sustainability
Article
(In)Equitable Accessibility to Sustainable Transport from
Universities in the Guadalajara Metropolitan Area, Mexico
Hugo de Alba-Martínez 1 , Alejandro L. Grindlay 2 and Gabriela Ochoa-Covarrubias 3,*
1 Department of Technological and Industrial Processes, ITESO, the Jesuit University of Guadalajara,
45604 Tlaquepaque, Mexico; hdealba@iteso.mx
2 Department of Urban and Regional Planning, University of Granada, 18071 Granada, Spain; grindlay@ugr.es
3 Department of Habitat and Urban Development, ITESO, the Jesuit University of Guadalajara,
45604 Tlaquepaque, Mexico
* Correspondence: ochoagabriela@iteso.mx; Tel.: +34-651-416-116
Abstract: The equitable accessibility to higher education favours social fairness in economic opportu-
nities. This paper provides an empirical approach to the assessment of the (in)equity of accessibility
from universities to sustainable transport modes: Light Rail Transit, Bus Rapid Transit, buses, and bi-
cycle infrastructure in the Guadalajara Metropolitan Area (Mexico). In particular, the study designed
and calculated an Access to Sustainable Transport from University Index by combining governmental
and crowdsourced Open Access Data. It used spatial analysis techniques within a Geographic
Information Systems environment, and multivariate statistical methods such as Principal Component
Analysis and Cluster Analysis. The findings highlight the weakness in the accessibility to sustainable
transport modes from the universities in the Metropolitan Area. Furthermore, this study revealed
an unfavourable bias in the location of sustainable transport stations/stops in the vicinity of public
universities. The results provide a methodology and empirical evidence for transport policy makers
to reduce inequalities and therefore transport-related social exclusion in this under-represented, but
socially relevant, student community.






Keywords: accessibility; social exclusion; university; inequity; sustainable transport; principal
Citation: de Alba-Martínez, H.; component analysis; geographic information system; crowdsourcing; open access data
Grindlay, A.L.; Ochoa-Covarrubias, G.
(In)Equitable Accessibility to Sustainable
Transport from Universities in the
Guadalajara Metropolitan Area, 1. Introduction
Mexico. Sustainability 2021, 13, 55.
https://dx.doi.org/10.3390/su13010055 Equitable accessibility to university facilities (UFs) guarantees social fairness in eco-
nomic opportunities [1]. Students suffer transport-related social exclusion when transport
Received: 10 December 2020 services are non-existent or severely restricted [2] due, for example, to the distance to
Accepted: 19 December 2020 reach them [3] or the limited multi-modal transport options [4]. According to Litman [5],
Published: 23 December 2020 equity refers to the fairness with which benefits are distributed. The spatial equity of
accessibility is the provision of consistent access throughout a geographic space. Since
Publisher’s Note: MDPI stays neu- Wachs and Kumagai [6], up to the present day, the equity of accessibility has been a subject
tral with regard to jurisdictional claims of research interest [7–10] and, more recently, public policies [11].
in published maps and institutional The literature concerning accessibility is extensive [7,8,12–18], along with empiri-
affiliations. cal reviews worldwide [19–25] and in Latin America [26–39]. Nevertheless, there is no
consensus on the definition of accessibility [15,40], mainly due to its relationship to the
multidimensionality of transport equity. In this study, accessibility was defined as the
Copyright: © 2020 by the authors. Li- possibility to reach a station/stop (henceforth ‘node’) of a sustainable transport mode from
censee MDPI, Basel, Switzerland. This UFs. Three elements are included in this definition, i.e., the number of destinations around
article is an open access article distributed the UFs; the ease to reach them by walking or cycling within a walking-time threshold; and
under the terms and conditions of the the quality of each node, as defined by the number of routes and bicycle lanes.
Creative Commons Attribution (CC BY) Access to sustainable transport mode (STM) infrastructure from universities is fun-
license (https://creativecommons.org/ damental in order to reduce the social exclusion of students in the current era of the
licenses/by/4.0/). sustainable mobility paradigm [41]. The promotion of STMs in the vicinity of UFs, e.g.,
Sustainability 2021, 13, 55. https://dx.doi.org/10.3390/su13010055 https://www.mdpi.com/journal/sustainability
Sustainability 2021, 13, 55 2 of 18
Light Rail Transit (LRT), Bus Rapid Transit (BRT), buses and bicycle infrastructure, greatly
benefits not only the students, but the city as a whole [42]. First, students need STMs
to commute to UFs, due to their limited incomes and transport choices [43,44]. Second,
the promotion of STMs contributes to shifting the future travel behaviour of current stu-
dents [45]. Third, the sustainable mobility paradigm promotes more equitable and livable
cities [41,42,46,47].
The sharing mobility paradigm, e.g., park and ride (PR) systems or bike-sharing
systems (BSS), is growing worldwide as a solution for sustainable mobility in cities as
a complete system. Macioszek and Kurek [48], and Ibrahim et al. [49] analysed the use
of PR in Cracow (Poland) and Putrajaya (Malaysia), respectively, as an option for the
improvement of accessibility to STM. In Warsaw (Poland), the BSS is an element enhanc-
ing sustainable mobility [50]. Politis et al. [51] studied the willingness to shift to BSS in
Thessaloniki (Greece). They found that the BSS promotes sustainability because they are
an active mode of transport. Moreover, in the current COVID-19 pandemic, BSS is a safe
mobility option [52]. In accordance with these global trends, the individual transport
systems—e.g., assisted bicycles, segways, and scooters—have been integrated into pub-
lic policies in the Guadalajara Metropolitan Area (GMA), and they must operate in the
city under the principles of accessibility, equity, and security [53,54]. University students’
transport-related inequities have been studied in developed countries [43,45,55–63]. Some
authors have tackled the conceptual frame [5,6,8,14,16,17], while others have improved the
concepts through empirical studies worldwide [4,60,64–67] and in Latin America [34,68].
Little scholarly research was found to have been published regarding this subject in de-
veloping countries [69–72], and very little scientific evidence was found with regard to
the (in)equity of accessibility to sustainable transport means from UFs in Latin American
cities. In particular, few quantitative studies have explored university students’ travel
needs in the Guadalajara Metropolitan Area (GMA) [73], in which the urban transport
system clearly generates social inequities [74] in one of the largest metropolitan areas in
Latin America [75].
