An Intelligent Visualisation Tool to Analyse the Sustainability of Road Transportation

  Ci a ion: Alonso de A miño, C.; U da, D.; Alcalde, R.; Ga cía, S.; He e o, Á. An In elligen Visualisa ion Tool o Analyse he Sus ainabili y o Road T anspo a ion. Sus ainabili y 2022,14, 777. h ps:// doi.o g/10.3390/su14020777 Academic Edi o : Ma c A. Rosen Recei ed: 7 Oc obe 2021 Accep ed: 7 Janua y 2022 Published: 11 Janua y 2022 Publishe ’s No e: MDPI s ays neu al wi h ega d o ju isdic ional claims in published maps and ins i u ional a il- ia ions. Copy igh : © 2022 by he au ho s. Licensee MDPI, Basel, Swi ze land. This a icle is an open access a icle dis ibu ed unde he e ms and condi ions o he C ea i e Commons A ibu ion (CC BY) license (h ps:// c ea i ecommons.o g/licenses/by/ 4.0/). sus ainabili y A icle An In elligen Visualisa ion Tool o Analyse he Sus ainabili y o Road T anspo a ion Ca los Alonso de A miño 1, Daniel U da 2,* , Robe o Alcalde 3, San iago Ga cía1and Ál a o He e o 2 1Depa amen o de Ingenie ía de O ganización, Escuela Poli écnica Supe io , Uni e sidad de Bu gos, A . Can ab ia S/N, 09006 Bu gos, Spain; [email p o ec ed] (C.A.d.A.); [email p o ec ed] (S.G.) 2G upo de In eligencia Compu acional Aplicada-GICAP, Depa amen o de Ingenie ía In o má ica, Escuela Poli écnica Supe io , Uni e sidad de Bu gos, A . Can ab ia S/N, 09006 Bu gos, Spain; [email p o ec ed] 3Depa amen o de Economía y Adminis ación de Emp esas, Facul ad de Ciencias Económicas y Emp esa iales, Uni e sidad de Bu gos, Pza. de la In an a Dª. Elena, S/N, 09001 Bu gos, Spain; [email p o ec ed] *Co espondence: du [email p o ec ed] Abs ac : Road anspo is an in eg al pa o economic ac i i y and is he e o e essen ial o i s de elopmen . On he downside, i accoun s o 30% o he wo ld’s GHG emissions, almos a hi d o which co espond o he anspo o eigh in hea y goods ehicles by oad. Addi ionally, means o anspo a e s ill e ol ing echnically and a e subjec o e e mo e demanding egula ions, which aim o educe hei emissions. In o de o analyse he sus ainabili y o his ac i i y, his s udy p oposes he applica ion o no el A i icial In elligence echniques (mo e speci ically, Machine Lea ning). In his esea ch, he use o Hyb id Unsupe ised Explo a o y Plo s is b oadened wi h new Explo a o y P ojec ion Pu sui echniques. These, oge he wi h clus e ing echniques, o m an in elligen isualisa ion ool ha allows knowledge o be ob ained om a p e iously unknown da ase . The p oposal is es ed wi h a la ge da ase om he o icial su ey o oad anspo in Spain, which was conduc ed o e a pe iod o 7 yea s. The esul s ob ained a e in e es ing and p o ide encou aging e idence o he use o his ool as a means o in elligen analysis on he subjec o de elopmen s in he sus ainabili y o oad anspo a ion. Keywo ds: a i icial in elligence; unsupe ised machine lea ning; explo a o y p ojec ion pu sui ; clus e ing; oad anspo a ion; anspo sus ainabili y; age o anspo means 1. In oduc ion & P e ious Wo k The 17 Sus ainable De elopmen Goals o he UN’s mas e plan include a de e mined line o ac ion o hal global wa ming, wi h a di ec link o educing CO 2 emissions. The Uni ed Na ions F amewo k Con en ion on Clima e Change (UNFCCC) and he Kyo o P o ocol, bo h signed in 2002, poin in he same di ec ion. Mo eo e , oad anspo is consubs an ial o economic ac i i y and, hus, essen ial o he u u e o ou ci ilisa ion. I s coun e pa accoun ed o 30% [ 1 ] o global CO 2 emissions in 2015, and 29% o he 314,529 kilo- onnes o GHG emi ed in 2019 in Spain, wi h a o al o 91,372 kilo- onnes o CO 2 -equi alen emissions. In u n, i is es ima ed a a global and an EU le el ha nea ly a qua e o hese emissions co espond o eigh anspo a ion in hea y goods ehicles and buses [ 2 , 3 ], wi h his being one o he a eas ha has eco ded sus ained g ow h in ecen yea s. Meanwhile, o he modes o anspo a e p og essi ely educing hei sha e, wi h an es ima ed inc ease o ou imes hei cu en emissions by 2050 i no measu es a e adop ed on he ma e . In iew o hese da a and o ecas s, pa o he ocus o scien i ic s udies and lines o ac ion o go e nance ha e cen ed on his a ea, wi h p og ess being made on he de elopmen o p oposals o egula e emissions o he equipmen ha pe o ms his ac i i y, which clea ly accoun s o 6% o he Eu opean Union’s o al GHG emissions [4]. Sus ainabili y 2022,14, 777. h ps://doi.o g/10.3390/su14020777 h ps://www.mdpi.com/jou nal/sus ainabili y Sus ainabili y 2022,14, 777 2 o 15 1.1. Sus ainabili y in T anspo a ion The sys ema ic e iew o in e en ion sys ems o he sus ainabili y o oad eigh anspo e ealed ha he mos ob ious line o ac ion is he applica ion o eme ging ech- nologies combined wi h decisi e ene gy policies [ 5 ]. O he complemen a y lines o ac ion ocus