Reducing Vehicle Emissions and Fuel Consumption in the City by Using Particle Swarm Optimization

Reducing Vehicle Emissions and Fuel Consump ion in he Ci y by Using Pa icle Swa m Op imiza ion A. Ca olina Oli e a 1,∗ Depa amen o de Ciencias e Ingenie ´ıa de la Compu aci´on, Uni e sidad Nacional del Su , A . Alem 1253, 8000, Bah´ıa Blanca, A gen ina J. Ga c´ıa-Nie o, E. Alba Dep . de Lenguajes y Ciencias de la Compu aci´on, Uni e si y o Malaga, ETSI In o m´a ica, Campus de Tea inos, Malaga - 29071, Spain Abs ac Nowadays in cu en ci ies he inc easing le els o pollu ion emissions and uel consump ion de i ed om he oad affic di ec ly affec o he ai quali y, he economy, and specially he heal h o ci izens. The e o e, imp o ing he affic flow is a manda o y ask in o de o mi iga e such c i ical p oblems. In his wo k, we p opose a Swa m In elligence app oach o op imizing signal ligh iming p og ams in me opoli an a eas. In his way, we can imp o e he affic flow o ehicles wi h he global a ge o educing hei uel consump ion and gas emissions (CO and NOx). In his a icle we op imize he iming p og ams o signal ligh s and analyze hei effec in pollu ion by ollowing he s anda d HBEFA as affic emission model. In conc e e, we a e ocused he e on wo la ge and he e ogeneous u ban ins ances loca ed in he ci ies o Malaga and Se ille (in Spain). In compa ison wi h iming p og ams o signal ligh s p edefined by expe s (close o eal ones), ou p oposal ob ains significan educ ions in e ms o he emission a e and he o al uel consump ion. Key wo ds: T affic Signal Timing, Pa icle Swa m Op imiza ion, SUMO Mic oscopic Simula o o U ban MObili y, HBEFA T affic Emission Model. ∗Co esponding au ho . Email add esses: [email p o ec ed], {jnie o,ea }@lcc.uma.es (J. Ga c´ıa-Nie o, E. Alba). 1Au ho s acknowledge unds om he CICE o he Jun a de Andalucia, unde con ac P07-TIC- 03044 (DIRICOM h p://di icom.lcc.uma.es) and Span- ish Minis y o Sciences and Inno a ion (MICINN) and FEDER unde con ac s TIN2011-28194 (RoadMe h p:// oadme.lcc.uma.es) and TIN2008-06491-C04-01 (M* h p://ms a .lcc.uma.es). Jos´e Ga c´ıa-Nie o is sup- po ed by g an BES-2009-018767 om he MICINN. Ana 1. In oduc ion In cu en me opoli an a eas, he inc easing le els o ai con amina ion and uel consump ion de i ed om he u ban oad affic ha e become highly se ious p oblems ha di ec ly affec o he C. Oli e a acknowledges CONICET, he ANPCyT o G an PICT 2011 Ca ego y I-B and SeCyT (UNS) o G an PGI 24/N026. P ep in submi ed o Else ie Science July 2, 2012 ai quali y, he economy, he building/s uc u e main enance, and especially o he heal h o ci - izens. Imp o ing he affic flow o ehicles is a manda o y ask in o de o mi iga e such c i ical issues. T adi ionally, affic conges ion has been deal wi h changes in u ban in as uc u es (e.g. sense o affic in s ee s o oundabou s), al hough his is usually no possible and always expensi e. Recen ly, a numbe o wo ks in he li e a u e p oposed he op imiza ion o iming p og ams o signal ligh s as one o he mos influen me hods o imp o e he flow o ehicles [20,22,23,28]. In his sense, he use o au oma ic in elligen me hods ha e demons a ed hei use ulness o he op imiza ion o iming p og ams o affic ligh s [3,23]. Howe e , au ho s in gene al ha e add essed specific cases o s udy wi h ew in e - sec ions and small numbe o signal ligh s [5], and mos o hem apply ad-hoc algo i hms de- signed only o one specific ins ance [3,23]. The use o a ificial in elligen echniques o la ge and he e ogeneous u ban a eas is s ill an open issue. Mo eo e , he op imiza ion o iming p og ams om he pe spec i e o he educ ion o gas emis- sion and hyd oca bons consump ion ha e no e e been deal , o he bes o ou knowledge. All his mo i a ed us o p opose in his wo k an op imiza ion s a egy, based in a Pa icle Swa m Op imiza ion (PSO) algo i hm [16], o find suc- cess ul signal ligh iming p og