Validation of road transport statistics through energy efficiency calculations

Energy 26 (2001) 467–491www.elsevier.com/locate/energy

Validation of road transport statistics through energyefficiency calculations

Theodoros Zachariadis, Zissis Samaras *

Laboratory of Applied Thermodynamics, Department of Mechanical Engineering, Aristotle University, 54006Thessaloniki, Greece

Received 24 February 2000

Abstract

The paper presents an approach to validate vehicle-kilometre statistics of motor vehicles by comparingthem with official energy consumption data. The comparisons were carried out for the 15 European Unioncountries by computing the energy efficiency and the fuel economy of individual vehicle categories andcomparing them with real-world data and model calculations. The major outcome of the study was thatestimates of vehicle-kilometres are often unreliable and yield unrealistic energy efficiency and specific fuelconsumption results. Based on these conclusions, some basic guidelines are given for energy and roadtransport analyses: use fuel economy estimates based on measurements of in-use cars under actual drivingconditions, collect as many travel data as possible and check their consistency with the aid of energybalances. 2001 Elsevier Science Ltd. All rights reserved.

1. Introduction

Mobility is a major human need that is growing rapidly, boosted by the increasing per capitaincome in many developing countries and a globalised world economy that enables internationalcontacts and trade and, therefore, transport of passengers and goods. Road transport satisfies alarge part of the total mobility and hence is responsible for a significant — and still rising —portion of transport’s energy and environmental impacts. In the 1990s, about 30% of energyconsumption and CO2 emissions in countries in the Organisation for Economic Cooperation andDevelopment (OECD) came from transport, and 80% of this was due to road transport [1].

This explains to a large extent why so many international and national organisations, industrial-ists, consultants and academics need reliable transport data for their studies and, at the same time,

* Corresponding author: Tel.: +30-31-996014; fax: +30-310996019.E-mail address: [email protected] (Z. Samaras).

0360-5442/01/$ - see front matter 2001 Elsevier Science Ltd. All rights reserved.PII: S0 360- 544 2(01 )000 12- 3

468 T. Zachariadis, Z. Samaras / Energy 26 (2001) 467–491

why so many of them collect and publish such data. Road transport, however, is an inherentlycomplex sector as it deals with a very large number of individual vehicles. Hence, apart fromvehicle stock data, which are generally of good quality in most OECD countries, other importantparameters such as vehicle-, passenger- and tonne-kilometres have to be approximated by estimat-ing the behaviour of an ‘average’ car or truck or by conducting travel surveys and generalisingtheir findings.

We recently conducted a study for the European Commission, trying to evaluate all road trans-port statistics of European Union (EU) countries, propose the most ‘reliable’ time series of mainparameters from 1970 to 1997, and provide projections of the possible developments up to 2010[2]. We collected data from national statistical services and automobile, truck and motorcycleassociations, as well as from international organisations such as EUROSTAT (the statistical officeof the European Communities), the OECD, the European Conference of Ministers of Transport(ECMT), the International Road Federation (IRF) and the Association of European AutomobileManufacturers (ACEA). Although one would assume that the data are reliable when most of thestatistical sources converge to similar figures, it seems that this is not always the case. Based onthat study and going one step further, we attempted to examine whether vehicle-kilometre stat-istics, when combined with energy statistics, yield reasonable results of energy efficiency andspecific fuel consumption (expressed in litres per 100 km). It was striking that in some countriesthis analysis led to unrealistic figures, thus indicating that vehicle-kilometre statistics most prob-ably need significant revisions. This paper describes the analysis we carried out, discusses theresults and provides some basic directions for achieving reliable traffic volume estimates.

2. The basic traffic–fuel relationship

Fuel consumption of road transport can be calculated with the following formula:

FCi��j

�k

CRijk·VPijk·VMijk, (1)

whereFCi estimated total consumption of fuel i (gasoline, diesel or liquified petroleum gas (LPG))

in a given year (grams or kilotonnes per year)CRijk consumption rate of vehicles using fuel i that belong to type j (passenger cars, light trucks,

heavy trucks or powered two-wheelers) and technology k (grams per kilometre driven)VPijk number of vehicles using fuel i that belong to type j and technology k in a given year

(vehicles per year)VMijk average distance driven annually by each vehicle using fuel i of type j and technology k

(kilometres per vehicle and year).

The four variables of Eq. (1) form what Schipper et al. [3] called the ‘vicious circle of fueluse’, meaning that the estimation of any one of these variables with the aid of the other three is

469T. Zachariadis, Z. Samaras / Energy 26 (2001) 467–491

often unreliable, because the three ‘independent variables’ are not estimated really independentlyand also contain a number of unresolved uncertainties. The quality of the calculations can beimproved if fuel consumption is allocated correctly among the different vehicle types, if theconsumption rates are based on real-world measurements and not on standardised test data, if thenumber of vehicles is known accurately, and if average mileage is estimated reliably.

