AIRPORT CHOICE BEHAVIOURS IN A MULTI-AIRPORT SYSTEM:
A SET OF CHOICE MODELS
Stefano de Luca
Department of Civil Engineering – University of Salerno
1
INTRODUCTION AND MOTIVATIONS
In recent years a huge number of analyses and models have focused on
comprehending and simulating the phenomenon of airport choice. In the last
decade many researchers have employed models based on random utility
theory, developing simple structures such as Multinomial Logit models (MNL),
or more complex ones allowing for correlation between different alternatives
(airports) and accommodating the fact that passenger behaviour varies across
different groups of travellers. Hierarchical Logit models (HL) and Cross-Nested Logit (CNL) models have been proposed to take into account the
influence of other dimensions of travel behaviour on a passenger’s choice of
airport. Mixed Multinomial Logit models (MMNL) have been proposed to simulate whether and how passenger behaviour varies randomly within
individual groups of travellers. Combinations of two (airport and airline; airport
and access mode) or three choice dimensions (airport, airline and access
mode) have been investigated, and models have been proposed for different
user classes, different trip purposes (see table 1).
Most of the models proposed in the literature have allowed broad
understanding of the phenomenon, the influence of other choice dimensions,
the relevance of specific attributes and their reciprocal weights. Much has
been done, nevertheless some considerations should be made.
Much work has been developed starting from aggregate data (demand flow
and aggregate level of service attributes) and it is mainly based on MNL
models (logistic functions). Such an approach should be interpreted as an
effective regressive method, but it does not allow for user characteristics, trip
characteristics or the incidence of relevant attributes such as air-fare, travel
time and waiting time.
Contributions based on disaggregate data use mainly revealed preference
data (RP), whereas there are very few contributions based on stated
preferences (SP) (Algers and Beser, 1997; Hess et al., 2007). Although a
wide variety of models can be found in the literature, the main drawback of RP
data is the difficulty estimating level of service attributes for the alternative
chosen and especially for the alternatives not chosen. Most research using
RP data, cannot rely on suitable detailed information on level-of-service
(airfares) and, in particular, it interpret flight availability or capacity problems
through frequency attributes, airline dummy variables or through the airfare
itself.
Among the implemented models, it is necessary to distinguish models that
simulate airport choice only, and the models that simulate the combination of
different choice dimensions such as airport-airline, airport-flight, airportairline-flight. As regards the latter models, it should be noted that the bestperforming ones present complex utility functions, which are not easy to apply:
they require a large amount of information, which if available in the survey,
© Association for European Transport and contributors 2009
1
might not be easily known by the analyst and/or might not be easily forecasted
in operational scenarios (airfares, frequency). As a consequence, the models
cannot be applied to strategic planning scenarios, particularly when it is not
possible to have any information on services offered. Moreover, more
complex models applied to more complex choice sets (CNL and MML) do not
seem to outperform the MNL (or HL) model sharply. In many cases the
differences are negligible and no elasticity analysis is carried out, while tradeoff analysis highlight small differences. Finally, most of the existing
applications are carried out on the same area (San Francisco or London),
where the choice process is among airports belonging to a multi-airport region
which is well defined and consolidated in the user’s mind, and where the lowcost phenomenon is also consolidated
Table 1 - Classification of main contributions found in the literature
model
origin
+access + dest. +
airport mode
airport
airline
Skinner, 1976; Harvey,
1987 Ashford and
Benchemann, 1987
Ozoka and Ashford,
1989
Innes and Doucet, 1990
Thompson and Caves,
1993 Windle and
Dresner, 1995
Cohas et al., 1995
Basar and Bhat, 2004
Hess and Polak, 2005a
Furuichi and
Koppelmann, 1994
Pels et al.,1997, 2000
and 2001
Hess et al., 2007
Ndoh et al., 1990
Mandel, 1999; Bondzio,
1996
Veldhuis et al., 1999
Hess and Polak, 2005b
Pels et al., 2003
+
flight
MNL
×
not
behavioral
choice
set
+MNL
MMNL
NL
NL
×
×
×
×
×
×
×
NL
NL
×
×
×
NL
CNL
NL
×
×
×
×
×
×
×
×
×
×
×
In this paper a set of models simulating airport choice behaviours is proposed.
The models are based on random utility theory, are easy to implement, and
address the main issues discussed above. The proposed set of models cope
with a choice-set, constituted by airports of different type (intercontinental
airport, regional airport and city airport) that compete with one another on
medium/short haul trips at a European scale, and aim to investigate
• travel behaviours in the presence of direct connections and of non-direct
connections,
• the incidence of different trip type (departure in week 1 and trip duration of
3 days, departure in 3 months and trip duration 7 days),
• non-linear transformations of level of service attributes,
• the incidence of users’ past experience,
• direct and cross-elasticity of implemented models.
© Association for European Transport and contributors 2009
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The models are calibrated on disaggregate data obtained by a SP survey. In
all, 800 users from Campania (southern Italy) were asked to face realist
choice scenarios, built from real data taken from the main web-sellers. Each
respondent had to choose the preferred solution to fly towards a given
European capital city. The trip purpose analyzed was leisure and the choice
set was defined by analyzing the services offered by the airports of Naples,
Rome Fiumicino and Rome Ciampino. The quoted airports belong to different
Italian regions (Campania and Lazio), they represent a realistic choice set for
the users interviewed and present different characteristics with respect to
accessibility, flight frequencies and services supplied. The choice context is
interesting since it allows us to interpret and simulate competition among
larger yet congested airports, city airports and regional airports typically used
by low-cost airlines. Such a scenario is growing rapidly in Italy (the low-cost
phenomenon is quite new and increasing rapidly) and in many European
regions. Hence the results may give useful insights or even be transferred to
similar choice contexts.
The paper is organized as follows: in section 2 study area and survey data are
introduced, models are described in section 3, conclusions are drawn in
section 4.