on he dedica ion and adap a ion o in as uc u es o he e ec i e de elopmen o his ac i i y and he p omo ion o in e modali y in he e icien managemen o supply chains [ 6 ]. In many cases, hese a e cen ed on a i m commi men o synch o-modali y [ 7 ] and a e e en inclined o measu e he sus ainabili y o anspo on he basis o i s in e - modali y [ 8 ]. A dis inc ion is also made be ween he e ec i eness o di e en measu es in a ou o sus ainabili y in collec ion and deli e y anspo , wi h imp o ed applicabili y o collabo a i e echniques suppo ed by in o ma ion sys ems, as opposed o long-dis ance anspo , whe e he esul s o echnological op imisa ion in e ms o anspo equipmen consump ion and he sui abili y o in as uc u es a e mo e con incing [ 9 ]. Some models, e lec ing he close connec ion be ween he economy and anspo a ion, a e based on he analysis o an economic sus ainabili y app oach o logis ics models [ 10 ] and, along he same lines, some o hem ocus on he analysis o ce ain goods based on hei p oduc ion s uc u e and he dis ibu ion o goods by oad, analysing hei en i e ac i i y [ 11 ]. The e a e also s udies aimed a op imizing he anspo p ocess based on s eamlining some o i s sub-p ocesses such as loading [12,13]. To his ex en , we could say ha we a e ollowing a classic pe spec i e in he sea ch o sus ainabili y, which Co lu [ 14 ] poin s o in his e iew o he s a e o he a on op imising ene gy consump ion in anspo p ocesses, as a combina ion o h ee main lines o ac ion o oad anspo : (i) imp o ing he load ac o , unde s ood as a educe o emissions based on maximum occupancy o he means o anspo , (ii) he use o collabo a i e echniques o op imise he alloca ion o means o anspo and (iii) de ining and moni o ing sus ainabili y objec i es in he de elopmen o anspo ope a ions. Ne e heless, he mos ema kable aspec o he ensemble is ha all he models and s udies poin , in sho , o a common unde lying objec i e; he op imisa ion o ene gy consumed in he p ocess o eigh anspo ope a ions. A line o esea ch di ec ly associa ed wi h he cen al objec i e o ene gy imp o emen is he s udy o emissions om anspo equipmen based on hei age. Hassani e al. [ 15 ] de e mined ha emissions in ligh ehicles can be up o i e imes highe depending on hei age. Owing o his inding, his idea (sus ainabili y based on he ehicles age) is gi en special ele ance in his s udy. These esul s a e no a bi a y; ins ead, hey a e he esul o he implemen a ion o policies adop ed by he ehicle manu ac u ing sec o . Back in 2002, Ang-Olson and Sch oee [ 16 ] de e mined ha he p ope implemen a ion o echnical solu ions in he p oduc ion o hea y goods oad anspo ehicles could lead o a educ ion o mo e han 11 billion li es o annual uel consump ion wi hin a pe iod o 10 yea s, associa ed wi h a dec ease o 8.3 million onnes o g eenhouse gas emissions in he Uni ed S a es (US) alone. Since hen, he go e nance mechanisms o he US and he EU ha e con inued o impose manda es on he p oduc ion o anspo ehicles, aimed a educing consump ion and emissions [ 17 ]. As a esul o hese manda es, he so-called Eu o emission s anda ds ha e been p og essi ely de eloped, wi h successi ely mo e demanding equi emen s on consump ion and emissions o anspo elemen s, as shown in Table 1, on ca bon monoxide (CO), ni ogen oxides (NOX) and pa icula e ma e (PM). I we ocus speci ically on hea y goods ehicles, Haugen and Bishop [ 19 ] es ablish wo comple e ehicle emission samplings a loading and unloading poin s wi h signi ican eigh mo emen s, inally de e mining ha a educ ion om an a e age age o 7.8 o 6 yea s o hese ehicles esul s in a signi ican educ ion in emissions o up o 87% o suspended pa icula e ma e . A subsequen s udy [ 20 ] also de e mined ha he educ ion in ha m ul NOX emissions dec eased om 38 g o 9 g pe kg o diesel uel consumed by hea y goods ehicles om 2005 o 2020, ep esen ing a educ ion o 76.3% in ehicles Sus ainabili y 2022,14, 777 3 o 15 p oduced be ween hese da es, i.e., an a e age educ ion o 5% in emissions o each yea o ehicle p oduc ion du ing his pe iod. Table 1. Emission s anda ds adop ed by he EU o diesel ca s and hea y goods ehicles. Sou ce: Own elabo a ion on he da a o [18]. Emission S anda ds o Diesel Ca s S anda d Da e CO g/Km NOX g/Km PM g/Km Eu o 4 2005 0.50 0.30 0.025 Eu o 5 2010 0.50 0.23 0.005 Eu o 6 2015 0.50 0.17 0.005 Emission S anda ds o Hea y Goods Vehicles S anda d Da e CO g/KWh NOX g/KWh PM g/KWh Eu o IV 2005 1.50 3.50 0.020 Eu o V 2008 1.50 2.00 0.020 Eu o VI 2013 1.50 0.40 0.010 Wi h possible di e ences in he quan i ica ion o emission imp o emen s, he e is one clea conclusion; hea y goods ehicles a e a signi ican con ibu o o he emission o ha m ul gases, and he age o said ehicles is also a de e mining ac o in