A framework for quantifying Hyperloop's socio-economic impact in smart cities using GDP modeling

Vesjolijs, Aleksejs; S ukalina, Yulia; Ze ina, Olga
A icle
A amewo k o quan i ying Hype loop's socio-economic
impac in sma ci ies using GDP modeling
Economies
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Sugges ed Ci a ion: Vesjolijs, Aleksejs; S ukalina, Yulia; Ze ina, Olga (2025) : A amewo k o
quan i ying Hype loop's socio-economic impac in sma ci ies using GDP modeling, Economies,
ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 8, pp. 1-20,
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Ci a ion: Vesjolijs, A., S ukalina, Y., &
Ze ina, O. (2025). A F amewo k o
Quan i ying Hype loop’s Socio-
Economic Impac in Sma Ci ies Using
GDP Modeling. Economies,13(8), 228.
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A icle
A F amewo k o Quan i ying Hype loop’s Socio-Economic
Impac in Sma Ci ies Using GDP Modeling
Aleksejs Vesjolijs 1, Yulia S ukalina 2,* and Olga Ze ina 2
1Enginee ing Facul y, T anspo and Telecommunica ion Ins i u e, 2 Lau as S ee , LV-1019 Riga, La ia;
[email p o ec ed]
2T anspo and Managemen Facul y, T anspo and Telecommunica ion Ins i u e, 2 Lau as S ee ,
LV-1019 Riga, La ia; [email p o ec ed]
*Co espondence: [email p o ec ed]
Abs ac
Hype loop ul a-high-speed anspo p esen s a ans o ma i e oppo uni y o u u e
mobili y sys ems in sma ci ies. Howe e , assessing i s socio-economic impac emains chal-
lenging due o Hype loop’s unique echnological, modal, and ope a ional cha ac e is ics.
As a no el, i h mode o anspo a ion—dis inc om bo h a ia ion and ail—Hype loop
equi es ailo ed e alua ion ools o policymake s. This s udy p oposes a cus om-designed
amewo k o quan i y i s mac oeconomic e ec s h ough changes in g oss domes ic p od-
uc (GDP) a he ci y le el. Unlike adi ional economic models, he p oposed app oach
is speci ically adap ed o Hype loop’s mul imodali y, in as uc u e, speed p o ile, and
digi al-g een oo p in . A Poisson pseudo-maximum likelihood (PPML) model is de el-
oped and applied a wo echnology eadiness le els (TRL-6 and TRL-9). Case s udies o
Glasgow, Be lin, and Busan a e used o simula e impac s based on geo-spa ial ea u es and
ci y-speci ic ade and accessibili y indica o s. Resul s indica e subs an ial GDP inc eases
d i en by ac o s such as expanded 60 min commu e ca chmen zones, imp o ed ade
lows, and connec i i y node densi y. Fo ins ance, unde TRL-9 condi ions, GDP upli
eaches o e 260% in ce ain scena ios. The amewo k o e s a scalable, ep oducible ool
o policymake s and u ban planne s o e alua e he economic po en ial o Hype loop
wi hin he con ex o sus ainable sma ci y de elopmen .
Keywo ds: socio-economic impac s; sma ci y; logis ics; u ban de elopmen ; hype loop
1. In oduc ion
The concep o he sma ci y has e ol ed o e he pas wo decades as a s a egic
amewo k o imp o ing he e iciency, sus ainabili y, and li abili y o u ban spaces. In
2024, he wo ld wi nessed a con inued accele a ion in u baniza ion and digi al ans o ma-
ion ac oss mul iple egions, d i en by in as uc u e demands, popula ion g ow h, and
en i onmen al impe a i es. Recen s udies show ha u ban demands—including mobili y
in as uc u e, housing, ene gy consump ion, and digi al se ices—ha e apidly inc eased
om 2023 o he i s qua e o 2025 (UN DESA,2023). This g ow h is d i en by ongoing
u ban popula ion expansion, pos -pandemic eco e y policies, and he accele a ion o
digi al ans o ma ion ac oss ci ies wo ldwide. These p essu es unde sco e he need o
scalable, sus ainable, and high-speed anspo in as uc u e solu ions such as Hype loop.
By 2050, nea ly 70% o he global popula ion is expec ed o li e in u ban a eas, placing
immense p essu e on exis ing in as uc u e, pa icula ly in he a eas o anspo a ion and
Economies 2025,13, 228 h ps://doi.o g/10.3390/economies13080228
Economies 2025,13, 228 2 o 20
logis ics (Wo ld Bank,2020). Among he mos ele an ad ances in high-speed g ound
anspo a ion is Hype loop echnology—a sys em ha p oposes nea -supe sonic a el
in acuum ubes, combining he bene i s o ail and ai anspo . Ini ially p oposed as a
u u is ic concep in 2012 by Tesla (Tesla.com,2012), Hype loop has apidly e ol ed in o a
angible inno a ion, eaching Technology Readiness Le el 6 (TRL) (Ho izon,2020).
Acco ding o (Planing e al.,2025), Hype loop accep ance eached 51.4% ac oss Eu ope,
indica ing expec a ions in high speed, com o , and en i onmen al aspec s despi e isks
associa ed wi h i (Kang,2025).
