TELKOM
NIKA Indonesia
n
Journal of
Electrical En
gineering
Vol. 12, No. 10, Octobe
r 20
14, pp. 7242
~ 724
8
DOI: 10.115
9
1
/telkomni
ka.
v
12i8.566
6
7242
Re
cei
v
ed
Jan
uary 24, 201
4
;
Revi
sed
Ju
n
e
28, 2014; A
c
cepted
Jul
y
20, 2014
Study on Cooperation between Traffic Control and
Route Guidance Based on Real-time Speed
Cao Jie
1
, Wa
ng Chua
n*
2
1
Colle
ge of Co
mputer an
d Co
mmunicati
on,
L
anzh
ou U
n
iver
sit
y
of T
e
chnol
og
y,
Lanz
ho
u 730
0
50, chin
a
；
2
Colle
ge of Ele
c
trical an
d Information En
gin
e
e
rin
g
, Lanz
hou
Universit
y
of T
e
chn
o
lo
g
y
,
Lanz
ho
u 730
0
50, Gansu, Ch
i
n
a
*
Corres
p
o
ndi
n
g
author,
e-ma
i
l
: w
a
c
1
9
00@
1
26.com
A
b
st
r
a
ct
Aiming
at
mi
ni
mi
z
i
ng
the
total trav
el ti
me
of the r
oad
n
e
t
w
o
rk, a coop
eratio
n
mo
del
of traffic
control
an
d r
oute
gui
da
nce
is bu
ilt b
a
s
ed o
n
re
al
-ti
m
e
spe
ed
ob
taine
d
fro
m
c
oop
erative
ve
hicl
e
infrastructure system
. Gene
tic
alg
o
rith
m
is u
s
ed to s
o
lve
th
e coo
per
ation
mo
de
l to g
e
t the o
p
ti
ma
l gre
e
n
ratio
and
gu
id
a
n
ce r
a
te of fl
o
w
through
tran
sformi
ng
ge
net
ic al
gorit
h
m
w
i
th co
nstraints
i
n
to u
n
constr
ai
ned
gen
etic al
gorit
hm by
pen
alty
function. T
h
e
simu
lati
on res
u
lts of an exp
e
ri
ment
al si
mu
latio
n
on a s
m
al
l
netw
o
rk show
that this meth
od ca
n effecti
v
ely b
a
la
nce t
he n
e
tw
ork flow
, reduce tota
l travel ti
me
a
n
d
improve th
e efficiency
of road
netw
o
rk.
Ke
y
w
ords
:
coop
erative ve
hicle i
n
frastruc
ture system, tra
ffic control, route gu
ida
n
ce,
genetic al
gor
i
t
hm,
pen
alty functio
n
Copy
right
©
2014 In
stitu
t
e o
f
Ad
van
ced
En
g
i
n
eerin
g and
Scien
ce. All
rig
h
t
s reser
ve
d
.
1. Introduc
tion
Traffic
contro
l and traffic fl
ow g
u
idan
ce
are
im
porta
nt mean
s to affect traffic flo
w
. The
former affe
cts the indu
ction strate
gy of traffic
flow
by changi
ng
time distributi
on of the traffic
flow, while th
e latter affects the cont
rol
strategy of traffic flow by
chan
ging a
r
rival time and
quantity of vehicle at i
n
tersectio
n
s.
Gra
s
ping t
he su
pplem
entary effect
and sp
ace-time
relation
shi
p
b
e
twee
n the traffic control
and traffi
c gu
idan
ce, comb
ining the
traffic
control wit
h
traffic guid
a
n
c
e h
a
s a
n
i
m
porta
nt sig
n
ifican
ce fo
r
safety, high
efficien
cy an
d smo
o
thne
ss of
traffic
.