2
DATA
2.1 Study area
As a region, Campania consists of 5 provinces, 518 municipalities and 5.7
million residents. There are 2 municipalities with more than 100,000
inhabitants, the biggest being the region capital, Naples (≈ 1,000,000), and
more than 3 million people live within a 1 hour (40 km) ride from the capital.
There is one airport, Naples-Capodichino (figure 1)., which served 5.8 million
passenger in 2007 (+13% more than in 2006), providing connections with the
main Italian cities (16), European capitals and other European destinations
(32), and towards a few intercontinental destinations (1). The services are
provided by 28 airline companies, including traditional airline companies and
low-cost airlines and, except for specific destinations, service frequencies are
less than 2 flights per day. Travellers in this area are generally concerned with
high airfares and low frequencies for flying into and out of the region, and it is
not unusual for travellers to use out-of-region airports. Indeed they may
choose among several alternatives, including Naples-Capodichino, Rome
Fiumicino and Rome Ciampino (see figure 2).
© Association for European Transport and contributors 2009
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Naples
Figure 1 – Case study and air routes (©Google 2009)
Rome-Fiumicino, in the region of Lazio, is the intercontinental airport for Rome
and guarantees connections towards the main European cities with a higher
frequency than Naples. Traditional and low-cost airlines provide the described
services. The airport is 245 km from Naples and can be reached in 2h30m h
by car and in 1.45h by train. Rome Ciampino is Rome’s second airport: it is
smaller than Naples-Capodichino, but its traffic demand is rapidly increasing
due to the activity of several low-cost airlines. The airport is 218 km from
Naples and can be reached in 2h20m by car and in 1.1h hour by train plus
45m on bus from Rome train station.
Rome
Fiumicino
Rome
Ciampino
Naples
Capodichino
Figure 2 – the multi-airport system (©Google 2009)
The described choice set can be assumed as a multi-airport system where
each airport can be reached in less than 2.5 hours. It is interesting since the
airports belong to different regions, have different accessibility and each of
© Association for European Transport and contributors 2009
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them offers completely different services: Naples-Capodichino offers greater
accessibility, traditional airlines, low-cost carriers, lower frequencies; RomeFiumicino, lower accessibility, traditional airlines and higher frequencies;
Rome-Ciampino, the lowest accessibility, low-cost carriers and higher
frequencies.
2.2 Survey data
The survey data used in this analysis were collected from a sample of around
800 individuals aged 18 and over, who were asked to choose which flight they
would choose to reach four different European destinations (London, Berlin,
Amsterdam and Paris, figure 3). The survey is not based on revealed
preferences but on stated preferences in respect to real scenarios.
Amsterdam
Berlin
London
Paris
Figure 3 – considered destinations (©Google 2009)
The scenarios were built by fixing the destinations and searching for all the
services available (direct and non-direct) from the airports introduced in the
previous section to the main airport which serve the destinations in question:
London Heathrow, Paris Charles de Gaulle, Amsterdam Schiphol, Berlin
Tegel. The main search engines were used (Obodo, Expedia, Last Minute),
and the following information was extrapolated :
• type of connections (direct or non-direct);
• airfare (average compared with the results obtained by the search
engines), travel time to destinations, transfer waiting time, number of
transfers;
• airline company, time of first flight, frequencies.
Each set of information was investigated in respect of the following contexts:
departure in 3 days, trip length: 3 days (2 nights); departure in 3 days, trip
length: 7 days (6 nights); departure in 3 months, trip length: 7 days.
Two questionnaires were built, one for direct connections only and one for
non-direct connections. Non-competitive services (more than one transfer,
airfare 50% higher, travel time greater than 100%, waiting transfer time
greater than 50% of flight travel time) were removed from the database.
© Association for European Transport and contributors 2009
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Respondents were asked to answer to both questionnaires. The attributes
used to describe the alternatives include flight time, the number of
connections, the airfare, the frequencies, waiting time, access time and
access monetary cost. An example of the questionnaire for direct connections
is proposed in figure 4.
The first stage of the survey collected data on air travel behaviour with respect
to the most recent domestic flight, along with socio-demographic information.
Then assuming a leisure trip purpose, respondents were asked to choose
among the air services available and the transport mode available to reach
the departure airport. The survey revealed the access mode, the flight chosen
and hence the airline chosen as well as the departure airport. Finally, travel
distance and travel time were estimated for each available transport mode
from the trip origin to the departure airport.
The survey in question was an intercept-survey conducted at main sites,
stations, squares and offices within the main Campania cities (except Naples)
of the region of Campania. Two undergraduates worked on 6-h shifts for four
weeks. Obviously, the shifts were scheduled to cover all the days of the week
and all times of the day.
AMSTERDAM !!
3 DAYS!!
DEPARTURE IN 1 WEEK
DIRECT FLIGHTS
AIRLINE
TRANSAVIA
ALITALIA
KLM
TRAVEL TIME
Naples C. Rome F./C.
2’40min
AIRFARE [€]
Naples C. Rome F./C.
13.30
189
Fiumicino
2h40min
Fiumicino
2h45min
NAPLES CAPODICHINO
1st flight
Naples C. Rome F./C.
Number of flights
Naples C. Rome F./C.
1
255
8.30
4
262
8.45
6
ROME FIUMICINO
ROME CIAMPINO
Figure 4 – example of the questionnaire (airport choice part)
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3
MODELS
The analysis makes use of two types of discrete choice model belonging to
the family of Random Utility models, namely Multinomial Logit model (MNL)
and Hierarchical Logit model (HL). Different models were calibrated according
to different air travel connections, direct connection vs non-direct connection,
and to different trip types, departure in week 1 and trip duration of 3 days,
departure in 3 months and trip duration 7 days.