In 2025, Eu opean Hype loop p ojec s a e being de eloped ac oss di e en egions,
o example, Zele os (Spain) (Zele os.com,2025), Ne omo (Poland) (Ne omo.com,2025),
HARDT ( he Ne he lands) (Ha d .global,2025), TUM (Ge many) (Tumhype loop.com,
2025), Ins i u e o Hype loop Technology (Ge many) (Ih -emden.de,2025), Swisspod
(Swi ze land) (Swisspod.com,2025), and o he s. Hype loop s a e-o - he-a includes ea-
sibili y s udies, pilo p ojec s, expe imen al uns, es acks (Tumhype loop.com,2023),
and public–p i a e in es men s also accele a ing i s po en ial deploymen in a ious global
egions including he USA (TT Hype loop) (Hype loop .com,2025), India (A ishka (2023),
Hype link (2023)), and China (CASIC) (S a ,2024). Hype loop echnology, wi h i s inno-
a i e design, ul a-high speeds, and mul imodali y, has he po en ial o change exis ing
in e ci y a el and ans o m he logis ics and eigh sys ems o sma ci ies (P emsaga ,
2022,2023). Fu he , i s sus ainable design suppo s he goals o he Eu opean G een
Deal (EC,2024b) and g een (EC,2023b) and digi al ans o ma ion (EC,2023a), which is
e lec ed in ecen s udies co e ing Hype loop’s ole in sma ci y logis ics (Hansen,2020),
sus ainabili y modeling (Ba bosa,2020), high-speed anspo in eg a ion (Noland,2021),
and EU-aligned de elopmen s a egies (Vesjolijs & Sko oboga o a,2025).
The applica ion o Hype loop echnology o sma ci y de elopmen has become
a p ominen opic a scien i ic and indus y con e ences dedica ed o ul a-high-speed
anspo a ion. No ably, he Hype loop Con e ence 2023, held in Busan, Sou h Ko ea,
b ough oge he a di e se a ay o s akeholde s—including enginee s, esea che s, in-
dus y leade s, and policy expe s— om ac oss No h Ame ica, Asia, and Eu ope. The
con e ence p o ided bo h heo e ical insigh s and empi ical da a d awn om ac i e pilo
implemen a ions and easibili y s udies on he in eg a ion o Hype loop mobili y wi hin
sma ci y amewo ks. The case o Busan’s sma ci y ini ia i es, highligh ed du ing he
e en , showcased he p ac ical iabili y o Hype loop implemen a ion in dense u ban
en i onmen s and o e ed aluable pe spec i es on logis ical op imiza ion, en i onmen al
sus ainabili y, and in as uc u e planning. La e , Hype loop p ojec s p esen ed a he
Eu opean Hype loop Week e en s in 2023 and 2024 showcased p ac ical applica ions ac oss
di e se geog aphies, including Ge many, he UK, he Ne he lands, Canada, and India. The
co esponding s udies highligh ed use-case inno a ion (Vesjolijs & Sko oboga o a,2025),
c oss-bo de pilo collabo a ions (Hype loopcon e ences.com,2025), and egion-speci ic
in as uc u e adap a ion (EHW,2023).
Recen ad ances in legal amewo ks o Hype loop in he EU demons a e Hype loop
applica ion po en ial o sma ci ies om a egula o y pe spec i e. The Eu opean Commis-
sion adop ed sus ainable u ban mobili y and anspo in eg a ion p inciples as main a eas
o sma ci ies de elopmen in he EU (EC,2025) and p io i ized he design o Hype loop
p omo ion and de elopmen s a egy (EC,2024a). Analysis o schola li e a u e in he
ield highligh s he lack o esea ch pape s dedica ed owa ds legisla ion and economic
de elopmen o Hype loop; cu en ly, mo e s udies a e being dedica ed o Hype loop’s
sys em pe o mance (Mi opoulos e al.,2021).
The cohe ence o sma ci y p inciples wi h Hype loop echnology p ojec s p esen s a
unique oppo uni y o ede ine u ban logis ics, pa icula ly in he con ex o mobili y as a
Economies 2025,13, 228 3 o 20
se ice, mul imodal in eg a ion, and sus ainable economic g ow h. Acco ding o Mas ub
e al. (2025), Hype loop also con ibu es o an “eco- iendly u u e socie y ene gy ne wo k”,
which co esponds wi h sma ci y echnological inno a ion goals. Hype loop has also
ecei ed wide co e age in media, echnical easibili y epo s, and public in as uc u e
whi e pape s— ocusing on p ojec ed ene gy e iciency (P emsaga ,2022), socio-economic
po en ial (Hansen,2020), and planning easibili y wi hin u ban ecosys ems (Ba bosa,2020).
Howe e , i s ole in d i ing economic change—pa icula ly h ough imp o ed u ban
logis ics and accessibili y— emains unde explo ed in academic esea ch.