Allsop i
s
the
first Briti
s
h
scholar who di
d
int
egrated
re
sea
r
ch o
n
tra
ffic sign
al p
r
o
c
e
ssi
ng
and traffic fl
ow eq
uilibri
u
m
. After years of devel
o
p
ment, som
e
collab
o
rative
model of traffi
c
control a
nd
route gui
dan
ce have
eme
r
ged, for insta
n
ce, a
collab
o
rative mo
de
l in which ro
ute
guida
nce pre
dominate
s
[1
], a collabo
ra
tive model
in which co
ntrol pred
omin
a
t
es [2], iterative
optimizatio
n
and all
o
catio
n
process m
odel [3, 4]
a
nd glo
bal o
p
timization
mo
del [5]. Although
these m
odel
s link the traffic control
with
route
g
u
ida
n
c
e, mo
st of them do
not
put co
ntrol a
nd
guida
nce in t
he eq
ually im
portant
po
sition. Some
mo
dels
avoid
ne
twork O
D
ne
eds [6] and
fa
il to
achi
eve the real co
upling
of the tr
affic control an
d ro
ute guida
nce.
Based
on p
r
evious
studi
es o
n
the
coope
ra
tion
b
e
twee
n traffi
c control an
d route
guida
nce, this arti
cle wi
ll introdu
ce
collab
o
rative vehiclei
nfrastru
ctu
r
e
system into
the
coo
peration betwe
en
t
r
affic cont
rol and
ro
ute
g
u
id
a
n
c
e. A
coll
abo
rative mo
del
of traffic co
ntro
l
and
route
g
u
idan
ce i
s
b
u
ilt on traffic
volume, veh
i
cle
spe
ed a
nd othe
r inf
o
rmatio
n whi
c
h
colle
cted a
n
d
transmitted
by advanced
data acq
u
isi
t
ion and co
mmuni
cation
technol
ogie
s
of
colla
borative vehicl
einf
ra
stru
cture
system. The
collabo
rative
model
is so
lved by g
e
n
e
tic
algorith
m
to
get the
be
st
para
m
eter of
traffic
control
and
ro
ute g
u
i
dan
ce to
a
c
hi
eve rea
s
ona
b
l
e
optimizatio
n of traffic flow and re
du
ction
of traffic con
gestio
n
.
2. The Estab
lishment of
Collabora
t
iv
e Model of T
r
affic
Con
t
ro
l and Route Guidanc
e
un
der
the Env
i
ron
m
ent of
Collabora
t
iv
e Ve
hicle Infras
tr
uctur
e
Sy
stem
There are ma
ny indicato
rs
to evaluate the
performan
ce of road net
works. For ex
ample,
total travel time, total cost
of driving, total delay
time and so on. T
he model in t
h
is arti
cle will
use
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Study on Coo
peratio
n between Traffic Control
an
d Ro
ute Guida
n
ce
Based on
… (Cao
Jie)
7243
total travel ti
me (total
trav
el time i
n
cl
ud
es
driv
ing
tim
e
an
d d
e
lay t
i
me)
a
s
o
b
je
ctive function
to
evaluate the
perfo
rman
ce
of road net
wo
rks.
2.1. The Co
mposition of Trav
el Time
A single
roa
d
is sh
own in
figure 1. In th
e gra
ph, the
box rep
r
e
s
en
ts a vehicl
e.
l
i
is the
vehicle’
s leng
th.
v
i
is the vehicl
e’s
spee
d.
L
is the ro
ad’s le
ngth.
l
is the que
uei
ng length.
T
a1
is
the driving ti
me whi
c
h i
s
runtime of the
vehicl
e in th
e road’
s up
stream area wit
h
low de
nsity.
T
a2
is the delay time whi
c
h is
runtime of vehicle in
the ro
a
d
’s do
wn
stre
am are
a
with
high de
nsity.
Travel time of
the whole
ro
ad is
comp
osed of
driving t
i
me and d
e
la
y time, therefore, the
travel time of road A is:
2
1
a
a
T
T
T
(1)
2.1.1. Driv
in
g time
T
a1
Most of the p
r
eviou
s
mo
de
ls solved d
r
ivi
ng time ba
se
d on the
relat
i
onship bet
ween the
traffic volum
e
and
vehi
cle
spe
ed. O
ne
o
f
the mo
st
co
mmonly u
s
e
d
model
s is th
e line
a
r
rel
a
tion
model p
r
op
o
s
ed by G
r
ee
n Shields. B
e
ca
use of
sp
eed an
d trav
el time indire
ctly obtained
by
traffic volume
, these mod
e
l
s lack di
re
ctness an
d pr
e
c
isi
on. Thi
s
a
r
ticle introdu
ces collab
o
rati
ve
vehiclei
nfra
structu
r
e syste
m
in
to the
coope
ration
b
e
twee
n traffic cont
rol a
nd
route
guid
a
n
c
e.