3.1 Utility functions
The utility function is expressed as a function Vj(Xkj) of attributes Xkj relative to
the alternatives and the decision-maker. The function Vj(Xkj) may be of any
type. For analytical and statistical convenience, it is usually assumed that the
systematic utility Vj is a linear function in the coefficients βk of the attributes Xkj
or of their functional transformations fk(Xkj):
V j = ∑ h β h X hj + ∑k β k f k ( X kj )
One useful parametric functional transformation for non-negative variables is
the Box-Cox one:
f k ( X kj ) = ( X kjλk − 1) / λ k
if λ k ≠ 0
f k ( X kj ) = log( X kj )
if λ k = 0
The attributes contained in the vector Xj can be classified into different sets:
•
level of service or performance attributes (times, costs, service
frequency), attributes related to the service offered by the land-side and
air-side transport system;
•
socio-economic attributes, attributes related to the decision-maker.
In the present case study, the following attributes were used:
attribute
airfare
travel time
frequency
AF
TT
FREQ
unit
€/100
h
-
access time
access distance
gender
agex
never flown
number of trips
car availability
income (proxy2)
AT
AD
Gen
Age
NV
N_TRIP
CAV
INC
min
Km/100
binary
binary
binary
cont.
cont.
cont.
fidelity cards
airline
FC
AIRL
binary
binary
description
airfare
flight time
number of flights in a day
(to the destination)
access time by car
access distance by car
1 if male
1 if under x years old
1 if the user has never flown
# of trip done in the past year
# of cars / number of households
# of household members employed / # of
households
1 if the users own a fidelity cards
1 if the airline is a traditional company
The airfare is what the user pays to fly to the predefined destination. It was
estimated by calculating the average values offered by the most important
© Association for European Transport and contributors 2009
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web-sellers (e.g. Expedia, Opodo, Lastminute, etc.). Fares were calculated for
each type of trip (3 days, 7 days) and for each departure time horizon (within 1
week or 3 months). If more than one flight is supplied by an airport, the airfare
is calculated as the average of airfares available, if necessary weighted on
flight frequency.
Travel time is the flight time from the origin airport to the destination airport.
The attribute is expected to have scarce significance for direct connections,
but not for non-direct connections.
The frequency is the number of flights that departs each day from each airport
towards the predefined destination. As usual, the frequency attribute can be
seen as a proxy variable representing two different and opposite phenomena:
the distribution of available departure times, but also a higher risk of more
congestion at origin or destination airports.
Car access time and/or access distance were used to measure airport
accessibility. Although it is well known that, consistent with a behavioural
approach, a satisfaction (logsum) variable should be estimated, such an
approach is advisable if mode choice towards the airports exists. In our case
study, car and car as passenger are the most used transport modes, thus no
mode choice model turned out to be statistically significant. Moreover, many
contributions present in literature suggest that travel time by car is a realistic
accessibility measure towards the departure airport, and it is adequate to
simulate airport choice. The significance of both distance and time was
explored, and they were estimated through a network model which takes into
account congestion effects that mainly occur in the urban area which
encompasses Naples airport. Together with access attributes, the car
availability attribute was investigated. This is equal to the ratio between the
number of car and number of household members. Because of the major role
played by car in modal split towards departure airports, such an attribute can
be interpreted as a proxy attribute of accessibility, representing a measure of
how easily a user has available a car to reach the departure airport.
User inertia to fly is accomplished by introducing never flown and experience
attributes. The former is a binary attribute that allows users to be classified
into two different classes; the latter is a continuous attribute that measures
how many trips have been made in the past, hence how familiar the user is
with air travel, supposing that past experiences influence, for instance, the
choice of nearer airports or low-cost carriers. It should be noted that the use of
these attributes leads to a less general, flexible model, since such information
is difficult to obtain for model application.
Age may be useful to separate the users into multiple homogeneous classes.
In this work various segmentations were attempted. The most significant ones
are reported in the following sections.
Income is often used to weight monetary attributes, but in many cases it is not
easily obtainable from interviews. Therefore, proxy attributes should be
introduced. In this work two types of attribute were explored: the ratio of the
number of household members employed to the number of households; the
ratio of the number of cars to the number of households. The former attribute
can be easily computed from census data; the latter is the same introduced
above. The idea is to link income to the number of cars owned by each family.
Finally, the incidence of frequent flyer programmes and of airline brands was
investigated.
© Association for European Transport and contributors 2009
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3.2 Models for direct connections
This section describes the findings of the estimation process. As introduce
before, MNL and HL structures were used, while the use of mixture models,
lies beyond the scope of this paper and will be addressed in a future paper.
The choice set consists of the three airports introduced before: NaplesCapodichino (65% share), Rome-Fiumicino (11%) and Rome-Ciampino
(14%). All models presented in this paper were calibrated on 800
observations; the results are presented in table 3.
3.2.1 MNL models
Starting from the simplest MNL model (MNL[1]), different MNL models are
proposed as utility functions change in their complexity. The aim is to highlight
what goodness of fit can be achieved with simpler but immediately applicable
models, or with more complex ones that require sophisticated attributes or the
implementation of other transportation models (e.g. network models,
assignment models, mode choice models).
The utility functions can be derived from table 2.
Table 2 – systematic utility functions
attribute
AF
airfare
TT
travel time
FREQ
frequency
AT
access time
AD
access distance
Gen
gender
Age
agex
NV
never flown
N_TRIP
number of trips
CAV
car availability
INC
income
* n.s. : not statistically significant
VNAPLES
n.s.
n.s.
n.s.
VROME-FIUMICINO
n.s.
n.s.
n.s.
-
VROME-CIAMPINO
n.s.
n.s.
n.s.
-
The simplest model, MNL[1] (see table 3), was calibrated using the main
continuous variables, including access time, air-fare and access distance. As
expected, flight time was not statistically significant, whereas the analysis
showed that the use of a log-transform for frequency and access distance
leads to significant gains in model performance, confirming decreasing
marginal returns for the associated attributes. The proposed model, although
very crude in the accessibility attribute, can be easily implemented (does not
require any network model) and shows similar goodness of fit compared with
the more complex ones that are shown below.
Model MNL[2] (table 3) introduce access time (by car) and investigates the
possibility that access time to Naples-Capodichino and to Rome’s airport might be analyzed through non-linear transformations. Several solutions were investigated: the same transformations for all choice alternatives, the same
transformations but different parameters for each choice alternative, a
combination of different transformations (e.g. inverse power, Box-Cox).
