Applying Stated Preference Methods to the Valuation of
Noise: Some Lessons to Date
Mark Wardmana, Abigail Bristowb, Elisabete Arsenioc
a
b
Institute for Transport Studies, University of Leeds, Leeds, LS2 9JT, UK
Transport Studies Group, Department of Civil and Building Engineering, Loughborough
University, Leicestershire, LE11 3TU, UK
c
LNEC, Lisbon, Portugal
a
[email protected]; [email protected]; [email protected]
Abstract Few studies have applied stated preference methods to the valuation of noise.
This paper draws upon the experiences of three studies which have estimated willingness
to pay for reductions in road traffic and aircraft noise. Such valuations can be used in the
economic appraisal of infrastructure and operating decisions using social cost benefit
analysis. The emphasis is upon methodological issues. In particular, this papers covers: the
insights obtained by the three studies into the presentation of noise in a survey setting;
whether marginal valuations depend on the size and sign of the change in noise levels and
the noise level from which variations occur; the impact of socio-economic variables on
valuations; and the comparison of the valuations obtained using stated preference methods
with those derived using the contingent valuation method.
1. INTRODUCTION
There have been few applications of stated preference (SP) methods to the valuation of
environmental externalities. The purpose of this paper is to report the experiences obtained
from three novel but quite disparate studies which have used the SP method to estimate the
monetary valuations individuals and households place upon transport related noise. Two of
the studies related to road traffic noise and one to aircraft noise and they were conducted
between 1995 and 2003. The structure of the paper is as follows. Section 2 explains briefly
what SP is and how it compares with other valuation methodologies. The three SP exercises
and the data collection are described in section 3. Section 4 covers the experiences and
insights gained from these studies, with an emphasis on methodological issues. In particular,
it covers: issues involved in presenting noise to individuals in survey contexts; what are
termed size, sign and reference effects; the impacts of a range of socio-economic variables on
the monetary valuations of noise; and within study comparisons of SP based valuations with
those derived using the contingent valuation method.
2. WHAT IS STATED PREFERENCE?
SP has its roots in marketing research and mathematical psychology, where it is commonly
termed conjoint analysis [1]. It offers individuals a series of situations to evaluate, typically a
choice between two alternatives, with the alternatives characterised by a range of relevant
attributes which influence choice and are of interest to the analyst. The responses supplied
indicate the importance attached to each of the attributes and are therefore important for
product design, policy appraisal and forecasting behaviour.
SP has a number of advantages, not least for environmental valuation that it can create a
hypothetical market in which these attributes can be traded where no real market exists.
Other advantages include the ability to control the attributes and the attribute levels offered to
respondents, thereby obtaining more ‘ideal’ trade-offs with lower inter-attribute correlations
than would occur in the real world. The experimental design can ensure there is sufficient
variation in attributes, so as to allow precise estimates to be obtained and to support specific
analysis such as the investigation of non-linear effects, whilst multiple observations are
obtained per person and variables which are not of primary interest can be ‘designed out’.
The main drawback is that the weights estimated to each attribute do not reflect individuals’
true weights due to response bias made possible because respondents are not committed to
behave in line with their statements. Chief amongst these concerns are the incentives to
register protest responses or to bias answers in a strategic fashion to influence policy makers.
Applications of SP to environmental valuation in general and noise valuation in particular are
comparatively recent but growing [2]. Its chief rivals are the hedonic pricing approach and
the contingent valuation method (CVM).
The hedonic pricing approach has been widely used to value noise from the impact on
willingness to pay in the surrogate housing market [3]. However, the method has been
questioned on several counts, including imperfect knowledge of the attributes of each
location and other market imperfections, correlation of explanatory variables, and the
difficulty of measuring intangible influences and individuals’ perceptions of them. In
addition, there is only a single observation per person whilst there can be problems of selfselectivity since those who have high valuations of noise will tend to live in quieter locations.
CVM has for many years been used to obtain valuations particularly of goods not traded in
the market-place [4]. Respondents are given information about the contingent market and are
asked to provide a willingness to pay for the good or service in question. The CVM can take
two forms, either open-ended which asks for a maximum willingness to pay or iterative
bidding where the respondent is asked whether a series of payments would be accepted.
