I
A
E
S
I
n
t
e
r
n
at
io
n
al
Jou
r
n
al
of
A
r
t
if
ic
ia
l
I
n
t
e
ll
ig
e
n
c
e
(
I
J
-
A
I
)
V
ol
. 13, No. 4, D
e
c
e
m
be
r
2024, pp.
4019
~
4028
I
S
S
N
:
2252
-
8938
,
D
O
I
:
10.11591/
ij
a
i.
v13.i4.pp
4019
-
4028
4019
Jou
r
n
al
h
om
e
page
:
ht
tp
:
//
ij
ai
.
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1
D
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pa
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m
e
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c
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e
nc
e
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e
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of
C
om
put
i
ng a
nd C
om
m
uni
c
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t
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on
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s
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s
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o, N
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i
a
3
D
e
pa
r
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m
e
nt
of
O
pe
r
a
t
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ons
a
nd Q
ua
l
i
t
y M
a
na
ge
m
e
nt
, D
ur
ba
n U
ni
v
e
r
s
i
t
y of
T
e
c
hnol
ogy, D
ur
ba
n, S
out
h A
f
r
i
c
a
4
C
e
nt
r
e
f
or
E
c
ol
ogi
c
a
l
I
nt
e
l
l
i
ge
nc
e
,
D
e
pa
r
t
m
e
nt
of
E
l
e
c
t
r
i
c
a
l
a
nd E
l
e
c
t
r
oni
c
E
n
gi
ne
e
r
i
ng S
c
i
e
nc
e
,
F
a
c
ul
t
y of
E
ngi
ne
e
r
i
ng a
nd t
he
B
ui
l
d
E
nvi
r
onm
e
nt
, U
ni
ve
r
s
i
t
y of
J
oha
nne
s
bur
g, J
oha
nne
s
bur
g, S
out
h A
f
r
i
c
a
A
r
t
ic
le
I
n
f
o
A
B
S
T
R
A
C
T
A
r
ti
c
le
h
is
to
r
y
:
R
e
c
e
iv
e
d
J
a
n 21, 2024
R
e
vi
s
e
d
A
pr
18,
2024
A
c
c
e
pt
e
d
J
un 14, 2024
This
research
presents
a
model
for
an
adaptive
traffic
signal
control
system
aimed
at
improving
urban
traffic
regulation.
It
dynamically
adjusts
signal
timing
based
on
vehicle
volume
at
intersections,
prioritizing
eme
rgency
vehicles
by
allowing
them
immedi
ate
passage.
Utilizing
Arduino
codi
ng,
the
system controls traf
fic light intensity acc
ording
to the traffic
flow,
enh
ancing
road
safety
and
efficiency.
This
innovative
approach
not
only
facilitate
s
faster
clearance
for
emergency
services
without
human
inte
rvention
but
also
r
educes
congesti
on
and
accident
rates.
This
research
creates
a
model
for
a
prio
ritized
traffic
signal
control
system.
When
the
vehicular
volume
at
the
inter
section
varies,
the
signal
time
alters
autonomously.
It
identifie
s
the
ambulance/
emergency
vehicles
and
allows
the
g
reen
light
for
eme
rgency
vehicles
like
ambulances,
and
fire
engines.
This
approach
may
be
used
to
detect
traffic
accidents
and
infractions
of
automobile
spiral
motions.
When
erected
on
the
road,
the
entire
syste
m
allows
fo
r
quick
traffic
cleari
ng
for
rescue
vehicles
without
requiring
a
policeman.
The
system
'
s
design
eli
minates
the
need
for
sensors
or
radio
frequency
identification
(
RFID)
tags,
simp
lifying
traffic
manageme
nt.
Simulations
validate
that
emergen
cy
vehicle
trav
el
time
is
significantly
reduced,
proving
the
system'
s
effectiveness
in
strea
mlining
urban traffic flows.
K
e
y
w
o
r
ds
:
E
m
e
r
ge
nc
y
S
im
ul
a
ti
on
T
r
a
f
f
ic
l
ig
ht
T
r
a
ns
por
ta
ti
on
V
e
hi
c
le
s
This is an
open
-
access
article under the
CC BY
-
SA
license.
C
or
r
e
s
pon
di
n
g A
u
th
or
:
O
lu
w
a
s
e
gun J
ul
iu
s
A
r
oba
C
e
nt
r
e
f
or
E
c
ol
ogi
c
a
l
I
nt
e
ll
ig
e
nc
e
,
D
e
pa
r
tm
e
nt
of
E
le
c
tr
ic
a
l
a
nd E
le
c
tr
oni
c
E
ngi
ne
e
r
in
g S
c
ie
nc
e
F
a
c
ul
ty
of
E
ngi
ne
e
r
in
g a
nd t
he
B
ui
ld
E
nvi
r
onm
e
nt
,
U
ni
ve
r
s
it
y
of
J
oha
nne
s
bur
g
A
uc
kl
a
nd P
a
r
k C
a
m
pus
,
J
oh
a
nne
s
bur
g,
S
out
h A
f
r
ic
a
E
m
a
il
:
a
r
os
e
gl
in
ks
@
ya
hoo.c
o.uk
1.
I
N
T
R
O
D
U
C
T
I
O
N
T
he
ur
ba
n
e
nvi
r
onm
e
nt
hum
s
w
it
h
th
e
pul
s
e
of
m
obi
li
ty
.
V
e
hi
c
le
s
s
w
ir
l
a
c
r
os
s
a
pa
tc
hw
or
k
of
s
tr
e
e
ts
,
th
e
ir
f
lo
w
gove
r
ne
d
by
th
e
tr
a
f
f
ic
s
ig
na
l,
th
e
qui
e
t
c
onduc
to
r
.
H
ow
e
ve
r
,
in
to
da
y'
s
m
ode
r
n
c
it
y,
w
he
n
ti
m
e
is
va
lu
a
bl
e
a
nd
f
ue
l
is
s
hor
t,
a
one
-
s
iz
e
-
f
it
s
-
a
ll
s
tr
a
te
gy
f
or
tr
a
f
f
i
c
r
e
gul
a
ti
on
is
no
lo
nge
r
a
de
qua
te
.
E
nt
e
r
th
e
pr
io
r
it
iz
e
d
tr
a
f
f
ic
li
ght
c
ont
r
ol
s
ys
te
m
m
ode
l
(
P
T
L
C
S
M
)
,
a
ga
m
e
-
c
ha
ngi
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id
e
a
th
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t
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s
th
e
pot
e
nt
ia
l
to
r
e
th
in
k
th
e
ur
ba
n
s
ym
phony
of
m
ove
m
e
nt
.
T
r
a
f
f
ic
s
ig
na
l
s
ys
te
m
s
a
r
e
us
e
d
to
gove
r
n
m
o
r
e
th
a
n
two
ve
hi
c
le
pa
s
s
in
g
c
ha
nne
ls
or
w
he
n
p
e
de
s
tr
ia
ns
c
r
os
s
a
r
oa
d
[
1]
–
[
3]
.
I
n
th
e
c
a
s
e
of
a
f
our
-
w
a
y
la
ne
,
it
is
a
ls
o
ut
il
iz
e
d
w
he
n
two
pa
th
w
a
ys
i
nt
e
r
s
e
c
t.
T
r
a
f
f
ic
c
onge
s
ti
on i
s
r
e
g
a
r
de
d a
s
one
of
t
he
m
os
t
s
e
r
io
us
i
s
s
ue
s
i
n m
e
tr
opol
it
a
n a
r
e
a
s
[
4]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
. 13, No. 4, D
e
c
e
m
be
r
2024:
4019
-
4028
4020
T
r
a
f
f
ic
c
onge
s
ti
on
is
pr
e
di
c
te
d
to
w
or
s
e
n
a
s
a
r
e
s
ul
t
of
th
e
r
is
in
g
num
be
r
of
m
ode
s
of
tr
a
ns
por
ta
ti
o
n
a
nd t
he
e
xi
s
ti
ng poor
c
ondi
ti
on o
f
r
oa
d
in
f
r
a
s
tr
uc
tu
r
e
. F
u
r
th
e
r
m
or
e
, nume
r
ous
s
tu
di
e
s
a
nd s
ta
ti
s
ti
c
s
ha
ve
be
e
n
c
r
e
a
te
d
in
de
ve
lo
pi
ng
na
ti
ons
pr
ovi
ng
th
a
t
th
e
m
a
jo
r
it
y
of
r
oa
d
f
a
ta
li
ti
e
s
a
r
e
c
a
u
s
e
d
by
li
m
it
e
d
r
oa
ds
a
nd
th
e
ha
r
m
f
ul
e
xpa
ns
io
n
in
tr
a
n
s
por
ta
ti
on
m
e
th
ods
.
A
s
y
s
te
m
w
it
h
f
le
xi
bi
li
ty
c
a
pa
bl
e
of
m
a
na
gi
ng
tr
a
f
f
ic
de
n
s
it
y
is
ne
c
e
s
s
a
r
y
f
or
bi
g
tr
a
f
f
ic
s
ys
te
m
s
.
T
he
f
unda
m
e
nt
a
l
di
s
a
dv
a
nt
a
g
e
of
a
ge
ne
r
a
li
z
e
d
tr
a
f
f
ic
c
ont
r
ol
s
ys
te
m
is
th
e
di
f
f
ic
ul
ty
in
de
te
c
ti
ng
hi
gh
-
pr
io
r
it
y
e
ve
nt
s
a
nd
e
m
e
r
ge
nc
y
s
c
e
n
a
r
io
s
[
5]
.
T
hi
s
e
m
pha
s
iz
e
s
th
e
ne
c
e
s
s
it
y
f
or
a
s
m
a
r
t
tr
a
f
f
ic
m
a
na
ge
m
e
nt
s
ys
te
m
t
ha
t
c
a
n h
a
ndl
e
a
ll
s
c
e
na
r
io
s
a
nd ma
ke
j
udgme
nt
s
a
ut
onomous
ly
.
A
tr
a
f
f
ic
m
a
na
ge
m
e
nt
s
ys
te
m
'
s
goa
l
is
to
m
a
na
ge
ve
hi
c
l
e
m
ove
m
e
nt
w
it
hi
n
a
n
in
te
r
s
e
c
ti
on
a
nd
to
a
voi
d
c
ol
li
s
io
ns
or
r
oa
d
bl
oc
ka
g
e
s
.
T
he
s
ig
na
l
li
ght
s
us
e
d
f
or
th
e
c
ur
r
e
nt
tr
a
f
f
ic
s
ys
te
m
s
a
r
e
not
a
bl
y,
r
e
d,
ye
ll
ow
,
a
nd
gr
e
e
n
f
or
s
to
p,
ge
t
r
e
a
dy,
a
nd
go
a
c
c
or
d
in
gl
y,
w
it
h
e
a
c
h
li
ght
ope
r
a
ti
ng
one
a
f
te
r
a
not
he
r
f
or
a
s
pe
c
if
ie
d
le
ngt
h
of
ti
m
e
in
m
os
t
de
ve
lo
pi
ng
na
ti
ons
.
T
he
de
la
y
c
ha
r
a
c
te
r
is
ti
c
s
th
a
t
a
f
f
e
c
t
tr
a
f
f
ic
lo
a
d
in
c
lu
de
ti
m
e
,
da
y,
s
e
a
s
on,
w
e
a
th
e
r
,
a
nd
unf
or
e
s
e
e
a
bl
e
e
ve
nt
s
li
ke
a
s
a
c
c
id
e
nt
s
or
c
ons
tr
uc
ti
on
ope
r
a
ti
ons
.
C
onge
s
ti
on
c
a
n
be
a
ll
e
vi
a
te
d
by
c
ons
tr
uc
ti
ng
ne
w
r
oa
dw
a
ys
[
6]
.
T
h
e
onl
y
di
s
a
dv
a
nt
a
ge
is
th
a
t
th
e
e
nvi
r
ons
ge
t
m
or
e
c
r
ow
de
d.
I
ns
te
a
d of
bui
ld
in
g
a
ddi
ti
ona
l
f
a
c
il
it
ie
s
t
w
ic
e
, i
t
is
ne
c
e
s
s
a
r
y t
o
m
odi
f
y t
he
t
r
a
f
f
ic
s
ys
te
m
. T
he
s
e
i
s
s
ue
s
c
a
n be
r
e
m
e
di
e
d
by
c
ont
in
ua
ll
y
m
oni
to
r
in
g
a
nd
m
odi
f
yi
ng
tr
a
f
f
ic
s
ig
n
a
l
ti
m
in
gs
ba
s
e
d
on
r
e
a
l
tr
a
f
f
ic
de
m
a
nd,
w
hi
c
h
im
a
ge
de
te
c
ti
on s
y
s
te
m
s
c
a
n do.
T
he
m
a
jo
r
goa
l
of
th
is
s
tu
dy
is
to
e
xa
m
in
e
tr
a
f
f
ic
li
ght
s
a
nd
m
or
e
c
onge
s
te
d
a
r
e
a
s
a
nd
to
pr
ovi
de
pr
e
c
e
de
nc
e
to
e
m
e
r
ge
nc
y
ve
hi
c
le
s
a
nd
a
m
bul
a
n
c
e
s
a
t
tr
a
f
f
ic
in
te
r
s
e
c
ti
ons
.
T
hi
s
r
e
qui
r
e
s
ju
s
t
m
in
or
phys
i
c
a
l
a
lt
e
r
a
ti
ons
t
o t
he
c
r
os
s
in
g a
nd
de
li
ve
r
s
t
he
qui
c
k
e
s
t
c
le
a
r
in
g t
o
v
e
hi
c
le
/p
e
de
s
tr
ia
n
t
r
a
f
f
ic
i
n a
ll
di
r
e
c
ti
ons
.