Estimation results show that Box-Cox transformation is the most effective, and
© Association for European Transport and contributors 2009
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that a parameter greater than 1 (λ = 2) is statistically significant for access
time toward Rome, whereas a parameter lower than 1 (λ = 0.8) is significant
toward Naples. In this case it is assumed that the marginal utility of access
time to Naples airport decreases as time increases. In other words, one more
minute of access time is more tolerated towards nearer destinations, than
farther ones, such as Rome airports. The use of such transformations could
be seen as controversial, given that time is time, and its perceived value may
vary as it increases and should have the same perception independently of
the destination. Nevertheless, the obtained results can be easily explained:
Naples Capodichino is always the nearest airport from the trip origin. In such a
context, users perceive the access time to the nearest airport (Naples)
differently from the farthest one (Rome). Values of λ show that the marginal
utility decreases as access time towards Naples airport increases, whereas it
increases with a higher rate as access time towards Rome airports increases.
The result can be generalized to similar case studies, where one airport can
be identified nearer to the trip origin than all the others.
Table 3 – estimation results
attribute*
airfare
travel time
frequency
access time
access distance
gender
age23
never flown
experience
car avail.
income
ASC
ρ2
2
ρ -correct
MNL[1]
- 0.18 (1.2)
■
0.36 (2.7)
logarithmic
■
MNL[2]
-0.62 (4.4)
■
0.22 (1.99)
logarithmic
-0.72 (13)
MNL[3]
-0.34 (2.0)
■
0.31 (2.7)
logarithmic
-0.47 (13)
MNL[4]
-0.36 (2.1)
■
0.30 (2.6)
logarithmic
-0.58 (9.8)
MNL[5]
-0.46 (2.4)
■
0.27 (2.3)
logarithmic
-0.31 (4.1)
Box-Cox
λNA = 0.8
λRMF= 2.0
λRMC= 2.0
Box-Cox
λNA = 0.8
λRMF= 2.0
λRMC= 2.0
Box-Cox
λNA = 0.8
λRMF= 2.0
λRMC= 2.0
Box-Cox
λNA = 0.8
λRMF= 2.0
λRMC= 2.0
-0.85 (1.4)
logarithmic
■
■
■
■
■
■
■
0.355
■
■
■
■
■
■
■
■
■
■
■
0.345
■
-0.48 (2.2)
■
-0.15 (1.6)
■
■
■
0.353
■
-0.51 (2.4)
■
-0.17 (1.9)
-0.66 (1.7)
-0.23 (1.1)
■
0.356
■
-0.53 (2.5)
■
-0.17 (1.8)
-0.73 (2.0)
-0.14 (1.0)
-1.24 (1.4)
0.362
0.352
0.342
0.348
0.349
0.351
* in parenthesis the t-student test value
Starting from MNL[2] different solutions are proposed by introducing socioeconomic attributes and attributes based on past experience: age and
experience (MNL[3], see table 3), car availability and income (MNL[4]).
The age attribute divides users into two classes, introducing a threshold at 23
years old. Such a value guarantees the best goodness of fit and, at the same
time, it is a realistic threshold which allows us to separate students or young
workers from all other users. The attribute is negative for Naples airport, showing that younger users prefer airports with lower airfares and higher
© Association for European Transport and contributors 2009
10
frequencies, instead of nearer airports. In this case age can also be
interpreted as a further proxy attribute of income, assuming that users under
23 years old cannot rely on a job or on a high salary.
As regard past experience, while never flown does not prove statistically
significant, the attribute number of trips plays an interesting role. The
coefficient is negative for Rome’s airports, indicating that users with less
experience prefer nearer airports (such as Naples). It should be noted that
such an attribute cannot be easily used to apply the model.
As regards car availability, the coefficient is statistically significant and shows
a negative value for Naples airport, meaning that the systematic utility of
Naples airport decreases (with respect to the other airports) as car availability
increases. This result points out that if a car is not available the users prefer to
choose the nearest airport (Naples), and this happens because Rome’s
airports often have no transit option (from trip origin) available or one which is
competitive with car mode.
As regards income, the proxy attribute introduced in the previous section gave
a good result and showed negative coefficients, meaning that the systematic
utility of Naples airport decreases as incomes increase. Users with higher
income are less affected by accessibility cost to departure airport, hence they
prefer farther solutions where better services are offered in terms of frequency
(Rome-Ciampino and Rome-Fiumicino) at the expense of more expensive
airfares (Rome Fiumicino).
The best performing model is MNL[5]. As well as the attributes discussed
above, an alternative specific constant is introduced in the Naples-Capodichino alternative. On the one hand, the constant makes the model less general
and transferable, on the other it highlights the fact that Naples-Capodichino,
albeit the most accessible airport, pays disutilities not measurable easily but
well perceived in the user’s mind: the lack of traditional and consolidated
airline companies, the scarce reliability and frequency supplied by transit
services and the limited capacity of existing parking lots; finally, many people
from outer Naples do not like entering the city.
Starting from MNL[5] model an elasticity analysis was carried out estimating
the proportional change in airports market shares for a proportional change of
airfare, flight frequency and access time. Direct elasticity and cross elasticity
were computed with increasing attributes values up to 50%. Results are
reported in table 4 for each attribute involved and for each airport; direct
elasticities are reported in coloured columns. In figures 5,6 and 7 direct
elasticities for each airport and for each attribute are plotted in order to make
easier a comparison.
Increase of airfare has a greater effect for Naples airport and smaller for
Rome Ciampino. An increase of 50% causes a market share decrease equal
to 5% for Naples airport and equal to 2.4% for Rome Ciampino. When Naples
airfares increase, Rome airports equally share out the captured demand. The
same phenomenon does not happen if we increase airfare for Rome airports.
If on the one hand market share decreases less than what happen to Naples
airport, the lost demand is almost totally absorbed by Naples airport. Such
results can be easily interpreted since Naples airport is the most accessible
one and Rome airports are usually chosen due to the greater flight frequency
or due to cheaper airfares (Rome Ciampino).