ams wi h ega ds o wo main ac o s: emissions o CO and NOx, plus he global amoun o uel consumed by ehi- cles. Se e al ea u es led us o use PSO ins ead o o he e olu iona y me hods: – Fi s o all, using a Fi ness Cloud p elimina y analysis [31], we es ed ha PSO is able o ackle he signal ligh iming p oblem (SLTP) efficien ly. A desc ip ion o his analysis is gi en in Sec ion 5.3. – Second, he PSO is a well-known algo i hm shown o pe o m a as con e ge o quasi- op imal solu ions [8]. This is a highly desi able ea u e o he op imal iming p og am o affic ligh s, whe e new adap i e (and au oma ically compu ed) schedules should be equi ed o ace upda ing e en s in affic scena ios. – Thi d, he S anda d PSO is easy o implemen , and equi es ew uning pa ame e s [8,16]. – Fou h, PSO is a kind o Swa m In elligence al- go i hm ha can in o m us on u u e issues o deal wi h his p oblem by using independen agen s o online adap a ion (a p omising line o esea ch). Coupled wi h PSO, we use in ou op imiza ion s a egy he mic oscopic simula o SUMO (Simu- la o o U ban Mobili y) [4] o he e alua ion o op imized iming p og ams codified as ec o so- lu ions. Such iming p og ams a e used in signal ligh s ha con ol he flow o ehicles h ough a gi en scena io (u ban ins ance). As done in o he simila ini ia i es [13,14,21], we use a affic simu- la o since i p o ides an immedia e and con inu- ous sou ce o in o ma ion abou he ehicles flow. In he case o SUMO, we can also wo k wi h he affic emission model HBEFA (HandBook Emis- sion FAc o s) [15] o oad anspo in o de o ec- ollec in o ma ion abou he emission a es and he uel consump ion. This in o ma ion is used by PSO o e alua e he iming p og ams o signal ligh s. As main con ibu ions o his wo k, we can men- ion he ollowing ones: – We p opose an op imiza ion s a egy o he e- duc ion o emissions in la ge and he e ogeneous u ban a eas wi h hund eds o ehicles and signal ligh s (high dimensionali y and complexi y). – We use eal in o ma ion: we ha e modeled wo u ban scena ios loca ed in he ci ies o Se ille and Malaga, in Spain. Ou op imiza ion s a egy has been hen e alua ed on ealis ic ins ances. – In compa ison wi h p edefined (by expe s) im- ing p og ams close o eal ones, ou PSO will be shown o ob ain quan i a i e imp o emen s in e ms o he wo main objec i es: educing he emission a es and he global uel consump ion. – We conside o he fi s ime he use o a swa m in elligen app oach coupled wi h he affic emission model HBEFA [9], o he educ ion o pollu ion and uel consump ion in u ban a eas. The s uc u e o his a icle is as ollows. In Sec- ion 2, a e iew o ela ed wo ks in he li e a u e is p esen ed. Sec ion 3 explains he SUMO simula- ion ool and he HBEFA emission model. Then in Sec ion 4, ou op imiza ion app oach is desc ibed. Expe imen s and analysis o esul s a e de ailed in Sec ion 5. Finally, concluding ema ks and u u e wo k a e gi en in Sec ion 6. 2 2. S a e o he A In he las decade, a numbe o wo ks can be ound in he ela ed li e a u e ha deal wi h he affic conges ion p oblem by means o accu a e signal ligh s iming p og ams [6,18,23,24,26,29]. In all hese app oaches, global ip imes and wai - ing imes o ehicles in affic ligh s a e op imized, al hough none o hem conside ed he influence o solu ions on emissions and uel consump ion ac o s. On he con a y, a ew o wo ks can be ound ha inco po a ed emission/consuming ac- o s in o he affic con ol s a egies by enhancing he affic flow [11,20] wi h diffe en esul s. In [11], jus o one in e sec ion (c oss oad), he imp o e- men o affic ligh s iming p og ams and hei impac on he final emission a es we e examined. In [20], he au ho s p oposed a signal ime model ha educe he ehicles’ delays, he uel consump- ion, and he gas emissions by conside ing he cy- cle leng h and he g een ime o affic ligh s in one in e sec ion in Nanking ci y (China). In [7], a mic oscopic