I
nte
rna
t
io
na
l J
o
urna
l o
f
I
nfo
r
m
a
t
ics a
nd
Co
mm
u
n
ica
t
io
n T
ec
hn
o
lo
g
y
(
I
J
-
I
CT
)
Vo
l.
5
,
No
.
3
,
Dec
em
b
er
201
6
,
p
p
.
124
~
128
I
SS
N:
2252
-
8776
124
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ia
e
s
jo
u
r
n
a
l.c
o
m/o
n
lin
e/in
d
ex
.
p
h
p
/I
J
I
C
T
Urba
n Tra
ff
i
c Si
m
ula
tors
Chitla
H
a
rsh
it
ha
*
1
,
R.
G
iris
riniv
a
a
s
2
,
V.
P
a
rt
hip
a
n
3
1,
2
B.
E
C
o
m
p
u
ter S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
,
S
a
v
e
e
th
a
S
c
h
o
o
l
o
f
En
g
in
e
e
rin
g
,
S
a
v
e
e
th
a
Un
iv
e
rsit
y
,
In
d
ia
3
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter S
c
ien
c
e
a
n
d
E
n
g
in
n
e
ri
n
g
,
S
a
v
e
e
th
a
S
c
h
o
o
l
Of
En
g
in
e
e
rin
g
,
S
a
v
e
e
th
a
Un
iv
e
rsit
y
,
In
d
ia
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Au
g
u
s
t
2
,
2
0
1
6
R
ev
i
s
ed
Octo
b
er
7
,
2
0
1
6
A
cc
ep
ted
No
v
e
m
b
er
1
,
2
0
1
6
Co
m
p
u
ter
traff
ic
si
m
u
latio
n
is
a
w
id
e
l
y
u
se
d
tec
h
n
iq
u
e
in
m
o
d
e
li
n
g
,
c
o
m
in
g
u
p
w
it
h
a
n
d
d
e
v
e
lo
p
m
e
n
t
o
f
tr
a
ff
ic
n
e
t
w
o
rk
s
a
n
d
sy
ste
m
s.
It
c
o
n
tain
s
a
v
a
rio
u
s
a
p
p
li
c
a
ti
o
n
s,
li
k
e
traf
f
i
c
f
o
re
c
a
stin
g
,
v
e
h
icle
n
a
v
ig
a
ti
o
n
d
e
v
ice
s
a
n
d
so
o
n
.
T
h
is
p
a
p
e
r
d
isc
u
ss
e
s
a
b
o
u
t
th
e
v
a
rio
u
s
ty
p
e
s
o
f
tra
ff
i
c
s
im
u
lato
rs.
A
n
o
th
e
r
d
ra
w
b
a
c
k
is
th
a
t
th
e
f
a
c
to
rs
l
ik
e
a
c
c
id
e
n
ts,
p
u
b
li
c
e
v
e
n
ts,
a
n
d
ro
a
d
c
lo
su
re
s.
In
a
d
d
it
io
n
,
w
e
a
re
in
tro
d
u
c
i
n
g
a
lg
o
rit
h
m
ic
c
o
n
c
e
p
ts,
q
u
a
n
ti
f
iab
le
m
e
tri
c
s an
d
d
a
ta stru
c
tu
ra
l
a
p
p
ro
a
c
h
e
s th
a
t
m
ig
h
t
b
e
a
p
p
li
e
d
to
t
h
e
sim
u
latio
n
s
y
ste
m
s.
K
ey
w
o
r
d
:
A
l
g
o
r
ith
m
Me
tr
ics
So
f
t
w
ar
e
r
ev
ie
w
T
r
af
f
ic
s
i
m
u
la
tio
n
Veh
ic
u
lar
t
r
af
f
ic
Co
p
y
rig
h
t
©
2
0
1
6
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts
re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
C
h
i
tla
Har
s
h
it
h
a
,
.
B
.
E
C
o
m
p
u
ter
Scien
ce
a
n
d
E
n
g
in
ee
r
i
n
g
,
Sav
ee
th
a
Sc
h
o
o
l o
f
E
n
g
i
n
ee
r
i
n
g
,
Sav
ee
t
h
a
Un
iv
er
s
it
y
,
I
n
d
ia
.
E
m
ail:
h
ar
s
h
ip
u
p
p
y
8
@
g
m
ail.
c
o
m
1.
I
NT
RO
D
UCT
I
O
N
T
r
af
f
ic
s
i
m
u
latio
n
s
y
s
te
m
s
ar
e
in
itiated
o
v
er
4
0
y
ea
r
s
ag
o
an
d
it
is
v
er
y
i
m
p
o
r
ta
n
t
f
o
r
tr
af
f
ic
an
d
tr
an
s
p
o
r
tatio
n
d
esi
g
n
in
g
i
n
t
o
d
ay
’
s
w
o
r
ld
.
