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ted
Mar
14
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2
0
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Traffic
c
o
n
g
e
stio
n
lea
d
s
t
o
wa
ste
d
ti
m
e
,
p
o
l
lu
ti
o
n
,
a
n
d
in
c
re
a
se
d
fu
e
l
c
o
n
su
m
p
ti
o
n
.
Traffic
c
o
n
g
e
stio
n
p
re
d
ictio
n
h
a
s
b
e
c
o
m
e
a
d
e
v
e
lo
p
in
g
re
se
a
rc
h
to
p
ic
in
re
c
e
n
t
y
e
a
rs,
p
a
rti
c
u
larl
y
in
th
e
field
o
f
m
a
c
h
in
e
lea
rn
in
g
(
M
L).
Th
e
e
v
a
lu
a
ti
o
n
o
f
v
a
ri
o
u
s
traffic
p
a
ra
m
e
ters
is
u
se
d
to
p
re
d
ict
traffic
c
o
n
g
e
sti
o
n
b
y
re
l
y
in
g
o
n
h
ist
o
rica
l
d
a
ta.
In
t
h
is
stu
d
y
,
we
will
p
re
d
ict
traffic
c
o
n
g
e
sti
o
n
in
Am
m
a
n
Cit
y
,
sp
e
c
ifi
c
a
ll
y
a
t
t
h
e
8
th
c
ircle
,
u
si
n
g
d
iffere
n
t
M
L
c
las
sifiers
.
Th
e
8
th
c
ircle
li
n
k
s
fo
u
r
m
a
in
str
e
e
ts:
Wes
tb
o
u
n
d
,
N
o
rth
b
o
u
n
d
,
E
a
stb
o
u
n
d
,
a
n
d
S
o
u
t
h
b
o
u
n
d
.
Da
tas
e
ts
we
re
c
o
ll
e
c
ted
fro
m
t
h
e
g
re
a
ter
Am
m
a
n
m
u
n
icip
a
li
t
y
h
o
u
rl
y
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h
e
l
o
g
ist
ic
re
g
re
ss
io
n
(LR)
,
k
-
n
e
a
re
st
n
e
i
g
h
b
o
r
(KN
N),
d
e
c
isio
n
tree
(DT),
ra
n
d
o
m
f
o
re
st
(RF
),
su
p
p
o
r
t
v
e
c
to
r
m
a
c
h
i
n
e
(S
VM),
a
n
d
m
u
lt
i
-
lay
e
r
p
e
rc
e
p
tro
n
(M
L
P
)
c
las
sifiers
h
a
v
e
b
e
e
n
c
h
o
se
n
to
p
re
d
ict
traffic
c
o
n
g
e
sti
o
n
a
t
e
a
c
h
stre
e
t
li
n
k
e
d
with
th
e
8
th
c
ircle
.
Th
e
wa
ik
a
to
e
n
v
ir
o
n
m
e
n
t
fo
r
k
n
o
wle
d
g
e
a
n
a
ly
sis
(W
EKA)
d
a
ta
m
in
i
n
g
to
o
l
is
u
se
d
to
e
v
a
l
u
a
te
c
h
o
se
n
c
las
sifiers
b
y
d
e
term
in
in
g
a
c
c
u
r
a
c
y
,
F
-
m
e
a
su
re
,
se
n
siti
v
it
y
,
a
n
d
p
re
c
isio
n
e
v
a
lu
a
ti
o
n
m
e
tri
c
s.
Th
e
re
su
lt
s
o
b
tain
e
d
fr
o
m
a
ll
e
x
p
e
rime
n
ts
h
a
v
e
d
e
m
o
n
stra
ted
t
h
a
t
S
V
M
is
t
h
e
b
e
s
t
c
las
sifier
to
p
re
d
ict
traffic
c
o
n
g
e
stio
n
.
Th
e
a
c
c
u
ra
c
y
o
f
S
VM
to
p
re
d
ict
traffic
c
o
n
g
e
stio
n
a
t
Wes
tb
o
u
n
d
S
t
re
e
t,
No
rth
b
o
u
n
d
S
tree
t,
Eas
tb
o
u
n
d
S
tree
t,
a
n
d
S
o
u
t
h
b
o
u
n
d
S
tree
t
wa
s
9
9
.
4
%
,
9
9
.
7
%
,
9
9
.
6
%
,
a
n
d
9
9
.
1
%
,
re
sp
e
c
ti
v
e
ly
.
K
ey
w
o
r
d
s
:
Alg
o
r
ith
m
K
-
n
ea
r
est n
eig
h
b
o
r
L
o
g
is
tic
r
eg
r
ess
io
n
Ma
ch
in
e
lear
n
in
g
Mu
lti
-
lay
er
p
er
ce
p
tr
o
n
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Ar
ee
n
Ar
ab
iat
Dep
ar
tm
en
t o
f
C
o
m
m
u
n
icatio
n
s
an
d
C
o
m
p
u
te
r
E
n
g
i
n
ee
r
in
g
,
Facu
lty
o
f
E
n
g
in
ee
r
in
g
Al
-
Ah
liy
y
a
Am
m
an
Un
i
v
er
s
ity
Al
-
Sar
o
,
Al
-
Salt,
Am
m
an
,
J
o
r
d
an
E
m
ail: a
.
ar
ab
iat@
am
m
an
u
.
ed
u
.
jo
1.
