The city-airport connection in the low-cost carrier era: Implications for urban transport planning

The ci y-ai po connec ion in he low-cos ca ie e a: Implica ions o u ban anspo planning José I. Cas illo-Manzano * Facul ad de Ciencias Económicas y Emp esa iales, Uni e si y o Se ille, A da. Ramón y Cajal, 1, 41018 Se ille, Spain Keywo ds: Ai po g ound access U ban anspo planning Passenge beha io Low-cos ai lines abs ac This a icle, examines di e ences be ween he beha io o passenge s o low-cos and ne wo k ai lines when choosing hei anspo mode o a el o ai po s. I is ound ha a passenge flying wi h a low- cos ca ie is 6% less likely o ake a axi o he ai po , bu mo e han 4% mo e likely o d i e a en ed ca and 2% mo e likely o use public anspo han a use o a ne wo k ca ie . Ó2010 Else ie L d. All igh s ese ed. 1. In oduc ion One o he ea u es o ai anspo de egula ion has been he de elopmen o low-cos ca ie s (LCCs) and hei new manage- men model, he main cha ac e is ics o which ha e unde mined belie in he indus y’s p e ious s uc u e, p ocedu es, and business models and b ough abou majo changes in he s a egies and beha io o he di e en economic agen s on bo h he supply and he demand sides o he ai anspo indus y. On he supply side, o example, he e has been an upsu ge in he use and impo ance o many seconda y, o en unde used (F ancis e al., 2004), egional ai po s compa ed o he hubs (Reynolds-Feighan, 2001). These ai po s ha e seen hei ba gaining powe diminished by he agg essi e ba gaining me hods o LCCs (Ba e , 2004) seeking o achie ing minimum ai po cha ges. B oadly speaking, compe i ion be ween ai po s o a ac LCCs can be said o be on he inc ease (Pels e al., 2009). LCCs’use o seconda y ai po s also means ai line a ficis u he sca e ed ac oss mul iple ai po s se ing he same me opoli an a ea. This is a d awback o ai po access planning, he main pu pose o which is o educe he ma ke sha e o p i a e ehicle use by bo h passenge s and ai po s a (Humph eys and Ison, 2005 on policies o changing ai po employee a el beha io ), as p i a e ehicles a e he modes o anspo a ion ha mos con ibu e o noise, conges ion le els, and ai pollu ion (G aham, 2008) in ai po hin e lands. On he demand side, one o he mos impo an changes b ough abou by he LCCs o ou analysis is he inc eased demand o ai anspo se ices by younge and p ice-sensi i e a ele s (O’Connell and Williams, 2005). A p io i his migh be su mised o impac on he ma ke sha es o he a ious modes o anspo a ion ha passenge s use o ge o ai po s and benefi he less expensi e. This would explain he in e es ha some LCCs in Eu ope ha e in ei he de eloping o wo king in coope a ion wi h bus and coach companies (such as Te a ision, igh ly linked o he Ryanai g oup, o he Easybus connec ions o he ai po s a ound London whe e Easyje ope a es). This pape looks a di e ences in he beha io o LCC and ne wo k ai line passenge s when choosing o a mode o anspo a ion o an ai po ? And i hese di e ences do exis , wha a e hey, and wha e ec s do hey ha e on ai po g ound access planning? Apa om some heo e ical conside a ions, he e ha e been ewe empi ical s udies looking a hese ques ions. 1 2. Da a The da abase comp ised 20,383 passenge s, 6247 o whom we e LCC passenge s. All o hese we e in e iewed in depa u e lounges *Tel.: þ34 678542833. E-mail add ess: [email p o ec ed] 1 Eche a ne (2008) supposed ha logic would dic a e ha low-cos passenge s would gene a e a g ea e demand o ca pa king acili ies a egional and seconda y ai po s whils adi ional ai