0144-1647 Print/1464-5327 Online/04/030261-14 © 2004 Taylor & Francis Ltd

DOI: 10.1080/0144164032000101193

Transport Reviews, Vol. 24, No. 3, 261–274, May 2004

Correspondence Address: Daniel J. Graham, Centre for Transport Studies, Imperial College of Science,

Technology and Medicine, London SW7 2BU, UK. Email: d.j.graham@ic.ac.uk

Road Traffic Demand Elasticity Estimates: A Review

DANIEL J. GRAHAM AND STEPHEN GLAISTER

Centre for Transport Studies, Imperial College of Science, Technology and Medicine, London, UK

(Received 29 October 2002; revised 25 March 2003; accepted 10 April 2003)

ABSTRACT A brief summary of road traffic-related elasticity estimates as reported in the

international literature is given. An indication of the orders of magnitude of these

elasticities is outlined and the variation in estimates commonly found is emphasized. The

results of previous extensive surveys are collated, but a wider scope of traffic-related

research is provided by reviewing recent work and including research that has received less

attention. A variety of elasticity measures related to car travel, car ownership, freight

traffic and fuel demand are reported. Based on the review, some important themes

underpinning the demand for road traffic are revealed.

1. Introduction

Elasticities associated with road traffic demand have recently received a great

deal of attention in the academic literature. TRACE (1998) and de Jong and Gunn

(2001) provide up-to-date surveys of research concerned with fuel price and car

time elasticities of car traffic, while Graham and Glaister (2002a) review the

international literature on fuel demand elasticity estimates.

A sound understanding of road user responses to changes in prices or in

standards of living is crucial to making transport policy decisions. Such

knowledge can inform attempts to achieve emissions reductions and help show

how traffic levels might be manipulated by making some change to the cost of

driving. It can help public policy-makers reach decisions about the allocation of

investment and can be used to forecast how the demand for fuel and road travel

will change as prices or standards of living change. It can also shed light on the

implications of road-related fiscal policy for certain groups in society or certain

geographical locations.

The purpose of the present paper is to build upon the recent literature surveys

cited above, which are concerned with quite specific dimensions of road traffic

demand, by collating and comparing their basic findings and by addressing their

various omissions. Research not reviewed in these surveys is summarized,

specifically emphasizing the demand for freight traffic that has received less
262 D. J. Graham and S. Glaister

attention in recent major survey articles. Also included within the scope of this

paper is a much broader range of traffic-related research. Through doing so, this

review provides a brief and substantially comprehensive review of the road traffic

elasticity literature.

The paper is based upon research undertaken as part of a larger piece of work

commissioned by the Department of Transport on road traffic demand (Graham

and Glaister 2002b). The purpose of Graham and Glaister’s report, along with that

of the parallel study by Hanly et al. (2002), was to inform the Department of

Transport about the likely magnitude of road traffic elasticities and ranges of

estimated values that are found across studies, and to distinguish separate

elasticities by traffic type and by different definitions of cost and price.

The paper is structured as follows. Section 2 summarizes the findings on the

elasticities of car travel, and elasticities of car ownership are reviewed in Section

3. Section 4 addresses the demand for road freight services and is followed by a

brief discussion of fuel demand elasticities in Section 5. A summary is then

presented in Section 6 that synthesizes and compares these findings and draws

out some important themes. Conclusions are offered in Section 7.

2. Elasticities of Car Travel

This section summarizes the results of two recent publications by TRACE (1998)

and de Jong and Gunn (2001) that have provided comprehensive and up-to-date

surveys of car time and fuel price elasticities of car travel. They reviewed

evidence from more than 50 recent studies (1985 and later) for Member States of

the European Union, reporting average unweighted values of short- and long-run

elasticities for car trips and car-km. (The short run included only mode choice

effects; the long run included some combination of mode choice, destination

choice, travel frequency choice, relocation of population and retail and service

activities.)

Regarding the effects of changes in fuel price, they found that in the short-run,

car trips and car-km respond in much the same way. The short-run fuel price

elasticity was –0.16 for both car trips and car-km. In the long run, the elasticity of

car-km to fuel prices increases quite substantially, to –0.26, but only marginally

for car trips (–0.19). Thus, the immediate consumer response to a fuel price

change is to modify the number of trips made, but over time they make even more

substantial changes to the distance travelled. De Jong and Gunn (2001) argued

that this may be due to adaptations in some combination of mode choice,

destination choice, relocation of population and retail and service activities. Note

that the elasticities of car-km with respect to fuel price are broadly consistent with

values quoted in previous surveys by Graham and Glaister (2002a) and Goodwin

(1992) (–0.15 in the short run, –0.31 in the long run).