The aim of this empirical study was to measure the inequity of accessibility to sustain-
able transport as an indicator of student transport-related social exclusion in the GMA. An
Access to Sustainable Transport from University Index (ASTUI) was calculated by measur-
ing the access, in the vicinity of UFs, to ST nodes, i.e., LRT, BRT and bicycle-sharing stations,
as well as high-quality and conventional bus stops. The ST nodes were weighted by means
of Principal Component Analysis. Thus, the ASTUI included the two main aspects of the
broader concept of accessibility, i.e., walking/cycling distance, and the number of nodes
and their quality, in terms of the quantity of the number of routes at each node.
This study provides methodological and empirical contributions. On the one hand,
the methodology includes advanced spatial and statistical analyses using crowdsourced
Open Access Data, i.e., data which was produced and reviewed by the community. On the
other hand, it also contributes to the understanding of mobility in the GMA. The findings
highlight the weakness in accessibility to sustainable transport from the universities in the
metropolitan area. Furthermore, this study reveals an unfavorable bias in the location of
sustainable transport stations/stops in the vicinity of public universities.
This paper includes five sections. After this introduction, Section Two describes
the state of the art, the study area, the methodology, and the data for the calculations of
the (in)equity of the spatial accessibility from UFs to STMs. Section Three presents the
findings of horizontal and vertical (in)equity by means of maps and charts. Next, Section
Four includes discussions. Finally, Section Five provides conclusions, implications and
recommendations for further research.
2. Materials and Methods
2.1. Study Area
The Guadalajara Metropolitan Area (GMA)—the capital of the state of Jalisco, one of
32 states of the Mexican Republic—has 4.5 million inhabitants spread over 3365 square
Sustainability 2020, 12, x FOR PEER REVIEW 3 of 19 
2. Materials and Methods 
Sustainability 2021, 13, 55 3 of 18
2.1. Study Area 
The Guadalajara Metropolitan Area (GMA)—the capital of the state of Jalisco, one of 
32 states of the Mexican Republic—has 4.5 million inhabitants spread over 3365 square 
kilometres [76] k(Filiogmureetr1e)s. [T7h6e] (GFiMguAreis 1s).e Trvheed GbMyAa  mis useltrivmeodd bayl atr manuslptiomrtosdyaslt termannspetowrto sryks.tem network. 
In accordance wIni tahccinortedrannactei ownaitlht rienntedrsn,astuiosntaail ntarebnledstr, asnussptaoirntamblee atnrasnasrpeoprtr ommeoatnesd abrey promoted by 
university authuonriitvieerssaitsya awutahyortiotireesd ausc ae wenavyi rtoon rmedeunctea leinmvpiraocntms oenntcailt iiemsp[7a1ct]s.  Tonh ucsit,itehs e[71]. Thus, the 
modes of transmpoordtecso onfs itdraenresdpoirnt tchoinsssidtuedreydw ine rtehitsh setuLdRyT ,wthereeB tRheT ,LhRigTh, -tqhue aBlRityT, bhuigsehs-,quality buses, 
conventional bcuosnevs,enantidonbailc byuclseess., aAncdc boircdyicnlgest. oAtchcoerOdifnfigc etoo tfhAe gOrfafirciea no,f TAegrrraitroiarina,l Taenrdritorial and Ur-
Urban Developbmanen Dte(vSeElDopAmTeUn)t [(7S7E]D, JAalTisUco) [i7s7a],m Jaolnisgcot hise asmtaotensgw thiteh sttahteesh wigihthes tthfee hdiegrhaelst federal and 
and local publicloincvale sptumbelnict iinnvseussttmaiennatb ilne  msuosbtialiintyabplreo jmecotbs,ilpitayr tpicruoljaercltys,i npaprutibcluiclatrrlayn isnp oprutblic transport 
(PT) and bicycle(PlTan) easn.d bicycle lanes. 
 
FigureF1ig. uTrhee 1G. Tuhaed aGlaujaadraalMajaertaro MpoeltirtoapnoAlitraena A(GrMeaA (G).MSoAu)r. cSeo:uaructeh: oarust,hboarsse, dbaosned[7 o8n– 8[17]8.–81]. 
Figures 2–4 illuFsitgruatresS 2T–M4 iilnlutshtreaGteM STAM. F inrs th, teh Ge MmAas. sFtirastn, stphoer mt saysst etrmanksnporwt nsyasstem known as 
SITEUR (UrbanSEITleEctUriRc T(UraribnaSny Estlemct,rifcr oTmraiitns iSnyitsitaelms i,n frSopman iitssh i)nciotinaslsis itns  oSfptahnreisehL) RcTonlisnisets of three LRT 
and one BRT linlien(eas saencdo nodneB BRRTTli nlieneis (ac usrerceonntdly BuRnTd leirnceo ins sctururrcetniotlny) ukndoewrn coanssTtreunctLioigne)r oknown as Tren 
and Macrobús, rLesigpeercot iavnedly M. Saeccrobnúds,, hriegshp-eqcutiavleitly.b Suesceosn, dk,n hoiwghn-aqsuSaliittryen b,uasneds,c konovwennt iaosn Salitren, and con-
buses serve thevceintyt,iowniathl b2u5s5erso suetrevse itnhteo ctiatly. ,T whiitrhd ,2t5h5e rbouictyecsl ein-s thoatariln. gThsiyrsdt,e tmhe,  kbnicoywclne-assharing system, 
MiBici, encompkansosewsn2 8a7s dMoicBkiciin, genstcaotmiopnassasensd 2a871 9d4o-ckkminbgi csytactlieonlasn aennde atw 1o9r4k-k. mTh beicoyuctler lane network. 
suburbs are alsoThser ovuedterb ysuobtuhrebrsp arirvea atleso ns-edrvemeda nbyd otrtahnesrp porritvmateo doens-dthemataanrde  btreaynosnpdortth me odes that are 
scope of this stubdey.ond the scope of this study. 
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Sustainability 2020, 12, x FOR PEER REVIEW 4 of 19 
 
Figure 2. Mass Transport System in the Guadalajara Metropolitan Area (GMA). Source: authors, based on [78–82]. 
