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s
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f
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CC B
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A
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Su
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it Gu
p
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Dep
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1.
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R
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way
ca
p
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k
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p
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c
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m
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ter
tim
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an
d
city
b
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g
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[
1
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.
Mu
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f
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b
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d
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to
f
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-
tim
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cy
cle:
q
u
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s
ted
f
u
el
,
an
d
s
to
p
-
s
tar
t
p
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llu
tio
n
[
2
]
th
at
tak
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p
lace
,
b
e
it
a
r
o
ad
with
tr
af
f
ic
o
r
o
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e
with
n
o
n
e.
C
en
tr
alize
d
co
n
tr
o
l
r
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m
s
ar
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f
in
e
as
lo
n
g
as
th
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ca
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b
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co
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ed
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b
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t
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r
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id
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g
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o
win
g
city
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o
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ar
ah
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b
o
th
in
e
x
ten
t a
n
d
in
r
es
p
o
n
s
e
[
3
]
.
An
o
t
h
e
r
o
p
ti
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is
to
d
ela
y
th
e
d
e
cisi
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s
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ti
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,
ev
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wi
th
in
t
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v
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cl
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a
n
d
th
is
is
t
h
e
task
t
h
is
wo
r
k
t
r
i
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t
o
ac
c
o
m
p
l
is
h
.
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ap
ti
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f
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m
r
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ase
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h
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th
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[
4
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d
es
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f
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th
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n
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t
o
f
t
h
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s
(
I
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w
h
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v
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f
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b
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o
w
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en
v
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icl
es
a
n
d
r
o
ad
-
s
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u
n
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ts
[
5
]
.
No
ce
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t
r
a
l
a
u
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h
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r
it
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is
i
n
v
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g
ai
n
ac
c
ess
t
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r
s
e
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o
n
s
[
6
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.
P
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r
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ai
n
s
at
t
h
e
e
d
g
e,
n
e
x
t
t
o
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r
ce
,
Evaluation Warning : The document was created with Spire.PDF for Python.
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all
o
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o
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it
t
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[
8
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T
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d
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eq
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city
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in
m
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u
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s
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f
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[
9
]
.
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I
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1
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(
SUMO
)
lo
ad
s
wh
ile
u
s
in
g
ab
o
u
t 2
5
% less
f
u
el.
T
h
e
u
s
e
o
f
v
eh
icle
-
to
-
v
eh
icle
(
V2
V
)
an
d
v
eh
icle
-
to
-
i
n
f
r
astru
ctu
r
e
(
V2
I
)
co
m
m
u
n
icatio
n
in
city
-
tr
af
f
ic
is
a
lo
n
g
h
is
to
r
y
.
Kh
o
d
ab
ak
h
s
h
lo
o
an
d
Fateh
i
[
8
]
wer
e
r
esp
o
n
s
ib
le
f
o
r
VT
L
s
,
in
wh
ich
v
eh
icles
co
-
o
r
d
in
atin
g
with
ea
ch
o
th
er
b
ea
t
to
d
a
y
'
s
tr
af
f
ic
lig
h
t
s
y
s
tem
,
an
d
Fer
r
eir
a
an
d
d
’
Or
ey
[
1
3
]
t
h
en
co
n
v
er
ted
ca
r
b
o
n
s
av
in
g
s
in
to
f
ig
u
r
es.
I
n
an
ad
ap
tiv
e
ap
p
r
o
ac
h
,
Nico
lo
s
i
et
a
l
.
[
6
]
d
ev
elo
p
ed
a
s
ig
n
al
s
ch
em
e
b
ased
o
n
a
n
I
o
T
co
n
ce
p
t
an
d
wo
n
ea
s
ily
o
v
er
f
i
x
ed
c
y
cles.
Me
an
wh
ile
,
th
r
ea
d
h
as
estab
lis
h
ed
a
r
ep
u
tatio
n
in
t
h
e
I
o
T
w
o
r
ld
f
o
r
its
ec
o
n
o
m
ical
p
o
wer
co
n
s
u
m
p
tio
n
an
d
ea
s
y
s
ca
lab
ilit
y
[
1
4
]
an
d
its
h
ig
h
-
r
ate
an
d
r
eliab
le
ex
c
h
an
g
e
in
s
m
ar
t
cit
y
en
v
ir
o
n
m
en
ts
was
d
em
o
n
s
tr
at
ed
[
1
]
.
