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ORIGIN AIRPORT CHOICE IN A MULTI-AIRPORT REGION
Article · January 2007
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Edoardo Marcucci Valerio Gatta Università Degli Studi Roma Tre Università Degli Studi Roma Tre
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ORIGIN AIRPORT CHOICE IN A MULTI-AIRPORT REGION
Edoardo MARCUCCI 1 e Valerio GATTA 2
1 Università Roma Tre, Via G. Chiabrera 199 - 00145 Roma 2 Università di Roma, “La Sapienza”, P.le Aldo Moro 5 - 00185 Roma
SUMMARY
Airports are the nodes on which air transport networks are based. Airport choice decisions are particularly relevant with an increasing air travel demand world-wide. The analysis of origin airport in multi-airport regions has a well established tradition in transportation and regional economics. The main goals of the paper pertain to the estimation of the relevance of the different attributes in determining origin airport choice including the specific role of attribute cut-offs; the estimation of statistically significant alternative specific variables to be used for policy simulations; the inference of significant socioeconomic segmentations to provide valuable suggestions for demand segmentation purposes. We used stated choice experiments to elicit agents’ preferences, and employed multinomial logit models for estimation. The study concentrates on a multi-airport region in central Italy (Marche and Emilia Romagna) where 4 competing airports are located. The models estimated on the 165 available interviews provided relevant information for all the research goals.
1 1. INTRODUCTION
Airports are the fundamental building blocks of air transport networks and, as such, of the entire aviation sector. In the last fifty years air traffic world-wide, has increased tremendously at a much greater rate than any other mode of transport. In the 1975-2002 period (Doganis, 2002) air transport demand has shown growth rates, on average, twice as great as world economic growth. Even if air transport demand is strongly influenced by highly unpredictable variables (terrorist attacks, wars, atmospheric fluctuations, economic recessions) nevertheless its growth is commonly forecasted as high by the major institutions in this sector (IATA, 2006; Boeing, 2006; Airbus, 2006). One possible way to accommodate extant demand increases is by raising available seat- kilometers even if, after a while, this necessarily conflicts with the limited runaway and terminal capacity available. Expansion is on the agenda of many busy airports throughout the world (UK Department for transport, 2003; Regional Airport Planning Committee, 2000). The issue of capacity expansion is complex in general and especially so when considering airports serving multi-airport regions. Bearing in mind morphology and population dispersion in Italy this issue becomes particularly relevant. The analysis of airport choice mechanisms adopted by air travellers is a crucial element both for long term planning strategies, for metropolitan areas served by more than one airport, as well as for airport managers confronting themselves with competing airports when formulating marketing strategies. Airport choice in a multi-airport region can be influenced by the following elements: capacity expansion, parking policies, ground transportation improvements, faster and less expensive ground services, higher connectivity, etc. Airport structure and development is influenced both by market and regulatory forces especially in a multi-airport region where each airport actively compete for origin passengers. No wonder air travel choice analysis has become increasingly popular world-wide over the last years even if in Italy there has been no such a specific attention paid to this issue with few but remarkable exceptions (Lupi, 2006; Postorino, 2001). Early studies adopted basic models dealing with simple choice of departure airport (Skinner, 1976). Subsequently more realistic choice situations have been modeled, including airport, airline (Pels et al., 2001) and, more recently, ground transportation mode choice (Hess and Polak, 2006). More sophisticated models estimated (Hess and Polak, 2005a, b, c) have also shown that passenger behavior varies deterministically for homogeneous though diversified groups (business/leisure) but also stochastically within specific groups. The results obtained for a behavioral model estimation depend not only on the methodology used but also on the aggregation and quality of the data employed. This statement is particularly true for airport choice studies and this is testified by the consistent number of works dealing with the San
2 Francisco Bay Area given the availability of detailed and good quality data from the 1995 Airline Passenger Survey conducted by the Metropolitan Transport Commission (MTC, 1995). The most important aim of this work consists in creating an extensive and high quality original database for Italy with specific relation to a well determined multi-airport region in the centre of Italy (Marche and Emilia Romagna). It is important to diversify and validate results in various institutional and regulatory contexts as well as in diverse geographical areas characterised by differentiated demand peculiarities. The present study makes use of stated preference (SP) data, implements a classical segmentation approach and introduces the concept of cut-offs recently suggested by Swait (2001) to take in due consideration the distinction between compensatory and non-compensatory decisions. The results obtained suggest that cut-offs issues are relevant in this research field as, for other aspects, also suggested by Ba şar and Bhat (2004) when proposing a probabilistic choice set multinomial logit (PCMNL) to analyze the choice (consideration) set formation of travellers. Similarly to other studies we concentrate on departing passengers given that data concerning arriving passengers are not available and connecting passengers issues are not relevant to the context studied. The paper is organized as follows. Section 2 provides a synthetic literature review illustrating where the research frontier lies and explaining how and where the present work innovates. The third section briefly describes the methodology used. Dataset illustration and econometric results are presented in section 4. Policy simulations are treated in section 5 and 6 describes future research plans. Section 7 concludes.
