Published in IET In elligen T anspo Sys ems
Recei ed on 11 h No embe 2009
Re ised on 12 h May 2010
doi: 10.1049/ie -i s.2009.0102
ISSN 1751-956X
Cu en pa adigms in in elligen
anspo a ion sys ems
S.L. To al
1
M.R. Ma ı
´nez To es
2
F.J. Ba e o
1
M.R. A ahal
1
1
E.S. Ingenie os, Uni e si y o Se ille, A da. Camino de los Descub imien os s/n, Se ille 41092, Spain
2
E.U.E. Emp esa iales, Uni e si y o Se ille, A da. San F ancisco Ja ie s/n, Se ille 41018, Spain
E-mail: [email p o ec ed]
Abs ac : In elligen anspo a ion sys ems (ITS) cons i u e oday a mul idisciplina y field o s udy in ol ing a
la ge numbe o di e en esea ch a eas. As a consequence, i is di ficul o achie e a s uc u ed iew o ITS,
which is necessa y o uni y e o s and as guidance o u u e de elopmen s. This s udy aims o iden i y he
main pa adigms in he field o ITS by seman ically analysing s udies ela ed o his gene al opic. An
unde s anding abou which esea ch is conside ed aluable by he esea ch communi y o build upon may
p o ide aluable insigh s in his field. As a esul o he s a is ical ea men o da a, up o 13 pa adigms a e
ob ained. The scope o hese pa adigms and he ela ionships be ween hem ha e also been de ailed,
p o iding a s uc u ed ision o ITS syn hesised in a map o m.
1 In oduc ion
In elligen anspo a ion sys ems (ITS) ha e been
in es iga ed o many yea s in Eu ope, No h Ame ica and
Japan, wi h he aim o imp o ing he sa e y and e ficiency
o oad anspo and en i onmen al conse a ion. To his
end, new echnologies and compu e powe ha e been
applied o eeway, a fic and ansi sys ems [1, 2]. ITS
can be conside ed a global phenomenon, a ac ing
wo ldwide in e es om anspo a ion p o essionals, he
au omo i e indus y and poli ical decision make s [3].
ITS in ol es a la ge numbe o esea ch a eas sp ead o e
many di e en echnological sec o s such as elec onics,
con ol, communica ions, sensing, obo ics, signal
p ocessing and in o ma ion sys ems [4, 5]. This
mul idisciplina y na u e inc eases he p oblem’s complexi y
because i equi es knowledge ans e and coope a ion
among di e en esea ch a eas [6]. One o he main
p oblems o being a complex and mul idisciplina y field is
he di ficul y o dealing wi h ITS as a de elopmen and
esea ch a ea [7, 8]. T adi ional opics o in e es a e
changing and new ones a e eme ging due o he con inuous
ad ance o eme gen echnologies and he economical,
social and en i onmen al implica ions o ITS.
The complexi y o he opic sugges s ha i will be o
benefi o define a global, s uc u ed iew. This iew will be
use ul o suppo close in eg a ion o ITS wi h con en ional
anspo a ion ini ia i es as well as o p o ide guidance o
u u e ITS deploymen s. Sha ing a common s uc u ed iew
will also help he p omo ion o ITS s anda ds de elopmen ,
he iden ifica ion and confi ma ion o needs, p oblems,
objec i es and issues, and he alignmen o esea che s,
companies and use s o syne gy [3].
The idea o ob aining his s uc u ed iew o a pa icula
esea ch and de elopmen a ea is no new, bu nowadays is
aking on majo impo ance. The mos ecen and
ambi ious a emp has been made by he Eu opean
commission by launching he echnological pla o m called
ARTEMIS [9]. The aim o ARTEMIS is o de elop and
d i e a join Eu opean ision on embedded sys ems
connec ing esea ch and de elopmen wi h inno a ion o
align agmen ed R&D e o s along common s a egic
agenda and looking o imp o emen s in Eu opean
companies’ e ficiency and compe i i eness. ARTEMIS is
ollowing a bo om-up scheme in which he mos
p ominen Eu opean companies a e ge ing in ol ed in he
defini ion o his unique Eu opean ision h ough he
de elopmen o a s a egic esea ch agenda (SRA). In
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pa icula , ITS can be loca ed in one o he ou applica ions
con ex s iden ified by ARTEMIS SRA de o ed o public
in as uc u e [9].