D
o
m
ín
g
u
ez
a
n
d
San
g
u
in
o
[
7
]
u
s
ed
a
r
tific
ial
in
tellig
e
n
ce
(
AI
)
-
co
n
tr
o
lled
s
ig
n
als
to
ea
s
e
co
n
g
esti
o
n
in
d
en
s
e
ar
ea
s
,
wh
ile
Alam
'
s
[
9
]
a
n
aly
s
is
o
f
v
e
h
icle
-
to
-
ev
er
y
th
in
g
(
V2
X
)
p
r
o
t
o
co
ls
s
h
o
wed
th
at
en
er
g
y
is
th
e
b
ett
er
m
etr
ic
f
o
r
r
ea
l
-
tim
e
tr
af
f
ic
m
ess
ag
in
g
,
th
is
tim
e
f
o
r
th
e
th
r
ea
d
p
r
o
to
co
l.
T
h
er
e
ar
e
s
ev
er
al
o
th
e
r
lin
es
o
f
r
elate
d
wo
r
k
,
s
u
ch
as
s
m
ar
t
p
a
r
k
in
g
[
1
0
]
u
s
in
g
d
ee
p
lear
n
in
g
,
r
ed
u
cin
g
laten
cy
an
d
en
er
g
y
r
e
q
u
ir
e
d
f
o
r
u
r
b
a
n
m
o
b
ilit
y
n
etwo
r
k
s
[
1
1
]
,
an
d
SUMO
-
s
u
p
p
o
r
ted
ev
id
e
n
ce
[
1
2
]
th
at
VT
L
s
r
ed
u
ce
p
r
ess
u
r
e
at
in
ter
s
ec
tio
n
s
.
C
h
o
u
d
h
ar
y
et
a
l
.
[
1
5
]
d
em
o
n
s
tr
ated
ed
g
e
-
m
a
n
ag
ed
VT
L
s
with
o
u
t a
n
y
ce
n
tr
al
co
n
tr
o
ller
.
All
o
f
th
em
ar
e
m
is
s
in
g
o
n
e
f
r
am
e
th
at
h
o
ld
s
r
ein
f
o
r
ce
m
en
t
lea
r
n
in
g
d
ec
is
io
n
s
,
th
r
e
ad
V2
X
m
ess
ag
in
g
an
d
ed
g
e
co
m
p
u
ti
n
g
in
o
n
e,
an
d
th
at
is
wh
at
t
h
ese
p
ap
er
f
o
c
u
s
es
on.
T
h
e
r
em
ain
in
g
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
ws:
s
ec
tio
n
2
p
r
esen
ts
th
e
r
elate
d
w
o
r
k
in
m
eto
d
e.
Sectio
n
3
d
is
cu
s
s
es
th
e
r
esu
lts
an
d
p
r
o
v
id
es
a
d
etailed
an
aly
s
is
.
Sectio
n
4
c
o
n
clu
d
es th
e
s
tu
d
y
an
d
o
u
tlin
e
s
f
u
tu
r
e
s
co
p
e.
2.
M
E
T
H
O
D
R
ein
f
o
r
ce
m
en
t
lear
n
in
g
an
d
t
h
r
ea
d
-
b
ased
V2
X
lin
k
s
ar
e
t
h
e
two
p
illar
s
o
f
th
e
h
y
b
r
id
tr
af
f
ic
f
lo
w
o
p
tim
izatio
n
(
HT
FO)
s
y
s
tem
,
wh
ich
h
a
n
d
les
V2
V
a
n
d
V2
I
ex
ch
an
g
e
alik
e
[
1
6
]
.
Pu
t
b
r
ie
f
ly
,
s
ig
n
als
s
h
o
u
l
d
an
s
wer
to
th
e
r
o
ad
r
ath
er
th
a
n
th
e
clo
ck
,
an
d
liv
e
d
ata
p
lu
s
lear
n
ed
d
ec
is
io
n
r
u
les
ar
e
p
o
in
ted
at
th
e
u
s
u
al
tr
o
u
b
les:
co
n
g
esti
o
n
,
l
o
n
g
waits,
an
d
wasted
f
u
el
[
1
7
]
.