Vehicle
spee
d and
length
(incl
ude
vehi
cle
spa
c
in
g)
are
obtain
ed
by the vehi
cl
e termin
al. T
hese
informatio
n a
r
e colle
cted b
y
road
side te
rminal throug
h
wirel
e
ss
com
m
unication te
chn
o
logy
su
ch
as
zig
bee
[7]. The ave
r
a
g
e
spee
d a
n
d
que
ueing
le
ngth g
o
t fro
m
the
roa
d
si
de terminal
are
acq
u
ire
d
to g
e
t the driving
time to
impro
v
e t
he pre
c
i
s
i
on. In the mo
del, the drivin
g time T
a1
is the
road le
ngth di
vided by the averag
e sp
ee
d
v
. Its
expres
s
i
on is
as
follows
:
v
l
L
T
a
1
(2)
In this
expre
ssi
on, the
ro
ad le
ngth i
s
equal
to the
differen
c
e
be
tween
the
ro
ad a
c
tual
length
L
an
d
queu
eing l
e
n
g
th
l.
This pa
per
doe
s n
o
t
take the
a
s
su
mption of p
o
i
n
t queu
eing
for
the queu
eing
length. The
collab
o
rativ
e
vehicle inf
r
ast
r
u
c
ture
system lets
v
0
be a velocity
threshold, th
e vehicl
e
wo
uld be
in the
queu
eing
stat
us
whe
n
the
vehicle
sp
ee
d is l
e
ss th
an
v
0
(assu
m
ing
qu
euein
g
vehi
cl
es
are
lo
cate
d in the
do
wn
strea
m
inte
rsection
). Th
e
queu
eing l
e
n
g
th
L is e
qual to
the sum
of q
ueuei
ng vehi
cle
le
ngth (l
e
ngth contain
s
vehicle
spa
c
ing)
l
1
, l
2
, l
3
,..
. l
n
divided by the numbe
r of lane
s a
r
. Average
spe
ed
v
is the avera
ge of spe
ed
whi
c
h is g
r
ea
ter
than
v
0
.
To s
u
m up, the driving time is
as
follows
:
1
1
2
1
1
2
1
1
n
n
v
v
v
a
l
l
l
L
v
l
L
T
n
n
r
n
a
(3)
In the expre
ssi
on,
n
1
i
s
t
he nu
mbe
r
o
f
queuei
ng v
ehicl
es;
n
is the total n
u
m
ber
of
vehicle
s
.
In the actual travel, vehicle
cong
estio
n
and del
ay
s often occu
r at interse
c
tion, th
erefo
r
e,
to
a
l
a
rg
e extent,
the level of intersecti
on sm
o
o
thne
ss determin
e
s the
length
of travel time.
Signal inte
rsection
delay
cal
c
ulatio
n is an imp
o
rta
n
t part
of the
study on traffic flow theo
ry in
traffic en
gine
ering. T
he d
e
l
ay time is no
t merely
an i
m
porta
nt ind
e
x of interse
c
tion se
rvice l
e
vel
evaluation, b
u
t also a key
part of the
urba
n roa
d
tr
avel time calculatio
n[8]. In
this article, the
delay time ca
lculatio
n use
s
the HCM (1
9
85 edition
) de
lay calcul
atio
n formula[9]. Its computati
o
n
formula is
as
follows
:
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 12, No. 10, Octobe
r 2014: 724
2
– 7248
7244
2
2
2
2
1
0.
38
1
16
173
1
1
a
Tc
X
X
XX
X
s
(4)
In the formula,
c
refers to si
gnal interse
c
t
i
on cycl
e;
refers
to green ratio;
X
refers to
the degree of
saturatio
n
;
s
refers
to s
a
turated traffic
v
o
lume of s
i
gnal intersec
tion.