Figure 2. Mass Transport System in the Guadalajara Metropolitan Area (GMA). Source: authors,
based on [78–82].
Sustainability 2020, 12, x FOR PEER REVIEW 5 of 19 
 
Figure 3. HFiigghu-qrueali3ty. aHndig chon-vqeuntaiolnitayl bauns dnectwoonrkv einn tthieo Gnuaaldbaluajsaran eMtewtrooproklitainn Athreea G(GuMaAd). aSloaujracre:a aMuthors, based on [78–83]. etropolitan Area (GMA).
Source: authors, based on [78–83].
Sustainability 2021, 13, 55 5 of 18
Sustainability 2020, 12, x FOR PEER REVIEW 6 of 19 
 
Figure 4F. Bigicuyrcele4 .syBsitceymcl enestywsotermk in etthwe oGrukaidnatlhajearGa uMadetarloapjaorlaitaMn eAtrroepa o(GlitManAA). rSeoau(rGceM: aAu)t.hSoorsu,r bcaes: eadu othno [r7s8,–81,84]. 
based on [78–81,84].
In contrast with the students’ transport modal split in North American universities 
In contras[t7w1]i, tshtutdhensttsu idne ontthse’ rt raengisopnosr thamvoed taol rseplyli tonin pNubolricth trAanmspeorircta onr ubniciyvcelresi -for their com-
ties [71], studenmtsutien to tuhneirvreergsitoiens [h72a]v. eFetowr meloydoanl sppulibt lsitcutdriaenss opfo urnt iovrerbsiictyc slteusdfeonrtsth inei trhe GMA were 
commute to unfiovuernsdit. iIens a[7c2co].rdFeawncem woditahl tshpel iJtaslitsucdo iGesoovferunnmiveenrts i[t8y5]s,t 2u0d.5e%nt sofi nPTth uesGerMs iAn the GMA are 
were found. Instaucdceonrdtsa, nbcuet owniltyh 8t.h9e%J ualsies cPoTG forv secrhnomoel ncotm[8m5]u, t2in0.g5 %theorfe.P MTousst esrtsudinentthse walk between 
GMA are stude1n atns,db 1u5t moninl yto8 r.9e%achu stheePirT dfeosrtinscahtioonl. cMoamnmy usttuindgentthse urese.  tMheoisr tbsictuycdlens ttso complement 
walk between 1thaenidr t1r5ipms. iHn atolf roefa tchhe tshtueidrednets tsiungatgieosnt.edM iamnpyrsotvuedmenentstsu tsoe stchheeirdubilceys calneds  tforequency. Gar-
complement thceíiar-Mtrioprsa.leHs a[l7f3o] fsthuedisetdu dae snatms spuleg goef sttherdeei munpirvoevresmitiesn tws ittoh srcehsepdecutl etos  athnedir modal split. 
frequency. GarcTíah-eM aourtahloers s[7ta3t]esdt uthdaiet dthae smamodpalel sopfltiht rveaeriuens ifvreormsi toiensew uinthivreerspiteyc tot oanthoethirer, depending 
modal split. Thme aiuntlhyo orns tahtee dactcheasstitbhielitmy otod palusbplilcit tvranrisepsofrrto amndo bniecyucnleiv learnseitsy into thaen ovtichienri,ty of the facil-
depending maiintyly, aosn wtheell acsc oenss tihbiel ittypteo opfu abdlmicitnriasntrsaptoiornt ,a in.ed., bai cpyucblelicla onre ps riinvathte ovrigcianniitsyation. 
of the facility, as welTl haseroen atrhee dtiyffpeereonfta sdomuricneis treagtiaornd,ini.eg. ,uanpivuebrsliictyo srtpatrisvtaictes. oArgccaonridsaintigo nto.  the National 
There are diIfnfesrteitnuttseo oufr cSetsartiesgtaicrsd ianngdu Gnievoegrsriatyphstya t(iIsNticEs.GAI)c c[8o6rd],i nthgetroet ahreeN 2a7t4io UnaFlsI nins ttihtuet eGMA (see Fig-
of Statistics and Gureeo g5r)a. pOhnyly(I N26E%G Io)f[ 8th6e],mth aere aprueb2l7i4clUy FfisnianntcheedG, eMvAen( stheeouFigghu rpeu5b).liOc nanlyd2 p6%rivate universi-
of them are pubtliiecsly sefirnvaen 1c6ed3,6e7v8e anntdh o8u6,g7h38p sutbuldiceanntsd, rpersipvaetcetiuvneliyv e[r8s7it]i.e Bsostehr vpeu1b6li3c, 6a7n8da pnrdivate universi-
86,738 students, rteiesps epcrtoivveildye[ 8s7e]r.vBicoeths tpou sbtluicdaenndtsp wrivitahte fiunnainvceirasilt dieisffpicrouvltidiees,s earnvdic mesotosts otuf dthene tpsrivate univer-
with financial disffiitciueslt ihesa,vaen dscmhoolsatroshf itphe pprroivgaratemumnievse rfsoirti eusnhdaevrepsrcivhiolelagresdhi pstpurdoegnrtasm, em.ge.s, fIoTrESO (Techno-
underprivilegedlosgtuicdaeln atsn,de .Hg.i,gIhTeErS SOtu(Tdeiechs nInoslotigtuictael oafn dthHe Wigheesrt fSrtoumdi eitss Iinnsittiitaultse ionf StphaenWisehs)t where almost 
from its initials ihnaSlfp oafn itshhe) swtuhdeerentasl mreocsetivhea lfionfanthceiaslt uaidde.n Atssr sehceoiwvenf ina Fnicgiaulraei d5,. tAhosushgohw thne UFs are dis-
in Figure 5, thoutrgihbuthteedU inFs tharee cdeinstraibl umteudnincipthaelictieenst roafl tmheu nGiMcipAa,l itiheesroe fisth ae cGoMncAen, trhaetrieonis of public UFs 
a concentration of public UFs in the central area, and private ones are located mainly in the
Sustainability 2021, 13, 55 6 of 18
Sustainability 2020, 12, x FOR PEER REVIEW 7 of 19 
in the central area, and private ones are located mainly in the municipalities of Guadala-
municipalities ojfaGrau aanddal aZjaarpaoapnadn.Z Tahpeo pmaind.-Tsohuetmh iodf- stohue tchitoyf itsh esecrivtyedis bseyr voendlyb ay foenwly paufbelwic UFs, and the 
public UFs, andsthoeutsho,u ethas, te aasntda nndorntohr othf othfeth GeMGAM Ahahvaev feefwew UUFsF.s .