line passenge s would p esen a highe axi usage. De Neu ille (2006) simila ly su mised ha p ice-conscious a ele s on low- cos ca ie s a e he passenge s mos likely o use public anspo , and ha he ma ke o ai po access is now mo e closely aligned wi h he adi ional ma ke o u ban public anspo . Con en s lis s a ailable a ScienceDi ec Jou nal o Ai T anspo Managemen jou nal homepage: www.else ie .com/loca e/jai aman 0969-6997/$ esee on ma e Ó2010 Else ie L d. All igh s ese ed. doi:10.1016/j.jai aman.2010.02.005 Jou nal o Ai T anspo Managemen 16 (2010) 295e298 a se en di e en Spanish seconda y ai po s, none o which had e ficien ail-based public anspo a ion o he ci y a he ime he su ey campaign was conduc ed (Table 1). 2 The size o he sample and he la ge numbe o passenge a ibu es and cha ac e is ics p o ide an e ec i e da abase o an analysis o he ai anspo indus y in Medi e anean coun ies (see Cas illo-Manzano and López-Valpues a (in p ess) o ano he applica ion o his da abase). Themain ea u eso AENA’s( heSpanish PublicAi po Au ho i y) 2005e2007 su ey ha is used o cons uc he da abase a e lis ed in Table 1. AENA has o a ed annual su eys ound some o i s ai po s since 1996. The me hodology used has, he e o e, finely honed and he e has been su ficien unding o ob ain samples ha closely app oxima e o a andom p ocess o a la ge popula ion and wi h a low sampling e o . As wi h simila da abases, each obse a ion was weigh ed acco ding o he numbe o passenge s on he fligh so ha he sample could be expanded o ep esen he popula ion. 3. Me hodology Ai po g ound access sys ems play an impo an ole in ai po planning and managemen (Tsamboulas and Nikole is, 2008) and a e inc easingly seen as a majo p oblem wo ldwide. Howe e , ew s udies ha e been done o da e on he choice o means o ai po access. T adi ionally, a ele s’choices o modes o anspo a ion ha e been he objec o mic oeconome ic analyses using disc e e choice models. Since he ocus o ea lie s udies has been on ai po g ound access and possible di e ences in he decisions and beha io o passenge s acco ding o whe he hey a e business o ou ism/ leisu e a ele s i makes sense o complemen his wo k wi h a s udy o di e ences in passenge s’ai po g ound access mode choice beha io depending on whe he hey fly on low cos o ne wo k ca ie s. LCC g ow h is al e ing he classic dis inc ion be ween business and ou is passenge s. Fi s ly, he e a e gene - ally no specific sea s assigned o business passenge s in hese ai planes making i di ficul o dis inguish business a ele s om o he passenge s on low-cos ai line fligh s. Despi e his, as he ma ke sha e o LCCs ises, business passenge s inc easingly choose hei se ices. Wi hou ejec ing disc e e demand models, he p oposed me hodology is amed by s a is ical causal in e ence and based on an es ima ion o he e ec ha a specific measu e o ac can ha e on one o mo e ele an a iables. In con as wi h adi ional analyses, his me hodology allows consis en es ima o s o he e ec s o he e alua ed measu e o be ob ained (Ro ni zky and Robins, 1995) by de e mining and isola ing he possible impac o addi ional con amina ing a iables. S a ing wi h an N-size andom sample (Table 2)wedefined he bina y a iable D ha indica es whe he he obse a ion co esponds oapassenge flyingwi hanLCC(D i ¼1)o a adi ionalai line(D i ¼0). Thus, ou Nobse a ions we e di ided in o N 1 and N 0 obse a ions (LCC s. adi ional ai line). In ou case, N 1 s ands o he 6247 passenge s who used an LCC, while N 0 ep esen s he emaining 14,136passenge