Si
m
u
latio
n
is
f
a
m
o
u
s
m
et
h
o
d
in
t
h
e
ar
ea
o
f
s
cie
n
ce
.
T
r
af
f
ic
s
i
m
u
lato
r
s
ar
e
u
s
ed
to
d
esi
g
n
t
h
e
tr
a
n
s
p
o
r
tatio
n
s
y
s
te
m
m
o
d
el
v
ir
t
u
all
y
u
s
i
n
g
t
h
e
co
m
p
u
te
r
s
o
f
t
w
ar
e
p
ac
k
ag
e
.
T
r
an
s
p
o
r
tatio
n
s
y
s
te
m
m
o
d
eli
n
g
u
s
e
s
th
e
s
e
s
i
m
u
lati
n
g
en
v
i
r
o
n
m
e
n
ts
to
v
er
i
f
y
th
e
tr
a
n
s
p
o
r
tatio
n
m
o
d
el
s
i
n
o
r
d
er
to
p
r
o
v
e
th
eir
p
r
o
p
er
ties
.
No
w
ad
a
y
s
lar
g
e
m
ec
h
a
n
ical
p
o
w
er
p
r
o
v
id
es i
n
d
iv
id
u
als
t
h
e
ab
ilit
y
to
s
i
m
u
late
an
en
v
ir
o
n
m
e
n
t q
u
ic
k
er
th
a
n
t
h
e
r
ea
l e
n
v
ir
o
n
m
en
t.
2.
T
RA
F
F
I
C
S
I
MU
L
AT
I
O
N
M
O
DE
L
S
T
r
af
f
ic
s
i
m
u
lato
r
m
o
d
elin
g
is
a
f
a
m
o
u
s
a
n
d
ef
f
ec
tiv
e
to
o
l
f
o
r
an
al
y
s
is
o
f
d
y
n
a
m
ical
is
s
u
es
in
t
h
e
co
m
p
le
x
p
r
o
ce
s
s
e
s
w
h
ic
h
ca
n
’
t
r
ea
d
il
y
b
e
d
escr
ib
ed
in
t
h
e
an
al
y
tical
ter
m
s
.
T
h
ese
co
m
p
lex
p
r
o
ce
s
s
es
ar
e
ch
ar
ac
ter
ized
b
y
th
e
co
m
m
u
n
ica
tio
n
o
f
s
y
s
te
m
ele
m
e
n
ts
th
a
t
in
ter
ac
tio
n
s
ar
e
co
m
p
licated
in
n
at
u
r
e.
Si
m
u
lato
r
s
m
o
d
els
ar
e
th
e
m
a
th
e
m
atica
l
r
ep
r
esen
tatio
n
s
o
f
th
e
r
ea
l
-
w
o
r
ld
s
y
s
te
m
s
w
h
ic
h
tak
es
th
e
s
h
ap
e
o
f
s
i
m
u
lat
io
n
s
o
f
t
w
ar
e
p
ac
k
ag
e
t
h
at
ar
e
ex
ec
u
ted
o
n
a
co
m
p
u
te
r
as a
n
ex
p
er
i
m
e
n
t.
3.
NE
S
SA
SI
T
Y
F
O
R
TR
A
F
F
I
C
S
I
M
UL
AT
OR
S
T
r
af
f
ic
s
i
m
u
lato
r
m
o
d
el
h
a
s
a
v
er
it
y
o
f
ap
p
licatio
n
s
in
t
h
e
v
er
it
y
o
f
f
ield
s
.
No
w
ad
a
y
s
s
i
m
u
lato
r
s
b
ec
o
m
e
i
m
p
o
r
ta
n
t
to
o
l
f
o
r
r
e
s
ea
r
ch
a
n
d
i
n
ter
p
r
etatio
n
o
f
r
ea
l
w
o
r
ld
en
v
ir
o
n
m
e
n
t
p
ar
tic
u
lar
l
y
i
n
t
h
e
tr
af
f
ic
en
g
i
n
ee
r
i
n
g
.
T
h
e
s
u
b
s
eq
u
e
n
t
s
it
u
atio
n
s
w
h
er
e
tr
af
f
ic
s
i
m
u
latio
n
m
o
d
el
w
ill
n
o
tice
t
h
eir
s
co
p
e.
W
h
en
an
an
al
y
tical
tr
ea
t
m
e
n
t
o
f
a
p
r
o
b
le
m
is
f
o
u
n
d
a
n
i
n
ad
eq
u
ate
d
u
e
to
its
co
m
p
licated
n
at
u
r
e.
