Resea ch A icle
In Sea ch o Se e i y Dimensions o T a ic Con lic s o Di e en
Simula ed Mixed Flee s In ol ing Connec ed and
Au onomous Vehicles
Tasneem Miqdady , Roc´
ıo de Oña , and Juan de Oña
TRYSE Resea ch G oup, Uni e si y o G anada, ETSI Caminos, Canales y Pue os, Campus de Fuen enue a, s/n,
G anada 18071, Spain
Co espondence should be add essed o Roc´
ıo de Oña; [email p o ec ed]
Recei ed 13 Feb ua y 2023; Re ised 25 Ap il 2023; Accep ed 8 May 2023; Published 20 May 2023
Academic Edi o : Yanyong Guo
Copy igh ©2023 Tasneem Miqdady e al. Tis is an open access a icle dis ibu ed unde he C ea i e Commons A ibu ion
License, which pe mi s un es ic ed use, dis ibu ion, and ep oduc ion in any medium, p o ided he o iginal wo k is p ope ly
ci ed.
Tis s udy aims o es ima e he se e i y o con ic s ha may a ise om he in oduc ion o connec ed and au oma ed ehicles
(CAVs) by examining he ehicle pa hs gene a ed by mic osimula ions o mixed ee s o human-d i en ehicles and CAVs wi h
di e en le els o au oma ion (L1-L4 ehicles). Te s udy assesses he se e i y o con ic s using a holis ic app oach ha conside s
h ee dimensions: (1) p oximi y o collision, ia he ime- o-collision (TTC) indica o ; (2) po en ial consequences o a con ic , ia
single su oga e sa e y measu es such as maximum speed (MaxS) and ehicle speed di e ence (Del aS); and (3) a combina ion o
bo h dimensions o assign se e i y sco es, ia TTC and eloci y ec o s. Te s udy’s ndings sugges ha mode a e pene a ion
a es o L3 and L4 ehicles (35–55%) show signi can di e ences in he numbe o a c con ic s wi h a ying TTC alues.
Addi ionally, high pene a ion a es o L3 and L4 ehicles (abo e 55%) esul in lowe alues o con ic consequences measu es
such as MaxS and Del aS. Fu he mo e, he s udy shows ha con ic consequences dec ease i he ollowe is a L3 o L4 ehicle.
Te s udy’s ndings also e eal ha he e is a conside able educ ion in high se e i y con ic s when he pene a ion a e o CAV
le els eaches 50%, and he ull ope a ion o L4 ehicles esul s in a 75.5% educ ion in high se e i y con ic s. Te e o e, his s udy
p o ides aluable insigh in o he po en ial se e e con ic s du ing he ansi ion pe iod om manual ehicle ope a ion o ull
CAV ope a ion. O e all, he s udy’s ndings highligh he impo ance o assessing he se e i y o po en ial con ic s a ising om
he in oduc ion o CAVs. By conside ing he p oximi y o collision and he po en ial consequences o con ic s, he s udy p o ides
a comp ehensi e assessmen o he se e i y o con ic s. Tis in o ma ion can in o m he de elopmen o policies and s a egies o
ensu e he sa e and esponsible in oduc ion o CAVs in o ou anspo a ion sys ems.
1. In oduc ion
Te o hcoming in oduc ion o connec ed and au o-
ma ed ehicles (CAVs) on oads has mo i a ed e-
sea che s o in es iga e hei a ious implica ions, such
as a c delay, conges ion, uel emissions, and a c
sa e y. Al hough CAV manu ac u e s ha e p og essed
om CAV esea ch o ehicle p o o ype p oduc ion
wi hin se e al au oma ion le els [1], he a ailable
(beha io al and c ash) da a can no su cien ly cla i y
he ambigui y su ounding he c ash isks
in ol ing CAVs.
Acco dingly, many s udies ha e used he su oga e sa e y
assessmen model (SSAM), de eloped by he Fede al
Highway Adminis a ion, o analyze he ehicle ajec o ies
ga he ed om a mic osimula ion pla o m o de e mine
a c sa e y. Se e al su oga e sa e y measu es (SSMs) (e.g.,
ime- o-collision (TTC), pos enc oachmen ime (PET), and
decele a ion a e) ha e been applied o es ima e he p ob-
abili y o con ic . A a c con ic is an e iden ins ance in
which wo o mo e oad use s o ehicles a e nea each o he
in e ms o space and ime o he ex en ha he isk o
collision exis s i hei mo emen s do no change [2]. In
a c simula ion-based s udies, a c con ic s can be
Hindawi
Jou nal o Ad anced T anspo a ion
Volume 2023, A icle ID 4116108, 21 pages
h ps://doi.o g/10.1155/2023/4116108
de e mined by modeling a c ow acking o ex ac
ehicle pa hways o e ime. Acco dingly, SSMs ha e been
ex ensi ely employed o iden i y po en ial a c con ic s
when CAVs sha e oads. In mos p e ious s udies, CAVs
ypically ha e a high au oma ion le el (i.e., L4) [3–7].
Howe e , o he s udies ha e also included se e al le els o
au oma ion [8–10]. In gene al, hey ound ha inc easing
he pene a ion a es o CAV can signi can ly educe he
numbe o po en ial con ic s.
Al hough he impac o CAVs on a c sa e y has been
widely s udied, o he bes o he au ho s’ knowledge, no s udy
has ho oughly assessed he se e i y o con ic s in a c
s eams esul ing om he p og essi e in oduc ion o CAVs.
Te no el y o his s udy is i s comp ehensi e analysis o
con ic se e i y in ol ing CAVs unde di e en simula ed
mixed ee s (i.e., human-d i en ehicles (HDVs) and CAVs o
di e en le els). In a e iew conduc ed by Zheng e al. [11], hey
highligh ed ha i is necessa y o es ablish an adequa e a c
con ic echnique o measu ing a c con ic se e i y, ap-
plying a sensi i i y analysis o selec SSMs h eshold and he
u iliza ion o a mul idimensional de ni ion o se e i y. Tus,
his s udy conside s hese wo esea ch di ec ions o de ise
a eliable echnique o assessing he a c con ic among
CAVs. Te p esen app oach uses h ee dimensions o ana-
lyzing con ic ’s se e i y: (1) he p oximi y o a collision; (2)
po en ial con ic consequences; and (3) a combina ion o
p oximi y and consequences. Te TTC h eshold is conside ed
he ma gin alue o se ious con ic s [12–14]. So, ini ially
di e en TTC h esholds a e es ed in his s udy wi h he
in oduc ion o CAVs o a ious le els on oads. A e iden-
i ying he key alues o di e en TTC h esholds, he s udy
examines he consequences o con ic se e i y using some
SSMs, namely maximum speed (MaxS) and ehicle speed
di e ence (Del aS). Te s udy hen compa es hese alues in
a ious scena ios and ypes o ehicle in e ac ion o gain insigh
in o he impac o CAVs on a c sa e y. Finally, a ime- o-
collision (TTC) o eloci y change a collision (Del aV) dia-
g am (i.e., TTC/Del aV cha ) is de eloped o each au oma-
ion le el o de i e a con ic se e i y sco e.