hei e iciency and sus ainabili y. 1.2. P e ious Wo k on Digi isa ion New pe spec i es eme ge when inco po a ing he digi alisa ion app oach o oad anspo a ion. A s udy by a panel o 52 expe s [ 21 ], highligh s he alue o p ocess au oma ion, he comple e collec ion o da a on digi al in o ma ion and he basis o he applica ion o a i icial in elligence o adequa e pe o mance and planning. Taking a s ep u he in his di ec ion, and wi h a clea ocus on imp o ing he managemen o supply chains, a s udy was ca ied ou on he da a collec ed by he EU in he Pe manen Su eys o Goods T anspo by Road [ 22 ]. Wi h his pu pose, a modelling was ca ied ou on his ac i i y, which was compiled by EUROSTAT o i s membe coun ies be ween 2011 and 2014. I was ca ied ou unde a Ho izon al Collabo a ion model [ 23 ] and analysed he imp o emen s ha would ha e esul ed om he applica ion o Pooling echniques, simila o op imised eigh g oupings, and an implemen a ion model o he so-called Physical In e ne [ 24 ]. This was done by de eloping compu e models ha simula e each op ion, which a e used o es ima e a calcula ion o emissions. The end esul is a clea ad an age o he cu en ly pu ely heo e ical physical in e ne model. Mo e speci ically, p e ious con ibu ions ha e been made conce ning he use o A i i- cial In elligence (AI) in gene al and Machine Lea ning (ML) in pa icula , in o de o add ess sus ainabili y issues in oad eigh anspo . In [ 25 ], an in eg a ed uzzy ailu e mode and e ec s analysis app oach was p oposed o he selec ion o isk mi iga ion s a egies in ack and ace asks in he indus y, which aimed o help manage s choose a s a egy conside ing he c i icali y o he isks unde a limi ed budge . In addi ion, [ 26 ] p esen ed a no el con aine anspo op imisa ion model ha inco po a es he oad ne wo k along wi h connec i i y me ics, aiming o minimise o al ip dis ance, uck uel cos , con aine en al cos , and con aine mo emen s be ween mul iple consignees and haule s. Mo e ecen ly, [ 27 ] p oposed a no el app oach o p edic he p o i ma gin, on a cus ome basis, in he sus ainable oad eigh anspo sec o by combining di e en ML me hods. This helps manage s o ob ain use ul in o ma ion on s a egic and sus ainable de elopmen pe spec i es. Di e ing om his p e ious s udy, he au ho s o his a icle ha e applied di e en ML echniques [ 28 ] o he same da a amily (see Sec ion 2). Signi ican esul s ha e been ob ained wi h espec o hei link wi h he economic ac i i y cycles. The economic u ning poin s ha occu ed du ing he G ea Recession in Spain ha e also been iden i ied, based exclusi ely on a ious clus e ing echniques om he oad eigh anspo da ase . A Sus ainabili y 2022,14, 777 4 o 15 s ong ecession was obse ed un il he second qua e o 2012, ollowed by a se e e dep ession du ing he ollowing pe iod un il he second qua e o 2013, which was hen ollowed by a g adual eco e y un il in he second qua e o 2015, whe e a clea phase o economic g ow h eme ged. The da ase showing he a e age lee age o hea y goods oad ehicles ne e ceased o inc ease a any gi en momen du ing he pe iod in ques ion (al hough i inc eased a di e en a es). I almos exac ly coincided a he poin s o economic in lec ion wi h he qua ile (Q) dis ibu ion o he da a, as can be seen in Figu e 1. This accele a ed o sus ained g ow h co esponds o no hing o he han a pa e n o o e -amo isa ion o he means o p oduc ion in imes o economic ecession, as a measu e o p o ec he p o i abili y o i s economic ac i i y. Sus ainabili y2022,14,xFORPEERREVIEW4o 15  me hods.Thishelpsmanage s oob ainuse ulin o ma ionons a egicandsus ainable de elopmen pe spec i es. Di e ing om hisp e iouss udy, heau ho so  hisa icleha eapplieddi e en  ML echniques[28] o hesameda a amily(seeSec ion2).Signi ican  esul sha ebeen ob ainedwi h espec  o hei linkwi h heeconomicac i i ycycles.Theeconomic u ning poin s ha occu eddu ing heG ea RecessioninSpainha ealsobeeniden i ied,based exclusi elyon a iousclus e ing echniques om he oad eigh  anspo da ase .A s ong ecessionwasobse edun il hesecondqua e o 2012, ollowedbyase e ede‐ p essiondu ing he ollowingpe iodun il hesecondqua e o 2013,whichwas hen ollowedbyag adual eco e yun ilin hesecondqua e o 2015,whe eaclea phaseo  economicg ow heme ged. Theda ase showing hea e age lee ageo hea ygoods oad ehiclesne e ceased oinc easea anygi enmomen du ing hepe iodinques ion(al houghi inc easeda  di e en  a es).I almos exac lycoincideda  hepoin so economicin lec ionwi h he qua ile(Q)dis ibu iono  heda a,ascanbeseeninFigu e1.Thisaccele a edo sus‐ ainedg ow hco esponds ono hingo he  hanapa e no o e ‐amo isa iono  he meanso p oduc ionin imeso economic ecession,asameasu e op o ec  hep o i a‐ bili yo i seconomicac i i y.  