s. Thus, hecondi ion ha s a es ha “N 0 isa leas he same o de o magni ude o N 1 ”is sa isfied (Abadie and Imbens, 2006). We defined he ou come a iable Y j as he decision o use a specific mode o anspo a ion o a gi en ci y-ai po ans e . In ou case, we conside ed fi e possible modes o anspo a ion: axi, public bus, ho el bus, p i a e ca and en ed ca . Using he po en ial-ou come no a ion o he RCM (Rubin, 1974), he esponse a iable was gi en as Y ij (1) when ideno ed a passenge using an LCC and as Y ij (0) when ico esponded o a passenge flying on a adi ional ai line. Hence, Y ij was equal o Yij ¼DijYijð1Þþ1DijYijð0Þ(1) The A e age T ea men E ec (ATE) o he LCC on he selec ed sample o each o he analyzed modes o anspo a ion was (Imbens, 2004) a j¼EYijð1ÞYijð0Þ¼1 NX N i¼1Yijð1ÞYijð0Þ(2) We also defined a K-dimensional ec o o obse ed co a ia es as X. A iad was he e o e obse ed o each indi idual (D ij ,Y ij ,X ij ). The aim was o gua an ee wo condi ions in he e alua ion p ocess. Fi s ly, he uncon oundedness condi ion (Rosenbaum and Rubin, 1983), also known as he condi ional independence assump ion,i.e.D ðYð1Þ;Yð0ÞÞjX. Secondly,we equi edcompliance wi h he o e lap condi ion, acco ding o which e e y alue o ec o Xis associa ed wi h a posi i e p obabili y ha allows (Ho z e al., 2005). The condi ion can be ead as ollows: 0 <PðD¼1jXÞ<1. To gua an ee bo h condi ions, he e alua ion p ocess con o med o he ollowing phases (Heckman and Vy lacil, 2005). 3.1. Es ima ion o p opensi y sco e Fi s ly, we no ed whe he he obse a ions co esponded o a passenge using an LCC (D i ¼1) o a legacy ai line (D i ¼0). We hen es ima ed he p opensi y sco e, defined by Rosenbaum and Table 1 Technical da a su ey. Ai po Alican e Bilbao Se ille Valencia San iago Valladolid Za agoza Ai po a fic in 2008 9,578,308 4172,901 4,391,794 5779,336 1,917,434 479,716 594,952 In o ma ion ga he ing Ques ionnai e A ailable in 12 languages A ailable in 6 languages A ailable in 5 languages A ailable in 4 languages Gene al Depa ing passenge s >15 yea s o age. Sampling (be o e weigh ing) Sample size 2420 3182 4140 4965 3497 1042 1137 Sampling me hod S a ified by a fic segmen s wi h selec ions o fligh s o each ou e and g oups o passenge pa icipan s done by sys ema ic sampling. Sampling e o a 2% 1.7% 1.5% 1.4% 1.5% 2.7% 2.5% Numbe o wa es 1 Field wo k Time pe iod Sep . 22e28 May 4e10 June 6e12 July 12e18 June 28eJuly 4 June 15e21 June 15e21 Loca ion Depa u e lounges. Time able MoneSun. 6ame10pm shi s ex ended du ing peak a fic pe iods. Yea 2006 2007 2006 2006 2006 2005 2006 a E o ¼Kffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðNnÞ=ðNnÞ p ffiffiffiffiffiffiffiffiffiffiffi pq=n p;whe e: N¼popula ion size; n¼sample size; p¼q¼0.5 complemen a y p obabili ies o he answe o an e en a he poin o g ea es inde e minacy; k¼pa ame e o he le el o answe o an e en , whe e k¼2 o a 95.45% confidence le el. 2 A subway line connec ing Valencia o i s ai po was opened a ew mon hs a e he su ey was conduc ed. J.I. Cas illo-Manzano / Jou nal o Ai T anspo Managemen 16 (2010) 295e298296 Rubin (1983) as he condi ional p obabili y o “pa icipa ing in he e alua ed measu e,”gi en a ec o Xo obse ed co a ia es. Di e en bina y esponse models can be used o es ima e he p opensi y sco e depending on he choice o hypo hesis ega ding he configu a ion o he Fdis ibu ion unc ion. In his case, we used he bina y esponse model (e.g. logi o p obi ) ha maxi- mized he log pseudo-likelihood: 3 (X)¼P(D¼1jX)¼F( b X) (3) whe e b is he