T
hi
s
,
in
tu
r
n,
of
f
e
r
s
a
va
r
ia
bl
e
tr
a
f
f
ic
li
ght
s
ys
te
m
th
a
t
e
ns
ur
e
s
va
r
ia
ti
ons
in
tr
a
f
f
ic
de
ns
it
y
w
hi
le
a
ls
o
r
e
duc
in
g
tr
a
f
f
ic
w
a
r
de
n s
tr
e
s
s
a
nd a
c
c
id
e
nt
s
. T
r
a
f
f
ic
c
onge
s
ti
on c
a
us
e
s
no pr
obl
e
m
s
i
n
th
e
e
ve
nt
of
a
n e
m
e
r
ge
nc
y a
nd r
e
duc
e
s
f
a
ta
li
ti
e
s
[
7]
. A
s
a
r
e
s
ul
t,
t
r
a
f
f
ic
m
a
y be
c
ont
r
ol
le
d w
it
hout
t
he
ne
e
d f
or
a
s
e
r
ge
a
nt
, a
nd t
he
s
ugge
s
te
d s
ol
ut
io
n
s
a
ve
s
p
e
opl
e
'
s
l
iv
e
s
a
nd va
lu
a
bl
e
t
im
e
.
P
r
e
vi
ou
s
s
tu
di
e
s
a
nd
pr
a
c
ti
c
a
l
a
ppl
i
c
a
ti
ons
h
a
ve
e
s
t
a
bl
i
s
he
d
th
e
p
ot
e
nt
i
a
l
of
r
e
a
l
-
ti
m
e
,
tr
a
f
f
ic
-
r
e
s
pon
s
iv
e
s
ig
n
a
l
op
ti
m
iz
a
ti
on
s
tr
a
te
g
ie
s
to
m
in
im
i
z
e
dr
iv
e
r
pa
us
e
s
a
nd
de
l
a
ys
a
t
pa
r
ti
c
ul
a
r
s
i
gna
li
z
e
d
ju
n
c
ti
on
s
[
8]
.
A
s
a
r
e
s
ul
t
,
it
i
s
r
e
a
li
s
ti
c
a
ll
y
c
r
it
ic
a
l
to
bui
ld
,
c
h
e
c
k,
a
nd
v
a
li
da
te
s
im
pl
e
y
e
t
po
w
e
r
f
ul
m
ode
l
s
th
a
t
a
id
in
th
e
de
s
ig
n
a
nd
im
pr
ov
e
m
e
nt
of
pub
li
c
tr
a
ns
it
s
e
c
ur
it
y
a
nd
e
f
f
e
c
t
iv
e
n
e
s
s
,
w
hi
le
r
e
s
c
u
e
ve
hi
c
l
e
s
a
r
e
ig
nor
e
d
.
A
m
bul
a
n
c
e
s
,
pol
ic
e
c
a
r
s
,
f
ir
e
e
n
gi
n
e
s
,
a
nd
o
th
e
r
e
m
e
r
g
e
nc
y
ve
h
ic
l
e
s
t
ha
t
b
e
c
o
m
e
c
a
ught
i
n
tr
a
f
f
ic
a
nd
a
r
e
de
l
a
ye
d
in
r
e
a
c
hi
ng
th
e
ir
de
s
ti
n
a
ti
on
c
a
n
r
e
s
ul
t
in
t
he
lo
s
s
of
pr
o
pe
r
ty
a
nd
va
l
ue
d
li
v
e
s
[
9]
,
[
10]
.
R
is
i
ng
c
a
r
n
um
be
r
not
onl
y
e
n
ha
n
c
e
s
th
e
t
im
e
ta
k
e
n f
or
r
e
s
c
u
e
v
e
hi
c
l
e
s
but
a
ls
o i
m
pr
ov
e
s
t
h
e
ir
c
ha
n
c
e
s
of
b
e
in
g i
nvol
v
e
d i
n f
a
ta
li
t
ie
s
[
11]
–
[
13]
.
T
h
e
e
m
e
r
g
e
nc
y
c
a
r
goi
ng i
n
to
a
j
un
c
ti
on a
t
hi
gh s
p
e
e
d on a
r
e
d l
ig
h
t
e
nda
n
ge
r
s
t
r
a
f
f
ic
on oth
e
r
r
oa
d
s
a
nd
c
a
n c
a
u
s
e
a
c
c
i
de
nt
s
.
B
a
s
e
d
on t
he
pr
e
s
e
nt
i
s
s
ue
in
t
hi
s
s
e
c
ti
on,
i
t
i
s
c
le
a
r
t
h
a
t
a
s
ophi
s
ti
c
a
t
e
d t
r
a
f
f
ic
m
a
n
a
ge
m
e
nt
s
y
s
t
e
m
i
s
r
e
qui
r
e
d
f
or
e
f
f
ic
ie
n
t
c
ont
r
o
l
of
bo
th
t
yp
ic
a
l
a
n
d e
m
e
r
ge
n
c
y
a
ut
o
m
obi
le
s
[
14]
–
[
1
6]
.
T
hi
s
pa
pe
r
pr
opos
e
s
a
m
e
th
od
f
or
s
c
he
dul
in
g
e
m
e
r
ge
nc
y
v
e
hi
c
le
s
in
gr
id
lo
c
k.
T
he
te
c
hnol
ogy
in
te
gr
a
te
s
vi
s
ua
l
de
te
c
ti
ng
te
c
hni
que
s
f
or
m
e
a
s
ur
in
g
th
e
di
s
ta
nc
e
th
a
t
e
xi
s
ts
be
twe
e
n
th
e
r
e
s
c
ue
c
a
r
a
nd
a
n
in
te
r
s
e
c
ti
on,
a
ut
om
obi
le
c
ount
in
g,
a
nd
ti
m
e
-
s
e
n
s
it
iv
e
w
a
r
ni
ng
tr
a
ns
m
is
s
io
n
w
it
hi
n
th
e
s
e
ns
or
ne
twor
k
s
.
T
o
r
e
c
ogni
z
e
e
m
e
r
ge
nc
y
ve
hi
c
le
s
,
th
e
s
e
ns
or
s
c
a
pt
ur
e
s
ir
e
n
s
ig
na
l
s
a
nd
tr
a
ns
f
e
r
th
e
m
to
th
e
m
e
a
s
ur
in
g c
ont
r
ol
le
r
(
A
r
dui
no
U
N
O
)
.
A
nd
pa
s
s
e
s
th
e
m
ove
r
to
th
e
f
r
e
que
nc
y
m
e
a
s
ur
e
m
e
nt
c
ont
r
ol
le
r
.
T
he
e
m
e
r
ge
nc
y
ve
hi
c
le
'
s
s
ir
e
n
w
a
ve
le
ngt
hs
a
r
e
de
te
c
te
d
by
th
e
c
ont
r
ol
de
vi
c
e
.
T
he
c
o
nt
r
ol
uni
t
e
s
ti
m
a
te
s
th
e
a
ve
r
a
ge
of
obs
e
r
ve
d
w
a
ve
le
ngt
hs
a
f
te
r
m
e
a
s
ur
in
g
th
e
f
r
e
que
nc
ie
s
of
s
ir
e
n
s
ounds
.
I
f
th
e
f
r
e
que
nc
y
is
be
twe
e
n
ye
lp
a
nd
w
a
il
,
th
e
f
r
e
que
nc
y
m
e
a
s
ur
e
m
e
nt
c
ont
r
ol
le
r
s
e
nds
a
n
a
le
r
t
s
ig
na
l
to
th
e
tr
a
f
f
ic
s
ig
na
l
c
ont
r
ol
le
r
(
A
r
dui
no
M
e
ga
)
.
O
n
r
e
c
e
ip
t
of
a
ppr
oa
c
hi
ng
e
m
e
r
ge
nc
y
ve
hi
c
le
in
f
or
m
a
ti
on,
th
e
tr
a
f
f
ic
s
ig
na
l
c
ont
r
ol
le
r
s
to
ps
th
e
pr
e
s
e
t
s
e
qu
e
nc
e
a
nd
li
ght
le
ngt
h
a
lg
or
it
hm
a
nd
c
o
nduc
ts
th
e
e
m
e
r
ge
nc
y
ve
hi
c
le
di
s
pa
tc
hi
ng
a
lg
or
it
hm
.
T
he
s
ugge
s
te
d
a
lg
or
it
hm
is
e
xe
c
ut
e
d
by t
he
c
ont
r
ol
le
r
, a
nd i
ts
j
udgme
nt
i
s
s
e
nt
t
o t
r
a
f
f
ic
l
ig
ht
s
. T
he
s
ys
te
m
r
e
s
um
e
s
r
e
gul
a
r
f
unc
ti
oni
ng
f
ol
lo
w
in
g
th
e
pa
s
s
in
g
of
a
n
e
m
e
r
ge
nc
y
ve
hi
c
le
,
a
nd
w
e
d
e
s
c
r
ib
e
di
s
ta
nc
e
m
e
a
s
ur
in
g
te
c
hni
que
s
,
ve
hi
c
le
c
ount
in
g
a
ppr
oa
c
he
s
,
a
di
s
ta
n
c
e
-
ba
s
e
d
e
m
e
r
ge
nc
y
ve
hi
c
le
di
s
pa
tc
hi
ng
m
e
c
ha
ni
s
m
,
a
nd
th
e
s
im
ul
a
ti
on
e
nvi
r
onm
e
nt
.
T
he
P
T
L
C
S
M
goe
s
be
yond
th
e
c
ons
tr
a
in
ts
of
ty
pi
c
a
l
r
e
d,
y
e
ll
ow
,
a
nd
gr
e
e
n.
I
t
a
n
a
ly
z
e
s
r
e
a
l
-
ti
m
e
da
ta
f
r
om
a
va
r
ie
ty
o
f
s
our
c
e
s
,
in
c
lu
di
ng
c
a
m
e
r
a
s
,
s
e
ns
or
s
e
m
b
e
dde
d
in
th
e
pa
ve
m
e
nt
,
a
nd
G
P
S
-
e
qui
ppe
d
a
ut
om
obi
le
s
.
T
hi
s
da
ta
in
c
lu
de
s
in
f
or
m
a
ti
on
s
uc
h
a
s
tr
a
f
f
i
c
d
e
ns
it
y,
ve
hi
c
le
ty
pe
,
pr
oxi
m
it
y
to
e
m
e
r
ge
nc
y
ve
hi
c
le
s
,
a
nd
e
v
e
n
e
nvi
r
onm
e
nt
a
l
p
a
r
a
m
e
te
r
s
s
uc
h
a
s
w
e
a
th
e
r
c
ondi
ti
ons
.
W
it
h
th
is
r
e
a
l
-
ti
m
e
knowle
dge
,
th
e
P
T
L
C
S
M
i
m
pr
ove
s
t
r
a
f
f
ic
s
ig
na
l
ti
m
in
g a
nd s
e
que
nc
in
g on a
n i
nt
e
r
s
e
c
ti
on
-
by
-
in
te
r
s
e
c
ti
on ba
s
is
.
T
he
s
tu
dy'
s
be
ne
f
it
is
th
a
t
it
gi
ve
s
a
nove
l
m
e
th
od f
or
c
a
lc
ul
a
ti
n
g
th
e
di
s
ta
nc
e
be
twe
e
n
th
e
e
m
e
r
ge
nc
y
ve
hi
c
le
a
nd
th
e
ju
nc
ti
on.
T
he
m
e
th
od
w
il
l
a
ll
ow
f
or
th
e
e
xa
m
in
a
ti
on
of
a
n
a
im
e
d
a
t
c
r
e
a
ti
ng
a
c
r
e
a
te
a
f
r
a
m
e
w
or
k
f
o
r
a
f
r
e
que
nc
y
m
e
a
s
ur
in
g
c
ont
r
ol
le
r
to
id
e
nt
if
y
e
m
e
r
ge
nc
y
c
a
r
s
.
A
s
e
lf
-
or
ga
ni
z
e
d
tr
a
f
f
ic
c
ont
r
ol
m
e
th
od
th
a
t
a
id
s
in
th
e
f
unc
ti
oni
ng
of
e
m
e
r
ge
nc
y
r
e
s
pons
e
v
e
hi
c
le
s
;
a
ll
ow
s
e
m
e
r
ge
nc
y
ve
hi
c
le
s
to
tr
a
ve
l
th
r
ough
tr
a
f
f
ic
.
A
te
c
hni
que
f
or
r
e
s
ol
vi
ng
ju
nc
ti
on
-
e
ns
ur
in
g
d
is
put
e
s
a
nd
im
pl
e
m
e
nt
in
g
a
pr
io
r
it
y
pl
a
n
f
o
r
pr
io
r
it
iz
in
g e
m
e
r
ge
nc
y ve
hi
c
le
s
a
t
in
te
r
s
e
c
ti
ons
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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J
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D
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H
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w
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A
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4021
T
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a
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r
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pot
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in
li
f
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ni
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c
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c
um
s
ta
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F
ur
th
e
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m
or
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it
im
pr
ove
s
th
e
e
f
f
ic
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publ
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tr
a
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por
ta
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a
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a
m
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m
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pe
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to
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m
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ta
in
a
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e
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n
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c
ol
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T
he
r
e
m
a
in
de
r
of
th
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s
s
tu
dy
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s
tr
uc
tu
r
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in
to
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e
di
s
c
us
s
e
s
th
e
m
e
th
odol
ogy
u
s
e
d
f
or
th
e
pr
io
r
it
iz
e
d
tr
a
f
f
ic
c
on
tr
ol
m
ode
l,
s
e
c
ti
on
f
our
de
s
c
r
ib
e
s
th
e
r
e
s
ul
ts
obt
a
in
e
d f
r
om
t
he
s
im
ul
a
ti
on, a
nd s
e
c
ti
on f
iv
e
c
onc
lu
de
s
t
he
s
tu
dy.