Regarding elasticities of car trips with respect to car time, they found that the

short-run elasticity (–0.6) was substantially higher than that of the long run

(–0.29). Thus, having made a change in destinations, travel frequency and landuse locations, car drivers begin to make more trips of lower duration, switching

from long to short trips, while some public transport users switch back to the car

for short trips. For car-km elasticities, however, the long-run travel time elasticity

of car-km was more than three time greater in magnitude that for the short run,

–0.74 compared with –0.20. This implies that drivers and perhaps land uses adjust

over time to minimize travel distance.
Road Traffic Demand Elasticity Estimates 263

Thus, comparing the fuel price and car time elasticities de Jong and Gunn’s

results show that in the long run the elasticity of car-km with respect to car time

is much higher than the elasticity of car-km with respect to fuel price.

The effect of income on car travel has been reviewed by Hanly et al. (2002),

whose results, again based on a literature survey, give a short-run mean elasticity

of demand for car-km with respect to income (real GDP) (from seven estimates)

of 0.30, and long-run mean figures (from seven estimates) of 0.73. These

elasticities confirm the conventional wisdom that standards of living have a

strong impact on the demand for road travel.

3. Demand for Car Ownership

Several studies have estimated elasticities of demand for car ownership as distinct

from car use. Recent research in this area is reviewed here to give an indication of

the relative influences of income, price and costs. One difficulty in drawing this

research together is that the definitions of ‘cost’ and ‘price’ used are often not

consistent across studies and this hinders proper comparability of estimates.

A good starting point in the present coverage of car ownership elasticities is the

early survey of price responses provided by Goodwin (1992), which collected 93

elasticities of car ownership from a variety of countries. The mean unweighted

price elasticity of car ownership was quoted as –0.89, with most estimates falling

in the range –0.4 to –1.6. The survey did not distinguish short- and long-run

effects and acknowledged that there were differing definitions of ‘car price’ across

the studies.

Two recent studies of car ownership were conducted using British and UK data.

(Britain comprises England, Wales and Scotland; the UK includes all of Britain

and Northern Ireland.) First, Romily et al. (1998) estimated a model of car

ownership for Britain based on time-series data from 1953 to 1988. Their model

related per capita car ownership to real personal disposable income per capita

and the real motoring cost index. Their estimated income elasticity was 0.34 in the

short run and 1.14 in the long run. The short-run price elasticity was –0.29, the

long run –2.19. Note that the average value of the short- and long-run price

elasticities was –1.24, slightly higher than Goodwin’s figure of –0.89.

A second recent study concerning car ownership in the UK carried out by

Dargay and Vythoulkas (1999) was based on cross-sectional time series data. They

applied a ‘pseudo-panel’ approach to estimate a dynamic model of car ownership

for the UK based on repeated cross-sectional data from the Family Expenditure

Survey (FES) between 1982 and 1993. For middle-income groups, they estimated

a short-run elasticity of car ownership with respect to income of 0.24 and a longrun figure of 0.65. The elasticity of car ownership with respect to the cost of

purchase was estimated at –0.12 in the short run and –0.33 in the long run, and

with respect to running costs –0.19 and –0.51, respectively. Note that these values

for both costs and income are generally low compared with those estimated by

both Goodwin (1992) and Romilly et al. (1998).

It is worth considering how these results for Britain and the UK compare with

those for other geographical areas from some notable recent studies. Johansson

and Schipper (1997) estimated vehicle stock per capita elasticities as part of their

analysis of fuel demand in 12 OECD countries between 1973 and 1992. They

estimate a long-run income elasticity of approximately 1.0 and a long-run fuel

price elasticity of about –0.1.
264 D. J. Graham and S. Glaister

Aggregate car ownership demand elasticities for Norway were examined by

Fridstrøm (1998) using pooled data on a cross-section of countries between 1973

and 1994. The explanatory variables included personal income, interest rates, fuel

cost (the real price of fuel times the average fuel consumption per vehicle-km

driven), and vehicle taxes. The results give a long-run income elasticity of

demand for cars of 1.2 averaged over the sample, and a total long-run fuel cost

demand elasticity of –0.24.