 
Figure 5. FUingiuvreers5i.tyU fnaicvileirtiseisty (UfaFcsi)l iitnie tsh(eU GFus)aidnaltahjearGa uMadetarloapjaorlaitaMne Atrroepao (lGitManAA).r Seoau(rGcMe: Aau)t.hSorusr, cbea:saeudt ohno r[s7,8–81,86]. 
based on [78–81,86].
2.2. Methodology 
2.2. Methodology Transport-related (in)equity is usually measured by means of accessibility [3]. Sev-
Transport-reerlaalt emde(tihno)edqsu oift yacisceusssuibaillliytym aesasesussrmedenbty wmeeraen ids eonftaifciceeds sinib itlhitey li[t3e]r.aStuevree.r aMl ost of the au-
methods of accetshsoibrisl irteyfaesr steos sfomuern atpwperroeaicdheenst,i fii.eed., pinrothxiemliitteyr-abtuasree.dM [5o,4st2]o,f atlhseo arueftehrorresdr etofe ars cumulative-
to four approacbhaesse, di. e[.3,4p,8ro8x];i mgriatyv-ibtya-sbeadse[5d,;4 u2]s,earl-sboasreedfe;r arendd tsopaacsec–utimuel aatpivper-obaacsheeds [[324],.8 I8n] ;addition, Sun 
gravity-based; ueste ar-l.b [a7s2e]d r;eafnedr tsop “accoe–mtipmeetitaiponp-rboaascehde”s [a2s]s.eIsnsmadednitt iaotn t,rSaunnspeotratl .n[o7d2]esre. fFeorrt ofurther discus-
“competition-basseiodn” aosfs aepsspmroenacthaetst rtaon sapcocertssniobdileitsy.  Fmoreafusurtrheemr denistc, ursesaiodnerosf aarpep rreofaecrhreesdt otoa, ca-mong others, 
cessibility measuthreemsee naut,trheoardse [r9s,a2r4e,2r5e,f6e8rr,8e8d–t9o4,]a. mong others, these authors [9,24,25,68,88–94].
In this study, thIen nthoidse sst,urdoyu,t tehse,  annoddebsi,c ryoculetelsa,n aensdw beirceyccloeu lnanteeds awseSreT Mcosu.nTthede AasS STTUMI s. The ASTUI 
was calculatedwbays tchaelcuplraotxeidm biyty thaen pdrocxoimpiteyt iatniodn c-bomaspedetimtioenth-boadsse.d Tmheetsheodmse. Tthhoedses mnoetthods not only 
only measure thmeetaostuarlen tuhme tboetar lo nfutrmanbespr oorf ttrnaondsepsoratv naoildaebsl eavinaialanbalere ian wanit ahrienaa wdiethsiinra ab ldeesirable walk-
walking or cycliinngg otri mcyecl[i4n,g5, 4ti2m],eb [u4,t5a,4ls2o], ebsutti malastoe etshtieminattee nthsiet yinotefnospitpyo orft uonpiptioerstuonritthieesi ror their attrac-
attractiveness [t1i5v]e.nTehsse [m15o]r. eThroeu mteosrteh reoruetaerse thaetrae satroep a,tt ah estmopo,r tehae tmtraocreti vatetriat cisti.vTeh itu iss,. tThheus, the ASTUI 
ASTUI was assewsasse dasbsyescsaeldcu blayt icnaglctuhleatninugm tbheer nofusmubsetar inoaf bsluesttraainnaspbloer ttrnaondspeso,rat snwodelelsa, sas well as the 
the number of rnouumtebseorr obf ircoyuctleesl aonr ebsicayvcaliel alabnleesin avthaeilaabrelea ionf tihnefl aureenac oefo ifneflaucehnUceF o[f5 e,4a2c]h. UF [5,42]. 
Figure 6 illustraFtiegsurthe e6 ciollnucsetrpattuesa lthmeo cdoenlcoefptthuealA mSoTdUeIl. oFfi rtshte,  tAhSeTsUerIv. iFciersatr, etahse wseerrveice areas were 
calculated fromctahlecu2l7a4teUdF sfrtohmro uthgeh 2th7e4 sUtrFese ttnhertowugorhk ,thweh sictrheiest anbeetwtteorrpk,r ewdhicitcohr oisf ha ubmetatner predictor of 
behavior than thhuemuasne obfehEauvciloidr itahnand itshtea nucsee aolfo Enuec[l9id5i]a.nT dheisnta, nthcee aalcocnuem [u95la].t iTohneonf, tnhoed aecscumulation of 
SSuussttaaiinnaabbiilliittyy 22002210,, 1132,, 5x5 FOR PEER REVIEW 87 of 1189 
nodes and routes for each mode and service area was spatially joined through a Model-
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mwoerdee pl wroipthoisneda,G inetoegrrparpehteidc  Iannfdor smelaetciotendS. yTshtiesm. Next, these variables were examined in aPrincipal Component Analysis (PCA), from w hmicehthpordin isc iupsaeldc obmy pthoen Nenattsiownearle Cporuonpcoils efodr, 
itnhtee rEpvraeltuedataionnd osfe lSeoccteiadl. DTehvisemlopetmhoednti sPuosliecdy b[9y6t]h. eFiNnaatliloyn, tahl eC AouSnTcUilIfso wr tehree Ecavlacluulaattieodn boyf  
SmoecaianlsD oefv weleoipgmhteendt vPaorliiacbyle[9s6 f]r.oFmin tahlley ,PtCheAA. STUIs were calculated by means of weighted
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an saolyftswisa. rFei rwsta,st heme spelrovyiecde  aforer aths ea nPdrinthceipnaul Cmobmerpoof-
SnTenMt iAnnfraalsytsruisc taunrdes twhee rcelucsotmerp aunteadlybsyis.m Feiarsnts, othf eN seetwrvoicrek Aarneaalsy sainsd. T thheen n, tuhmebweeri gohf tSinTgMs  
oinffvraasrtiraubclteusrwese wreecroe mcopmutpeudtewdi tbhy SmtaetaGnrsa pofh iNcse®tw. oFrinka Allyn,atlhyseisin. Teqhuenit,i etshew wereeigahntainlygsse dof, 
variables were computed with StatGraphics®through clusters. . Finally, the inequities were analysed, 
through clusters. 