E
s
s
ie
n
a
nd
U
lo
ko
[
17]
e
nt
a
il
e
d
d
e
ve
lo
pi
ng
a
m
e
th
od
to
r
e
duc
e
th
e
li
ke
li
hood
of
a
c
c
id
e
nt
s
pr
e
va
le
nt
a
t
tr
a
f
f
ic
s
ig
na
l
ju
nc
ti
ons
,
w
h
e
r
e
ve
hi
c
le
s
f
r
e
que
nt
ly
ne
e
d
to
s
w
e
r
ve
to
m
a
ke
s
pa
c
e
f
or
e
m
e
r
ge
nc
y
ve
hi
c
le
s
f
ol
lo
w
in
g
a
pr
e
de
te
r
m
in
e
d
pa
th
.
T
he
s
tu
dy
w
a
s
s
uc
c
e
s
s
f
ul
in
a
na
ly
z
in
g,
s
im
ul
a
ti
ng,
a
nd
im
pl
e
m
e
nt
in
g
w
ir
e
le
s
s
c
om
m
uni
c
a
ti
on
s
ys
te
m
s
f
or
tr
a
f
f
ic
li
ght
m
a
na
ge
m
e
nt
.
T
he
r
e
a
li
z
e
d
m
ode
l
us
e
s
r
a
di
o
f
r
e
que
nc
y
id
e
nt
if
ic
a
ti
on
(
R
F
I
D
)
c
om
m
uni
c
a
ti
on,
ope
r
a
te
s
in
c
onc
e
r
t
w
it
h
th
e
s
e
que
nt
i
a
l
m
ode
of
s
ig
na
ls
to
c
ha
nge
th
e
tr
a
f
f
ic
li
ght
s
e
r
ie
s
,
a
nd
r
e
s
to
r
e
s
th
e
tr
a
f
f
ic
s
ig
na
ls
to
th
e
ir
or
ig
in
a
l
s
e
que
n
c
e
a
f
te
r
th
e
e
m
e
r
ge
nc
y
ve
hi
c
le
ha
s
pa
s
s
e
d
th
r
ough
th
e
in
te
r
s
e
c
ti
ons
.
T
he
s
y
s
te
m
m
a
de
us
e
of
a
R
F
I
D
s
e
n
s
or
a
nd
a
r
e
duc
e
d
in
s
tr
uc
ti
on
s
e
t
c
om
put
in
g
(
R
I
S
C
)
a
r
c
hi
te
c
tu
r
e
m
ic
r
oc
ont
r
ol
le
r
to
s
im
pl
if
y
th
e
ope
r
a
ti
on
of
a
3
-
w
a
y
in
te
r
s
e
c
ti
on
tr
a
f
f
ic
li
ght
a
nd
to
gi
ve
r
e
s
c
ue
ve
hi
c
le
s
pr
io
r
it
y.
M
oha
m
e
d
a
nd
R
a
dw
a
n
[
18]
pr
ovi
de
a
uni
que
tr
a
f
f
ic
l
ig
ht
c
o
nt
r
ol
(
T
L
C
)
te
c
hnol
ogy
f
or
di
f
f
e
r
e
nt
tr
a
f
f
ic
s
c
e
na
r
io
s
th
a
t
is
r
e
s
pons
iv
e
to
c
ur
r
e
nt
tr
a
f
f
ic
c
ondi
ti
ons
a
t
a
c
r
os
s
r
oa
ds
a
nd
pr
io
r
it
iz
e
s
e
s
s
e
nt
ia
l
c
a
r
s
a
bove
nor
m
a
l
tr
a
f
f
ic
. T
h
e
s
ugg
e
s
te
d
m
e
th
od a
ll
ow
s
f
or
s
e
ve
r
a
l
de
gr
e
e
s
of
pr
io
r
it
y a
m
ong pr
io
r
it
y ve
hi
c
le
s
a
nd
e
f
f
ic
ie
nt
ly
a
r
r
a
nge
s
tr
a
f
f
ic
s
ig
na
ls
a
t
a
ju
nc
ti
on
s
o
th
a
t
ot
h
e
r
or
di
na
r
y
tr
a
f
f
ic
is
not
di
s
r
upt
e
d.
T
he
s
tu
dy
doe
s
not
s
e
r
vi
c
e
pr
io
r
it
y
ve
hi
c
le
s
s
uc
h
a
s
bus
e
s
or
e
m
e
r
ge
n
c
y
ve
hi
c
le
s
(
a
m
bul
a
nc
e
s
a
nd
f
ir
e
e
ngi
ne
s
)
.
T
hi
s
w
or
k
in
ve
s
ti
ga
te
s
th
e
tr
a
f
f
ic
pa
tt
e
r
ns
in
le
s
s
-
di
s
c
ip
li
ne
d,
di
ve
r
s
e
tr
a
f
f
ic
(
two
-
th
r
e
e
-
f
our
-
w
he
e
le
r
c
a
r
s
)
,
w
hi
c
h
i
s
c
om
m
on i
n m
os
t
de
ve
lo
pi
ng na
ti
ons
. A
c
c
or
di
ng t
o
th
e
s
im
ul
a
ti
on f
in
di
ngs
, t
he
s
ugge
s
te
d m
e
th
od c
a
n t
ol
e
r
a
te
a
20%
m
is
ta
ke
in
th
e
pa
s
s
e
nge
r
c
a
r
uni
t
c
ount
w
it
hout
a
f
f
e
c
ti
ng
pe
r
f
or
m
a
nc
e
.
F
ur
th
e
r
m
or
e
,
th
is
s
tu
dy
in
di
c
a
te
s
th
a
t
th
e
r
e
qui
s
it
e
pr
e
c
is
io
n
in
tr
a
f
f
ic
in
f
or
m
a
ti
on
c
a
n
be
ha
ndl
e
d
in
r
e
a
l
-
time
ut
il
iz
in
g
th
e
a
va
il
a
bl
e
m
ic
r
oc
ont
r
ol
le
r
s
.
B
is
ht
e
t
al
.
[
19]
,
th
e
in
ve
s
ti
ga
ti
on
s
tr
a
te
gy
h
a
s
pl
a
c
e
d
a
hi
g
h
f
oc
us
on
pl
a
nni
ng,
a
na
ly
z
in
g,
a
nd
r
e
por
ti
ng
out
c
om
e
s
.
A
c
c
or
di
ng
to
th
e
s
tu
dy'
s
f
in
di
ngs
,
a
s
pe
c
ia
li
z
e
d
de
s
ig
n
th
a
t
m
oni
to
r
s
tr
a
f
f
ic
on
r
oa
ds
a
nd
gi
ve
s
in
te
ll
ig
e
nt
a
lt
e
r
na
ti
ve
s
ha
s
ye
t
to
be
e
s
t
a
bl
is
he
d.
T
h
e
s
ta
nda
r
d
de
s
ig
n
m
e
th
od
la
c
ks
in
te
ll
ig
e
nt
pe
r
f
or
m
a
nc
e
,
a
dj
us
ta
bl
e
ti
m
e
f
r
a
m
e
s
,
le
a
r
ni
ng
c
a
p
a
bi
li
ti
e
s
,
e
m
e
r
ge
nc
y
pr
io
r
it
iz
a
ti
on
a
dm
in
is
tr
a
ti
on,
a
nd
dyna
m
ic
dur
a
ti
on
s
e
que
nc
e
s
.
W
hi
le
th
e
a
da
pt
iv
e
s
e
lf
-
or
ga
ni
z
a
ti
on
s
tr
a
te
gy
di
s
r
e
ga
r
de
d
le
a
r
ni
ng
s
ki
ll
s
.
H
ow
e
ve
r
,
th
e
va
s
t
m
a
jo
r
it
y
of
c
le
ve
r
ly
pl
a
nne
d
e
xp
e
r
im
e
nt
s
la
c
ke
d
c
ogni
ti
ve
s
tr
e
s
s
r
e
s
pon
s
e
s
a
nd
f
e
e
dba
c
k
c
a
pa
bi
li
ti
e
s
.
A
r
if
f
in
e
t
al
.
[
20
]
of
f
e
r
a
n i
nt
e
ll
ig
e
nt
s
ys
te
m
t
ha
t
dyna
m
ic
a
ll
y m
odi
f
ie
s
t
he
c
yc
le
l
e
ngt
h f
or
e
a
c
h l
a
ne
a
t
a
ju
nc
ti
on
b
a
s
e
d
on
tr
a
f
f
ic
de
n
s
it
y
a
nd
gi
ve
s
di
f
f
e
r
e
nt
ty
pe
s
o
f
e
m
e
r
ge
nc
y
ve
hi
c
l
e
s
a
c
c
e
s
s
to
th
e
c
r
os
s
r
oa
d
s
by
a
s
s
ig
ni
ng
va
r
yi
ng
pr
io
r
it
y.
T
he
s
ys
te
m
i
s
di
vi
de
d
in
to
th
r
e
e
m
odul
e
s
:
a
ut
om
a
ti
on
of
tr
a
f
f
ic
li
ght
s
,
e
m
e
r
ge
nc
y
R
F
I
D
,
a
nd
th
e
in
te
r
ne
t.
U
s
in
g
ul
tr
a
s
oni
c
s
e
ns
or
s
,
th
e
tr
a
f
f
ic
li
g
ht
r
e
gul
a
ti
on
m
odul
e
s
de
te
c
t
m
ot
o
r
is
t
de
ns
it
y
a
nd
pr
ovi
de
a
n
e
vol
vi
ng
s
e
t
of
c
yc
le
le
ngt
h
s
a
c
c
or
di
ng
to
th
e
s
p
e
c
if
ic
la
ne
de
n
s
it
y
s
it
ua
t
io
n.
E
m
e
r
ge
nc
y
R
F
I
D
s
a
r
e
f
it
te
d
on
s
e
ve
r
a
l
s
or
ts
of
e
m
e
r
ge
nc
y
ve
hi
c
le
s
ba
s
e
d
on
pr
e
de
t
e
r
m
in
e
d
pr
io
r
it
y
w
e
ig
ht
s
.
T
he
in
te
r
ne
t
m
odul
e
e
na
bl
e
s
a
ut
hor
iz
e
d
s
ta
f
f
t
o m
a
na
ge
t
he
dyna
m
ic
t
r
a
f
f
ic
s
ig
na
l
s
y
s
te
m
i
n
r
e
a
l
ti
m
e
.
K
um
a
r
e
t
al
.
[
21]
us
e
a
dyna
m
ic
a
nd
in
te
ll
ig
e
nt
t
r
a
f
f
ic
l
ig
ht
c
ont
r
ol
s
ys
te
m
(
D
I
T
L
C
S
)
th
a
t
ope
r
a
te
s
in
th
r
e
e
f
or
m
s
:
F
a
ir
m
ode
(
F
M
)
,
pr
io
r
it
y
m
ode
(
P
M
)
,
a
nd
e
m
e
r
ge
n
c
y
m
ode
(
E
M
)
,
in
w
hi
c
h
a
ll
ve
hi
c
le
s
ha
ve
e
qua
l
pr
io
r
it
y,
m
ot
or
c
yc
le
s
of
va
r
io
us
c
a
te
gor
iz
a
ti
ons
ha
ve
di
f
f
e
r
e
nt
de
gr
e
e
s
of
im
por
ta
nc
e
,
a
nd
m
e
di
c
a
l
ve
hi
c
le
s
ha
ve
th
e
hi
ghe
s
t
pr
io
r
it
y.
A
c
om
pl
e
x
r
e
in
f
or
c
e
m
e
nt
le
a
r
ni
ng
a
ppr
oa
c
h
is
a
ls
o
de
m
ons
tr
a
te
d
to
to
ggl
e
tr
a
f
f
ic
li
ght
s
in
di
s
ti
nc
t
pha
s
e
s
(
r
e
d,
gr
e
e
n,
a
nd
ye
ll
ow
)
,
a
nd
a
f
uz
z
y
in
f
e
r
e
nc
e
s
ys
te
m
is
de
m
ons
tr
a
te
d
to
pi
c
k
one
m
ode
be
twe
e
n
th
e
th
r
e
e
opt
io
ns
(
F
M
,
P
M
,
a
nd
E
M
)
ba
s
e
d
on
tr
a
f
f
ic
da
ta
.
T
he
s
im
ul
a
ti
on
r
e
s
ul
ts
de
m
ons
tr
a
te
D
I
T
L
C
S
'
s
e
f
f
ic
ie
nc
y i
n c
om
pa
r
is
on t
o ot
he
r
s
ta
te
-
of
-
th
e
-
a
r
t
a
lg
or
it
hm
s
on a
va
r
ie
ty
of
pe
r
f
or
m
a
nc
e
m
e
tr
ic
s
.
A
c
c
or
di
ng t
o
P
e
te
r
e
t
al
.