Al hough some s uc u ed iew o ITS has been p oposed
based on ma ke a eas [3] o on da abases and cumula i e
expe ience [10], his s udy p oposes a quan i a i e and
sys ema ic me hodology o iden i y he main pa adigms
wi hin he b oade field o ITS. The s a ing poin is he
in o ma ion p o ided by he Ins i u e o Scien ific
In o ma ion’s (ISI) massi e da ase s. The abs ac s o he
published pape s included in da ase s will ha e been
analysed using ex ca ego isa ions ools o ob ain he final
pa adigms. To mee hese objec i es, he pape has been
s uc u ed as ollows. In Sec ion 2, ITS will be analysed as
a field s udy, desc ibing p e ious a emp s o s uc u ing
his esea ch opic. In Sec ion 3, he p oposed me hodology
will be desc ibed in de ail, including a discussion abou
some o he app oaches. The ob ained pa adigms in ITS
will be p esen ed in Sec ion 4, h ough he applica ion o
he p oposed me hodology. Finally, he main conclusions
o he wo k a e included in Sec ion 5.
2 Analysis o ITS as a field s udy
The e a e di e en ways o classi y and segmen he ITS field.
Six majo ca ego ies we e e iewed in [6] om a
echnological pe spec i e:
†Ad anced a fic managemen sys ems (ATMS), used o
imp o e a fic se ice quali y and o educe a fic delays.
†Ad anced a elle s in o ma ion sys ems (ATIS), used o
supply eal- ime a fic in o ma ion o a elle s.
†Comme cial ehicles ope a ion (CVO), sys ems ha use
di e en ITS echnologies o inc ease he sa e y and
e ficiency o comme cial ehicles and flee s.
†Ad anced public anspo a ions sys ems (APTS), which
make use o elec onic echnologies o imp o e he
ope a ion and e ficiency o high-occupa ion anspo s, such
as buses and ains.
†Ad anced ehicles con ol sys ems (AVCS), which join
senso s, compu e s and con ol sys ems in d i ing assis ance
solu ions.
†Ad anced u al anspo s sys ems (ARTS), used o sol e
p oblems a ising in u al zones (s eep g ades, blind co ne s,
cu es, sca ce na iga ional signs, mix o use s, lack o
al e na i e ou es).
Al hough his classifica ion is shaped by eme ging
echnologies and is use ul om he iewpoin o sys em
designe s, some in e es ing opics ela ed o ITS a e
excluded. Fo ins ance, anspo policy and planning,
a fic modelling and o ecas ing, and he sociological and
beha iou al influences o ITS a e no included in he
p e ious classifica ion. The equi emen s and p e e ences o
ITS use s may also be gi en inadequa e a en ion.
Ano he way o looking a ITS is o conside ma ke a eas
as a ep esen a ion o ITS use s and ope a o s wi h simila
needs. Nine majo ma ke a eas, de ailed in Table 1,a e
defined in [3].
Simila classifica ions can also be ound in [10], al hough
hey conside up o 13 a eas by sub-di iding se e al o he
nine ma ke a eas defined in Table 1.
As a di e ence o he p e ious classifica ions, mainly
ocused on one aspec o ITS, an in eg al analysis o he
ITS field is p oposed in his pape . The s a ing poin will
be he abs ac s and keywo ds o published pape s ela ed
o ITS included in he ISI Web o Science da abase
[11, 12]. ISI Web o Science includes jou nals o almos
Table 1 Ma ke a eas in ITS
Ma ke a ea Goal
a ea 1 a fic managemen manage he en i e oad ne wo k on behal o he gene al public
a ea 2 eme gency managemen espond o incidences and eme gencies (fi e, police, ambulance)
a ea 3 anspo a ion planning ma ch anspo a ion supply wi h demand bo h now and in he u u e
a ea 4 a elle in o ma ion supply in o ma ion o a elle and subsc ibe s
a ea 5 comme cial ehicles p o ide a el in o ma ion and flee managemen se ices
a ea 6 ansi managemen plan and ope a e ansi sys ems in bo h u ban and u al a eas
a ea 7 in elligen ehicles enhance he capabili ies o oad ehicles h ough he use o elec onics, senso s,
communica ions and con ol ac ua o echnologies
a ea 8 inciden managemen conce ned wi h e ficiency and sa e y o he oadway ne wo k
a ea 9 paymen sys ems encompass all he people ha ake money in e u n o p o iding a se ice ( oll oad
ope a o s, ansi agencies, ca pa king ope a o s, e c.)
202 IET In ell. T ansp. Sys ., 2010, Vol. 4, Iss. 3, pp. 201–211
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each scien ific field. Consequen ly, no only echnological
and ma ke app oaches o ITS will be ga he ed, bu any
scien ific discipline s ongly o weakly ela ed o ITS, such
as planning and logis ic, psychology o social sciences. The
selec ed pape s will be p ocessed using a s a is ical ex
ca ego isa ion ool and, as a esul , majo pa adigms in he
field o ITS will be iden ified.
