2.benchmarking efficiency

Benchmarking efficiencyof public passenger transport

in larger citiesOlli-Pekka Hilmola

Kouvola Research Unit, Lappeenranta University of Technology,Kouvola, Finland

Abstract

Purpose – The purpose of this paper is to evaluate public transportation efficiency in larger cities.Global agreements to decrease environmental emissions in the future (CO2), world-wide decreasingreserves of oil, and growing population in larger cities is the main motivation to develop efficiencybenchmarking measurement models for public transportation systems, and gives reason for thisresearch work. Also, from the point of view of the city, data envelopment analysis (DEA) basedefficiency measurement has not been researched earlier, which is another motivation for this studyfrom the method development perspective.

Design/methodology/approach – Four different DEA-based efficiency benchmarking models areused to evaluate public transportation efficiency in larger cities. Data are from year 2001, and amountof analyzed cities in smaller DEA model is 52 and in larger 43. This gives statistical significance andefficiency measurement confidence over the results.

Findings – Medium-sized, old and central European cities such as Bern, Munich, Prague and Zurichshow frontier performance in all four models. Mega-cities fail to reach frontier and/or good performancein small “services used” DEA model. However, some other medium-sized cities show contrarianbehaviour for “space used” DEA model. Lowest performance is more divergent in the analyses, but isfound from Spanish cities, Athens, Middle East and North America. The author also found support fromregression analysis that higher DEA efficiency results in lower share of private car use in large cities.

Research limitations/implications – This research work uses only year 2001 data, and should berepeated in the future as public transportation data18base is being updated. The research is alsolimited on the use of DEA method, and other efficiency measurement methods should be used to verifythe results further.

Originality/value – According to the author’s knowledge, this research work is seminal from thecity-level DEA efficiency benchmarking studies concerning public passenger transportation systems.Earlier research works have concerned actors (e.g. bus companies or rail operators), but the overallpicture from the city level has not been researched before.

Keywords Cities, Passenger transport, Benchmarking, Europe, Middle East, North America

Paper type Research paper

1. IntroductionTypically public passenger transport is significantly dependent on the amount of potentialusers in its sphere of influence (Lao and Liu, 2009; Karathodorou et al., 2010; Karttunenet al., 2010), and therefore it is not surprise that mega-cities (Jain et al., 2008) or largerentities (Odek, 2008) have been analyzed to be most efficient in previous benchmarkingstudies. Although, privatization and deregulation processes are catching up in global scalein transportation industry overall, e.g. rail-based passenger transport is still having ratherminor share of private companies operating (Approx. 13 per cent from produced volumebased on Amos and Thompson (2007)). Thus, research findings are giving their support

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Efficiencyof publictransport

23

Benchmarking: An InternationalJournal

Vol. 18 No. 1, 2011pp. 23-41

q Emerald Group Publishing Limited1463-5771

DOI 10.1108/14635771111109805

for more deregulated and privatized transportation systems (Jain et al., 2008; Cowie, 1999),particularly in bus industry (Cowie and Asenova, 1999; Odek, 2008). However, makingpublic passenger transportation system profitable one is extremely difficult task, andbased on our knowledge Guangshen Railway Company in Hong Kong is among the few tohave achieved this (also listed in stock exchange). For example, passenger operatorAmtrak in the USA has produced massive annual losses for decades (Amtrak, 2008;Rhoades et al., 2006) and does not have any end in horizon with this regard. Similarly,George and Rangaraj (2008) concluded from Indian railways that passenger transporthurts regional efficiency considerably, and those regions transporting more freight werehaving higher performance. Veolia’s (2009) transportation segment (including passengerand freight transport both in road and rail; one of the largest private operators in the world)has produced Approx. 2.5 per cent operating income in year 2008, not too flourishing resultas thinking about investments needed for operations.

It should be noted that public transportation is only increasing its importance, due tocontinuing urbanization and for the need to connect suburbs and regions into centers(Qin, 2008). Among this, increasing environmental pressure from road transports(CO2 emissions), road transports’ very significant dependency on oil (especially privatecars; Sandalow, 2008), and estimated decline in oil availability in the world scale(Maggio and Cacciola, 2009) are all increasing the reasons to investigate the efficiency ofpublic short-distance passenger transportation systems. This is particularly concern inlarger cities, and population concentration centers of prospering emerging economies(Kenworthy, 2002; Hu et al., 2009); frightening scenario is that these emerging cities willadapt to use within large-scale private cars by following the examples of West(Cameron et al., 2003, 2004). It should be highlighted that transportation in general hasnothing but increased its CO2 emissions within previous two decades time period(generally in other sectors contrarian development has been reached) – for example,even in EU (2010), which has showed proactive role in emission prevention, haverecorded 30 per cent increases from year 1990 levels. In general, increased emissions arecaused by road transportation and aviation.