Te emainde o his pape is o ganized as ollows. In
Sec ion 2, he analysis o a c sa e y and con ic se e i y o
CAVs and manually d i en ehicles epo ed in he exis ing
li e a u e is discussed. Te s udy con ex modeled by Aimsun
[15] and he CAV con ol algo i hms and alida ion p ocesses
used a e p esen ed in Sec ion 3. In Sec ion 4, he se e i y analysis
and i s esul s a e discussed. Finally, Sec ion 5 summa izes he
conclusions and limi a ions o he s udy as well as he ecom-
mended u u e di ec ions o CAV a c sa e y esea ch.
2. Li e a u e Re iew
Tis sec ion p esen s he SSMs used o iden i y a c
con ic se e i y. A e wa ds, he ex en o which he con ic
se e i y in he CAV eld can be p edic ed is discussed.
2.1. SSMs and Con ic Se e i y. C ash a e and se e i y a e
di ec indica o s o a c sa e y pe o mance. Howe e ,
c ashes a e a e, and da a on alea o y e en s leading o
c ashes a e no always s a is ically su cien o s udies.
Because o his, SSMs a e used o iden i y a c con ic s and
es ima e hei se e i y by analyzing eco ded ideos o
a eal- ime analysis [16, 17], and/o a c simula ion ou -
pu s. In ac , by ex ac ing ehicle pa hways o e ime and
e alua ing hei p oximi y and mo emen s, ehicles close o
collisions and wi h je ky mo emen s a e conside ed o be
in ol ed in mo e se e e con ic s [18].
Mos s udies ha used SSMs as a c sa e y assessmen
ools implied ha a subs an ial co ela ion exis s be ween
se ious con ic s and c ash se e i y [19–24]. Howe e , de-
i ing con ic se e i y om SSMs as an indica o has been
widely deba ed, and se e al a c con ic echniques ha e
been de eloped o e he decades.
P e ious esea ch has p oposed se e al SSM h esholds
o delinea e isky/non isky con ic s. Uni o m and non-
uni o m con ic se e i y zones we e also c ea ed ollowing
a ious a c con ic echniques and SSM indica o s [25].
To p edic con ic se e i y, ime-based SSMs (e.g., TTC [26],
PET [27], ime-in eg a ed TTC (TIT), and ime exposed
TTC (TET) [28]), decele a ion-based SSMs (e.g., de-
cele a ion a e o a oid c ash [29], maximum decele a ion
a e [30], and ea -end collision isk index [31]), and ene gy-
based SSMs (e.g., Del aV, ex ended Del aV [32, 33], and
con ic index [34]) ha e been used [35]. Acco dingly, a c
con ic se e i y has been de ned in e ms o h ee di e en
ypes o SSM.
Time-based and decele a ion-based SSMs de ne se e i y
as he p oximi y wi h espec o a c ash. Tis is he mos
p e alen indica ion o s udying a c acciden s and
con ic se e i y [33]. Howe e , he ea ly decision-making
c i e ia o se e e/nonse e e con ic s mainly depended on
he assessmen o human obse e s by iden i ying se e e
e en s based on hei p oximi y o a collision [19, 23].
Mo eo e , a ime o space h eshold ha is commonly
employed o align se e e con ic s has mul iple assump ions
and alida ion alues.
Ene gy-based SSMs de ne se e i y by ano he di-
mension: he consequences o he isk esul ing om an
in e ac ion (con ic ). Te idea is ha high kinema ic o ces
esul ing om ehicle in e ac ions conside ably a ec oad
use s and p obably esul in se e e inju ies and a ali ies [2].
O e he yea s, esea che s ha e indica ed hei high con-
dence in his ype o indica o o p edic ing c ash se e i y.
Ca lson [36] a emp ed o de elop models o es ima ing he
p obabili y o inju ies o a ali ies in a c ash based on
a iables, such as impac speed and ehicle mass; hence,
Del aV was used o p edic inju ies and a ali ies. E an [37]
subsequen ly ed se e al models using Del aV o p edic
inju ies and a ali ies a ising om con ic s. Ne e heless,
because his indica o was no used o a c con ic
analysis un il i s ecen inco po a ion in o SSAM [2], he
de elopmen o new equa ions was no dis inc ly pu sued.
Consequen ly, he classical E an models [37] emained
in use.
Finally, he hi d de ni ion o a c con ic se e i y is
ela ed o he concu en p opo ioning o alues o p ox-
imi y and p opensi y dimensions and gene a ing di e en
se e i y le els. Con ic s wi h po en ially high consequences
2Jou nal o Ad anced T anspo a ion
Table 1: Summa y o p e ious s udies abou se e i y wi hin CAV’s analysis.
Re e ences Da a sou ce CAV
conside ed Con ex Se e i y dimension Se e i y measu es
Sinha e al. [6] Simula ion L4 2-Lane mo o way P oximi y and consequences TTC, Del a S
El-Hansali e al. [43] Simula ion L4 6-Lane eeway Consequences MaxS, MaxD, MaxDel aV
Rahman e al. [44] Simula ion L1, L2 A e ial (61.15 km) P oximi y and consequences TET, TIT, TERCRI, LCC, and NCJ
Zhang e al. [45] Simula ion L4 4-Lane eeway (7 km) P oximi y and consequences TET, TIT, TERCRI, and LCC
Lau eshyn e al. [32] Video analysis — U ban in e sec ion P oximi y, consequences and le els o
se e i y
T, Del aV, ex ended Del aV (T/
Del aV)
Souley e e and Hochs ein [38] Simula ion — Exp essway
in e sec ions Le els o se e i y TTC/MaxDel aV
an de Ho s and K aay e al.
[39]
Manual con ic ’
obse a ion — Va ious Le els o se e i y TTC and speeds a con ic
Sinha e al. [46] Field da a (c ash da a) L4 U ban ne wo k Consequences Machine lea ning classi e s
Chen e al. [47] Field da a (c ash da a) L4 U ban ne wo k Consequences Machine lea ning classi e s
TTC: ime- o-collision, Del aS: di e ence in ehicle speeds as obse ed a MinTTC, MaxS: maximum speed o ei he ehicle h oughou he con ic , MaxD: maximum decele a ion o he ollowe ehicle, Del aV:
eloci y change a collision, MaxDel aV: maximum Del aV alue o ei he ehicle in he con ic , TET: ime-exposed- ime- o-collision, TIT: ime-in eg a ed- ime- o-collision, TERCRI: ime exposed ea -end c ash
isk index, LCC: lane changing con ic , NCJ: numbe o c i ical je ks, T: he expec ed ime o he second (la es ) ehicle o a i e a he con ic poin , and ex ended Del aV: models o he in eg a ion o T/
Del aV da a.