3 Me hodology
The s a ing poin o he p oposed me hodology consis s o
pape ex ac ion om ISI da abases. Pa icula ly, pape s
ela ed o he opic ‘in elligen anspo a ion sys ems’.
Ins ead o analysing he ull ex , which would be an
eno mous ask, a ep esen a i e piece o ex summa ising
he whole pape has been selec ed, i.e. he abs ac s and
keywo ds o index e ms. An abs ac is a condensed
e sion o a pape ha highligh s he majo poin s co e ed
and concisely desc ibes he con en and scope o he
w i ing, while keywo ds e iew he w i ing’s con en s in
abb e ia ed o m. Keywo ds a e included because hey
emphasise he con en o he pape . Al hough keywo ds
hemsel es can cons i u e an adequa e desc ip ion o he
pape con en [13], hey can also es ic he numbe o
di e en opics o be ob ained. No ice ha some imes
keywo ds mus be chosen among a closed lis p o ided by
he jou nal publishe . Consequen ly, he p oposed
me hodology will conside bo h abs ac s and keywo ds
wi h he aim o lea ing open he numbe o opics o be
ob ained. I is implici ly assumed ha au ho s w i e good
abs ac s ( ep esen a i e o he con en o he pape ) and
ha keywo ds a e ca e ully chosen.
In gene al, bibliome ic esea ch is de o ed o quan i a i e
s udies o li e a u e. Se e al empi ical me hods can be ound
in he li e a u e. Co-ci a ion me hods a e pe haps he mos
employed [14], and hey ha e been equen ly used o
analyse he in ellec ual s uc u e o many disciplines [15].
The basis o co-ci a ion me hods consis s o coun ing he
numbe o imes ce ain ma ke s occu o co-occu , gi ing
ise o in o ma ion on such au ho co-ci a ion [16], jou nal
co-ci a ion, keywo d co-ci a ion, e c. [13].
The main d awback o adi ional bibliome ic echniques,
such as au ho o jou nal co-ci a ion me hods, is ha hey a e
no conce ned abou he con en o conside ed pape s bu on
e e ences usually delayed be ween 2 and 5 yea s a e a pape
is fi s d a ed. Al hough hey lead o in e es ing esul s, hey
do no p o ide an immedia e pic u e o he ac ual con en o
he esea ch opic deal wi h in he li e a u e. As a di e ence,
seman ic analysis based on co-wo ds analysis (co-occu ences
o wo ds in he publica ions on a gi en subjec ) has he
po en ial o sol ing his kind o p oblem [13–17].
Seman ic analysis usually employs a ec o space model
[18], in which documen s a e summa ised and ep esen ed
by ec o s o wo ds ( e m ec o s). Howe e , a cen al
p oblem in his kind o s a is ical analysis is he high
dimensionali y o he ea u e space (one dimension o each
unique wo d). The e o e, i is desi able o fi s p ojec he
documen s in o a lowe -dimensional subspace in which he
seman ic s uc u e o he documen space becomes clea
[19]. In he low-dimensional seman ic space, he
adi ional clus e ing algo i hms can hen be applied. To
his end, spec al clus e ing [20, 21], clus e ing using la en
seman ic indexing (LSI) [22] and clus e ing based on non-
nega i e ma ix ac o isa ion [23, 24] a e he mos well-
known echniques. Pa icula ly, LSI decomposes a e m
documen ma ix using a echnique called singula alue
decomposi ion o cons uc new ea u es as combina ions o
he o iginal ea u es, significan ly educing he high-
dimensionali y p oblem o he ea u e space [25].
Mo eo e , LSI conside s documen s ha ha e many wo ds
in common o be ‘seman ically close’, and ones wi h ew
wo ds in common o be ‘seman ically dis an ’. The LSI
app oach makes h ee basic claims: ha seman ic
in o ma ion can be de i ed om a wo d-documen
co-occu ence ma ix; ha dimensionali y educ ion is an
essen ial pa o his de i a ion; and ha wo ds and
documen s can be ep esen ed as poin s in a Euclidean
me ic space.
A di e en app oach has been applied in his pape . This
app oach is consis en wi h he fi s wo o hese claims, bu i
di e s in he hi d, desc ibing a class o s a is ical models in
which he seman ic p ope ies o wo ds and documen s a e
exp essed in e ms o p obabilis ic opics [26]. The opic
model is a s a is ical language model ha ela es wo ds and
documen s h ough opics. I is based upon he idea ha
documen s a e mix u es o opics, whe e a opic is a
p obabili y dis ibu ion o e wo ds [26–28].
In his pape , he me hodology p oposed o ITS
pa adigms iden ifica ion consis s o a ou -s ep p ocedu e
based on he la en Di ichle alloca ion (LDA) me hod
o [26] (see Appendix o mo e de ails) and illus a ed in
Fig. 1.