2.2. The Esta
blishment of the Collabor
ativ
e Model
The a
r
ticle
wi
ll take time a
s
the
cost fun
c
tion of road
and evalu
a
tio
n
index, and t
a
ke th
e
freque
ntly-u
sed total trav
el time of th
e ro
ad n
e
twork a
s
mo
de
ling obj
ective
function.
Th
e
obje
c
tive fun
c
tion
will exp
r
ess traffic
control
st
rate
g
y
throug
h green
ratio of i
n
tersectio
n
,
and
expre
s
s ro
ute guid
a
n
c
e
strategy throug
h rate
of
traffic inflo
w
. Co
n
s
traint
co
nditi
on 1
and
2 a
r
e
use
d
to limit the distri
butio
n of the traffic
on the
net
work by
setting thre
sh
old
of the mean
and
varian
ce of saturation. Constraint co
n
d
ition
3 is the con
s
traint
for network flow balan
ce
.
Con
s
trai
nt co
ndition 4 re
st
rict
s the relat
i
onship
bet
ween the rate
of traffic outflow and traffic
volume. Co
n
s
traint
con
d
ition 5 an
d 6 a
r
e the th
re
sh
old ra
nge
of gree
n ratio a
nd traffic volu
me.
In summa
ry, colla
borative model is e
s
ta
blish
ed a
s
follows:
a
n
a
a
a
n
a
n
a
n
B
a
n
a
n
k
k
A
a
n
a
N
i
n
i
n
a
a
a
x
u
T
t
x
t
v
t
v
OD
t
u
X
X
N
S
X
N
X
t
s
T
T
f
,
0
1
0
/
)
(
)
(
)
(
)
(
)
(
1
1
.
.
min
1
)
(
)
(
1
2
2
1
1
2
1
(5)
3. Solv
ing the Collabora
t
i
v
e
Model on the Basis of
Gene
tic Alg
o
rithm
Geneti
c
algo
rithm is a ne
w rand
om se
arch
an
d optimi
z
ation al
gorit
hm develop
e
d
rapi
dly
in recent yea
r
s. Its ba
si
c i
dea i
s
ba
sed
on
Da
rwin’
s
evolutiona
ry theory
and
M
endel’
s
hered
ity
theory. The
geneti
c
algo
ri
thm provide
s
a gene
ral
framework for
solving p
r
o
b
l
e
ms of
comp
lex
system
optim
ization. It i
s
i
ndep
ende
nt
of sp
ecifi
c
fiel
ds
of p
r
oble
m
and
h
a
s a
stron
g
rob
u
st
ness
for the types
of proble
m
s,
hen
ce this a
r
ticle
u
s
e
s
the geneti
c
algo
ri
thm to solve the model.
Geneti
c
al
gorithm only
sea
r
ch
es by u
s
in
g the
fitne
s
s
of individual
s in the
pop
ul
ation on
the ba
si
s of fi
tness fun
c
tio
n
. The
obj
ecti
ve functio
n
i
s
the n
e
two
r
k
travel time i
n
this p
ape
r, a
nd
its value is positive. For the minimum point pr
ob
lem, the objective functio
n
can be di
rectly
trans
formed into fitnes
s
func
tion.
n
a
a
a
T
T
fitness
1
2
1
min
(6)
For the o
p
timization
pro
b
lem with
constraints, th
e penalty fu
nction m
e
tho
d
is a
comm
only u
s
ed techniq
u
e
.
Esse
ntially, it is that
by
puni
shin
g in
feasibl
e
solut
i
on to conve
r
t
con
s
trai
ned
probl
em
s into uncon
strai
ned proble
m
s.