[
22]
,
a
n
a
tt
e
m
pt
i
s
m
a
de
i
n s
tu
dy
t
o e
n
ha
nc
e
t
he
K
a
duna
R
e
f
in
e
r
y
J
unc
ti
on'
s
(
K
R
J
)
c
ur
r
e
nt
pr
ogr
a
m
m
e
d
s
ta
ti
ona
r
y
r
oa
d
tr
a
f
f
ic
s
ig
na
l
m
a
na
ge
m
e
nt
s
y
s
te
m
.
T
h
e
K
R
J
is
th
e
m
a
in
r
oa
d
c
onne
c
ti
ng
th
e
s
out
h
e
r
n
r
e
gi
on
of
th
e
s
ta
te
to
K
a
duna
'
s
m
a
jo
r
to
w
n.
D
ur
in
g
th
e
w
or
kda
y,
ve
hi
c
le
s
f
r
om
ot
he
r
pa
r
ts
of
t
he
c
ount
r
y, publi
c
a
nd pr
iv
a
te
s
e
r
va
nt
s
,
s
tu
de
nt
s
,
m
e
r
c
ha
nt
s
, a
nd oth
e
r
s
t
r
a
ve
li
ng t
o ot
he
r
a
r
e
a
s
of
t
he
c
ount
r
y
vi
a
th
e
s
ta
te
'
s
s
out
he
r
n
ha
lf
c
onv
e
r
ge
a
t
th
e
K
R
J
.
A
n
e
c
o
nom
ic
a
l
m
ode
l
of
th
e
f
uz
z
y
lo
gi
c
(
F
L
)
m
e
th
od
is
c
r
e
a
te
d
f
or
th
e
m
os
t
e
f
f
e
c
ti
ve
pl
a
nni
ng
of
T
L
C
s
y
s
te
m
s
us
i
ng
T
r
a
C
I
4M
A
T
L
A
B
a
nd
s
im
ul
a
ti
on
of
ur
ba
n
m
obi
li
ty
(
S
U
M
O
)
.
T
he
a
ve
r
a
ge
im
pr
ove
m
e
nt
ove
r
th
e
pr
e
vi
ou
s
r
e
s
ul
t
w
a
s
2.74%
.
W
he
n
e
m
e
r
ge
nc
y
ve
hi
c
le
s
w
e
r
e
pr
io
r
it
iz
e
d,
th
e
r
e
w
a
s
a
66.79%
im
pr
ove
m
e
nt
ove
r
s
ta
ti
c
p
ha
s
e
s
c
he
dul
in
g.
T
hi
s
de
m
on
s
tr
a
te
s
th
a
t
F
L
c
a
n
be
us
e
f
ul
i
n t
r
a
f
f
ic
c
ont
r
ol
s
ys
te
m
s
.
L
i
e
t
al
.
[
23]
pl
a
nne
d e
f
f
o
r
t,
w
hi
c
h
is
ba
s
e
d on a
ut
om
obi
le
pr
io
r
it
y, i
nt
e
nds
t
o
pr
ovi
de
a
s
m
a
r
t
t
r
a
f
f
ic
s
ig
na
l
s
ys
te
m
t
o w
hi
c
h t
he
hi
gh pr
io
r
it
y ve
hi
c
le
s
(
H
P
V
)
m
a
y
r
e
que
s
t
be
in
g l
oa
de
d a
t
a
c
r
os
s
r
oa
ds
. D
e
pe
ndi
ng
on
th
e
gr
e
a
te
s
t
pr
io
r
it
y,
th
e
s
ys
te
m
w
oul
d
c
h
a
nge
th
e
tr
a
f
f
ic
s
i
gna
l
gr
e
e
n
to
c
le
a
r
th
e
r
oa
d
s
e
gm
e
nt
(
R
S
)
a
nd
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
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I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
. 13, No. 4, D
e
c
e
m
be
r
2024:
4019
-
4028
4022
s
a
ve
H
P
V
tr
a
ve
l
ti
m
e
.
T
h
e
s
ys
t
e
m
is
e
va
lu
a
t
e
d
us
in
g
th
e
P
yt
ho
n
tr
a
f
f
ic
c
ont
r
ol
in
te
r
f
a
c
e
(
T
r
a
C
I
)
a
nd
S
U
M
O
.
T
he
f
in
di
ngs
de
m
ons
tr
a
te
th
e
e
f
f
ic
ie
nc
y
of
th
e
in
te
ll
ig
e
nt
tr
a
f
f
ic
s
ig
na
l
s
ys
te
m
.
I
n
th
e
f
ut
ur
e
,
it
m
a
y
ha
ve
im
por
ta
nt
t
he
or
e
ti
c
a
l
a
s
w
e
ll
a
s
pr
a
c
ti
c
a
l
ut
il
it
y f
or
i
nt
e
ll
ig
e
nt
t
r
a
ns
por
ta
ti
on s
ys
te
m
s
(
I
T
S
)
.
T
r
a
f
f
ic
li
ght
s
a
r
e
in
te
nde
d
to
k
e
e
p
c
it
ie
s
m
ovi
ng
s
m
oot
hl
y
[
24]
.
T
he
e
xi
s
ti
ng
tr
a
f
f
ic
s
ys
te
m
h
a
s
b
e
e
n
in
pl
a
c
e
f
or
s
om
e
ye
a
r
s
[
25
]
-
[
29
]
.
A
s
th
e
num
be
r
of
c
a
r
s
on
th
e
r
oa
d
in
c
r
e
a
s
e
s
,
th
is
m
e
th
od
is
f
a
il
in
g
to
a
ddr
e
s
s
tr
a
f
f
ic
c
onge
s
ti
on
is
s
ue
s
,
p
a
r
ti
c
ul
a
r
ly
a
t
ju
nc
ti
ons
.
H
P
V
be
c
om
e
s
c
a
ught
in
tr
a
f
f
ic
a
s
a
r
e
s
ul
t
of
tr
a
f
f
ic
c
onge
s
ti
on,
c
a
us
in
g
de
la
ys
in
th
e
ir
s
e
r
vi
c
e
s
.
H
P
V
s
,
s
uc
h
th
e
a
m
bul
a
nc
e
s
a
nd
f
ir
e
tr
uc
ks
,
m
us
t
r
e
s
pond
to
a
va
r
ie
ty
of
i
nc
id
e
nt
s
. H
P
V
m
u
s
t
a
r
r
iv
e
on t
im
e
.
T
he
r
e
i
s
a
ne
e
d f
or
a
s
ys
te
m
t
ha
t
pr
ovi
de
s
a
c
onduit
f
or
H
P
V
to
tr
a
ve
l
a
s
s
oon
a
s
f
e
a
s
ib
le
.
T
he
s
ugg
e
s
te
d
w
or
k
ta
k
e
s
a
pr
io
r
it
iz
e
d
a
ppr
oa
c
h.
I
t
s
e
e
k
s
to
pr
ovi
de
a
us
e
r
-
in
te
r
a
c
ti
ve
H
P
V
s
ys
te
m
in
w
hi
c
h
a
n
H
P
V
d
r
iv
e
r
m
a
y
s
ubm
it
r
e
que
s
ts
to
th
e
s
ys
te
m
a
nd
th
e
s
ys
te
m
r
e
a
c
ts
in
te
ll
ig
e
nt
ly
.
A
t
a
ju
nc
ti
on,
th
e
pr
io
r
it
y
of
R
S
i
s
de
te
r
m
in
e
d,
a
nd
th
e
tr
a
f
f
ic
s
ig
na
l
tu
r
ns
gr
e
e
n
f
or
th
e
R
S
w
it
h
th
e
hi
gh
e
s
t
pr
io
r
it
y.
T
he
m
e
th
od
w
a
s
e
va
l
u
a
te
d
on
S
U
M
O
a
nd
yi
e
ld
e
d
pos
it
iv
e
r
e
s
ul
ts
,
s
a
vi
ng
m
or
e
th
a
n
50%
of
th
e
ti
m
e
a
c
r
os
s
a
ll
tr
a
f
f
ic
c
onc
e
nt
r
a
ti
ons
(
lo
w
, m
ode
r
a
te
, a
nd high)
.
2.
M
E
T
H
O
D
T
he
pr
oc
e
s
s
e
m
pl
oye
d
in
th
is
r
e
s
e
a
r
c
h
in
c
lu
de
s
m
a
na
gi
ng
tr
a
f
f
ic
c
e
nt
e
r
pe
r
f
or
m
a
nc
e
,
di
s
ta
nc
e
m
e
a
s
ur
in
g
te
c
hni
que
s
,
a
nd
a
di
s
ta
nc
e
-
ba
s
e
d
r
e
s
c
ue
ve
hi
c
le
,
de
te
r
m
in
in
g
th
e
pos
it
io
n
of
th
e
e
m
e
r
ge
nc
y
c
a
r
,
a
nd
m
e
th
ods
f
or
e
m
e
r
ge
nc
y
ve
hi
c
le
id
e
nt
if
ic
a
ti
on
ba
s
e
d
on
s
ir
e
n s
o
unds
.
T
h
e
a
m
ount
of
ti
m
e
it
ta
k
e
s
to
ge
t
to
th
e
ne
xt
s
e
t
of
in
te
r
s
e
c
ti
ons
.
T
he
tr
a
f
f
ic
s
ig
na
l
w
il
l
r
e
ve
r
t
to
it
s
us
ua
l
s
e
que
nc
e
a
f
te
r
th
e
e
m
e
r
ge
nc
y
ve
hi
c
le
h
a
s
pa
s
s
e
d. F
or
t
he
s
e
tt
in
g up of
t
hi
s
s
ys
te
m
, a
n A
R
D
I
N
U
O
U
N
O
w
a
s
e
m
pl
oye
d.
2.1.
T
r
af
f
ic
d
e
t
e
c
t
io
n
T
he
s
uppor
t
m
ode
l,
li
ke
ot
he
r
t
r
a
f
f
ic
-
r
e
s
pons
iv
e
f
la
g
m
a
na
ge
m
e
nt
s
ys
te
m
s
,
de
te
r
m
in
e
s
s
ig
na
l
-
s
w
it
c
hi
ng
c
hoi
c
e
s
ba
s
e
d
m
a
in
ly
on
e
xpe
c
te
d
ve
hi
c
le
c
om
in
g
da
ta
.
T
he
da
t
a
is
a
c
qui
r
e
d
vi
a
tr
a
f
f
ic
de
te
c
to
r
s
s
tr
a
te
gi
c
a
ll
y
pl
a
c
e
d
a
lo
ng
th
e
r
oa
d
le
a
di
ng
to
th
e
c
o
nt
r
ol
le
d
ju
nc
ti
on.
T
he
c
onc
e
pt
a
ll
ow
s
f
o
r
th
e
de
pl
oym
e
nt
of
one
or
m
or
e
tr
a
f
f
ic
de
te
c
to
r
s
a
lo
ng
ju
nc
ti
on
a
ppr
oa
c
he
s
a
t
a
ny
poi
nt
.
S
uppor
t
r
e
c
or
ds
th
e
de
te
c
ti
ng
ti
m
e
a
nd
ki
nd
o
f
c
a
r
w
he
ne
ve
r
th
e
c
a
r
t
r
a
ve
ls
ove
r
a
s
e
ns
or
.
T
he
s
im
ul
a
to
r
is
e
nt
it
y
-
o
r
ie
nt
e
d
a
nd
t
a
ke
s
in
to
a
c
c
ount
t
hr
e
e
obj
e
c
t
ty
pe
s
:
c
a
r
s
, s
e
gm
e
nt
s
, a
nd
ve
hi
c
le
a
c
ti
ons
. T
he
t
ot
a
l
a
m
ount
of
s
e
gm
e
nt
s
a
nd
ve
hi
c
le
ope
r
a
ti
ons
a
r
e
s
e
t
by
th
e
us
e
r
.
V
e
hi
c
le
s
a
r
e
c
r
e
a
t
e
d
a
ut
om
a
ti
c
a
ll
y
dur
in
g
s
im
ul
a
ti
on
ba
s
e
d
on
u
s
e
r
-
de
f
in
e
d
f
lo
w
pa
r
a
m
e
te
r
s
(
f
or
e
xa
m
pl
e
,
s
ta
ti
s
ti
c
a
l
di
s
tr
ib
ut
io
ns
of
in
te
r
-
a
r
r
iv
a
l
dur
a
ti
ons
,
pl
a
to
on
f
e
a
tu
r
e
s
,
a
nd
a
r
r
iv
a
l
li
s
ts
)
.
T
he
s
im
ul
a
to
r
c
a
n
s
im
ul
a
te
th
e
e
f
f
e
c
ts
th
a
t
tr
a
ns
it
ve
hi
c
le
s
ha
ve
on
ot
he
r
c
a
r
s
w
he
n
th
e
y
s
to
p
on
th
e
c
or
r
e
c
t
a
m
ount
of
r
oa
d t
o boa
r
d a
nd r
e
le
a
s
e
pe
opl
e
.
2.2.
R
u
le
-
b
as
e
d
s
ig
n
al
-
s
w
it
c
h
in
g d
e
c
is
io
n
p
r
oc
e
s
s
T
he
s
uppor
t
pa
r
a
di
gm
de
c
id
e
s
on
s
ig
na
l
f
li
ppi
ng
vi
a
a
he
ur
is
ti
c
r
ul
e
-
ba
s
e
d
s
ig
na
l
opt
im
iz
a
ti
on
a
ppr
oa
c
h.