Public transportation in cities has been subject of some number of earlier dataenvelopment analysis (DEA) based efficiency studies (Cowie and Asenova, 1999;Jain et al., 2008; Odek, 2008; Lao and Liu, 2009), but these have been concentrating onservice production through actors in some selected cities or inside of one country.However, big picture from the perspective of a city has not been considered, and this is themain motivation behind this research work. Short-distance public transport is typicallyonly concentrated on passenger transport, and problematic incorporation of joint inputswith freight segment is therefore avoided (George and Rangaraj, 2008; Yu and Lin, 2008).Among passenger transportation service production efficiency, we are interested fromused amount of land, which is also scarce resource in ever enlarging cities. We couldassume a priori that efficiency benchmarking based on DEA method will reveal greatdifferences between cities, since analyzed area involves significant public influence, butalso private sector presence. In previous studies, e.g. gas distribution in Ukraine(Goncharuk, 2008), beer production in Ukraine (Goncharuk, 2008) and railway sector inlarger Europe and entire world (Hilmola, 2007, 2009; Yu, 2008) has illustrated such resultsin efficiency benchmarking studies. In comparison, intensively competed service sectorconsisting only private companies has showed much lower differences between analyzedactors (Keh and Chu, 2003; Joo et al., 2007; Debnath and Shankar, 2008).

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This manuscript is structured as follows: in Section 2, literature research is completedfrom public transportation issues in larger cities. Thereafter, in Section 3 used researchmethodology, data and public passenger transport measurement models from cityperspective are being presented. Section 4 shows the results of four used efficiencymeasurement models in larger cities. Results of these models are altogether discussed inSection 5. Finally, Section 6 concludes our work, and proposes further areas for research.

2. Literature review – urban transportMajor concern of larger cities, but also countries within passenger transport, is theincreasing popularity of private car-based road transports. For example, if China doesnot bother to do nothing with this respect, then in year 2030 it will have 400 millionpassenger cars on roads, hungry for gasoline (Hu et al., 2009). In city level situation isshowing similar frightening growth potential, based on Kenworthy (2002) in the USAper capita consumption of energy for private cars is 60,000 MJ (in cities), while in China itis only 2,500 MJ. Only strict policies and regulations have been able to constrain thisdevelopment; in Hong Kong and Singapore amount of private cars is five to ten timeslower compared to cities in Europe and the USA (Cameron et al., 2004). But these only dueto very unfavourable cost implications of owning and driving private car. Amongpolicies and regulations, careful planning of urban areas and closeness of people livingbesides each other as well as services, decreases throughout the world traveling byprivate car (Cameron et al., 2003; Karathodorou et al., 2010). However, even using UITP(2005) database, and analyzing private cars per 1,000 persons in larger cities gives clearcausal message (see Figure 1, including also Singapore and Hong Kong): higher theeconomic prosperity, typically higher is the amount of private cars. de Jong and van de Riet(2008) confirm this with extensive literature survey and statistical analysis; only hope

Figure 1.Economic prosperitydrives car ownership

in larger cities

y = 0.0053x + 310.62R2 = 0.1498

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GDP per Capita (City)Note: n = 52Source: UITP (2005)


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lies in increasing amounts of older population, and their inability to use private cars,but this potential positive direction could easily be substituted by smaller sizedhouseholds.

Although, being against of private car usage, research has not given that manyanswers, how and by what manner city-level short-distance public transportationsystems should be built. Such issues as number of stop points, round-trip time, routesand operating hours need to be carefully planned to have needed utilization for publictransportation system (Lao and Liu, 2009; Karttunen et al., 2010). However, rail-basedsystems need considerable investments before they could operate (comprehensiveup-to-date analysis, see Flyvbjerg et al. (2008), especially in situations where they areoperating mostly underground (e.g. Jubilee line extension in London being built duringperiod of 2002-2007 has total length of 22 kilometer, and total cost of GBP 3,600 million,resulting in per kilometer cost of GBP 163.6 million). Bus-based public transportationsystem is much cheaper to construct, since typically infrastructure has already beenpaid by road investments (and in most of the cases by private cars), and, e.g. stations aremuch easier and cheaper to add in the system.

Earlier research has shown (Lao and Liu, 2009; Karttunen et al., 2010) that inside citythere exist some routes, which are extremely popular, and even could be profitable tooperate. However, this is not complete solution and package, since numerous other routesneed to be operated too in order to satisfy transportation needs, and their popularity andprofitability could be questionable. Efficiency is not the ultimate answer here, in otherwords efficiency of actor level (or route level, like George and Rangaraj (2008) illustrated);most interesting is how efficient public passenger transportation systems are fromsystem’s perspective (city level). This efficiency concerns both objectives: publicpassenger transportation system services used, and space needed to build this system tooperate. These objectives could have tradeoffs as well, where favouring efficiency ofanother one will have decreasing efficiency in other respect.

3. Research methodology and used dataInternational Association of Public Transport (UITP, 2005) maintains databaseconcerning public transportation in larger cities. Latest version from year 2001 of thisdatabase contains 52 cities around the world. However, most of these are from Europe(47 of total). This database has been used in earlier scientific studies (Kenworthy, 2002;Cameron et al., 2004; Karathodorou et al., 2010; Albalate and Bel, 2010) and could beassumed to have needed reliability of indicators gathered. From most recent studiesKarathodorou et al. (2010) and Albalate and Bel (2010) both used UITP database asmain source within their large-scale statistical analysis, not only describing the largecities public transport environment as has been the case in the earlier studies(Kenworthy, 2002; Cameron et al., 2004).