3. Fuel balances

Traffic volume (i.e., vehicle-kilometres per year) is the product of vehicle population timesaverage mileage. Despite some problems in vehicle registrations and deregistrations in a fewcountries, which may lead to an overestimation of the number of in-use vehicles by about 5–10%[3,4], vehicle stock is generally a reliable statistical data set, provided also that one differentiatespassenger cars from light trucks and vans. Hence, the main difficulty associated with the calcu-lation of traffic volume lies in the estimation of the average distance driven by each vehicle(average mileage).

Vehicle mileage data are often collected in different ways: travel surveys, traffic counts andquestionnaires to drivers are some of these methods. While it is useful to gather information indifferent ways in order to obtain a more representative picture, it is also necessary to conductconsistency checks in order to ensure that the different approaches lead to reasonable results.Such checks may include the calculation of several indices like vehicle-kilometres per capita orper gross domestic product (GDP) unit. However, these are rather qualitative in nature and hencehave only a relative importance. The most reliable consistency checks, which ensure quantitativecomparisons, are those related with energy considerations.

One such consideration is associated with energy consumption: according to Eq. (1), estimatedvehicle-kilometres, combined with experimentally derived fuel consumption rates for each vehiclecategory, result in a total (calculated) fuel consumption that can be compared with fuel sales ineach country. This check presupposes that the amount of fuel sold in a country is equal to theamount that was actually consumed there; the assumption is generally valid as long as:

1. the country examined is not very small (e.g., Luxembourg);2. there is no great difference in fuel prices between that country and neighbouring regions —

in Hungary, for example, about 5% of fuel consumed in 1990 was estimated to have beenbought from neighbouring countries with considerably lower fuel prices [5]; and

3. international transit traffic, particularly freight traffic, is correctly accounted for; this is dis-cussed further in Section 4.

We have carried out such consistency checks before, notably in the frame of the MEET [6] study.MEET (Methodologies for the Estimation of Emissions from Transport) was an EU-financedproject that has provided a basic, Europe-wide procedure for evaluating the impact of transporton air pollution. It brought together the most comprehensive and up-to-date information on trans-port vehicle emission rates and activity statistics which, together, make it possible to estimate theemissions resulting from almost any transport operation. The results of this work have givenEuropean decision-makers the information necessary to test alternative transport options.


Fuel balances within MEET have been conducted in order to produce reliable estimations ofvehicle-kilometres for use in air emissions calculations. The fuel consumption rates that wereapplied for this purpose were those used throughout Europe by the European Environment Agencyand the United Nations Economic Commission for Europe [7], and some of these are illustratedin Fig. 1. They were derived from laboratory measurements on a wide variety of driving cyclesthat are based on real-world recordings of driving behaviour, with average speeds ranging from11 km/h to 120 km/h [8]. These tests had all been conducted on in-use cars, in order to avoidthe significant discrepancies in fuel economy between new cars on standardised tests and in-usecars in real-world driving [9].

Table 1 shows estimates of total vehicle mileage according to MEET and official statistics.The MEET figures were derived by multiplying the number of vehicles of a given engine sizeand technology category with an assumed average annual mileage per vehicle of that category.In this way, different mileage estimates were made for each model year group, taking also intoaccount the well-reported fact that cars tend to be driven less as they grow older [6,10], so thatan age-dependent annual mileage was assumed.

It is evident from Table 1 that the MEET mileage figures are often different from those ofofficial statistical sources. One has to keep in mind, however, that the MEET figures should notbe regarded as the best possible estimates, since they were produced for use in emission calcu-lations and therefore an accuracy of ±10% in the resulting fuel consumption was regarded assatisfactory. Moreover, MEET had to produce emission estimates similar to those delivered byEU Member States to other emissions-related activities, so this was an additional limitation tothe accuracy of MEET’s vehicle-kilometres. This agreement in emissions data had to be fulfilledfor three calculation years, i.e., 1985, 1990 and 1995, which further limited the quality of theestimates, so that in some countries the discrepancy between official and calculated fuel consump-tion even exceeded ±30%. Still, MEET constitutes a consistent basis of satisfactory quality, and

Fig. 1. Fuel consumption rates of MEET for various vehicle categories, as a function of average driving speed [6].


Tab

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in fact it is the most advanced and harmonised database of vehicle data from all EU countries.For these reasons we shall use several MEET data sets for comparisons in this paper.

The differences in estimated vehicle-kilometres observed in Table 1 can help to explain someof the discrepancies described in the following section, where we attempt to derive energyefficiency and specific fuel consumption figures from official statistics and compare them withreal-world efficiency and fuel economy of motor vehicles.

4. Energy efficiency calculations

A second energy-related consistency check has to do with the efficiency of a vehicle engine.By considering the possible losses when driving a car (rolling resistance, wind resistance or drag,and power consumption during vehicle accelerations), and adjusting the relations in order to makeuse of annual vehicle-kilometre figures, the engine efficiency of a vehicle category is describedby the following formula:

h�Total road load energy

Total energy consumption�

(f·m·g+1/2·r·cw·A·u2+m·a)·(vehicle-kilometres/year)(annual fuel consumption)·hu

, (2)

where

h engine efficiencyf friction coefficientm vehicle massg acceleration of gravityr density of aircw drag coefficientA vehicle frontal areau vehicle speeda vehicle accelerationhu lower heating value of a fuel (gasoline, diesel or LPG).