Bjørner (1999) estimated the demand for car ownership in Denmark using data

from surveys of Danish car use carried out in 1992 and 1993. Elasticity estimates

based on this data were compared with estimates from two previous studies

using the same model: one based on Dutch data by de Jong (1990) and one based

on Norwegian data by Ramjerdi and Rand (1992). The model estimates elasticities

of car ownership against variable costs (fuel, replacement, repairs and maintenance) and fixed costs (depreciation, annual ownership tax and insurance). No

distinction was made between short- and long-run effects. The results (Table 1)

were consistent with respect to sign, but they showed variation in the estimated

elasticities of car ownership.

From the studies reviewed here, some observations on the demand elasticities

of car ownership can be made. (Results from Johansson and Schipper (1997) are

not included as their estimates were far outside the range indicated by the other

studies.):

Estimates of the long-run income elasticity of demand for car ownership fall

within the range 0.3 to 1.1. The mean across the studies reviewed is 0.74.

Short-run income elasticity was much smaller than the long run, taking a mean

of 0.28 and falling within a fairly narrow range of 0.24 to 0.34.

Ignoring for the moment the definitional issues surrounding ‘price’ and ‘cost’,

the mean long-run price/cost elasticity of car ownership is –0.90 (range –0.24

to –2.65), while the short-run mean is –0.20 (range –0.35 to –0.09).

4. Freight Traffic Demand Elasticities

This section reviews evidence on road freight demand elasticities. It is often

assumed that the demand for road freight is price inelastic, or at least more so

than general traffic. The studies reviewed here indicate that in fact this may not

be the case. The price demand elasticity estimates are, almost without exception,

negative and in many cases exceed unity.

However, as regards the actual magnitude of price elasticity estimates, it must

be stated at the outset that this section demonstrates the very wide variation that

Table 1. Elasticities of car ownership

Ramjerdi and Rand

(1992)

Bjørner

(1999)

de Jong

(1990)

Disposable income 0.33 0.41 0.15

Variable costs –1.33 –0.78 –0.41

Fixed costs –2.65 –1.29 –0.80
Road Traffic Demand Elasticity Estimates 265

exists. This variation is almost certainly due to the differences between studies in

the models estimated, in the type of data used (e.g. aggregate or disaggregate), in

the level and definition of commodity group aggregation, in market coverage,

and in demand definitions.

Owing to the small number of studies dealing with freight elasticities, it is

extremely difficult to disentangle the influence of any of these particular effects.

There is not enough evidence to form coherent grouping of studies according to

these criteria, and thus we are constrained to compare estimates from one very

specific study with those of another very specific study.

In what follows, we simply report the evidence we have collected and draw out

the apparent common themes. The focus in freight demand studies has

principally been on identifying different elasticities for different commodity

groups. A small number of articles have estimated elasticities separately for

different trip lengths, or have distinguished demand between freight transport

(tonnage) and freight traffic (tonne-km). The treatment of time is generally

ambiguous as there is little evidence on the distinction between short- and longrun effects.

Given the small number of studies, and with no obvious groupings to structure

this survey, the articles are reviewed chronologically by year of publication.

Oum (1979) formulated a demand model for intercity freight transport as an

intermediate input to the production and distribution sectors of the economy. He

considered three modes of freight transport: railways, highways and waterway

carriers (excluding ocean shipping). His data described the price indices and

revenue share of the three modes of freight transport in Canada from 1945 to 1974.

He found that the price sensitivity of trucking services increased over time. In

fact, his estimates showed radically different effects over the years examined with

an estimated own price elasticity for highway freight services of 1.11 in 1950 and

–0.16 by 1970.

Friedlaender and Spady (1980) looked in more depth at the price effect by

providing a breakdown of elasticity estimates by the type of commodity being

carried. They analysed truck and rail transport demand using data from a crosssection of manufacturing industries in 1972 in five regions of the USA.

Interestingly, they found that the estimates of own price elasticities of demand for

freight truck services fell within a fairly narrow band across regions and

commodities. The mean of their 44 estimates was –1.12 with a standard deviation

of 0.2, and in fact with few exceptions, they found that estimates were generally

close to unity.