Sp
ee
d
,
p
o
s
itio
n
an
d
lo
ca
lly
v
is
ib
le
co
n
g
esti
o
n
g
o
o
u
t
f
r
o
m
ev
er
y
ca
r
o
v
e
r
th
r
ea
d
,
a
p
r
o
to
co
l
b
u
ilt
f
o
r
q
u
ick
ex
ch
an
g
es
o
n
v
er
y
litt
le
p
o
wer
[
1
8
]
.
No
th
in
g
tr
av
els
f
ar
;
th
e
n
u
m
b
er
-
c
r
u
n
c
h
in
g
h
ap
p
en
s
at
th
e
in
ter
s
ec
tio
n
,
s
o
r
esp
o
n
s
es
s
tay
q
u
ick
an
d
n
o
r
em
o
te
s
er
v
er
is
lean
ed
o
n
[
1
9
]
.
An
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
p
o
licy
,
tr
ain
ed
o
n
f
lo
ws
p
ast
an
d
p
r
esen
t,
d
e
cid
es
wh
eth
er
a
VT
L
n
ee
d
s
to
co
m
e
o
n
at
all,
a
n
d
id
lin
g
tim
e
an
d
f
u
el
b
u
r
n
d
r
o
p
a
s
a
r
esu
lt
[
2
0
]
.
2
.
1
.
Sy
s
t
e
m
a
rc
hite
ct
ure
T
h
e
b
asic
ar
ch
itectu
r
e
o
f
th
e
s
y
s
tem
is
d
ep
icted
in
Fig
u
r
e
1
.
As
illu
s
tr
ated
,
th
e
s
y
s
tem
s
tack
s
f
o
u
r
lay
er
s
in
its
d
esig
n
[
1
8
]
,
[
1
9
]
.
All
f
o
u
r
co
r
e
lay
er
s
ar
e
d
escr
ib
ed
in
th
e
f
ig
u
r
e.
Fig
u
r
e
1
.
L
a
y
er
ed
a
r
ch
itectu
r
e
o
f
th
e
p
r
o
p
o
s
ed
s
y
s
tem
2.
1
.
1
.
P
er
ce
ptio
n
la
y
er
Data
g
ath
er
in
g
liv
es
at
th
is
l
ay
er
o
f
th
e
p
r
o
p
o
s
ed
ar
c
h
itectu
r
e.
C
ar
s
an
d
R
SUs
ar
e
eq
u
i
p
p
ed
with
s
en
s
o
r
s
,
GPS,
an
d
o
n
b
o
ar
d
u
n
its
th
at
r
ec
o
r
d
s
p
ee
d
,
p
o
s
itio
n
,
an
d
r
o
a
d
o
cc
u
p
an
cy
[
5
]
.
T
h
r
ea
d
V2
X
th
en
cir
cu
lates th
ese
r
ea
d
in
g
s
q
u
ick
ly
,
ef
f
icien
tly
,
a
n
d
with
o
u
t r
el
y
in
g
o
n
a
ce
n
tr
al
r
elay
.
2
.
1
.
2
.
E
dg
e
co
m
pu
t
ing
la
y
er
C
o
m
p
u
tatio
n
s
its
b
y
th
e
r
o
ad
s
id
e
h
er
e
[
2
1
]
.
T
h
e
b
u
s
ier
ju
n
ctio
n
s
g
et
th
ei
r
r
ea
d
in
g
s
h
a
n
d
led
o
n
th
e
s
p
o
t
b
y
R
SUs
,
a
p
r
e
d
ictio
n
m
o
d
u
le
f
lag
s
wh
er
e
f
lo
w
is
th
ick
en
i
n
g
[
1
9
]
.
T
h
e
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
a
g
en
t r
aises
o
r
d
r
o
p
s
VT
L
s
as th
e
m
o
m
e
n
t d
e
m
an
d
s
[
1
2
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J Ar
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I
n
tell
I
SS
N:
2252
-
8
9
3
8
Tr
a
ffic flo
w
o
p
timi
z
a
tio
n
u
s
in
g
r
ein
fo
r
ce
men
t a
n
d
t
h
r
ea
d
-
b
a
s
ed
V
2
X
co
mmu
n
ica
tio
n
f
o
r
…
(
P
a
r
u
l A
r
o
r
a
)
4063
2
.