In order to apply Eq. (2) appropriately, one has to account for the different technological categ-ories of vehicles as technology clearly affects fuel economy. Therefore, all vehicle types wereclassified into further categories (model year groups) according to their engine and/or emissioncontrol technologies, as required by national and international emission regulations, mainly thoseof the United Nations Economic Commission for Europe (UN ECE). In total, 58 different techno-logical categories were considered; these are described in Table 2.

Table 3 summarises the average (over all subcategories) values of the coefficients of Eq. (2)that we used for each basic vehicle category. In accordance with literature data [11–13], weapplied different values of f, m, cw and A depending on a vehicle’s model year and size: inautomobiles, for example, f and cw were assumed to decrease from 0.017 and 0.45 respectivelyin 1970 model year cars to 0.014 and 0.38 respectively in 1995 cars. Vehicle mass m was assumed


Table 2The vehicle subcategories used by MEET and their corresponding approximate model year groups (GVW: GrossVehicle Weight)

Vehicle category Model year Vehicle category Model year

Gasoline passenger cars �1.4 litres LPG passenger carsPRE ECE pre 1972 Conventional pre 1986ECE 15/00-01 1972–1977 EURO I 1986–1997ECE 15/02 1978–1980 EURO II 1997–1999ECE 15/03 1981–1984 Gasoline light duty vehicles �3.5 t GVWECE 15/04 1985–1990 Conventional pre 1995Improved conventional 1986–1990 EURO I (93/59/EEC) 1995–1997Open loop 1986–1990 EURO II (96/69/EC) 1998–EURO I (national — 91/441/EEC) 1986–1995a Diesel light duty vehicles �3.5 t GVWEURO II 1996–1999 Conventional pre 1995

Gasoline passenger cars 1.4–2.0 litres EURO I (93/59/EEC) 1995–1997PRE ECE pre 1972 EURO II (96/69/EC) 1998–ECE 15/00-01 1972–1977 Diesel heavy duty vehicles 3.5–7.5 t GVWECE 15/02 1978–1980 Conventional pre 1995ECE 15/03 1981–1984 EURO I (91/542/EEC Stage 1) 1995–1997ECE 15/04 1985–1990 EURO II (91/542/EEC Stage 2) 1998–Improved conventional 1986–1990 Diesel heavy duty vehicles 7.5–16 t GVWOpen loop 1986–1990 Conventional pre 1995EURO I (national — 91/441/EEC) 1986–1995a EURO I (91/542/EEC Stage 1) 1995–1997EURO II 1996–1999 EURO II (91/542/EEC Stage 2) 1998–

Gasoline passenger cars �2.0 litres Diesel heavy duty vehicles 16–32 t GVWPRE ECE pre 1972 Conventional pre 1995ECE 15/00-01 1972–1977 EURO I (91/542/EEC Stage 1) 1995–1997ECE 15/02 1978–1980 EURO II (91/542/EEC Stage 2) 1998–ECE 15/03 1981–1984 Diesel heavy duty vehicles �32 t GVWECE 15/04 1985–1990 Conventional pre 1995EURO I (national — 91/441/EEC) 1986–1995a EURO I (91/542/EEC Stage 1) 1995–1997EURO II 1996–1999 EURO II (91/542/EEC Stage 2) 1998–

Diesel passenger cars �2.0 litres BusesConventional pre 1986 Conventional pre 1993EURO I (national — 91/441/EEC) 1986–1997a EURO I (91/542/EEC Stage 1) 1993–1996EURO II 1997–1999 EURO II (91/542/EEC Stage 2) 1997–

Diesel passenger cars �2.0 litres CoachesConventional pre 1986 Conventional pre 1993EURO I (national — 91/441/EEC) 1986–1997a EURO I (91/542/EEC Stage 1) 1993–1996EURO II 1997–1999 EURO II (91/542/EEC Stage 2) 1997–

a Introduction of these technologies has come into effect at different years in different European countries, mainlydue to national incentives taken before the implementation date of the common European directive.

to decrease by 10% from 1970 to 1990 model year vehicles, and from 1990 onwards it was keptconstant since further weight reduction, caused by the use of lighter components, was counterbal-anced by increased demand for safety and comfort. A was also reduced slightly (10% in eachvehicle size class between 1970 and 1995 model year vehicles).