In Figures 2 and 3 two DEA efficiency measurement models of this research workare shown; one concerning space utilization efficiency and second one service usageefficiency. We altered these models in a manner that amounts of inputs were either two(population and urban population density) or all five. In all of the following efficiencymeasurement models, outputs are the same in each case (space and service used per se),four of these outputs are rail related and one concerns busses. Of course, walking andbiking should be part of public passenger transportation system, but we were forced toleave them out of measurement due to lack of data.

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As in larger efficiency measurement models five inputs and five outputs were beingused, some cities were not included in the efficiency analysis. Mostly, reason forexcluding was the lack of data concerning inputs of “urban population plus job density”and “proportion of jobs in the central business district”. Therefore, our analysis in thelarger models consist 43 cities. However, in smaller models all of the database cities wereinvolved.

In the following analysis, we have only analyzed constant return on scale (CCR)performance of actors using DEA efficiency analysis method. CCR assumes that scaleeconomics is linear and constant and does not take into account that larger decision-makingunits (in this case cities) have much better probability for higher performance than smallerones. However, authors cannot recognize this as a caveat, since outputs in all four modelsare scaled with area or population, and therefore probability for scale economicsconcerning larger decision making units should not be that present (this is actuallysupported by following analysis). However, variable return on scale, Banker, Charnes andCooper (BCC), is other alternative to take into account scale economics, but it is not appliedin the following. It should be reminded that CCR is original method for DEA efficiencyanalysis (Charnes et al., 1978), and BCC development followed as derivative later on(Banker et al., 1984). Thus, seminal research manuscript from DEA discussed aboutscale efficiency function already five decades ago (Farrell, 1957).

Idea in developed two DEA models is following: identified inputs (two/five) of UITP(2005) databases drive the public passenger transportation needs. These needs arefulfilled by transportation system, which could be based on road transportation (e.g. busesand private cars), mechanical transportation modes (like bikes), rail or by walking. In ourstudy, we are interested from bus and rail (tram, light rail, metro or sub-urban rail) basedsystems, and their ability to fulfil public transportation need. Introduced two differentmeasurement models are developed with an idea that they could be used in any sizedcity – inputs are just enablers of public transportation need, and these are converted

Figure 3.DEA measurement model

of public passengertransport concerning

efficiency oftransportation services

used per inhabitant

OutputsInputs

Publictransportationprocess

Tramway vehicle kilometers per inhabitant

Light rail vehicle kilometers per inhabitant

Metro vehicle kilometers per inhabitant

Suburban railway vehicle kilometers per inhabitant

Population

Urban population density

Urban population +job density

Proportion of jobs in thecentral business district

GDP per inhabitant

Bus kilometers per inhabitant

Figure 2.DEA measurement model

of public passengertransport concerning

efficiency of space beingused

Publictransportationprocess

OutputsInputs

Tramway vehicle kilometers per urban hectare

Population

Urban population density

Urban population +job density

Proportion of jobs in thecentral business district

Light rail vehicle kilometers per urban hectare

Metro vehicle kilometers per urban hectare

Suburban railway vehicle kilometers per urban hectare

GDP per inhabitant

Bus kilometers per urban hectare


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into transactions with the transportation system. So, basically our model treats smallerand more spread around cities similarly to those of having well concentrated and highamount of urban population. Results in the following mostly concern, how efficient publictransportation system is in the space use and in service use. These both are scaled witharea or population. So, therefore results are not necessarily supporting earlier studies,which give more significance on volume (absolute, like number of passengers and/orpassenger-kilometers).

4. Benchmarking efficiency of public passenger transports in large citiesSmall DEA efficiency measurement modelAs analyzing results of Tables I and II in overall, it could be concluded that between citiesin both models, there exist quite significant variation, and some cities seem to outperformwhole observation group. This outperforming group of efficiency frontier performance inboth of the models is following: Bern, Munich, Prague and Zurich. Among these four,Stockholm is nearly frontier in both of the models. All of these cities are medium-sizedamong larger cities database, and they are located in central Europe (if Stockholm is nottaken into account). Also, common in all five identified high-performing cities is thedominance of rail-based public transportation (as all railway transportation modes aresummed together) over busses. This is particularly the case with Bern, Munich and Zurich.

Lowest performing cities are not that homogenous group in Tables I and II, but threeSpanish cities (Barcelona, Sevilla and Valencia) as well as Athens could be identifiedpresenting such group. However, there does not exist any “easy explanation” behindthis low efficiency. It is true that Athens and Sevilla rely on public transport in busses,but Barcelona and Valencia are having quite significant proportion for bothtransportation modes (bus and rail).

Interesting further insight in the analysis of smaller DEA results is the high performanceof mega-cities in space utilization, but very low performance in the services used. Citiesbehaving in such manner are following: Hong Kong, Moscow, London, Singapore,Sao Paulo, Berlin and Madrid. Contrarian behaviour also exists, as like Melbourne, Dublinand Newcastle illustrate. Only explaining factor in these is that in very large-scale citiestransportation use per inhabitant is just smaller. However, counter argumentation couldalso be stated, e.g. Melbourne is behaving oppositely although being mega-city.