In genetic algorithm, p
enalty functio
n
is
use
d
to
ke
ep
part
s
of
infe
asibl
e
solutio
n
in
ea
ch
ge
neratio
n p
o
p
u
lation. Th
e
geneti
c
sea
r
ching,
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Study on Coo
peratio
n between Traffic Control
an
d Ro
ute Guida
n
ce
Based on
… (Cao
Jie)
7245
therefo
r
e, ca
n achieve th
e optimal
sol
u
tion from
b
o
th sid
e
s of
the infea
s
ible
regio
n
an
d the
feasibl
e
regi
o
n
. Usin
g the penalty func
ti
on, the fitness functio
n
be
come
s:
12
1
22
mi
n
(
)
(
(
m
a
x
(
0
,
(
)
))
(
(
))
)
n
aa
a
ij
fit
n
e
s
s
T
T
Mg
x
h
x
(7)
In the fo
rmul
a,
g
i
(x
)
is inequality constraints,
h
j
(x
)
i
s
equ
ality co
n
s
traint
s,
M
is a large
positive num
ber. When th
e
x
is fea
s
ibl
e
, the penalty is 0; wh
en
x
i
s
not fea
s
ible
, the penalty is a
numbe
r great
er than 0.
M
is 100
0 in this paper.
4. Small Road Net
w
o
r
k T
est
In orde
r to validate the coll
aborative mo
del
and its al
gorithm, a small roa
d
network
will
be b
u
ilt to ex
perim
ent in
th
e VISSIM microsco
pic tr
affic
s
i
mulation softwar
e.
Figu
re 2
sho
w
s the
small ro
ad n
e
twork. The
netwo
rk
cont
ains fou
r
interse
c
tion a
nd four two
-
way
road
s with two
lane
s. Every intersectio
n
is two pha
se in
terse
c
tion.
Th
e initial gree
n
ratio is 0.47.
Cycle i
s
fixed.
Figure 2. Dia
g
ram of Smal
l Netwo
r
k
In VISSIM, th
e data dete
c
ting point and
queuei
ng co
unter a
r
e set to simulate the dat
a
acq
u
isitio
n device of colla
borative vehi
cle infra
s
tru
c
t
u
re sy
stem. There are three data dete
c
ting
points in
ea
ch road
to
coll
ect ave
r
a
ge
speed.
Th
e
qu
euein
g
cou
n
ter
i
s
set in the down
s
trea
m of
the road to
co
llect que
uein
g
length.
Its config
uratio
n is sh
own in Figure 3.
Figure 3. Dia
g
ram of Data Dete
ction Poi
n
t
In this article,
the small ro
a
d
netwo
rk h
a
s
four O
D
de
mand
s, name
l
y, 1-3, 2-4, 3-1 and
4-2. The p
a
th
s of 1-3 a
r
e 1
-
2-3 and 1
-
4
-
3, its OD dem
and is 1.2
ve
hicle/
s
. The p
a
ths of 2-4 are
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Vol. 12, No. 10, Octobe
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2
– 7248
7246
2-3
-
4 an
d 2-1
-
4, its OD d
e
m
and is 1.4
vehicl
e/s
. The
paths of 3
-
1 a
r
e 3-2-1 a
nd
3-4
-
1, its OD
deman
d is 1.
3
vehi
cle/
s
. The path
s
of 4-2 are 4
-
3-2 a
nd 4-1
-
2, its
OD de
mand i
s
1.1vehi
cle/s.
The O
D
dem
and can be
si
mulated by setting the pat
h in VISSIM.
As sho
w
n in
Figure 4, there
are 4 path
s
from the left road of intersection 1 to
interse
c
tion 3. Th
e prop
ortio
n
of a vehicle'
s
choi
ce of pat
h can b
e
sim
u
lated by sett
ing
these pat
h flow distri
bu
tions re
sp
ecti
vely.
Figure 4. Dia
g
ram of the P
a
th 1-3
In VISSIM simulation, th
e time is
5
minute
s
. T
h
rou
gh
setti
ng the
co
rre
spo
ndin
g
evaluation p
a
r
amete
r
s, the
data re
cord files will b
e
ob
tained by the end of sim
u
la
tion. The initial
data of spe
e
d
and roa
d
net
work obtai
ne
d by sort
ing d
a
ta are
sho
w
n in the following table.