I
NT
RO
D
UCT
I
O
N
As
cities
g
r
o
w
an
d
p
eo
p
le
m
o
v
e
f
r
o
m
r
u
r
al
to
u
r
b
an
ar
ea
s
,
tr
af
f
ic
co
n
g
esti
o
n
m
ay
i
n
cr
ea
s
e,
lead
in
g
to
d
ec
r
ea
s
ed
p
r
o
d
u
ctiv
ity
,
air
p
o
l
lu
tio
n
,
a
n
d
i
n
cr
ea
s
ed
f
u
el
c
o
n
s
u
m
p
tio
n
[
1
]
.
C
o
n
g
esti
o
n
is
a
m
ajo
r
tr
a
n
s
p
o
r
tatio
n
co
n
ce
r
n
in
in
d
u
s
tr
ial
co
u
n
t
r
ies,
co
s
tin
g
$
1
2
1
b
illi
o
n
in
t
h
e
U
S
alo
n
e.
I
n
ef
f
icien
t
tr
a
n
s
p
o
r
tat
io
n
,
g
r
ee
n
h
o
u
s
e
g
as
em
is
s
io
n
s
,
an
d
a
lo
wer
q
u
ality
o
f
life
f
o
r
city
d
weller
s
ar
e
all
co
n
s
eq
u
en
ce
s
o
f
tr
af
f
ic
co
n
g
e
s
tio
n
[
2
]
.
C
o
n
g
esti
o
n
h
as
b
ee
n
ch
ar
ac
ter
ized
in
a
v
ar
iety
o
f
way
s
b
y
r
e
s
ea
r
ch
er
s
.
T
h
e
m
o
s
t
co
m
m
o
n
co
n
ce
p
t
o
f
tr
af
f
ic
co
n
g
esti
o
n
is
wh
en
th
e
d
em
an
d
f
o
r
tr
an
s
p
o
r
t
ex
ce
e
d
s
th
e
ca
p
ac
ity
o
f
th
e
r
o
ad
s
.
Desp
ite
s
ig
n
if
ican
t
im
p
r
o
v
em
e
n
ts
in
tr
a
n
s
p
o
r
tatio
n
in
f
r
astru
ct
u
r
e,
t
r
af
f
ic
c
o
n
g
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s
tio
n
co
n
tin
u
es
t
o
b
e
a
m
ajo
r
s
o
cieta
l
an
d
p
o
licy
p
r
o
b
lem
[
3
]
.
I
n
g
en
er
al,
tr
af
f
i
c
co
n
g
esti
o
n
ca
n
b
e
class
if
ied
in
to
two
ty
p
es:
r
ec
u
r
r
e
n
t
co
n
g
esti
o
n
s
,
wh
ich
a
r
e
u
s
u
ally
ca
u
s
ed
b
y
a
m
o
b
ilit
y
d
em
an
d
,
a
n
d
in
ter
m
itten
t
co
n
g
esti
o
n
s
,
wh
ich
ar
e
u
s
u
ally
ca
u
s
ed
b
y
a
lack
o
f
m
o
b
ilit
y
th
at
s
u
r
p
ass
es
th
e
r
o
a
d
n
etwo
r
k
'
s
ca
p
ac
ity
,
in
clu
d
in
g
n
o
n
r
ec
u
r
r
in
g
tr
af
f
ic
jam
s
ca
u
s
ed
b
y
in
cid
en
ts
o
r
s
p
ec
ial
ev
en
ts
[
4
]
.
T
o
r
e
d
u
ce
co
n
g
esti
o
n
,
th
r
ee
o
p
tio
n
s
ar
e
s
u
g
g
ested
:
en
h
an
cin
g
in
f
r
astru
ctu
r
e,
p
r
o
m
o
tin
g
p
u
b
lic
tr
an
s
p
o
r
tatio
n
in
lar
g
e
cities,
an
d
p
r
ed
ictin
g
f
u
tu
r
e
r
o
ad
s
eg
m
en
ts
'
s
tatu
s
.
Ho
wev
er
,
th
ese
o
p
tio
n
s
m
ay
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2
2
5
2
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8
9
3
8
WEKA
-
b
a
s
ed
ma
ch
in
e
lea
r
n
in
g
fo
r
tr
a
ffic c
o
n
g
esti
o
n
p
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ed
ict
io
n
in
A
mma
n
C
ity
(
A
r
ee
n
A
r
a
b
ia
t)
4423
r
eq
u
ir
e
s
ig
n
if
ican
t
in
v
estme
n
t
an
d
m
ay
n
o
t
alwa
y
s
b
e
f
ea
s
ib
le
[
5
]
.
Air
p
o
llu
tio
n
an
d
s
u
s
tain
ab
ilit
y
ar
e
b
o
th
b
ad
ly
im
p
ac
ted
b
y
tr
af
f
ic
co
n
g
esti
o
n
o
n
r
o
a
d
n
etwo
r
k
s
.
E
f
f
i
cien
t
tr
af
f
ic
c
o
n
tr
o
l
ca
n
aid
in
lo
wer
in
g
p
o
llu
tio
n
lev
els.
T
h
e
p
r
o
life
r
atio
n
o
f
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
d
ev
ices
p
r
o
v
id
es
d
ata
s
ets
f
o
r
in
tellig
en
t,
en
v
ir
o
n
m
e
n
tally
f
r
ien
d
ly
t
r
an
s
p
o
r
tatio
n
o
p
tio
n
s
.
Dep
en
d
in
g
o
n
th
e
d
esig
n
o
f
th
e
r
o
ad
s
,
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
n
etwo
r
k
s
ca
n
f
o
r
ec
ast
th
e
s
p
r
e
ad
o
f
co
n
g
esti
o
n
o
v
e
r
a
n
etwo
r
k
o
f
r
o
ad
s
with
a
n
ac
cu
r
ac
y
o
f
8
4
–
9
5
%.