Large DEA efficiency measurement modelAs further developed DEA model is used, differences between evaluated cities arebecoming smaller (Tables III and IV). In larger models 12 cities make the frontiersimultaneously in both of the models, and London as well as Copenhagen are rather nearof this group too. Basically, mega-cities start to show much better performance asadditional three input items are included in the models. In mega-city performanceappraisal cases, reason is simply that these actors do show much lower performance injobs located in central business district (as these cities have several job concentrationareas) as well as in some cases in gross domestic product per capita.

Lowest performance in both of the larger DEA models has now more cities included,but all lower performing actors, which were included in both smaller and largeranalyses, are still in the worst performance group (Athens, Barcelona and Sevilla).However, in larger DEA model lowest performance is present in Dubai, but efficiencyof Chicago is not good either. These two mentioned cities were not performing showing

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City Space used (%)

1 Bern 100.02 Gent 100.03 Hong Kong 100.04 Krakow 100.05 London 100.06 Moscow 100.07 Munich 100.08 Prague 100.09 Vienna 100.0

10 Zurich 100.011 Tallinn 99.112 Stockholm 98.413 Warsaw 94.814 Brussels 94.715 Berlin 93.316 Singapore 84.217 Copenhagen 83.818 Amsterdam 79.319 Graz 76.520 Budapest 75.821 Helsinki 75.322 Stuttgart 68.123 Bologna 67.824 Glasgow 67.325 Nantes 64.926 Clermont-Ferrand 60.527 Oslo 59.328 Paris 59.229 Sao Paulo 58.930 Hamburg 57.531 Marseilles 57.332 Geneva 56.733 Milan 53.634 Madrid 53.335 Newcastle 52.536 Manchester 46.937 Lille 46.438 Rome 43.539 Dublin 40.440 Lisbon 40.041 Bilbao 36.742 Barcelona 36.243 Tunis 36.044 Lyons 35.445 Melbourne 34.746 Rotterdam 33.147 Turin 30.648 Athens 25.249 Valencia 24.950 Chicago 24.551 Dubai 18.352 Se villa 17.3

Notes: Small DEA; n ¼ 52

Table I.Efficiency of analyzed

cities with respect ofspace used by public

transportation system


29

City Services used (%)

1 Bern 100.02 Gent 100.03 Munich 100.04 Prague 100.05 Zurich 100.06 Tallinn 100.07 Stockholm 100.08 Copenhagen 100.09 Graz 100.0

10 Helsinki 100.011 Melbourne 100.012 Krakow 97.513 Glasgow 89.414 Newcastle 83.515 Warsaw 81.616 Dublin 81.317 Stuttgart 81.118 Oslo 80.919 Nantes 77.420 Vienna 75.021 Budapest 67.122 Amsterdam 66.723 Brussels 62.524 Clermont-Ferrand 60.825 Berlin 56.926 Bologna 56.527 Marseilles 50.328 Manchester 49.629 Geneva 49.130 Hamburg 47.831 Lisbon 46.832 London 45.533 Lille 43.734 Paris 43.635 Lyons 41.836 Singapore 38.937 Bilbao 37.738 Rotterdam 37.539 Dubai 36.240 Turin 35.841 Chicago 35.842 Milan 30.443 Rome 29.144 Sao Paulo 26.745 Hong Kong 24.846 Madrid 22.647 Valencia 22.548 Sevilla 22.249 Moscow 19.250 Tunis 17.651 Athens 15.852 Barcelona 13.8

Notes: Small DEA; n ¼ 52

Table II.Efficiency of analyzedcities with respect of usedtransportation services ofpublic transportationsystem

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high performance in smaller DEA, but in enlarged models their performance was clearlybelonging into lowest performing cities of this evaluated group. However, it should benoted that in current analysis “services used” model shows much better efficiencyin overall; it is clearly on the higher level on the average as compared to “space used”model, and the lowest performing cities have also significantly better starting level as in

City Space used (%)

1 Bern 100.02 Brussels 100.03 Budapest 100.04 Graz 100.05 Hong Kong 100.06 London 100.07 Moscow 100.08 Munich 100.09 Prague 100.0

10 Stockholm 100.011 Stuttgart 100.012 Vienna 100.013 Warsaw 100.014 Zurich 100.015 Copenhagen 90.216 Singapore 84.217 Bologna 81.918 Clermont-Ferrand 81.319 Amsterdam 79.820 Helsinki 76.421 Sao Paulo 76.022 Nantes 75.523 Glasgow 72.124 Geneva 71.225 Paris 62.526 Lille 62.227 Oslo 59.328 Marseilles 57.329 Madrid 56.830 Newcastle 54.631 Manchester 50.032 Rome 46.933 Lisbon 43.634 Bilbao 41.235 Melbourne 39.036 Barcelona 38.337 Lyons 37.438 Rotterdam 36.239 Turin 34.440 Athens 27.341 Chicago 25.242 Sevilla 19.043 Dubai 18.3

Notes: Large DEA; n ¼ 43

Table III.Efficiency of analyzed

cities with respect ofspace used by public

transportation system


31

the “space used” model. So, based on the analysis it could be argued that publictransportation has regarding to service use some more brighter future, but land use isstill challenge in numerous cities.