Table 1. Data sources.
Table 1. Data sources. 
Data. Source
Data. Street Network SoOurpceen StreetMap, October 2020 [98]
University Facilities National Statistical Directory of Economic Units [86]
Street Network OpenStreetLMigahpt, ROacitloSbyesrt e2m020 [98]  Mass Transport * and High-quality Buses (SITEUR from its initials inUniversity Facilities National Statistical Directory of EScopnaonmishic) U[9n9i–t1s0 [18]6]  
Mass Transport * and High-qualiCtyo nBvuesnesti onLailgBhuts Resail System (SITEUR froMme ittrso pinoiltiitaalns iPnl aSnpnainigshI)n s[9ti9t–u1te01[]8 3]
Conventional Buicsyecsl e-sharing Stations and LanMesetropolitan PlaMnnetirnogp IonlisttaitnuPtel a[n83n]i n g Institute [84]
Bicycle-sharing Stations a*nMd aLssanTreasn sport data included liMnees,trsotaptioolnitsa, nst oPplsa,nfnreiqnuge nIncystaitnudtev e[h8i4c]l e capacity.
* Mass Transport data included lines, stations, stops, frequency and vehicle capacity. 
The calculation of the ASTUIs required the specification of the time threshold that
the stTuhdee ncatslcaurleatwioinll ionfg thtoe wAaSlTkUoIrs cryecqlue,iraendd ththee saptetcriafcictiavteionne sosf otfhea tcimh ne othdreeashnodldro tuhtaet. 
Dthees pstiutedtehnets eanrsei twiviiltlyinagn tdo iwmaplokr toarn cyecolef,t hanisdc rthite raiottnra, tchtievreniessas loafc keaocfhc onnosdeen asunsd ornouthte. 
dDiestsapnitce tthea tsepnesoiptilveiatyre awndil liimngptoortwanaclke toof rtehaisc hcrpituebrliiocnt,r tahnesrpeo irst ain laaccko omf pcolenxsceonnsutesx ot nsu tchhe 
adsistthaencGeM thAat[ 1p8e].oTpolet haerea uwtihlloirnsg’ btoes wt kanlko wtol erdegaec,ht hpeurbeliisc ntroasntsapnodrat ridn oaf acoccmespsliebxi lictoynttheaxtt 
SuSsutsatianianbaibliitlyity2 022012,01, 31,25, 5x FOR PEER REVIEW 9 o8fo 1f91 8
such as the GMA [18]. To the authors’ best knowledge, there is no standard of accessibility 
itshbart oisa dblryoaadcclye patcecdepatnedd uansedd uisnedu ribna unrabnadn atrnadn strpaonrstppolratn pnlianngn. iSnign.c Seinthcee Tthrea nTsriatnOsriti eOnrtie-d
Denetveedl oDpemveelnotp(mTOenDt )(TpOarDad) ipgamradhiagsma lhreaasd aylrbeaedeny abpeepnli eadppinliesdo mine saormeaes aorefatsh eofc itthye [c1i0ty2 ],
t[h1i0s2s],t uthdiys ustsueddyt huesetidm teheth triemseh othldretshhaotldw athsadt ewfinase ddemfionsetdo mfteonstb oyfttehne bTyO Dthea nTdOaDlr aenaddy 
aaplrpelaidedy ianppoltiheedr irne gotiohners r[e4g2i]o. nAs s[4s2h]o. wAsn sihnoTwanb lien 2T,atbhlee A2,S tThUe IAsSiTnUclIusd iendcluthdeedfu tlhfiel mfueln- t
ofiflmtheenTt OofD thseta TnOdaDr dstfaonrdtahredd fiosrt atnhcee dfirsotmancuen firvoemrs iutineisvteorstihtieesS TtoM tsh,ei .SeT.,Mfifst,e ie.en.,m fiifnteuetne s
wmailnkuintegs fwoarlmkiansgs ftorra mnsapsosr ttr,asnuscphorats, sLuRcTh aans dLRBTR Tan; fid vBeRmT;i nfiuvtee ms winaultkeisn wgafolkrinhgig fho-rq huiaglhit-y
aqnudalciotyn vaenndt icoonnavlebnutsioens;aal nbdustweso; amnidn utwteos cmycinliuntgesf ocryMcliinBgic ifosrta MtioiBniscia nstdatciyocnlse alannde sc.ycle 
lanes. 
Table 2. MaximTuabmle ti2m. eM tahxreimshuomldsti bmye mthordees hoof ltdrsanbsypmorot.d Seooufrctrea: nasupthoortr.sS, aoduarcpet:edau ftrhoomr s[,1a8d,4a2p].t ed from [18,42].
Sustainable TranspoSrut s tainSaubslteaTinraanblsep oTrrtanspSourstt Mainoadbe lIenTfrraasn-sporWt Malkoidneg or WCyalckliinngg ToirmCey TclhinregshToimlde  Threshold
Mode  Mode tructure (STIMnf) ra structure (STM) (Minutes) (Minutes)
LRT * LRT * Station Station 15 15
BRT * BRT * Station Station 15 15
High-quality Bus * High-quality Bus * Stop  Stop 5 5
Conventional Bus ConventionalSBtoupss and RouteSsto *p* s and Routes ** 5 5
Bicycle BicySctlaetions and BicSytaclteio Lnasnaensd **B icycle Lanes ** 2 2
* SITEUR: Tren Ligero, Macrob*úSsIT aEnUdR S: iTtrreennL. i*g*e rToh, Me accornobvúesnatniodnSaitlr benu.s*e*sT ahnedco tnhvee bntiicoyncalleb suhsaersianngd stthaetiboincysc aleres htahrein ognsltya tmionosdes 
with more than one route/lanaere atth tehoen slytamtioodness/swtoitphsm. ore than one route/lane at the stations/stops.