Jou nal o Ad anced T anspo a ion 3
and hose ha a e obse ed close o he occu ence o
c ashes a e ound o ha e a high p obabili y o se e i y
du ing he in e ac ion [32]. In he pas , a simple human
decision-making app oach was employed o iden i y wo
zones dis inguishing se e e con ic s om he es o he
con ic s conside ing only he p oximi y h eshold alue o
ime. Subsequen ly, he In e na ional Commi ee on T a c
Con ic Techniques con ibu ed o he de elopmen o
se e al con ic echniques ha aided in unde s anding c ash
occu ence and i s po en ial se e i y manually (by obse -
a ion). Te objec i e was o es ablish se e i y in e ms o
se e al le els ins ead o simply spli ing i in o wo ca ego ies
(se e e/nonse e e) [25, 32, 38]. Ten, he le els we e ali-
da ed by s udies conduc ed ab oad. In he Du ch echnique
(i.e., DOCTOR), he con ic s in which speed is high and
TTC is less han he h eshold alue a e as deemed se e e
[39]. In addi ion, bo h DOCTOR and he Canadian a c
con ic echnique [19] inco po a e a subjec i e assessmen
in which a sco e ( anging 1–5) de e mines he p obable
con ic consequences based on e asi e ac ion, maneu e ing,
obse ed speed, and objec i e nea ness-in- ime indica o .
Te Swedish a c con ic echnique [21, 40–42] conside s
bo h he p oximi y in ime and speed a which he con ic
occu s o indica e se e i y and e ec he po en ial conse-
quences implici ly. Equidis an pa allel se e i y zones we e
es ablished by di iding he esul ing sco es in o se e al
le els. Te indica o s used o de i ing he se e i y le els
we e a ied (e.g., ehicle speed and dis ance om a con ic
si e, equi ed decele a ion, and ic ion coe cien ) [25].
Mo eo e , se e al p oximi y- o-collision h esholds ha e
been p oposed in a c con ic echnique esea ch [33].
O he app oaches ha e been employed by o he e-
sea che s o indica e se e i y le els. Fo example, Souley e e
and Hochs ein [38] de eloped an assessmen sco e by de-
ning and adding TTC and Del aV sco es. Ten, some
se e i y lines we e ed by d awing con ou s o equal
assessmen sco e a eas. Simila ly, Lau eshyn e al. [32] in-
co po a ed he minimum ime leading o an acciden and
Del aV in a gu e, hus o e ing he ex ended Del aV
alues as se e i y lines o de e mining se e i y le els.
2.2. CAV C ash/Con ic Se e i y. Table 1 p o ides a sum-
ma y o p e ious s udies ha discussed he se e i y e ms
and a c con ic echniques, especially, hose conside ing
CAVs in hei analysis.
Te e a e some unde going CAVs’ es s on public oads
in se e al loca ions in he Uni ed S a es. In hose cases, some
s udies a e able o analyze CAVs’ c ash se e i y based on eal
da a. Sinha e al. [46] conduc ed a de ailed sa e y analysis
using he da a om he Cali o nia Depa men o Mo o
Vehicles (2014–2019). Te epo ed da a we e used o de-
elop a ious au oma ed ehicle c ash se e i y models ha
ocused on he inju ies o all c ash ypes. Howe e , owing o
insu cien da a on c ashes in ol ing CAVs, he ac o s ha
con ibu e o he se e i y o a CAV’s c ash a e no well
de ned. Ne e heless, a ious machine lea ning app oaches
ha e been used o be e unde s and CAV c ash se e i y.
Chen e al. [47] used a simila app oach and ound ha
among all he es ed classi e s, X eme g adien boos ing,
a decision ee classi ca ion model, pe o ms be e in
de ec ing inju ies occu ing in CAV c ashes. Tei ndings
show ha i wo au oma ed ehicles c ash a an in e sec ion
o a e unde ad e se wea he condi ions (e.g., og and snow),
he se e i y o he c ash signi can ly inc eases. Fu he mo e,
c ashes esul ing in inju ies a e mo e likely o occu in
loca ions wi h a ious land use pa e ns. Di e se land use
(e.g., esiden ial, comme cial, and public) esul s in mixed
a c beha io s and changes in egional a c ow, sub-
s an ially a ec ing a c sa e y.
By con as , as esea che s ex ensi ely employ SSMs o
unde s and he sa e y implica ions o new a c designs and
al e na i e sa e y emedies be e , modeling he sa e y
consequences o CAVs and hei in e ac ions wi h HDVs is
a ele an applica ion o SSMs. In addi ion, owing o he
limi ed in oduc ion o CAVs, a c mic osimula ion
ou pu s ha e been used o p oduce SSMs a he han ana-
lyzing ideos.
Bo h p oximi y and consequences dimensions ha e been
used o analyze he se e i y o CAV con ic s. Se e al
p oximi y SSM indica o s ha e been employed, wi h TTC
being he mos p e alen indica o . TIT and TET ha e been
also widely employed in pa allel wi h TTC [44, 45, 48]. By
con as , he dis ibu ions o eme gency b aking [49], ea -
end collision isk index [44, 50, 51], sideswipe (lane-change
con ic s) a c condi ion [51], and ime exposed ea -end
c ash isk index [51] a e all examples o decele a ion-based
SSMs o e alua ing CAV a c sa e y [51]. O he su oga e
sa e y indica o s, such as s anda d de ia ion o speed
[51, 52], MaxS, and Del aS [43, 46, 53], ha e been used as
consequence indica o s o assess CAV sa e y implica ions.
Howe e , o he bes knowledge o he au ho s, no s udies
ha e combined all he se e i y dimensions in CAV a c
sa e y analysis.
In mos p e ious s udies, CAVs and HDVs we e assessed
using he same SSMs and h esholds (e.g., TTC �1.5 s), and
no speci c alues we e conside ed o CAVs’ con ic
analysis [5, 43, 45]. By con as , some esea che s sugges
ha in dealing wi h CAVs, he de aul TTC alue should be
educed because o hei as e eac ion imes and sho e
headways. Fo ins ance, Mo ando e al. [4] es ed he en-
suing con ic s o L4 ehicle pene a ion using h ee TTC
h esholds: 1.50 s o any con ic in ol ing HDVs and wo
lowe alues (i.e., 1.00 and 0.75 s) o L4–L4 in e ac ions.
Tey indica ed ha he TTC h eshold is an impo an ac o
in demons a ing he bene o CAV in oduc ion in e ms
o sa e y. Gu´
e iau and Duspa ic [8] and Weije ma s e al.
[54] p oposed 0.75 s o de e mining con ic s in ol ing
CAVs. By con as , Vi di e al. [7] used 0.50 s, and hey
claimed ha ega ding hei assump ion ha he headway
kep by CAVs is educed o one- hi d, hen he h eshold
de ning he con ic should be also p opo ionally educed.