This s udy is in ended o b idge his gap by in es iga ing how Hype loop echnology
can con ibu e o he logis ics ecosys em o sma ci ies. I add esses a c i ical gap a he
in e sec ion o in as uc u e inno a ion and u ban economic modeling. Hype loop has
been widely discussed in he enginee ing and easibili y li e a u e; howe e , ew academic
s udies o e a ep oducible me hod o quan i y i s mac oeconomic e ec s, pa icula ly a
he ci y le el in he con ex o sma ci y u ban de elopmen . To b idge his gap, he p esen
esea ch in oduces a scalable modeling amewo k ha in eg a es geo-spa ial analysis,
anspo accessibili y, and ade elas ici y in o a GDP-based es ima ion ool ailo ed o
sma ci ies. By doing so, he s udy p o ides policymake s wi h a no el, da a-d i en
mechanism o e alua e he socio-economic impac o Hype loop deploymen and in o m
in as uc u e planning wi hin he sma ci y agenda.
The emainde o his pape is s uc u ed as ollows. Sec ion 2ou lines he esea ch
me hodology, including he sys ema ic li e a u e e iew and he de elopmen o he g oss
domes ic p oduc (GDP) impac modeling amewo k. Sec ion 3p esen s he use-case
analysis o Hype loop applica ions wi hin sma ci ies, based on echnical eadiness and
anspo unc ionali y. Fu he mo e, i in oduces he p oposed GDP assessmen s a egy
and explains i s in eg a ion wi h geo-spa ial and socio-economic da a laye s. Sec ion 4
desc ibes he case s udies o Glasgow, Be lin, and Busan and demons a es he applica ion
o he model. Sec ion 5discusses he esul s, compa ing GDP impac s ac oss ci ies and
iden i ying key d i e s o economic change. A summa y o he indings, limi a ions, and
p oposing di ec ions o u u e esea ch is gi en in he Conclusions.
2. Ma e ials and Me hods
To achie e he aim o he s udy, he au ho s de eloped he ou -s age me hodology
p esen ed in Figu e 1. While a ious me hods such as cos –bene i analysis, compu able
gene al equilib ium (CGE) models, and sys em dynamics a e commonly applied in ans-
po economics, he p esen s udy equi ed a scena io-d i en, spa ially disagg ega ed, and
modula app oach aligned wi h sma ci y u ban de elopmen cha ac e is ics. The e o e,
a ou -s age me hodology was de eloped o mee hese speci ic equi emen s. S ep 1
in ol ed a sys ema ic li e a u e e iew pe o med by P e e ed Repo ing I ems o Sys-
ema ic e iews and Me a-Analyses (PRISMA) guidelines, by sc eening 184 jou nal a icles,
whi e pape s, and echnical epo s published be ween 2016 and 2025.
The e iew had wo objec i es: (i) o ca alogue socio-economic mechanisms ha he
Hype loop li e a u e claims could a ec u ban pe o mance, co e ing passenge bene i s,
eigh logis ics, ene gy demand, land-use change, and emissions, and (ii) o iden i y he
quan i a i e p oxies mos equen ly used o measu e hose mechanisms. This scan yielded
44 dis inc indica o s and con i med a me hodological gap in linking hem cohe en ly
o g oss-domes ic-p oduc ou comes. Abs ac and ull- ex sc eening we e conduc ed
manually by he au ho s using p e-de ined inclusion c i e ia in acco dance wi h PRISMA
guidelines. No au oma ed e iew ools (e.g., Abs ack , BIBOT, e c.) we e used in he
selec ion p ocess.
Economies 2025,13, 228 4 o 20
Figu e 1. Resea ch me hodology o e iew. Use case selec ion sou ce: Waseem e al. (2025) (au ho s’
elabo a ion).
Li e a u e analysis was suppo ed by p ima y sou ce da a om Hype loop Con e ence
2023 in Busan, Hype loop Con e ence 2024 in Gdansk (Hype loopcon e ences.com,2025),
and Eu opean Hype loop Week 2023 (EHW,2023) in Edinbu gh, UK (EHW,2023). EHW
2023 b ough oge he o e 400 s uden s, esea che s, and p o essionals o compe e and
collabo a e on di e se Hype loop opics om enginee ing o socio-economic modeling.
P ima y sou ce da a om EHW 2023 p o ided he necessa y in o ma ion o Hype loop
echnology applicabili y o sma ci ies in he UK, Ge many, and he Ne he lands. These
insigh s in o med he de elopmen o he s udy’s classi ica ion amewo k, use-case ma ix,
and GDP-impac assessmen model.
Du ing S ep 2, he indica o s we e classi ied in o he Eu opean Commission’s sma
ci y ame: ICT, ci izen ocus, in as uc u e, anspo , and mobili y. The esul s o he
analyses we e consolida ed in o a sho lis o passenge and eigh use-cases, which
con i med he po en ial applica ion o Hype loop o sma ci ies. Wi h he in oduc ion o
GenAI o science, esea ch me hods a e also ad ancing, allowing o be e de e mina ion
o blind spo s in scien i ic a eas and iden i ica ion o p e iously unknown phenomena.