T
h
e
tr
af
f
ic
s
i
m
u
lato
r
s
ar
e
u
s
ed
in
lar
g
e
v
ar
iet
y
o
f
ap
p
licatio
n
s
li
k
e
e
v
alu
a
tio
n
o
f
a
l
ter
n
ati
v
e
tr
ea
t
m
e
n
t
s
an
d
tes
t
in
g
n
e
w
d
e
s
ig
n
s
a
s
an
en
ti
t
y
o
f
th
e
d
esi
g
n
p
r
o
ce
s
s
,
em
b
ed
d
ed
in
o
th
er
to
o
ls
,
s
a
f
e
t
y
an
al
y
s
is
a
n
d
s
o
o
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
IJ
-
I
C
T
I
SS
N:
2252
-
8776
Urb
a
n
Tr
a
ffic S
imu
la
to
r
s
(
C
h
itla
Ha
r
s
h
ith
a
)
125
4.
CAT
E
G
O
R
I
E
S O
F
T
RAF
F
I
C
SI
M
UL
AT
I
O
N
M
O
DE
L
4
.
1
.
M
a
cr
o
s
co
pic Si
m
ula
t
io
n
M
o
del
M
a
c
r
o
s
c
o
p
i
c
m
od
el
d
o
n
'
t
co
n
s
i
d
e
r
c
a
r
f
o
r
f
o
ll
o
w
i
n
g
p
r
o
p
e
r
ti
es
in
d
eta
il;
h
o
w
e
v
er
tr
a
f
f
ic
m
od
el
as
a
m
i
x
t
u
r
e
o
f
f
l
u
id
f
l
o
w
.
I
t
d
e
s
c
r
i
b
es
t
h
e
a
c
t
i
v
iti
e
s
a
n
d
c
o
mm
un
icati
o
n
o
f
e
n
ti
t
y
at
a
lo
w
l
e
v
el
o
f
d
etail.
T
o
un
d
e
r
s
t
a
n
d
t
h
e
p
r
o
p
e
r
tie
s
o
f
tr
a
f
f
ic
a
n
a
l
ys
is
f
l
o
w
c
o
n
d
itio
n
i
n
a
s
p
ir
ited
w
a
y
,
t
i
m
e
m
od
el,
ei
t
h
er
n
o
n
c
o
m
p
l
ic
a
ted
o
r
h
i
g
h
-
o
r
d
e
r
,
a
r
e
s
o
m
et
i
m
e
s
us
ed
in
t
h
e
m
a
c
r
o
s
c
op
ic
s
i
m
u
lat
i
n
g
m
od
el
.
T
h
e
s
i
m
p
le
tr
a
f
f
ic
m
od
el
c
o
n
tai
n
s
an
e
q
u
ati
o
n
r
e
pr
ese
n
ti
n
g
a
li
n
k
i
n
-
b
e
t
w
e
e
n
t
h
e
s
p
e
ed
,
d
e
ns
i
t
y
,
a
n
d
f
l
o
w
g
e
n
e
r
a
ti
o
n
r
at
e
.
T
h
is
m
od
el
w
as
e
v
en
us
ed
i
n
t
h
e
K
R
O
N
O
S.
T
h
e
tr
a
f
f
ic
m
od
el
do
es
n
't
c
o
ns
i
d
er
t
h
e
a
c
c
e
le
r
ati
o
n
a
n
d
t
h
e
s
p
ir
ited
s
p
e
ed
-
d
e
ns
i
t
y
r
ela
ti
o
n
s
h
i
p
s
d
ete
r
m
i
n
ed
i
n
t
h
e
r
e
a
l
w
or
ld
tr
a
f
f
ic
f
l
o
w
.
A
l
t
h
o
u
g
h
t
h
e
e
x
i
s
t
i
n
g
m
od
el
l
ook
s
p
r
o
m
i
s
i
n
g
b
u
t
it
n
e
e
d
n
o
t
y
et
p
r
o
v
en
a
c
t
u
al
l
y
s
up
e
r
i
o
r
to
t
h
e
s
i
m
p
le
tr
a
f
f
ic
m
od
e
l
s
at
least
i
n
m
e
d
i
um
-
t
o
-
c
o
ng
e
s
ted
f
l
o
w
c
o
n
d
itio
n
s.
4
.
2
.
M
icr
o
Sco
pic Si
m
ula
t
io
n M
o
del
T
h
is
t
y
p
e
o
f
s
i
m
u
latio
n
p
r
o
v
id
es
atten
tio
n
to
an
in
d
i
v
id
u
a
l
v
eh
ic
le
an
d
th
e
ir
co
m
m
u
n
i
ca
tio
n
s
.