E iden ly, o dis inguish be ween se e e and nonse e e
sa e y c i ical e en s (con ic s), a su cien h eshold le el
mus be de ned. Te de ni ion o his alue is a cu en
challenge conce ning con ic s in ol ing CAVs ha mus be
scien i cally add essed.
4Jou nal o Ad anced T anspo a ion
As ega ds he esul s o p e ious s udies analyzing
a c con ic se e i y in CAV a eas, Rahman e al. [44]
used TTC-de i ed measu es (e.g., TET and TIT) as
p oximi y indica o s in addi ion o e asi e ac ion in-
dica o s (e.g., numbe o c i ical je ks and ime exposed
ea -end c ash isk index) as consequence indica o s in
es ima ing a c con ic se e i y when L1 and L2 ehicles
en e a a c s eam. Te esul s e eal ha CAV pen-
e a ion exceeding 60% signi can ly educes he con ic
se e i y a a e ial segmen s and in e sec ions. Sinha e al.
[6] s udied se e al SSMs (e.g., TTC, PET, MaxS) by an-
alyzing hei dis ibu ions a di e en pene a ion a es
and es ima ing he co esponding c ash a es o assess he
con ic se e i y o L4 in oduc ion. Tei ndings
showed ha a c sa e y imp o es, and con ic se e i y
and c ash a es dec ease when he oads a e ully ope a ed
wi h L4 ehicles. Howe e , he con ic s in ol ing HDVs
did no dec ease in e ms o equency and se e i y. El-
Hansali e al. [43] in es iga ed a c sa e y by compa ing
HDVs and L4 ehicles ope a ing independen ly a
a eeway sec ion (i.e., 100% HDVs s. 100% L4 ehicles).
Con a y o expec a ions, g ea e alues o se e i y in-
dica o s we e obse ed in he case o L4 ehicles han in
he case o HDVs. Fo example, a highe MaxS was ob-
ained o ei he ehicle ype du ing con ic s, and a highe
MaxD was obse ed when only L4 ehicles occupied he
oad. Zhang e al. [45] conduc ed a s udy ha ocused on
oadway con gu a ion. Using p oximi y and conse-
quences indica o s (i.e., TTC, TIT, TET, ime exposed
ea -end c ash isk index, and lane-changing con ic s),
hey in es iga ed he sa e y o lanes dedica ed o L4
ehicles wi h di e en pene a ion a es. Tey emphasized
ha es ablishing e en one exclusi e lane could inc ease
sa e y because he con ic se e i y was signi can ly e-
duced in e ms o longi udinal and la e al mo emen s.
3. Me hodology
Aimsun [15] has been used o mic osimula ion o es i-
ma e ajec o ies o he di e en ypes o ehicles con-
side ed. Subsequen ly, SSAM [18] was applied o ex ac
su oga e sa e y indica o s o he se e i y es ima ion
p ocess.
3.1. S udy Con ex . As a es co ido , he s udy a ea o
a h ee-lane wo-way mo o way segmen (20.27 km o GR-
30, an impo an oad leading o G anada Ci y, Spain) (il-
lus a ed in Figu e 1) was modeled using Aimsun Nex . Te
geome ic design cha ac e is ics o he segmen (e.g., oad
p o le, cu es, and lane de ailing) we e in oduced using an
impo ed Open S ee Map o he segmen . Te chosen
segmen has 14 on- amps and o - amps and wo majo
en y poin s. T a c ow da a we e ga he ed using nine
de ec o s ins alled in he a ea by he Gene al T a c Di-
ec o a e (Di ecci´
on Gene al de T ´
a co (DGT)). Te de-
ec o s egis e ins an aneous speeds, a c olumes, and
ehicle ype dis ibu ions (hea y ehicles s. passenge ca s)
a 15-min in e als.
Te impo ed le da a om he DGT senso s o ali-
da ion we e selec ed o a egula day (Tuesday) and o -peak
hou (10:00-11:00 am) because his s udy modeled a ee-
ow condi ion. Te a e age ins an aneous speed ange was
83–118 km/h, and he a c coun was eco ded e e y
15 min: 547−3570 pc/h and 89–260 h /h we e egis e ed o
he GR-30 no hbound ehicles, and 809–3281 pc/h and
93–499 h /h we e egis e ed o he GR-30 sou hbound
ehicles.
3.2. Mic osimula ion Model and Scena ios. Aimsun was
selec ed o calib a e he di e en au oma ion le els o e-
hicles ( om L0 o L4) because i p o ides specialized ools
o CAVs. V2X ex ension was employed o model he
connec i i y be ween ehicles. Te p oposed analy ical pe-
iod o he mic osimula ion is 1 h; howe e , o a c
alida ion ( olume and speed), his pe iod was b oken down
o 15-min in e als o e ec he eal a c da a eco ded by
he DGT’s de ec o s be e . T a c ope a ion da a a e gen-
e a ed using a small ime s ep (i.e., 0.1 s, ollowing p e ious
s udies [4, 5] o inc ease simula ion accu acy and educe he
isk o losing ehicle mo emen de ails). Te wa m-up ime
was se o 18 min ollowing Wunde lich e al. [55] (based on
he oad sec ion leng h and he a e age speed o ehicles).
Fu he mo e, he model ope a ions we e calib a ed and
alida ed ollowing he modeling guidelines o Roads and
Ma i ime Se ices [56]. Miqdady e al. [10] p o ide mo e
de ails abou his s ep.
A e checking he alidi y o he modeled ne wo k, he
“ca - ollowing and lane-change models” o Gipps [57, 58]
ha a e a ian s o each a el condi ion a e calib a ed o all
wi hin he p oposed s ochas ic dynamic en elopes o CAVs.
Fo ins ance, CAVs a e supposed o ha e sho eac ion
imes, accep sho gaps, coope a e in lane changes, e c.
Tables 4 and 5 (in Appendix A) show all he pa ame e s ha
a e a ec ed by di e en le els o ehicle au oma ion in he
ca - ollowing and lane-changing models o Gipps based on
p e ious esea ch [4, 5, 8, 43–45, 54, 59] and logic. Te
pa ame e de ni ions a e summa ized om he Aimsun
G anada
GR-30
Spain
Figu e 1: S udy a ea: GR-30 oadway segmen in G anada (Spain).
Jou nal o Ad anced T anspo a ion 5

use manual. Te alues summa ized in Tables 4 and 5 a e
he inpu s o he mic osimula ion models. Tey a e p o-
ided as means and s anda d de ia ions and a e no mally
dis ibu ed as sugges ed by Gipps models o bo h passenge
ca s and hea y ehicles.
Te analysis a emp ed o co e a g adually in oduc ion
o CAVs wi h a ious ee mixes ha he eal wo ld may
encoun e . Acco dingly, as jus i ed in Miqdady e al. [10],
nine mixed ee scena ios wi h di e en CAV pene a ion
a es we e sugges ed. Table 2 lis s he combina ions o HDVs
and ehicles wi h di e en au oma ion le els (L1–L4) in
each scena io.