Fiigure  7  iillllustrates the numericall modell fforr tthee ASSTUII ccaallccuullaattioionn. .ItI tininccluluddees sththrere e
ppaarrttiiccullarities of the ssttuuddyy aarreeaa. .FFirisrts,t g, igvievne nthtahta ptepoepolpe laerea rweiwlliinllgin tog wtoawlka ulkp utop 5t om5inm toin 
troearceha cbhubseuss, etsh,etyh weyouwldou alldsoa wlsaolkw tahliks tdhisistadnicset atno creatcohr aea bcihcyaclbei-csyhcalrein-sgh satraitniogns taot cionmt-o
cpolmetep ltehte jtohuernjoeuyr nbeyy cybcylicnygc.l iTnhge.rTefhoerree, ftohree ,MthiBeiMci isBtaictiosntas twioenrse wcoeurentceodu wntietdhiwn itthhisin sethr-is
sveircvei acreear. eSae.cSoencdo, nbdic,ybcilceys calrees aalrloewaleldo wone dLRoTn aLnRdT thane dhitghhe-qhuigahli-tqy ubaulsiteys,b i.ues.,e Ssi,tir.een.,. CSiotrne-n.
Cseoqnusenqtulyen, tlhye,steh etwseot wtyopetys poefs sotaftsiotantsio annsda nstdopstso wpserwe ecroemcopmutpedut feodr ftohret sheervseicrev iacreear oeaf 2o f
2mmini ncycyclcilnign.g T. hTihsi sisi snonto ththe ecacsaes efofor rBBRRTT oor rccoonnvveenntitoionnala lbbuusesse.s .TThhiridrd, t,hthereer eisi soonnlyly oonne e
rroouutteea attt hteheL RLTR,TB,R BTRaTn danSidtr eSnitsrteant isotnasti.oTnhsu. sT,hthues,r othuete rsofuortetsh efsoer mthoedses mwoedreesn owt ecoreu ntoetd ,
icnouorndtedr ,t oina ovrodiedr btoia asvesoiidn bthiaesePsC iAn .thIne cPoCnAtr. aIsnt ,ctohnetreasits, uthseurael liys umsouraelltyh manoroen tehraonu otenea t
trhoeucteo navt ethneti oconnalvbenutsiosntoapl sbuans dstmoposr eanthda mn ornee tchyacnle olnaen ecyactleth leanMe iBati ctihset aMtioiBnisci. Tsthautiso,ntsh. e
rTohuutess, tahned rocuytcelse alannde csywcler leancoesu wnterde fcorubnotetdh fmoro bdoetsh.  Bmaosdeeds.o Bnatsheeds eonp athrteisceu plaarrittiiceusl,atrh-e
siptiaetsi,a tlhaen saplyastiiaslf aonratlhyesisse frovri ctehea rseearsviacned artheaesn aunmd bthere onfuSmTbMesr owfe SrTeMcosm wpeurtee cdobmypmuteeadn bsyo f
Nmeetawnosr okf ANneatwlyosrt,kw Ainthaltyhset,M woitdhe tlBheu iMldoedreflrBoumildEesri f®r.om Esri®. 
 
Figure 7. Access to Sustainable Transport from University Index (ASTUI) Numerical Model. Source: the authors.
Sustainability 2021, 13, 55 9 of 18
The Principal Component Analysis (PCA) describes the correlations between the STM
infrastructure variables referred to in Table 3 by creating new components that propose
the weights of the original variables for the 274 UFs [91,96,103–106]. The purpose of the
PCA is to obtain a small number of linear combinations of the 12 variables that account
for most of the variability in the data. Each component can be interpreted as one part of
the accessibility phenomenon. A negative value means that this variable negatively affects
accessibility. Three components were retained, since their eigenvalues were greater than or
equal to 1.0, as shown in Table 4. Together, they account for 72.4925% of the variability in
the original data.
Table 3. Input variables of the service area and the Principal Component Analysis. Source: the authors.
STM id Sustainable Transport Mode t = 15 t = 5 t = 2
[STid] Infrastructures [Walking Minutes] [Walking Minutes] [Cycling Minutes]
A Number of LRT Stations in F15_LRT
B Service Area t (Tren Ligero) F5_LRT
C B2_LRT
D Number of BRT Stations in F15_BRT
E Service Area t (Macrobús) F5_BRT
F Number of High-quality Bus F5_HQB
G Stops in Service Area t (Sitren) B2_HQB
H Number of Conventional BusStops in Service Area t F5_CNBs
I Number of Conventional BusRoutes in Service Area t F5_CNBr
J Number of MiBici Stations in F5_ BYKs
K Service Area t B2_ BYKs
L Number of Bicycle Lanes inService Area t B2_BYKl
Table 4. Principal Component Analysis Components. Source: the authors.
Component. Variance Cumulative
Number Eigenvalue [%] Variance[%]
1 5.19051 43.254 43.254
2 2.21364 18.447 61.701
3 1.29495 10.791 72.493
4 0.966089 8.051 80.543
5 0.7064 5.887 86.430
6 0.404834 3.374 89.804
7 0.319993 2.667 92.470
8 0.288061 2.401 94.871
9 0.240288 2.002 96.873
10 0.218669 1.822 98.695
11 0.0839818 0.700 99.395
12 0.072579 0.605 100.000
The factorability tests provide an indication of whether or not it is likely to be worth-
while to attempt to extract factors from a set of variables. The Kaiser-Meyer-Olkin (KMO)
Measure of Sampling Adequacy statistic delivers an indication of how much common
variance is present. For the factorisation to be worthwhile, the KMO should normally
be at least 0.6. Since the KMO = 0.786828, factorisation is likely to provide meaningful
information about any underlying factors.
The three components that were retained were interpreted by the variable weights
shown in Table 5. They represent the relationship between the original variables and
Sustainability 2021, 13, 55 10 of 18
the principal components. Component 1, named the ‘Multimodal Transport System’,
represents all of the modes of transport retained in the study with less importance than
the BRT, as the values of its variables are smaller than the others. Component 2, named
the ‘Mass Transport System’, favours mass modes of transport, i.e., the Tren Ligero and the
Macrobús, and gives negative or very low values to modes linked to bicycles and buses,
both Sitren and conventional. Component 3, named the ‘Bus Rapid Transit’ component,
prioritises this mode, and thus it complements Component 1.