T
h
is
m
ay
b
e
a
cr
u
cial
p
a
r
t
o
f
tr
af
f
ic
m
o
d
el
in
g
in
th
e
f
u
tu
r
e
f
o
r
s
m
ar
t
cities
[
6
]
.
T
r
a
f
f
ic
co
n
g
esti
o
n
p
r
ed
ictio
n
h
as
g
r
o
w
n
s
ig
n
if
ican
tly
in
r
ec
en
t
d
ec
a
d
es
d
u
e
to
b
ig
d
ata
f
r
o
m
s
en
s
o
r
s
an
d
n
ew
ar
tific
ial
in
tellig
en
ce
(
AI
)
m
o
d
els.
Ma
ch
in
e
lear
n
in
g
(
ML
)
,
a
p
ar
t
o
f
AI
,
is
u
s
ed
to
e
v
alu
ate
tr
af
f
ic
p
ar
am
eter
s
an
d
p
r
ed
ict
s
h
o
r
t
-
ter
m
co
n
g
esti
o
n
.
AI
h
as
m
a
d
e
s
ig
n
if
ica
n
t
ad
v
a
n
ce
s
in
ML
,
d
ata
m
in
in
g
,
c
o
m
p
u
ter
v
is
io
n
,
e
x
p
er
t
s
y
s
tem
s
,
n
atu
r
al
la
n
g
u
a
g
e
p
r
o
ce
s
s
in
g
,
an
d
r
o
b
o
tics
.
Pre
d
ictio
n
is
s
u
es c
an
b
e
class
if
ied
as M
L
class
if
icatio
n
s
o
r
r
eg
r
ess
io
n
m
o
d
els
[
7
]
.
Mo
u
m
en
et
a
l
.
[
8
]
s
h
o
ws
h
o
w
co
m
b
in
in
g
AI
an
d
I
o
T
r
o
ad
tr
af
f
ic
d
ata
m
ig
h
t
e
n
h
an
ce
u
r
b
a
n
m
o
b
ilit
y
th
r
o
u
g
h
tr
af
f
ic
p
r
ed
ictio
n
in
s
m
ar
t
city
s
ettin
g
s
.
T
h
e
L
STM
m
o
d
el
en
h
a
n
ce
s
r
ea
l
-
tim
e
v
eh
icle
co
u
n
t
esti
m
ates
an
d
p
r
o
jects
f
u
tu
r
e
tr
af
f
ic
p
att
er
n
s
u
s
in
g
I
o
T
s
en
s
o
r
s
a
n
d
d
ee
p
lear
n
i
n
g
al
g
o
r
ith
m
s
,
allo
wi
n
g
f
o
r
well
-
in
f
o
r
m
e
d
d
ec
is
io
n
-
m
ak
in
g
.
T
h
e
p
o
te
n
tial
b
en
ef
its
o
f
in
teg
r
atin
g
I
o
T
an
d
AI
ar
e
en
o
r
m
o
u
s
,
in
clu
d
i
n
g
im
p
r
o
v
ed
u
r
b
a
n
m
o
b
ilit
y
,
less
tr
af
f
ic,
less
co
n
g
esti
o
n
,
an
d
ef
f
ec
tiv
e
tr
af
f
ic
m
a
n
ag
em
en
t.
I
n
th
is
s
tu
d
y
,
L
ST
M
ac
cu
r
ac
y
ac
h
iev
ed
9
1
%,
wh
er
ea
s
lin
ea
r
r
eg
r
ess
io
n
,
k
-
n
ea
r
est
n
eig
h
b
o
r
(
KNN)
,
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
e
(
SVM)
ac
cu
r
ac
y
r
ea
ch
ed
4
1
%,
4
3
%,
an
d
4
6
%
o
f
th
e
to
tal,
r
esp
ec
tiv
ely
.
Najm
et
a
l.
[
9
]
id
en
tifie
d
s
ev
er
al
ML
alg
o
r
ith
m
s
in
o
r
d
e
r
to
p
r
ed
ict
th
e
b
est
c
o
n
g
esti
o
n
c
o
n
tr
o
l
f
o
r
5
G
I
o
T
wir
eless
s
en
s
o
r
s
.
T
h
is
s
tu
d
y
s
u
g
g
ests
a
n
o
v
e
l
ML
m
o
d
el
b
ased
o
n
th
e
d
ec
is
io
n
tr
ee
(
DT
)
m
eth
o
d
.
Ov
er
9
2
%
ac
c
u
r
ac
y
an
d
r
e
ca
ll
wer
e
attain
ed
b
y
th
e
m
o
d
e
l
af
ter
it
was
tr
ain
ed
o
n
a
tr
ain
in
g
d
ataset.
I
n
Helsin
k
i,
Fin
lan
d
,
a
s
tu
d
y
in
tr
o
d
u
ce
d
th
e
c
r
itical
p
ath
m
eth
o
d
u
s
in
g
co
n
v
o
l
u
tio
n
al
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
C
PM
-
C
o
n
v
L
STM
)
,
a
s
p
atio
tem
p
o
r
al
m
o
d
el
th
at
p
r
ed
icts
co
n
g
esti
o
n
lev
els
in
ea
ch
r
o
ad
s
eg
m
en
t
in
th
e
s
h
o
r
t
r
u
n
o
u
tp
e
r
f
o
r
m
s
s
ix
r
iv
als
in
p
r
e
d
ictio
n
ac
cu
r
ac
y
b
ased
o
n
tr
af
f
ic
d
ata
[
1
0
]
.