The SP approach has a number of advantages over CVM. The iterative bidding form of CVM
is a special case of SP with only one variable in addition to the monetary term and where the
(environmental) variable to be valued takes only two levels. SP tends to examine several
attributes and each tend to have more levels than is typical on CVM. The SP approach
therefore supports the analysis of non-linear and interaction effects whilst it places less
emphasis on any specific attribute which it is often felt reduces incentives to bias since the
purpose of the exercise is less transparent and the study does not ‘fixate’ on a single variable.
Open-ended CVM asks for the strength of preference whilst SP tends to ask for the order of
preference. Whilst the strength of preference contains more information and involves only a
single question rather than a whole series, SP responses can be expected to be more reliable
than this form of CVM response for two principal reasons. Firstly, it is simpler to indicate the
order than the strength of preference. Secondly, individuals routinely make choices but are
rarely required to establish the strength of preference in real life decision-making.
3. THE THREE STATED PREFERENCE EXERCISES
The first of our SP exercises was conducted in Edinburgh in 1996 as one of the first
applications of SP to value traffic related noise [2]. Households were offered choices between
two housing options differing in terms of indoor noise levels, neighbourhood air quality,
accessibility levels by bus and car, and local council tax. Noise was presented as a percentage
change from the existing situation. A sample of 398 households yielded 4175 choices.
The second SP study covered here built upon the Edinburgh experience in terms of its
presentation of noise [5]. It was based upon residents’ choice of apartments in Lisbon but
instead asked them to consider noise levels as they would be in different apartments in their
block with different exposures to traffic. The different apartments were characterised in
terms of traffic noise, view, exposure to sunlight and housing service charge. The survey was
conducted in 1999 and obtained a sample of 412 households and 4944 SP choices.
The third study was instead aimed at valuing aircraft noise [6]. Residents around the airports
of Manchester, Lyon and Bucharest were interviewed in a ‘group hall test’ and completed
two SP exercises. The first SP couched aircraft movements within a broader quality of life
dimension, alongside factors such a local school quality, neighbourhood crime levels, local
road congestion, air quality, road traffic noise, and local health and recreation facilities. A
range of improvements and subsequently a range of deteriorations were ranked in order of
preference. The aim of this exercise was to mask the purpose of the study and thereby reduce
the incentive to response bias. The second SP took a more conventional form, and offered
choices between two alternatives differing in terms of movements of three types of aircraft
and local tax levels. Around 200 interviews were completed at each of the three locations.
4. LESSONS FROM OUR EXPERIENCES
The logit model has been used to estimate the parameters of each variable in the SP
exercises. A variant upon this, the ordered logit model, was used to analyse the ranking data
of the quality of life SP exercise. The Alogit package has been used throughout [7], with its
jack-knife procedure correcting for the repeated observations nature of the SP data.
4.1 Presentation of Noise
One of the main challenges facing the valuation of noise within a survey context is that of
presenting it in what respondents take to be a realistic and understandable fashion. Unlike SP
exercises based around market goods and services, where the attributes can generally be
presented in their natural units, noise cannot be sensibly presented in the units in which it is
usually measured. There are several means of introducing noise into an SP exercise.
A simple approach is to use categorical scales, such as ‘very noisy’, ‘noisy’, ‘quite noisy’ and
so on. The main problem is to relate these scales to actual levels of noise and to be able to
know when an actual change causes an individual to experience one category of the variable
instead of another. Specifying proportionate changes is a common approach, although
disadvantages are respondents’ difficulties in understanding percentage changes and
translating these changes into an objective measure. Respondents can experience noise at
different levels under experimental ‘laboratory controlled’ conditions. However, noise
simulation tends to be an expensive approach whilst respondents may be affected by the
artificial and usually limited exposure. What is termed the location method is an attractive
approach. It can take a spatial dimension, whereby the respondent is asked to compare
different locations with different noise levels, or else a temporal dimension, where at the
same location there is variation in exposure over time. Ideally, the respondent would be
familiar with the different levels of noise. Finally, we can use a proxy measure, such as
traffic or aircraft movements, and variations in movements are used to imply variations in
noise. Physical noise measures are then taken or estimated for the different movements.
The Edinburgh study wanted to use the location method on the grounds of realism. However,
it was felt that households would be familiar only with outside noise levels in other homes
and these would not be consistent with indoor noise levels at their home. The proportionate
change method was instead used. However, as part of the study, both the proportionate
change and location methods were used to present variations in air quality. There were
significant differences in the valuations obtained by each method which were taken to
confirm a preference for the location method over the proportionate change method.