1
.
3
.
Dec
is
io
n
la
y
er
I
n
th
is
lay
er
,
it
is
d
ec
id
ed
wh
ich
co
n
tr
o
l
ac
tio
n
s
ar
e
tak
e
n
.
T
h
e
VT
L
is
o
n
ly
ac
tiv
ate
d
wh
en
tr
a
f
f
ic
r
eq
u
ir
es
it,
av
o
i
d
in
g
u
n
n
ec
ess
ar
y
d
elay
s
[
2
2
]
.
L
iv
e
p
r
e
d
ictio
n
s
d
iv
id
e
u
p
th
e
g
r
ee
n
tim
e,
a
n
d
th
e
s
tan
d
in
g
r
u
les
r
o
u
te
am
b
u
lan
ce
s
th
r
o
u
g
h
f
ir
s
t,
with
th
e
h
ea
v
ier
r
o
u
tes g
ettin
g
ex
tr
a
g
r
ee
n
[
2
3
]
.
2
.
1
.
4
.
Clo
ud
a
nd
da
t
a
a
na
ly
t
ics la
y
er
T
h
e
lo
n
g
-
ter
m
r
ec
o
r
d
is
s
to
r
ed
in
th
e
clo
u
d
.
Fed
er
ated
lear
n
i
n
g
th
en
m
er
g
es
to
g
eth
er
wh
at
th
e
m
o
d
els
at
d
if
f
er
en
t
in
ter
s
ec
tio
n
s
h
av
e
lear
n
ed
.
C
i
ty
p
er
s
o
n
n
el
ca
n
m
o
n
ito
r
th
e
en
tire
n
etwo
r
k
o
n
a
d
ash
b
o
ar
d
,
wh
en
n
ec
ess
ar
y
,
th
ey
ca
n
s
tep
in
to
h
elp
.
2
.
2
.
H
y
brid
t
ra
f
f
ic
f
l
o
w
o
pti
m
iza
t
io
n
:
a
ma
t
hem
a
t
ica
l mo
del
I
n
th
e
HT
FO
m
o
d
el,
th
er
e
a
r
e
o
n
ly
th
r
ee
co
s
ts
:
tim
e
lo
s
t
at
r
ed
,
s
tan
d
i
n
g
q
u
eu
e
,
an
d
en
er
g
y
s
p
e
n
t.
E
ac
h
s
ig
n
al
d
ec
is
io
n
is
ev
alu
ated
b
ased
o
n
t
h
e
tr
af
f
ic
t
h
at
is
ac
tu
ally
p
r
esen
t.
T
h
ese
m
o
d
els
ar
e
ex
p
lain
e
d
th
r
o
u
g
h
m
ath
em
atica
l m
o
d
elli
n
g
in
s
ec
tio
n
s
as f
o
llo
ws
.
2
.
2
.
1
.
T
ra
f
f
ic
f
lo
w
o
ptim
iza
t
i
o
n m
o
del
Simp
le
ac
co
u
n
tin
g
o
p
en
s
th
e
m
o
d
el.
Ar
r
iv
als
b
ey
o
n
d
d
e
p
ar
tu
r
es
len
g
th
e
n
a
q
u
eu
e;
t
h
e
r
ev
er
s
e
s
h
o
r
ten
s
it.
T
h
at
is
f
lo
w
co
n
s
er
v
atio
n
o
f
(
1
)
.
−
=
(
1
)
W
h
er
e
d
en
o
tes
ar
r
iv
als
at
th
e
in
ter
s
ec
tio
n
,
d
ep
ar
tu
r
es,
an
d
th
e
q
u
eu
e
s
tan
d
in
g
th
er
e.
T
h
e
q
u
an
tity
m
in
im
ized
is
waitin
g
tim
e
s
u
m
m
ed
ac
r
o
s
s
in
ter
s
ec
tio
n
s
,
as
in
(
2
)
.
∑
(
2
)
Qu
eu
es a
r
e
n
ev
e
r
allo
wed
to
r
u
n
awa
y
,
as in
(
3
)
.
≤
(
3
)
2
.
2
.
2
.