© Association for European Transport and contributors 2009
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As regards flight frequency, because of the less than linear perception of such
attribute, the effect on market shares is negligible. An increase of 50% leads
to market shares increase always smaller than 2% for Naples airport and
equal to 1% for Rome airports.
As regards access time, the effect are significant for all airports but in
particular for Naples airport. The effect is not linear: 10% increase has similar
effects on all airports (direct elasticity), 50% increase has a large effect only
on Naples airport (-21%). As for airfare, demand lost by Naples airport is
shared out between Rome airports, demand lost by Rome Fiumicino or Rome
Ciampino is almost totally captured by Naples airport. Such a result is an
important warning to Naples airport managers, since Naples airport
accessibility suffers from road congestion phenomena that, if increase, may
influence Naples competitiveness.
Table 4 – elasticity analysis (direct elasticities and cross-elasticities)
Increase of
10%
20%
30%
40%
50%
Increase of
10%
20%
30%
40%
50%
Increase of
10%
20%
30%
40%
50%
Naples airfare
Rome Fium. airfare
∆RMF ∆RMC
∆ΝΑ ∆RMF ∆RMC ∆ΝΑ
-0.9% 0.4% 0.5%
0.7%
-0.8%
0.1%
-1.9% 0.8% 1.0%
1.3%
-1.5%
0.3%
-2.8% 1.3% 1.6%
1.8%
-2.2%
0.4%
-3.8% 1.7% 2.1%
2.4%
-2.9%
0.5%
-4.8% 2.1% 2.7%
2.9%
-3.5%
0.6%
Rome Ciamp. airfare
∆ΝΑ ∆RMF ∆RMC
0.4% 0.1% -0.5%
0.9% 0.1% -1.0%
1.3% 0.2% -1.5%
1.6% 0.3% -1.9%
2.0% 0.3% -2.4%
Naples flight freq.
Rome F. flight freq.
∆RMF ∆RMC
∆ΝΑ ∆RMF ∆RMC ∆ΝΑ
0.4% -0.2% -0.2% -0.2%
0.2%
0.0%
0.9% -0.4% -0.5% -0.4%
0.5%
-0.1%
1.2% -0.5% -0.7% -0.6%
0.7%
-0.1%
1.6% -0.7% -0.9% -0.7%
0.9%
-0.2%
1.9% -0.8% -1.0% -0.9%
1.1%
-0.2%
Rome C. flight freq.
∆ΝΑ ∆RMF ∆RMC
-0.3% 0.0% 0.3%
-0.5% -0.1% 0.6%
-0.7% -0.1% 0.8%
-0.9% -0.2% 1.1%
-1.1% -0.2% 1.3%
Naples access time
Rome F. access time
∆RMF ∆RMC
∆ΝΑ ∆RMF ∆RMC ∆ΝΑ
-4.1%
1.8% 2.3% 2.6% -3.2%
0.6%
-8.3%
3.7% 4.6% 4.5% -5.5%
1.0%
-12.6% 5.6% 6.9% 5.8% -7.1%
1.3%
-16.8% 7.5% 9.3% 6.7% -8.2%
1.5%
-21.0% 9.4% 11.6% 7.3% -8.9%
1.6%
Rome C. access time
∆ΝΑ ∆RMF ∆RMC
3.4% 0.6%
-4.1%
5.8% 1.1%
-6.9%
7.5% 1.4%
-8.9%
8.7% 1.6% -10.3%
9.5% 1.7% -11.2%
The following figures confirm previous remarks. Access time is the only
attribute that can considerably change market shares, flight frequency does
not play any role, airfare have effects with increase greater than 30%.
© Association for European Transport and contributors 2009
12
Naples Capodichino choice probability
percentual variation
5.0%
0.4%
0.9%
10%
-0.9%
20%
-1.9%
1.2%
1.6%
1.9%
30%
40%
50%
0.0%
-5.0%
-4.1%
-10.0%
-2.8%
-3.8%
-4.8%
-8.3%
-12.6%
-15.0%
-16.8%
-20.0%
-21.0%
-25.0%
% increase of ...
airfare
frequency
access time
Figure 5 – direct elasticities (Naples Capodichino)
5.0%
0.2%
0.5%
0.7%
0.9%
1.1%
10%
-0.8%
20%
-1.5%
30%
-2.2%
40%
50%
-2.9%
-3.5%
-8.2%
-8.9%
Rome Fiumicino choice probability
percentual variation
0.0%
-5.0%
-3.2%
-5.5%
-7.1%
-10.0%
-15.0%
-20.0%
-25.0%
% increase of...
airfare
frequency
access time
Figure 6 – direct elasticities (Rome Fiumicino)
5.0%
0.3%
0.6%
0.8%
1.1%
1.3%
10%
-0.5%
20%
-1.0%
30%
-1.5%
40%
-1.9%
50%
-2.4%
Rome Ciampino choice probability
percentual variation
0.0%
-5.0%
-4.1%
-6.9%
-10.0%
-8.9%
-10.3%
-11.2%
-15.0%
-20.0%
-25.0%
% increase of...
airfare
frequency
access time
Figure 7 – direct elasticities (Rome Ciampino)
© Association for European Transport and contributors 2009
13
3.2.2 HL models
Starting from previous specifications, Hierarchical Logit (HL) models were
calibrated in order to (i) evaluate correlation between alternatives perceived
utility, (ii) compare different correlation structures, (iii) compare HLs goodness
fit and forecasting elasticity with respect to MNL approach.
Three different nesting criteria were investigated (figure 8): geographical
(same region and contiguity), operating airlines (mainly low-cost companies
vs. mainly traditional carriers) , airport type (dimension, notoriety). Based on
such criteria the following nests were introduced: Rome Fiumicino and Rome
Ciampino because they are close together (RM); Naples and Rome Ciampino
because they are mainly served by low-cost/non-traditional airlines (NTA);
Naples and Rome Fiumicino because they are more consolidated in users
perception (UP).