In the Lisbon study respondents evaluated different apartments in the same block with which
they would be familiar. These different noise levels were then used in the SP exercise. An
advantage of this approach is that we can relate valuations to actual noise levels as measured
by Leq since respondents will have experienced the different physical noise levels.
Separate models were estimated to perceived noise levels, as rated on a 1-100 scale, and
measured noise levels in Leq. The model based on perceived levels performed better, in
terms of goodness of fit and precision of parameter estimates, than the model based on indoor
Leq which in turn was better than the model based on outdoor Leq. Whilst the value of noise
on the rating scale was €1.96 with a 95% confidence interval of ±0.81, the value based on
indoor Leq was €9.44 but with a very large 95% confidence interval of ±15.7.
An interesting feature of this study was the estimation of the relationship between perceived
and measured noise. Such a relationship is necessary to operationalise a model calibrated to
perceived measures as represented by the ratings. It estimated a model of the form:
DR = α ( Leq C − Leq i ) + β d I ( Leq C − Leq i ) + γ ( Leq C − Leq i ) 2 + δLeq C ( Leq C − Leq i )
DR is the difference in the rating of perceived noise between the current (c) and some
alternative (i) apartment which is regressed on the difference in the measured indoor noise
levels. The dummy term dI denotes an increase in noise on the current situation and thus β
indicates whether any sign effect is present. The squared term indicates whether there is any
support for a size effect whilst the final term specifies an interaction with the noise level at
the current apartment and tests for the presence of reference effects.
The results are reported in Table 1. There was no statistical support for a size effect but it
emerged that an increase in noise had a larger impact on the ratings than an equivalent
reduction and that a given change in Leq implied a larger change in ratings when the current
apartment was noisier. The magnitude of the variation is apparent in Table 3 below where we
subsequently make use of these results to derive monetary valuations expressed in Leq units
Table 1: Regression Model of Ratings on Leq Indoor Measures
α
β (Sign Effect)
δ (Reference Effect))
Adj R2
Obs
n.s.
-0.8904 (4.88)
-0.0576 (18.35)
0.460
824
The aircraft study used variations in aircraft movements as the means of introducing noise
level variations. The conventional study offered variations in three types of aircraft and, in
order to assist in the discrimination between the noise associated with different aircraft type,
half the sample were played simulations of aircraft noise prior to undertaking the SP exercise.
Table 2 reports the results of the analysis in terms of money values and associated t ratios.
Table 2: Impact of Noise Simulations (€)
4 Engined-Simulation
2 Engined-Simulation
Propeller-Simulation
4 Engined-None
2 Engined-None
Propeller-None
Manchester
2.58 (1.5)
1.01 (2.1)
1.13 (1.0)
-0.18 (0.1)
-0.24 (0.8)
2.53 (2.0)
Lyon
1.78 (1.9)
2.02 (2.6)
1.97 (1.1)
4.84 (2.6)
0.40 (0.5)
-0.56 (0.4)
Bucharest
0.28 (1.0)
0.10 (0.9)
0.20 (1.3)
0.16 (0.5)
0.33 (1.6)
0.08 (0.5)
In Manchester, the noise simulation did have an impact. When there was no simulation, it is
only the smallest and least noisy aircraft which had a significant effect and indeed the other
two aircraft had wrong sign values. For those who heard the noise simulation, all three values
are correct sign and the four engined jets are disliked most as expected. Although only one
value is statistically significant, the t ratios for the other two are reasonably respectable. The
noise simulation also impacted upon the Lyon SP responses. All three values are correct sign
and two are significant for those who heard the noise simulation prior to completing the SP
exercise, although the values are similar to each other. On the other hand, two of the values
where noise simulation was not heard are far from statistically significant and the value of a
unit changed in four engined planes does seem rather high at €4.84. In the case of Bucharest,
all six values are correct sign. On balance, the t statistics are better where the simulation was
played but there is little to choose between the two sets of results.
4.2 Size, Sign and Reference Effects
The utility function most commonly adopted in practical discrete choice modelling is linearadditive and this constrains the marginal monetary valuations to be constant as the ratio of
the constant marginal utility (coefficient) for the variable of interest and the constant
marginal utility (coefficient) for cost.