AI
-
driv
en
re
info
rc
em
e
nt
lea
rning
m
o
del
E
v
er
y
in
ter
s
ec
tio
n
is
its
o
wn
ag
en
t.
I
t
watc
h
es
f
o
u
r
th
i
n
g
s
:
th
e
s
tan
d
in
g
q
u
eu
e
(
)
,
v
eh
icl
e
s
p
ee
d
s
(
)
,
lin
k
q
u
ality
to
war
d
ca
r
s
a
n
d
in
f
r
astru
ctu
r
e
(
)
,
an
d
th
e
t
r
a
n
s
m
it
p
o
wer
i
n
u
s
e
at
ea
ch
v
e
h
icle
(
).
Fo
u
r
m
o
v
es
ar
e
o
p
en
to
it:
s
witch
t
h
e
s
ig
n
al,
h
o
ld
it,
o
r
r
aise
o
r
d
r
o
p
a
VT
L
.
R
ewa
r
d
clim
b
s
as
waitin
g
tim
e,
q
u
e
u
e
len
g
th
,
o
r
en
er
g
y
s
p
en
d
f
alls
,
as f
o
r
m
u
lated
in
(
4
)
;
h
er
e,
t
h
e
weig
h
ts
,
an
d
s
et
th
e
r
elativ
e
p
r
io
r
ities
.
=
−
(
∑
+
ν
∑
+
ξ
∑
)
(
4
)
Dee
p
Q
-
l
ea
r
n
in
g
h
an
d
les th
e
tr
ain
in
g
an
d
r
e
p
lay
ed
e
x
p
er
ien
ce
th
e
s
h
ar
p
en
i
n
g
o
f
th
e
p
o
licy
.
2
.
2
.
3
.
E
nerg
y
-
ef
f
icient
t
hrea
d
-
ba
s
ed
co
m
m
un
ica
t
io
n m
o
del
R
ad
io
en
er
g
y
g
ets
its
o
wn
tr
e
atm
en
t
[
1
1
]
.
A
v
eh
icle
tr
an
s
m
its
n
o
h
ar
d
er
th
a
n
its
d
is
tan
ce
f
r
o
m
th
e
in
ter
s
ec
tio
n
r
eq
u
ir
es,
in
p
lace
o
f
an
y
f
ix
ed
lev
el
,
as in
(
5
)
.
=
+
η
(
1
−
)
(
5
)
I
n
wh
ich
is
th
e
f
lo
o
r
p
o
wer
,
a
tu
n
in
g
co
n
s
tan
t,
th
e
v
eh
icl
e
-
to
-
in
ter
s
ec
tio
n
d
is
tan
ce
,
an
d
th
e
r
ea
ch
o
f
th
e
co
m
m
u
n
icatio
n
r
a
n
g
e.
2
.
3
.
F
l
o
w
o
f
t
he
pro
po
s
ed
a
lg
o
rit
hm
Veh
icles
s
en
s
in
g
co
n
g
esti
o
n
ar
e
wh
at
s
tar
t
a
cy
cle.
T
h
eir
s
p
ee
d
an
d
p
o
s
itio
n
r
e
p
o
r
ts
r
e
ac
h
n
ea
r
b
y
R
SU
s
o
v
er
th
r
ea
d
,
o
n
e
ch
an
n
e
l
ca
r
r
y
in
g
V2
V
an
d
V2
I
alik
e
[
1
5
]
.
T
h
e
R
SU
ad
d
s
h
ar
d
ly
an
y
r
elay
d
elay
[
2
4
]
.
At
th
e
ed
g
e,
m
ac
h
in
es
b
y
t
h
e
in
ter
s
ec
tio
n
t
h
en
ta
k
e
s
to
ck
,
co
u
n
tin
g
v
e
h
icles,
g
au
g
in
g
s
p
ee
d
s
,
watc
h
in
g
th
e
q
u
eu
e
g
r
o
w,
an
d
n
o
s
er
v
er
r
o
u
n
d
tr
ip
en
ter
s
in
to
it
[
2
2
]
.
Fr
o
m
th
at
p
ictu
r
e
,
th
e
AI
f
ix
es
t
h
e
s
ig
n
al
p
lan
[
2
3
]
;
VT
L
s
r
is
e
o
r
f
all,
an
d
v
eh
icles g
et
th
r
o
u
g
h
with
f
ewe
r
h
alts
[
2
5
]
.
