour
,
which
were
distrib
uted
as
presented
in
T
able
4.
It
also
had
3708
pedestrian
flo
w
per
hour
that
were
distrib
uted
into
four
junctions/nodes:
636,
1386,
415,
and
860
people
for
node
1,
2,
3
and
4,
respecti
v
ely
.
The
turning
percentage
of
VF
per
hour
of
each
road
in
Ooe
T
oroku
road
netw
ork
w
as
set
by
real
data
that
were
obtained
from
the
Oee
T
oroku
site
which
were
manually
counted
at
peaks
sessions
(8:00
AM
to
9:00
AM)[1,
8].
In
this
sim
u
l
ation,
the
H-MEGA
w
as
compared
to
the
related
w
orks:
MEGA
(base-line)[8]
and
PSO[1].
The
fitness
of
population
e
v
aluation
during
the
simulation
sho
w
that
all
methods
tend
to
find
best
traf
fic
light
signal
parameters
of
real
road
netw
ork,
as
presented
in
the
Fig.
4.
The
H-MEGA
tends
to
gi
v
e
better
performance
in
terms
F
F
than
MEGA
and
PSO,
because
the
fitness
of
H-MEGA
is
sm
aller
than
that
of
the
others.
F
actually
,
by
using
the
best
traf
fic
light
signal
parame
ters
for
Ooe
T
oroku
netw
ork
obtained
by
H-MEGA
(presented
T
able
5),
the
F
F
of
H-MEGA
(92.64%)
is
much
higher
than
that
of
MEGA
(82.63%)
and
PSO
(85.76%)
while
the
real
F
F
is
about
71.02%
(see
T
able
6).
From
T
able
6,
the
proposed
method
can
impro
v
e
significantly
the
traf
fic
congestion
of
real
Ooe
T
oroku
road
netw
ork
by
about
21.61%
of
the
real
F
F
.
While
the
MEGA
and
PSO
can
impro
v
e
by
about
11.60%
and
14.74%
of
IJECE
V
ol.
8,
No.
1,
February
2018:
246
–
253
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
251
the
real
F
F
.
This
simulation
results
are
inline
to
simple
road
netw
ork
achie
v
ement.
It
reconfirms
that
the
H-MEGA
not
only
can
obtain
the
best
traf
fic
light
signal
parameters
for
solving
the
traf
fic
congestion
b
ut
also
can
impro
v
e
the
performances
of
MEGA
and
PSO
for
real
Ooe
T
oroku
Road
Netw
ork.
1
100
10000
1
0
0
0
0
0
0
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
Fi
t
t
ne
s
s
Ge
ne
ra
ti
ons
M
E
GA
P
S
O
H
-ME
GA
Figure
4.
Fitness
of
H-MEGA
compared
to
e
xisting
methods.
T
able
4.
The
v
ehicles
flo
w
of
Ooe
T
oroku
road
netw
ork[1,
7,
17].
S
h
e
e
t
1
P
a
g
e
1
V
e
h
i
c
l
e
s
F
l
o
w
/
h
ou
r
of
R
oad
ID
*
T
o
t
al
297
298
304
307
310
314
316
319
C
a
r
486
586
1594
1122
318
164
432
456
5158
B
us
8
18
24
30
0
0
2
12
94
T
r
uc
k
24
28
64
64
12
6
30
30
258
*:
R
o
a
d
I
D
o
f
Fi
g
.
3
(
b
)
T
able
5.
The
best
traf
fic
light
signal
parameters
of
Ooe
T
oroku
obtained
by
H-MEGA.
Ju
n
c
ti
o
n
s
Of
f
se
t
(s)
Cy
c
le
(s)
M
a
in
S
p
li
t
(%
)
S
u
b
S
p
li
t
(%
)
No
d
e
1
0
135
65
35
No
d
e
2
13
95
45
40
No
d
e
3
86
175
70
30
No
d
e
4
14
70
60
30
The
traf
fic
congestion
condition
of
before
and
after
optimization
were
v
erified
by
simulating
the
Ooe
T
oroku
road
netw
ork
using
the
original
traf
fic
light
signal
parameters[1]
and
the
best
one
(T
able
5).
The
simulation
results
were
compared
in
Fig.
5,
which
sho
w
that
the
real
traf
fic
congestions
are
happen
in
the
8
roads
singed
by
red
roman
number
(see
Fig.
5(a)).The
hea
vy
traf
fic
congestions
are
happen
in
road
section
I,
II,
III,
V
,
VII
and
VIII
which
is
indicated
by
man
y
v
ehicles
queue
symbolized
by
small
blue
rectangular
.