As for the work consumed due to accelerations, since it depends on the driving pattern (i.e.,


Table 3The average (over all subcategories) values that were used for the engine efficiency calculations (GVW: GrossVehicle Weight)

Friction Mass (kg) Drag Frontal Average speed (km/h)coefficient coefficient area (m2)

Urban Rural Highway

Passenger carsGasoline �1.4 l 0.015 900 0.4 1.9 25 50 100Gasoline 1.4–2.0 l 0.015 1100 0.4 2.0 25 50 105Gasoline �2.0 l 0.015 1300 0.4 2.1 25 50 110Diesel �2.0 l 0.015 1200 0.4 2.0 25 50 110Diesel �2.0 l 0.015 1400 0.4 2.1 25 50 110LPG 0.015 1200 0.4 2.0 25 50 110

Light duty vehiclesGasoline 0.015 2000 0.5 2.2 20 60 90Diesel 0.015 2000 0.5 2.2 20 60 90

Heavy duty vehiclesDiesel 3.5–7.5 t GVW 0.012 5500 0.8 7 20 60 90Diesel 7.5–16 t GVW 0.012 11,750 0.9 7 20 60 90Diesel 16–32 t GVW 0.012 24,000 1.2 7 20 60 90Diesel �32 t GVW 0.012 36,000 1.2 7 20 60 90Buses and coaches 0.012 10,000 0.6 7 20 60 90

the frequency and magnitude of accelerations encountered when driving a vehicle in differentroad types), we assumed appropriate values for the fraction of total road load power that is due toaccelerations. Taking into account literature data [12], some sample calculations and the relevantexperience from our laboratory’s experimental data from emission tests, we came up with thefigures shown in Table 4. For this, we examined various driving patterns, both legislated (suchas the US Federal Test Procedure and the New European Driving Cycle [11,14]) and non-legis-lated [15] ones. For those driving cycles that we had used in laboratory tests, the measuredinstantaneous power consumption was available for about 100 different passenger cars [16]. We

Table 4Estimated fraction of power consumed for vehicle acceleration over the total road load power, for various vehiclecategories and driving patterns (GVW: Gross Vehicle Weight)

Vehicle category Urban driving Rural driving Highway driving

Passenger cars 40% 30% 20%Heavy duty vehicles

3.5–7.5 t GVW 41% 26% 19%7.5–16 t GVW 45% 34% 26%16–32 t GVW 47% 38% 31%Over 32 t GVW 48% 41% 34%Buses and coaches 39% 36% 29%


used these data further to calibrate road load calculations for other driving cycles as well as forother vehicle types. We used the figures of Table 4 for all model years: older vehicles wereheavier, but at the same time they also had higher values of f, cw and A, so that the overallacceleration power fraction remained essentially unchanged over the years. Road loads for heavyduty vehicles were calculated on the basis of driving cycles specifically designed for these vehicles(e.g., the Heavy Duty Urban Dynamometer Driving Schedule used in the US), as well as oncycles that such a vehicle can actually drive under real-world conditions.

With the aid of these data it was possible to compute the engine efficiency according to (1)MEET and (2) the official vehicle-kilometre statistics that we considered to be the most reliableand consistent, and to compare the results with expected figures. The average efficiency of theengine of a European in-use gasoline car, a diesel car and a diesel heavy duty truck would nor-mally be in the range of 10–20%, 15–25% and 35–45%, respectively, while the correspondingranges for specific fuel consumption would be 8–11, 5–8 and 25–35 litres per 100 km, respect-ively [17,18].

In the former case, both estimated vehicle-kilometres and calculated fuel consumption of MEETwere used, while in the latter case vehicle-kilometres and fuel consumption data came mainlyfrom EUROSTAT [19] (which as a rule puts together the corresponding data from national statisti-cal services) or other official sources [2]. Table 5 displays the EUROSTAT energy consumptionfigures by fuel. Since official statistical data in many countries do not include a breakdown ofvehicles in more detailed categories according to engine type and size, the fuel consumption ofeach one of these vehicle categories was estimated for both cases with the aid of the correspondingMEET ratios, which have been produced by national experts. Note that motorcycles were notincluded in the evaluation, but their share in road gasoline consumption is about 1–2% in mostEU countries and up to 4% in Southern EU countries such as Italy, Spain and Greece [18]. Hence,omitting two-wheeled vehicles from the calculation does not introduce a sizeable error.

The results of computed engine efficiency are shown for the major vehicle categories in Figs.2–4. Additionally, Figs. 5–7 present calculations of specific fuel consumption (expressed in litresper 100 km); this is inversely proportional to energy efficiency, although not by a constant factorbecause of the differing fractions of various vehicle types per country and different specific fuelconsumption of each vehicle type. Note that specific fuel consumption was not calculated with theaid of road load estimates, but just by dividing the fuel consumption of a vehicle type (converted tolitres with the aid of fuel density) with the corresponding vehicle-kilometres.