Some studies have explored for what are termed sign, size and level effects. The sign effect
denotes an asymmetry between the valuations of gains and losses in the level of an attribute.
A size effect is present where the unit value of a change in an attribute depends upon how
large the change is. The level or reference effect indicates that the sensitivity to a change in
an attribute depends on the level from which it varies. There are a number of reasons why
we might expect size, sign and level effects but it is essentially a matter for empirical testing.
Where noise is a continuous variable, we can specify the utility function as say:
U = α 1 d G X λ + α 2 d L X λ + β 1 d G ( X − X base ) 2 + β 2 d L ( X − X base ) 2
dG and dL are dummy variables denoting whether X is a gain on the current situation or a
loss. The expressions for the marginal utility of X for gains (MUXG) and losses (MUXL) are:
MU XG =
∂U
= α1λX λ −1 + 2 β1 ( X − X base )
∂X
MU XL =
∂U
= α 2 λX λ −1 + 2 β 2 ( X − X base )
∂X
Comparison of these marginal utilities indicates the extent to which there is a sign effect. The
parameter λ allows the sensitivity to changes in X to depend upon the level of X whilst β1
and β2 denote size effects.
Where a noise measure is unavailable or unreliable, we use a dummy variable specification.
For example, in the Edinburgh study noise entered at three levels of proportionate change and
the current situation. The utility function was therefore specified as:
U = γ 1 d +50 + γ 2 d +100 + γ 3 d −50
where dummy variables are specified for a 50% increase in noise (d+50), a 100% increase
(d+100) and a 50% reduction (d-50). Comparison of γ1 and γ3 provides a test of sign effects
whilst comparison of γ1 and γ2 indicates whether a size effect is present. By definition, we
cannot here examine level effects.
The Edinburgh study found that the unit valuation of noise did not vary between a 50% and
100% increase. Whilst there was some evidence to support noise valuations being larger for
increases in noise than reductions, it was not particularly convincing and it was concluded
that “for most of the population, gains and losses in noise would be valued the same”.
The Lisbon study explored whether sign, size and level effects were present in the model
based on ratings of perceived noise. It found noise valuations to be higher for increases than
equivalent reductions but only by 10% and the difference was not statistically significant.
There no support for size or level effects. However, matters are different when actual noise
measures are brought into the equation.
In order to operationalise the model, it is necessary to relate perceived noise levels to indoor
levels of Leq as reported in section 4.1 and Table 1. That model recovered sign and reference
effects. The extent of these effects are illustrated in Table 3, and they are quite pronounced.
Table 3: Household Monthly Valuations for a unit change in Leq (€)
Change
in
Leq
Deterioration
40 to 41
40 to 42
40 to 43
Improvement
40 to 39
40 to 38
40 to 37
Change in
ratings
Unit
Value
3.19
6.39
9.58
6.80
6.80
6.80
2.30
4.61
6.91
4.91
4.91
4.91
Change in
Leq
Levels
30 to 31
35 to 36
40 to 41
45 to 46
50 to 51
Change in
ratings
Unit
Value
2.62
2.91
3.19
3.48
3.77
5.58
6.20
6.79
7.41
8.03
The aircraft noise models estimated in the quality of life SP exercise estimated separate
models for improvements and deteriorations. For Manchester, significant parameters could
not be recovered for deterioration in evening noise and for Bucharest it was not possible to
discern significant effects for either of the evening values. The results do not provide any
convincing evidence for a sign effect.
Table 4: Marginal Monetary Values (€ per Household per Week)
Manchester
Lyon
Bucharest
Improvements
Daytime
Evening
1.08 ±0.60
0.41 ±0.41
0.91 ±0.32
1.31 ±0.28
0.48 ±0.20
n.s.
Deteriorations
Daytime
Evening
0.81 ±0.35
n.s.
1.28 ±0.45
1.20 ±0.41
0.03 ±0.02
n.s.
The results here are not as clear-cut as we would like. On balance, we would have to
conclude that there is little support for size, sign and reference effects as far as perceived
values are concerned. However, we can tentatively point to sign and reference effects when
we are dealing in units of Leq. This may stem from the non-linear nature of the index.