simula o was used o he e alua- ion o affic con ol s a egies in a sub-ne wo k selec ed om he Haidian dis ic o Beijing. This las wo k was ocused on analyzing he ela ion be- ween ehicles’ emissions and hei ins an aneous speeds/accele a ions, al hough affic signal op i- miza ion was no conside ed and only wo con ol s a egies we e s udied. A he same ime, ad anced algo i hms ha e eme ged as accu a e echniques o sol ing a - fic ligh s scheduling and affic con ol p ob- lems [23,26]. Howe e , he en i onmen al impac o he affic flow is igno ed o pa ially conside ed. An example o his can be ound in [32] whe e a Gene ic Algo i hm (GA) was used o he ai pollu ion educ ion conside ing he op imiza ion o affic signals in one in e sec ion. In [33], he au ho s showed how he cycle p og ams o affic ligh s affec he gas concen a ions on a gi en oad in e sec ion by using Neu al Ne wo ks. Conce ning Swa m In elligence app oaches, ew o hem can also be ound o he schedule o affic ligh s. One o he mos ep esen a i e was p oposed in [6], whe e he au ho s applied a PSO o aining a uzzy logic con olle loca ed in each in e sec ion by de e mining he effec i e ime o g een o each phase o he affic ligh s. Peng e al. [25] p esen ed a PSO wi h isola ion niches o he schedule o affic ligh s. In ha wo k, a pu ely academic small ins ance wi h a es ic i e one-way oad wi h wo in e sec ions was used o es he PSO. Mo e ecen ly, an An Colony Op- imiza ion (ACO) [12] has been p oposed o he signal ligh iming. In his wo k, wo in e es ing unce ain y and con e gence analysis we e pe - o med, al hough in he scope o one simple affic in e sec ion. In hese las wo ks, en i onmen ac- o s we e no conside ed a all, and only academic ins ances we e s udied. All hese app oaches ocused on diffe en aspec s o he affic ligh scheduling. As a summa y, ou limi a ions can be ound in gene al: – They ackled limi ed ehicula ne wo ks wi h e y ew affic ligh s and a small numbe o o he elemen s ( oads, in e sec ions, di ec ions, e c.). In con as , ou PSO can find op imized iming p og ams o la ge scena ios wi h hun- d eds o affic ligh s, ehicles, and o he ci y el- emen s. – They we e designed o only one specific sce- na io. Some o hem s udied he influence o he affic densi y. Ou app oach can be eas- ily adap ed o diffe en scena io opologies and ci ies. – In mos o he cases, exis ing wo ks we e no compa ed agains o he echniques. Ou PSO is compa ed he e agains wo diffe en app oaches: a Random Sea ch algo i hm ( o show ha i is in elligen ), and he cycle p og am gene a- o p o ided by SUMO ( ha uses human expe knowledge). – P e ious wo ks did no conside he op imiza- ion o en i onmen al ac o s. Ou app oach con- side s a se ies o ac o s (CO,NOx, and uel con- sump ion) ha , coupled wi h pu e affic flow in- dica o s ( ehicles a i ing a des ina ions, global ip imes, e c.), p o ide he expe wi h op i- mized signal ligh iming p og ams: a small s ep o he u u e sma ci y. 3 … … … … … … 40 5 40 10 36 6 22 … … … … … … … in e sec ion id=“i+1” in e sec ion id=“i” phasedu a ion=“36” Solu ion:a pa icle posi ion o he PSO algo i hm Cu en s a e o in e sec ion i=“ GG GG ” Figu e 1. Timing p og am (phase du a ion) o signal ligh s wi hin in e sec ions. In ege codifica ion inside a PSO solu ion 3. SLTP: Timing and Emission Models A u ban affic scena io is basically composed by: in e sec ions, affic ligh s, oads, di ec ions, and ehicles mo ing h ough hei own diffe en ou es. The affic ligh s a e loca ed in in e sec- ions and con ol he flow o ehicles by ollowing hei p og ams o colo s a es, and iming cycles o phase du a ions. In his con ex , all affic ligh s loca ed in he same in e sec ion a e go e ned by a common p og am, since hey ha e o be necessa - ily synch onized o affic secu i y. In addi ion, o all he affic ligh s in an in e sec ion, he combina- ion o colo s a es du ing a cycle pe iod is always