Recen s udies in liquid AI discussed by scien is s and academia ep esen a i es a he
Fu u e o In o ma ion and Communica ion Con e ence 2025 (Saicon e ence.com,2025),
speci ically he me hod p oposed by Waseem e al. (2025), we e applied in his s udy o
classi y and ank Hype loop use-cases based on echnical eadiness and da a a ailabili y.
Each selec ed use-case was mapped o a Hype loop con ibu ion a ea, c ea ing he
b idge be ween aw enginee ing a ibu es and u ban policy goals, which helps in unde -
s anding how o de elop a s a egy o GDP calcula ion and wha i s componen s should
be. This mapping connec s Hype loop’s echnical cha ac e is ics o u ban de elopmen
goals and enables he o mula ion o a model o es ima e GDP- ela ed impac s o Hype -
loop deploymen . While GDP as a mac oeconomic measu e has es ablished me hods and
componen s, he p oposed amewo k builds on hese ounda ions o cap u e ci y-le el
changes ha may a ise.
Du ing S ep 3, he classi ica ion esul s we e ope a ionalized using he GDP-impac
calcula ion amewo k de eloped by he au ho s in he ea lie phase o he s udy. The
wo-laye s a egy combines a 0.33 km
2
geo-spa ial g id wi h a ci y-speci ic Poisson pseudo-
maximum likelihood (PPML) module p oposed by au ho s (Vesjolijs e al.,2025) and
a ade- e-balance block; i s s ep-by-s ep implemen a ion is summa ized in a de ailed
algo i hm wi h speci ic ac ions and ou pu s. Since he solu ion is ocused on ci ies and
no by coun ies (as in classical in e na ional ade g a i y models), he model is applied
ci y-by-ci y, meaning no pooled c oss-sec ional s uc u e is imposed. Each ci y’s baseline
and Hype loop-enhanced ows a e e alua ed independen ly using ci y-speci ic eg essions,
which helps isola e e ec s and a oids in e dependence ac oss spa ial uni s. This also aligns

Economies 2025,13, 228 5 o 20
wi h ou goal o de eloping a scalable, modula ool applicable o di e se u ban se ings.
Addi ionally, he PPML speci ica ion is pa icula ly sui able o ade low modeling wi h
he e oskedas ici y and ze o alues, as shown in Sil a and Ten ey o (2006).
A wo king p o o ype was de eloped unde a con en ional so wa e-de elopmen
li e-cycle (SDLC) (Olo unshola & Ogwueleka,2022) du ing S ep 4. The equi emen s we e
o malized in uni ied modeling language (UML) use-case diag ams (Koç e al.,2021); da a-
inges ion, compu a ion, and isualiza ion laye s we e coded in Py hon 3.12 (Py hon.o g,
2025); uni and in eg a ion es s we e execu ed wi h PyTes and Pandas-based ix u es;
and alida ion was pe o med on he Glasgow, Be lin, and Busan case s udies. The SDLC
me hod, oge he wi h s ep-by-s ep algo i hm o p o o ype de elopmen , allows ep oduc-
ing he p oposed solu ion and modeling uning (Olo unshola & Ogwueleka,2022).
The au ho s hen summa ized he use-case analysis, case s udy, s a egy, algo i hm,
and p o o ype in o an in eg a ed amewo k p oposed o GDP-impac e alua ion o
Hype loop deploymen in sma ci ies (S ep 5).
3. The Role o Hype loop Technology o Sma Ci ies and U ban
De elopmen and Examina ion o Possible Implica ions o Economic
De elopmen
In his sec ion, he au ho s p esen he esul s o a sys ema ic li e a u e e iew, apply
use-case classi ica ion using Waseem e al.’s (2025) me hod, and de elop he logic o he
empi ical assessmen o he GDP calcula ion and e alua ion o mul i ace ed ac o s.
3.1. Role o Hype loop Technology o Sma Ci ies
Use-case analysis p o ides an o e iew o he po en ial ha Hype loop o e s o sma
ci ies, con ibu ing o he de elopmen o he solu ion p oposed by he au ho s (Table 1).
3.2. Hype loop Impac on GDP in he Con ex o Sma Ci ies
GDP is a well-es ablished mac oeconomic indica o used o measu e he o al alue o
goods and se ices p oduced wi hin a coun y o egion. T adi ional GDP measu emen
encompasses a ious me hods (expendi u e, income, and p oduc ion app oaches) and
has also aced c i iques ega ding i s adequacy in cap u ing en i onmen al, social, and
dis ibu ional dimensions. This s udy does no aim o al e he heo e ical ounda ions
o GDP calcula ion. Ins ead, i in oduces a modeling amewo k ha es ima es he po-
en ial change in GDP a he u ban le el esul ing om he deploymen o Hype loop
in as uc u e. The model is designed o ope a e wi hin exis ing GDP amewo ks while
accoun ing o spa ial accessibili y, ade e ec s, and ime-e iciency gains enabled by he
new anspo mode.