ec o o pa ame e s associa ed wi h X. He e, X comp ised he 20 co a ia es p esen ed in Table 2 along wi h hei desc ip i e s a is ics. 3.2. Es ima ion o a e age ea men e ec In a second phase, we calcula ed he a e age ea men e ec o he measu e being e alua ed on he esponse a iable, in ou case, he p obabili y o a passenge choosing a specific mode o ans- po a ion o ge o he ai po . The a e age e ec on he selec ed sample was es ima ed using: a ¼E½ a ðXÞ (4) In ou case, wi h Ybeing a disc e e choice a iable (using one o he fi e possible modes o anspo a ion), we used a mul inomial logi model o es ima e he a e age ea men e ec . The e o e, acco ding o Hi ano and Imbens (2001), he mul inomial logi p obabili y o mula o a passenge iwhen a pe son chooses a mode o anspo a ion j o fi e ca ego y ou comes and equency weigh s is pij ¼P ðyi¼jÞ¼(1=1þS5 m¼2eðx i s m Þ;i j ¼1 eðx 0 i s m Þ=1þS5 m¼2eðx i s m Þ;i js1(5) whe e: x’ i s m¼ s m0þ a Diþ s m1b 3 ðxiÞþ s m2b 3 ðxiÞEhb 3 ðxÞiDiþuij (6) Table 2 Co a ia es and hei desc ip i e s a is ics. Va iable Desc ip ion Mean S d. De . a) Socio-demog aphic ac o s and employmen s a us. Base ca ego y includes he unemployed. Sex 1 ¼male; 0 ¼ emale. 0.537 0.498 Age 1 ¼30 and unde ; 2 ¼31-49; 3 ¼50e64; 4¼65 and abo e. 1.985 0.825 Non-Spanish 1 ¼non-Spanish passenge ; 0 o he wise. 0.300 0.458 F equen flye Numbe o fligh s aken by passenge in p e ious wel e mon hs: 1¼0fligh s; 2 ¼1-3; 3 ¼4-12; and 4 ¼o e 12 fligh s. 2.433 1.003 Homemake 1 ¼passenge is homemake ; 0 o he wise. 0.032 0.175 Sel -employed 1 ¼passenge is non-sala ied, gene ally sel -employed; 0 o he wise. 0.168 0.374 Sala ied wo ke 1 ¼passenge is sala ied wo ke ; 0 o he wise. 0.584 0.493 S uden 1 ¼passenge is s uden ; 0 o he wise. 0.108 0.311 Re i ed 1 ¼passenge is e i ed; 0 o he wise. 0.082 0.274 b) T ip ca ego y. Base ca ego y includes passenge s isi ing iends and ela i es (VFR) on a ne wo k ca ie . Low-cos ca ie 1 ¼passenge is flying wi h a low-cos ca ie (LCC); 0 o he wise. 0.306 0.461 Vaca ion 1 ¼ aca ion ip; 0 o he wise. 0.441 0.497 Business 1 ¼business ip; 0 o he wise. 0.310 0.462 Leng h o s ay (LOS) 1 ¼passenge e u ns a e one nigh o ea lie ; 2 ¼ wo nigh s o a week; 3 ¼se en o 14 days; 4 ¼mo e han wo weeks bu less han a mon h; 5 ¼mo e han a mon h. 2.408 0.955 c) Social in e ac ion. Base ca ego y includes passenge s a eling wi hou child en and no accompanied o/ om he ai po . G oup size 1 ¼ a eling alone; 2 ¼ wo people; 3 ¼ h ee o mo e people. 1.684 0.741 Child en 1 ¼ a eling wi h child en; 0 o he wise. 0.078 0.267 Seen o 1 ¼someone sees passenge o a ai po ; 0 o he wise. 0.279 0.448 d) En i onmen . Base ca ego y includes passenge s a eling on wo kdays. Weekend 1¼su ey aken on Sa u day o Sunday, when axi a e (fla o egula ) is highe ; 0 o he wise. 0.265 0.441 Ho el 1 ¼passenge depa s om a ho el, boa ding house, o o he paid accommoda ion; 0 o he wise. 0.214 0.410 Home 1 ¼passenge depa s om own p ima y o seconda y home; 0 o he wise. 0.523 0.499 F iends o amily 1 ¼passenge depa s om home o iends o ela i es; 0 o he wise. 0.142 0.349 Table 3 P obi es ima ion o he p opensi y sco e. Co a ia e Coe ficien Co a ia e Coe ficien Sex 0.031 Business 0.502*** Age 0.039 Leng h o s ay 0.077*** Non-Spanish 0.885*** G oup Size 0.063*** F equen flye 0.029*** Child en 0.110 Homemake 0.089 Seen o 0.004 Sel -employed 0.028 Weekend 0.092** Sala ied wo ke 0.027 Ho el 0.183*** S uden 0.205** Home 0.035 Re i ed 0.018 F iends o amily 0.155*** Vaca ion 0.038 Cons an 0.907*** No. o