I
t
an
al
y
s
is
th
e
co
m
m
u
n
icatio
n
b
et
w
ee
n
t
h
e
d
r
iv
er
to
d
r
iv
er
o
n
r
o
ad
.
T
h
is
s
u
p
p
o
r
ts
lan
e
c
h
an
g
i
n
g
t
h
eo
r
ies
an
d
ca
r
f
o
llo
w
i
n
g
t
h
eo
r
ies
th
at
m
i
g
h
t
r
ep
r
esen
t
t
h
e
tr
af
f
ic
f
l
o
w
s
a
n
d
v
eh
icle
b
eh
a
v
io
r
s
i
n
d
etail.
T
h
is
ca
r
-
f
o
llo
w
in
g
t
h
eo
r
y
s
h
o
w
s
t
h
e
lo
n
g
i
tu
d
i
n
al
m
o
v
e
m
en
t
o
f
v
eh
ic
le
an
d
its
ap
p
r
o
ac
h
is
q
u
ite
s
im
p
le,
th
at
is
,
ev
er
y
v
eh
ic
les
atte
m
p
ts
to
in
cr
ea
s
es
at
its
d
esire
d
s
p
ee
d
w
h
ile
m
ain
tai
n
i
n
g
a
s
ec
u
r
e
f
o
llo
w
i
n
g
d
is
tan
ce
f
r
o
m
th
e
v
eh
ic
le
ah
ea
d
.
T
h
e
lan
e
ch
an
g
in
g
t
h
eo
r
y
s
h
o
w
s
t
h
e
later
al
tr
af
f
ic
b
eh
av
io
r
.
T
h
is
co
u
ld
b
e
co
n
s
id
er
ed
in
ter
m
o
f
v
ar
iet
y
o
f
p
er
ce
p
tio
n
th
r
e
s
h
o
ld
s
g
o
v
er
n
in
g
t
h
e
co
n
s
id
er
atio
n
o
f
t
h
e
c
h
an
ce
o
f
ac
c
ep
tin
g
a
g
ap
i
n
a
n
eig
h
b
o
r
in
g
lan
e.
T
o
g
en
er
ate
r
an
d
o
m
n
u
m
b
er
s
f
o
r
r
ep
r
esen
ti
n
g
t
h
e
d
r
iv
er
/v
e
h
icle
b
eh
av
io
r
in
r
ea
l
tr
af
f
ic
co
n
d
itio
n
s
.
5.
CL
AS
SI
F
I
CAT
I
O
N
O
F
SI
M
UL
AT
I
O
N
M
O
DE
L
S
5
.
1
.
SU
M
O
SUMO
co
u
ld
b
e
a
s
tr
ictl
y
m
i
cr
o
s
co
p
ic
tr
af
f
ic
s
i
m
u
latio
n
.
I
t
w
a
s
i
n
itialized
i
n
2
0
0
1
,
w
it
h
a
p
r
im
ar
y
o
p
en
s
o
u
r
ce
r
elea
s
e
in
2
0
0
2
.
SUMO
is
a
tr
af
f
ic
s
i
m
u
latio
n
.
I
t
is
an
op
en
s
o
u
r
ce
,
p
o
r
tab
l
e
an
d
m
icr
o
s
co
p
ic
r
o
ad
tr
af
f
ic
s
i
m
u
latio
n
p
ac
k
a
g
e
d
esig
n
ed
to
m
o
d
el
th
e
lar
g
e
r
o
ad
n
et
w
o
r
k
s
.
T
h
er
e
ar
e
2
r
e
aso
n
s
f
o
r
m
a
k
i
n
g
it
as
an
o
p
en
s
o
u
r
ce
,
b
ec
au
s
e
it
ca
n
b
e
im
p
le
m
e
n
ted
w
it
h
o
u
r
o
w
n
alg
o
r
it
h
m
.
S
UM
O
tr
af
f
ic
s
i
m
u
lato
r
u
s
e
s
n
etg
e
n
f
o
r
g
en
er
ati
n
g
r
o
ad
n
et
w
o
r
k
w
i
th
t
h
e
d
ig
ital
r
o
ad
m
ap
.
Net
co
n
v
er
ter
is
u
s
ed
a
s
th
e
r
o
ad
n
et
w
o
r
k
i
m
p
o
r
ter
,
w
h
ich
p
er
m
i
ts
r
ea
d
in
g
n
et
w
o
r
k
s
f
r
o
m
o
t
h
er
tr
af
f
i
c
s
i
m
u
lato
r
s
.