Lewis and Widup (1982) found less elastic estimates for the US shipment of

assembled automobiles using annual time series data for 1955–75. They estimated

a translog transport demand model to measure price and quality-of-service

demand elasticities for rail and motor carrier shipments of assembled automobiles. Their estimated own price elasticities for truck freight transport (share

within total freight transport) related to this commodity ranged from –0.52 to

–0.67.

Other specific price elasticity estimates for the USA are reported in the

literature. For aggregate goods, Winston (1985) reported estimates by Levin (1978)

of –0.25 to –0.35 (share of road in total freight), for leather rubber and plastic

products –2.97 (truck volume), and for machinery –0.4 (truck volume) (Winston

1981). Wilson et al. (1988) estimated a price elasticity of demand for US road

freight grain (wheat) transport (truck volume) of –0.73.
266 D. J. Graham and S. Glaister

Thus, very different estimates are found across different commodities and for

different geographical areas. One consistent area of concern in the freight demand

literature has focused on the influence of aggregation and methodology on the

magnitude of estimates. Oum (1989) addressed this issue comparing the use of

four commonly used aggregate models of transport demand in the context of

freight services: the linear demand model, the log-linear demand model, the

aggregate logit model and the translog demand system.

He used data on interregional freight flows in Canada in 1979 to investigate the

total freight flows of all commodities as well as the flows of one single commodity

group: fruits, vegetables and edible foods. The purpose of this comparison is to

help to identify the effect of aggregation over commodities on the relative

performance of the four alternative models. The estimated price demand (truck

volume) elasticities are shown in Table 2.

Oum pointed out that since the linear model excluded all quality of service

variables, the price elasticity estimates were likely to be biased. He noted that the

log-linear estimates appeared on the high side and that the translog estimates

seemed more reasonable. However, crucially, he concluded that the magnitude of

the elasticity estimate depended on the functional form being used for estimation.

Of the four models compared, he believed that the translog demand system

performed best.

Oum et al. (1992) returned to this theme providing a review of estimates of the

demand elasticity of truck freight. They demonstrated an important point about

the elasticity of freight demand put forward in the literature: that estimates can

vary as much within the same commodity group as they do across commodities

and across estimation methods. Thus, for instance, own price elasticities for

aggregate truck freight (all commodities) were –1.43 for the log-linear model,

–0.93 for the aggregate tobit and –0.69 for the translog. On the other hand, the

translog estimates for the food industry itself ranged from –0.52 to –1.00, and for

wood and wood products from –0.56 to –1.55.

The issue of variance in estimates was addressed by Abdelwahab (1998), who

presented empirical estimates of market elasticities of demand and elasticities of

mode choice probabilities in the intercity freight transport market. The analysis

was based on the estimation of a mixed discrete/continuous choice model of

mode and shipment size, where the mode choice component of the full model was

specified as a binary probit function. The analysis considered two modes (rail and

truck) using data describing the transport of individual commodities at the

Standard Metropolitan Statistical Area level.

Abdelwahab found that the estimated market own-price elasticity of demand

for truck freight transport (truck volume) is greater than unity in all except three

market segments out of eight, with a mean of –1.06. He went onto present a

comparison of price elasticity estimates for the iron and steel sector based on

Table 2. Elasticities of demand for road freight with respect to the freight rate

Translog Log-linear Linear Logit

Elasticity (all commodities) –0.692 (46.1) –1.341 (31.9) –0.048 –0.928

Elasticity (single commodity) –0.652 (18.6) –1.542 (9.0) –0.381 –0.970
Road Traffic Demand Elasticity Estimates 267

previous studies by Friedlaender and Spady (1980), Oum (1980), Chaing et al.

(1981), Winston (1981) and Kim (1984, 1987) (Table 3).

Abdelwahab pointed to the great variation in the magnitude of elasticities

estimated by the various studies. However, he regarded this as unsurprising

given the differences in model type, data type (e.g. aggregate or disaggregate),

grouping and definition of commodity group, market coverage and demand

definitions. In conclusion, he argued the comparison served to emphasize the

importance of examining the various elasticity measures within a specific

economic context (i.e. commodity type, market coverage, type of data, etc.).