Figu e 1 Da aflow o he p oposed me hodology
IET In ell. T ansp. Sys ., 2010, Vol. 4, Iss. 3, pp. 201–211 203
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1. In o ma ion ex ac ion: he ex ac ion p ocess in ol es
access o ISI da abases o anno a e he abs ac and
keywo ds o a pape dealing wi h he opic ‘in elligen
anspo a ion sys ems’.
2. On ology: an on ology defines he basic e ms and ela ions
comp ising he ocabula y o a opic a ea as well as he ules
o combining e ms and ela ions be ween e ms [29].An
on ological model o he domain is used as a acili a o
h oughou all he p ocesses. This s ep p o ides a common
ocabula y and specifies he seman ics o key ela ionships
wi hin he domain. The selec ion is based on he ob ained
equency-o -occu ence- a es o wo ds o e he o al
co pus o ex ac ed ex s.
3. S uc u ing and p ocessing in o ma ion: in s a is ical na u al
language p ocessing, one common way o modelling he
con ibu ions o di e en opics o a documen is o ea
each opic as a p obabili y dis ibu ion o e wo ds, iewing a
documen as a p obabilis ic mix u e o hese opics [27].
4. Ca ego isa ion: The algo i hm ou lined abo e can be used
o find he opics ha accoun o he wo ds used in a se o
documen s. In his s udy, e e y abs ac and i s associa ed
keywo ds a e conside ed a documen .
4 Pa adigms in ITS
A o al o 1147 pape s ela ed o he opic ‘in elligen
anspo a ion sys ems’ has been ob ained om ISI da abases,
co e ing subjec a eas like enginee ing, anspo a ion,
compu e science, elecommunica ions, au oma ion and
con ol sys ems, and ope a ions esea ch and managemen
science. The majo i y o hem belong o he gene al ca ego y
o Science & Technology, bu a small pe cen age is
associa ed o Social Sciences and A s & Humani ies.
Acco ding o he explained p ocedu e, abs ac s and
keywo ds ha e been used as documen s in he e minology
o seman ic analysis. Up o 64 132 wo ds ha e been
ex ac ed om hese documen s, leading o a ocabula y
(non- epea ed wo ds) o 5485 wo ds. Wo king wi h such
an amoun o wo ds would be p ohibi i e in e ms o
compu ing ime, so on ology is selec ed o acili a e he
implemen a ion o he p ocedu e. The c i e ion o he
on ology selec ion was based on he equency o e ms in
he documen collec ion. Pa icula ly, wo ds ha occu ed
in mo e han 15 documen s we e conside ed [30].Asa
esul , an on ology o 267 wo ds we e ob ained. This is he
mos manual-s ep o he p ocedu e, because he final esul
mus be supe ised o emo e wo ds ha a e no di ec ly
ela ed o ITS issues. Table 2 shows he key dimensions
used in he opic model.
T and ITER ep esen opic model un pa ame e s. The
numbe o Gibbs sample i e a ions was chosen o be
ITER ¼200. This is a la ge enough alue o gua an ee he
con e gence o he algo i hm [21]. The numbe o opics
was selec ed using he pe plexi y alue. Pe plexi y is a
s anda d measu e o pe o mance o s a is ical models o
na u al language [31, 32] defined by (1).
pplex =exp −1
W
W
n=1
log P(wn|dn)

(1)
The ole o pe plexi y has mos ly been discussed on an
in ui i e le el as a e age unce ain y when p edic ing he
nex wo d gi en i s his o y. Pe plexi y indica es he
unce ain y in p edic ing a single wo d. A lowe pe plexi y
sco e indica es be e gene alisa ion pe o mance. Pe plexi y
a ies om 1 o W; lowe pe plexi y is be e , and he
maximum pe plexi y o Wis eached when all wo ds in he
ocabula y a e equally likely. In ou case s udy, he LDA
algo i hm was un o a numbe o opics a ying be ween
1 and 30. The esul s a e illus a ed in Fig. 2.
The minimum pe plexi y alue is eached o a numbe o
opics equal o 13. Consequen ly, 13 was chosen as he
numbe o selec ed opics. Table 3 shows he 13 opics
ob ained, wi h he mos likely wo ds in each opic, and
hei p obabili ies P(w| ).