4.3 Socio-Economic Impacts on Valuations
A feature of each of the studies has been an attempt to discern the impact of a range of socioeconomic variables on the valuation of noise. A key variable is the influence of income on
monetary valuations. We expect that as households become wealthier and therefore less
sensitive to cost variations, the amount that they are prepared to pay for improvements in
noise will increase. There is also the issue of whether income per household member or
overall household income provides a better account of households’ willingness to pay. Cost
can be entered into the utility function along with some measure of income (Y) as:
U =γ
C
Yλ
The marginal utility of money will fall and monetary values will increase as income
increases, and λ denotes the elasticity of the marginal value of noise with respect to income.
In the Lisbon study, it emerged that adjusted household income per person provided a better
fit than household income or unweighted household income per person. The search process
across different λ’s identified the best fitting model to be for an income elasticity of 0.5. The
Edinburgh study found a somewhat similar income elasticity of 0.7, although in contrast
household income provided a somewhat better fit than household income per person.
In the study of aircraft valuations, the quality of life SP exercise found the income elasticity
to be 0.5 for household income per person in both the Manchester improvements and
deteriorations models. For Lyon, no effect was discerned in the improvements model and the
elasticity was 0.3 for income per person for the deteriorations model. For Bucharest, there
was no income effect for improvements but it was 0.6 for deteriorations. In the more
conventional aircraft noise SP exercise, the income elasticity for Manchester was 0.7 and
household income provided a better fit than income per person whilst in Lyon it was 0.9
based on adjusted income. No income effect could be found in Bucharest but this might be
due to the limited variation in incomes.
Not only is there an encouraging degree of consensus across our studies that income does
affect valuations and the extent to which it impacts, but the results are very much in line with
other evidence that environmental externalities have an income elasticity greater than zero
but less than one. A value around 0.5 seems to be the central estimate.
We might expect household size to impact on values since larger households will have more
people affected by noise. The presence of children might also raise concerns about and hence
valuations of noise. In Edinburgh, two adult households with children had values around 50%
higher than those without children whilst two adult households had much higher values than
single adult households, in some instances more than twice as large. However, the other two
studies failed to detect variations in values according to household size.
In the Edinburgh study, those who had undertaken alleviation measures, such as the
installation of double glazing, had higher valuations. This is consistent with the selfselectivity effect found in Lisbon whereupon those who lived at the quieter side of the
apartment block had appreciably higher values.
The other consistent finding that emerges across our studies is that the number of factors that
influence the valuation of noise appears to be limited. In the Lisbon study, despite exhaustive
testing of a wide range of socio-economic and residential variables, only income and the
household’s location relative to noise had any impact. This is not to say that values do not
vary widely across individuals, just that they cannot be systematically related to observed
factors. The mixed logit model estimated revealed the random variation in preferences across
households to be considerable. What does emerge across our studies is that estimating
significant and theoretically consistent incremental effects is challenging
4.4 SP versus CVM
The CVM was used in the Edinburgh study to elicit willingness to pay values for 50%
improvements to noise levels. Table 5 contains weekly household valuations and associated
95% confidence intervals for the entire sample except those who stated that noise levels
could not be improved in this way (CVM1) whilst the other sample (CVM2) additionally
removes those who are not prepared to pay more council tax. It can be seen that the CVM
values for both samples are considerably lower than the SP value, even though the SP sample
does not remove those with a genuine reason for providing a zero response.
Table 5: Edinburgh Noise Valuations for 50% Improvements
Noise
CVM 1
£1.48 (±0.34)
CVM 2
£2.55 (±0.54)
SP
£3.17 (±1.94)
In the aircraft study, respondents were asked how much they would pay per week to halve the
number of daytime and evening flights. Table 6 reports the CVM and SP values and 95%
confidence intervals. The former include the entire sample (CVM1) and also the sample
(CVM2) which excludes those who stated they did not think any change would occur or they
had a right to peace and quiet. The results are more mixed here. However, it must be borne in
mind that the SP models contain those who have been removed from the CVM2 sample. It
therefore seems reasonable to conclude that open-ended CVM tends to produce lower values.