2
.
4
.
E
x
perim
ent
a
l,
m
a
t
hema
t
ica
l
,
a
nd
s
im
ula
t
io
n mo
dellin
g
Sy
n
th
etic
tr
af
f
ic
n
o
w
ex
e
r
cises
th
e
m
o
d
el
in
s
id
e
SUMO,
with
s
tatic
s
ig
n
al
co
n
tr
o
l
s
er
v
in
g
as
th
e
b
aselin
e
f
o
r
th
e
p
r
o
p
o
s
ed
tr
a
f
f
ic
f
lo
w
o
p
tim
izatio
n
(
T
FO
)
f
r
am
ewo
r
k
.
E
v
er
y
ex
p
er
im
e
n
t
is
co
n
d
u
cted
in
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
5
,
No
.
5
,
Octo
b
er
2
0
2
6
:
4
0
6
1
-
4
0
7
0
4064
SUMO.
Op
en
Stre
etM
ap
(
OSM)
d
ata
f
o
r
a
m
id
-
s
ized
city
g
av
e
th
e
r
o
a
d
n
etwo
r
k
its
in
ter
co
n
n
ec
ted
j
u
n
ctio
n
s
,
s
ig
n
alize
d
cr
o
s
s
in
g
s
,
an
d
two
-
way
s
tr
ee
ts
; Fig
u
r
e
2
s
h
o
ws th
e
lay
o
u
t w
ith
its
d
o
m
in
a
n
t f
lo
ws.
Fig
u
r
e
2
.
R
o
ad
n
etwo
r
k
u
s
ed
i
n
th
e
s
im
u
latio
n
2
.
4
.
1
.
Da
t
a
g
ener
a
t
io
n
R
ep
ea
tab
ilit
y
is
wh
y
th
e
d
em
a
n
d
is
s
y
n
t
h
etic.
Sev
er
al
in
ter
s
ec
tio
n
s
r
ec
eiv
ed
g
en
e
r
ated
tr
a
f
f
ic
wh
o
s
e
ar
r
iv
al
r
ates,
q
u
e
u
e
len
g
th
s
an
d
v
e
h
icle
m
ix
,
em
er
g
en
cy
v
e
h
icles
am
o
n
g
th
em
,
ch
a
n
g
ed
s
lo
t
b
y
s
lo
t
ac
r
o
s
s
p
ea
k
an
d
o
f
f
-
p
ea
k
p
er
i
o
d
s
.
Par
am
et
er
v
alu
es,
th
e
q
u
eu
e
ca
p
ac
ity
(
)
,
th
e
m
i
n
im
u
m
tr
a
n
s
m
it
p
o
w
er
(
)
an
d
th
e
r
ewa
r
d
weig
h
ts
(
,
,
)
,
wer
e
f
ix
ed
f
r
o
m
t
h
e
liter
atu
r
e
an
d
f
r
o
m
p
r
ac
tical
u
r
b
a
n
lim
its
.
T
ab
le
1
ca
r
r
ies th
e
f
ig
u
r
es f
o
r
ea
ch
1
5
-
m
i
n
u
te
s
lo
t
.
T
ab
le
1
.
Sy
n
t
h
etic
tr
af
f
ic
d
e
m
an
d
u
s
ed
in
th
e
r
u
n
s
I
n
t
e
r
sec
t
i
o
n
Ti
me
s
l
o
t
V
e
h
i
c
l
e
a
r
r
i
v
a
l
s (
c
a
r
s/
h
r
)
Emerg
e
n
c
y
v
e
h
i
c
l
e
s
(
p
e
r
h
r
)
B
u
s
e
s (
p
e
r
h
r
)
(
max
q
u
e
u
e
)
A
8
–
9
A
M
2
8
0
6
20
25
A
9
–
1
0
A
M
3
2
0
8
18
30
B
8
–
9
A
M
2
1
0
3
12
20
B
9
–
1
0
A
M
2
4
0
5
10
22
C
8
–
9
A
M
1
9
5
4
9
18
C
9
–
1
0
A
M
2
2
5
5
11
21
2
.
4
.
2
.