Usi
n
g
a
d
r
iv
er
m
o
d
el
th
at
u
s
es
SVM
to
an
ticip
ate
tr
af
f
ic
c
o
n
g
esti
o
n
with
o
u
t
r
ely
in
g
o
n
tr
af
f
ic
f
lo
w
m
o
n
ito
r
in
g
tech
n
o
lo
g
y
.
T
h
e
m
o
d
el
u
s
es
s
teer
in
g
,
th
r
o
ttle,
an
d
s
p
ee
d
in
p
u
t
f
r
e
q
u
e
n
cy
ch
an
g
es
in
d
r
iv
i
n
g
s
im
u
l
ato
r
s
,
r
eq
u
ir
in
g
n
o
ad
d
itio
n
al
s
en
s
o
r
s
o
r
i
n
f
r
astru
ctu
r
e
[
1
1
]
.
A
tr
af
f
ic
co
n
g
esti
o
n
p
r
ed
ictio
n
m
o
d
el
is
d
ev
elo
p
ed
u
s
in
g
r
an
d
o
m
f
o
r
est
(
R
F)
,
a
r
o
b
u
s
t
an
d
h
ig
h
-
p
er
f
o
r
m
an
ce
ML
ap
p
r
o
ac
h
.
T
h
e
m
o
d
el,
in
co
r
p
o
r
atin
g
wea
th
er
,
p
er
io
d
,
s
p
ec
ial
r
o
ad
co
n
d
itio
n
s
,
r
o
ad
q
u
ality
,
an
d
h
o
lid
ay
s
,
ac
h
iev
e
d
an
ac
c
u
r
ac
y
o
f
8
7
.
5
%
an
d
a
lo
w
g
en
er
aliza
tio
n
er
r
o
r
[
1
2
]
.
C
o
n
g
est
io
n
m
atr
ices
u
s
in
g
d
if
f
er
en
t
m
et
h
o
d
s
ar
e
d
ev
elo
p
ed
o
n
r
eg
io
n
al
tr
af
f
ic
n
etwo
r
k
s
u
s
in
g
r
elativ
e
p
o
s
itio
n
s
f
o
r
r
o
ad
n
o
d
es.
T
h
ey
u
s
ed
a
co
n
v
o
lu
tio
n
al
lo
n
g
-
s
h
o
r
t
-
ter
m
m
em
o
r
y
n
etwo
r
k
to
p
r
ed
ic
t
co
n
g
esti
o
n
in
all
s
ec
tio
n
s
o
f
th
e
n
etwo
r
k
.
T
h
e
ap
p
r
o
ac
h
o
u
tp
er
f
o
r
m
e
d
b
aselin
e
m
o
d
els
an
d
ac
c
u
r
ately
ca
p
t
u
r
ed
tr
af
f
ic'
s
tem
p
o
r
al
an
d
s
p
a
tial
ch
ar
ac
ter
is
tics
,
d
em
o
n
s
tr
atin
g
its
in
ter
p
r
etab
il
ity
f
o
r
co
n
g
esti
o
n
p
r
ed
ictio
n
[
1
3
]
.
An
o
t
h
er
s
tu
d
y
u
s
ed
s
ev
en
ML
alg
o
r
ith
m
s
to
p
r
ed
ict
tr
af
f
ic
co
n
g
esti
o
n
u
s
in
g
a
b
in
a
r
y
class
if
icatio
n
p
r
o
b
l
em
.
T
h
e
alg
o
r
ith
m
s
in
clu
d
ed
KNN,
DT
,
ar
tific
ial
n
eu
r
al
n
etwo
r
k
(
ANN)
,
s
to
ch
a
s
tic
g
r
ad
ien
t
d
escen
t
(
SGD)
,
f
u
zz
y
u
n
o
r
d
e
r
ed
r
u
le
in
d
u
ctio
n
a
lg
o
r
ith
m
(
FUR
I
A)
,
B
ay
esian
n
etwo
r
k
(
B
N)
,
a
n
d
S
VM
.
E
n
s
em
b
le
lear
n
in
g
alg
o
r
i
th
m
s
lik
e
b
ag
g
in
g
,
b
o
o
s
tin
g
,
p
i
lin
g
,
an
d
PTS
wer
e
u
s
ed
to
en
h
an
ce
t
h
e
p
r
ed
ictio
n
ac
cu
r
ac
y
[
1
4
]
.
Usi
n
g
th
e
lo
g
i
s
tic
r
eg
r
ess
io
n
(
L
R
)
tech
n
iq
u
e
in
[
1
5
]
r
e
v
ea
ls
th
at
it
h
as
a
9
1
%
ac
cu
r
ac
y
r
ate
in
tr
af
f
ic
an
aly
s
is
,
p
r
o
v
id
i
n
g
th
e
q
u
ick
est
an
d
m
o
s
t
d
ir
ec
t
r
o
u
te
t
o
d
esire
d
lo
ca
tio
n
s
.
T
h
is
m
eth
o
d
r
ed
u
ce
s
tr
av
el
tim
e,
n
o
is
e
p
o
llu
tio
n
,
ca
r
b
o
n
d
i
o
x
id
e
e
m
is
s
io
n
s
,
an
d
f
u
el
c
o
n
s
u
m
p
tio
n
.