Du
e
to
t
h
e
lack
o
f
ap
p
licatio
n
s
,
th
e
s
u
p
p
o
r
t
fo
r
T
I
GE
R
n
et
w
o
r
k
s
wa
s
d
r
o
p
p
ed
.
5
.
2
.
Q
ua
d
s
t
o
n
e
P
a
r
a
m
i
c
s
Qu
ad
s
to
n
e
P
ar
a
m
u
s
co
u
ld
b
e
a
lead
in
g
m
icr
o
s
co
p
ic
tr
af
f
ic
an
d
p
ed
estrian
.
P
ar
am
u
s
is
th
e
m
o
s
t
s
y
s
te
m
a
ticall
y
d
ep
en
d
ab
le
tr
af
f
ic
d
esi
g
n
i
n
g
ap
p
licatio
n
o
f
f
er
ed
n
o
w
ad
a
y
s
.
Qu
ad
s
to
n
e
P
ar
a
m
u
s
also
d
ev
elo
p
s
p
ed
estrian
m
icr
o
s
i
m
u
latio
n
s
o
f
t
w
ar
e
p
ac
k
ag
e
ca
lled
t
h
e
U
r
b
an
An
al
y
tics
Fra
m
e
w
o
r
k
.
U
s
ed
in
o
v
er
eig
h
t
y
co
u
n
tr
ies
w
o
r
ld
-
w
id
e
b
y
th
o
u
s
an
d
s
o
f
co
n
s
u
m
er
s
i
n
cl
u
d
in
g
c
o
m
m
er
cial
co
n
s
u
lta
n
ts
,
5
.
3
.
V
I
SS
I
M
VI
SS
I
M
is
a
s
i
m
u
la
to
r
u
s
ed
f
o
r
th
e
d
esig
n
o
f
tr
a
f
f
ic
co
n
tr
o
l
s
y
s
te
m
s
.
I
t
is
also
o
n
e
o
f
th
e
w
o
r
ld
's
lead
in
g
s
o
f
t
w
ar
e
f
o
r
m
icr
o
s
co
p
ic
tr
af
f
ic
s
i
m
u
lat
io
n
.
5
.
4
.
S
y
s
te
m
A
r
c
h
i
t
ec
t
ur
e
of
V
I
SS
I
M
T
h
e
f
ir
s
t
m
o
d
el
is
th
e
tr
af
f
ic
f
l
o
w
m
o
d
el
an
d
th
e
s
ec
o
n
d
m
o
d
el
is
th
e
s
ig
n
al
m
a
n
a
g
e
m
e
n
t
m
o
d
el.
I
t’
s
th
e
m
aster
p
r
o
g
r
a
m
t
h
at
s
e
n
d
s
v
alu
e
s
d
etec
ted
b
y
th
e
d
etec
t
o
r
f
o
r
ea
ch
an
d
ev
er
y
s
ec
o
n
d
t
o
th
e
s
i
g
n
al
co
n
tr
o
l
p
r
o
g
r
am
.
T
h
e
s
ig
n
a
l
co
n
tr
o
l
p
r
o
g
r
a
m
u
s
e
s
th
e
v
al
u
es
d
etec
ted
f
r
o
m
t
h
e
d
etec
to
r
to
g
et
th
e
p
r
esen
t
s
ig
n
al
asp
ec
ts
.
VI
SS
I
M
r
ec
ei
v
es
th
e
s
ig
n
al
a
s
p
ec
ts
a
n
d
t
h
e
n
e
x
t
ite
r
atio
n
o
f
tr
a
f
f
ic
-
f
lo
w
w
ill
b
e
i
n
itiated
.
T
h
e
tr
af
f
ic
f
lo
w
m
o
d
el
an
d
th
e
s
i
g
n
al
co
n
tr
o
l
co
m
m
u
n
icate
v
ia
s
ta
n
d
ar
d
ized
in
ter
f
ac
es.
Flex
ib
ilit
y
i
s
t
h
e
b
asic
ad
v
a
n
tag
e
o
f
s
p
litt
i
n
g
t
h
e
2
tas
k
s
i
n
to
2
p
r
o
g
r
a
m
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8776
IJ
-
I
C
T
Vo
l.
5
,
No
.
3
,
Dec
em
b
er
2
0
1
6
:
124
–
1
2
8
126
Fig
u
r
e
1
.
T
r
af
f
ic
Flo
w
Mo
d
el
Fig
u
r
e
2
.
T
h
e
Sig
n
al
Ma
n
ag
e
m
en
t M
o
d
el
5
.
4
.
1
.
Appl
ica
t
io
ns
o
f
Vis
s
i
m
a.