Appl
ica
t
io
n
o
f
t
he
ma
t
hem
a
t
ica
l
m
o
del
E
ac
h
s
im
u
latio
n
tim
estep
wo
r
k
s
th
r
o
u
g
h
th
e
f
o
llo
win
g
:
i)
T
r
af
f
ic
f
lo
w
o
p
tim
izatio
n
–
Ar
r
iv
als (
)
an
d
d
ep
ar
t
u
r
es (
)
ar
e
tallied
at
ev
er
y
in
ter
s
ec
tio
n
,
th
e
q
u
eu
e
u
p
d
atin
g
as
(
6
)
.
(
+
1
)
=
(
)
+
−
(
6
)
–
W
aitin
g
tim
e
co
m
es o
u
t o
f
th
e
q
u
eu
e
d
y
n
a
m
ics an
d
s
er
v
ice
r
ates.
–
A
q
u
eu
e
s
atu
r
ates a
t
,
as a
r
ea
l r
o
ad
wo
u
ld
.
ii)
R
e
i
n
f
o
r
c
e
m
e
n
t
l
e
a
r
n
i
n
g
c
o
n
t
r
o
l
:
o
n
e
r
e
i
n
f
o
r
c
e
m
e
n
t
l
e
a
r
n
i
n
g
a
g
e
n
t
s
e
r
v
es
e
a
c
h
i
n
t
e
r
s
ec
t
i
o
n
.
Sta
t
e
:
[
q
u
e
u
e
,
m
ea
n
s
p
ee
d
,
lin
k
q
u
ality
,
tr
an
s
m
it
p
o
wer
]
;
ac
tio
n
s
:
{switch
s
ig
n
al,
h
o
l
d
,
VT
L
o
n
/o
f
f
}
.
T
h
e
r
ewa
r
d
is
th
at
o
f
(
4
)
,
an
d
d
ee
p
Q
-
l
ea
r
n
in
g
lifts th
e
p
o
licy
o
v
e
r
s
im
u
lated
ep
is
o
d
es.
iii)
T
h
r
ea
d
-
b
ase
d
V2
X
en
er
g
y
c
o
n
tr
o
l:
tr
an
s
m
it
p
o
wer
p
er
v
eh
icle
f
o
llo
ws
(
5
)
(
=
0
.
5
W
,
=
2
,
=
2
5
0
m
;
r
ea
d
liv
e)
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
en
tire
HT
FO
s
tack
was
tr
an
s
p
o
r
ted
b
y
SUMO,
s
y
n
th
etic
d
em
an
d
was
in
jecte
d
v
ia
q
u
eu
e
lo
g
ic,
an
d
r
ein
f
o
r
ce
m
en
t
lear
n
i
n
g
s
ig
n
al
co
n
tr
o
l
was
u
s
ed
to
co
n
tr
o
l
th
e
s
im
u
lato
r
,
wh
ich
was
s
t
ee
r
ed
b
y
th
e
tr
af
f
ic
co
n
tr
o
l
in
ter
f
a
ce
(
T
r
aCI)
.
T
h
e
two
h
o
u
r
s
o
f
t
h
e
m
o
r
n
in
g
r
u
s
h
ar
e
r
ep
lay
ed
i
n
ea
ch
r
u
n
,
an
d
tr
ajec
to
r
ies,
s
ig
n
al
s
tates,
q
u
eu
e
len
g
th
s
,
waitin
g
t
im
es,
v
eh
icles
s
er
v
ed
,
as
well
as
f
u
el
an
d
e
m
is
s
io
n
s
p
er
v
eh
i
cle,
ar
e
r
ec
o
r
d
ed
f
o
r
ev
er
y
s
tep
.
T
h
e
SUMO
f
lo
atin
g
ca
r
d
ata
(
FC
D
)
o
u
tp
u
t,
w
h
o
s
e
d
ata
is
th
e
s
o
u
r
ce
o
f
Fig
u
r
e
3
,
s
h
o
ws
th
e
m
o
v
em
en
t
o
f
v
e
h
icles
with
in
t
h
e
n
etwo
r
k
at
p
ea
k
h
o
u
r
;
co
n
g
esti
o
n
p
o
c
k
ets,
f
av
o
u
r
e
d
r
o
u
te
s
o
r
b
o
ttlen
ec
k
s
i
n
th
e
m
ak
in
g
ar
e
all
d
is
ce
r
n
ib
le.