T
he
te
c
hni
que
i
s
ba
s
e
d
on
th
e
knowle
dg
e
th
a
t
s
ig
n
a
l
c
ha
nge
s
of
te
n
oc
c
ur
a
f
te
r
th
e
r
e
a
li
z
a
ti
on
of
s
pe
c
if
ie
d
di
s
c
r
e
te
oc
c
ur
r
e
nc
e
s
,
s
uc
h
a
s
w
h
e
n
a
li
ne
of
c
a
r
s
r
e
a
c
he
s
a
gi
v
e
n
s
iz
e
w
he
n
a
que
ue
di
s
s
ip
a
te
s
,
or
w
he
n
a
n
a
r
r
iv
in
g
tr
a
ns
it
ve
hi
c
le
is
de
te
c
te
d.
T
he
a
ppr
oa
c
h
a
ll
ow
s
th
e
s
uppor
t
m
ode
l
to
gr
e
a
tl
y
m
in
im
iz
e
th
e
num
be
r
of
a
lt
e
r
na
ti
ve
s
w
it
c
hi
ng
pe
r
m
ut
a
ti
ons
th
a
t
m
us
t
be
in
ve
s
ti
ga
te
d
to
id
e
nt
if
y
a
ne
a
r
-
opt
im
um
s
ol
ut
io
n
b
y
di
s
c
a
r
di
ng
th
os
e
e
v
e
nt
s
th
a
t
h
a
ve
no
s
ig
ni
f
ic
a
nc
e
f
or
s
ig
na
l
f
u
nc
ti
oni
ng.
T
he
m
ode
l
pa
r
ti
c
ul
a
r
ly
a
na
ly
z
e
s
th
e
f
ol
lo
w
in
g r
e
a
c
ti
ons
t
o s
ig
ni
f
ic
a
nt
t
r
a
f
f
ic
e
ve
nt
s
t
ha
t
oc
c
ur
on a
pa
r
ti
c
ul
a
r
j
unc
ti
on s
tr
a
te
gy:
−
S
te
p
1:
I
f
th
e
r
e
is
a
n
n
-
ve
hi
c
le
s
to
p
li
ne
que
u
e
th
a
t
is
not
be
in
g of
f
e
r
e
d,
be
gi
n
s
e
r
vi
c
in
g
th
e
que
u
e
a
s
s
oon
a
s
f
e
a
s
ib
le
. I
f
t
he
s
to
p l
in
e
que
u
e
r
e
a
c
he
s
a
us
e
r
-
de
f
in
e
d l
e
ngt
h, t
ur
n t
he
s
ig
na
l
di
s
pl
a
y gr
e
e
n.
−
S
te
p
2:
K
e
e
p
th
e
pr
e
s
e
nt
gr
e
e
n
s
ig
na
l
in
di
c
a
to
r
in
a
di
r
e
c
ti
on
w
he
r
e
th
e
ups
tr
e
a
m
que
ue
'
s
r
e
a
c
h
e
xc
e
e
ds
a
us
e
r
-
de
f
in
e
d pos
it
io
n.
−
S
te
p
3:
I
f
a
ba
c
kl
og
on
one
of
th
e
m
e
th
od'
s
u
s
e
r
-
de
f
in
e
d
m
a
in
e
xi
t
c
onne
c
ti
ons
im
pe
nd
s
to
le
a
k
ba
c
k
ove
r
th
e
j
unc
ti
on, t
ur
n t
he
l
ig
ht
r
e
d.
−
S
te
p
4:
W
he
n
a
gr
oup
of
n
or
m
or
e
c
a
r
s
a
ppr
oa
c
h
th
e
ju
nc
ti
on,
tu
r
n
th
e
s
ig
na
l
s
how
to
gr
e
e
n
a
t
a
m
om
e
nt
th
a
t
a
ll
ow
s
t
he
pl
a
to
on t
o pa
s
s
t
he
j
unc
ti
on w
it
hout
be
in
g i
nf
lu
e
nc
e
d by ve
hi
c
le
s
h
a
lt
e
d a
t
th
e
s
to
p l
in
e
.
−
S
te
p 5:
I
f
a
ve
hi
c
le
l
in
e
i
s
be
in
g s
e
r
ve
d, c
ont
in
ue
t
o
s
e
r
ve
t
he
q
ue
ue
.
−
S
te
p 6:
I
f
you a
r
e
s
e
r
vi
ng a
pl
a
to
on of
ve
hi
c
le
s
, ke
e
p doing s
o.
−
S
te
p
7:
A
s
a
tr
a
n
s
it
ve
hi
c
le
a
ppr
oa
c
he
s
it
s
tr
a
ns
it
s
to
p,
tu
r
n
th
e
s
i
gna
l
s
how
to
gr
e
e
n
a
t
a
m
om
e
nt
th
a
t
a
ll
ow
s
th
e
ve
hi
c
le
t
o c
ont
in
ue
up t
o i
ts
put
ti
ng pos
it
io
n undis
tu
r
be
d.
−
S
te
p 8:
A
s
a
t
r
a
ns
it
ve
hi
c
l
e
a
ppr
oa
c
he
s
t
he
s
to
p l
in
e
, t
ur
n t
he
s
i
gna
l
di
s
pl
a
y t
o gr
e
e
n a
t
a
m
om
e
nt
w
he
n t
he
ve
hi
c
le
c
a
n c
r
os
s
t
he
r
oa
dw
a
y w
it
hout
s
to
ppi
ng.
−
S
te
p
9:
T
o
a
c
c
ount
f
or
th
e
r
e
a
li
ty
th
a
t
not
a
ll
t
r
a
f
f
ic
oc
c
ur
r
e
nc
e
s
a
r
e
e
qua
l
in
im
por
ta
nc
e
,
e
a
c
h
e
ve
nt
is
gi
ve
n
a
pr
io
r
it
y or
w
e
ig
ht
in
g.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
D
e
v
e
lo
pm
e
nt
of
a pr
io
r
it
iz
e
d t
r
af
fi
c
l
ig
ht
c
ont
r
ol
s
y
s
t
e
m
f
or
e
m
e
r
ge
nc
y
…
(
H
al
le
lu
y
ah O
lu
w
at
obi
A
w
or
in
de
)
4023
−
S
te
p
10:
B
y
th
e
de
te
c
ti
on
of
a
ny
pr
io
r
it
y
oc
c
ur
r
e
nc
e
dur
in
g
t
he
pe
r
io
d
dur
in
g
w
hi
c
h
tr
a
f
f
ic
a
r
r
iv
e
s
,
th
e
s
ig
na
l
opt
im
iz
a
ti
on
m
e
th
od
pr
ovi
de
s
r
e
que
s
ts
f
or
e
it
he
r
a
gr
e
e
n
or
a
r
e
d
s
ig
na
l
in
di
c
a
ti
on
on
c
e
r
ta
in
a
ppr
oa
c
he
s
a
t
s
pe
c
if
ie
d t
im
e
s
.
2.3.
M
u
lt
i
-
ob
j
e
c
t
iv
e
op
t
im
iz
at
io
n
p
r
oc
e
s
s
W
hi
le
th
e
s
uppor
t
a
ppr
oa
c
h
c
a
n
id
e
nt
if
y
th
e
r
e
la
ti
ve
s
ig
ni
f
ic
a
n
c
e
of
m
ul
ti
pl
e
tr
a
f
f
ic
e
ve
nt
s
by
u
s
in
g
pr
io
r
it
iz
e
d
li
s
ti
ngs
of
in
c
id
e
nt
s
,
it
is
f
r
e
que
nt
ly
im
pos
s
ib
le
to
k
now
w
hi
c
h
e
v
e
nt
ought
to
r
e
c
e
iv
e
th
e
gr
e
a
te
s
t
pr
io
r
it
y
a
he
a
d
of
ti
m
e
.
T
he
us
e
r
c
a
n
c
hoos
e
th
e
r
e
la
ti
ve
w
e
ig
h
ts
f
or
pa
us
e
s
,
de
la
ys
,
a
nd
tr
a
ve
l
ti
m
e
(
kd,
ks
,
kT
T
)
.
T
r
a
ve
l
ti
m
e
is
not
ta
ke
n
in
to
a
c
c
ount
in
a
ny
of
th
e
a
s
s
e
s
s
m
e
nt
s
in
th
is
a
r
ti
c
le
(
i.
e
.
kT
T
=
0)
.
T
he
ope
r
a
ti
on
a
ddi
ti
ona
ll
y
in
c
or
por
a
te
s
a
te
r
m
in
a
l
c
os
t
c
om
pon
e
nt
th
a
t
pr
oj
e
c
t
io
ns
th
e
de
la
ys
in
c
ur
r
e
d
by
e
ve
r
y
c
a
r
le
f
t
in
th
e
que
ue
out
s
id
e
th
e
e
nd
of
th
e
c
hoi
c
e
hor
iz
on
unde
r
th
e
pr
e
s
um
pt
io
n
th
a
t
no
a
ddi
ti
ona
l
c
a
r
s
jo
in
th
e
que
u
e
be
yond
th
e
e
nd of
t
he
c
hoi
c
e
hor
iz
on a
nd t
ha
t
que
u
e
e
xpe
ll
e
d ha
pp
e
ns
a
t
th
e
gr
e
a
te
s
t
r
a
te
. T
h
e
t
e
r
m
in
a
l
c
os
t
is
u
s
e
d t
o
ove
r
c
om
e
a
bi
a
s
th
a
t
m
ig
ht
c
a
us
e
th
e
s
ig
na
l
opt
im
iz
a
ti
on
pr
oc
e
dur
e
to
pi
c
k
s
ig
na
l
-
s
w
it
c
hi
ng
opt
io
ns
th
a
t
pr
oduc
e
a
l
ow
c
os
t
in
t
he
s
hor
t
te
r
m
but
a
hi
gh c
os
t
la
t
e
r
on.
2.4.
A
r
c
h
it
e
c
t
u
r
al
d
e
s
ig
n
of
t
h
e
s
ys
t
e
m
T
he
s
ys
te
m
a
r
c
hi
te
c
tu
r
e
of
th
e
pr
opos
e
d
s
ys
te
m
s
how
s
how
t
he
s
ys
t
e
m
be
ha
v
e
s
w
it
h
th
e
a
id
of
a
s
im
pl
e
de
s
ig
n.
A
s
s
how
n
in
F
ig
ur
e
1,
th
e
tr
a
f
f
ic
s
ys
te
m
m
us
t
pr
io
r
it
iz
e
e
m
e
r
ge
nc
y
c
ir
c
um
s
ta
nc
e
s
.
T
h
e
a
dm
in
is
tr
a
to
r
lo
gi
ns
t
o t
he
la
pt
op
a
nd c
onn
e
c
ts
t
o t
he
A
r
dui
no b
oa
r
d
in
w
hi
c
h a
ll
t
h
e
w
ir
in
g of
a
ll
f
our
l
a
ne
s
i
s
c
onne
c
te
d
to
th
e
A
r
dui
no
boa
r
d
a
nd
a
ls
o
to
th
e
gr
ound
on
th
e
A
r
dui
no
boa
r
d,
th
e
n
th
e
tr
a
f
f
ic
li
gh
t
f
or
th
e
f
our
-
la
ne
w
or
ks
.
O
n
th
e
pr
e
e
m
pt
io
n
of
th
e
a
m
bul
a
nc
e
,
it
tr
ig
ge
r
s
th
e
li
ght
a
nd
c
ha
nge
s
to
gr
e
e
n
f
or
th
e
pa
s
s
a
ge
of
t
he
a
m
bul
a
nc
e
a
nd r
e
tu
r
ns
t
o t
he
nor
m
a
l
tr
a
f
f
ic
f
lo
w
.
F
ig
ur
e
1. A
r
c
hi
te
c
tu
r
a
l
di
a
gr
a
m
of
a
pr
io
r
i
ti
z
e
d t
r
a
f
f
ic
f
lo
w
T
he
a
lg
or
it
hm
de
te
r
m
in
e
s
w
hi
c
h
r
out
e
s
houl
d
be
gr
a
nt
e
d
a
gr
e
e
n l
ig
ht
ba
s
e
d
on que
u
e
l
e
ngt
h, v
e
hi
c
le
s
pe
e
d,
v
e
hi
c
le
a
c
c
e
le
r
a
ti
on,
a
nd
c
a
r
di
s
ta
nc
e
s
f
r
om
th
e
ju
n
c
ti
on.
I
t
de
f
in
e
s
th
e
s
e
qu
e
nc
e
in
w
hi
c
h
th
e
va
r
io
us
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
. 13, No. 4, D
e
c
e
m
be
r
2024:
4019
-
4028
4024
gr
e
e
n s
ta
ge
s
ought t
o be
c
om
pl
e
te
d a
nd how long they s
houl
d l
a
s
t.
T
he
pr
oc
e
dur
e
i
s
t
he
n r
e
pe
a
te
d w
h
e
n t
he
s
e
s
te
ps
ha
ve
be
e
n
c
om
pl
e
te
d.
T
he
s
e
que
nc
e
of
pha
s
e
s
is
de
te
r
m
in
e
d
by
th
e
le
ngt
h
of
e
a
c
h
que
ue
a
ppr
oa
c
hi
ng
th
e
in
te
r
s
e
c
ti
on,
w
it
h
a
bi
gge
r
que
ue
r
e
c
e
iv
in
g
m
or
e
pr
io
r
it
y
th
a
n
a
s
m
a
ll
e
r
one
,
a
nd
th
e
a
m
bul
a
nc
e
s
w
hi
c
h
a
ppr
oa
c
h
th
e
r
oa
d
f
ir
s
t.