As number of cities was able to upgrade their performance, the contrasting resultsbetween two models were getting less frequent. However, some still remain to show

City Services used (%)

1 Bern 100.02 Budapest 100.03 Graz 100.04 Hong Kong 100.05 Moscow 100.06 Munich 100.07 Prague 100.08 Stockholm 100.09 Stuttgart 100.0

10 Vienna 100.011 Warsaw 100.012 Zurich 100.013 Copenhagen 100.014 Helsinki 100.015 Sao Paulo 100.016 Glasgow 100.017 Newcastle 100.018 Melbourne 100.019 Manchester 99.120 London 94.821 Nantes 93.822 Singapore 86.723 Clermont-Ferrand 84.124 Bologna 81.225 Oslo 81.126 Amsterdam 75.227 Lisbon 73.128 Brussels 71.929 Lille 69.530 Paris 67.631 Geneva 67.032 Madrid 65.133 Bilbao 56.034 Marseilles 54.735 Turin 51.636 Rome 47.937 Sevilla 46.138 Barcelona 45.639 Lyons 45.040 Athens 44.041 Rotterdam 40.442 Chicago 39.543 Dubai 39.3

Notes: Large DEA; n ¼ 43

Table IV.Efficiency of analyzedcities with respect of usedtransportation services ofpublic transportationsystem

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such behaviour, like Newcastle, Manchester and Melbourne. Also quite number ofother cities shows similar, much higher performance in services used model, but dohave problems regarding to the use of space in public transportation system. Oppositesituation occurs only in significant terms in Brussels, where space utilization efficiencyis high, and service utilization 28.1 percentage points lower.

5. DiscussionUsed four efficiency measurement models yielded similar findings, and this was verifiedby correlation analysis too (Table V and Figure 3). Especially, strong connection wasfound between two “space used” DEA models, positive correlation co-efficient was above0.958. Difference between small and large models is present in correlation analysis; strongpositive correlation exist between “space used” and “service used” larger models (0.806),but interestingly between smaller counterparts this co-efficient was much lower (0.532).

It should be emphasized that all of the used models were having relationshipsbetween each other, and these were analyzed to be statistically significant too. However,as it is evident from Figure 4, in some cases higher efficiency does not lead to higherperformance in other measure. This is caused by different variance in error term as wemove on higher values of efficiency – this in turn decreases the predictability ofbehaviour in all situations. For cities, e.g. this means that any efficiency measurementmodel is good enough to follow with low-efficiency standard, but as performanceimproves further, increasing number of efficiency measurement models is needed tomeasure performance and implications of decisions correctly.

As Table VI illustrates, only four cities from central Europe (all of these belong tomedium-sized group of large cities) reached the frontier in all four efficiencymeasurement models, and these are namely Bern, Munich, Prague and Zurich. Lack ofmega-cities in the frontiers of all four used models was earlier explained with the amountof public transportation used per inhabitant. Similar central European favouringfindings were reported in the most recent public transportation supply/demand study(Albalate and Bel, 2010), which used regression analysis with same UITP (2005)database, and illustrated that supply of public transport mostly occurs in the center

CorrelationsUH_large I_large UH_small I small

UH_large Pearson correlation 1 0.806 0.958 * 0.547 *

Sig. (two-tailed) 0.000 0.000 0.000N 43 43 43 43

I_large Pearson correlation 0.806 * 1 0.768 * 0.681 *

Sig. (two-tailed) 0.000 0.000 0.000N 43 43 43 43

UH_small Pearson correlation 0.958 * 0.768 * 1 0.532 *

Sig. (two-tailed) 0.000 0.000 0.000N 43 43 43 43

I_small pearson correlation 0.547 * 0.581 * 0.532 * 1Sig. (two-tailed) 0.000 0.000 0.000N 43 43 43 43

Notes: *Correlation is significant at the 0.01 level (two-tailed); denotation, UH ¼ urban hectare andI ¼ inhabitant per vehicle kilometre

Table V.Correlations are positiveand significant between

all four publictransportation DEA

models


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CitySpace used

(large)Services used

(large)Space used

(small)Services used

(small)

1 Bern 1.000 1.000 1.000 1.0002 Munich 1.000 1.000 1.000 1.0003 Prague 1.000 1.000 1.000 1.0004 Zurich 1.000 1.000 1.000 1.0005 Vienna 1.000 1.000 1.000 0.7506 Hong Kong 1.000 1.000 1.000 0.2487 Moscow 1.000 1.000 1.000 0.1928 London 1.000 0.948 1.000 0.4559 Stockholm 1.000 1.000 0.984 1.000

10 Warsaw 1.000 1.000 0.948 0.81611 Brussels 1.000 0.719 0.947 0.62512 Graz 1.000 1.000 0.765 1.00013 Budapest 1.000 1.000 0.758 0.67114 Stuttgart 1.000 1.000 0.681 0.81115 Copenhagen 0.902 1.000 0.838 1.00016 Singapore 0.842 0.867 0.842 0.38917 Bologna 0.819 0.812 0.678 0.56518 Clermont-Ferrand 0.813 0.841 0.605 0.60819 Amsterdam 0.798 0.752 0.793 0.66720 Helsinki 0.764 1.000 0.753 1.000

Table VI.The most efficient20 public transportationcities sorted first withspace used models(small/large) and secondby servicesused (small/large)

Figure 4.Correlations in matrixfigure between fourdifferent publictransportation DEAefficiency measurementmodels

UH_large

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of Europe rather than in its peripheries. Interestingly, our research work confirmed thatthis supply has also in quite many cities needed frontier efficiency.