Figu e1.T anspo  lee ageda ase ies,di idedin oqua ilesand ela ed o hephasesde e mined o  heG ea Dep ession.Sou ce:Ownelabo a ion. Weha edecided o ocusou s udieson hisda ase iesg oupedin oqua ilesowing o he ollowing easons:Fi s ly,i isspeci icallylinked o hesus ainabili yo  anspo  ac i i y;andsecondly,i sdis ibu iono da abyqua ilescoincideswi h heeconomic phases.Fu he mo e, hes udyo  hisse ies,whichiskey o hein e p e a iono  hesus‐ ainabili yo  he lee o  eigh  anspo  ehicles,wasneglec ed om hee olu ionand s udyo  he es o  hese ies ha  e lec edspeci icinc easeso dec easesin hedep ession phase,whichmakesi ad isable oapplycomplemen a yanalysis echniques oi . Toadd ess hisp oblem,pionee  isualisa ion oolsbasedonMLa epu  o wa din hiss udy.Mo especi ically,clus e ingandExplo a o yP ojec ionPu sui (EPP) ech‐ niquesha ebeencombined o  he i s  ime,unde  he ameo Hyb idUnsupe ised Explo a o yPlo s(HUEPs), osuppo  he isualanalysiso sus ainabili yda a ega ding oad anspo a ion.Likewise, heo iginal o mula iono HUEPsisex ended,aswellas newp ojec ion echniquesbeingp oposedand alida ed. Thus a , esea che sha ewidelys udiedclus e ingandEPPme hods,concluding someo  hem ha p ojec ionme hodsa eno ause ulino de  o educe hedimension‐ ali yo da a o a ollowingclus e ing[29,30].Al hough hiss a emen maybe uein Figu e 1. T anspo lee age da a se ies, di ided in o qua iles and ela ed o he phases de e mined o he G ea Dep ession. Sou ce: Own elabo a ion. We ha e decided o ocus ou s udies on his da a se ies g ouped in o qua iles owing o he ollowing easons: Fi s ly, i is speci ically linked o he sus ainabili y o anspo ac i i y; and secondly, i s dis ibu ion o da a by qua iles coincides wi h he economic phases. Fu he mo e, he s udy o his se ies, which is key o he in e p e a ion o he sus ainabili y o he lee o eigh anspo ehicles, was neglec ed om he e olu ion and s udy o he es o he se ies ha e lec ed speci ic inc eases o dec eases in he dep ession phase, which makes i ad isable o apply complemen a y analysis echniques o i . To add ess his p oblem, pionee isualisa ion ools based on ML a e pu o wa d in his s udy. Mo e speci ically, clus e ing and Explo a o y P ojec ion Pu sui (EPP) ech- niques ha e been combined o he i s ime, unde he ame o Hyb id Unsupe ised Explo a o y Plo s (HUEPs), o suppo he isual analysis o sus ainabili y da a ega ding oad anspo a ion. Likewise, he o iginal o mula ion o HUEPs is ex ended, as well as new p ojec ion echniques being p oposed and alida ed. Thus a , esea che s ha e widely s udied clus e ing and EPP me hods, concluding some o hem ha p ojec ion me hods a e no a use ul in o de o educe he dimensionali y o da a o a ollowing clus e ing [ 29 , 30 ]. Al hough his s a emen may be ue in some cases, some o he combina ions o such me hods ha e been p e iously p oposed, di e en om his sequen ial applica ion o me hods. Tha is he case o [ 31 , 32 ], whe e he ou pu o clus- e ing me hods (i.e., he assigned clus e o each da a ins ance) is added o he p ojec ion ob ained by EPP me hods, ha could be 2D o 3D. On he o he hand, o he au ho s ha e p oposed [ 33 , 34 ] he simul aneous applica ion o clus e ing and dimensionali y- educ ion me hods. As opposed o hese p e ious ideas, HUEPs ha e been ecen ly p oposed o he combina ion o clus e ing and p ojec ion me hods, being independen ly applied. Sus ainabili y 2022,14, 777 5 o 15 The emaining sec ions o his a icle a e o ganised as ollows: he me hods employed, oge he wi h he da a on which hey a e applied, a e desc ibed in Sec ion 2. The esul s ob ained in he expe imen al s udy a e p esen ed in Sec ion 3, and he main conclusions in ela ion o hese, as well as some p oposals o u u e wo k, a e p esen ed in Sec ion 4. 2. Ma e ials and Me hods As p e iously s a ed, oad anspo a ion ac i i y was esea ched in his s udy, wi h a speci ic ocus on i s sus ainabili y. This was done by analysing a da ase desc ibed in Sec ion 2.1 wi h he no el echniques ha a e p esen ed in Sec ion 2.2. 2.1. Da ase Da a we e e ie ed om wo di e en sou ces: • The Minis y o T anspo , Mobili y and U ban Agenda (Minis e io de T anspo es, Mo ilidad y Agenda U bana) o Spain, h ough i s Gene al Sub-Di ec o a e o Economic S udies and S a is ics. • The Eu opean Road F eigh T anspo su ey (ERFT). This su ey ela es o he ac i i y o hea y goods ehicles licenced in Spain o he anspo o goods. I has a su icien ly high sampling le el o be o s a is ical ep esen a i eness o each Au onomous Region, in o de o measu e hei anspo ope a ions. Wi h his aim, he su ey egis e s he mo emen o a single class o goods, om a depa u e poin o a des ina ion. The esea ch was conduc ed in acco dance wi h he co esponding egula ion [ 35 ] and i s subsequen e ision [ 36 ]. The o al numbe o eco ds included