Table 2 Dimensions o he opic model
Pa ame e Desc ip ion Value
Dnumbe o documen s in co pus 449
N o al numbe o wo ds in co pus 34 132
La e age leng h o documen in
wo ds (L¼N/D)
76
Vnumbe o wo ds in ocabula y 5485
Won ology 267
Tnumbe o opics –
ITER numbe o i e a ions 200
Figu e 2 Pe plexi y as a unc ion o he numbe o opics
204 IET In ell. T ansp. Sys ., 2010, Vol. 4, Iss. 3, pp. 201–211
&The Ins i u ion o Enginee ing and Technology 2010 doi: 10.1049/ie -i s.2009.0102
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Table 3 Ob ained opics and p obabili ies om he LDA algo i hm
TOPIC 1 TOPIC 2 TOPIC 3 TOPIC 4
me hod 0.165 sys em 0.101 ehicle 0.265 inciden 0.079
e ec 0.087 se ice 0.074 ehicles 0.118 pe o mance 0.067
benefi 0.073 sys ems 0.073 sys em 0.069 de ec ion 0.065
analysis 0.072 anspo 0.059 in o ma ion 0.069 echnique 0.061
me hods 0.055 decision 0.054 ansi 0.048 a fic 0.053
benefi s 0.049 in o ma ion 0.049 mobile 0.038 eeway 0.049
e alua ion 0.047 amewo k 0.039 ese ed 0.027 ne wo k 0.047
e ec i e 0.047 suppo 0.039 au oma ic 0.025 managemen 0.047
esul 0.037 en i onmen 0.037 ision 0.025 condi ions 0.047
me hodology 0.032 assess 0.036 in as uc u e 0.025 neu al 0.040
in elligen 0.031 in elligen 0.032 app oach 0.022 sys em 0.037
s a ion 0.028 se ices 0.031 igh s 0.021 eal- ime 0.033
po en ial 0.026 e alua e 0.030 unc ion 0.021 echniques 0.033
e ec s 0.024 ope a ions 0.023 changes 0.020 cha ac e is ics 0.031
accu acy 0.022 impac s 0.020 co ido 0.020 delay 0.031
al e na i e 0.022 echnologies 0.019 mo ion 0.018 de ec o 0.027
e ec i eness 0.021 analysis 0.018 mo ing 0.017 p esen ed 0.027
expe imen al 0.021 wea he 0.018 scena ios 0.017 s a egies 0.023
TOPIC 5 TOPIC 6 TOPIC 7 TOPIC 8
sys em 0.191 algo i hm 0.187 anspo 0.127 a fic 0.294
sys ems 0.134 algo i hms 0.073 anspo a ion 0.126 simula ion 0.118
design 0.078 p ocess 0.059 de elop 0.089 signal 0.072
communica ion 0.060 p esen 0.057 sys em 0.066 esul s 0.047
p esen 0.051 de elop 0.049 s a e 0.053 es ima e 0.043
echnology 0.046 app oach 0.045 de elopmen 0.048 esul 0.038
posi ion 0.034 applica ion 0.045 planning 0.041 pa ame e s 0.034
highway 0.032 de eloped 0.044 egion 0.039 es ima es 0.032
message 0.028 senso 0.043 a chi ec u e 0.038 in elligen 0.030
esea ch 0.024 pe o mance 0.041 in elligen 0.032 a e ial 0.028
in eg a ed 0.023 e ficien 0.036 deploymen 0.031 empo al 0.024
p esen ed 0.020 complex 0.036 de eloping 0.028 oadway 0.023
implemen a ion 0.020 applica ions 0.030 p og am 0.026 in e sec ion 0.022
po en ial 0.019 anspo a ion 0.026 issues 0.025 mic oscopic 0.020
communica ions 0.019 in elligen 0.022 managemen 0.023 spa ial 0.019
global 0.017 scheme 0.022 p ocess 0.022 empi ical 0.017
Con inued
IET In ell. T ansp. Sys ., 2010, Vol. 4, Iss. 3, pp. 201–211 205
doi: 10.1049/ie -i s.2009.0102 &The Ins i u ion o Enginee ing and Technology 2010
www.ie dl.o g

Table 3 Con inued
TOPIC 5 TOPIC 6 TOPIC 7 TOPIC 8
con ex 0.017 p ocessing 0.021 public 0.019 emissions 0.017
wi eless 0.016 senso s 0.019 egional 0.019 e alua ed 0.014
TOPIC 9 TOPIC 10 TOPIC 11 TOPIC 12
anspo a ion 0.144 con ol 0.138 ne wo k 0.164 model 0.356
anspo 0.142 d i e 0.092 p oblem 0.116 models 0.121
sys em 0.087 sys em 0.087 dynamic 0.089 a fic 0.067
applica ion 0.070 ehicle 0.067 ne wo ks 0.069 de elop 0.056
sys ems 0.068 sys ems 0.065 p oblems 0.042 anspo a ion 0.054
loca ion 0.045 esul 0.053 p esen 0.040 app oach 0.039
in elligen 0.044 d i ing 0.052 compu a ion 0.035 modelling 0.036
echnologies 0.043 d i e s 0.041 solu ion 0.033 de eloped 0.035
use 0.042 esul s 0.039 ou ing 0.033 anspo 0.030