If smaller DEA concerning services used is not taken into account, then we have citiesfrom positions 5-10 as potential frontier and exemplary actors of public transports.However, it should be emphasized that cities having positions from 11-20 in Table VI arenot poor performers either. Some of these might have weakness in either service used orspace used side, but overall show high performance, and have possibly reached frontier inone of the two measurement dimensions. This research work also gives some guidance forthese lower performing (but still meeting good standard) cities, how to proceed further withpublic transportation system efficiency improvement. For example, lower performance inspace used DEA models indicates that all of the routes in the transportation system arenot necessarily needed and/or overlapping in routes for the regions between publictransportation modes should be carefully examined. In other situation, when “space used”DEA is indicating frontier performance and “services used” is lacking behind, then newalternative routes and/or modification of current service structure should be examinedfurther.

What then could very low-performing cities do with the results of this study? Aftertaking into account special characteristics of the particular city, it should either be startto develop public transportation system further from the perspective of “space used” or“services used”. For example, in new mega-cities of Asia “space used” sounds reasonable(Kenworthy, 2002) as medium-sized cities in Europe could follow “services used”strategy. However, in some cities, like in the USA (e.g. Chicago and Phoenix)and Australia (e.g. Perth), new bold steps to develop public transportation system asfunctional, should simply need to be taken (analyzed in details in Cameron et al. (2004)).

Argued development steps are further supported from the environmental perspectivetoo; we analyzed relationships of different measures of private car use, and foundinteresting as well as statistically strong connection between share of private car use(or motorized vehicle) and measured DEA efficiency. In Figure 5 is shown thisrelationship between space used DEA model (small) and share of private car use – linearregression enjoys R 2-value of 35 per cent (this regression relationship was also found tobe , 0.001 statistically significant in regression analysis, see Appendix). As couldbe clearly noted, lower the efficiency of the public transportation DEA model, thecorrespondingly higher use of private cars. This relationship holds very nicely until thelevel of 0.9 DEA efficiency – interestingly some cities having frontier performance couldhave high car use or other way around. Similar statistically significant relationshipswere found within both larger DEA models (space and services; see Appendix forregression analysis); these also repeated similar causal relationship of private car useand DEA efficiency, which was having strong explanation power until frontierefficiency. Only DEA model from four used, smaller services used DEA, did not have anyrelationship with the share of private car use. However, it should be remembered that inthe earlier correlation analysis it was showed, that all DEA models are in statisticallystrong and positive relationship with others, and therefore we cannot neglect largerservices used DEA from the further use. Thus, in the future further analyses should becompleted to reveal whether it has connection with the private car use or not.

Based on this research work, we are in environmental terms slightly in favour ofspace used DEA model, since both large and small were in statistically strong andsignificant relationship with share of private car use; interesting fact, among the lack


35

of relationship with smaller services used DEA model, is that even larger services usedDEA model was having lower statistical significance with private car use (,0.01).

6. ConclusionsProblem with economic growth is that larger amounts of people will get an opportunityto use private car transportation – in larger scale it has also been shown several timesthat economic prosperity leads into increased private car sales. However, this isfrightening development for larger cities, as climate change and scarcity of oil arebecoming as the fact in following decade. Therefore, we need to have much more efficientpublic passenger transport systems to support the traveling needs of inhabitants.As argued by Ausubel and Marchetti (2001) even middle-aged people in cities werehaving needs to daily trips and size of wall protected cities was Approx. 2.5 kilometerradius circle (resulted in 1 hour of traveling each day). Currently, this medieval circle isinsufficiently sized, since millions of people are living in one city only. However, we needto modify our transportation systems in a manner that travel need is fulfilled by themost environmentally friendly means. This does not necessarily mean that private cartravel would not be existing, but surely not having the magnitude of 600-700 carsper 1,000 persons living. First, cities need to offer good enough covered publictransportation system, which reaches as many people as possible with the least amountof space being used. This research work has pointed some frontier cities, which otherscould follow in their planning, implementation and enhancement processes. Problematicpart with public transport is that it has costs, and these costs are seldom covered by theusage fees. Therefore, cities in general have temptation to select cheapest possibleconfiguration, which favours road transports. However, there does not exist any supportthat these sorts of systems could favour nor support the objectives of year 2020 or 2030 interms of transportation sustainability.