3.3. Se e i y Analysis. To e alua e he a c con ic se e i y
in he simula ed scena ios (i.e., he po en ial ma ke in-
oduc ion scena ios o CAV), his s udy conside ed he
h ee ques ions p esen ed by Lau eshyn e al. [32]: (i) How
can he p oximi y o a c ash be measu ed? (ii) How can he
se e i y o he consequences o a po en ial c ash be mea-
su ed? (iii) How can bo h dimensions be me ged?
A ew s udies ha e analyzed he ex en o con ic se e i y
in he CAV con ex [6, 44]. Howe e , hey ha e nei he
conside ed all dimensions o se e i y combined no ana-
lyzed all le els o au oma ion. Fo he nine p oposed sce-
na ios, se e al SSM indica o s we e applied o de e mine
bo h p oximi y and consequence dimensions a each con-
ic . Te ollowing sec ion illus a es how his s udy ad-
d esses he p e ious esea ch ques ions. Te de ailed
amewo k is illus a ed in Figu e 2. Te nex sec ion ex-
plains he app oach ollowed o explo e each se e i y di-
mension and p esen s he esul s ob ained a e applying
hese app oaches.
4. CAV Se e i y Dimensions
4.1. P oximi y T eshold. Te mos widely used indica o o
in es iga ing a c p oximi y and con ic se e i y in HDV
and CAV con ic analysis is TTC [35]. Tis indica o is
de ned as “ he ime ha emains un il a collision could
occu i wo successi e ehicles main ain a speed di e ence”
[28]. I is gi en by he ollowing:
TTCi( ) �
xi−1( ) − xi( ) − li−1
i( ) − i −1( ),i i( )> i −1( ),
∞,i i( )≤ i −1( ),
⎧⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎩
(1)
whe e TTCi( ) deno es he TTC alue o he ollowing e-
hicle, i, a a ime ins an ; ,x, and deno e he ime, posi ion,
and eloci y o he ehicles, espec i ely; and l
i−1
ep esen s
he leng h o he leading ehicle. A small TTC alue indica es
a high isk o collision a a gi en ime ins an .
To assess he se e i y o ehicle- ollowing e en s, a TTC
h eshold mus be de ned o dis inguish be ween se e e and
nonse e e con ic s [60]. Se ing an uni e sal TTC h eshold
o assessing con ic se e i y has become a ma e o con-
en ion, pa icula ly in he case o CAV in oduc ion. A
e iew o p e ious esea ch e eals ha se e al h esholds
anging 0.9–5.0 and 0.5–1.5 s ha e been p oposed o a ious
HDV a c and d i ing condi ions and o CAV scena ios,
espec i ely [60]. Al hough his s udy analyzes a c sa e y
o CAV in oduc ion scena ios, a unique alue (i.e., 1.5 s) is
p oposed o con ic s in ol ing HDVs o ehicles wi h low
au oma ion (L1 and L2) as ollowe ehicles (low CAVs,
LCAV). Te mos commonly used alue o HDV is 1.5 s
[18]; and i is also he de aul alue used in he SSAM.
Sensi i i y analysis was conduc ed o de ne a easonable
h eshold o con ic s whe e a high le el o au oma ion
ehicle (L3 and L4) is he ollowe (high CAVs, HCAV). Fi e
di e en alues (0.5, 0.75, 1.0, 1.25, and 1.5 s) we e examined
o he TTC h eshold unde each scena io o emphasize he
app op ia e alue unde a ious ci cums ances. Table 3
summa izes he numbe o con ic s when applying he
di e en TTC alues o de e mine whe he he e a e sig-
ni can changes by using one-way analysis o a iance o
each scena io. Te changes esul ing om applying any
alue and he base alue (1.5 s) a e lis ed in Table 3.
Table 3 shows ha
(i) TTC does no p esen a signi can in uence on he
numbe o con ic s a scena ios wi h low pene-
a ion a es o HCAV (scena ios D o below)
(ii) TTC p esen s a e y signi can in uence on he
numbe o con ic s a scena ios wi h high pene-
a ion a es o HCAV (scena ios G o o e )
(iii) A in e media e scena ios (E o F), ep esen ing
mode a e pene a ion a es o HCAV, he numbe
o a c con ic s s a s o p esen signi can di -
e ences i he TTC alue is below 1.0 s.
Tese esul s emphasize he impo ance o using
di e en TTC alues o ob ain a eliable assessmen o
a c sa e y ela ed o high pene a ion o HCAV.
Mo eo e , he esul s e i y he heo e ical ision o CAV
in oduc ion: when CAV pene a ion a e is high, a c
ow imp o es by achie ing mo e ha monized speeds and
by educing eac ion imes ha p obably ha e a di ec
e ec on he TTC h eshold. Tese esul s ag ee wi h he
alues sugges ed in p e ious s udies. Mo ando e al. [4]
used wo TTC alues (0.75 and 1.0 s) o iden i y con ic s
in ol ing CAVs; bo h alues we e assumed o be ap-
p op ia e. O he s udies used 0.75 s as he TTC alue [8]
o xed con ic s wi h CAV pa icipa ion, and o he
s udies educed his h eshold o 0.5 s [7, 61]. Papazikou
Table 2: Te s udied mixed ee s’ simula ed scena ios.
Scena ios HDV (%) L1 (%) L2 (%) L3 (%) L4 (%)
A 100 0 0 0 0
B 75 10 10 5 0
C 50 10 25 10 5
D 40 15 20 15 10
E 20 20 25 20 15
F 5 10 30 30 25
G 0 0 10 40 50
H 0 0 0 25 75
I 0 0 0 0 100
6Jou nal o Ad anced T anspo a ion
e al. [61] claimed ha CAVs ope a ing wi h asse i e
d i ing s yles could lead o di e en ci cums ances
esul ing in a lowe TTC h eshold.
4.2. Se e i y Consequences Indica o s. Te p oximi y o
a collision ha esul s in a sligh c ash mus no be equa ed o
a c ash wi h a po en ially se e e inju y. Te e o e, he se e i y
measu ed by he po en ial consequences o a c ash mus be
accoun ed by some o he means [32]. Se e al SSMs can be used
o ex ac he dynamic consequences o a con ic [18, 35].
Following p e ious s udies [42, 62], his esea ch uses MaxS
and Del aS o measu e he esul ing se e i y o con ic s ela ed
o di e en ypes o ehicles (HDVs and L1, L2, L3, and L4
ehicles). Te o me is de ned as he maximum speed o any
o he ehicles h oughou he con ic , whe eas he la e is he
di e ence in ehicle speed (i.e., he di e ence in he eloci y o
ehicles in con ic ) obse ed a he minimum alue egis e ed
o TTC. Bo h indica o s a e ou pu s o he SSAM and simula e
he esul ing dynamics.