F
ur
th
e
r
m
or
e
,
th
e
a
lg
or
it
hm
ut
il
iz
e
s
th
e
jo
ur
ne
y
dur
a
ti
on
of
e
a
c
h
la
ne
a
c
ti
ve
in
th
e
s
ta
ge
is
de
te
r
m
in
e
d
by
th
e
ve
hi
c
le
ve
lo
c
it
y
a
nd
di
s
ta
nc
e
to
th
e
ju
nc
ti
on.
T
he
lo
nge
s
t
tr
a
ve
r
s
in
g
ti
m
e
i
s
th
e
n
pi
c
ke
d
a
s
t
he
s
ta
ge
t
im
e
f
r
a
m
e
, a
s
l
ong a
s
i
t
is
l
e
s
s
t
ha
n a
pr
e
s
e
t
opt
im
um
gr
e
e
n t
im
e
o
f
60 s
e
c
onds
. T
hr
e
e
s
e
c
onds
i
s
th
e
ye
ll
ow
t
im
e
a
nd r
e
d i
s
s
e
t
to
90
s
e
c
onds
.
3.
R
E
S
U
L
T
S
A
N
D
D
I
S
C
U
S
S
I
O
N
T
he
m
ode
l
f
or
th
e
pr
io
r
it
iz
e
d
T
L
C
s
ys
te
m
is
de
s
ig
n
e
d
us
in
g
a
m
ic
r
oc
ont
r
ol
le
r
a
nd
a
n
in
te
gr
a
te
d
c
ir
c
ui
t
i.
e
.
A
r
dui
no,
a
br
e
a
dboa
r
d
,
ju
m
pe
r
w
ir
e
s
,
a
m
ot
or
c
a
r
,
L
E
D
l
ig
ht
s
,
a
nd
Wi
-
F
i
m
odul
e
(
T
)
.
T
hi
s
s
y
s
te
m
is
e
m
pl
oye
d
be
c
a
us
e
of
it
s
e
f
f
ic
a
c
y,
s
pe
e
d,
a
nd
e
f
f
ic
ie
nc
y.
I
ts
f
unc
ti
ona
li
ty
a
nd
m
e
th
od
a
r
e
s
im
pl
e
a
nd
e
a
s
y
to
us
e
.
3.1.
T
e
s
t
in
g an
d
e
val
u
at
io
n
of
t
h
e
s
ys
t
e
m
T
hi
s
s
ys
te
m
w
a
s
li
nke
d
u
s
in
g
ju
m
pe
r
c
a
bl
e
s
,
a
nd
e
a
c
h
e
le
m
e
nt
o
f
th
e
c
onne
c
ti
on
f
unc
ti
one
d
w
e
ll
.
T
he
f
unc
ti
oni
ng
of
th
e
s
ys
te
m
w
a
s
s
how
n
on
a
ti
ny
c
a
r
ve
d
tr
a
f
f
ic
li
g
ht
boa
r
d,
a
nd
i
ts
out
c
om
e
s
w
e
r
e
c
a
r
e
f
ul
ly
te
s
te
d
a
ga
in
s
t
e
s
ta
bl
is
he
d
c
ir
c
um
s
ta
nc
e
s
,
a
nd
a
t
a
ny
po
s
it
iv
e
of
a
s
e
t
c
o
ndi
ti
ons
s
om
e
th
in
g
is
tr
ig
ge
r
e
d.
F
ig
ur
e
2
s
how
s
th
e
c
onne
c
ti
on
of
L
E
D
li
gh
ts
to
th
e
b
r
e
a
dboa
r
d
a
nd
ju
m
pe
r
w
ir
e
s
c
onne
c
te
d
to
th
e
br
e
a
dboa
r
d
a
ll
c
onne
c
te
d
to
th
e
A
r
dui
no.
T
he
ju
m
pe
r
w
ir
e
s
a
r
e
c
onne
c
te
d
r
e
s
pe
c
ti
ve
ly
to
t
he
num
be
r
s
e
a
c
h
on
th
e
A
r
d
ui
no,
e
a
c
h
la
ne
is
c
onne
c
te
d
to
3
s
p
a
c
e
s
e
a
c
h
on
th
e
A
r
dui
no
boa
r
d
.
T
he
c
onn
e
c
ti
on
of
th
e
A
r
dui
no
boa
r
d
w
it
h
ju
m
pe
r
w
ir
e
s
to
th
e
r
e
s
pe
c
ti
ve
4
la
ne
s
of
th
e
tr
a
f
f
ic
or
de
r
a
nd
c
onne
c
ti
ng
th
e
A
r
dui
no
to
th
e
la
pt
op
w
he
r
e
th
e
c
ode
is
w
r
it
te
n,
th
e
w
ir
e
s
a
r
e
c
onne
c
te
d t
o t
he
numbe
r
s
on t
he
A
r
dui
no
r
e
s
pe
c
ti
ve
ly
, a
nd a
w
ir
e
is
c
onne
c
te
d t
o t
he
gr
ound
on
th
e
A
r
dui
no boa
r
d a
s
s
how
n i
n F
ig
ur
e
3.
F
ig
ur
e
2. T
r
a
f
f
ic
c
ont
r
ol
i
m
pl
e
m
e
nt
a
ti
on boa
r
d
F
ig
ur
e
3. T
r
a
f
f
ic
c
ont
r
ol
i
m
pl
e
m
e
nt
a
ti
on boa
r
d
T
he
w
ir
e
s
a
r
e
a
lr
e
a
dy
c
onne
c
t
e
d;
th
e
A
r
dui
no
boa
r
d
is
a
lr
e
a
dy
c
onne
c
te
d
to
th
e
s
ys
te
m
a
ls
o;
it
f
ur
th
e
r
s
how
s
th
e
li
ght
f
or
th
e
f
our
la
ne
s
;
th
e
f
ir
s
t
la
ne
is
on
li
ght
,
th
e
s
e
c
ond
la
ne
i
s
on
r
e
d
li
ght
,
th
e
th
ir
d
la
ne
is
on
ye
ll
ow
li
ght
a
bout
to
c
ha
nge
to
gr
e
e
nl
ig
ht
,
th
e
n
th
e
la
s
t
la
ne
is
o
n
r
e
d
li
ght
a
s
s
how
n
in
F
ig
ur
e
4.
T
he
w
ir
e
s
a
r
e
a
lr
e
a
dy
c
onne
c
te
d;
th
e
A
r
dui
no
boa
r
d
is
a
lr
e
a
dy
c
onne
c
te
d
to
t
h
e
s
ys
te
m
a
ls
o;
I
t
f
ur
th
e
r
s
how
s
th
e
li
ght
f
o
r
th
e
f
our
la
ne
s
;
T
he
f
ir
s
t
la
ne
i
s
on
a
ye
ll
ow
li
ght
a
bout
to
c
ha
nge
to
a
r
e
d
li
gh
t,
a
nd
th
e
s
e
c
ond
la
ne
i
s
on
a
r
e
d
li
ght
,
th
e
t
hi
r
d l
a
ne
i
s
on r
e
d l
ig
ht
a
bout
t
o c
h
a
nge
t
o gr
e
e
nl
ig
ht
, t
he
n
th
e
l
a
s
t
la
ne
i
s
on ye
ll
ow
l
ig
ht
a
bout
t
o
c
ha
nge
to
gr
e
e
nl
ig
ht
a
s
s
how
n
in
F
ig
ur
e
5.
F
ig
ur
e
6
s
how
s
th
e
li
ght
s
;
T
he
w
ir
e
s
a
lr
e
a
dy
c
onne
c
te
d;
A
r
dui
n
o
boa
r
d
a
lr
e
a
dy
c
onne
c
te
d
to
th
e
s
ys
te
m
a
l
s
o
.
I
t
f
ur
th
e
r
s
how
s
w
he
n
th
e
a
m
bul
a
nc
e
is
a
ppr
oa
c
hi
ng
a
pa
r
ti
c
ul
a
r
la
ne
w
hi
c
h
is
on
r
e
d;
w
he
n
it
r
e
c
e
iv
e
s
th
e
s
ig
na
l
th
a
t
th
e
a
m
bul
a
nc
e
is
a
ppr
oa
c
hi
ng
f
r
om
th
e
c
ode
it
c
ha
nge
s
to
gr
e
e
n
li
ght
f
or
t
he
pa
s
s
a
ge
of
t
he
a
m
bul
a
nc
e
a
nd goe
s
ba
c
k t
o t
he
nor
m
a
l
pos
it
io
n.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
D
e
v
e
lo
pm
e
nt
of
a pr
io
r
it
iz
e
d t
r
af
fi
c
l
ig
ht
c
ont
r
ol
s
y
s
t
e
m
f
or
e
m
e
r
ge
nc
y
…
(
H
al
le
lu
y
ah O
lu
w
at
obi
A
w
or
in
de
)
4025
F
ig
ur
e
4. T
r
a
f
f
ic
c
ont
r
ol
boa
r
d s
im
ul
a
ti
on w
it
h
pr
io
r
it
iz
e
d m
ode
l
F
ig
ur
e
5. T
he
t
r
a
f
f
ic
l
ig
ht
boa
r
d s
how
s
a
ye
ll
ow
li
ght
on t
he
f
ir
s
t
la
ne
F
ig
ur
e
6. T
r
a
f
f
ic
l
ig
ht
s
ig
na
li
ng t
o t
he
a
m
bul
a
nc
e
a
ppr
oa
c
hi
ng
3.2.
S
t
at
is
t
ic
al
an
al
ys
is
O
ne
s
a
m
pl
e
t
-
te
s
t
w
a
s
c
onduc
te
d
to
de
t
e
r
m
in
e
th
e
a
v
e
r
a
ge
di
s
ta
n
c
e
a
t
w
hi
c
h
th
e
a
m
bul
a
nc
e
is
de
te
c
t
e
d.
R
e
s
ul
ts
s
how
th
a
t
t=
-
1,
df
=
14,
p
-
va
lu
e
=
0.3343
w
it
h
a
95%
c
onf
id
e
nc
e
in
te
r
va
l
(
298.9517,
300.3816)
f
or
th
e
m
e
a
n.
S
in
c
e
th
e
p
-
va
lu
e
=
0.3343
is
gr
e
a
te
r
th
a
n
t
he
s
ig
ni
f
ic
a
nc
e
le
ve
l,
0.05,
it
w
a
s
obs
e
r
ve
d
th
a
t
th
e
a
ve
r
a
ge
di
s
ta
nc
e
a
t
w
hi
c
h
a
n
a
m
bul
a
nc
e
i
s
de
te
c
te
d
is
300
m
e
t
e
r
s
.
F
or
th
e
a
c
c
ur
a
c
y
of
th
e
nor
m
a
l
tr
a
f
f
ic
f
lo
w
,
a
one
-
s
a
m
pl
e
t
-
te
s
t
w
a
s
c
onduc
te
d.
A
c
c
ur
a
c
y
of
r
e
d
tr
a
f
f
ic
li
ght
s
,
th
e
r
e
w
a
s
no
ne
e
d
to
c
o
ndu
c
t
th
e
t
-
te
s
t
s
in
c
e
a
ll
t
he
t
im
e
s
r
e
c
or
de
d w
e
r
e
90 s
e
c
onds
.
T
hi
s
i
m
pl
ie
s
t
ha
t
th
e
r
e
d t
r
a
f
f
ic
l
ig
ht
i
s
a
c
c
ur
a
te
i
n i
ts
t
im
e
de
la
y.
F
or
a
c
c
ur
a
c
y
of
th
e
ye
ll
ow
li
ght
f
or
th
e
nor
m
a
l
tr
a
f
f
ic
f
lo
w
:
r
e
s
ul
ts
s
how
th
a
t
t
=
-
1.8708,
df
=
14,
p
-
va
lu
e
=
0.08242
w
it
h
a
95%
c
onf
id
e
nc
e
in
te
r
va
l
(
2.570713,
3.
029287)
f
or
th
e
m
e
a
n.
S
in
c
e
p
-
va
lu
e
=
0.08242
is
gr
e
a
te
r
th
a
n
th
e
s
ig
ni
f
ic
a
nc
e
le
ve
l,
0.05,
w
e
c
on
c
lu
de
th
a
t
th
e
ye
ll
ow
li
ght
de
la
y
is
3
s
e
c
ond
s
.
A
s
s
u
c
h
th
e
ye
ll
ow
l
ig
ht
de
la
y t
im
e
i
s
a
c
c
ur
a
te
.
F
or
a
c
c
ur
a
c
y
of
th
e
gr
e
e
n
li
ght
f
o
r
th
e
nor
m
a
l
t
r
a
f
f
ic
f
l
ow
:
r
e
s
ul
ts
s
how
th
a
t
t=
1,
df
=
14,
p
-
va
lu
e
=
0.3343
w
it
h
a
95%
c
onf
id
e
nc
e
in
te
r
va
l
(
29.92368,
30.
20965)
f
or
th
e
m
e
a
n.
S
in
c
e
p
-
va
lu
e
=
0.3343
is
gr
e
a
te
r
th
a
n
th
e
s
ig
ni
f
ic
a
nc
e
le
ve
l,
0.05,
w
e
c
onc
lu
de
th
a
t
th
e
gr
e
e
n
li
ght
de
la
y
is
30
s
e
c
onds
.
F
or
th
e
g
r
e
e
n
li
ght
pa
s
s
a
ge
f
or
th
e
a
m
bul
a
nc
e
,
a
one
-
s
a
m
pl
e
t
-
te
s
t
w
a
s
a
ls
o
c
onduc
te
d.
R
e
s
ul
ts
s
how
th
a
t
t=
-
1.4446,
df
=
14,
p
-
va
lu
e
=
0.1706
w
it
h
a
95%
c
onf
id
e
nc
e
in
te
r
va
l
(
28.01227,
30.