In general, energy efficiency and specific fuel consumption of each vehicle category are quiteoften found to be out of the expected ranges that were mentioned above. More specifically:

� the picture is mixed in gasoline passenger cars — in theory, countries with a higher share ofold cars (e.g., Italy, Spain, Portugal and Greece) should demonstrate lower efficiency and higherspecific fuel consumption. Keeping this in mind, official data seem to be more realistic thanMEET in the case of Spain, while MEET produces more reasonable efficiency and consumptionfigures for Luxembourg (which has to be attributed to the fuel consumption problem mentionedabove), France, The Netherlands and Portugal. Both data sets seem to give too high figuresfor Ireland. In any case, the high increase in official efficiency figures from 1980 to 1995 inFrance, Italy, The Netherlands, Portugal and Sweden is most probably unjustified, for reasonsthat will be explained in the next section.


Tab

le5

Roa

dtr

ansp

ort

ener

gyco

nsum

ptio

nin

EU

coun

trie

s(s

ourc

e:E

UR

OST

AT

[19]

)

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

Roa

dtr

ansp

ort

gaso

line

cons

umpt

ion

(PJ)

Aus

tria

103

101

103

104

105

106

108

110

113

114

113

123

118

113

109

106

Bel

gium

129

119

117

113

114

110

120

125

129

127

120

120

128

125

125

125

Ger

man

ya10

6299

910

1910

2510

5310

3110

7611

1411

5611

5411

9113

7113

7313

7813

0213

15D

enm

ark

6462

6161

6366

6465

6667

7075

7780

8384

Finl

and

5959

6060

6366

7276

7985

8787

8782

8483

Fran

ce77

178

778

879

279

678

280

380

581

880

379

172

170

174

064

961

1G

reec

e58

6165

6771

7680

8692

100

104

108

111

114

116

120

Irel

and

4545

4341

3937

3837

3838

3940

4342

4346

Ital

y52

452

151

249

849

750

650

750

251

353

456

760

666

569

471

374

2L

uxem

bour

g12

1414

1313

1314

1414

1718

2123

2324

23N

ethe

rlan

ds16

916

216

015

815

914

915

315

014

815

015

215

215

816

717

217

9Po

rtug

al33

3233

3336

3841

4651

5660

6674

7981

83Sw

eden

147

148

151

154

159

165

173

180

187

194

183

187

191

181

183

187

Spai

n23

824

925

225

225

525

928

130

133

534

235

837

540

538

940

337

5U

K84

282

484

786

189

089

894

397

610

2310

5310

7010

5710

5210

4610

0596

7E

U15

4259

4181

4224

4232

4312

4302

4474

4586

4760

4831

4924

5110

5207

5252

5093

5044

(con

tinu

edon

next

page

)


Tab

le5

(con

tinu

ed)

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

Roa

dtr

ansp

ort

dies

elco

nsum

ptio

n(P

J)A

ustr

ia65

6564

6465

6266

6574

7986

9610

110

911

111

9B

elgi

um76

7882

8891

101

114

118

136

146

147

150

153

162

168

169

Ger

man

ya41

842

643

446

547

950

054

256

658

161

866

178

582

688

492

195

1D

enm

ark

2929

3134

4149

4749

5057

6359

5959

6364

Finl

and

4950

5252

5455

5863

6265

6561

6063

6564

Fran

ce38

539

740

541

443

444

548

552

559

365

972

177

382

185

790

194

9G

reec

e37

3638

4649

5155

5556

5758

6666

6768

70Ir

elan

d16

1615

1516

2220

1320

2426

2729

3032

26It

aly

360

386

475

413

447

486

553

571

612

645

635

623

633

629

605

602

Lux

embo

urg

56

66

78

810

1114

1822

2425

2524

Net

herl

ands

8495

8999

104

127

104

115

128

139

143

145

154

155

157

161

Port

ugal

4845

5154

5149

5154

6063

6670

7578

8489

Swed

en57

5758

5960

6067

6673

7471

6571

7785

82Sp

ain

193

190

208

212

219

233

233

237

326

363

380

403

419

422

440

478

UK

248

235

242

268

286

301

333

358

396

428

451

452

469

499

546

569

EU

1520

6821

1022

5122

8124

0325

4627

3428

6631

7834

3135

9237

9639

6041

1742

7244

17R

oad

tran

spor

tL

PGco

nsum

ptio

n(P

J)A

ustr

ia0

00

11

11

11

00

11

11

1B

elgi

um2

33

44

33

32

33

22

22

3G

erm

anya

01

11

11

00

00

00

00

33

Den

mar

k3

34

43

32

11

10

00

00

0Fi

nlan

d0

00

00

00

00

00

00

00

0Fr

ance

12

33

33

33

32

22

22

11

Gre

ece

01

11

11

11

11

12

22

22

Irel

and

11

11

11

10

01

00

00

00

Ital

y34

4045

4544

4446

5556

6162

5955

6064

68L

uxem

bour

g0

00

00

00

00

00

00

00

0N

ethe

rlan

ds32

3439

4140

3739

3839

4342

4140

3836

35Po

rtug

al0

00

00

00

00

00

00

00

0Sw

eden

00

00

00

00

00

00

00

00

Spai

n5

54

44

33

22

11

22

33

3U

K0

00

00

00

00

00

00

00

0E

U15

7790

103

104

101

9798

104

106

113

112

109

104

107

113

116

aG

erm

anda

taup

to19

90re

fer

tofo

rmer

Wes

tG

erm

any

only

.