Table 5. Component Weights. Source: the authors.
Component Weight ID
[id] Variable Component_1 Component_2 Component_3
WA F15_LRT 0.318819 0.296047 −0.197854
WB F5_LRT 0.225533 0.302996 −0.383931
WC B2_LRT 0.288487 0.224921 −0.386717
WD F15_BRT 0.135682 0.443007 0.488484
WE F5_BRT 0.047106 0.311531 0.576449
WF F5_HQB 0.255624 −0.380555 0.174918
WG B2_HQB 0.283082 −0.378544 0.155742
WH F5_CNBS 0.352379 0.0976065 0.0679395
WI F5_CNBR 0.329129 0.225656 0.00105742
WJ F5_ BYKS 0.361178 −0.140043 0.106823
WK B2_BYKS 0.378923 −0.207403 0.116437
WL B2_BYKL 0.3027 −0.255853 −0.0924796
Equation (1) shows an example of the first principal component, where the values of
the variables in the equation must be standardised by subtracting their means and dividing
by their standard deviations.
The three components were calculated by means of Equation (2).
Component 1 = 0.318819 * F15_LRTStat + 0.225533 * F5_LRTStat + 0.288487 * B2_LRTStat + 0.135682 *
F15_BRTStat + 0.047106 * F5_BRTStat + 0.255624 * F5_HQBStop + 0.283082 * B2_HQBStop + 0.352379 *
(1)
F5_CVBStop + 0.329129 * F5_CVBRout + 0.361178 * F5_ BYKStat + 0.378923 * B2_BYKStat + 0.3027 *
B2_BYKLane
Component_n = A * WAn + B * WBn + C * WCn + D * WDn + E * WEn + F * WFn + G * WGn + H * WHn + I * (2)
WIn + J * WJ n+ K * WKn + L * WLn
where n = the number of components from Table 4, i.e., {1, 2, 3}.
A to K = the variables listed in Table 2, standardised by subtracting their means and
dividing by their standard deviations.
WAn to WLn = the weight of each variable A–L at component n, as shown in Table 4.
An average of the three components was retained for the ASTUI calculation at UF i,
as shown in Equation (3).
ASTUI i = [(Eigenvalue_1 * Component_1) + (Eigenvalue_2 * Component_2) + (Eigenvalue_3 *
(3)
Component_3)]/[Eigenvalue_1 + Eigenvalue_2 + Eigenvalue_3]
Recent literature refers to statistics for the measurement ofthe (in)equity of spatial
accessibility [2,34,72], particularly cluster analyses [24,96]. These computations served to
identify homogeneous areas, reducing the number of the 274 UFs by the classification of 5
strata, thus simplifying the data interpretation of the (in)equity of the accessibility to STMs
from UFs.
The clustering method was Ward’s, with the squared Euclidean distance metric and
non-standardised observations. According to the StatGraphics® report, the procedure
began with each observation in a separate group. It then combined the two observations
that were closest together to form a new group. After recomputing the distance between the
groups, the two groups that were now closest together were combined. This process was
Sustainability 2021, 13, 55 11 of 18
repeated until only 5 groups remained. The computations served to identify homogeneous
areas, reducing the number of the 274 UFs by the classification of the limited number of
strata shown in Table 6. Then, the vertical equity of the spatial accessibility was calculated
by statistical comparisons of the ASTUIs at private/public universities.
Table 6. Cluster analysis results. Source: the authors.
Strata Centroid of ASTUI Cluster Members Percent [%]
Very Low −1.26353 2 105 38.32
Low −0.349644 4 62 22.63
Medium 0.686179 3 60 21.90
High 1.54135 1 25 9.12
Very High 3.39289 5 22 8.03
3. Results and Discussion
According to Litman [5] and recent literature [2,47,107], transport equity can be hori-
zontal or vertical. Horizontal equity assumes that everyone has the same right to access
basic goods, or that the group has equal abilities and needs. Since the accessibility of UFs is
recognised as a basic good [108], horizontal equity means that the STMs do not favour any
UF. Every student should have equitable access to sustainable transport means from their
UF in the current era of the Sustainable Development Goals.
Figure 8 shows the ASTUI spatial pattern for the analysis of horizontal (in)equity.
Even though the public UFs seem to be ‘near’ SITEUR, this study ascertained that the
vicinity is not within the standards recommended in the ASTUI. The figure also shows
that almost half of the UFs have Low and Very Low accessibility, while less than 10%
of the UFs meet the standard of the Transport Oriented Development paradigm. This is
empirical evidence of the transport-related social exclusion of university students, who
have traditionally been under-represented in public transport policies.
Sustainability 2020, 12, x FOR PEER REVIEW 13 of 19 
 
Figure 8. Access toFigSuures 8ta. Ainccaesbsl teo STursataninsapbloe rTtrafnrsport from University Index (ASTUI) in the Guadalajara Metropolitan Area (GMA). Source: the authors, based on [78–81]o. m University Index (ASTUI) in the Guadalajara Metropolitan Area (GMA).
Source: the authors, based on [78–81].
 
Figure 9. Inequity distribution of the Access to Sustainable Transport from University Index (ASTUI) in the Guadalajara 
Metropolitan Area (GMA). Source: the authors. 
Sustainability 2020, 12, x FOR PEER REVIEW 13 of 19 
Sustainability 2021, 13, 55 12 of 18
Vertical equity refers to social and/or income class [5,91]. It suggests that the distri-
bution of STMs should favour a specific group of students. The ASTUIs were compared
across the public and private UFs. An egalitarian accessibility policy should ensure that
the location of the STMs does not negatively impact students attending universities with
public funds. Other factors, such as the number of students and their gender, are relevant
to transport equity analyses. However, only funding sources were financial factors were
computed in this project, given the data availability.
According to the calculation of the ASTUIs and their classification by clusters, most
of the university infrastructure has low and very low ASTUI values, i.e., 62.0% and 42.3%
for public and private infrastructure, respectively. Nevertheless, as shown in Figure 9,
there is no spatial pattern of inequity between public and private infrastructure. Although
more private infrastructure is observed in the three most disadvantaged strata, the relative
difference between them is small, i.e., 5%, 8%, and 3% for ‘Very Low’, ‘Low’ and ‘Medium’,
respectively. There are 14 percentage points of difference in the ‘High’ stratum that favours
public infrastructure. In the ‘Very High’ stratum, private infrastructure is favoured over
Figure 8. Access to Sustainable Transport from University Index (ASTUI) in the Guadalajara Metropolitan Area (GMA). 