Di e en ehicle in e ac ions can esul in a ied a c
ow dynamics, and consequen ly, he se e i y le els di e .
A he end, high MaxS and Del aS alues indica e ha he
con ic s esul in high se e i y.
Te a ia ions in MaxS and Del aS o di e en ehicles
in ol ed in a con ic wi hin di e en a c ee scena ios
a e shown in Figu e 3. Fo simplici y and cla i y in p e-
sen ing he esul s, L1 and L2 ehicles a e g ouped as low
CAVs and L3 and L4 ehicles as high CAVs, shown as LCAV
and HCAV in Figu e 3, espec i ely. Te shown alues (o
MaxS and Del aS) a e he mean alues o 15 uns in each
scena io. Te blue-yellow- ed scale indica es he inc ease in
se e i y owa ds he ed colo . Las ly, in each gu e, he
alues a e ca ego ized by he ollowe ehicle in he con ic :
-HDV, -LCAV, and -HCAV, indica ing ha he ollowe
ehicle is a HDV, LCAV, and HCAV, espec i ely. Figu e 7
in Appendix B shows an example o he mic osimula ion
esul s as equency dis ibu ions o MaxS and Del aS.
Tese dis ibu ions show also he hea maps’ alues (i.e., he
mean alues exhibi ed in Figu e 3).
Examining
Se e i y dimensions
among he lee mixes
Mic osimula ion
scena ios
(Ex ac ing ehicles ajec o ies)
SSAM analysis
(Ex ac ing SSMs)
Tes ing con lic
consequences
Tes ing p oximi y
Tes ing con lic
se e i y sco e
(CAV a ic con lic
echnique)
One-way ANOVA
o es con lic s
esul ed by
di e en TTC
h eshold (0.50
,0.75, 1.00,
1.25, & 1.50 s)
Hea maps o MaxS
and Del aS
by scena io and
ehicle in e ac ion
Ob aining TTC sco e: in lec ion
poin s om he TTC cumula i e
dis ibu ion a pu e ehicle ype
simula ion (HDV, L1, L2,L3, o
L4 ehicle, exclusi ely).
Es ablishing MaxDel aV sco e.
Se e i y o e all sco es cha s
based on he sum o TTC sco e
and MaxDel aV sco e.
Classi ica ion o con lic s by
se e i y sco e a he simula ed
scena ios.
(i)
(ii)
(iii)
(i )
Figu e 2: F amewo k o simula ion-based a c con ic se e i y es ima ion o CAV.
Jou nal o Ad anced T anspo a ion 7
Rega ding he con ic consequences ex ac ed a he
di e en scena ios, Figu e 3 shows ha
(i) Te highe MaxS du ing con ic s is ypically ob-
se ed in scena ios in which he pene a ion a e o
HCAV is om low o mode a e (less han 55%, o
scena io F) (see Figu e 3(a))
(ii) By con as , high pene a ion a es o HCAV
(scena ios G, H, and I) esul in lowe MaxS du ing
con ic s (see Figu e 3(a))
(iii) Simila conclusions could be ob ained om Del aS’s
esul s in Figu e 3(b)
Sinha e al. [6] epo ed a simila pa e n. Tey ob-
ained low c ash a es and a dis ibu ions o Del aS
alues as he pene a ion a es o L4 ehicles inc eased.
Rahman e al. [44] obse ed, using o he su oga e sa e y
indica o s (e.g., TET, TIT, numbe o c i ical je ks, and
ime exposed ea -end c ash isk index), ha he inc ease
in he pene a ion a e o ehicles wi h low au oma ion
le els (i.e., L1 and L2 ehicles) dec eased he con ic
se e i y. Tey ound ha he highes educ ion in se e i y
was achie ed when he pene a ion a e was 100% CAV.
By con as , he educ ion was insigni can when he
pene a ion a e was less han 40%.
Table 3: Sensi i i y analysis o di e en alues o TTC h eshold o HCAV (-L3 and -L4 ehicles).
Scena io TTC h eshold o HCAV No. o con ic s % Change
A(0)∗— 3251 —
B(5)
0.50 2636 −0.69
0.75 2637 −0.68
1.00 2637 −0.60
1.25 2640 −0.56
1.50 2655 —
C(15)
0.50 1671 −6.08
0.75 1675 −5.86
1.00 1697 −4.62
1.25 1724 −3.11
1.50 1779 —
D(25)
0.50 1131 −11.10
0.75 1137 −10.61
1.00 1156 −8.40
1.25 1200 −5.64
1.50 1272 —
E(35)
0.50 890a∗∗ −16.85
0.75 900a −15.91
1.00 935a −12.69
1.25 980a,b −8.50
1.50 1071b —
F(55)
0.50 628a −31.22
0.75 648a −28.29
1.00 709a,b −22.36
1.25 770b −15.66
1.50 913c —
G(90)
0.50 255a −66.13
0.75 298a −60.46
1.00 415b −44.88
1.25 528c −29.99
1.50 754d —
H(100)
0.50 149a −79.03
0.75 198b −72.02
1.00 341c −51.91
1.25 467d −34.06
1.50 709e —
I(100)
0.50 133a −82.79
0.75 192b −75.12
1.00 365c −52.67
1.25 517d −32.99
1.50 771e —
∗Te alue in ( ) deno es o he pe cen ages o HCAVs in he scena io. ∗∗Fo each alue con aining a, b, . . ., le e in a scena io (in he no. o con ic s column),
i deno es alues o s a is ically signi can di e ences (p<0.05). Two o mo e alues wi h he same le e deno e a homogeneous subg oup. No e. TTC
h eshold �1.5 s is es ablished when he ollowe ehicle is a HDV o a LCAV (L1 o L2 ehicle).
8Jou nal o Ad anced T anspo a ion
Vehicles in ol ed
-HDV MaxS (m/s)
20 22 21 19 23 0 0 0 0
0 2124232127 0 0 0
0 1122212222 0 0 0
HCAV-HCAV
LCAV-HCAV
HDV-HCAV
0
5
10
15
20
25
GH IEFCBDA
Scena io
-LCAV MaxS (m/s)
0
0
0
21 20 22 18 12 000
24 16 20 22 23 14 00
0011 23 19 21 20 8.7
Vehicles in ol ed
HCAV-HCAV
LCAV-HCAV
HDV-HCAV
0
5
10
15
20
25
BCDEFGHIA
Scena io
-HCAV MaxS (m/s)
0
0
0
11 8.6 11 8.9 7.6 0 0 0
6.2 9.6 11 13 15 8.4 0 0
0 9.1 16 19 17 7.1 7.7 0
Vehicles in ol ed
HCAV-HCAV
LCAV-HCAV
HDV-HCAV
0
5
10
15
20
25
BCDEFGHIA
Scena io
(a)
Figu e 3: Con inued.
Jou nal o Ad anced T anspo a ion 9
Table 6: Te assigned TTC sco e by ehicle ype.