The p oposed modeling amewo k in eg a es geospa ial disagg ega ion wi h socio-
economic impac assessmen o es ima e he mac o-le el e u ns ha a passenge - and
eigh -Hype loop co ido could gene a e o indi idual sma ci ies. Analysis o sma
ci ies a ea maps om Glasgow (UK), Be lin (Ge many), Busan (Sou h Ko ea), Singapo e,
Oslo (No way), Ams e dam ( he Ne he lands), Helsinki (Finland), Zü ich (Swi ze land),
B idgewa e (Canada), Columbus (USA), B isbane (Aus alia), Fujisawa (Japan), Shenzhen
(China), and o he loca ions e ealed ha each sma ci y has unique a ea, geo-spa ial
ea u es, and changing bounda ies, and hey a e no homogenous. Ano he complexi y
ha some ci ies migh ha e is combined zones. Fo example, Busan a ea con ains a F ee
T ade Zone, which, in ac , acili a es geog aphical ad an ages o di e en dis ic s inside
one a ea, which can be conside ed as sepa a e en i ies.
Economies 2025,13, 228 6 o 20
Table 1. Possible Hype loop use-cases o sma ci y in eg a ion based on Waseem e al. (2025) me hodology. PHS—passenge Hype loop; FHS— eigh Hype loop
(au ho s’ cons uc ion).
ID PHS Use-Case Sou ce ID FHS Use-Case Sou ce
1Ul a-high-speed daily commu ing
be ween sma ci y a eas
(Swissloop.ch,2023;S. Chen e al.,2022;Kuma &
Singh,2022)1 C i ical pa cel deli e y (Tumhype loop.com,2023;Hype loop .com,2025;Ba bosa,
2020;Bhuiya e al.,2022)
2Ai po o ci y hype
shu le connec o (Celes in,2023;Ki ika & Suwa cha apinun,2024) 2 F esh p oduce cold
chain logis ics (Hansen,2020;Ba bosa,2020;Noland,2021)
3 In e ci y egional passenge co ido s (Hansen,2020;Ba bosa,2020;Noland,2021;
Vesjolijs e al.,2025;Sil a & Ten ey o,2006)3 Pods con aine shu le (Tumhype loop.com,2023;S. Chen e al.,2022)
4 E en -based c owd anspo (Celes in,2023) 4 Ai po ca go o down own
consolida ion hub (Tumhype loop.com,2023;Hype loop .com,2025;S. Chen
e al.,2022;Ki ika & Suwa cha apinun,2024)
5 Eme gency medical e acua ion pods (P emsaga ,2022,2023;Noland,2021;Kuma &
Singh,2022)5 Manu ac u ing supply line (EC,2023b;Kuma & Singh,2022;Bhuiya e al.,2022;Singh
e al.,2021)
6 Uni e si y connec i i y (Singh e al.,2021;Tudo & Paolone,2019;J. Li
e al.,2021;Ai ken e al.,2016)6 High- alue elec onics secu e ansi (EC,2023b;Hansen,2020;Ki ika & Suwa cha apinun,2024)
7 Tou ism enhanced access (Cao e al.,2023;A ma˘gan,2020) 7 Was e emo al (EC,2023b;Hansen,2020;A ma˘gan,2020)
8 In eg a ed knowledge hub (Hansen,2020;Bhuiya e al.,2022;Ki ika &
Suwa cha apinun,2024;A ma˘gan,2020;Goha &
Nencioni,2021)8Pha maceu ical
dis ibu ion (Zhang e al.,2007;Ra ho e e al.,2015;Edoh,2017;
Schlingensiepen e al.,2016)
9 Ru al accessibili y (Tumhype loop.com,2023;Hype loop .com,
2025;EC,2023b;Vesjolijs e al.,2025;Ki ika &
Suwa cha apinun,2024;Lingli,2015;Edoh,2017)9 Re e se logis ics
(Hype loop .com,2025;EC,2023b;Vesjolijs e al.,2025;Sil a
& Ten ey o,2006;Singh e al.,2021;Ra ho e e al.,2015)
10 Inclusion se ices (Ra ho e e al.,2015;Schlingensiepen e al.,2016) 10 Medical o gans anspo a ion (Cao e al.,2023;Ra ho e e al.,2015;Sheikh e al.,2022;
Willems,2021;Sha ma e al.,2024)
11 MaaS icke ing
(Tumhype loop.com,2023;Hype loop .com,2025;
EC,2023b;Hansen,2020;Sil a & Ten ey o,2006)11 Disas e elie supply chain suppo (EC,2023b;Hansen,2020;Py hon.o g,2025;Ki ika &
Suwa cha apinun,2024)
12 Nigh ime pod se ice (EC,2023b;Hansen,2020;Niki as e al.,2017;
McGillycuddy,2024)12 Au onomous
wa ehouse- o-wa ehouse hype lanes
(Ki ika & Suwa cha apinun,2024;Sane,2020;Mogaji,2024)
13 Wo k o ce bel expansion (Sil a & Ten ey o,2006;Schlingensiepen e al.,
2016;Mogaji,2024)13 In eg a ion wi h u ban consolida ion
cen e s
(Tumhype loop.com,2023;Hype loop .com,2025;Hansen,
2020;Ki ika & Suwa cha apinun,2024;Schlingensiepen
e al.,2016)
14 High- equency business ush-hou