Source: the authors, based otnh e[7p8–u8b1l]i.c infrastructure by 9 percentage points.
 
FFiigguurree 99.. IInneeqquuiittyy ddiissttrriibbuuttiioonn ooff tthhee AAcccceessss ttoo SSuussttaaiinnaabbllee TTrraannssppoorrtt ffrroomm UUnniivveerrssiittyy IInnddeexx ((AASSTTUUII)) iinn tthhee GGuuaaddaallaajjaarraa 
MMeettrrooppoolliittaann AArreeaa ((GGMMAA)).. SSoouurrccee:: tthhee aauutthhoorrss.. 
According to Guthrie et al. [4], the results highlight that university students in the
GMA are under-represented in transport planning. In spite of the investments in the
SITEUR and the governmental subsidies for students to use public transport [77] and
MiBici, this study revealed that transport-related social exclusion persists within this
disadvantaged group. Several reasons were identified that discourage the use of sustainable
transport by university students. First, despite the extensive area served by conventional
buses, they offer a low-quality service [109]. Second, most of the UFs are further away than
a 2-min bicycle ride from the MiBici stations and bicycle lanes. Third, notwithstanding the
high-quality service offered by SITEUR [38], the LRT, BRT and Sitren cover only a small
proportion of the GMA.
The public transport system is considered to be a promoter of social inclusion. This
is more relevant in areas that are traditionally characterised by high levels of structural
inequalities, such as the GMA. This research identified low access levels for the university
student population, which means that the study detected transport-related social exclu-
sion [110]. The authors propose general actions to enhance the equity of opportunities not
just for university students but also for other groups with transport-related disadvantages.
Sustainability 2021, 13, 55 13 of 18
Guaranteeing the quality of the conventional buses and building a safe, interconnected, and
larger bicycle lane network will reduce the transport disadvantages of university students.
The researchers also propose the incorporation of university students and authorities into
the decision-making process for transport planning and policies, in order to reduce the
accessibility disparities experienced by this under-represented, but socially relevant [111],
community.
4. Conclusions
According to this research, the promotion of sustainable transport modes in the vicinity
of UFs is not supported by the GMA’s public transport policies, nor is the intention to
modify the future travel behaviour of current students. Indeed, the GMA is very far from
the paradigm of a more equitable and livable city when it comes to sustainable mobility in
the vicinity of UF. As expected, university students in the GMA suffer transport-related
social exclusion when they access LRT, BRT, buses, and bicycles. It affects all students,
since the ASTUI is similar in the public and private UI, except for the average values of the
index, where the public UI is favoured.
The analysis does not fully confirm the inequity between private and public UFs,
and a deeper analysis must be carried out in future research, since this study has some
limitations. First, the number of students at each UF was not available in Open Data
sources, and thus the analysis did not distinguish according to the ‘size’ of each UF. Second,
the results may be significantly different, as the service areas vary. Third, the ASTUIs did
not include walking or travel impedances, such as transport fees, topography, security,
or other urban quality indicators. Fourth, (in)equity is difficult to measure due to the
subjective (individual) nature of accessibility. Therefore, the results of this research are
plausible, but not yet completely established.
Although this paper represents a significant advancement of the prior work in the
GMA, there is still room for improvement. Future tasks that could enhance this research
include:
• Considering the topography and urban environment as elements of effective mobility
for the measurement of hindrances affecting the walking time to the stops/stations.
• Computing a sensitivity analysis for different service areas.
• Including the BRT line currently under construction. According to Jalisco [112], this
line will serve more than 49,000 students.
• Considering income, gender, physical ability, or other vulnerabilities in the student
population.
• Accounting for the impact of transport fees in the model.
• Including data from smartcards and General Transit Feed Specifications as soon as
they are available.
• Transferring the proposed methodology to the calculation of indices for other basic
goods, such as health, cultural, or basic educational infrastructure.
It is necessary to shift the mobility paradigm, taking advantage of the adaptation
inertia in the post-COVID era. This study proposed a simple and powerful approach to
improve the practice of transport planning and policies. It is easy to understand, interpret
and replicate for policy makers in metropolitan areas similar to the GMA who seek to
reduce inequality in the form of transport-related social exclusion.
Author Contributions: Conceptualization, H.d.A.-M. and G.O.-C.; Data curation, H.d.A.-M. and
G.O.-C.; Formal analysis, H.d.A.-M. and G.O.-C.; Funding acquisition, H.d.A.-M. and A.L.G.; Inves-
tigation G.O.-C.; Methodology, H.d.A.-M. and G.O.-C.; Project administration, G.O.-C.; Resources,
H.d.A.-M. and A.L.G.; Supervision, H.d.A.-M. and A.L.G.; Validation, H.d.A.-M. A.L.G. and G.O.-C.;
Visualization, H.d.A.-M. and G.O.-C.; Writing—original draft, G.O.-C.; Writing—review and editing,
H.d.A.-M. and A.L.G. All authors have read and agreed to the published version of the manuscript.
Funding: This research was partially supported by CONACyT (México), the Alliance for Training and
Research in Infrastructure for Development of Mexico, A.C. (FIIDEM). This research project also ben-
Sustainability 2021, 13, 55 14 of 18
efited from ITESO, the Jesuit University of Guadalajara, for Research Support Program scholarships
for Ivana Georgina Kroepfly Cota and the SUPA program for Gabriela Ochoa-Covarrubias.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable
Data Availability Statement: The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to repository administrative procedures.
They will be published as soon as possible.
Acknowledgments: We gratefully thank Ivana Georgina Kroepfly Cota, Juan Pablo Jiménez González
and Orlando Andrade Barraza for the provision of the bicycle and conventional bus data. We also
acknowledge Associate Professor Emilio Molero Melgarejo (University of Granada) and the reviewers
for their contribution of professional guidance and suggestions.
Conflicts of Interest: The authors declare no conflict of interest.
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