Ho
we
v
er
,
when
using
the
best
traf
fic
light
signal
parameters,
the
traf
fic
congestions
decrease
significantly
,
as
sho
wn
in
Fig.
5(b).
In
det
ail,
hea
vy
traf
fic
congestions
are
just
happen
in
road
section
I
and
VI.
It
is
still
happen
because
the
v
ehicles
flo
w
from
in
the
section
I
(Road
ID
316
and
317
(Fig.
3(b)),
is
high
enough
432
with
road
width
just
3
meter
which
mean
the
road
density
is
o
v
erflo
w
.
From
this
v
erification,
the
traf
fic
congestion
can
be
solv
ed
by
resetting
the
traf
fic
light
signal
parameters
using
appropriate
ones
which
can
be
searched
by
artificial
intelligence
such
GA,
PSO,
Neural
Netw
ork,
etc.
Ov
er
all,
this
v
erification
supports
the
pre
vious
conclusion
that
the
H-MEGA
is
alternati
v
e
solution
for
searching
the
optimum
t
raf
fic
light
signal
parameters
and
it
also
can
impro
v
e
the
performances
of
MEGA[8]
and
PSO[1].
T
able
6.
Throughput
of
H-MEGA
on
Ooe
T
oroku
road
netw
ork
compared
to
mostly
related
w
orks.
N
o
M
e
t
ho
ds
V
e
hi
c
l
e
V
F
V
g
o
V
in
V
w
o
=
V
F-
V
g
o
D
e
l
a
y
T
i
m
e
D
T
r
t
o
t
(
k
m
)
F
F
(
%
)
1
Re
a
l
Bu
s
9
4
8
1
2
0
3
2
1
3
N
A
N
A
7
1
.
0
2
Ca
r
5
1
5
8
3
0
8
5
1
1
3
2
1
3
4
0
2
0
7
3
T
ru
c
k
2
5
8
1
5
9
5
5
6
5
9
9
Pe
d
e
s
t
r
i
a
n
3
7
0
8
3
2
3
9
3
4
0
4
6
9
T
o
t
a
l
9
2
1
8
6
5
6
4
1
2
4
1
1
4
3
7
2
6
5
4
N
A
2
Ba
s
e
L
i
n
e
[8
]
Bu
s
9
4
8
6
1
2
5
8
9
1
3
.
9
9
5
7
9
0
.
4
3
8
2
.
6
3
Ca
r
5
1
5
8
3
7
8
4
8
9
4
3
8
1
1
3
7
4
T
ru
c
k
2
5
8
2
5
4
3
7
2
3
4
Pe
d
e
s
t
r
i
a
n
3
7
0
8
3
4
5
3
1
6
2
7
9
2
5
5
T
o
t
a
l
9
2
1
8
7
5
7
7
1
1
0
5
4
8
8
1
6
4
1
0
.
2
2
2
*
3
PSO
[
1
]
Bu
s
9
4
8
2
7
5
1
2
1
6
8
2
.
4
2
6
0
8
9
.
8
3
8
5
.
7
6
Ca
r
5
1
5
8
4
0
5
2
7
4
2
3
2
9
1
1
0
6
T
ru
c
k
2
5
8
2
7
3
5
0
1
7
-
1
5
Pe
d
e
s
t
r
i
a
n
3
7
0
8
3
5
0
2
1
5
0
1
3
2
0
6
T
o
t
a
l
9
2
1
8
7
9
0
9
9
4
9
3
6
4
1
3
0
9
0
.
2
1
2
*
4
H-
M
EGA
Bu
s
9
4
9
7
6
0
-
3
7
9
5
.
2
0
6
9
6
1
.
8
1
9
2
.
6
4
Ca
r
5
1
5
8
4
5
4
3
5
2
8
4
5
6
1
5
T
ru
c
k
2
5
8
2
9
4
2
9
3
-
3
6
Pe
d
e
s
t
r
i
a
n
3
7
0
8
3
6
2
3
6
9
0
8
5
T
o
t
a
l
9
2
1
8
8
5
5
7
6
3
2
4
8
6
6
1
0
.
0
9
3
*
Note:
*
The
delay
ti
m
e
div
id
e
d
by
V
go
5.
CONCLUSION
AND
FUTURE
W
ORKS
The
proposed
traf
fic
light
signal
parameters
optimization
using
H-MEGA
has
been
implemented
successfully
to
find
the
best
traf
fic
light
signal
parameters,
which
is
sho
wn
by
higher
throughput
of
both
simple
and
real
road
T
r
af
fic
Light
Signal
P
ar
ameter
s
Optimization
Using
Modification
...
(I
Gede
P
asek
Suta
W
ijaya)
Evaluation Warning : The document was created with Spire.PDF for Python.