I
t
ca
lls
v
e
h
icle
s
i
g
n
al
co
n
tr
o
l
s
tr
ateg
ie
s
t
h
at
ar
e
id
en
t
ical
to
th
e
i
m
p
le
m
e
n
tatio
n
s
i
n
th
e
co
n
tr
o
ller
.
W
h
ile
test
i
n
g
w
i
th
g
en
er
ated
tr
af
f
ic
f
lo
w
o
n
e
ca
n
test
b
y
m
a
n
u
a
ll
y
s
tar
tin
g
t
h
e
d
etec
to
r
s
.
b.
T
h
e
tr
ig
g
er
in
g
o
f
t
h
e
d
etec
to
r
s
is
r
ep
o
r
ted
in
m
ac
r
o
f
ile
s
w
h
ic
h
ca
n
b
e
u
s
ed
f
o
r
r
u
n
n
i
n
g
id
en
tical
te
s
t
s
itu
a
tio
n
w
it
h
alter
ed
s
i
g
n
al
co
n
tr
o
l p
ar
am
e
ter
s
.
5
.
5
.
V
is
u
m
I
t
is
also
o
n
e
o
f
th
e
f
a
m
o
u
s
tr
af
f
ic
s
i
m
u
la
to
r
an
d
GI
S
–
b
ased
d
ata
m
an
a
g
e
m
en
t.
T
h
is
VI
S
UM
s
h
o
w
s
all
th
e
co
m
m
u
n
icatio
n
o
f
u
s
er
s
an
d
h
a
s
b
ec
o
m
e
a
r
ec
o
g
n
ize
d
s
tan
d
ar
d
in
tr
an
s
p
o
r
tatio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
IJ
-
I
C
T
I
SS
N:
2252
-
8776
Urb
a
n
Tr
a
ffic S
imu
la
to
r
s
(
C
h
itla
Ha
r
s
h
ith
a
)
127
5
.
5
.
1
.
A
dv
a
n
t
a
g
es
a.
I
n
v
e
st
m
e
n
t p
r
o
te
c
t
i
ve
b.
S
tr
o
n
g
se
r
v
i
c
e
a
v
a
i
l
a
b
le
c.
D
e
s
c
r
ip
tiv
e
a
nd
c
o
n
v
in
c
i
ng
r
e
s
u
l
t
s
.
5
.
6
.
CO
RSI
M
C
OR
SIM
is
a
p
o
p
u
lar
m
o
d
el
f
o
r
test
in
g
t
h
e
s
it
u
atio
n
s
in
v
o
lv
in
g
i
n
v
ar
io
u
s
g
eo
m
etr
ic
co
n
f
i
g
u
r
atio
n
s
,
w
o
r
k
zo
n
e
i
m
p
ac
ts
,
an
d
v
a
r
io
u
s
r
a
m
p
m
eter
i
n
g
o
p
tio
n
s
.
I
t
also
u
s
ed
f
o
r
test
in
g
s
ce
n
ar
io
s
in
v
o
lv
i
n
g
in
ter
s
ec
tio
n
s
t
y
le,
s
i
g
n
a
l
co
o
r
d
in
atio
n
o
p
tio
n
s
,
a
n
d
tr
an
s
it
m
o
d
eli
n
g
f
o
r
ex
cl
u
s
iv
e
la
n
es
o
r
m
i
x
ed
in
tr
af
f
ic.
I
t
w
il
l
as
s
es
s
ad
v
a
n
ce
d
co
n
tr
o
l
s
itu
a
tio
n
s
i
n
t
h
at
t
h
e
r
o
u
te
is
f
i
x
ed
.
I
t
m
o
d
el
s
4
v
er
ities
o
f
o
n
-
r
a
m
p
f
r
ee
w
a
y
m
eter
i
n
g
C
OR
SIM
h
a
s
th
e
m
o
s
t
s
o
p
h
is
ticated
ca
r
-
f
o
llo
w
in
g
an
d
lan
e
-
ch
a
n
g
in
g
lo
g
ic
to
s
i
m
u
l
ate
t
w
e
n
t
y
n
i
n
e
v
eh
ic
le
m
o
v
e
m
e
n
t
s
o
n
a
s
ec
o
n
d
-
by
-
s
ec
o
n
d
b
asic
.
5
.
7
.
A
I
MS
U
N
2
A
I
MS
UN
w
as
f
ir
s
t
cr
ea
ted
b
y
J
.
B
A
R
C
E
L
O
a
n
d
J
.
L
.
FER
R
E
R
I
N
B
AR
C
E
L
ON
A
.