kep alid [19] and i mus ollow he specific affic ules o in e sec ions, in o de o a oid ehicle col- lisions and acciden s. In his sense, we wo k only wi h alid combina ions o colo s a es o each in- e sec ion, which a e kep easible du ing he op i- miza ion p ocess. This a oids in alid combina ions o colo s a es and es ic s he op imiza ion ap- p oach o wo k only wi h easible s a es. F om an en i onmen al poin o iew, since di - e en iming p og ams lead o diffe en flow o e- hicles, hei unde lying speeds, accele a ions, and decele a ions po en ially esul in diffe en le els o emissions [33]. In sho , decele a ions occu be- o e ed ligh s, whe eas g een ligh s cause he ac- cele a ion o ehicles. The e o e, affic emissions a e likely influenced by iming p og am o a - fic ligh s [7]. In his con ex , iming and emission models a e de ailed in he ollowing subsec ions. 3.1. Timing Model Ou main objec i e is o find op imized iming p og ams (TP) o all he signal ligh s loca ed in a gi en u ban a ea wi h he aim o educing he emis- sions and he uel consump ion o ehicles. Specifi- cally, iming p og ams a e e e eed o he ime span ha a se o signal ligh s (in a junc ion) keep hei colo s a es. A he same ime, hese p og ams ha e o coo dina e signal ligh s in adjacen in e sec ions wi h he aim o imp o ing he global flow o ehi- cles ci cula ing acco ding o affic egula ions. Fo his eason, we ha e ocused on a mic oscopic iew o he managemen o affic agen s bu , a he same ime, we wan o e alua e he beha io o all he ehicles in he comple e u ban scena io du ing a gi en ime in e al (mac oscopic analysis). An example o his mechanism can be obse ed in Figu e 1, whe e he in e sec ion wi h id="i" con ains se en phases wi h du a ions 40, 5, 40, 10, 36, 6, and 22 seconds (simula ion s eps). In hese phases, he s a es ha e wel e signals (colo s), co - esponding each one o hem o one o he wel e signal ligh s loca ed in he s udied in e sec ion. These s a es a e he alid ones gene a ed by SUMO (Simula ion o U ban Mobili y) [4] a ending o eal affic ules. In his ins ance, he fi h phase con ains he s a e “G GG G GG” meaning ha six affic ligh s a e in g een (G), and he six o he s a e in ed ( ) du ing 36 seconds. The ol- lowing phase changes he s a e o he ou a - fic ligh s o o he alid combina ion, o example, 4 “yGGG yGGG ” (ymeans yellow) du ing 6 seconds, and so on. The las phase is ollowed by he fi s one, and his cycle ( iming) is epea ed du ing all he analysis ime. All he in e sec ions in he comple e scena io pe o m hei own iming cy- cles o phases a he same ime, hence con o ming he global schedule o signal ligh s. As commen ed be o e, compu ing TP consis s in op imizing he combina ion o phase du a ions o all affic ligh s (in all in e sec ions) wi h he aim o imp o ing he global flow o ehicles. A final indica ion in his sense conce ns he be- ha io o he ehicles in ol ed in a SUMO simu- la ion, ha depends on bo h oad di ec ions and speed. SUMO employs a space-disc e e ex ended model as in oduced by K auß e al. [17]. In his model, he s ee s a e di ided in o cells and he e- hicles ci cula ing h ough he s ee s go om one cell o ano he i bo h, he sense and he di ec- ion a e allowed. The speed o each ehicle depends on i s dis ance o he ehicle in on o i , wi h a p ees ablished maximum speed ypical o u ban a eas (50 km/h in ou s udy). 