applica ions 0.040 sa e y 0.033 guidance 0.030 u ban 0.029
ad anced 0.025 condi ions 0.022 assignmen 0.030 in elligen 0.028
esea ch 0.024 significan 0.021 s uc u e 0.028 o ecas ing 0.025
expe ience 0.022 beha iou 0.020 op imal 0.027 esea ch 0.024
unc ion 0.020 acking 0.019 p esen ed 0.024 demand 0.017
equi emen s 0.019 came a 0.016 eal- ime 0.022 sys ems 0.016
quali y 0.017 au onomous 0.016 op imisa ion 0.022 eg ession 0.015
comme cial 0.016 dynamics 0.015 sho es 0.020 p esen 0.012
solu ion 0.015 human 0.015 compu a ional 0.020 easibili y 0.012
TOPIC 13
a el 0.205 – – – – – –
p edic 0.088 – – – – – –
in o ma ion 0.071 – – – – – –
imes 0.068 – – – – – –
p edic ion 0.045 – – – – – –
a is 0.045 – – – – – –
esul 0.042 – – – – – –
a elle 0.040 – – – – – –
choice 0.035 – – – – – –
esul s 0.031 – – – – – –
es ima e 0.025 – – – – – –
a el- ime 0.021 – – – – – –
u u e 0.020 – – – – – –
s a is ical 0.019 – – – – – –
Con inued
206 IET In ell. T ansp. Sys ., 2010, Vol. 4, Iss. 3, pp. 201–211
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Each opic can be de i ed om i s co esponding bag o
wo ds, leading o he ollowing opic ca ego isa ion lis :
†Topic 1. E alua ion o e ec i eness and benefi s o ITS: This
opic deals wi h he analysis o implica ions and po en ial use
o ITS including new challenges and oppo uni ies, and he
an icipa ion o use beha iou in he p ocess o designing
new ITS echnologies.
†Topic 2. S udy o sys ems and ools suppo ing decision making
and anspo a ion planning: I includes he de elopmen o
models o a el demand decision and anspo a ion
planning modelling ools, echnology in eg a ion and
da abases suppo ing a chi ed da a use se ices.
†Topic 3. Au oma ic ehicle de ec ion sys ems: Vehicle
de ec ion appea s o be one o he mos p omising a eas in
a fic su eillance and con ol. This concep en ails he
de ec ion o ehicles and ex ac ion o a fic pa ame e s in
eal- ime om images gene a ed by ideo came as
o e looking a a fic scene.
†Topic 4. Inciden managemen : Inciden managemen
includes eme gency esponse deploymen and e ou ing o
bypass he a ec ed a ea, impac o eeway lane closu es,
and inciden de ec ion algo i hms.
†Topic 5. Mobile and wi eless communica ion in ITS: This
opic co e s in e ehicle communica ion ne wo ks,
in- ehicle communica ions ), oad- o- ehicle
communica ions, wi eless p o ocols, GPRS and hi d-
gene a ion sys ems.
†Topic 6. P ocessing algo i hms o ITS applica ions: This
opic is de o ed o ad anced p ocessing algo i hms o
sol ing ITS p oblems, such as pa h p oblems in dynamic
ne wo ks, o igin–des ina ion es ima ion and p edic ion,
and image and ideo p ocessing algo i hms.
†Topic 7. Ad anced a fic managemen sys ems (ATMS):
They a e ocused on he de elopmen o ITS o imp o e
sa e y and quali y o se ice as well as he e ficiency o
exis ing oadway u ilisa ion.
†Topic 8. T a fic simula ion: A a fic simula ion sys em
consis s o a a fic-flow simula ion code, which is able o
simula e a fic on a eeway ne wo k. I usually conside s a
mic oscopic ep esen a ion (whe e each indi idual ehicle is
ep esen ed) o a mac oscopic model cap u ing a fic
dynamics. The pu pose o hese sys ems consis s o
pe o ming a fic-flow simula ion o applica ions like
a fic condi ions p edic ion in eal- ime, a fic con ol and
d i e s’ guidance, link a el ime calcula ion and signal
con ol s a egy. This opic also co e s issues like modelling
d i e beha iou unde he influence o ex e nal ac o s.
†Topic 9. Comme cial Vehicles Ope a ion (CVO): This opic is
ocused on he impac o ITS on comme cial ehicles and
flee s o imp o ing anspo a ion sa e y and e ficiency.