Table 6: Aircraft Noise Willingness to Pay Values in €
CVM1
CVM2
SP
Manchester
Day
Evening
1.96 (±0.6)
1.99 (±0.7)
3.26 (±1.1)
3.41 (±1.2)
7.95 (±4.4)
2.71 (±2.7)
Lyon
Day
Evening
5.31 (±2.7)
5.97 (±3.0)
9.90 (±5.4) 11.75 (±6.2)
5.02 (±1.8) 8.80 (±1.9)
Bucharest
Day
Evening
0.05 (±0.03) 0.04 (±0.02)
0.079 (±0.05) 0.06 (±0.03)
0.72 (±0.3)
0.0
Two further issues concern us about the CVM results. First is the high proportion of zero
responses. This reduces the Manchester, Lyon and Bucharest data sets by 53%, 52% and 30%
respectively. We have doubts as to whether these are genuine zero valuations. Secondly, the
correlations between the daytime and evening values were extraordinarily high, at around
0.9, indicating a lack of discrimination in responses which raises concerns about their quality.
5. CONCLUSIONS
In this paper, we have attempted to demonstrate some important lessons learnt in three
studies that we have recently been involved in which have estimated the monetary valuation
that residents place on noise.
As far as presentation is concerned, we have a preference for the location method both on the
grounds of realism and because it allows valuations to be linked to physical noise measures
since the noise has actually been experienced. Simulation does seem to have a useful role to
play whilst models based on perceived levels of noise are superior.
Our experiences of exploring size, sign and level effects are mixed. On balance there is no
strong support for them as far as values based on perceived noise is concerned but there is
some evidence that they are more likely to be present when physical noise indices are used.
Our studies have examined a wide range of socio-economic and residential impacts on
estimated valuations. It is clear that values increase with income and there is a high degree of
consistency in the income elasticity around a figure of 0.5. The other noticeable feature of the
study findings is that few other significant incremental effects can be discerned.
It seems that open-ended CVM values are lower than equivalent SP values. The results here
confirm evidence from environmental studies in general that open-ended CVM provides
lower values than SP, even when the large proportion of protest zeros common with the
former are removed. Our view is that the reduced emphasis on increased taxes means that
there is a lesser incentive to bias willingness to pay in SP studies.
Our experiences and the limited amount of research in this area lead us to recommend a
number of areas for further research. Firstly, there is a need for further testing of different
means of presenting noise, and within this the contribution that noise simulation can make
should be explored. Secondly, the relationship between perceived and physical noise
measures requires more attention, and as part of this the most appropriate index of physical
noise measurement must be identified. Thirdly, the values of noise estimated are not trivial. If
they are an accurate reflection of true preferences their influence ought to be detected in
relevant real-world markets, such as in the choice between different houses. Corroboration of
SP values from revealed preferences is urgently required. Fourthly, the evidence relating to
sign, size and reference effects is not conclusive, and further work should be backed up with
appropriate in-depth qualitative research. Fifthly, we are often dealing with household
impacts and household decision making but the SP exercise is based around individual
responses. The dynamics of group decision making and distinguishing between individual
and household values requires further research. This may lie behind the failure to detect more
than a limited impact from household characteristics on valuations. Finally, although we have
doubts about open-ended CVM, it does provide the basis for probing the reasons for certain
types of response which is valuable information that could be used to enhance SP modelling.
REFERENCES
[1]
Wittink, D.R. and Cattin, P. (1989) Commercial Use of Conjoint Analysis: An Update. Journal of
Marketing 53 (July), pp.91-6.
[2]
Wardman, M and Bristow, A.L. (2004) Traffic Related Noise and Air Quality Valuations: Evidence from
Stated Preference Residential Choice Models. Transportation Research D, 19(1), pp.1-27.
[3]
Bateman, I., Day, B., Lake, I. and Lovett, A. (2001) The Effect of Road Traffic on Residential Property
Values: A Literature Review and Hedonic Pricing Study. Report to the Scottish Executive, Edinburgh.
[4]
Bateman, I.J. and Willis, K.G. (1999) Valuing Environmental Preference: Theory and Practice of the
Contingent Valuation Methods in the US, EU and Developing Countries. Oxford University Press.
[5]
Arsenio, E. (2002) The Valuation of Environmental Externalities: A Stated Preference Case Study on
Traffic Noise in Lisbon. PhD Thesis, Institute for Transport Studies, University of Leeds.
[6]
Bristow, A.L. and Wardman, M. (2003) Attitudes Towards and Values of Aircraft Annoyance and Noise
Nuisance. Prepared for EUROCONTROL Experimental Centre, France.
[7]
Hague Consulting Group (2000) ALOGIT 4.0EC, The Hague.