L
i
n
ea
r
L
R
p
r
ed
icts
p
r
o
b
a
b
ilit
y
v
alu
es
u
s
in
g
a
lin
ea
r
co
m
b
in
atio
n
o
f
f
ea
tu
r
es,
with
ch
an
ce
s
r
an
g
in
g
f
r
o
m
ze
r
o
to
i
n
f
in
it
y
[
1
5
]
.
T
h
e
p
r
ed
ictin
g
o
f
tr
af
f
ic
co
n
g
esti
o
n
u
s
in
g
r
eg
r
ess
io
n
m
o
d
els
ar
e
b
ec
o
m
i
n
g
i
n
cr
ea
s
in
g
ly
in
ef
f
ec
tiv
e
as
th
e
am
o
u
n
t
o
f
d
ata
a
n
d
its
co
m
p
l
ex
ity
r
is
e.
Ma
p
p
in
g
n
o
n
lin
ea
r
d
ata
to
a
h
ig
h
-
d
im
en
s
io
n
al
li
n
ea
r
s
p
ac
e
w
h
er
e
it
m
ay
b
e
lin
ea
r
l
y
ca
teg
o
r
ized
u
s
in
g
h
y
p
er
p
lan
es is
th
e
f
u
n
d
a
m
en
tal
co
n
ce
p
t
o
f
SVM
[
1
6
]
,
[
1
7
]
.
Mo
d
er
n
ML
alg
o
r
ith
m
s
an
d
d
ata
p
r
ep
r
o
ce
s
s
in
g
to
o
ls
ar
e
a
r
r
an
g
ed
in
an
o
r
d
e
r
ly
m
a
n
n
er
o
n
th
e
W
ek
a
wo
r
k
b
en
c
h
.
Usi
n
g
t
h
ese
m
eth
o
d
s
f
r
o
m
th
e
c
o
m
m
an
d
lin
e
is
th
e
p
r
im
ar
y
m
eth
o
d
o
f
in
ter
ac
t
in
g
with
th
em
.
B
u
t
th
er
e
ar
e
also
ea
s
y
-
to
-
u
s
e
in
ter
ac
tiv
e
g
r
ap
h
ical
u
s
er
i
n
ter
f
ac
es
av
ailab
le
f
o
r
d
ata
ex
p
lo
r
atio
n
,
lar
g
e
-
s
ca
le
ex
p
er
im
en
t
s
etu
p
o
n
d
is
tr
ib
u
t
ed
co
m
p
u
tin
g
p
latf
o
r
m
s
,
a
n
d
s
tr
ea
m
ed
d
ata
p
r
o
ce
s
s
in
g
co
n
f
ig
u
r
atio
n
d
esig
n
.
T
h
ese
in
ter
f
ac
es p
r
o
v
id
e
a
s
o
p
h
is
ticated
s
ettin
g
f
o
r
d
ata
m
in
in
g
ex
p
e
r
im
en
ts
.
T
h
e
GNU
g
en
er
al
p
u
b
l
ic
licen
s
e
g
o
v
er
n
s
th
e
d
is
tr
ib
u
tio
n
o
f
th
e
J
av
a
-
wr
itten
s
y
s
tem
[
1
8
]
.
T
h
is
s
tu
d
y
aim
s
to
p
r
ed
ict
c
o
n
g
esti
o
n
u
s
in
g
AI
tech
n
iq
u
es,
esp
ec
ially
m
ac
h
in
e
an
d
d
ee
p
lear
n
in
g
,
u
s
in
g
th
e
W
ek
a
to
o
l
f
o
r
d
ata
th
at
was
co
llected
f
r
o
m
Gr
ea
t
er
Am
m
an
M
u
n
icip
ality
.
T
h
e
d
ata
wer
e
clea
n
ed
,
p
r
o
ce
s
s
ed
,
an
d
i
n
s
er
ted
in
t
o
th
e
W
E
KA
to
o
l
u
s
in
g
th
e
s
elec
ted
class
if
ier
s
.
Usi
n
g
a
v
ar
iety
o
f
s
tatis
t
ical
m
etr
ics,
in
clu
d
in
g
ac
cu
r
ac
y
,
s
p
ec
if
icity
,
s
en
s
itiv
ity
,
an
d
th
e
F1
-
m
ea
s
u
r
e,
class
if
ier
s
wer
e
em
p
lo
y
ed
to
f
in
d
th
e
b
est
o
n
e
to
p
r
ed
ict
tr
af
f
ic
c
o
n
g
esti
o
n
o
n
all
r
o
a
d
s
th
at
en
ter
th
e
ei
g
h
t
h
cir
cle
in
Am
m
an
C
ity
.
T
h
ese
m
atr
ices
m
ak
e
it
p
o
s
s
ib
le
to
id
en
tif
y
co
n
g
esti
o
n
m
o
r
e
p
r
ec
is
ely
.
T
h
en
th
e
b
est
clas
s
if
ier
wil
l
b
e
d
eter
m
in
ed
ac
co
r
d
in
g
to
th
e
h
ig
h
est ac
cu
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d
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ata
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llected
o
v
er
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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M
E
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H
O
D
2
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1
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ra
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f
ic
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estio
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dict
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h
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aim
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at
Am
m
an
'
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cle
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ter
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ec
tio
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if
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a
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tili
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t
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p
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th
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n
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ataset
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Gr
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ter
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m
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M
u
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K
A.