shu les be ween bo lenecks (McGillycuddy,2024;Sane,2020) 14 High-densi y ene gy cell anspo (Goha & Nencioni,2021;Mogaji,2024)
15 Rapid disas e a ea
e acua ion (Ga zy & Scalea,2022) 15 Pos deli e y (Sil a & Ten ey o,2006;Goha & Nencioni,2021;
Mogaji,2024)
16 MaaS pods o educed mobili y pe sons (Ca a e al.,2011) 16 U ban mic o ul ilmen node
connec o
(Hype link,2023;Ki ika & Suwa cha apinun,2024;Ya uz &
Öz ü k,2021;Kale,2019)
17
On-demand au onomous passenge pods
(A ishka ,2023;A. Chen e al.,2023;Niki as e al.,
2017;We ne e al.,2016)17 Ca bon-neu al eigh co ido s
(Ya uz & Öz ü k,2021;Kale,2019;Hype loop De elopmen
P og am,2022;Ab aham e al.,2024)
18 In eg a ed pa k and ide hype loop hubs (Niki as e al.,2017;Kale,2019) 18 P i a e anspo
eloca ion (Rocha e al.,2021;Swi ube,2024)
19 Educa ion suppo o
u al–u ban connec ions (Rocha e al.,2021;Legaspi e al.,2020) 19 U ban– u al hubs
connec i i y (A ishka ,2023;S a ,2024;Hansen,2020;Noland,2021;
Ki ika & Suwa cha apinun,2024)
20 Ca bon-neu al na u e pa ks connec ions (B kljaˇci´c e al.,2020;Alawad e al.,2023) 20 D one eloca ion (Na h e al.,2023)
21 Cybe pa ks (B kljaˇci´c e al.,2020;Alawad e al.,2023) -/- -/- -/-
22 Aging popula ion
suppo (Ghosh,2004) -/- -/- -/-
23 AR/VR MaaS expe ience (An hopoulos,2017) -/- -/- -/-
Economies 2025,13, 228 7 o 20
The e o e, one o he key equi emen s o he solu ion is ha GDP calcula ion should
ake in o accoun u ban a ea geo-spa ial speci ics and cap u e di e en Hype loop de-
ploymen s. Fo example, a ci cle-closed-loop Hype loop can ha e he same leng h as a
s aigh -line Hype loop om poin A o poin B bu a he same ime co e a di e en
a ea. Also, inco po a ing geo-spa ial da a has he po en ial o mo e accu a ely ep esen
he anspo a ion ne wo k and ene gy g id i necessa y. Fu he , depending on equi e-
men s, use s can calcula e he applica ion o Hype loop o a speci ic dis ic a ea o zone
and o he whole u ban a ea. As a esul , he au ho s p oposed a solu ion ha acili a es
he abo e-men ioned equi emen s. I uses an in eg a ed mul i-laye app oach, allowing
o cap u e GDP alloca ion gi en he geo-spa ial speci ics o he exac sma ci y a ea. A
high-le el o e iew is p esen ed in Figu e 2.
Figu e 2. S a egy o GDP impac s assessmen ha couples a geo-spa ial g id wi h a ci y-speci ic
PPML socio-economic laye (au ho s’ cons uc ion). (Legend o geo-spa ial laye on 1 km
2
sec o :
squa es—1/3 km
2
g id sec o s wi hin a sma ci y and sec o s u he subdi ision o 1/9; black
ci cles—cu en anspo connec ion nodes, pu ple ci cle—node ep esen ing s a o a Hype loop
ou e; g een diamond—new connec ion nodes; o ange lines—g aphs ep esen ing node connec ion
by iden i ied Hype loop ou e; g ey sec o —ou o sma ci y anspo a ion ne wo k).
A he ou se , each ci y is g anula ed in o a g id o 0.33 km
2
sec o s using a pa h inding
algo i hm p oposed by Ho zyk and Mon ebello (2025) o d ones’ na iga ion. The me hod
has bo h empi ical and heo e ical use-cases. Fo uncha ed a eas o sma ci ies, his can be
used o de ine a g id using he unmanned lying ehicles, and o hose a eas ha al eady
ha e a de ailed g id, i can be applied o mapping. This g anula ep esen a ion allows
bo h he exis ing u ban s uc u e and he planned Hype loop alignmen o be mapped on o
a common spa ial can as. Wi hin his laye , he model de i es se e al baseline desc ip o s:
he o al numbe o sec o s (ci y g id size), he sha e o hose cells ha lie wi hin a 60 min
su ace-commu e ca chmen , and he s ock o mul imodal connec ion nodes, exp essed
as densi y pe squa e kilome e . The Hype loop igh -o -way is hen supe imposed, and
addi ional passenge -s a ion and eigh - e minal nodes a e alloca ed along i s oo p in ,
c ea ing a modi ied ne wo k opology wi hou al e ing he economic co e o he ci y.