Beuthe et al. (2001) addressed the theme of variation in estimates across

commodity groups and also explored the implications of trip type. They

implemented a detailed multimodal geographic information system (GIS)

network model of freight transport in Belgium. They assumed that shippers

minimize the generalized cost of their transport and optimize an assignment of

flows between freight modes, types of vehicles, or their combinations, and routes.

Hence, the elasticity estimates obtained were with respect to generalized cost rather

than to price.

Table 4 shows their aggregate road freight total cost demand elasticities for

short- and long-distance trips ( > 300 km), and for transport effects (tonnes) as

well as traffic effects (tonne-km).

Beuthe et al. stated that road transport tonnage demand was inelastic, while

it was elastic when computed in tonne-km. This result shows substantially

Table 3. Comparison of price elasticity estimates for the iron and steel sector

Reference Model type

Commodity

group Market Price elasticity

Oum (1980) Aggregate

translog

function

steel and iron

alloys

Canada –0.994

Friedlaender and

Spady (1980)

Aggregate

translog

function

iron and steel

products

ICC official –1.091

Chiang et al. (1981) MNL mode and

shipment size

model

agricultural

fertilisers

Chicago–

Houston

–1.143

Winston (1981) MNP mode

choice model

primary and

fabrication,

metal

USA –0.18–0.775

Kim (1984) Nested logit

and shipment

size

primary and

fabrication

metal

ICC official –0.101

Kim (1984) Generalized

Cobb–Douglas

primary and

fabrication

metal

USA –0.664

Abdelwahab (1998) Simultaneous

equations

primary and

fabrication

metal

ICC official –0.797
268 D. J. Graham and S. Glaister

larger elasticities for freight traffic than for freight transport. They also indicated

that the elasticity for long-distance road freight was higher than for shortdistance ones.

Disaggregating their analysis for separate commodity groups, Beuthe et al.

found great variation between commodity type and trip type in the magnitude of

total cost elasticity estimates. For some long-distance commodities such as solid

fuel and iron products, the elasticity was quoted as zero, while over short-run

distances the estimate for petroleum products was –7.92, for metallurgical

products was –2.38 an for food products was –0.14.

Commenting on the variation between commodities, the authors warned that

the use of more global estimates in forecasting exercises may lead to mistaken

assessments when applied to a particular situation. However, similar variation

may well exist within their own disaggregation, defined such as it is by only 10

commodities. In fact, Beuthe et al. have shown tremendous differences in the

magnitude of elasticity estimates depending on the length of trip, even for the

same commodity. Thus, it is not clear that estimates disaggregated at the level

presented are any more informative than the aggregate figures.

From the material presented above, 143 price elasticity estimates of the demand

for road freight have been collected. Summary statistics of these estimates are

presented in Table 5.

The price elasticity estimates given in the literature reviewed above range from

–7.92 to 1.72. The mean was –1.07.

Figure 1 is a histogram of these estimates. Despite the great range in magnitude

of estimates, it shows that the majority of estimates, 66%, fall between –0.5 and

–1.3. Forty-two per cent of estimates fall within the range –0.4 to –0.8. Only 13%

of estimates are greater than –0.4 and 2% of estimates are positive.

Can it then be concluded on the basis of the elasticity estimates reviewed here

(Figure 1) that the order of magnitude of the price demand elasticity of freight

traffic falls somewhere between –0.5 and –1.5? Certainly, the studies reviewed

herein, spanning a range of different commodities and countries, indicate that the

price elasticity of demand for freight is negative and relatively elastic. However,

we are also forced to conclude that different elasticities emerge for different

Table 4. Aggregate demand elasticities of road freight

with respect to total cost

Short distance Long distance

Tonnes –0.58 –0.63

Tonne-km –1.06 –1.31

Table 5. Price elasticities of demand for freight services

Number Mean Maximum Minimum Median SD

143 –1.07 1.72 –7.92 –1.05 0.84
Road Traffic Demand Elasticity Estimates 269

commodity groups, for different trip types and for different levels of market

coverage. Thus, the specifics of any particular context have an important bearing

on the magnitude of estimates. However, other issues raised here counsel a

cautious approach in the interpretation of the elasticity estimates. Evidence has

been found that estimates may be highly sensitive to the modelling approach

used and to the level of data aggregation. The treatment of time and the

distinction between long- and short-run effects is generally vague. One must also

recognize that road freight demand studies are still relatively scarce. For these

reasons, we believe that it would be imprudent to offer a firm conclusion about

the order of magnitude of the price elasticity of demand for road freight

movement.