obse a ions (be o e weigh ing) 19930 Log pseudo-likelihood 14640197 Pseudo R 2 0.137 Wald Chi 2 wi hou clus e s (p- alue) 4638753.72 (0.000) No e: *, **, o *** indica e coe ficien significance a he 10%, 5%, and 1% le els calcula ed om s anda d e o s obus o he e oskedas ici y and clus e ed by ai po o o igin. Table 4 Mul inomial es ima ion o ele an e ec s. LCC (D i ) Cons an b 3 ðx i Þð b 3 ðx i ÞE½b 3 ðxÞÞD i Public Bus 0.227* 1.825*** 0.794 1.154*** Ho el Bus 0.017 3.290*** 4.584*** 2.912*** Ren -a- ca 0.169* 1.251 1.966* 0.178 Taxi 0.218*** 0.080 0.795* 0.428 No e: *,**,o *** indica ecoe ficien significancea he10%,5%, and1%le els,calcula ed om s anda d e o s obus o he e oskedas ici y and clus e ed by ai po o o igin. J.I. Cas illo-Manzano / Jou nal o Ai T anspo Managemen 16 (2010) 295e298 297 3.3. The ma ginal e ec o b a As in bina y ou come models, only he sign o he coe ficien is di ec ly in e p e ed in mul inomial models. Thus, a posi i e coe ficien in he mul inomial logi means ha , as he eg esso inc eases, al e na i e jis mo e likely o be chosen han al e - na i e k. In o de o make he eading o he esul s easie , he odds a ios o ela i e- isk a ios o he bina y a iable D i (using an LCC) a e also conside ed. Following Came on and T i edi (2009), he ela i e p obabili y o odds a io o choosing al e - na i e j a he han al e na i e 1, also called he base ou come, is gi en by P ðyi¼jÞ P ðyi¼1Þ¼ex 0 i s j (7) Howe e , mul inomial logi coe ficien s and odds a ios only allow he subs i u abili y ela ionship be ween pai s o op ions o be s udied, ha is, he ela ionship be ween each op ion and he base ca ego y, which in ou case is he p i a e ca . In o de o o e come his ocus on pai wise opposi ions, we calcula ed he ma ginal e ec s o D i ac oss all conside ed op ions. This enabled he di ec subs i u abili y ela ionship, should one exis , o be ob ained be ween he fi e modes o anspo a ion o LCC passenge s (D i ¼1), as opposed o he con ol g oup including all passenge s flying wi h legacy ai lines. Acco ding o Came on and T i edi (2009), he ma ginal e ec s a he mean (MEMs) o he mul inomial logi model a e: d pij d Di ¼pij a j a i(8) a i¼Xpil a l; whe e is a p obabili y-weigh ed a e age o a l . 4. Resul s Table 3 summa izes he esul s o p opensi y sco e es ima ion (Model 3), in he con ex o he 20 co a ia es in Table 2. A p obi specifica ion was op ed o as i maximized he log pseudo- likelihood. A mul inomial logi specifica ion (Models 5 and 6) was hen used o es ima e he a e age ea men e ec s. The esul s a e p esen ed in Table 4. Finally, he ma ginal e ec a he mean o an LCC passenge is es ima ed by applying Model 8 (Table 5). 5. Conclusions The esul s show ha being a LLC passenge educes he p ob- abili y o choosing a axi o go o he ai po by 5.85%, bu inc eases he likelihood o a LLC passenge and ha o choosing a en ed ca o a public mode o anspo a ion by abou 4% and 2%, espec i ely. The ou come canno be explained by ac o s such as LCC passenge s ha ing a lowe income le el han he ne wo k ca ie passenge s because he sco es a e co ec ed using income le el p oxy a iables. I would he e o e be mo e app op ia e o speak o passenge s who a e inc easingly p ice-conscious. The e could, howe e , be a h eshold ype p ice e ec on mode choice i he e is a s ong psychological shock in paying mo e o a 10 km axi ide han o a 1000 km ai plane fligh . The a e age ci y-ai po dis ance o he se en ai po s in ou sample is 9.86 km, wi h a s anda d de ia ion o only 1.21. Gene ally-speaking, 