Recen s udies ha e also explo ed ad anced a ic modeling echniques using
Lag ange-coo dina e-based cellula au oma a amewo ks, such as he mul i-lane model
p oposed by X. Li e al. (2021). While he Lag ange-coo dina e-based a ic modeling
app oach p oposed by X. Li e al. (2021) o e s aluable insigh s in o mul i-lane low dy-
namics unde empi ical a ic condi ions, he g aph-based pa h inding me hod by Ho zyk
and Mon ebello (2025) is mo e sui able o ou s udy. The Ho zyk and Mon ebello me hod
enables sec o -le el spa ial disagg ega ion, node- o-node ou ing, and adap a ion o new
Economies 2025,13, 228 8 o 20
o hypo he ical anspo ne wo ks such as he p oposed Hype loop g id, which lacks
his o ical low da a and ope a es unde a undamen ally di e en in as uc u e model.
Building upon his geo-spa ial g id, he amewo k p ocesses physical accessibili y
changes in o high-impac GDP e ec s. Accessibili y is measu ed by he numbe o g id
sec o s ha a ep esen a i e wo ke can each wi hin he designa ed commu e ime; he
Hype loop scena ios enla ge his oppo uni y se in p opo ion o hei highe a el speeds.
Because commu ing occupies only a ac ion o he 24 h day, he aw each expansion is
a enua ed by a ime-budge ac o ha ecognizes p ac ical limi s on human mobili y. The
model u he adds a modes line-alignmen e ec , e lec ing p ope y- alue upli and
s a ion-a ea ac i i y ha end o acc ue along high-capaci y co ido s. These s eps a e
ca ied ou independen ly since e e y ci y eco d is scalable, and i can be applied o he
speci ic dis ic s.
To con e accessibili y impac s in o GDP le els, he algo i hm elies on a Poisson
pseudo-maximum-likelihood speci ica ion ha is calib a ed sepa a ely o each ci y using
only i s own baseline and Hype loop scena io ows. This ci y-speci ic eg ession links
obse ed g oss p oduc o changes in eachable dis ance, ime sa ings, and he p esence
o he Hype loop dummy, he eby yielding in e nally consis en p edic ions ha emain
ee om c oss-ci y con amina ion. The PPML s age ac s as a s uc u al b idge be ween
he spa ial laye and he economic laye , egula izing andom sec o -le el a ia ions while
p ese ing ela i e di e ences gene a ed by he anspo in e en ion.
The PPML model p oposed by he au ho s (Vesjolijs e al.,2025) was chosen o calcula e
ade wi hin he sma ci y. A high-le el equa ion o e alua ing ade be ween wo economic
ac o s is p esen ed below (Mi opoulos e al.,2021).
lnT adeij, =α0+α1ln (GPDi)+α2ln GPDj+α3lnDis ancei,j+∈ij, (1)
whe e
T adeij,
is he expo o impo low om poin i o jin yea ;
∈ij,
is he e o e m.
Fo he e ec e alua ion o Hype loop on ade, he ollowing o m is used (Ba bosa,
2020):
T adeij, =exp(β0+β1ln (GPDi, ) + β2ln(GPDj, ) + β3
ln (Dis ancei,j) + β4HLij, +β3ln (TimeReduc ionij, +1) + . . .) + ∈ij,
(2)
whe e
T adeij,
is he expo o impo low om coun y i o jin yea ;
β4
and
β5
cap u e
e ec o po en ial Hype loop connec i i y on ade lows;
exp (. . .)
means he exponen ial
link o PPML is used;
∈ij,
is he e o e m, acco ding o (Vesjolijs e al.,2025). The
calcula ion app oach adjus ed o sma ci ies answe s he ollowing ques ions:
•How will he wo k o ce mass?
•Wha is a educ ion in anspo a ion ime?
•Wha is he e ec on ade?
•Wha is he e ec on anspo a ion cos s?
•Wha is he e ec on CO2emissions?
•How does GDP change?
The amewo k is ex ensible and has he e o e been con igu ed o include GDP-impac
me hodology ad anced by T ansPod (A. Chen e al.,2023;Delas e al.,2019), whe eby
he wel a e e ec s o a e y-high-speed sys em a e decomposed in o (i) consume su plus
om lowe icke p ices, (ii) mone ized en i onmen al ex e nali ies, and (iii) p oduc i i y
gains a ibu able o a el- ime sa ings. In p ac ice, he p esen model embeds a a i
module ha i s benchma ks Hype loop a es agains p e ailing ail/ai p ices on he
same OD pai s and hen eeds he di e en ial in o a demand-elas ici y ou ine o ob ain a
s a ic consume -su plus es ima e. A pa allel emissions block con e s mode-shi onnage
in o a oided CO
2
using li e-cycle emission ac o s consis en wi h EU axonomy guidelines.
Economies 2025,13, 228 15 o 20
6. Conclusions
An end- o-end me hod has been p oposed o es ima e how Hype loop deploymen
may al e he mac oeconomic dynamics o da a-d i en sma ci ies. The s udy’s main
objec i e was achie ed by in oducing a amewo k designed o suppo GDP-impac
calcula ion o Hype loop implemen a ion in he con ex o sma ci y de elopmen .