TTC
sco e
HDV L1 L2 L3 L4
T esholds
Sample
size
(%)
T esholds
Sample
size
(%)
T esholds
Sample
size
(%)
T esholds
Sample
size
(%)
T esholds
Sample
size
(%)
0 4.0 <TTC ≤5.0 30.0 4.2 <TTC ≤5.0 28.9 4.2 <TTC ≤5.0 30.4 4.3 <TTC ≤5.0 29.9 4.3 <TTC ≤5.0 32.8
1 2.5 <TTC ≤4.0 26.9 2.5 <TTC ≤4.2 31.9 2.5 <TTC ≤4.2 31.1 2.6 <TTC ≤4.3 33.6 2.6 <TTC ≤4.3 31.1
2 1.5 <TTC ≤2.5 27.6 1.0 <TTC ≤2.5 32.4 1.0 <TTC ≤2.5 32.3 0.75 <TTC ≤2.6 31.5 0.75 <TTC ≤2.6 31.5
3 TTC ≤1.50 15.3 TTC ≤1.0 6.6 TTC ≤1.0 6.1 TTC ≤0.75 4.8 TTC ≤0.75 4.4
16 Jou nal o Ad anced T anspo a ion

1
2
TTC sco e
1
23
4
23
4
5
01234
MaxDel aV sco e
(a)
MaxDel aV sub-sco e
TTC sub-sco e
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
5
5
5
5
55
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
1234
(b)
Figu e 8: Concep ual illus a ion o conduc ing he o e all se e i y sco e: (a) o e all sco e by egions and (b) s ep-g aded lines om he
subsco es.
HDV
1
2
3
4
5
6
123450
TTC (s)
0
20
40
60
80
100
120
MaxDel aV (Km/h )
y = 27.273x - 76.364
y = 27.273x - 46.364
y = 25.532x - 7.6596
y = 24.194x + 29.032
y = 23.377x + 63.117
(a)
LCAV (L1/L2)
0
20
40
60
80
100
120
MaxDel aV (Km/h )
123450
TTC (s)
y = 27.778x - 78.889
y = 24.324x - 31.622
y = 25x - 5
y = 25.862x + 20.69
y = 25x + 55
(b)
HCAV (L3/L4)
123450
TTC (s)
0
20
40
60
80
100
120
MaxDel aV (Km/h )
y = 29.126x - 85.631
y = 23.196x - 25.979
y = 24.742x - 3.7113
y = 26.786x + 16.071
y = 24.896x + 55.519
(c)
Figu e 9: Se e i y sco es (SS) o di e en ypes o ehicles: (a) o HDV, (b) o L1 & L2 ehicles (LCAV), and (c) o L3 & L4 ehicles
(HCAV).
Jou nal o Ad anced T anspo a ion 17
5. Conclusion
Tis s udy in es iga es he ex en o con ic se e i y
esul ing om he in oduc ion o CAVs in o a c s eams.
I p esen s an analysis o he po en ial a c con ic s ha
occu when oads comple ely ope a ing wi h HDVs an-
si ion in o ull L4 ehicle ope a ion. T ee dimensions o
se e i y a e examined: p oximi y o collision, consequences
o collision, and p oximi y/consequence o collision classi-
ed by se e i y sco e. Owing o he lack o c ash da a in-
ol ing CAVs, his s udy implemen ed a a c
mic osimula ion app oach ollowed by SSAM analysis. Te
speci c ou pu s o he SSAM (e.g., TTC, MaxS, Del aS, and
MaxDel aV) a e used o es ima e con ic se e i y. Te e-
sul s o se e al mixed ee ope a ion scena ios a e compa ed
o de e mine a c sa e y when he eal and cu en ex en
o pene a ion o CAVs on oads is exceeded.
Te key ndings o his s udy a e as ollows. Te sen-
si i i y analysis o he TTC h eshold in scena ios whe e
HCAV is he ollowe ehicle yields in e es ing esul s. I he
p esence o HCAV on he oad is low (less han 35%), he
di e ence in he numbe o iden i ed con ic s be ween he
applied TTC h eshold alues (i.e., 0.5, 0.75, 1.0, 1.25, and
1.5 s) is no s a is ically signi can . By con as , he scena ios
whe e HCAVs ha e mode a e sha ing pe cen ages (35%–
55%) s a o show a signi can di e ence a 1.0 s. Te
scena ios whe e he ope a ion pe cen age o hese ehicles is
high lead o signi can di e ences in he numbe o con ic s
among all he es ed TTC alues. Te e o e, he impo ance
o applying di e en TTC h eshold alues o such sce-
na ios mus be ecognized. Te MaxS and Del aS alues we e
discussed as con ic consequence indica o s wi hin he
p oposed scena ios and se e al ehicle in e ac ions. Tese
indica o s show ha he scena ios whe e 55% o mo e
HCAV sha e he oad esul in con ic s wi h low se e i y
(low speeds and low speed di e ences among ehicles in-
ol ed in con ic s). In addi ion, he con ic s whe e HDVs
a e he ollowe ehicles yield he highes se e i y con ic s,
ollowed by he con ic s whe e he ollowe ehicles
a e LCAV.
Finally, p oximi y/consequence (TTC/MaxDel aV)
cha s ela ed o di e en ehicle ypes ha e been de eloped.
Tese cha s ha e been used o classi y he esul ing con ic s
in o se e i y sco es in each scena io. Te esul s indica e ha
inc easing he sha ed pe cen ages o CAVs ope a ing on he
oad signi can ly dec eases he numbe o con ic s wi h
high se e i y. When app oxima ely 100% o HCAV ope a e
on oads, se e e con ic s a e an icipa ed o disappea , and
hose wi h low se e i y a e educed.
Tis s udy p esen ed a comp ehensi e in es iga ion o
a c con ic se e i y dimensions and analyzed he con ic
se e i y ela ed o se e al le els o au oma ion wi hin a ious
mixed ee ope a ion scena ios. Ne e heless, his s udy
p esen s some limi a ions ha should be conside ed o
u u e esea ch. Fi s ly, whe he he SSMs h esholds unde
con en ional a c condi ions a e applicable when mod-
eling sa e y in mixed o ully au oma ed a c emains
unclea . Di e en TTC h eshold alues ha e been es ed
and applied o sol e his p oblem. Howe e , when eal da a
become a ailable, he alidi y o SSM should be ho oughly
e iewed and e i ed. Te e o e, new da a sou ces ela ed o
CAV da a will be c ucial o he de elopmen o an uni e sal
SSM se ha can sa is y all au oma ion le els. Secondly, o
pa icula a c scena ios, he in es iga ion o SSMs, such as
he la e al sa e y p o ided by lane changing and me ging
maneu e s, mus be implemen ed. Bo h HDVs and CAVs
can exhibi di e en le els o la e al sa e y, pa icula ly in
a mixed au onomy a c. And nally, he calib a ion
p ocess could be imp o ed wi h eld TTC da a [63, 64]. Tis
should be conside ed in simila u u e esea ch.