10 km is a easonable ci y-ai po dis ance measu e o Spanish seconda y ai po s. Acknowledgemen s The au ho would like o exp ess his g a i ude o AENA o i s suppo . The au ho is also g a e ul o Kenne h Bu on, Guido Imbens and he anonymous e iewe s o e y help ul commen s. Re e ences Abadie, A., Imbens, G.W., 2006. La ge sample p ope ies o ma ching es ima o s o a e age ea men e ec s. Econome ica 74, 235e267. Ba e , S.D., 2004. How do he demands o ai po se ices di e be ween ull- se ice ca ie s and low-cos ca ie s? Jou nal o Ai T anspo Managemen 10, 33e39. Came on, A.C., T i edi, P.K., 2009. Mic oeconome ics Using S a a. S a a Publica ion, College S a ion. Cas illo-Manzano, J.I., López Valpues a, L. The decline o he adi ional a el agen mode. T anspo a ion Resea ch E in p ess, doi:10.1016/j. e.2009.12.009. De Neu ille, R., 2006. Planning ai po access in an e a o low-cos ai lines. Jou nal o he Ame ican Planning Associa ion 72, 347e356. Eche a ne, R., 2008. The impac o a ac ing low cos ca ie s o ai po s. In: G aham, A., Papa heodou u, A., Fo sy h, P. (Eds.), A ia ion and Tou ism. Ashga e Publishing, Hampshi e. F ancis, G., Humph eys, I., Ison, S., 2004. Ai po s’pe spec i es on he g ow h o low-cos ai lines and he emodelling o he ai po eai line ela ionship. Tou ism Managemen 25, 507e514. G aham, A., 2008. Managing Ai po s: An In e na ional Pe spec i e, hi d ed. Bu e wo h Heinemann-Else ie , Ox o d. Heckman, J.J., Vy lacil, E., 2005. S uc u al equa ions, ea men e ec s, and econome ic policy e alua ion. Econome ica 73, 669e738. Hi ano, K., Imbens, G.W., 2001. Es ima ion o causal e ec s using p opensi y sco e weigh ing: an applica ion o da a on igh hea ca he e iza ion. Heal h Se ices & Ou comes Resea ch Me hodology 2, 259e278. Ho z, V.J., Imbens, G.W., Mo ime , J.H., 2005. P edic ing he e ficacy o u u e aining p og ams using pas expe iences a o he loca ions. Jou nal o Econo- me ics and S a is ics 125, 241e270. Humph eys, I., Ison, S., 2005. Changing ai po employee a el beha iou : he ole o ai po su ace access s a egies. T anspo Policy 12, 1e9. Imbens, G.W., 2004. Nonpa ame ic es ima ion o a e age ea men e ec s unde exogenei y: a e iew. Re iew o Economics and S a is ics 86, 4e29. O’Connell, J.F., Williams, G., 2005. Passenge s’pe cep ions oflow cos ai lines and ull se ice ca ie s: a case s udy in ol ing Ryanai , Ae Lingus, Ai Asia and Malaysia Ai lines. Jou nal o Ai T anspo Managemen 11, 259e272. Pels, E., Njego an, N., Beh ens, C., 2009. Low-cos ai lines and ai po compe i ion. T anspo a ion Resea ch E 45, 335e344. Reynolds-Feighan, A., 2001. T a fic dis ibu ion in low-cos and ull-se ice ca ie ne wo ks in he US ai anspo a ion ma ke . Jou nal o Ai T anspo Managemen 7, 265e275. Rosenbaum, P.R., Rubin, D.B., 1983. The cen al ole o he p opensi y sco e in obse a ional s udies o causal e ec s. Biome ika 70, 41e55. Ro ni zky, A., Robins, J., 1995. Semipa ame ic eg ession es ima ion in he p esence o dependen censo ing. Biome ika 82, 805e820. Rubin, D.B., 1974. Es ima ing causal e ec s o ea men s in andomized and non- andomized s udies. Jou nal o Educa ional Psychology 66, 688e701. Tsamboulas, D.A., Nikole is, A., 2008. Passenge s’willingness o pay o ai po g ound access ime sa ings. T anspo a ion Resea ch A 42, 1274e1282. Table 5 Ma ginal e ec o b a : P i a e Ca 0.0003 /¼0.03% Public Bus 0.0185 / D 1.85% Ho el Bus 0.0010 /¼0.10% Ren -a-Ca 0.0385 / D 3.85% Taxi 0.0585 /V5.85% J.I. Cas illo-Manzano / Jou nal o Ai T anspo Managemen 16 (2010) 295e298298