RM
NAP
RMF
RMC
RMF
NAP
NTA
UP
RMC
RMF
NAP
RMC
Figure 8 – hierarchical structures
Calibration results show that only the first two structures were statistically
significant (see table 5).
The first structure (nest RM) shows goodness fit (ρ2 = 0.363) similar to MNL[5]
and points out a significant correlation between Rome airports perceived
utilities. HLRM model parameter (δ) is equal to 0.45 and systematic utility
parameters are quite different from those obtained by MNL calibration. In
particular, alternative specific constant (ASA) changes sign and increases its
relative weight (with respect to airfare) in systematic utility and the same
happens for all the attributes except for income and access time: ratio
between flight frequency coefficient and airfare coefficient triplicate, ratio
regarding access time slightly increases, ratio regarding all the others attribute
coefficients double their values. In conclusion, if on the one hand HLRM model
allows to simulate more flexible substitution patterns between alternative
perceived utilities, on the other hand it is characterized by higher values of all
those attribute that are independent from flight services offered (socioeconomic and alternative specific constant) and by smaller values of those
attributes that usually play a significant role in users perception (airfare and
access time).
The second structure (nest NTA) shows results similar to MNL[5] model in
terms of goodness fit (ρ2 = 0.362) and in terms of parameters values. As
reported in Table 5, HL model parameter is equal to 0.93 and the ratios
between coefficients of all attributes and airfare coefficient are similar to those
estimated for MNL[5].
© Association for European Transport and contributors 2009
14
Table 5 – estimation results
model\attribute
MNL
β/βfare
fare
freq
exp
inc
ASA
-0.46
1.00
0.27
-0.58
access
-0.31
0.68
age
-0.53
1.19
carAV
-0.53
1.57
-0.17
0.36
-0.12
0.26
-1.24
2.67
HLRM (δ = 0.45)
β/βfare
-0.28
1.00
0.54
-1.95
-0.13
0.46
-0.56
2.01
-0.63
2.24
-0.19
0.69
-0.14
0.50
-1.47
-5.24
HLNTA (δ = 0.93)
β/βfare
-0.66
1.00
0.24
-0.36
-0.31
0.47
-0.56
0.85
-0.68
1.03
-0.18
0.27
-0.16
0.24
-1.39
2.11
With respect to the most performing model an elasticity analysis and a
comparison with MNL[5] was carried out (figure 9). In the following figures,
proportional changes in airport choice probabilities are plotted for HLRM and
MNL[5] models due to proportional increase of airfare, flight frequency and
access time.
Airfare
Rome Fiumicino
Naples
10%
0.0%
-0.5%
-1.0%
-1.5%
-2.0%
-2.5%
-3.0%
-3.5%
-4.0%
-4.5%
-5.0%
20%
30%
40%
50%
-0.5%
-1.1%
-1.6%
-0.9%
-2.2%
-2.8%
-1.9%
-2.8%
-3.8%
0.0%
-0.5%
-1.0%
-1.5%
-2.0%
-2.5%
-3.0%
-3.5%
-4.0%
-4.5%
-5.0%
10%
-0.4%
20%
30%
Rome Ciampino
40%
50%
-0.7%
-1.1%
-1.4%
-1.7%
-0.8%
-1.5%
-2.2%
-2.9%
-3.5%
0.0%
-0.5%
-1.0%
-1.5%
-2.0%
-2.5%
-3.0%
-3.5%
-4.0%
-4.5%
-5.0%
10%
-0.2%
20%
-0.4%
30%
40%
-0.7%
-0.9%
50%
-1.1%
-0.5%
-1.0%
-1.5%
-1.9%
-2.4%
-4.8%
MNL
HL(rome)
MNL
MNL
HL(rome)
HL(rome)
Flight frequency
Rome Fiumicino
Naples
10%
5.0%
4.5%
4.0%
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
20%
30%
40%
3.6%
3.0%
2.4%
1.7%
0.9%
1.9%
1.6%
1.2%
0.9%
0.4%
MNL
10%
50%
5.0%
4.5%
4.0%
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
20%
30%
Rome Ciampino
40%
1.3%
1.6%
1.0%
0.7%
0.4%
0.2%
HL(rome)
0.5%
MNL
0.7%
0.9%
10%
50%
1.1%
5.0%
4.5%
4.0%
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
20%
30%
40%
50%
1.9%
1.5%
1.2%
0.8%
0.4%
0.3%
HL(rome)
0.6%
MNL
1.1%
0.8%
1.3%
HL(rome)
Access time
Rome Fiumicino
Naples
0.0%
-2.0%
-4.0%
-6.0%
-8.0%
-10.0%
-12.0%
-14.0%
-16.0%
-18.0%
-20.0%
10%
-1.7%
20%
30%
40%
50%
-3.4%
-5.1%
-6.7%
-4.1%
-8.4%
-8.3%
-12.6%
-16.8%
MNL
HLrome
0.0%
-2.0%
-4.0%
-6.0%
-8.0%
-10.0%
-12.0%
-14.0%
-16.0%
-18.0%
-20.0%
10%
-1.2%
20%
-2.2%
30%
-3.1%
Rome Ciampino
40%
-3.9%
50%
-4.6%
-3.2%
-5.5%
-7.1%
MNL
-8.2%
-8.9%
HLrome
0.0%
-2.0%
-4.0%
-6.0%
-8.0%
-10.0%
-12.0%
-14.0%
-16.0%
-18.0%
-20.0%
10%
-1.4%
20%
-2.7%
30%
-3.8%
40%
-4.8%
50%
-5.7%
-4.1%
-6.9%
-8.9%
-10.3%
MNL
-11.2%
HLrome
Figure 9 – direct elasticities: comparison between MNL[5] and HLRM models
© Association for European Transport and contributors 2009
15
Confirming what discussed for MNL model elasticities and what said on HLRM
coefficients values, it is interesting to note that HLRM model shows smaller
sensitivity with respect to airfare variation and access time, while shows an
higher variation with respect to flight frequency. As regards airfare and access
time, choice probabilities are similar for small change of attribute values but
are sensibly different (1/2 and 1/3 of what were for MNL[5]) for variation
greater than 30%.