on ha da abase was 1,932,671 ha has a sampling ep esen a i eness o 1,259,938,252 anspo ope a ions. Da a om be ween 2011 and 2017 we e used. All he da a ep esen ed qua e ly le els o agg ega ion, which he e o e included each a iable in he s udy, a o al o 28 alues. The a iables unde conside a ion we e: • T anspo a ion cos s (B): based on a 100 pe cen inc ease abo e he a e age yea ly p ices in 2000, as de e mined by he Minis y o De elopmen ’s qua e ly esea ch s udies. •Fuel cos s in Spain: qua e ly midpoin s weigh ed in cen imes o a eu o, as indica ed by he da a ga he ed by he Minis y o De elopmen . • Fuel cos s in he EU: qua e ly midpoin s weigh ed in cen imes o a eu o, as indica ed by he da a ga he ed by he Minis y o De elopmen . •Numbe o ons anspo ed (A, B, C): weigh o anspo ed goods. •Comple ed ips (A, B, C): numbe o anspo ope a ions and emp y dis ance. •Emp y dis ance (A, B): kilome es a elled wi hou goods. • Maximum load o anspo ope a ions (A, B): uppe weigh limi o comple ed ips in ons. • Maximum load o emp y dis ance (A, B): uppe weigh limi o emp y dis ance co e ed in ons. •Haulage dis ance (A, B): kilome es a elled. •Emp y haulage dis ance (A, B): kilome es a elled wi hou goods. •Quan i y o ehicles ep esen ed (A): numbe o ehicles ep esen ed. •Rep esen ed load capaci y (A): uppe load limi o he ep esen ed ehicles. • Tons-kms (A, B, C): o al ons anspo ed, and dis ance co e ed in each haulage ope a ion. • A e age lee age (A, B): a e age amoun o yea s elapsed since he egis a ion o he ehicles. As p e iously indica ed in Sec ion 1.1, his is an impo an da a inding in ega d o sus ainabili y. Owing o his, i is also used in he glyph me apho . • A e age lee age o emp y dis ance (A, B): a e age amoun o yea s elapsed since he egis a ion o he ehicles a elling wi hou goods. Sus ainabili y 2022,14, 777 6 o 15 The da a se ies wi h assigned le e s we e sub-di ided as acco ding: (A) Type o anspo : (A1) All anspo ; (A2) Own anspo ; (A3) Hi e o ewa d. (B) Dis ance ange: (B1) All dis ances; (B2) < 50 km; (B3) 51–100 km; (B4) 101–200 km; (B5) 201–300 km; (B6) > 300 km. (C) Geog aphic ca chmen : (C1) All ca chmen s; (C2) Municipal; (C3) Regional; (C4) Na ional; (C5) Impo a ion; (C6) Expo a ion; (C7) Cabo age. 113 anspo da a se ies we e calcula ed, wi h he alues o he 28 p e iously indica ed qua e s in each one. As a esul , a da ase wi h high dimensionali y is equi ed o be analysed in o de o in es iga e he sus ainabili y o oad anspo a ion. 2.2. Hyb id Unsupe ised Explo a o y Plo s Hyb id Unsupe ised Explo a o y Plo s (HUEPs) [ 37 ] ha e been ecen ly p oposed as a new isualisa ion ool o combine he ou pu s o Explo a o y P ojec ion Pu sui (EPP) and clus e ing me hods in a no el and in o ma i e way. To add ess he well-known “cu se o dimensionali y” challenge and ad ancing in desc ip i e da a analysis, bo h EPP and clus e ing me hods a e independen ly applied, and hei ou pu s combined in a new way. In pa icula , 3 EPP me hods we e pu o wa d, commonly known as P incipal Compo- nen Analysis (PCA), Maximum Likelihood Hebbian Lea ning (MLHL), and Coope a i e MLHL (CMLHL). The e a e di e en ways o implemen ing such me hods; in he o iginal o mula ion o HUEPs, hey we e implemen ed as A i icial Neu al Ne wo ks. Addi ionally, an ex ension o his s udy is included o imp o e he isualisa ion capabili y o HUEPs. In o de o gene a e he displays, each o iginal x ec o ( om he inpu space) is p ocessed as ollows: 1. 2D p ojec ion o he ec o is ob ained by he applied EPP me hod (yEPP 1,yEPP 2). 2. The ou pu o he clus e ing me hod (i.e., he assigned clus e numbe ) is calcula ed (yc). 3. The wo p e ious ou pu s a e combined in a 3D ec o ha is loca ed in he ou pu space (y1,y2,y3). 4. Op ionally, u he in o ma ion (sus ainabili y da a in he p esen s udy) is added o he isualisa ion by using he glyph me apho . O iginally, HUEPs we e concei ed as a new way o in ui i ely displaying da a by applying one pa i ional (k-means) o one hie a chical (agglome a i e) clus e ing me hod oge he wi h one EPP me hod. As an e olu ion o his ini ial p oposal, his s udy alida es he inco po a ion o complemen a y and well-known display me hods, namely Ke nel-PCA (KPCA) [38] and Sammon Mapping (SM) [39]. KPCA is a non-linea ex ension o con en ional PCA ha akes he majo i y o ke nel unc ions in o de o ob ain mo e in e es ing p ojec ions o da a by ex ac ing non-lineal p incipal componen s while keeping he compu a ion cos a a easonable le el. On he o he hand, SM was p oposed as a special case o he dis ance-based me ic Mul idimen- sional Scaling amily, being i sel one o he i s mani old lea ning p oposals. Fu he mo e, SM can be conside ed as he i s p oposed nonlinea mani old lea ning me