3.2. HBEFA: Road T affic Emission Model Many esea ch effo s ha e a emp ed o de elop emission o oad anspo a ion models. Due o hei simplici y, a mac oscopic poin o iew has become e y popula [2] in his sense. This kind o model compu es uel consump ion (FC) and emis- sions ac o (EF) based on a e age link speeds in a global way. Tha is, changes o ehicle’s speed and accele a ions le els a e compu ed as mean alues o he whole ne wo k. Fo his eason, many mic o- scopic models ha e been p oposed. In pa icula , HBEFA (Handbook o Emission Fac o s o Road T anspo ) p o ides emission ac o s o all cu en ehicle ca ego ies: PC (Passenge Ca ), LDV (ligh deli e y ehicles), HDV (hea y du y ehicles), u - ban buses, mo o cycles, and o a wide a ie y o affic si ua ions. The HBEFA allows expe s o se- lec diffe en ypes o emission ac o s (EFs). These EFs depend on many a iables o ehicles such as: size, ype, cylinde capaci y, uel mode o he ehi- cle (gasoline o diesel), ype o exhaus echnology (wi h/wi hou ca aly ic con e e ), d i ing s yle (accele a ion and speed), oad g adien , and he main enance [9]. SUMO e sion 0.12.0 [4] allows us o simula e ehicula en i onmen al ac o s based on HBEFA. The e o e, i is possible o define ehicles wi h in- o ma ion abou accele a ion, decele a ion imes, maximum eloci y, and e en hei HBEFA-based emission class (PC, LDV, HDV, e c). Then, a e a simula ion p ocedu e wi h SUMO, we can ob- ain in o ma ion abou CO,NOx, uel consump- ion, and o he pollu an agen s o e alua e he ob ained iming p og ams by ou PSO. Fo his s udy, we e ie e he in o ma ion abou CO and NOxemissions, and uel consump ion. 4. Op imiza ion S a egy This sec ion desc ibes ou op imiza ion ap- p oach o compu e he op imal iming p og ams o affic ligh s. I de ails he solu ion encoding, he fi ness unc ion, and finally he global op i- miza ion p ocedu e. 4.1. Solu ion Encoding In ou app oach, he op imal TP is encoded by means o a ec o o in ege s (see Figu e 1) ollow- ing he SUMO s uc u e o p og amming cycles ( iming), whe e each elemen ep esen s a phase du a ion o one s a e o he signal ligh s in ol ed in a gi en in e sec ion. In spi e o i s simplici y, his solu ion ep esen- a ion allows ou PSO o ake in o accoun he de- pendency o a iables (epis asis), no only be ween phase du a ions o a s a e o affic ligh s in an in- e sec ion, bu also be ween affic ligh s in adja- cen ones. 4.2. Fi ness Func ion In o de o e alua e each iming p og am solu- ion (s) gene a ed by ou PSO, he ollowing fi ness unc ion is minimized, which conside s he in o - ma ion ob ained om he e en s happening du ing he affic flow analyzed: 5 F p(s) = (CO+NOx+F u)(s)+ω·(G (s)+(C(s)×S ) V2(s) + P) (1) The main objec i e is o maximize he numbe o ehicles ha each hei des ina ions (V) and minimize bo h, emission le els (CO and NOx) and uel consump ion (Fu), du ing he simula ion ime (S ). The global ip ime o all he ehicles (G ) has o be also minimized. The numbe o ehicles ha a i e o hei des ina ions is squa ed (V2(s)) in o de o p io i ize i o e he o he e ms and ac o s. Ob iously, he numbe o ehicles ha do no each hei des ina ions and emain ci cula - ing C(s) a e he simula ion has o be minimized. The global ip ime conce ns an agg ega ion o he ip ime o ehicles ha each hei des ina ions du ing he simula ion p ocess. On he con a y, e- hicles wi h uncomple ed a els C(s) consume all he simula ion ime S and hen, an addi ional pe- naliza ion is induced by mul iplying hese wo ac- o s. I is wo h men ioning ha e ms in Equa- ion 1 a e in he ange o alues [1e+ 0 · · · 5e+ 2] and he e o e, addi ional weigh ing alues we e no conside ed in his o mula ion. Only he alue ω which is se o 0.5 is conside ed in o de o en- hancing en i onmen al e ms in he o e all fi ness compu a ion. Finally, he balanced p opo ion o colo s in he phase du a ion o he s a es should p omo e hose s a es wi h mo e affic ligh s in g een loca ed in s ee s wi h a high numbe o ehicles ci cula ing, and affic ligh s in ed loca ed in s ee s wi h a low numbe o ehicles mo ing. The p opo ion o colo s in each phase (ph) o all he l in e sec ions can be o mula ed as ollows: P= l ∑ k=0 ph ∑ j=0 sk,j ·(Gk,j k,j ),(2) whe e Gk,j is he numbe o affic ligh s in g een, and k,j is numbe o affic ligh s in ed in he phase s a e j(wi h du a ion sk,j) and in he in e sec ion k. The minimum alue o edk,j is 1 in o de o a oid di ision by 0. 