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h
en
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ataset
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ild
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q
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ir
ed
m
o
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el
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et
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ize
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et
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s
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wer
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s
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8
6
4
0
)
in
th
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d
ataset
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o
r
ea
c
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d
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a
p
p
lied
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in
cr
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th
e
p
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o
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m
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s
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g
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atr
ix
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at
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lts
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ter
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ter
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e
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m
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el
h
as
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ee
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c
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s
tr
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cted
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th
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o
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o
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ar
ativ
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is
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e
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d
u
cted
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ML
m
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el
ex
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its
th
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ig
h
est
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er
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o
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m
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ce
in
d
icato
r
s
.
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d
in
g
s
s
h
o
w
wh
ich
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o
r
ith
m
s
ar
e
th
e
b
est
to
p
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ed
ict
tr
af
f
ic
co
n
g
esti
o
n
,
am
o
n
g
o
th
er
s
.
Fig
u
r
e
1
d
ep
icts
th
e
tr
af
f
ic
co
n
g
esti
o
n
p
r
ed
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n
s
y
s
tem
ar
ch
itectu
r
e.
Fig
u
r
e
1
.
T
r
af
f
ic
co
n
g
esti
o
n
p
r
ed
ictio
n
s
y
s
tem
ar
ch
itectu
r
e
2
.
2
.
Da
t
a
s
et
T
h
e
d
ataset,
o
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tain
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o
m
t
h
e
Gr
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ter
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m
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Mu
n
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e
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io
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r
o
m
1
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0
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9
to
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,
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o
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en
f
o
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e
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f
icien
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d
p
r
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e
tr
af
f
ic
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g
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o
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p
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ed
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n
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I
t
in
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tr
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is
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ce
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ic
v
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m
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etec
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ic
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ted
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p
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2
4
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r
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th
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2
0
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9
.
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h
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o
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Fig
u
r
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wev
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ata
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p
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2
2
5
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8
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WEKA
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4425
Fig
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W
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o
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c
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Fig
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r
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3
.
Stu
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[
1
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ata
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u
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licate,
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lete
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ata
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n
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em
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ata
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ir
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t step
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is
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ee
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n
W
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h
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d
les
th
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p
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ce
s
s
,
ty
p
ically
s
av
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g
th
e
tr
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f
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ic
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C
SV
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ile.
Fig
u
r
e
3
d
ep
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a
lis
t
o
f
attr
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u
tes
a
n
d
class
es
f
o
r
c
o
n
g
esti
o
n
p
r
ed
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n
an
aly
s
is
;
Fig
u
r
e
4
s
h
o
ws
th
e
attr
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u
te
lis
t
v
is
u
aliza
tio
n
o
f
W
E
KA.
2
.
3
.
M
a
chine le
a
rni
ng
cla
s
s
if
iers
ML
is
a
tech
n
iq
u
e
f
o
r
teac
h
in
g
m
ac
h
in
es to
h
an
d
le
d
ata
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ett
er
.
ML
is
in
cr
ea
s
in
g
ly
p
o
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u
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e
ab
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n
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a
n
ce
o
f
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atasets
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u
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in
ess
es to
ex
tr
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p
o
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tan
t d
ata
[
2
0
]
.
ML
h
as b
ee
n
cr
u
cial
in
s
m
ar
t
tr
an
s
p
o
r
tatio
n
,
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n
v
esti
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atin
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m
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lex
in
ter
ac
tio
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s
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e
twee
n
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a
d
way
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n
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ir
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ic
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r
ash
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u
s
ed
in
ap
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licatio
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lik
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ata
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e
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o
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ith
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s
lear
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co
m
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lete
t
ask
s
in
d
ep
en
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en
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[
2
1
]
.
2
.
3
.
1
.
L
o
g
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re
s
s
io
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L
R
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f
u
n
d
am
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tal
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icatio
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tech
n
iq
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e
,
a
lin
ea
r
class
if
i
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th
at
u
s
es
p
r
o
b
ab
ilit
y
to
ca
teg
o
r
ize
d
ata
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to
b
in
ar
y
g
r
o
u
p
s
[
2
2
]
.
I
t
is
a
s
im
p
le
an
d
q
u
ick
m
eth
o
d
f
o
r
d
ata
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aly
s
is
,
allo
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f
o
r
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y
u
n
d
er
s
tan
d
in
g
o
f
f
in
d
in
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s
.
I
t
p
r
im
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r
ily
f
o
cu
s
es
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b
in
ar
y
class
if
icatio
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b
u
t
ca
n
also
s
o
lv
e
m
u
lticlas
s
is
s
u
es
[
2
3
]
.
T
h
e
b
asic
m
o
d
el
o
f
th
e
LR
esti
m
atio
n
is
as (
1
)
:
(
=
1
)
(
=
0
)
=
1
−
=
(
β0
+
β1
X1
+……+
βk
Xk
i)
(
1
)
wh
er
e
e
is
th
e
e
x
p
o
n
en
tial c
o
n
s
tan
t,
(
1
-
Pi)
is
t
h
e
ch
a
n
ce
t
h
at
Y
tak
es a
v
alu
e
o
f
0
,
an
d
Pi is
th
e
p
r
o
b
ab
ilit
y
th
at
Y
tak
es a
v
alu
e
o
f
1
[
2
4
]
.
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
3
,
No
.
4
,
Dec
em
b
er
2
0
2
4
:
4
4
2
2
-
4
4
3
4
4426
2
.
3
.
2
.
K
-
n
ea
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neig
hb
o
r
A
KNN
class
if
ier
is
a
r
eliab
le
d
ata
class
if
icatio
n
alg
o
r
ith
m
,
b
u
t its
ac
cu
r
ac
y
d
ep
en
d
s
o
n
th
e
ch
o
ice
o
f
n
ea
r
est
KNNs
[
2
5
]
.