Table 1. Net
w
ork Initial Data
Road
Length
(m)
Queueing
length(m)
T
r
affic volume
(vehicle/s)
Inflow
rate
(vehicle/s)
Cy
cle
(s
)
1-2
480
37
0.57
0.24
120
2-1
480
158 0.42 0.33
100
2-3
310
36
0.31
0.40
90
3-2
310
33
0.35
0.48
120
3-4
530
22
0.40
0.37
100
4-3
530
58
0.19
0.45
90
4-1
300
36
0.17
0.45
100
1-4
300
191 0.45 0.36
100
Table 2. Spe
ed of every Road
Road
Detecting point 1
(k
m/h)
Detecting point 2
(k
m/h)
Detecting point 3
(k
m/h)
Average
(k
m/h)
1-2
50.8
48.8
41.9
47.2
2-1
50.9
43.9
32.2
42.3
2-3
50.5
46.1
37.1
44.6
3-2
51.5
45.8
38.8
45.4
3-4
52.2
48.6
41.0
47.3
4-3
52.1
51.5
48.1
50.6
4-1
49.9
43.7
39.6
44.4
1-4
41.9
35.6
30.6
36.0
In sim
u
lation
softwa
r
e
MA
TLAB, obje
c
ti
ve functio
n
i
s
programme
d
with
average
sp
eed
and
que
uein
g
len
g
th
data
.
The
value
of
ψ
,
σ
and
τ
are
ψ
=0.4,
σ
=0.
0
5,
τ
=3. The co
nstrai
ned
probl
em i
s
tra
n
sformed i
n
to
the un
con
s
t
r
aine
d p
r
oble
m
throu
gh p
e
nalty function
. In the geneti
c
algorith
m
tool
box, the p
opu
lation
size i
s
30, the
cr
o
s
sover
pro
babili
ty is 0.6, m
u
tation p
r
ob
abil
i
ty
is 0.0
9
, the t
e
rmin
ation of
algeb
ra
is
1
000, an
d oth
e
r p
a
ra
meters a
r
e
default.
Ne
w inflo
w
rate,
traffic volum
e
and th
e g
r
e
en ratio a
r
e
got thro
ugh
571 time
s
of iteration
s
si
mulation, a
n
d
the
value is sho
w
n in Table 3.
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TELKOM
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ISSN:
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046
Study on Coo
peratio
n between Traffic Control
an
d Ro
ute Guida
n
ce
Based on
… (Cao
Jie)
7247
Table 3. Opti
mized
Network Data
Road
Traffic
volume
(vehicle/s)
Inflow
rate
(vehicle/s)
green r
a
tio
1-2
0.53
0.13
0.20
2-1
0.26
0.47
0.67
2-3
0.59
0.66
0.75
3-2
0.71
0.63
0.74
3-4
0.29
0.52
0.52
4-3
0.37
0.15
0.19
4-1
0.31
0.16
0.27
1-4
0.77
0.25
0.42
The
optimized inflow
rate is input int
o
the VISSIM through t
he path inflow. The
redi
strib
u
ted i
n
flow rate of
path 1-3 i
s
sh
own in Fig
u
re
5.
Figure 5. Traffic Flo
w
Distri
bution
Compa
r
ison of Path 1-3
Acco
rdi
ng to
the ne
w val
u
e of g
r
ee
n
ra
tio,
the intersection
si
gnal
timing is reset. The
setting of inte
rse
c
tion 1 i
s
shown in Figu
re 6.
Figure 6. Signal Timing of
Interse
c
tion 1
Simulating in
VISSIM for 5 minutes, the t
r
avel
time of
every se
ction
of the roa
d
n
e
twork
is sh
own in Figure 7. Fro
m
the figure,
the road
tra
v
el time is relatively balance
d
. The total
netwo
rk trave
l
time de
crea
sed f
r
om
895
.1s to 7
40.9
s
, a de
crea
se
of 17.2%. Th
e traffic
of ea
ch
road
is re
dist
ributed, th
ere
f
ore the
total
netwo
rk tr
ave
l
time is re
du
ced,
and th
e
efficien
cy of t
h
e
netwo
rk i
s
im
proved.