hod. These non-linea EPP me hods a e p oposed o he i s ime unde he ame o HUEPs as being ones o he main non-linea EPP me hods. They a e analysed in his s udy and alida ed wi h he da a p e iously desc ibed. 2.3. Meh odology In o de o alida e he p oposed applica ion o HUEPs in he p esen wo k, isual- iza ions ha e been ob ained by combining he p ojec ions o EPP me hods (PCA, MLHL, CMLHL, KPCA, and SM) wi h he ou pu o clus e ing me hods (k-means and agglome a- i e). Expe imen s ha e been pe o med uning each one o he me hods wi h he ollowing pa ame e alues. Sus ainabili y 2022,14, 777 7 o 15 PCA •Numbe o p incipal componen s o be ob ained: 2. MLHL •Numbe o p ojec ed dimensions o be ob ained: 2. •Lea ning a e: [0.01, 0.05]. •ppa ame e : [1, 2]. CMLHL •Numbe o p ojec ed dimensions o be ob ained: 2. •Lea ning a e: [0.01, 0.05]. •ppa ame e : [1, 2]. • au pa ame e : [0.00000001, 0.01]. KPCA •Numbe o p ojec ed dimensions o be ob ained: 2, 3. •Ke nel: linea , polynomial, gaussian. SM •Numbe o p ojec ed dimensions o be ob ained: 2, 3. •k-means. •Numbe o clus e s: 2, 3, 4, 6, 8. •Dis ance: sqEuclidean, Ci yblock, Cosine, Co ela ion. Agglome a i e •Numbe o clus e s (cu o ): 2, 3, 4, 6, 8. • Dis ance: Euclidean, sEuclidean, sqEuclidean, Ci yblock, Hamming, Jacca d, Minkowski, Chebyche , Spea man, Cosine, Co ela ion. •Linkage: A e age, Cen oid, Comple e, Median, Single, Wa d, Weigh ed. 3. Resul s The HUEP displays ob ained a e shown in his sec ion. Fi s ly, Figu e 2shows he HUEP display ob ained by combining agglome a i e clus e ing wi h di e en EPP ech- niques. As a esul , he in luence o he di e en EPP echniques on he ob ained esul s can be compa ed. Due o his, addi ional in o ma ion is no shown h ough he glyph me apho in his igu e, o enable he sole compa ison o he p ojec ions. Fo he sake o b e i y, he mos in e es ing g aphical displays a e shown and hose ob ained by some o he EPP me hods a e no included in Figu e 2. The display ob ained by KPCA can be conside ed he mos e ealing. I allows he s uc u e o he da a o be obse ed mo e clea ly, as i ep esen s he da a in a mo e compac o m and hus allows ends o be analysed wi h mo e cla i y. Since i is no possible o include all he esul s ob ained in his s udy, only esul s ob ained using KPCA a e shown in he es o his sec ion. These esul s alida e he main p oposal o he p esen esea ch: ex ending he o iginal HUEP o mula ion by adding new EPP me hods ha can imp o e he isualiza ion o a gi en da ase . Fo he da ase unde analysis, none o he EPP me hods in he o iginal HUEP o mula ion p o ides wi h he bes p ojec ion, bu one o he new ones (KPCA) ins ead. Sus ainabili y 2022,14, 777 8 o 15 Sus ainabili y2022,14,xFORPEERREVIEW8o 15   (a)  (b)  (c) Figu e2.HUEPsob ainedbyapplyingagglome a i eclus e ing(k=3,dis ance=sEuclidean,link‐ age=a e age) o heanalysedda ase , a ying heEPP echnique:(a)CMLHL,(b)KPCA,(c)SM. Thedisplayob ainedbyKPCAcanbeconside ed hemos  e ealing.I allows he s uc u eo  heda a obeobse edmo eclea ly,asi  ep esen s heda ainamo ecom‐ pac  o mand husallows ends obeanalysedwi hmo ecla i y.Sincei isno possible oincludeall he esul sob ainedin hiss udy,only esul sob ainedusingKPCAa e shownin he es o  hissec ion. These esul s alida e hemainp oposalo  hep esen  esea ch:ex ending heo ig‐ inalHUEP o mula ionbyaddingnewEPPme hods ha canimp o e he isualiza ion o agi enda ase .Fo  heda ase unde analysis,noneo  heEPPme hodsin heo iginal Figu e 2. HUEPs ob ained by applying agglome a i e clus e ing (k = 3, dis ance = sEuclidean, linkage = a e age) o he analysed da ase , a ying he EPP echnique: ( a ) CMLHL, ( b ) KPCA, ( c ) SM. Sus ainabili y 2022,14, 777 9 o 15 Resul s including he Glyph Me apho A e ha ing selec ed KPCA as he EPP model ha o e s he bes p ojec ions o he da a analysed, he esul s using he glyph me apho a e p esen ed in his sec ion. I is wo h men ioning ha o any o he da ase , his may no be he mos app op ia e EPP model. In his sec ion, addi ional in o ma ion on he Flee Age a iable (sus ainabili y da a) is inco po a ed in he ollowing g aphs. The symbols o each piece o da a a e di e en ia ed acco ding o he qua ile o which hey belong, consis en wi h he alue aken o ha a iable, in acco dance wi h he symbols shown in Table 2. Table 2. Legend o he g aphs using he glyph me apho acco ding o he alues o he sus ainabili y- ela ed ea u e (A e age age o he ehicle lee ). Q Glyph 1 Sus ainabili y2022,14,xFORPEERREVIEW9o 15  HUEP o mula ionp o ideswi h hebes p ojec ion,bu oneo  henewones(KPCA) ins ead. Resul sIncluding heGlyphMe apho  A e ha ingselec edKPCAas heEPPmodel ha o e s hebes p ojec ions o  he da aanalysed, he esul susing heglyphme apho a ep esen edin hissec ion.I is wo hmen ioning ha  o anyo he