5. Fuel Demand Elasticities

Graham and Glaister (2002a) surveyed the international fuel demand literature.

Their objective was to reach a view about the magnitude of fuel demand

elasticities as reported in that literature. Using material from this study and some

more recent articles, Graham and Glaister (2002b) gathered a literature survey of

113 studies published between 1966 and 2000, and collected 1083 fuel demandrelated elasticity estimates. These included 387 short-run price elasticities, 213

long-run price elasticities, 333 short-run income elasticities and 150 long-run

income elasticities.

Figure 1. Price elasticity of demand for road freight services.
270 D. J. Graham and S. Glaister

Table 6 shows summary descriptive statistics on the price elasticity estimates

collected from the literature review. The elasticities were estimated principally

with respect to all traffic, but in some cases were specific to the private car.

The mean of the 387 short-run price elasticities was –0.25, and although the

range was large, from 0.59 to –2.13, the median was very close to the mean. For

the long-run estimates, the mean of our estimates was –0.77, with a maximum of

0.85 and minimum of –22.0. The existence of positive price elasticities was

surprising. However, only very few short- and long-run estimates were positive,

approximately 2% in each case.

Table 7 shows the summary statistics describing the income demand elasticity

estimates.

The literature survey collected 333 short-run and 150 long-run income

elasticities for demand for fuel. The actual measure of income varied across

studies. Aggregate analysis tends to use data on GDP, while household studies are

often based on measures of personal or disposable income. The mean short-run

was 0.47 ranging from zero to 1.7, and the mean long-run was 0.93, ranging from

zero to 2.68.

Looking in more detail at the distribution of fuel demand elasticity estimates

reviewed here, the findings can be summarized in the following points:

Weight of evidence in the literature suggests that the long-run price elasticity of

demand for fuel falls between –0.6 and –0.8, and the short-run elasticity

between –0.2 and –0.3.

Income elasticity of fuel demand was between 0.3 and 0.5 in the short run and

between 0.5 and 1.5 in the long run.

Table 6. Summary statistics of price elasticity estimates from the literature

survey

Number Maximum Minimum Mean Median SD

Short run price 387 0.59 –2.13 –0.25 –0.21 0.24

Long run price 213 0.85 –22.00 –0.77 –0.55 1.65

Table 7. Summary statistics of income elasticity estimates from the literature

survey

Number Maximum Minimum Mean Median SD

Short run income 333 1.71 0.0 0.47 0.42 0.29

Long run income 150 2.68 0.0 0.93 0.91 0.49

6. Discussion: Determinants of Road Traffic Demand

This section summarizes the information from our literature review regarding the

determinants of road traffic demand and discusses some of the main implications.
Road Traffic Demand Elasticity Estimates 271

The elasticities derived from the survey are summarized in Table 8, which

distinguishes short- from long-run elasticities. In fact, the criteria used to make

these temporal distinctions differ across studies. As a general rule, anything less

than 1 year is usually regarded as short run. Other studies are based not so strictly

on time but on the type of change involved. Thus, for instance TRACE (1998) and

de Jong and Gunn (2002) allowed only for mode choice changes in the short run,

but included all other behavioural changes in the long run (i.e. destination choice,

travel frequency choice, land use change, etc.).

Table 8 also presents the means of road traffic-related elasticity estimates either

calculated by us for this survey or taken from other review articles. Car

ownership mean elasticities are also quoted, although it must be recognized that

these values are based on very few estimates and this necessarily tempers the

degree of confidence of these values. Throughout, this review has stressed that

there is a great deal of variations in estimates, but if for the moment these means

are accepted as being indicative of broad orders of magnitude, some interesting

themes can be drawn out.