38773)
f
or
th
e
m
e
a
n.
S
in
c
e
p
-
va
lu
e
=
0.1706
is
gr
e
a
te
r
t
ha
n t
he
s
ig
ni
f
ic
a
nc
e
l
e
v
e
l,
0.05, we
c
onc
lu
de
t
ha
t
th
e
gr
e
e
n l
ig
ht
pa
s
s
a
ge
t
im
e
f
or
t
he
a
m
bul
a
nc
e
i
s
30
s
e
c
onds
.
3.3.
D
is
c
u
s
s
io
n
T
he
c
r
e
a
ti
on of
a
P
T
L
C
S
M
h
a
s
e
nor
m
ous
pr
om
is
e
f
or
i
m
pr
ovi
ng t
r
a
f
f
ic
f
lo
w
, de
c
r
e
a
s
in
g c
onge
s
ti
on,
a
nd
s
hor
te
ni
ng
e
m
e
r
ge
nc
y
r
e
s
pons
e
ti
m
e
s
.
T
hi
s
s
ta
ti
s
ti
c
a
l
s
tu
dy
s
he
ds
li
ght
on
th
e
m
ode
l'
s
pe
r
f
o
r
m
a
nc
e
in
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
. 13, No. 4, D
e
c
e
m
be
r
2024:
4019
-
4028
4026
te
r
m
s
of
bot
h
r
e
gul
a
r
tr
a
f
f
ic
a
nd
a
m
bul
a
nc
e
pr
io
r
it
iz
in
g.
T
he
P
T
L
C
S
M
c
oul
d
be
u
s
e
d
to
c
r
e
a
te
s
ophi
s
ti
c
a
t
e
d
s
ys
te
m
s
th
a
t
ut
il
iz
e
a
dva
nc
e
d
te
c
hnol
ogy
a
nd
a
lg
or
it
hm
s
f
or
dyna
m
ic
tr
a
f
f
ic
s
ig
na
l
c
ont
r
ol
pr
io
r
it
iz
in
g
e
m
e
r
ge
nc
y
ve
hi
c
le
s
li
ke
a
m
bul
a
nc
e
s
but
a
ls
o
a
ll
ow
in
g
nor
m
a
l
ve
hi
c
ul
a
r
m
ove
m
e
nt
.
T
he
ke
y
c
om
pone
nt
s
of
t
he
P
T
L
C
S
M
a
r
e
;
i)
r
e
a
l
-
ti
m
e
da
ta
us
in
g
s
e
ns
or
s
:
e
m
pl
oys
a
ne
twor
k
of
tr
a
f
f
ic
s
e
ns
or
s
a
nd
c
a
m
e
r
a
s
to
ga
th
e
r
up
-
to
-
th
e
-
m
in
ut
e
r
e
c
or
ds
of
tr
a
f
f
ic
de
ns
it
y,
ve
hi
c
le
ty
pe
s
,
a
nd
on
-
r
oa
d
s
ta
tu
s
;
ii
)
t
e
c
hno
lo
gi
e
s
of
c
om
m
uni
c
a
ti
on:
ut
il
iz
e
s
v
e
hi
c
le
-
in
f
r
a
s
tr
uc
tu
r
e
c
om
m
uni
c
a
ti
o
n
(
V
2I
)
to
de
te
c
t
a
nd
pr
io
r
it
iz
e
e
m
e
r
ge
n
c
y
ve
hi
c
le
s
a
ppr
oa
c
hi
ng
in
te
r
s
e
c
ti
ons
;
a
nd
ii
i)
t
r
a
f
f
ic
m
a
na
ge
m
e
nt
ba
s
e
d
on
a
lg
or
it
hm
s
:
in
c
or
por
a
te
s
a
lg
or
it
hm
s
c
a
pa
bl
e
of
r
a
pi
dl
y
c
ha
ngi
ng
s
ig
na
l
pha
s
e
s
de
pe
ndi
ng
on
a
c
tu
a
l
r
oa
d
c
ondi
ti
ons
a
nd
r
e
qui
r
in
g
pr
io
r
it
ie
s
f
or
a
m
bul
a
nc
e
s
a
nd
ot
he
r
e
m
e
r
ge
nc
y
ve
hi
c
le
s
to
a
ll
ow
th
e
m
to
c
r
os
s
ju
nc
ti
ons
qui
c
kl
y
w
it
hout
a
ny
p
r
obl
e
m
s
.
T
he
or
ga
ni
z
a
ti
on P
T
L
C
S
M
gua
r
a
nt
e
e
s
t
he
s
e
ga
in
s
f
or
m
ove
m
e
nt
s
a
c
r
os
s
c
it
ie
s
. I
nc
lu
di
ng but not
l
im
it
e
d t
o;
be
tt
e
r
tr
a
f
f
ic
m
odul
a
ti
ng
e
f
f
ic
ie
nc
y
a
nd
m
ul
ti
-
s
e
c
to
r
a
l
in
f
lu
e
nc
in
g
f
a
c
to
r
s
;
s
uc
h
a
s
in
c
r
e
a
s
e
d
s
a
f
e
ty
w
h
e
r
e
qui
c
ke
r
c
le
a
r
a
nc
e
s
a
r
e
pr
ovi
de
d
f
or
a
m
bul
a
nc
e
s
th
u
s
pr
ovi
di
ng
good
p
a
ti
e
nt
r
e
s
ul
ts
in
c
a
s
e
of
m
e
di
c
a
l
e
m
e
r
ge
nc
ie
s
;
lo
w
e
r
e
d
C
O
2
e
m
is
s
io
n
s
th
a
t
a
r
e
a
tt
r
ib
ut
e
d
to
le
s
s
ti
m
e
s
pe
nt
id
li
ng
a
nd
m
or
e
f
lu
id
dr
iv
in
g
c
ondi
ti
ons
;
le
s
s
ti
m
e
s
pe
nt
i
n t
r
a
f
f
ic
c
a
n l
e
a
d t
o l
ow
e
r
f
ue
l
us
a
ge
a
nd t
hu
s
i
nc
r
e
a
s
e
d
pr
oduc
ti
vi
ty
a
t
a
n e
c
onomi
c
a
l
le
ve
l.
3.4.
K
e
y f
in
d
in
gs
T
he
m
a
jo
r
f
in
di
ngs
of
t
hi
s
r
e
s
e
a
r
c
h a
r
e
a
s
f
ol
lo
w
s
:
a.
A
m
bul
a
nc
e
de
te
c
ti
on
:
t
he
a
ve
r
a
g
e
di
s
ta
nc
e
a
t
w
hi
c
h
a
m
bul
a
nc
e
s
a
r
e
id
e
nt
if
ie
d
is
300
m
e
te
r
s
,
gi
vi
ng
th
e
s
ys
te
m
e
nough ti
m
e
t
o
pr
io
r
it
iz
e
t
r
a
f
f
ic
s
ig
na
ls
.
b.
N
or
m
a
l
tr
a
f
f
ic
f
lo
w
−
T
he
r
e
d
-
li
ght
de
la
y i
s
c
ons
i
s
te
nt
ly
90 s
e
c
onds
.
−
T
he
ye
ll
ow
l
ig
ht
de
la
y i
s
3
s
e
c
ond
s
.
−
T
he
gr
e
e
n l
ig
ht
de
la
y i
s
pr
e
c
is
e
a
t
30
s
e
c
ond
s
.
c.
P
r
io
r
it
iz
a
ti
on of
a
m
bul
a
nc
e
s
:
t
he
gr
e
e
n
l
ig
ht
pa
s
s
a
ge
dur
a
ti
on f
or
a
m
bul
a
nc
e
s
i
s
a
l
s
o c
or
r
e
c
t
a
t
30
s
e
c
ond
s
,
gua
r
a
nt
e
e
in
g a
s
m
oot
h a
nd quic
k t
r
a
n
s
it
.
A
c
c
or
di
ng
to
th
e
s
ta
ti
s
ti
c
a
l
a
na
ly
s
is
,
th
e
P
T
L
C
S
M
m
e
e
ts
it
s
obj
e
c
ti
ve
s
f
or
bot
h
r
e
gul
a
r
t
r
a
f
f
ic
f
lo
w
a
nd
a
m
bul
a
nc
e
pr
io
r
it
iz
in
g.
T
he
te
c
hnol
ogy
r
e
c
ogni
z
e
s
a
m
bul
a
nc
e
s
a
t
a
s
a
f
e
di
s
ta
nc
e
,
pr
io
r
it
iz
e
s
th
e
ir
pa
s
s
a
ge
th
r
ough gr
e
e
n l
ig
ht
s
, a
nd ke
e
ps
a
ll
t
r
a
f
f
ic
li
ght
s
on t
im
e
.
3.5.
F
u
r
t
h
e
r
c
on
s
id
e
r
at
io
n
s
W
hi
le
t
he
s
tu
dy yie
ld
s
e
nc
our
a
gi
ng r
e
s
ul
ts
, i
t
s
houl
d be
not
e
d
t
ha
t
it
i
s
ba
s
e
d on a
s
m
a
ll
s
a
m
pl
e
s
iz
e
.
L
a
r
ge
r
da
ta
s
e
t
te
s
ti
ng
m
ig
ht
r
e
in
f
or
c
e
th
e
c
onc
lu
s
io
ns
a
nd
gi
ve
m
or
e
ge
ne
r
a
li
z
a
bl
e
r
e
s
ul
ts
.
T
he
in
ve
s
ti
ga
ti
on
c
onc
e
nt
r
a
te
d on the
pr
e
c
is
io
n
of
t
im
in
g a
n
d de
te
c
ti
on. F
ut
ur
e
r
e
s
e
a
r
c
h m
ig
ht
l
ook
a
t
how
t
he
P
T
L
C
S
M
a
f
f
e
c
ts
ot
he
r
va
r
ia
bl
e
s
in
c
lu
di
ng
tr
a
f
f
ic
c
onge
s
ti
on,
tr
a
ve
l
ti
m
e
,
a
nd
p
ol
lu
ta
nt
s
.
T
he
P
T
L
C
S
M
'
s
e
f
f
ic
a
c
y
m
a
y
di
f
f
e
r
ba
s
e
d
on
f
a
c
to
r
s
s
uc
h
a
s
tr
a
f
f
ic
de
n
s
it
y,
r
oa
d
la
yout
,
a
nd
e
m
e
r
ge
nc
y
ve
hi
c
le
ty
pe
.
M
or
e
s
tu
dy
is
r
e
qui
r
e
d
to
unde
r
s
ta
nd how the
s
y
s
te
m
f
unc
ti
ons
i
n va
r
io
us
s
e
tt
in
gs
.
4.
C
O
N
C
L
U
S
I
O
N
T
he
P
T
L
C
S
M
ha
s
e
nor
m
ous
pot
e
nt
ia
l
to
r
e
vol
ut
io
ni
z
e
tr
a
f
f
ic
m
a
na
ge
m
e
nt
.
T
hi
s
s
ta
ti
s
ti
c
a
l
s
tu
dy
gi
ve
s
pos
it
iv
e
e
vi
de
nc
e
of
i
ts
pr
e
c
i
s
io
n a
nd e
f
f
ic
a
c
y. H
ow
e
ve
r
, f
ur
th
e
r
s
tu
dy i
s
r
e
qui
r
e
d t
o pr
ope
r
ly
c
om
pr
e
he
nd t
he
s
ys
te
m
'
s
s
tr
e
ngt
hs
a
nd
li
m
it
s
.
W
e
c
a
n
ge
t
c
lo
s
e
r
to
de
v
e
lo
pi
n
g
s
m
a
r
te
r
,
s
a
f
e
r
,
a
nd
m
or
e
e
f
f
ic
ie
nt
c
it
ie
s
f
or
e
ve
r
yone
if
w
e
c
ont
in
ue
to
e
nha
n
c
e
a
nd
im
pr
ove
th
e
P
T
L
C
S
M
.
A
c
c
or
di
ng
to
th
e
s
ta
ti
s
ti
c
a
l
a
na
ly
s
i
s
,
th
e
ti
m
in
gs
of
th
e
tr
a
f
f
ic
li
ght
s
ys
te
m
a
r
e
c
om
pa
ti
bl
e
w
it
h
th
e
c
r
it
e
r
ia
s
pe
c
if
i
e
d
f
or
th
e
r
e
d,
ye
ll
ow
,
a
nd
gr
e
e
n
li
ght
s
,
a
s
w
e
ll
a
s
th
e
a
m
bul
a
nc
e
de
te
c
ti
on
di
s
t
a
nc
e
.
T
he
f
a
c
t
th
a
t
th
e
p
-
va
lu
e
s
f
or
a
ll
te
s
ts
w
e
r
e
la
r
ge
r
th
a
n
0.05,
e
xc
e
pt
f
or
th
e
r
e
d
li
ght
(
w
he
r
e
th
e
te
s
t
w
a
s
not
r
e
le
va
nt
)
,
in
di
c
a
te
s
th
a
t
th
e
r
e
is
no
s
ubs
ta
nt
ia
l
e
vi
de
nc
e
th
a
t
th
e
ti
m
in
gs
de
vi
a
te
f
r
om
th
e
ir
r
e
s
pe
c
ti
ve
hypothe
s
iz
e
d
va
lu
e
s
.