†Topic 10. Ad anced ehicles con ol sys ems (AVCS): AVCS
a e based on sys ems ha p o ide inc eased sa e y and/o
con ol o he d i e ei he by means o imp o ing he
in o ma ion abou he d i ing en i onmen o by ac i ely
aiding he d i e in he d i ing ask. They include on-
boa d au onomous in elligen c uise con ol sys ems, ABS
and ac ion con ol sys ems, ac i e suspension sys ems,
ehicle s abili y sys ems, in- ehicle collision wa ning
sys ems, e c.
†Topic 11. Dynamic ou e selec ion algo i hms: Dynamic
ou e selec ion p oblems a e sea ch p oblems o finding an
op imal ou e om a s a ing o a des ina ion poin on a
oad map wi hin a ime limi . Since he ime o a e se a
link will depend upon a fic olume encoun e ed on ha
link, link imes a e dynamic.
†Topic 12. Models o a fic demand o ecas ing: T a fic
conges ion is a majo ope a ional p oblem on ITS.
Reducing conges ion e ec s equi es de eloping models
ha can accu a ely p edic a fic demand.
†Topic 13. Ad anced a elle s in o ma ion sys ems (ATIS):
The unc ion o ATIS is o assis a elle s wi h planning,
pe cep ion, analysis and decision making o imp o e he
con enience and e ficiency o a el.
The ob ained esul s depic an exhaus i e d aw o ITS
esea ch a eas. Some ma ke -specific opics a e supp essed
compa ed o p e ious classifica ion me hods desc ibed in
Sec ion 2, like APTS o ARTS in [6] o eme gency
managemen and paymen sys ems in [3–10], bu new
esea ch opics a e meanwhile iden ified. In ac , up o six
new esea ch a eas ha e now been de ec ed om he
ob ained 13 opics. Fo ins ance, specific opics like
e alua ion o e ec i eness and benefi s o ITS, mobile and
Table 3 Con inued
TOPIC 13
p opaga ion 0.019 – – – – – –
conges ion 0.018 – – – – – –
es ima ed 0.017 – – – – – –
igh s 0.017 – – – – – –
IET In ell. T ansp. Sys ., 2010, Vol. 4, Iss. 3, pp. 201–211 207
doi: 10.1049/ie -i s.2009.0102 &The Ins i u ion o Enginee ing and Technology 2010
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wi eless communica ions in ITS o models o a fic demand
o ecas ing ha e no been p e iously es ablished. They ha e
eme ged as he esul o he inco po a ion o new
echnologies, and also due o he necessi y o modelling
and assessing hei social and economical impac . The
p oposed ca ego isa ion should help esea che s and
p ac i ione s o cla i y hei posi ion o u u e wo k
because he ob ained 13 opics ep esen he mos ecen
issues and eme gen echnologies in he ITS wo ld.
The opic co ela ion ma ix (Table 4) shows ha opics
a e poo ly co ela ed wi h each o he , which means ha
opics a e well defined and hei scope is clea ly delimi ed.
Ne e heless, i is impossible o achie e pe ec ly delimi ed
opics wi h independen scopes. The e is always some
deg ee o o e lap. O e lapping can be used o ob ain
se e al majo pa adigms a ending o he opic simila i y.
Fo his pu pose, a mul i a ia e s a is ical echnique like
mul idimensional scaling was used [33]. This analysis
consis s o p ojec ing he wo ks on a wo-dimensional map,
using he da a om he co ela ion ma ix as inpu da a.
Fig. 3 shows he ob ained map. The ob ained RSQ
coe ficien (0.93430) and K uskal’s s ess (0.15374) sugges
ha goodness o fi is e y accep able ( he RSQ coe ficien
is he squa ed co ela ion index R
2
ha measu es he model
fi o he da a, and i s minimum accep able sco e is 0.6
Figu e 3 Mul idimensional scaling
Table 4 Topic co ela ion ma ix
T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13
T1 1.00 0.13 0.10 0.07 20.05 20.03 0.06 0.14 0.07 20.01 20.10 0.03 0.06
T2 0.13 1.00 0.01 0.00 0.12 20.07 0.16 0.07 0.16 20.07 0.03 0.10 0.04
T3 0.10 0.01 1.00 0.03 0.13 0.01 20.06 0.10 0.07 0.13 20.02 20.02 0.08
T4 0.07 0.00 0.03 1.00 20.05 0.14 20.05 0.19 20.02 0.05 20.02 0.21 0.21
T5 20.05 0.12 0.13 20.05 1.00 0.05 0.21 20.02 0.28 0.05 20.03 20.09 20.08
T6 20.03 20.07 0.01 0.14 0.05 1.00 20.04 0.04 0.01 0.03 0.34 20.02 0.00
T7 0.06 0.16 20.06 20.05 0.21 20.04 1.00 20.06 0.12 20.10 20.06 20.02 20.07
T8 0.14 0.07 0.10 0.19 20.02 0.04 20.06 1.00 20.01 0.08 0.03 0.25 0.15
T9 0.07 0.16 0.07 20.02 0.28 0.01 0.12 20.01 1.00 20.01 0.04 20.03 0.02
T10 20.01 20.07 0.13 0.05 0.05 0.03 20.10 0.08 20.01 1.00 20.04 0.00 20.10
T11 20.10 0.03 20.02 20.02 20.03 0.34 20.06 0.03 0.04 20.04 1.00 0.04 0.13
T12 0.03 0.10 20.02 0.21 20.09 20.02 20.02 0.25 20.03 0.00 0.04 1.00 0.30
T13 0.06 0.04 0.08 0.21 20.08 0.00 20.07 0.15 0.02 20.10 0.13 0.30 1.00
208 IET In ell. T ansp. Sys ., 2010, Vol. 4, Iss. 3, pp. 201–211
&The Ins i u ion o Enginee ing and Technology 2010 doi: 10.1049/ie -i s.2009.0102
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[34]) and ha he map exhibi s a good app oxima ion o
eali y.