4.3. Op imizing Timing P og ams wi h PSO The op imiza ion s a egy is composed by wo main pa s: he Pa icle Swa m Op imize (PSO), and he simula ion p ocedu e wi h he SUMO a - fic mic osimula o . The PSO algo i hm [16] is a popula ion-based me aheu is ic inspi ed by he social beha io o bi ds wi hin a flock, and was ini ially designed o con inuous op imiza ion p oblems. In PSO, each po en ial solu ion o he p oblem is called pa icle posi ion and he popula ion o pa icles is called he swa m. We ha e ollowed he specifica ion o he S anda d PSO 2011 [10]. In his algo i hm, each pa icle posi ion xiis upda ed each i e a ion gby means o he Equa ion 3. xi g+1 =xi g+ i g+1 (3) whe e e m i g+1 is he eloci y o he pa icle, gi en by he Equa ion 4. i g+1 =w· i g+G i g−xi g+HS(G , ∥G −xg∥) (4) wi h G i g=xi g+p′i g+l′i g 3(5) and p′i g=xi g+c·(pi g−xi g) (6) l′i g=xi g+c·(li g−xi g) (7) In his o mula, pi gis he bes solu ion ha he pa icle ihas seen so a , li gis he bes pa icle o a neighbo hood o ko he pa icles (also known as he social bes ) andomly (uni o m) selec ed om he swa m, and wis he ine ia weigh o he pa - icle (i con ols he ade-off be ween explo a ion and exploi a ion). The accele a ion coefficien c > 1 is a no mal (Gaussian) andom alue wi h µ= 1/2 and ρ= 1/12. This coefficien is sampled anew o each componen o he eloci y ec o . Finally, HS is a dis inc i e elemen o he S anda d PSO 2011 wi h ega ds o he p e ious ones. I is basi- cally a andom numbe gene a o wi hin a Hype - sphe e space, wi h G as cen e o g a i y. Tha is, G is calcula ed as he equidis an poin o p′ g,l′ g, and xg. This is a new o a ion in a iance mecha- nism p o ided by he S anda d PSO 2011 o (pos- 6 sibly) a oid he in insic coo dina e dependence showed by all p e ious e sions o PSO [10]. Since he op imal SLTP equi es solu ions en- coded wi h a ec o o in ege s ( ep esen ing phase du a ions), we ha e used he quan isa ion me hod p o ided in he s anda d specifica ion o PSO 2011 [10]. This quan isa ion is applied o each new gene a ed pa icle (in Equa ion 3), and ans o ms he con inuous alues o pa icles o disc e e ones. I consis s o a Mid-Th ead uni o m quan ise me hod as specified in Equa ion 8. The quan um s ep is se he e o ∆ = 1. Q(x)=∆· ⌊x/∆+0.5⌋(8) Algo i hm 1 S anda d PSO 2011 o he SLTP 1: ini ializeSwa m() 2: while g < maxI e a ions do 3: o each pa icle xi gdo 4: bn g=bes Neighbou Selec ion(xi g, n) 5: i g+1=upda eVeloci y(w, i g, xg, φ1, pg, φ2, bn g) 6: xi g+1=Q(upda ePosi ion(xi g, i g+1)) 7: e alua e(xi g+1) //SUMO Simula ion and Eq. 1 8: pi g+1=upda e(pi g) 9: end o 10: end while Algo i hm 1 desc ibes he pseudo-code o he S anda d PSO 2011 o he op imal SLTP. The al- go i hm s a s by ini ializing he swa m (Line 1). The co esponding elemen s o each pa icle (solu- ions) a e ini ialized wi h andom alues ep esen - ing he phase du a ions. These alues a e wi hin he ime in e al [5,60] ∈Z+, and cons i u e he ange o possible ime spans (in seconds) a affic ligh can kep a signal colo (only g een o ed, he ime o yellow is a cons an alue se in sumo o 5 seconds). Then, o a maximum numbe o i e - a ions, each pa icle flies h ough he sea ch space upda ing i s eloci y and posi ion (Lines 4, 5, and 6), i is hen e alua ed (Line 7), and i s pe sonal bes posi ion piis also upda ed (Line 8). Finally, he bes pa icle ound so a is e u ned. The simula ion p ocedu e is hen used o as- signing a quan i a i e quali y alue (fi ness) o he solu ions, hus leading o op imized iming p o- g ams ailo ed o a gi en u ban scena io ins ance. This ask is ackled by he SUMO mic oscopic a - fic simula o , which accep s new iming p og ams o affic ligh s and compu e he equi ed alues in Equa ion 1. When ou PSO gene a es a new solu ion, i is used o upda ing he iming p og am. Then, SUMO is s a ed o simula e he ins ance wi h s ee s, di ec ions, obs acles, affic ligh s, ehi- cles, speed, ou es, e c., unde he