The conduc ed esea ch con i med he bene i s o e ed by he Hype loop echnology
by applying i o a sma ci y con ex and mapping a ca alogue o passenge and eigh
use-cases using he liquid AI me hod, demons a ing mul idisciplina y and mul i ace ed
aspec s o ul a-high-speed echnology deploymen .
A s uc u ed GDP-impac assessmen s a egy ha couples a geo-spa ial g id wi h
a ci y-speci ic PPML socio-economic laye (Figu e 2) was de eloped, b idging he gap
be ween empi ical geo-spa ial pa h inding algo i hms and socio-economic aspec s o Hy-
pe loop. The s a egy was o malized in o a ep oducible 17-s ep algo i hm (Table 2),
beginning wi h sec o -le el each calcula ions and concluding wi h he adjus men o g oss
p oduc based on ne ade e ec s. The algo i hm is scalable and can be de eloped as
a digi al applica ion, which is con i med by he applied so wa e de elopmen li ecycle
me hod. The algo i hm was execu ed o h ee con as ing ye densi y-compa able sma
ci ies—Glasgow, Be lin, and Busan—demons a ing in e nal consis ency and e ealing
dis inc bene i pa hways: commu e - each expansion o Glasgow, co ido -led alue
cap u e and expo upli o Be lin, and eigh -domina ed gains o Busan.
The nume ical esul s con i m ha Hype loop can deli e signi ican GDP inc eases
(mo e han 30%) unde cu en HL TRL-6 and TRL-9 pe o mance assump ions. The
GDP e ec inc ease is achie ed by simul aneously densi ying mul imodal connec ion
nodes, expanding he commu e zone, and dec easing e ec i e a el imes inside he EU’s
60 min sma ci y benchma k. The inal solu ion p esen s a amewo k (Figu e 4) ha
posi ions Hype loop wi hin he Eu opean Commission’s clima e-neu al and inno a ion-
access agendas, and i is eady o di ec use by planne s and in es o s o sma ci ies
applica ions. The s udy p o ides he i s scalable, policy-aligned oolki o quan i ying
Hype loop’s economic le e age ac oss di e se u ban con ex s and he e o e ul ils he
esea ch aim.
Limi a ions. Calcula ions we e conduc ed o a 1-yea pe iod. While he cu en s udy
elies on 2023 s a ic inpu da a o illus a i e pu poses, he amewo k was designed o
be da a-agnos ic and can be applied using highe - equency, eal- ime, o longi udinal
da ase s as hey become a ailable. Resul s o he calcula ions hea ily depend on Hype loop
speci ica ion in he co esponding TRL. In case o Hype loop echnical speci ica ion changes,
he esul migh change d ama ically, e en o nega i e alues. Resea ch did no e alua e
sys em dynamics o Hype loop p ojec s o e ime.
Fu u e s udy. The nex s ep is o apply he solu ion o he mos ele an da a om
2025 when hey a e published by he co esponding sma ci ies. Fu u e esea ch will ocus
on in eg a ing dynamic u ban da a s eams o e ine he model’s empo al sensi i i y and
be e suppo p edic i e planning ools o sma ci ies. Fu he , he au ho s aim o p opose
a de elopmen s a egy o he Eu opean Commission o Hype loop implemen a ion and
an app op ia e amewo k o he exis ing Hype loop indus y p ojec s. Fu he , i is
ecommended o conduc a case s udy o mo e nuanced implemen a ion wi h speci ic
componen s, o example, wi h o wi hou acuum o Magle , and o conside quan i ying
social ac o s and use-cases’ impac on sma ci ies de elopmen .
Supplemen a y Ma e ials: The ollowing suppo ing in o ma ion can be downloaded a h ps://
gi hub.com/pi encode/hpl
_
sma
_
ci ies, accessed on 14 June 2025. sma _ci y_gdp_model.py:
Py hon P og am o GDP calcula ion o Hype loop on sma ci ies; /da a/smc_inpu .cs : Inpu da a

Economies 2025,13, 228 16 o 20
o case s udies; /da a/sma _ci y_me ics_ou pu _ppml.cs : Resul s o GDP calcula ion o case
s udy scena ios.
Au ho Con ibu ions: Concep ualiza ion, A.V. and Y.S.; me hodology, A.V.; so wa e, A.V.; alida-
ion, A.V.; o mal analysis, A.V.; in es iga ion, A.V.; esou ces, A.V.; da a cu a ion, A.V.; w i ing—
o iginal d a p epa a ion, A.V. and Y.S.; w i ing— e iew and edi ing, A.V., Y.S., and O.Z.; isual-
iza ion, A.V.; supe ision, Y.S.; p ojec adminis a ion, O.Z. All au ho s ha e ead and ag eed o he
published e sion o he manusc ip .
Funding: This esea ch ecei ed no ex e nal unding.
Ins i u ional Re iew Boa d S a emen : No applicable.
Da a A ailabili y S a emen : The o iginal con ibu ions p esen ed in his s udy a e included in he
a icle. Fu he inqui ies can be di ec ed o he co esponding au ho (s).
Con lic s o In e es : The au ho s decla e no con lic o in e es .
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