I
t
is
a
s
i
m
u
lat
io
n
to
o
l
th
at
r
ep
r
o
d
u
ce
s
r
ea
l
tr
af
f
i
c
co
n
d
itio
n
s
in
a
n
u
r
b
an
n
e
t
wo
r
k
th
at
co
n
tai
n
s
ex
p
r
es
s
w
a
y
s
an
d
ar
ter
ial
r
o
u
tes.
A
I
MS
UN
p
r
o
v
id
es
th
e
f
lo
w
,
s
p
ee
d
s
,
tr
av
el
ti
m
es
etc.
,
o
f
a
v
eh
icle.
I
t
d
is
tin
g
u
is
h
es
b
et
w
ee
n
v
ar
io
u
s
t
y
p
es
o
f
v
eh
ic
les
a
n
d
d
r
i
v
er
s
.
A
I
MSU
N2
h
as
b
ee
n
j
o
in
ed
to
UK
s
co
o
t
UT
C
s
y
s
te
m
.
I
t
is
tr
af
f
ic
s
i
m
u
lato
r
th
at
p
er
m
i
ts
y
o
u
to
d
esi
g
n
o
n
e
tr
af
f
ic
lan
e
t
o
a
co
m
p
lete
r
e
g
io
n
.
I
n
th
i
s
r
e
al
-
w
o
r
ld
ap
p
licatio
n
A
I
M
SU
N2
p
ass
ed
d
etails
o
f
tr
af
f
ic
f
lo
w
to
s
co
o
t
an
d
u
s
e
s
th
e
d
ata
th
at
w
as
r
et
u
r
n
ed
to
it
f
r
o
m
th
e
a
n
al
y
s
i
s
.
T
h
e
m
o
s
t
r
ec
en
t
v
er
s
io
n
-
A
I
MS
UN
eig
h
t.
1
-
w
as
r
ele
ased
in
J
u
n
e
2
0
1
5
.
E
co
n
o
m
i
ca
l
s
o
f
t
w
ar
e
p
ac
k
a
g
e
d
ev
elo
p
m
en
t
m
a
k
es
m
icr
o
s
i
m
u
lat
io
n
w
it
h
A
I
MSUN
ea
c
h
s
e
n
s
ib
le
an
d
c
h
ea
p
,
ev
en
o
n
to
d
ay
’
s
p
o
r
tab
le
co
m
p
u
ter
5
.
8
.
M
AT
SI
M
(
M
ulti
Ag
e
nt
M
icr
o
-
Si
m
ula
t
io
n)
I
t
p
r
o
v
id
es
a
f
r
a
m
e
w
o
r
k
to
i
m
p
le
m
en
t
lar
g
e
-
s
ca
le
ag
e
n
t
-
b
as
ed
tr
an
s
p
o
r
t
s
i
m
u
latio
n
s
.
T
h
e
f
r
a
m
e
w
o
r
k
co
n
s
is
ts
o
f
v
ar
io
u
s
m
o
d
u
le
s
w
h
ic
h
m
i
g
h
t
b
e
co
m
b
in
ed
o
r
u
s
ed
co
m
p
lete.
Mo
d
u
les
m
a
y
b
e
r
ep
lace
d
b
y
o
w
n
i
m
p
le
m
en
ta
tio
n
s
to
c
h
ec
k
s
in
g
le
a
s
p
ec
ts
o
f
y
o
u
r
o
w
n
w
o
r
k
.
C
u
r
r
en
tl
y
,
M
A
T
SIM
o
f
f
er
s
a
f
r
a
m
e
w
o
r
k
f
o
r
d
em
a
n
d
-
m
o
d
eli
n
g
,
ag
e
n
t
-
b
ase
d
m
o
b
ilit
y
-
s
i
m
u
latio
n
(
tr
af
f
ic
f
lo
w
s
i
m
u
latio
n
)
,
r
ep
lan
tin
g
,
an
d
a
co
n
tr
o
ller
t
o
iter
ativ
el
y
r
u
n
s
i
m
u
latio
n
s
as
w
ell
a
s
s
tr
ate
g
ies to
r
esear
ch
t
h
e
o
u
tp
u
t
g
e
n
er
ated
b
y
t
h
e
m
o
d
u
les.
Fig
u
r
e
3
.
Fra
m
e
w
o
r
k
o
f
L
ar
g
e
-
Scale
Ag
e
n
t
-
B
ased
T
r
an
s
p
o
r
t Si
m
u
latio
n
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8776
IJ
-
I
C
T
Vo
l.
5
,
No
.
3
,
Dec
em
b
er
2
0
1
6
:
124
–
1
2
8
128
6.