new defined schedule o iming p og ams. A e he simula ion, SUMO e u ns he global in o ma ion necessa y o compu e he fi ness unc ion. Each solu ion e alua ion (Line 7 a Algo i hm 1) equi es a sim- ula ion p ocedu e since ehicle ou es in SUMO a e gene a ed de e minis ically. Each new iming p og am is hen loaded o each simula ion p oce- du e. In his sense, wha eal affic ligh human schedule s ac ually demand a e cons an iming p og ams o specific a eas and o p ees ablished ime pe iods ( ush hou s, noc u ne pe iods, e c.), which led us o ake his ocus. 5. Expe imen s and Resul s In his sec ion we p esen he expe imen al amewo k ollowed o assess he pe o mance o ou PSO algo i hm o c ea ing op imized TPs. Fi s , we desc ibe he scena io ins ances, he im- plemen a ion de ails o ou app oach, and he pa- ame e se ings. La e , esul s and compa isons o o he echniques a e p esen ed. A s udy o he esul ing iming p og ams is also ca ied ou in o de o show he ac ual benefi s o using ou p o- posal and hei impac in o he li ing en i onmen o u ban a eas. 5.1. U ban Scena io Ins ances As we a e in e es ed in de eloping an op imiza- ion sol e capable o dealing wi h close- o- eali y gene ic u ban a eas, we ha e gene a ed wo sce- na ios by ex ac ing ac ual in o ma ion om eal digi al maps. These wo scena ios co e simila a - eas o app oxima ely 0.75 km2, and hey a e phys- ically loca ed in he ci ies o Malaga and Se ille, in Spain. The in o ma ion used conce ns: affic ules, affic elemen loca ions, buildings, oad di- ec ions, s ee s, in e sec ions, e c. Mo eo e , we 7 Figu e 2. P ocess o c ea ion o eal-wo ld ins ances o s udy. U ban cen e o Malaga (36◦43’01”N 4◦25’58”O) and Se ille (37◦38’14”S 5◦97’23”O) ins ance iews. A e selec ing he a ea o in e es (Google Ea h iew), i is in e p e ed by means o he OpenS ee Map ool, and hen expo ed o SUMO o ma ha e se he numbe o ehicles ci cula ing, as well as hei speeds by ollowing cu en specifica ions a ailable in he Mobili y Delega ion o he Ci y Hall o Malaga (h p://mo ilidad.malaga.eu/). This in o ma ion was collec ed om senso ized poin s in ce ain s ee s ob aining a measu e o affic densi y in se e al ime in e als. In he case o Se ille we consul ed he Mobili y Delega ion o Se ille Council (h p://www. ajano.com/). In Figu e 2, he selec ed a eas o he wo ci ies a e shown wi h hei co esponding snapsho s o Google Ea h, OpenS ee Map, and SUMO. This figu e illus a es he p ocess o gene a ing he a - fic ne wo k ins ances. The specific ea u es o hese a eas a e as ollows: (i) Malaga. In he zone be ween he ci y cen- e and he ha bo . This second scena io (Figu e 2, op) is composed by s ee s wi h diffe en wid hs and leng hs, and se e al oundabou s. I con ains junc ions including om 4 o 16 affic ligh s each one. The main a enues ound in his a ea a e: Andaluc´ıa, Am´e icas and Au o a a enues, Hile a, and Lehmbe g Ruiz s ee s. (ii) Se ille. Loca ed in he popula dis ic o Ne i´on in he ci y cen e o Se ille (Figu e 2, bo om), i is made up o in e sec ions be ween s ee s including each one om 4 o 17 affic ligh s. The comple e a ea shows a ep esen a i e o ganiza ion wi h almos all he junc ions connec ing be ween h ee and ou s ee s. The main a enues c ossing his neighbo hood a e: Men´endez Pelayo, Ed- ua do Da o, San F ancisco Ja ie , Mon o o, Gal ´an, and Buha ´ıa. We ha e chosen hese wo scena ios since hey cons i u e diffe en me opoli an a eas wi h he - e ogeneous s uc u es and affic o ganiza ions. The numbe o s udied in e sec ions is 70 o he wo ins ances, wi h 250 ci cula ing (PC and LDV ypes) ehicles h ough each one o hem. We ha e o no ice ha in spi e o ha ing in bo h ins ances a simila numbe o in e sec ions (70), he num- be o signal ligh s is no exac ly he same, since hey con ain diffe en in e sec ion shapes (304 affic ligh s in Malaga and 368 ones in he case o Se ille). In he s udy, each ehicle pe