First, the review clearly shows that growth in the standard of living is crucial

in explaining increasing road traffic demand. The level of car ownership is

heavily dependent upon income as is the demand for car travel and fuel

consumption. Table 8 shows that the mean elasticities of car ownership and carkm with respect to income are very similar in both the long and short runs, but

the fuel demand elasticities take higher values. Perhaps this supports the

Table 8. Summary of elasticities from the traffic demand literature

Short/long

run Elasticity Comments

Fuel demand with respect to fuel price SR –0.25 mean (n = 377)

LR –0.77 mean (n = 213)

Fuel demand with respect to income SR 0.47 mean (n = 333)

LR 0.93 mean (n = 150)

Traffic (car-km) with respect to fuel price SR –0.15 Graham and Glaister (2002a)

LR –0.31 Graham and Glaister (2002a)

Traffic (car trips) with respect to fuel price SR –0.16 de Jong and Gunn (2001)

LR –0.19 de Jong and Gunn (2001)

Traffic (car-km) with respect to car time SR –0.20 de Jong and Gunn (2001)

LR –0.74 de Jong and Gunn (2001)

Traffic (car trips) with respect to car time SR –0.60 de Jong and Gunn (2001)

LR –0.29 de Jong and Gunn (2001)

Traffic (car-km) with respect to income SR 0.30 Hanly et al. (2002)

Traffic (car-km) with respect to income LR 0.73 Hanly et al. (2002)

Freight traffic with respect to price N.A. –1.07 mean (n = 143)

Car ownership with respect to cost SR –0.20 mean (n = 7)

LR –0.90 mean (n = 8)

Car ownership with respect to income SR 0.28 mean (n = 5)

LR 0.74 mean (n = 5)
272 D. J. Graham and S. Glaister

hypothesis that as incomes grow, as well as buying more cars and thus creating

more traffic, consumers are more likely to purchase larger cars and cars that are

less fuel-efficient.

However, income affects road traffic demand in another important way

reflected in Table 8. This is through its influence on the value of time and thus on

the level of generalized cost of drivers. The mean long-run elasticity of car-km

with respect to car time is –0.74. The actual value of time along with road speed

determines the value of car time. Income growth has an important influence on

the average money value of time, but the exact nature of this relationship is the

subject of continuing research (e.g. Wardman, 1998). Assuming that values of time

grow roughly in line with disposable income, Graham and Glaister (2002b) have

inferred that the value of time per km for the average urban car driver has

increased from approximately 50% of the generalized cost of driving a vehicle-km

in 1960 to 65% by 2000. Thus, the value of time may be of greater relative

importance to the car driver now than in the past because it has grown faster than

many other components of generalized cost. Evidence is not available here to say

how these changing cost proportions have affected elasticities, but what is

indicated is that the elasticity of car-km with respect to car time in the long run

is much higher than the elasticity with respect to fuel price, by a factor of almost

2.5.

Regarding price elasticities, our review confirms the well-known result that

growth in fuel prices has a much stronger influence on fuel consumption than on

the number of kilometres driven. Car trips are also much less responsive to fuel

price changes in the long run than car-km, due perhaps to adaptations in mode

choice, destination choice and land-use location—people make less trips, but

travel much shorter distances. The car ownership elasticities with respect to cost

also indicate that in the long run consumers do respond to price changes.

However, what is also interesting in Table 8 is the relative magnitude of road

traffic-related price and income effects. Income elasticities of fuel demand are of

greater magnitude in both the short and long run than the price effects. This is

also true of the elasticities of car-km with respect to fuel price and income. The

indication may be that even to stabilize traffic and fuel consumption at present

levels prices would have to rise faster than incomes. However, the offsetting

factor may be, as mentioned above, the increase in values of time. Further

research is required to uncover the exact nature of this effect.

Finally, from Table 8, our review has emphasized elasticities of demand for

freight services. It has also been shown that a great deal of variation exists in the

magnitude of estimates reported in the literature and that there are a whole range

of factors that have a bearing on this, including the level of aggregation, the

methodological approach and the specific demand context. Nevertheless, we are

confident that from the studies reviewed, which span a variety of different

commodities and countries, the price elasticity of demand for freight is negative

and relatively elastic.

7. Conclusions

This paper has provided a brief review of road traffic and fuel demand elasticity

estimates from the international literature. An indication has been given of the

orders of magnitude of these effects and the factors underpinning variation in

estimates have been emphasized. A range of different elasticity estimates of
Road Traffic Demand Elasticity Estimates 273

relevance to road traffic has also been reported. By collating and comparing these

estimates, some important implications have been drawn out and some factors

underpinning the determinants of road traffic demand emphasized.

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