T
h
er
e
was
n
o
way
th
at
t
h
e
p
h
a
s
es
r
em
ain
ed
co
n
s
tan
t.
Dem
a
n
d
f
lu
ctu
ated
ar
o
u
n
d
,
an
d
it
was
th
e
le
n
g
th
en
i
n
g
a
n
d
s
h
o
r
ten
i
n
g
o
f
r
e
d
s
an
d
g
r
ee
n
s
(
p
lu
s
th
e
q
u
ieter
h
o
u
r
s
)
th
a
t
wer
e
r
em
em
b
er
ed
by
Fig
u
r
e
4
.
Mo
s
t
o
f
t
h
is
g
ain
is
p
r
o
v
id
e
d
b
y
tim
in
g
ad
a
p
tatio
n
.
As
ex
p
ec
t
ed
f
r
o
m
an
ag
en
t
th
at
ca
n
n
o
t
h
an
d
le
lo
ca
l
co
n
g
esti
o
n
,
th
e
r
ei
n
f
o
r
ce
m
en
t
lear
n
in
g
ag
en
t
co
n
tin
u
o
u
s
ly
ad
ap
ted
g
r
ee
n
d
u
r
atio
n
s
in
ev
er
y
R
SU
eq
u
ip
p
ed
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
Tr
a
ffic flo
w
o
p
timi
z
a
tio
n
u
s
in
g
r
ein
fo
r
ce
men
t a
n
d
t
h
r
ea
d
-
b
a
s
ed
V
2
X
co
mmu
n
ica
tio
n
f
o
r
…
(
P
a
r
u
l A
r
o
r
a
)
4065
with
a
th
r
ea
d
n
o
d
e
to
th
e
c
u
r
r
e
n
t
lev
el
o
f
co
n
g
esti
o
n
.
Fig
u
r
e
5
co
m
p
ar
es
th
e
two
at
a
m
aj
o
r
in
ter
s
ec
tio
n
:
ad
ap
tiv
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[
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a
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t,
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lh
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d
ia.
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it
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e
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se
rv
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d
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h
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h
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ls
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ley
train
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rin
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m
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g
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d
in
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ig
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sy
ste
m
s
.
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h
e
c
a
n
b
e
c
o
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tac
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a
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m
a
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:
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ti
k
a
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wa
so
n
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v
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m
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in
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2
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o
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v
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Tec
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)
in
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m
p
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g
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fro
m
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ra
p
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stit
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rm
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ra
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h
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m
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m
s
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c
a
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b
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o
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tac
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m
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v
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sh
a
r2
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2
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g
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o
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to
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Bh
a
ra
ti
Vi
d
y
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p
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stit
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m
p
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p
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s
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n
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g
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m
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t
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w
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p
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rtme
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ti
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d
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strial
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se
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in
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o
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m
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sin
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h
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h
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lh
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tern
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o
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n
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rm
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h
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(BJIT),
p
u
b
li
s
h
e
d
b
y
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p
rin
g
e
r
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tu
re
.
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c
a
n
b
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c
o
n
tac
ted
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t
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m
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c
a
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h
o
d
a
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m
a
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or
m
c
a
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a
c
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in
.
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m
it
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se
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n
d
AI.
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h
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s
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ted
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g
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ifi
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d
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d
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wo
r
k
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o
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se
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ra
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m
o
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ls
with
h
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g
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p
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rm
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m
p
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g
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e
n
h
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n
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d
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c
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-
m
a
k
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g
c
a
p
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s.
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in
terd
isc
ip
l
in
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ry
c
o
ll
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b
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ra
ti
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f
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L
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n
d
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field
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li
k
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lt
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re
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sm
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rt
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n
d
c
y
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rse
c
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rit
y
.
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rre
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tl
y
,
h
e
h
e
a
d
s
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p
a
rtme
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t
o
f
Re
se
a
rc
h
a
n
d
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v
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p
m
e
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t
,
Xs
y
s
S
o
ftwa
re
Tec
h
n
o
l
o
g
ies
,
Be
n
g
a
lu
r
u
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
su
m
it
2
k
4
@g
m
a
il
.
c
o
m
.
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