Figure 5.Scatter plot and linearregression relationshipbetween space used DEAmodel (small) and relativeshare of private roadtransportation

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y = –34.888x + 89.254R2 = 0.3453

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To continue further with our research work, we would like to take into accountpossible future scenarios and changes of public passenger transport. For example, onescenario could be the higher priced oil (e.g. 150 USD per barrel), while CO2 emissionregulations could be other changing factor (and corresponding into extra cost of emittingtoo much). So, this would lead us most probably in the development of two-staged DEAefficiency measurement model, where intermediate level (to our already tested DEAs)would consist these inputs (and number of others, like investments and labour used). Ourresearch work currently weights similarly rail-based emission free passenger transportwith busses using diesel oil or fuel. However, latter alternative could also be developedfurther using gas, electricity or alternative fuels and also being environmentallysustainable. Therefore, further research is needed to find most sustainable cities in theworld regarding to passenger transports – setting new standards for the future.

References

Albate, D. and Bel, G. (2010), “What shapes local public transportation in Europe? Economics,mobility, institutions and geography”,TransportationResearchPartE, Vol. 46 No. 5, pp. 775-90.

Amos, P. and Thompson, L. (2007), Railways in Development: Global Round-Up 1996-2005,World Bank Transport Note No. TRN-36, World Bank, Washington, DC.

Amtrak (2008), National Railroad Passenger Corporation and Subsidiaries (Amtrak) ConsolidatedFinancial Statements for the Years Ended September 30, 2008 and 2007, Amtrak,Washington, DC.

Ausubel, J.H. and Marchetti, C. (2001), “The evolution of transports”, The Industrial Physicist,Vol. 7 No. 2, pp. 20-4.

Banker, R.D., Charnes, R.F. and Cooper, W.W. (1984), “Some models for estimating technical andscale inefficiencies in data envelopment analysis”, Management Science, Vol. 30 No. 9,pp. 1078-92.

Cameron, I., Kenworthy, J.R. and Lyons, T.J. (2003), “Understanding and predicting privatemotorized urban mobility”, Transportation Research: Part D, Vol. 8 No. 4, pp. 267-83.

Cameron, I., Lyons, T.J. and Kenworthy, J.R. (2004), “Trends in vehicle kilometers travel in worldcities, 1960-1990: underlying drivers and policy responses”, Transport Policy, Vol. 11 No. 3,pp. 287-98.

Charnes, A., Cooper, W.W. and Rhodes, E. (1978), “Measuring the efficiency of decision makingunits”, European Journal of Operational Research, Vol. 2 No. 6, pp. 429-44.

Cowie, J. (1999), “The technical efficiency of public and private ownership in the rail industry”,Journal of Transport Economics and Policy, Vol. 33 No. 3, pp. 241-51.

Cowie, J. and Asenova, D. (1999), “Organisation form, scale effects and efficiency in the Britishbus industry”, Transportation, Vol. 26, pp. 231-48.

Debnath, R.M. and Shankar, R. (2008), “Benchmarking telecommunication service in India – anapplication of data envelopment analysis”, Benchmarking: An International Journal,Vol. 15 No. 5, pp. 584-98.

de Jong, G. and van de Riet, O. (2008), “The driving factors of passenger transport”, EuropeanJournal of Transport and Infrastructure Research, Vol. 8 No. 3, pp. 227-50.

EU (2010), Energy – Statistical Pocketbook, available at: http://ec.europa.eu/energy/publications/statistics/statistics_en.htm (accessed February 2010).

Farrell, M.J. (1957), “Measurement of productive efficiency”, Journal of the Royal StatisticalSociety: Series A (General), Vol. 120 No. 3, pp. 253-90.


37

Flyvbjerg, B., Bruzelius, N. and van Wee, B. (2008), “Comparison of capital costs perroute-kilometre in urban rail”, European Journal of Transport and Infrastructure Research,Vol. 8 No. 1, pp. 17-30.

George, S.A. and Rangaraj, N. (2008), “A performance benchmarking of Indian railway zones”,Benchmarking: An International Journal, Vol. 15 No. 5, pp. 599-617.

Goncharuk, A.G. (2008), “Performance benchmarking in gas distribution industry”,Benchmarking: An International Journal, Vol. 15 No. 5, pp. 548-59.

Hilmola, O.-P. (2007), “European railway freight transportation and adaptation to demand decline –efficiency and partial productivity analysis from period of 1980-2003”, International Journal ofProductivity and Performance Management, Vol. 56 No. 3, pp. 205-25.

Hilmola, O.-P. (2009), “Global railway passenger transports – efficiency analysis from period of1980-2004”, International Journal of Logistics Economics and Globalisation, Vol. 2 No. 1,pp. 23-39.

Hu, X., Chang, S., Li, J. and Qin, Y. (2009), “Energy for sustainable road transports in China:challenges, initiatives and policy implications”, Energy, Vol. 35 No. 11, pp. 4289-301.

Jain, P., Cullinane, S. and Cullinane, K. (2008), “The impact of governance development models onurban rail efficiency”, Transportation Research Part A, Vol. 42 No. 10, pp. 1283-94.

Joo, S.-J., Stoeberl, P.A. and Kwon, I.-W.G. (2007), “Benchmarking efficiencies and strategies forresale operations of a charity organization”, Benchmarking: An International Journal,Vol. 14 No. 4, pp. 455-64.

Karathodorou, N., Graham, D.J. and Noland, R.B. (2010), “Estimating the effect of urban densityon fuel demand”, Energy Economics, Vol. 32 No. 1, pp. 86-92.