da ase , hismayno be hemos app op ia eEPP model. In hissec ion,addi ionalin o ma ionon heFlee Age a iable(sus ainabili yda a) isinco po a edin he ollowingg aphs.Thesymbols o eachpieceo da aa edi e en i‐ a edacco ding o hequa ile owhich heybelong,consis en wi h he alue aken o  ha  a iable,inacco dancewi h hesymbolsshowninTable2. Table2.Legend o  heg aphsusing heglyphme apho acco ding o he alueso  hesus ainabil‐ i y‐ ela ed ea u e(A e ageageo  he ehicle lee ). QGlyph 1 2 3 4  Inacco dancewi h heabo e, hep e iouslyselec edHUEPg aphisshown(Figu e 2b),al houghwi h hesus ainabili y ea u enowinco po a ed.Addi ionally, he igu eis enhancedbya o m oguide he eade in hein e p e a iono  he esul s. InFigu e3i ispossible osee ha aclea di e en ia iono  heda aqua ileso  he se iesisob ained,andaclea lineo p og essioncanbema kedon he esul (dashed yellowline).Theyellowlineisassocia ed o he empo alp og essiono da a.F oma p ac icalpoin o  iew, heg aphshows ha  he isualisa ionob ainedisuse ulwhen de e mining hephaseso p og essiono  heagese ies;1. heini ialage,2. hephaseo  o e ‐amo isa iono meanso  anspo and3. hephaseo  henewa e ageageo  he lee .  Figu e3.HUEPob ainedbyapplyingKPCAandagglome a i eclus e ing(k=3,dis ance=sEu‐ clidean,linkage=a e age) o heanalysedda ase ,using hesus ainabili y ea u ein heglyphme ‐ apho .Theyellowlineisassocia ed o he empo alp og essiono da a. 2 Sus ainabili y2022,14,xFORPEERREVIEW9o 15  HUEP o mula ionp o ideswi h hebes p ojec ion,bu oneo  henewones(KPCA) ins ead. Resul sIncluding heGlyphMe apho  A e ha ingselec edKPCAas heEPPmodel ha o e s hebes p ojec ions o  he da aanalysed, he esul susing heglyphme apho a ep esen edin hissec ion.I is wo hmen ioning ha  o anyo he da ase , hismayno be hemos app op ia eEPP model. In hissec ion,addi ionalin o ma ionon heFlee Age a iable(sus ainabili yda a) isinco po a edin he ollowingg aphs.Thesymbols o eachpieceo da aa edi e en i‐ a edacco ding o hequa ile owhich heybelong,consis en wi h he alue aken o  ha  a iable,inacco dancewi h hesymbolsshowninTable2. Table2.Legend o  heg aphsusing heglyphme apho acco ding o he alueso  hesus ainabil‐ i y‐ ela ed ea u e(A e ageageo  he ehicle lee ). QGlyph 1 2 3 4  Inacco dancewi h heabo e, hep e iouslyselec edHUEPg aphisshown(Figu e 2b),al houghwi h hesus ainabili y ea u enowinco po a ed.Addi ionally, he igu eis enhancedbya o m oguide he eade in hein e p e a iono  he esul s. InFigu e3i ispossible osee ha aclea di e en ia iono  heda aqua ileso  he se iesisob ained,andaclea lineo p og essioncanbema kedon he esul (dashed yellowline).Theyellowlineisassocia ed o he empo alp og essiono da a.F oma p ac icalpoin o  iew, heg aphshows ha  he isualisa ionob ainedisuse ulwhen de e mining hephaseso p og essiono  heagese ies;1. heini ialage,2. hephaseo  o e ‐amo isa iono meanso  anspo and3. hephaseo  henewa e ageageo  he lee .  Figu e3.HUEPob ainedbyapplyingKPCAandagglome a i eclus e ing(k=3,dis ance=sEu‐ clidean,linkage=a e age) o heanalysedda ase ,using hesus ainabili y ea u ein heglyphme ‐ apho .Theyellowlineisassocia ed o he empo alp og essiono da a. 3 Sus ainabili y2022,14,xFORPEERREVIEW9o 15  HUEP o mula ionp o ideswi h hebes p ojec ion,bu oneo  henewones(KPCA) ins ead. Resul sIncluding heGlyphMe apho  A e ha ingselec edKPCAas heEPPmodel ha o e s hebes p ojec ions o  he da aanalysed, he esul susing heglyphme apho a ep esen edin hissec ion.I is wo hmen ioning ha  o anyo he da ase , hismayno be hemos app op ia eEPP model. In hissec ion,addi ionalin o ma ionon heFlee Age a iable(sus ainabili yda a) isinco po a edin he ollowingg aphs.Thesymbols o eachpieceo da aa edi e en i‐ a edacco ding o hequa ile owhich heybelong,consis en wi h he alue aken o  ha  a iable,inacco dancewi h hesymbolsshowninTable2. Table2.Legend o  heg aphsusing heglyphme apho acco ding o he alueso  hesus ainabil‐ i y‐ ela ed ea u e(A e ageageo  he ehicle lee ). QGlyph 1 2 3 4  Inacco dancewi h heabo e, hep e iouslyselec edHUEPg aphisshown(Figu e 2b),al houghwi h hesus ainabili y ea u enowinco po a ed.Addi ionally, he igu eis enhancedbya o m oguide he eade in hein e p e a iono  he esul s. InFigu e3i ispossible osee ha aclea di e en ia iono  heda aqua ileso  he se iesisob ained,andaclea lineo p og essioncanbema kedon he esul (dashed yellowline).Theyellowlineisassocia ed o he empo alp og essiono da a.F oma p ac icalpoin o  iew, heg aphshows ha  he isualisa ionob ainedisuse ulwhen de e mining hephaseso p og essiono  heagese ies;1. heini ialage,2. hephaseo  o e ‐amo isa iono meanso  anspo and3. hephaseo  henewa e ageageo  he lee .  