Figure 7. Tra
v
el Time
T
r
av
el
t
i
m
e
com
par
i
s
o
n
0
50
100
150
200
1
234
567
8
R
oad
Tra
vel
ti
m
e
Or
i
g
i
n
a
l
tra
v
e
l
ti
m
e
O
p
t
i
m
i
z
ed t
r
av
el
t
i
m
e
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r 2014: 724
2
– 7248
7248
5. Conclusio
n
Colla
borative vehicl
e infra
s
tru
c
ture
syst
em is
an
inte
lligent
syste
m
integ
r
ating
with
all
kind
s of
high
and n
e
w-te
ch
nology. It ca
n
instant
aneo
u
s
ly obtain
spe
ed qu
euei
ng l
ength
req
u
ire
d
by the co
ope
ration sy
stem
of traffic control
and route guida
nce
thro
ugh wirel
e
ss comm
uni
cati
on
technology such as Zigbee,
thus
it is an important part of inte
lligent transportation sy
stem. In the
article, the re
al-time sp
eed
and v
ehicle i
n
formatio
n of coope
rative
vehicle infrastructu
re sy
ste
m
are
used to
reesta
blish th
e travel time
model, a
nd th
e OD informa
t
ion of all
se
ctions i
s
a
pplie
d in
the coll
abo
ra
tive model. T
he arti
cle
sol
v
es the
colla
borative m
o
d
e
l with g
enet
ic alg
o
rithm t
o
optimize traffic paramete
r
s, adjust travel
time
of each
road an
d vehicle di
strib
u
tion of netwo
rk.
The net
wo
rk
total travel time is 1
7
.2%
less t
han
b
e
fore, an
d th
e efficien
cy
of the network is
improve
d
. In view of the complexity of traffi
c sy
stem, the impact o
f
bus and oth
e
r vehicl
es o
n
traffic flow in
modelin
g an
d simul
a
tion
are n
o
t fully con
s
id
ere
d
, a
nd advan
ce
d
techn
o
logy
and
real
-time dat
a provide
d
b
y
collabo
rative vehicle
infras
truc
ture s
y
s
t
em ar
e not
fully used, these
probl
em
s nee
d further
stud
y.
Referen
ces
:
[1]
Z
H
AO Xiao
hu
a, SAHNG
Yanzan
g, T
A
N Li
ngl
ong.
A Study on Co
oper
ation of Urb
a
n
T
r
affic Contr
o
l
and R
oute Gui
danc
e for Con
gested C
o
n
d
iti
on.
Procee
di
n
g
s of the 10
th
Internatio
na
l Confere
n
ce of
Chin
ese T
r
ansportatio
n
Profe
ssion
al.Resto
n
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i
et
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gin
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9-21
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[2]
BAO Li Xia, Y
A
NG Z
hao Sh
en, HU Ji
an M
en, et al
. Quas
i
-
optimal
al
gorit
hm for flo
w
g
u
i
danc
e do
ub
le-
obj
ectiVe
opti
m
izatio
n mo
de
l of traffi
c c
o
o
r
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ontro
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i
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hao She
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C
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wi
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T
F
G
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r
a
n
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lo
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1
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CHEN
Xin, YA
NG Z
hao
Sh
en
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A
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Y
ang,
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Co
ord
i
nati
on
of Urb
a
n
T
r
affic Co
ntr
o
l
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y
stem
w
i
t
h
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y
n
a
mic R
oute
Guida
n
ce S
y
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a
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ay
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E
I Yu Xi
ao.
Stud
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or
din
a
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an R
oad
T
r
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y
s
t
em
w
i
t
h
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out
e Guid
anc
e
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[6]
GU Yuan Li,
LI Shan Mei,
SHAO Chu
n
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u
. Stud
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perat
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r
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d R
o
u
t
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ce Bas
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go
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Joumal
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PENG Den
g
,
XU J
i
a
n
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LIN Pei Q
un.
Rese
ar
ch o
n
communic
a
tio
n
of cit
y
c
o
o
p
e
r
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hicl
e
infrastructure
s
y
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d it’
s
positio
nin
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lo
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uter Eng
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avel T
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HUANG Yan,
Z
E
N W
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arch On
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o
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a
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Jour
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Z
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iversit
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eeri
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g
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hao S
h
e
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ng
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ppl
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on Of Gen
e
tic
Algor
ithm In
Coor
din
a
tio
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e
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T
r
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l
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w
Guid
ance
An
d T
r
affic Control.
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urna
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Un
iversit
y
(Engi
neer
in
g a
nd T
e
chn
o
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