Fig. 2. Efficiency of gasoline passenger cars, as calculated with the aid of MEET and official transport parametersrespectively.

� MEET vehicle-kilometres of diesel passenger cars in Greece seem to be underestimated (ortoo much fuel consumption is attributed to these cars), thereby causing too low efficiency andtoo high specific fuel consumption figures. On the other hand, efficiency calculated with officialdata for diesel passenger cars is too high in Denmark and Ireland and too low in Italy, Luxem-bourg, Spain and Sweden, which shows that statistics of these vehicles have to be examinedcarefully.

� Official data for Ireland and Portugal lead to extremely high efficiency figures (exceeding unity!)and too low specific fuel consumption in heavy duty vehicles. This has to be attributed to theoverestimation of vehicle-kilometres of trucks and buses in official statistics, which can alsobe seen in the comparison of Table 1. On the contrary, most other official efficiency figuresare too low (to a large extent because of the much lower vehicle-kilometres estimated in com-parison to MEET — see again Table 1), with Belgium and Luxembourg having the mostoutstanding figures; as a result, the calculated specific fuel consumption reaches extremelyhigh levels.

� According to the MEET data, there is a general tendency towards increasing energy efficiency


Fig. 3. Efficiency of diesel passenger cars, as calculated with the aid of MEET and official transport parametersrespectively.

(and therefore declining specific fuel consumption) from 1980 to 1990, which then tends tostabilise or even to decrease slightly, particularly in gasoline cars. This seems to be in linewith other estimations [20,21]. Conversely, the picture is mixed in the official data: sharpincreases or decreases in energy efficiency are observed, which do not seem to be justified andmay have to do with the low reliability of the vehicle-kilometre figures.

5. Discussion

5.1. Uncertainties in the input data of the efficiency calculations

In this study, the most reliable data sets available for all countries are those of total roadgasoline consumption and vehicle population, whereas road diesel consumption statistics have tobe treated with more caution, as will be discussed later in this section. The breakdown of vehiclesinto different types of fuel and engine size (e.g., three-way catalyst gasoline passenger cars of


Fig. 4. Efficiency of diesel heavy duty vehicles (trucks, buses and coaches), as calculated with the aid of MEET andofficial transport parameters respectively.

engine capacity over 2 litres, trucks with gross weight over 32 tonnes, etc.) is performed on thebasis of official estimates or experts’ data; the same applies for vehicle-kilometres as well. Further-more, the allocation of fuel consumption into the vehicle categories shown in Table 2 is effectedthrough these experts’ data and experimentally determined fuel consumption rates that areincluded in the MEET database. All in all, many assumptions are necessary and it is thereforequite difficult to find which statistical parameters are mainly responsible for the inconsistent orunrealistic figures shown in Figs. 2–7.

Still, some observations are apparent: it is clear, for example, that the allocation of dieselconsumption between passenger cars and heavy duty vehicles is not responsible for the too highofficial diesel efficiency figures of Ireland and Portugal, nor would the picture improve if somevehicle-kilometres were attributed to gasoline passenger cars instead of diesel ones. The sameholds true for MEET’s efficiency figures for Spain (gasoline cars) and Greece (diesel cars). More-over, some drastic increases or decreases in official energy efficiency figures cannot be explainedthrough similar changes in fuel sales.

The input data we used for the energy efficiency calculations (vehicle mass, driving speeds,


Fig. 5. Specific fuel consumption of gasoline passenger cars, as calculated with the aid of MEET and official transportparameters respectively.

drag coefficients, etc.) are adequately precise. In the case of heavy duty vehicles, the averageweight of each class was taken equal to the mean gross weight of that class. Although this assump-tion may not be accurate, we conducted a sensitivity analysis and realised that a 20% error invehicle mass — already an exaggerated error margin — leads to a 8–13% change in total roadload, which would still be in an acceptable range for the calculations presented here. Other poss-ible errors in parameters, such as driving speed or the share of urban or highway driving, havean even smaller impact [22]. Hence the input data are not a serious source of errors in the com-puted energy efficiency values.

In view of these considerations, the most plausible interpretation for the inconsistenciesobserved in Figs. 2–7 seems to lie with the quality of traffic volume data (i.e., vehicle-kilometres).This is the parameter that is usually measured or estimated with various methods of unknownaccuracy. Apparently, official transport figures are not always validated against real energy con-sumption data, thus leading to results such as those shown in Figs. 2–7. Still, vehicle-kilometredata are often used in further studies such as predictions of passenger-kilometres and tonne-kilometres, air pollutant emissions, or the evaluation of traffic-related interventions; their impor-


Fig. 6. Specific fuel consumption of diesel passenger cars, as calculated with the aid of MEET and official transportparameters respectively.

tance is therefore obvious. In other words, the whole analysis of a transportation system maybecome ambiguous as a result of unreliable traffic volume data.