The p oximi ies o opics on he map show hei simila i y
and sugges a meaning o he axes. The ho izon al axis is
ela ed o he applica ion a ea. Topics loca ed o he le
pa o he map (T4, T8, T12 and T13) a e ocused on he
imp o emen o a fic in u ban a ea. Topics loca ed on he
cen e o he map conside ITS ools om wo di e en
pe spec i es, he implemen a ion de ails ep esen ed by
opics T3, T6, T10 and T11 a he op o he map and
hei benefi s and po en ial use ep esen ed by T1 and T2
a he bo om o he map. Finally, opics loca ed o he
igh pa o he map a e ocused on ehicle and a fic
managemen (T5, T6 and T7). The e ical axis is ela ed
o he le el o ha dwa e implemen a ion, as i can be clea ly
deduced om opics in he uppe hal o he map,
conce ned wi h he implemen a ion de ails o ITS ools
(T3, T6, T10 and T11) o he mobile and wi eless
communica ion possibili ies suppo ing a fic managemen
(T5).
Compa ing hese esul s wi h p e ious classifica ions
de ailed in Sec ion 2, no ice ha ‘Benefi s and po en ial use
o ITS ools’ we e no conside ed om a echnological
pe spec i e and he ‘implemen a ion de ails o ITS ools’
we e no conside ed om a ma ke a ea pe spec i e.
Consequen ly, he map o Fig. 3 summa ises a mo e
comple e ision o ITS.
5 Conclusion
The main con ibu ion o his pape is a global iew o ITS,
which could be used by u u e esea che s as a s a e o he a
o me hods, echniques and applica ion a eas. The analysis is
in ended o o e new pe spec i es in o wha is iewed as
impo an o build upon, p o iding aluable insigh s in o
bo h wha esea ch is impo an and whe e he field o ITS
is heading. The p oposed me hodology o p oducing his
global iew is based on he seman ic analysis o keywo ds
and abs ac s o pape s indexed by he ISI. As a di e ence
o au ho o jou nal co-ci a ion me hods, seman ic analysis
is ocused on he con en o pape s, so esul s a e no
biased by ci es o i ele an li e a u e o ecu en ci ed
pape s. A o al o 1147 pape s ha e been analysed co e ing
a la ge a ie y o opics ela ed o ITS, including he mos
ecen issues and eme gen echnologies. As a esul o
he analysis, 13 pa adigms we e ob ained. Using a
mul idimensional scaling, hey ha e been ep esen ed on a
bidimensional map, illus a ing he mos ela ed pa adigms
as well as he b idges among hem.
Fu he mo e, his s udy also defines a s a ing poin o
o he analyses aimed a a be e unde s anding o he ITS
field. This con inuous analysis is conside ed necessa y as
ITS is an e olu iona y field influenced by changes in
echnology, wi h changing se ices and suppo o end use s.
6 Acknowledgmen s
The au ho s g a e ully acknowledge suppo p o ided by he
Spanish Minis y o Educa ion and Science (p ojec wi h
e e ence DPI2007-60128) and he Conseje ı
´ade
Inno acio
´n, Ciencia y Emp esa (Resea ch P ojec wi h
e e ence P07-TIC-02621).
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IET In ell. T ansp. Sys ., 2010, Vol. 4, Iss. 3, pp. 201–211 209
doi: 10.1049/ie -i s.2009.0102 &The Ins i u ion o Enginee ing and Technology 2010
www.ie dl.o g