I
n
te
r
m
s
of
ti
m
in
g
a
c
c
ur
a
c
y,
th
e
r
e
s
e
a
r
c
h
a
pp
e
a
r
s
to
be
r
ig
or
ous
a
nd
s
how
s
a
w
e
ll
-
f
unc
ti
oni
ng t
r
a
f
f
ic
s
ig
na
l
s
ys
te
m
.
R
E
F
E
R
E
N
C
E
S
[
1]
G
. P
a
u, T
. C
a
m
pi
s
i
, A
.
C
a
na
l
e
,
A
. S
e
ve
r
i
no, M
.
C
ol
l
ot
t
a
, a
nd
G
. T
e
s
or
i
e
r
e
, “
S
m
a
r
t
pe
de
s
t
r
i
a
n c
r
os
s
i
ng m
a
na
ge
m
e
nt
a
t
t
r
a
f
f
i
c
l
i
ght
j
unc
t
i
ons
t
hr
ough a
f
uz
z
y
-
ba
s
e
d a
ppr
oa
c
h,”
F
ut
ur
e
I
nt
e
r
n
e
t
, vol
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:
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f
i
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2]
A
. A
. E
m
m
a
nue
l
e
t
al
.
, “
V
e
hi
c
l
e
-
c
a
us
e
d
r
oa
d a
c
c
i
de
nt
s
of
f
our
m
a
j
or
c
i
t
i
e
s
i
n
t
he
nor
t
h
-
c
e
nt
r
a
l
r
e
gi
on of
N
i
ge
r
i
a
. (
2010
–
2015)
,”
I
nt
e
r
nat
i
onal
J
our
nal
of
C
i
v
i
l
E
ngi
ne
e
r
i
ng and T
e
c
hnol
ogy
, vol
. 10, no. 2, pp.
124
–
134, 2019
.
[
3]
A
.
K
a
t
hur
i
a
a
nd
P
.
V
e
da
gi
r
i
,
“
E
va
l
ua
t
i
ng
pe
de
s
t
r
i
a
n
-
ve
hi
c
l
e
i
nt
e
r
a
c
t
i
on
dyna
m
i
c
s
a
t
un
-
s
i
gna
l
i
z
e
d
i
nt
e
r
s
e
c
t
i
ons
:
A
pr
oa
c
t
i
v
e
a
ppr
oa
c
h f
or
s
a
f
e
t
y a
na
l
ys
i
s
,”
A
c
c
i
de
nt
A
nal
y
s
i
s
and P
r
e
v
e
nt
i
on
, vol
. 134, 2020
, doi
:
10.1016/
j
.a
a
p.2019.105316.
[
4]
D
.
L
.
G
ui
doni
,
G
.
M
a
i
a
,
F
.
S
.
H
.
S
ouz
a
,
L
.
A
.
V
i
l
l
a
s
,
a
nd
A
.
A
.
F
.
L
our
e
i
r
o,
“
V
e
hi
c
ul
a
r
t
r
a
f
f
i
c
m
a
na
ge
m
e
nt
ba
s
e
d
on
t
r
a
f
f
i
c
e
ngi
ne
e
r
i
ng f
or
ve
hi
c
ul
a
r
a
d hoc
ne
t
w
or
ks
,”
I
E
E
E
A
c
c
e
s
s
, vol
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A
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E
S
S
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[
5]
K
.
N
e
l
l
or
e
a
nd
G
.
P
.
H
a
nc
k
e
,
“
A
s
ur
ve
y
on
ur
ba
n
t
r
a
f
f
i
c
m
a
na
ge
m
e
nt
s
y
s
t
e
m
u
s
i
ng
w
i
r
e
l
e
s
s
s
e
n
s
or
ne
t
w
or
ks
,”
S
e
ns
or
s
,
vol
.
16,
no.
2, 2016, doi
:
10.3390/
s
16020157.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
D
e
v
e
lo
pm
e
nt
of
a pr
io
r
it
iz
e
d t
r
af
fi
c
l
ig
ht
c
ont
r
ol
s
y
s
t
e
m
f
or
e
m
e
r
ge
nc
y
…
(
H
al
le
lu
y
ah O
lu
w
at
obi
A
w
or
in
de
)
4027
[
6]
R
. V
a
ni
, N
. T
he
ndr
a
l
, J
. C
. K
a
vi
t
ha
, a
nd N
. P
. G
.
B
ha
va
ni
, “
I
nt
e
l
l
i
ge
nt
t
r
a
f
f
i
c
c
ont
r
ol
s
ys
t
e
m
w
i
t
h pr
i
or
i
t
y t
o e
m
e
r
ge
nc
y ve
h
i
c
l
e
s
,
”
I
O
P
C
onf
e
r
e
nc
e
Se
r
i
e
s
:
M
at
e
r
i
al
s
Sc
i
e
n
c
e
and E
ngi
ne
e
r
i
ng
, vol
. 455, no. 1, 20
18, doi
:
10.1088/
1757
-
899X
/
455/
1/
012023.
[
7]
A
.
E
.
R
e
t
a
l
l
a
c
k
a
nd
B
.
O
s
t
e
ndor
f
,
“
C
ur
r
e
nt
unde
r
s
t
a
ndi
ng
of
t
he
e
f
f
e
c
t
s
of
c
onge
s
t
i
on
on
t
r
a
f
f
i
c
a
c
c
i
de
nt
s
,”
I
nt
e
r
nat
i
onal
J
our
na
l
of
E
nv
i
r
onm
e
nt
al
R
e
s
e
ar
c
h and P
ubl
i
c
H
e
al
t
h
, vol
. 16, no. 18, 2019, doi
:
10.3390/
i
j
e
r
ph16183400.
[
8]
A
.
A
l
m
ut
a
i
r
i
,
P
.
Y
i
,
a
nd
M
.
O
w
a
i
s
,
“
N
e
w
a
ppr
oa
c
h
f
or
e
s
t
i
m
a
t
i
ng
i
nt
e
r
s
e
c
t
i
o
n
c
ont
r
ol
de
l
a
y
f
r
om
pa
s
s
i
ve
t
r
a
f
f
i
c
s
e
ns
or
s
a
t
t
he
ne
t
w
or
k l
e
ve
l
,”
I
E
E
E
A
c
c
e
s
s
, vol
. 12, pp. 2882
–
2900, 2024, doi
:
10.1109/
A
C
C
E
S
S
.2024.3349499.
[
9]
G
.
P
.
N
i
t
hya
a
nd
A
.
C
.
M
i
ni
ya
da
n,
“
P
r
i
or
i
t
y
-
ba
s
e
d
i
nt
e
l
l
i
ge
nt
t
r
a
f
f
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de
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p
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e
i
nf
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c
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m
e
nt
l
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a
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d
t
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a
f
f
i
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c
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t
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por
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t
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duc
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f
f
i
c
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i
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c
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,”
i
n
I
nt
e
r
nat
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C
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w
i
t
z
e
r
l
a
nd,
2023
,
do
i
:
10.1007/
978
-
3
-
031
-
29857
-
8_82
.
[
29]
T.
T
.
A
de
l
i
yi
,
D
.
O
l
uw
a
de
l
e
,
K
.
I
gw
e
,
a
nd
O
.
J
.
A
r
oba
,
“
A
n
a
l
ys
i
s
of
r
oa
d
t
r
a
f
f
i
c
a
c
c
i
de
nt
s
s
e
ve
r
i
t
y
us
i
ng
a
pr
une
d
t
r
e
e
-
ba
s
e
d
m
ode
l
,
”
I
nt
e
r
nat
i
onal
J
our
nal
of
T
r
ans
por
t
D
e
v
e
l
opm
e
nt
and I
nt
e
gr
at
i
on
,
v
ol
. 7,
no.
2
,
2023
,
doi
:
10.18280/
i
j
t
di
.070208
.
B
I
O
G
R
A
P
H
I
E
S
O
F
A
U
T
H
O
R
S
Halleluyah
Oluwatobi
Aworinde
lectures
at
the
Computer
Scienc
e
Programme
College
of
Computing
and
Communicat
ion
Studies,
Bowen
Universi
ty,
Iwo.
He
has
a
B.Tech.
,
M.Sc.,
and
Ph.D.
in
Computer
Science.
His
resear
ch
interests
include
AI,
machine
learning,
intelligent
systems,
information
security,
pattern
recognition
,
image
proce
ssing
,
and
biometrics.
He
has
more
than
10
years
of
experience
in
teaching
a
nd
research.
He
can
be
contacted
at email
:
aworinde.h
alleluyah
@
bowen.edu.
ng
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
. 13, No. 4, D
e
c
e
m
be
r
2024:
4019
-
4028
4028
Abidemi
Emmanuel
Adeniyi
received
the
B.Sc.
and
M.Sc.
degree
s
in
computer
science
from
Ladoke
Akintola
University
of
Technology,
Ogbo
moso,
and
Landmark
University, Omu
-
Aran, Nigeria, in 2019
and 2021, respectivel
y, and h
e is currently a research
student
at
the
Department
of
Compu
ter
Science,
University
of
Ilori
n
and
a
lecturer
in
the
Department
of
Computer
Science,
Bowen
University,
Iwo,
Nigeria.
He
has
authored
or
co
-
authored
more
than
50
journal
and
conference
papers,
and
50
book
chapters
with
Elsevier
and
Springer.
His
research
interests
include
internet
of
things
securit
y
,
information
security,
applicati
ons
of
artificial
intell
igence,
applicati
on
of
machine
learning
for
intrusi
on
detection
,
internet
of
medical
things,
and
cryptogra
phic
algorithms.
He
can
be
contacte
d
at
email:
abidemi.
adeniyi@
bowen.edu.
ng
.
Segun
Adebayo
holds
a
Ph.D.
in
Electrical
and
Electronics
Engine
ering.
He
is
a
passionate
scholar
and
researcher
in
the
Department
of
Mechatronics
Engineering,
at
Bowen
University,
Iwo.
He
is
an
Assistant
Professor
with
research
interests
in energy,
communication
and
s
ignal
processing,
machine
learning,
robotics,
and
the
internet
of
things
.
He
is
a
member
of
the
Nigeria
Society
of
Engineers
(NSE),
the
Council
for
the
Regul
ation
of
Engineering
in
Nigeria
(COREN),
and
the
International
Association
of
Engin
eers
(IAENG).
He
can
be
contacted
at
email:
segun.adeb
ayo@
bowen.edu.
ng
or shegdabayo@yahoo.com
.
Faith
Adeniji
is
a
computer
science
professional
with
a
B
.
Sc
.
from
Bowen
University
and
an
M
.
Sc
.
from
the
University
of
Ibadan.
Her
research
in
terests
include
artificial
intelligence
,
machine
learning,
and
data
scien
ce,
with
a
passion
for
le
veraging
technology
to
drive
growth,
improve
efficiency,
and
enhance
lives.
She
can
b
e
contacted
at
email
:
faithadeniji
91@
gmail.com
.
Oluwasegun
Julius
Aroba
is
an
Artificial
Intelligence
and
Data
S
cience
Expert
at the Un
iversit
y of
Johannes
burg, was
recently
selected
as one
of the
t
op 10 S
outh Afri
can
AI
specialists
in
2024,
a
senior
lecturer,
and
an
Honorary
Research
Associate
at
Durban
University
of
Technology.
He
has
a
Ph.D.
in
Information
Technology.
A
gr
aduate
of
Information
Technology
University
from
the
prestigious
Coventry
University
United
Kingdom,
B
.
Sc.
Computer
Science
and
Technol
ogy
(
upper
-
class
division
)
Crawford
Un
iversity
whose
research
inclination
focus
area
are
into
wireless
sensor
networks,
project
management,
data
science,
machine
learning
,
SAP
specialist,
robotics,
and
artificial
intelligence
with
over
a
dec
ade
year
of
experience
in
the
higher
education
sector,
healthcare
industries,
go
vernment
parastatals,
a
consult
ant
across
the
globe,
a
graduate
member
of
IEEE,
IITPSA
So
uth
Africa,
Member
of
IET
UK.
He
has
autho
red
or
coauthored
more
than
30
journals,
conf
e
rence
papers,
and
book
chapters
with
Elsevier,
IEEE,
and
Springer
.
He
has
chaired
and
co
-
c
haired
international
and
national
conferences
.
He
is
a
board
member
of
SAIMC,
Lifestyle
Asset
Hub,
Founder
of
Aroseglinks S
ervices Limited and
Julius Aroba Lt
d,
Zill
ionaire’s
Grou
p, He is a
Supervisor
of
Ph.D.
and
Masters/MBA
s
tudents,
and
he
has
graduate
d
over
1000
st
udents
under
his
direct
and
indirect
supervision
from
honours,
bachelor,
masters
and
MBA
&
PhD’s.
He
is
a
global
mentor
to
SMEs
and
European
Afric
an,
Sisonke
NdabaX,
Guest
Edit
or
to
Q2
Journal
outlet
(Journal
of
Electrical
and
Computer
Engineering)
Scopus
index.
Seasoned
three
(3)
time
speaker
at
IndabaX
South
Africa,
Indaba,
and
a
seasone
d
guest
speaker
at
Global
Summit
o
n
Artificial
Intelligence
USA
,
and
AI
master
class
organizers
and
sp
eakers.
He
has
a
vast
colla
boration
network
across
Spain,
France,
Morocco,
Canada,
D
ubai,
Nigeria,
Ghana,
Rwanda,
German
y,
India,
USA,
The
UK,
South
Africa
,
India,
and
Swed
en.
He
can
be
contacted
at email
:
jaroba@
uj.ac.za
or
oluwaseguna@
dut.ac.za
.
Evaluation Warning : The document was created with Spire.PDF for Python.