5.2. Evolution of energy efficiency over the years

A basic issue that can help in this evaluation is the evolution of energy efficiency and specificfuel consumption over time. The efficiency of cars and light trucks in OECD countries hasincreased during the period 1980–1990, mainly due to reduced engine friction and improvedengine management. After 1990, however, the improvement seems to have stopped, because therewas a shift towards more powerful cars that are also heavier because of increased safety andcomfort requirements [20]. As for heavy duty vehicles (mainly trucks but also buses and coaches),the picture is mixed because efficiency is also determined by load factors of trucks and passengeroccupancy rates of buses. In general, countries with older vehicle fleets have experienced animprovement in specific fuel consumption because of the penetration of modern, more efficienttrucks and buses, while in other countries the improvements were not remarkable and in somecases specific consumption even increased because of inefficient utilisation of truck capacity [21].


Fig. 7. Specific fuel consumption of diesel heavy duty vehicles (trucks, buses and coaches), as calculated with theaid of MEET and official transport parameters respectively.

Bearing these in mind, Figs. 2–7 show clearly that there are numerous inconsistent results inthe figures calculated through the official traffic volume statistics. Sharp increases or decreaseswithin five years and mixed trends over the 15-year period under examination indicate that officialvehicle travel data in many cases have not been consistently checked against energy data.

5.3. The special case of heavy duty vehicles

The inconsistency of the official data for heavy duty vehicles is further demonstrated in Fig.8, where the specific fuel consumption of 1990, taken from Fig. 7, is plotted against the averagegross weight of all these vehicles in each country. Although it was already expected that theMEET fuel consumption data would correlate well with vehicle mass because they come from aharmonised database, they are illustrated here in order to show a reasonable picture of these twoparameters. This picture is completely mixed in the case of official data: there is effectively nocorrelation between specific fuel consumption and vehicle mass; or, in other words, if there is a


Fig. 8. Calculated specific fuel consumption of diesel heavy duty vehicles in 1990 and their average Gross VehicleWeight in each country.

correlation in some countries, this is hidden behind the many countries with inconsistent data.Although there are additional parameters influencing fuel consumption (such as driving speed andvehicle technology), a quite clear relationship between fuel consumption and vehicle mass shouldhave been apparent.

The issue of heavy goods traffic is further complicated by the following facts.

1. International goods traffic is not always accounted for in statistics. This traffic includes tripsthat started in one country and finished in another, possibly traversing third countries (for whichthe trip is considered part of ‘transit’ traffic). Such trips may be made by trucks registered inthe origin or destination country, or in a third country. The most comprehensive Europeanstudy that is known to us [23] attempted to estimate all tonne-kilometres of freight traffic percountry, accounting for domestic, international and transit traffic with the aid of origin–desti-nation matrices. According to that study, transit tonne-kilometres (and the correspondingvehicle-kilometres, too) are those usually not accounted for in statistics, and were responsiblefor over 10% of total road freight tonne-kilometres in Austria, France and Luxembourg, 5–10% of the total in Belgium and Germany, and less than 5% in all other EU countries inthe 1990s.

2. Irrespective of the gaps in freight statistics, road diesel consumption may be misallocatedbetween countries if trucks consume fuel in a given country that was purchased in a neighbour-ing country with lower diesel prices. It is difficult to assess how big the discrepancies couldbe. In a small country with heavy transit traffic like Luxembourg, the error may be large. Theproblem must be less pronounced in Austria and Belgium, particularly because of their highshare in diesel passenger cars, which lowers the overall error in diesel consumption. Finally,in larger countries such as France and Germany, domestic freight traffic and diesel passengertravel may limit the error to insignificant amounts.

The two major sources of discrepancies mentioned above require more detailed quantitative analy-


sis that is beyond the scope of this paper. Still, the results from official data shown in Figs. 4, 7and 8 demonstrate more general problems in heavy duty vehicle-kilometres that require in-depthstudy of the whole sector and not only of the errors outlined in this section.

5.4. Uncertainties in diesel consumption

As mentioned earlier, the reliability of diesel sales data is questionable, especially for the 1980sand in countries with large tax differences between road diesel and heating diesel, such asDenmark, Italy and Greece. Apart from this reason, diesel statistics may be inaccurate because apart of automotive diesel sold is consumed by non-road machinery used in agriculture, industry,forestry and recreation. In an earlier study [24] we had found considerable discrepancies betweenofficial and our own diesel consumption estimates, which had been based on detailed accountingof all non-road vehicles and machines — including railways and inland navigation. Table 6presents a summary of those estimates. Other national data that we had collected for that studyindicated also that non-road diesel consumption is approximately 35–45% of road diesel consump-tion, which means that the error may be large if diesel sales data do not consider correctly thevarious uses of diesel. Conversely, as is clear from Table 6, the problem is negligible in the caseof gasoline.