Appendix
A. Beha io Pa ame e s Used o CAV
Le els Modeling
Tis appendix con ains he CAV beha io pa ame e alues
as indica ed in Table 4 ( o passenge ca s) and Table 5 ( o
hea y ehicles).
B. A Sample o Mic osimula ion Resul s
Te ollowing esul s in Figu e 7 ep esen an example o he
mic osimula ion ou pu s ela ed o MaxS and Del aS ha we e
gene a ed a scena io E, when HDV is he ollowe ehicle in
he con ic s. MaxS and Del aS ou pu s a e p esen ed as dis-
ibu ion cha s o e ec he esul ed da a mo e desc ip i ely.
C. Se e i y Cha s o HDVs and CAVs
Tis appendix desc ibes he p ocedu e ollowed in de-
eloping he se e i y cha s (by ehicle ype) based on TTC
sco e/MaxDel aV sco e.
To ob ain he TTC sco e, di e en TTC h esholds we e
es ablished by ehicle ype. Te p ocedu e looks o he
in ec ion poin s o he TTC cumula i e dis ibu ion when
pu e ehicle ype scena ios we e modeled (i.e., all he e-
hicles in he simula ion a e exclusi ely HDVs, L1, L2, L3, o
L4 ehicles). Speci cally, 15 mic osimula ion uns we e
execu ed o each pu e scena io, and he TTC cumula i e
dis ibu ion cha s we e depic ed. All he con ic s iden i ed
wi h a TTC alue equal o o lowe han 5.0 s we e con-
side ed o he TTC dis ibu ion analysis.
Acco ding o he Hyd´
en [21] sa e y py amid, ex emely
se e e con ic s a e conside ably limi ed, whe eas less se e e
a c con ic s a e mo e equen . Acco ding o Souley e e
and Hochs ein [38], hese se e e con ic s can be ob ained
based on he in ec ion poin s o he TTC cumula i e dis-
ibu ion o he pu e scena ios o each ehicle ype. Tese
poin s a e used as h esholds o delinea e he ew se e e
con ic s om nonse e e ones. La e , he nonse e e con ic s
we e di ided in o h ee app oxima ely equal g oups. A TTC
sco e was assigned o each g oup (one se e e and h ee
nonse e e), which was la e used o ob ain he o e all sco e.
Table 6 summa izes he p oposed TTC sco es and h esholds
o de e mine he o e all sco es o he pu e ope a ion
scena ios.
18 Jou nal o Ad anced T anspo a ion
As lis ed in Table 6, he h esholds ha iden i y
se e e con ic s (wi h a TTC sco e equal o 3) di e
among he pu e ehicle ype ope a ional scena ios.
P ecisely, he in ec ion poin o HCAV (L3 and L4
ehicles) was lowe han hose o he o he ehicles,
indica ing hei imp o ed capabili ies. Te in ec ion
poin s o he pu e scena ios o HCAV, LCAV, and
HDVs we e 0.75, 1.0, and 1.5 s, espec i ely. Table 6
shows he a ia ion in he in ec ion poin a ec s o he
h esholds. Mo eo e , he dec ease in a se e e con ic
egion (wi h a sco e equal o 3) is clea ly achie ed by
inc easing he au oma ion le el, a ming he sa e y
bene o inco po a ing HCAV.
Fo he consequence sco e, Souley e e and Hochs ein
[38] used he equa ion o E an [37], which employs Max-
Del aV o calcula e he likelihood o c ash inju ies and a-
ali ies. Tei esul s showed ha MaxDel aV alues o
app oxima ely 30 and 60 km/h a e key (in ec ion) alues
ha signi can ly inc ease he p opensi y o se e e con ic s.
Because he E an equa ion only depends on MaxDel aV and
he consequences o a c ash wi h ce ain MaxDel aV alues
ha e he same e ec on HDVs and CAVs (wi h di e en
au oma ion le els), hese wo key alues can be conside ed
he same o all au oma ion le els and in e ac ions.
Te e o e, ollowing Souley e e and Hochs ein [38], he
se e i y alue o MaxDel aV is di ided in o h ee sco es: (1)
sco e 1, MaxDel aV anging 0–30 km/h; (2) sco e 2, Max-
Del aV anging 30–60 km/h; and (3) sco e 3, MaxDel aV
exceeding 60 km/h.
Te nex s ep is adding bo h sco es (TTC and Max-
Del aV sco es) o ob ain an o e all se e i y sco e. Te
esul ed o e all sco e is ep esen ed by egions in
Figu e 8(a), whe e each sco e a ea ep esen s he se e i y
sco e. Howe e , se e i y sco es a e be e iden i ed by lines
o cu es a he han by squa e a eas [32, 38]. Fo his
eason, hese squa e a eas a e con e ed in o se e i y isolines
as con ou lines (Figu e 9). P ecisely, each o e all squa e
a ea is eshaped o e subsco es (wi h an inc emen o 0.2
poin s) o each majo sco e (in bo h anges o TTC and
MaxDel aV sco es), see Figu e 8(b).
A e wa ds, he s ep-g aded lines esul ing om he
equal o e all sco es in Figu e 8(b) a e eshaped in o smoo h
con ou lines o HDVs, LCAV, and HCAV, as shown in
Figu e 9. In he gu e, he a ia ion o he ed colo om
ligh o da k ep esen s he inc emen in Se e i y Sco e (SS).
Da a A ailabili y
Te da a suppo ing he cu en s udy a e a ailable om he
co esponding au ho upon eques .
Disclosu e
Tis s udy is pa o he Resea ch P ojec PID2019-
110741RA-I00.
Con lic s o In e es
Te au ho s decla e ha he e a e no con ic s o in e es .
Au ho s’ Con ibu ions
Roc´
ıo de Oña and Juan de Oña concep ualized he s udy;
Tasneem Miqdady, Roc´
ıo de Oña, and Juan de Oña pe -
o med me hodology; Tasneem Miqdady, Roc´
ıo de Oña, and
Juan de Oña did o mal analysis and in es iga ion; Tasneem
Miqdady w o e he o iginal d a p epa a ion; Roc´
ıo de Oña
and Juan de Oña w o e he a icle and e iewed and edi ed
he a icle; Roc´
ıo de Oña did unding acquisi ion.
Acknowledgmen s
Te au ho s a e g a e ul o he Spanish Gene al Di ec o a e
o T a c (DGT) o p o iding he a c ows om se e al
GR-30 sec ions. Tasneem Miqdady app ecia es Aimsun o
p o ide hei pos g adua e s uden license o make his
wo k. Tis wo k was suppo ed by Resea ch P ojec
PID2019-110741RA-I00, nanced by he Spanish S a e Re-
sea ch Agency (MCIN/AEI/10.13039/501100011033). Open
Access unding enabled and o ganized by CRUE-CBUA
Gold.
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