3.3 Models for non-direct connections
Since Rome-Ciampino only offers direct connections, the choice set consists
of two airports: Naples-Capodichino (64% share) and Rome-Fiumicino (36%).
This section describes the findings of the estimation process for non-direct
connections. Only basic MNL structures were used, for the utility functions see
table 2.
Statistically significant attributes were as follows: access time, airfare and
travel time, never flown and income (table 6). Unlike the case of direct
connections, travel time plays a major role in user behaviour. This is hardly
surprising, since the connections offered by the different airports have much
more different travel times than direct connections travel times. Several nonlinear transformations were investigated, the most effective being the Box-Cox
with different parameters for the two alternatives: λ = 2.5 for NaplesCapodichino and λ = 0.5 for Rome-Fiumicino. The interpretation is
straightforward: for the users that choose the nearest airport (e.g. Naples; λ =
2.5) it can be assumed that they are more affected by longer trip travel times.
However, if they choose the farther airport (e.g. Rome) they have already
allowed for greater travel time. Hence they are less affected by longer trip
travel times.
Table 6 – estimation results for non-direct connections
attribute
airfare
travel time
frequency
access time
access distance
gender
age23
never flown
experience
car availability
income (proxy2)
ASC
MNL[6]*
- 1.1 (13)
- 0.047 (2.8)
Box-Cox
λNA = 2.5, λRM = 0.5
■
- 1.57 (8.4)
Box-Cox
λNA = 0.8 , λRMF = 2.0
■
■
■
■
-0.041 (1.7)
-0.33 (1.5)
■
■
ρ2
ρ2-correct
0.322
0.317
* in parenthesis the t-student test value
© Association for European Transport and contributors 2009
16
By the same token, access time is statistically significant and Box-Cox
transformation leads to a higher goodness of fit and better statistical results
for the remaining coefficients. Also in this case, different λ parameters were
calibrated for the two airports and, surprisingly, the values obtained are the
same as those calibrated for direct connections (0.8 for Naples and 2.5 for
Rome). This confirms what was proposed in the previous section and gives
robustness to our interpretation. Frequency does not seem to play any role.
This is consistent with what was expected: for non-direct connections
frequency is not greatly perceived by users. Moreover, frequency itself is the
same or comparable for both alternatives. As for direct connections, caravailability plays a role for farther airports (Rome), whereas experience has a
negative incidence on Rome’s systematic utility. The interpretations are
similar to those proposed in the previous section. Comparing the parameters
estimated for direct and non-direct connections, it can be noted that the
reciprocal weight with respect to the airfare coefficient decreases for caravailability and experience, whereas it increases for access time.
As for models for direct connections, an elasticity analysis was carried out. In
figure 10 market share percentual variations are plotted for Naples Airport as
airfare, flight time and access time increase. In figure 11 variations are plotted
for Rome Fiumicino airport. Cross-elasticities can be easily derived since
choice set is made up by two alternatives.
It is interesting to note that Naples airport choice probability is highly
influenced by flight time and airfare, moreover models sensitivity is quite the
same for each attribute: an increase of 10% leads to a decrease of about
10%, an increase of 50% leads to a decrease of about 30%. Such results can
be easily interpreted since Naples is mainly chosen because of better airfares,
due to more low-cost services, and because of a better accessibility, due to its
geographical position. For both reasons, users prefer to switch to Rome
airport, as these attributes increase.
As regards Rome Fiumicino, different kinds of remarks arise. Airfare and
access time play a major role, whereas flight time seems to have a negligible
effect on choice probabilities. Users accepting to reach Rome airport are more
willing to accept longer total travel time, thus longer flight time.
Naples Capodichino choice probability
percentual variation
0.0%
-5.0%
-3.9%
10%
-2.4%
20%
-5.2%
-8.9%
-4.8%
30%
40%
50%
-7.3%
-10.0%
-10.9%
-9.9%
-14.8%
-12.6%
-15.0%
-17.0%
-20.0%
-21.6%
-25.0%
-23.1%
-30.0%
% increase of ...
airfare
flight time
-29.0%
-29.1%
access time
Figure 10 – direct elasticities for non-direct connections model
© Association for European Transport and contributors 2009
17
5.0%
-0.2%
-0.3%
-0.5%
-0.6%
-0.8%
10%
-1.9%
-4.3%
20%
30%
40%
50%
-3.7%
Rome Fiumicino choice probability
percentual variation
0.0%
-5.0%
-5.6%
-10.0%
-7.5%
-8.0%
-9.3%
-11.1%
-15.0%
-13.7%
-15.9%
-20.0%
-25.0%
-30.0%
% increase of...
airfare
flight time
access time
Figure 11 – direct elasticities for non-direct connections model
In figure 12, a comparison between models calibrated for direct and non-direct
connections is proposed. It is interesting to note that users forced to travel on
non-direct connection are more sensitive to airfare than users travelling on
direct connections, while they are less sensitive with respect to access time
increase. Users that are forced to accept longer travel time try at least to
minimize travel expenses and are willing to accept greater access time.