This finding indicates that, in the case of diesel vehicles, and particularly heavy duty trucksand buses, vehicle-kilometre data are not necessarily responsible for the discrepancies observedin Figs. 4, 7 and 8; fuel use data could be equally inaccurate. As mentioned in previous sections,vehicle population and fuel consumption rates are generally accurate within a ±5–10% margin.When accounting for total gasoline consumption, which has a similar error margin, it is apparentfrom Eq. (1) that larger errors, such as those implied by some results of Figs. 2–7, have to beattributed to the uncertainties in vehicle mileage. When calculating diesel consumption, however,it is both mileage and total consumption that have large error margins, so that both parametershave to be checked more carefully.

Table 6Estimates of gasoline and diesel consumption of transport in the EU for the year 1990, according to EUROSTAT anda detailed study of the authors [24]. All figures are given in kilotonnes

EUROSTAT ‘Off road’ study

Diesel Gasoline Diesel Gasoline

Agriculture 9763 222 15,334 387Forestry 646 584Industry 9620 82 18,660 1796Household 57 427Railways 2151 0 4363 0Inland navigation 5217 387 1179 117Total non-road transport 26,751 691 40,239 3311Road transport 79,656 103,211 79,656 103,211Non-road/road transport 34% 1% 51% 3%


6. Implications for transport–energy analyses

It is clear that reliable real-world data of vehicle travel and fuel use are absolutely necessaryfor policy analysis. The study that we outlined in this paper does not resolve all problems, butprovides useful insight into the topic. We did not have access to detailed fuel sales statistics inorder to correct eventual fuel misallocations or double counting, nor is it possible to have thebackground information necessary to correct statistics of earlier years. It is possible, however, to:

� use representative, real-world fuel consumption rates in order to reflect actual the fuel economyof in-use cars; and

� collect as much information on traffic volumes as possible, and then compare estimated vehicle-kilometres with total fuel consumption through Eq. (1).

The MEET data fulfil these two requirements. Firstly, fuel consumption rates have been exper-imentally determined from measurements of in-use vehicles with accumulated mileage of 3000to 100,000 km in eight different countries (over 800 passenger cars and light trucks tested inabout 3000 driving cycles that reflect actual driving conditions, 36 heavy duty vehicle enginestested on legislated and actual test cycles). Thus it is reasonable to assume that fuel consumptionrates are the appropriate ones for this analysis.

Secondly, mileage per vehicle has been estimated within MEET on the basis of multipleapproaches. Transport authorities and individual experts have provided their best estimates accord-ing to available travel surveys, data from truck and bus companies, taxi drivers, motorcyclists,etc. These estimates were then entered into Eq. (1) and were iteratively adjusted in order to fulfilthe fuel balance. In a few cases it became evident from this procedure that fuel sales data couldnot be correct. We had this experience with Greek road diesel statistics in the 1980s, which wereapparently underestimated, yielded too low mileage figures and, therefore, we had to ignore them.In most cases, however, the fuel balance is an important checkpoint of mileage estimates.

For the reasons we outlined in Section 3, we do not consider the MEET database as the onethat provides the best possible vehicle-kilometre figures. It gives, however, a consistent methodfor resolving the discrepancies between fuel use and vehicle travel estimates. The calculationspresented in Section 4 and Figs. 2–7 have demonstrated the consistency of this method, in contrastto official estimates, which yield unrealistic figures in many cases.

7. Conclusion

The main finding of this study is that European traffic volume statistics are often of questionablequality: when cross-checked against real-world energy data such as specific fuel consumption andtotal fuel consumption figures, they often lead to unrealistic results. As vehicle-kilometre statisticsare frequently used for various important purposes (e.g., transportation planning, calculations ofvehicle emissions and energy consumption, forecasts of passenger- and tonne-kilometres, etc.), itis imperative that authorities and individuals conducting such studies employ transparent andconsistent methods and always validate their figures with the aid of energy balances. Additionalproblems are associated with estimates of freight traffic, where international road goods transport


and cross-border differences in fuel prices can lead to considerable under- or overestimation ofactual diesel consumption in a country. Diesel sales are also a more general problem, as thedistinction between road and non-road diesel consumption is often unreliable, so that, in the caseof diesel vehicles, traffic volume data are not the sole source of discrepancies and fuel salesstatistics may be erroneous as well.

Data of the MEET study, although not the best possible traffic volume estimates, provide thebasic guidelines for such analyses: use actual fuel economy data from in-use cars, collect asmany independent vehicle mileage data as possible and then check their consistency throughfuel balances.

Acknowledgements

The first considerations related to this paper were initiated during a study that was funded bythe European Commission’s Directorate General for Energy and Transport. The authors expressalso their thanks to Savas Geivanidis and Nicolas Papalazarou for preparing the data and per-forming the calculations.

References

[1] Environmental data compendium 1995. Paris (France): Organisation for Economic Cooperation and Development(OECD), 1995.

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Validation of road transport statistics through energy efficiency calculations

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