Figu e3.HUEPob ainedbyapplyingKPCAandagglome a i eclus e ing(k=3,dis ance=sEu‐ clidean,linkage=a e age) o heanalysedda ase ,using hesus ainabili y ea u ein heglyphme ‐ apho .Theyellowlineisassocia ed o he empo alp og essiono da a. 4 Sus ainabili y2022,14,xFORPEERREVIEW9o 15  HUEP o mula ionp o ideswi h hebes p ojec ion,bu oneo  henewones(KPCA) ins ead. Resul sIncluding heGlyphMe apho  A e ha ingselec edKPCAas heEPPmodel ha o e s hebes p ojec ions o  he da aanalysed, he esul susing heglyphme apho a ep esen edin hissec ion.I is wo hmen ioning ha  o anyo he da ase , hismayno be hemos app op ia eEPP model. In hissec ion,addi ionalin o ma ionon heFlee Age a iable(sus ainabili yda a) isinco po a edin he ollowingg aphs.Thesymbols o eachpieceo da aa edi e en i‐ a edacco ding o hequa ile owhich heybelong,consis en wi h he alue aken o  ha  a iable,inacco dancewi h hesymbolsshowninTable2. Table2.Legend o  heg aphsusing heglyphme apho acco ding o he alueso  hesus ainabil‐ i y‐ ela ed ea u e(A e ageageo  he ehicle lee ). QGlyph 1 2 3 4  Inacco dancewi h heabo e, hep e iouslyselec edHUEPg aphisshown(Figu e 2b),al houghwi h hesus ainabili y ea u enowinco po a ed.Addi ionally, he igu eis enhancedbya o m oguide he eade in hein e p e a iono  he esul s. InFigu e3i ispossible osee ha aclea di e en ia iono  heda aqua ileso  he se iesisob ained,andaclea lineo p og essioncanbema kedon he esul (dashed yellowline).Theyellowlineisassocia ed o he empo alp og essiono da a.F oma p ac icalpoin o  iew, heg aphshows ha  he isualisa ionob ainedisuse ulwhen de e mining hephaseso p og essiono  heagese ies;1. heini ialage,2. hephaseo  o e ‐amo isa iono meanso  anspo and3. hephaseo  henewa e ageageo  he lee .  Figu e3.HUEPob ainedbyapplyingKPCAandagglome a i eclus e ing(k=3,dis ance=sEu‐ clidean,linkage=a e age) o heanalysedda ase ,using hesus ainabili y ea u ein heglyphme ‐ apho .Theyellowlineisassocia ed o he empo alp og essiono da a. In acco dance wi h he abo e, he p e iously selec ed HUEP g aph is shown ( Figu e 2b ), al hough wi h he sus ainabili y ea u e now inco po a ed. Addi ionally, he igu e is en- hanced by a o m o guide he eade in he in e p e a ion o he esul s. In Figu e 3i is possible o see ha a clea di e en ia ion o he da a qua iles o he se ies is ob ained, and a clea line o p og ession can be ma ked on he esul (dashed yellow line). The yellow line is associa ed o he empo al p og ession o da a. F om a p ac ical poin o iew, he g aph shows ha he isualisa ion ob ained is use ul when de e mining he phases o p og ession o he age se ies; 1. he ini ial age, 2. he phase o o e -amo isa ion o means o anspo and 3. he phase o he new a e age age o he lee . Sus ainabili y2022,14,xFORPEERREVIEW9o 15  HUEP o mula ionp o ideswi h hebes p ojec ion,bu oneo  henewones(KPCA) ins ead. Resul sIncluding heGlyphMe apho  A e ha ingselec edKPCAas heEPPmodel ha o e s hebes p ojec ions o  he da aanalysed, he esul susing heglyphme apho a ep esen edin hissec ion.I is wo hmen ioning ha  o anyo he da ase , hismayno be hemos app op ia eEPP model. In hissec ion,addi ionalin o ma ionon heFlee Age a iable(sus ainabili yda a) isinco po a edin he ollowingg aphs.Thesymbols o eachpieceo da aa edi e en i‐ a edacco ding o hequa ile owhich heybelong,consis en wi h he alue aken o  ha  a iable,inacco dancewi h hesymbolsshowninTable2. Table2.Legend o  heg aphsusing heglyphme apho acco ding o he alueso  hesus ainabil‐ i y‐ ela ed ea u e(A e ageageo  he ehicle lee ). QGlyph 1 2 3 4  Inacco dancewi h heabo e, hep e iouslyselec edHUEPg aphisshown(Figu e 2b),al houghwi h hesus ainabili y ea u enowinco po a ed.Addi ionally, he igu eis enhancedbya o m oguide he eade in hein e p e a iono  he esul s. InFigu e3i ispossible osee ha aclea di e en ia iono  heda aqua ileso  he se iesisob ained,andaclea lineo p og essioncanbema kedon he esul (dashed yellowline).Theyellowlineisassocia ed o he empo alp og essiono da a.F oma p ac icalpoin o  iew, heg aphshows ha  he isualisa ionob ainedisuse ulwhen de e mining hephaseso p og essiono  heagese ies;1. heini ialage,2. hephaseo  o e ‐amo isa iono meanso  anspo and3. hephaseo  henewa e ageageo  he lee .  Figu e3.HUEPob ainedbyapplyingKPCAandagglome a i eclus e ing(k=3,dis ance=sEu‐ clidean,linkage=a e age) o heanalysedda ase ,using hesus ainabili y ea u ein heglyphme ‐ apho .Theyellowlineisassocia ed o he empo alp og essiono da a. Figu e 3. HUEP ob ained by applying KPCA and agglome a i e clus e ing (k = 3, dis ance = sEu- clidean, linkage = a e age) o he analysed da ase , using he sus ainabili y ea u e in he glyph me apho . The yellow line is associa ed o he empo al p og ession o da a. To app ecia e he impac o he k pa ame e o he clus e ing echnique on he display, di e en isualisa ions a e p esen ed below (Figu e 4), wi h he same EPP and clus e ing models, bu wi h a a ying numbe o clus e s.