252
ISSN:
2088-8708
(a)
Real
traf
fic
light
signal
(b)
The
best
traf
fic
light
signal
of
H-MEGA
Figure
5.
T
raf
fic
congestion
v
erification
of
Ooe
T
oroku
road
netw
ork
us
ing
Aimsun
6.1
simulator
using
real
and
best
traf
fic
light
signal
parameters.
netw
orks.
In
detail,
the
H-MEGA
can
increase
significantly
the
throughput
(
F
F
)
of
real
Ooe
T
oroku
road
netw
ork
by
about
21.62%
(from
71.02%
to
92.64%).
It
means,
the
H-MEGA
is
successfully
to
search
the
best
traf
fics
light
signal
parameters
of
considered
junctions
that
af
fects
the
decrease
traf
fic
congestion
on
the
Ooe
T
oroku
road
netw
ork.
In
terms
of
computational
time,
the
proposed
method
needs
much
shorter
time
for
accomplishing
the
sim
u
l
ation
among
mostly
related
methods
(MEGA
and
PSO).
In
future,
the
Aimsun
6.1
simulator
will
be
modeled
by
Neural
Netw
ork
for
decreasing
the
computat
ional
time
of
accomplishing
the
simulation.
In
addition,
the
proposed
methods
will
be
formulated
for
finding
the
best
traf
fic
light
signal
parameters
on
comple
x
road
netw
ork
.
A
CKNO
WLEDGMENT
W
e
w
ould
lik
e
to
s
end
our
great
thank
to
Japan
Students
Services
Or
g
anization
(J
ASSO)
for
funding
of
my
research
in
GSST
-K
umamoto
Uni
v
ersity
.
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19421948.
BIOGRAPHIES
OF
A
UTHORS
I
Gede
P
asek
Suta
W
ijaya
recei
v
ed
the
B.Eng.
de
grees
in
Electrical
Engineering
from
Gadjah
Mada
Uni
v
e
rsity
in
1997,
M.Eng.
de
grees
in
Computer
Informatics
System
from
Gadjah
Mada
Uni
v
ersity
in
2001,
and
Doctor
of
Engineering
de
grees
in
Computer
Science
from
K
umamoto
uni-
v
ersity
,
Japan
in
2010.
During
1998-1999
he
w
ork
ed
in
T
o
yota
Astra
Motor
Compan
y
in
Indonesia
as
Planning
Production
Control,
and
from
1999-2000,
ne
xt,
he
w
ork
ed
as
lecturer
assistance
in
Y
o-
gyakarta
National
T
echnology
Colle
ge
in
Indonesia,
and
since
2000
up
today
,
he
has
been
full
time
lecturer
and
stays
in
Expert
Systems
Laboratory
in
Informatics
Engineering
Department,
Mataram
Uni
v
ersity
,
Indonesia.
His
research
interests
are
pattern
recognition,
artificial
intelligence,
and
im-
age
processing
application
on
computer
vision.
K
eiichi
Uchimura
recei
v
ed
the
B.Eng.
and
M.Eng.
de
grees
from
K
umamoto
Uni
v
ersity
,
K
u-
mamoto,
Japan,
in
1975
and
1977,
respecti
v
ely
,
and
the
Ph.D.
de
gree
from
T
ohoku
Uni
v
ersity
,
Miyagi,
Japan,
in
1987.
He
is
currently
a
Professor
with
the
Graduate
School
of
Science
and
T
ech-
nology
,
K
umamoto
Uni
v
ersity
.
He
is
eng
aged
in
research
on
intelligent
transportation
systems,
and
computer
vision.
From
1992
to
1993,
he
w
as
a
V
isiting
Researcher
at
McMaster
Uni
v
ersity
,
Hamilton,
ON,
Canada.
His
research
interests
are
computer
vision
and
optimization
problems
in
the
Intelligence
T
ransport
System.
Gou
K
outaki
ecei
v
ed
the
B.Eng.,
M.Eng.,
and
Ph.D.de
gree
from
K
umamoto
Uni
v
ersity
,
K
u-
mamoto,
Japan,
in
2002,
2004,
and
2007,
respecti
v
ely
.
From
2007
to
2010,
he
w
as
with
Production
Engineering
Research
Laboratory
,
Hitachi
Ltd.
He
is
currently
an
Assistant
Professor
with
the
Graduate
School
of
Science
and
T
echnology
,
K
umamoto
Uni
v
ersity
.
He
is
eng
aged
in
research
on
image
processing
and
pattern
recognition
of
the
Intelligence
T
ransport
System.
T
r
af
fic
Light
Signal
P
ar
ameter
s
Optimization
Using
Modification
...
(I
Gede
P
asek
Suta
W
ijaya)
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