airfare
Rome Fiumicino
Naples
10%
20%
30%
40%
50%
-10.0%
20%
30%
40%
50%
-0.8%
-1.5%
-2.2%
-2.9%
-3.5%
0.0%
0.0%
-5.0%
10%
-0.9%
-1.9%
-2.8%
-5.2%
-5.0%
-3.8%
-4.3%
-4.8%
-10.0%
-10.9%
-15.0%
-8.0%
-11.1%
-15.0%
-13.7%
-20.0%
-25.0%
-15.9%
-20.0%
-17.0%
-25.0%
-23.1%
-30.0%
-30.0%
-29.1%
MNL
MNL
MNLno-direct
MNLno-direct
access time
Rome Fiumicino
Naples
10%
20%
30%
40%
0.0%
10%
-1.9%
-5.0%
-3.2%
50%
0.0%
20%
30%
40%
50%
-3.7%
-5.6%
-5.0%
-10.0%
-2.4%
-4.1%
-4.8%
-8.3%
-7.5%
-9.3%
-5.5%
-7.3%
-7.1%
-10.0%
-8.2%
-9.9%
-15.0%
-12.6%
-12.6%
-16.8%
-20.0%
-20.0%
-21.0%
-25.0%
MNL
MNLno-direct
-8.9%
-15.0%
-25.0%
MNL
MNLno-direct
Figure 12 – direct elasticities: direct vs non-direct connections models
© Association for European Transport and contributors 2009
18
3.4 Model for direct connections and trip types
In this section different MNL models are specified as trip type changes. It is
worth understanding whether and how user behaviour is affected in such
different choice contexts. The models have the same specification proposed
in the previous sections, as shown in table 7 the parameters have the same
sign in all the scenarios and differ according to the trip type.
Our findings show that the incidence of trip type is not negligible. To carry out
a comparison it is necessary to compare the ratio between coefficients in
order to normalize the incidence of MNL parameter (π2θ2/6) and of the
attributes missing. In table 8 the absolute ratios of all coefficients with respect
to access time are reported.
Table 7 – estimation results
3d-1w
7d-1w
7d-3m
airfare
-1.0
-0.3
-0.5
acc.time
-0.62
-0.82
-0.77
frequency
1.39
0.38
0.72
age
-0.91
-0.68
-0.63
* all parameters are statistically significant
car availability experience
ρ2
-1.82
0.28
-0.67
-0.15
0.27
-0.73
-0.19
0.27
Table 8 – ratio with respect to access time
3d-1w
7d-1w
7d-3m
airfare
1.6
0.4
0.6
access time
1
1
1
frequency
2.2
0.5
0.9
age
1.5
0.8
0.8
car availability
2.2
0.8
0.9
experience
0.2
0.2
* absolute values
The airfare is much more outstanding for those who wish to depart in 1 week
and for 3 days, its value of (access) time being more than double. The result
is understandable given the shorter trip length. As regards the other choice
scenarios, it is worth noting that airfare is greater in 7d-3m. This may be
interpreted in two ways: either users are more sensitive to airfares if they have
to choose where and how to fly 3 months before, or greater airfare variety can
be found with respect to the time horizon.
The same consideration may be made for frequency, which plays a much
more major role in the 3d-1w scenario. For shorter stays, users prefer more
flexible departure times. In other words, a departure time spread over the
whole day is perceived as more compatible with user needs. Comparing the
other two scenarios, frequency has a larger weight in the 7d-3m scenario, meaning that users prefer more flexibility if they have to book so far in advance.
Interestingly, socioeconomic variables (age and car availability) assume
different values among the three scenarios. Recalling that both attributes
define Naples airport’s systematic utility and that both have negative
coefficients, as for MNL models for direct connections, younger users prefer
airports with lower airfares, but it should be noted that the weights are quite
different between 7-day and 3-day trips. In particular, age has a higher
incidence for 3-day trips, meaning that as the trip duration decreases more
younger users prefer to choose cheaper solutions.
With regard to car availability, the utility of choosing one of Rome’s airports
increases as the number of cars available increases. This phenomenon is
much more marked for shorter trips (3 days), since they require fewer parking
days and hence lower parking fees. As regards past experience, the attribute
experience is statistically significant for 7d-1w and 7d-3m models only. In this
case, as expected, the values are the same.
© Association for European Transport and contributors 2009
19
4
CONCLUSIONS
In this paper a set of models to estimate the catchment area of an airport in
multi-airport system is proposed. The case study is composed by airports that
belong to different regions, have different accessibility and each of them offers
completely different services: Naples-Capodichino, with greater accessibility,
traditional airlines, low-cost carriers, lower frequencies; Rome-Fiumicino, with
lower accessibility, traditional airlines and higher frequencies; RomeCiampino, with the lowest accessibility, low-cost carriers and higher
frequencies.
The models are based on random utility theory and were calibrated on real
data obtained from an ad hoc stated preferences survey. Models for direct
connections and non-direct connections were investigated, and several facets
of the phenomenon were addressed, such as non-linearities, the incidence of
socio-economic attributes, and the incidence of trip type.
From calibration of models for direct connections the following conclusions
may be drawn:
• level of service, socio-economic and experience attributes turned out to
be statistically significant;
• non linear transformation of flight frequency (logarithmic) and of access
time (box-cox) sensibly improved model’s goodness of fit. For both the
involved attributes, box-cox transformation with different parameters for
different alternative sensibly improved model’s goodness of fit.
• Access time is the only attribute able to change considerably airports
market share, airfare may influence market share but great variation is
needed, flight frequency seems to play an irrelevant role,
• Two correlation structures were statistically significant: (i) nesting airports
belonging to the same region and close together, (ii) nesting airports
similar is users perception;
• MNL formulation shows direct and cross-elasticities greater than HL
formulation.
From calibration of models for non-direct connections the following
conclusions may be drawn:
• in addition to airfare, socio-economic and experience attributes, flight time
attribute turned out to be statistically significant.
• Non linear transformation of flight time (box-cox) and of access time (boxcox) improved sensibly model’s goodness of fit. For both the involved
attributes, box-cox transformation with different parameters for different
alternative sensibly improved model’s goodness of fit.
• Flight time and airfare are the only attribute able to change considerably
airports market share.
• With respect to models for direct connections, and differently from access
time, airfare has much greater effects on airports market shares.
As regards trip types, models parameters sensibly differ according to the trip
type. The airfare is much more outstanding for those who wish to depart in 1
week and for 3 days, the same consideration may be made for frequency,
which plays a much more major role in the 3d-1w scenario. Also
socioeconomic variables (age and car availability) assume different values
among the three scenarios: age and car availability have a higher incidence
for 3-day trips.
© Association for European Transport and contributors 2009
20
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