Karttunen, J., Hilmola, O-P. and Saranen, J. (2010), “Evaluating light rail as a short distancepassenger transportation solution in a midsized town”, World Review of IntermodalTransportation Research, Vol. 3 Nos 1/2, pp. 121-36.

Keh, H.T. and Chu, S. (2003), “Retail productivity and scale economics at the firm level: a DEAapproach”, Omega – The International Journal of Management Science, Vol. 31 No. 2,pp. 75-82.

Kenworthy, J. (2002), “Traffic 2042 – a more global perspective”, Transport Policy, Vol. 9 No. 1,pp. 11-15.

Lao, Y. and Liu, L. (2009), “Performance evaluation of bus lines with data envelopment analysisand geographic information systems”, Computers, Environment and Urban Systems,Vol. 33 No. 4, pp. 247-55.

Maggio, G. and Cacciola, G. (2009), “A variant of the Hubbert curve for oil production forecasts”,Energy Policy, Vol. 37 No. 11, pp. 4761-70.

Odek, J. (2008), “The effect of mergers on efficiency and productivity of public transportservices”, Transportation Research: Part A, Vol. 42 No. 4, pp. 696-708.

Qin, Z. (2008), “Improving public transit access to in-city villages”, International Journal of DataAnalysis Techniques and Strategies, Vol. 1 No. 2, pp. 141-52.

Rhoades, D.L., Williams, M.J. and Green, D.J. (2006), “Imperfect substitutes: competitive analysisfailure in US intercity passenger rail”, World Review of Intermodal TransportationResearch, Vol. 1 No. 1, pp. 82-93.

Sandalow, D. (2008), Freedom from Oil, McGraw-Hill, New York, NY.

UITP (2005), International Association of Public Transport – Mobility in Cities Database,Belgium, Brussels.

BIJ18,1

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Veolia (2009), Consolidated Financial Statements for the Half-year Ended June 30, , available at:www.veolia-finance.com/docs/CONSOLIDATES-FINANCIAL-STATEMENTS-FOR-THE-HALF-YEAR-ENDED-JUNE-0030-2009.pdf (accessed December 2009).

Yu, M.-M. (2008), “Assessing the technical efficiency, service effectiveness, and technicaleffectiveness of the world’s railways through NDEA analysis”, Transportation ResearchPart A, Vol. 42 No. 10, pp. 1283-94.

Yu, M.-M. and Lin, E.T.J. (2008), “Efficiency and effectiveness in railway performance using amulti-activity network DEA model”, Omega – International Journal of ManagementScience, Vol. 36 No. 6, pp. 1005-17.

Further reading

Goncharuk, A.G. (2009), “Improving of the efficiency through benchmarking: a case ofUkrainian breweries”, Benchmarking: An International Journal, Vol. 16 No. 1, pp. 70-87.

Appendix. Regression statistics from the causality of used four DEA models of thestudy and the share of private car use

Regression statisticsMultiple R 0.5876R 2 0.3453Adjusted R 2 0.3317SE 0.2232Observations 50ANOVA

df SS MS FSignificance

FRegression 1 1.2609 1.2609 25.3174 0.0000Residual 48 2.3905 0.0498Total 49 3.6514

Coefficients SE t-stat. p-value Lower 95%Upper95%

Lower95.0%

Intercept 1.3113 0.1345 9.7520 0.0000 1.0410 1.5817 1.0410Percentage of dailymechanised andmotorised trips by privatemotorised modes 20.0099 0.0020 25.0316 0.0000 20.0139 20.0059 20.0139

Table AI.Summary output – spaceused DEA modal (small)


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Coefficients SE t-stat. p-value Lower 95%Upper95% L%

Intercept 0.6978 0.1785 3.9091 0.0003 0.3389 1.0567 0.3389Percentage of dailymechanised andmotorised trips byprivate motorisedmodes 20.0015 0.0026 20.5737 0.5689 20.0067 0.0038 200067

Table AII.Summary output –services used DEAmodel (small)





Intercept 1.3596 0.1432 9.4927 0.0000 1.0703 16.488Percentage of daily mechanised andmotorised trips by private motorisedmodes 20.0099 0.0021 24.7600 0.0000 20.0140 20.0057

Table AIII.Summary output – spaceused DEA model (large)

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About the authorOlli-Pekka Hilmola PhD is an Acting Professor of Logistics in Lappeenranta University ofTechnology (LUT), in Kouvola, Finland. Concurrently he serves as a Visiting Professor ofLogistics in University of Skovde, Sweden. He is affiliated with numerous international journalsthrough editorial boards, including Baltic Journal of Management, Industrial Management andData Systems, as well as Decision Support Systems. Olli-Pekka Hilmola can be contacted at:[email protected]





Lower95.0%

Upper95.0%

Intercept 1.2060 0.1322 9.1205 0.0000 0.9389 1.4730 0.5389 1.4730Percentage ofdailymechanisedand motorisedtrips by privatemotorisedmodes 20.0061 0.0019 23.1957 0.0027 20.0100 20.0022 20.0100 20.0022

Table AIV.Summary output –services used DEA

model (large)

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2.benchmarking efficiency

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