CO
NCLU
SI
O
N
T
h
e
tr
af
f
ic
s
i
m
u
lato
r
is
t
h
e
lar
g
el
y
u
s
ed
m
et
h
o
d
f
o
r
s
i
m
u
lati
o
n
o
f
th
e
tr
a
f
f
ic
co
n
d
itio
n
s
in
t
h
e
p
ar
ticu
lar
ar
ea
.
T
h
e
ab
o
v
e
m
e
n
tio
n
ed
s
i
m
u
lato
r
s
ar
e
co
m
m
o
n
l
y
u
s
ed
f
o
r
th
e
tr
af
f
ic
s
i
m
u
lat
io
n
an
d
th
er
e
ad
v
an
ta
g
es a
n
d
d
is
ad
v
an
tag
e
s
ar
e
also
m
e
n
tio
n
ed
in
t
h
is
p
ap
er
.
Usi
n
g
th
e
s
e
s
i
m
u
lato
r
s
th
e
r
o
ad
n
et
w
o
r
k
is
d
esig
n
ed
an
d
th
e
tr
a
f
f
ic
co
n
d
it
io
n
is
ex
p
lai
n
ed
in
a
p
ar
ticu
lar
ar
ea
.
I
n
f
u
t
u
r
e
w
e
ar
e
g
o
i
n
g
to
i
m
p
l
e
m
en
t th
i
s
s
i
m
u
lato
r
f
o
r
d
esig
n
in
g
t
h
e
tr
af
f
ic
le
v
el
m
an
a
g
e
m
en
t
s
y
s
te
m
,
w
h
ic
h
d
eter
m
i
n
es t
h
e
tr
af
f
ic
co
n
d
itio
n
s
in
t
h
e
ar
ea
wh
er
e
th
e
u
s
er
is
tr
a
v
eli
n
g
a
n
d
it
w
il
l s
u
g
g
est t
h
e
alter
n
ati
v
e
s
h
o
r
test
p
ath
to
th
e
r
o
ad
tak
er
s
u
s
i
n
g
t
h
e
s
p
atial
v
is
u
al
izatio
n
.
RE
F
E
R
E
NC
E
S
[1
]
A
.
Ne
u
m
a
n
n
;
K.
Na
g
e
l;
D.
G
re
e
ter,
S
im
u
latio
n
o
f
Urb
a
n
T
ra
f
f
ic C
o
n
tr
o
l:
A
Qu
e
u
e
M
o
d
e
l
a
p
p
ro
a
c
h
T
ra
n
sp
o
rt
S
y
st
e
m
s
P
lan
n
i
n
g
a
n
d
T
ra
n
sp
o
rt
T
e
le
m
a
ti
c
s
.
[2
]
C
M
e
n
e
g
u
z
z
e
r,
Re
v
ie
w
o
f
M
o
d
e
ls
Co
m
b
in
in
g
T
ra
ff
i
c
As
sig
n
m
e
n
t
a
n
d
S
ig
n
a
l
Co
n
tro
l
,
J
o
u
rn
a
l
o
f
T
r
a
n
sp
o
rta
t
io
n
En
g
i
n
e
e
rin
g
,
n
o
.
1
2
3
,
v
o
l.
2
,
1
9
9
7
.
[3
]
T
Ra
jas
e
k
a
r
a
n
;
Dr.
A
Re
n
g
a
ra
jan
,
T
ra
ff
ic M
a
n
a
g
e
m
e
n
t
B
a
se
d
o
n
D
a
t
a
Ex
trac
ti
o
n
a
n
d
Visu
a
li
z
a
ti
o
n
.
[4
]
S
a
n
jay
Ku
m
a
r
Ya
d
a
v
;
M
a
d
h
a
v
i
S
in
g
h
,
E
ff
i
c
ien
t
Ro
u
t
e
F
in
d
e
r
S
ys
t
em
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
C
o
mp
u
ter
En
g
i
n
e
e
rin
g
a
n
d
T
e
c
h
n
o
lo
g
y
(
IJ
C
ET
),
v
o
l.
4
,
n
o
.
4
,
p
p
.
2
2
4
-
2
3
2
,
2
0
1
3
.
[5
]
Brian
M
A
K
;
Ho
n
g
K L
O,
P
a
ss
e
n
g
e
r
Ro
u
te G
u
id
a
n
c
e
S
y
ste
m
f
o
r
M
u
lt
im
o
d
a
l
T
ra
n
sit
Ne
tw
o
rk
s.
[6
]
Ov
e
rlo
a
d
T
ra
ff
ic M
a
n
a
g
e
m
e
n
t
f
o
r
S
e
n
so
r
Ne
tw
o
rk
s,
A
CM
T
r
a
n
sa
c
t
io
n
s
o
n
S
e
n
so
r
Ne
tw
o
rk
s,
No
.
4
,
2
0
0
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.