o ms i s own ou e om i s own o igin o des ina ion ci cula ing wi h a maximum speed o 50 km/h ( ypical in u ban a - eas). The ou es we e p e iously gene a ed by ol- lowing andom pa hs. The simula ion ime was se 8 Table 1 SUMO and PSO pa ame e s Sol e Phase Pa ame e Value Simula ion Time (s eps) 500 s A ea 0.75 km2 SUMO De ails Numbe o Vehicles 250 Vehicle Speed 0-50 km/h Vehicles Types PC/LDV N. o S udied In e sec ions 70 Max. N. o E alua ions 9,000 Swa m Size 30 Pa icle Size (N. T affic Ligh s) 368 304 PSO Pa ame e s Local Coefficien (φ1) 2.0 Social Coefficien (φ2) 2.0 Maximum Ine ia (wmax ) 0.5 Minimum Ine ia (wmin ) 0.1 Veloci y T unca ion Fac o (λ) 0.5 o 500 seconds (i e a ions o mic osimula ion) o each ins ance. This ime was de e mined as a max- imum ime o a ca o comple e i s ou e, e en i i mus s op in all he affic ligh s along i s way. Vehicles a e loca ed in hei own o igins and hey mo e om he ini ial simula ion s eps. When a e- hicle lea es he scena io ne wo k, i eaches i s des- ina ion and i will no appea again. 5.2. Expe imen al Se up We ha e used he implemen a ion in C++ o he PSO algo i hm p o ided by he MALLBA [1] amewo k. The simula ion phase is ca ied ou by execu ing ( o he e alua ion o pa icles) he a - fic simula o SUMO elease 0.12.0 o Linux. The expe imen s we e pe o med in he compu ing a- cili ies o he Depa men o Compu e Science o he Uni e si y o Malaga (Spain). Mos o hem a e equipped wi h mode n dual co e p ocesso s, 1GB RAM, and Linux Debian O.S. They ope a e unde a Condo [30] middlewa e pla o m ha ac s as a dis ibu ed ask schedule (each ask dealing wi h one independen un o PSO). Fo each scena io ins ance we ha e ca ied ou 30 independen uns o ou PSO. The swa m size was se o 30 pa icles pe o ming 300 i e a ion s eps, hence esul ing a numbe o 9,000 solu ion e alu- a ions (SUMO simula ions) pe un and ins ance. As p e iously men ioned, he pa icle size di ec ly depends on he numbe o affic ligh s o each in- Algo i hm 2 Pseudocode o RANDOM 1: gene a e(x) //ini ial solu ion 2: i←0 3: while i < Max Numbe o E alua ions do 4: gene a e(xi) //new solu ion 5: i (x)≥ (xi) hen 6: x←xi 7: end i 8: i←i+ 1 9: end while s ance. The emaining pa ame e s a e summa ized in Table 1. These pa ame e s we e se a e p elim- ina y execu ions. Specific pa ame e s o PSO we e selec ed as ecommended in he s udy abou he con e gence o his algo i hm in [8]. Addi ionally, we ha e implemen ed a Ran- dom Sea ch algo i hm, also in he scope o he MALLBA lib a y, wi h he aim o es ablishing compa isons agains ou PSO. Thus, by pe o m- ing he same expe imen a ion p ocedu e wi h PSO and Random Sea ch algo i hm we expec o ob ain some insigh s in o he powe o ou p oposal (how much in elligen i is). The pseudocode o he Ran- dom Sea ch algo i hm (RANDOM om now on) is shown in Algo i hm 2. The maximum numbe o e alua ions was se o 9,000, as o PSO. SUMO p o ides a de e minis ic algo i hm o gene a ing cycle p og ams (SCPG). Then we also compa e he cycle p og ams ob ained by ou PSO agains he ones ob ained by SUMO. This las al- go i hm basically consis s in assigning o he phase du a ions o he affic logics esh alues in he ange o [6,31], acco ding o h ee diffe en ac o s: (i) he p opo ion o g een s a es in he phases, (ii) he numbe o incoming lanes o he in e sec- ion, and (iii) he b aking ime o he ehicles app oaching o hei affic ligh s. Fu he in o ma ion abou his algo i hm can be ound in [4]. 5.3. E ol abili y o PSO on he SLTP Landscape P e ious o he pe o mance expe imen a ion, we ha e ca ied ou a Fi ness-Cloud analysis [31] wi h he aim o e i ying whe he ou op imiza ion s a egy wi h PSO is able o success ully ackle he signal ligh iming p oblem o no , o he scena io 9 [19] J. Leung, L. Kelly, and J. H. Ande son. Handbook o Scheduling: Algo i hms, Models, and Pe o mance Analysis. 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