I
t
ig
n
o
r
e
s
th
e
k
-
en
v
ir
o
n
m
en
t
d
is
tr
ib
u
tio
n
,
m
a
k
in
g
c
o
n
s
tan
t
k
n
u
m
b
er
s
u
n
s
u
itab
le
f
o
r
ir
r
eg
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lar
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atasets
.
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n
am
ic
k
s
elec
tio
n
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s
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g
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ested
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b
u
t
m
ax
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er
f
o
r
m
an
ce
is
c
h
allen
g
in
g
f
o
r
lar
g
e
d
atasets
[
2
6
]
.
KNN
ex
ce
ls
in
class
if
icat
io
n
d
u
e
to
its
s
im
p
licity
.
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wev
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lar
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e
s
am
p
le
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izes
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d
f
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tu
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e
attr
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tes
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n
h
in
d
er
its
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er
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o
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m
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ce
.
T
h
e
p
ar
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eter
k
,
wh
ic
h
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eter
m
in
es
th
e
n
u
m
b
er
o
f
K
NNs,
s
ig
n
if
ican
tly
im
p
ac
ts
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e
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o
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ith
m
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s
tic
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er
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m
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n
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eq
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en
o
v
e
r
f
itti
n
g
a
n
d
u
n
d
er
f
itti
n
g
[
2
7
]
.
2
.
3
.
3
.
Dec
is
io
n
t
re
e
DT
class
if
ier
s
ar
e
wid
ely
u
s
ed
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ata
class
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m
eth
o
d
s
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s
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m
ain
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atter
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n
g
esti
o
n
p
r
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ctio
n
.
T
h
ey
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o
n
s
is
t o
f
n
o
d
es a
n
d
b
r
a
n
ch
es a
n
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u
s
e
v
ar
io
u
s
class
if
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n
alg
o
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it
h
m
s
to
m
an
a
g
e
m
is
s
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g
v
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lu
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co
n
tin
u
o
u
s
an
d
p
er
io
d
i
c
p
r
o
p
er
ties
[
2
8
]
.
I
n
ter
p
r
etab
le
m
o
d
els
(
DT
)
a
r
e
wid
ely
u
s
ed
f
o
r
ca
teg
o
r
izatio
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d
ec
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m
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g
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u
t
th
ei
r
m
y
o
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ic
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d
u
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alg
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r
ith
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s
lead
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f
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en
tal
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icate
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ter
ac
tio
n
s
b
etwe
e
n
in
p
u
t f
ea
tu
r
es o
cc
u
r
[
2
8
]
,
[
2
9
]
.
2
.
3
.
4
.
Ra
nd
o
m
f
o
re
s
t
R
F
cla
s
s
if
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s
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r
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m
m
eth
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d
th
at
cr
ea
tes
m
u
ltip
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DT
s
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s
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g
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r
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d
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m
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to
r
to
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in
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r
r
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im
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r
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h
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iv
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o
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b
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r
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d
at
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r
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k
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llo
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r
s
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s
.
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e
g
o
al
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te
a
m
ix
tu
r
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o
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ec
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io
n
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m
a
k
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g
tr
ee
s
f
o
r
d
if
f
e
r
en
t f
o
r
ec
asts
[
3
0
]
–
[
3
2
]
.
2
.
3
.
5
.
Su
pp
o
rt
v
ec
t
o
r
m
a
chi
ne
SVMs
ar
e
s
u
p
er
v
is
ed
lear
n
in
g
alg
o
r
ith
m
s
th
at
m
ay
b
e
ap
p
lied
to
b
o
th
class
if
icatio
n
an
d
r
eg
r
ess
io
n
ap
p
licatio
n
s
.
T
h
e
b
asic
g
o
al
o
f
SVMs
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s
to
d
is
co
v
er
th
e
h
y
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er
p
lan
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th
at
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est
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ep
ar
ates
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e
class
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th
e
f
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tu
r
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ec
r
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s
in
g
th
e
a
m
o
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n
t
o
f
m
is
clas
s
if
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n
s
.
T
h
is
is
ac
co
m
p
lis
h
ed
b
y
s
o
l
v
in
g
an
o
p
tim
izatio
n
p
r
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b
lem
t
h
at
aim
s
to
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tify
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e
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y
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r
p
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h
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c
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th
e
m
ar
g
in
(
t
h
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d
is
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ce
b
et
wee
n
th
e
h
y
p
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lan
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an
d
th
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n
ea
r
est
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ata
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o
in
ts
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an
d
th
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er
r
o
r
r
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T
h
e
r
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o
r
e,
SVMs
ex
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l
at
h
an
d
lin
g
h
ig
h
-
d
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m
en
s
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al
d
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d
ar
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f
r
eq
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tly
f
a
v
o
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o
v
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o
t
h
er
ML
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o
r
ith
m
s
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wo
r
k
in
g
with
n
o
n
-
lin
ea
r
ly
s
ep
ar
a
b
l
e
d
atasets
[
3
3
]
,
[
3
4
]
.
2
.
3
.
6
.
M
ulti
-
la
y
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perc
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n
M
L
P
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[
3
5
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–
[
3
7
]
.
Fig
u
r
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4
.
Mu
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p
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r
al
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etwo
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c
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itectu
r
e
[
38
]
Evaluation Warning : The document was created with Spire.PDF for Python.
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[
39
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.
T
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[
40
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,
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41
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.
Fig
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o
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Fig
u
r
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ab
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l
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e
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t
e
d
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Evaluation Warning : The document was created with Spire.PDF for Python.
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4428
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u
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R
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ter
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ata
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icatio
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[
4
4
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4
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ier
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u
r
e
7
d
em
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ates th
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th
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ier
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c
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ier
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f
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ex
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er
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m
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DT
(
%)
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V
M
(
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(
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N
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LP
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o
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n
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S
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e
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2
9
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.
7
9
6
.
2
9
8
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7
9
5
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4
9
7
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6
Fig
u
r
e
7
.
Acc
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r
ac
y
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o
m
p
ar
is
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n
o
f
all
class
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ier
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r
all
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er
im
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ts
92.
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93.
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94.
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95.
00%
96.
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97.
00%
98.
00%
99.
00%
100
.00%
DT
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V
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RF
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r
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e
C
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o
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t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
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N:
2
2
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9
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WEKA
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b
a
s
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ch
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e
lea
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n
in
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r
tr
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o
n
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o
n
p
r
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n
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A
mma
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C
ity
(
A
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r
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4429
I
n
ter
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s
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e
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atio
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d
p
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ested
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ested
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as sh
o
wn
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3
)
[
4
6
]
.
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ab
le
3
s
h
o
ws th
e
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cc
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r
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ch
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im
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n
t
f
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r
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e
r
en
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ier
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.
Fig
u
r
e
8
d
em
o
n
s
tr
ates
th
e
ac
cu
r
ac
y
r
e
p
r
esen
tatio
n
o
f
all
class
if
ier
s
f
o
r
all
ex
p
er
im
en
ts
.
Pr
e
c
ision
=
TP
TP
+
FP
(
3
)
T
ab
le
3
.
Pre
cisi
o
n
co
m
p
ar
is
o
n
o
f
all
class
if
ier
s
f
o
r
all
ex
p
er
i
m
en
ts
D
T
(
%)
S
V
M
(
%)
R
F
(
%)
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N
N
(
%)
M
LP (
%)
LR
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W
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st
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n
d
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t
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e
e
t
9
8
.
4
9
9
.
4
9
8
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7
9
8
.
6
9
7
.
0
9
9
.
2
S
o
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h
b
o
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n
d
S
t
r
e
e
t
9
5
.
2
9
8
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8
9
7
.
2
9
8
.
3
9
6
.
9
9
7
.
8
Ea
st
b
o
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n
d
S
t
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e
e
t
1
0
0
.
0
1
0
0
.
0
1
0
0
.
0
1
0
0
.
0
9
9
.
9
1
0
0
.
0
N
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h
b
o
u
n
d
S
t
r
e
e
t
9
6
.
9
9
9
.
8
9
7
.
0
9
9
.
0
9
8
.
4
9
7
.
7
Fig
u
r
e
8
.
Pre
cisi
o
n
c
o
m
p
ar
is
o
n
o
f
all
class
if
ier
s
f
o
r
all
ex
p
er
im
en
ts
Sen
s
itiv
ity
is
an
o
th
er
p
er
f
o
r
m
an
ce
ev
alu
ati
o
n
u
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ed
in
th
is
s
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ich
is
co
m
p
u
ted
as
t
h
e
r
atio
o
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r
r
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tifie
d
p
o
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itiv
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v
alu
es
to
th
e
to
tal
n
u
m
b
er
o
f
p
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s
itiv
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v
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es.
T
h
e
s
en
s
itiv
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m
atr
ix
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eter
m
in
es
h
o
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e
class
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ier
ca
n
d
is
co
v
er
p
o
s
itiv
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as
s
h
o
wn
in
(
4
)
[
4
7
]
.
T
a
b
le
4
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o
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e
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m
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r
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f
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ier
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Fig
u
r
e
9
d
em
o
n
s
tr
ates
th
e
ac
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r
ac
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o
f
th
e
r
ep
r
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o
f
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ier
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f
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r
all
ex
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er
im
en
ts
.
Se
n
s
itivit
y
=
TP
TP
+
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4
)
T
ab
le
4
.
Sen
s
itiv
ity
co
m
p
ar
is
o
n
o
f
all
class
if
ier
s
f
o
r
all
ex
p
er
im
en
ts
D
T
(
%)
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V
M
(
%)
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F
(
%)
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N
N
(
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LP (
%)
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W
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st
b
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n
d
S
t
r
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e
t
9
8
.
4
9
9
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8
9
7
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8
9
8
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6
9
5
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3
9
6
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o
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n
d
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2
9
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3
9
6
.
5
9
8
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1
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4
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2
9
6
.
3
Ea
st
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n
d
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t
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e
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9
7
.
2
9
9
.
6
9
7
.
1
9
8
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3
9
6
.
4
9
6
.
6
N
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h
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o
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n
d
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e
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9
7
.
6
9
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5
9
5
.
4
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8
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5
9
2
.
5
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7
.
5
92.
00%
93.
00%
94.
00%
95.
00%
96.
00%
97.
00%
98.
00%
99.
00%
100
.00%
DT
S
V
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RF
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M
LP
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e
r
c
e
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l
as
si
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s
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un
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t
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E
a
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St
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e
t
N
o
r
t
hb
o
un
d
S
t
r
e
e
t
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
3
,
No
.
4
,
Dec
em
b
er
2
0
2
4
:
4
4
2
2
-
4
4
3
4
4430
Fig
u
r
e
9
.
Sen
s
itiv
ity
co
m
p
ar
is
o
n
o
f
all
class
if
ier
s
f
o
r
all
ex
p
er
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Evaluation Warning : The document was created with Spire.PDF for Python.