2. LITERATURE REVIEW
This section provides a short review of the extant research on air travel choice modeling. Among the first studies on airport choice one should recall the work of Skinner (1976) studying via a multinomial logit (MNL) model the Baltimore-Washington DC area airport choice and providing statistically relevant evidence of the role played by flight frequency and ground accessibility. Other studies inquiring airport choice mechanisms in multi-airport systems serving metropolitan areas include also Kanafani and Yuan (1977), Augustinus and Demakopolous (1978), Harvey (1987) who used both access time and frequency in a non- linear fashion in order to capture the diminishing marginal utility (disutility) of frequency (access time); Ashford and Bencheman (1987) instead used travel time to the airport, fare and frequency of service as explanatory variables; Ndoh et al. (1990) showed that a nested multinomial logit is statistically preferred to a MNL; Thompson and Caves (1993) found that for leisure travelers, access time to the airport, airfare and maximum number of seats available were statistically significant in explaining airport choice; Windle and Dresner (1995) used various ground access modes and parking as explanatory variables to explain
3 airport selection; Monteiro and Hansen (1996), Bondzio (1996), Pels et al. (2000) adopt a nested logit (NL) demand function to develop a competition model with airlines using both fares and frequencies as competitive instruments and airports using just airport taxes. Pels et al. (2001), Pels et al. (2003), Ba şar and Bhat (2004), Hess and Polak (2006, 2005a, c) concentrate the analysis of passenger airport choice departing from a multi-airport region using all the data collected for the San Francisco Bay Area implementing mixed logit (ML), NL, PCMNL, generally accounting for the additional choices along either the airline or the access-mode dimension. Hess and Polak (2005b) instead study the combined choice of airport, airline and access mode for the Greater London area adopting a Cross-Nested Logit (CNL) model. Recent and comprehensive reviews focusing on airport and airline choice dimensions can be found in Ba şar and Bhat (2004) and in Hess and Polak (2006). The present work innovates with reference to the following aspects: 1) it creates a new and detailed database concerning airport choice in a multi-airport region in Italy that for institutional, regulatory and market characteristics proves quite different from previously studied cases. The results obtained are useful in validating and testing the robustness of previous studies; 2) it specifically treats the compensatory/non-compensatory decision issues by asking detailed questions about the acceptability of attributes and levels employed in the estimation process; 3) the labelled SP choice experiments performed provide valuable information to airport managers as well as to policy makers. In fact, knowing the specific preference structure for each airport allows the latter to take informed decisions when planning or implementing regulatory/institutional changes and the former to use appropriate competitive strategies within a complex and interdependent environment.
3. METHODOLOGY
The methodology used for data acquisition is based on SP choice experiments describing a potential choice situation among the 4 airports considered. In SP surveys, respondents are asked to compare a set of alternatives and select the one providing the highest utility. The theoretical basis is represented by the micro-economic theory of choice and by the random utility theory (Louviere et al., 2000). Travellers preferences elicitation is done using choice-based conjoint analysis, a decompositional SP method which estimates the structure of an individual’s preferences given his/her choices between a set of alternatives that are pre-specified in terms of levels of different attributes through a formal experimental design. We have chosen a fractional factorial design and in particular we have adopted the complete enumeration scheme when constructing the full profile random design. This strategy considers all possible concepts (except those indicated as prohibited) and chooses each one so as to produce the most nearly orthogonal design for each respondent, in terms of main effects. The concepts within each
4 task are also kept as different as possible (minimal overlap); if an attribute has at least as many levels as the number of concepts in a task, then it is unlikely that any of its levels will appear more than once in any task. The selection of the potential most important attributes was done through focus groups and literature review, while the attribute levels were defined in order to create realistic choice sets. The 5 selected attributes are under control of the airport management: type of airline (low-cost, regular), connection capability (low, medium, high), waiting time (low, medium, high), type of parking area (small & fee-paying, large & fee- paying, small & free, large & free), flight frequency (one, two, three per day). Each pc-based interview is composed by a total of 8 hypothetical choice exercises in which the sample respondent is ask to evaluate the four airports and declare the most preferred one. Discrete choice models can be used to analyze the gathered choice data with the purpose of estimating the coefficients related to the attribute levels and discover their relative importance weights.
4. DATA DESCRIPTION AND ECONOMETRIC RESULTS
This paragraph gives a concise description of the sample used and reports the main econometric results. The area of study includes two regions in central Italy: Marche and Emilia-Romagna. The multi-airport region includes the airports of: Ancona, Rimini, Forlì and Bologna. In table 1 we describe the main socioeconomic variables characterising our sample segmented according to the airport chosen in the last trip made.
Table 1 - The most important socio-economic characteristics of the sample.
Ancona Bologna Forlì Rimini Total Count % Count % Count % Count % Count % GENDER Female 24 55,8% 28 38,9% 20 51,3% 4 36,4% 76 46,1% Male 19 44,2% 44 61,1% 19 48,7% 7 63,6% 89 53,9% Total 43 100,0% 72 100,0% 39 100,0% 11 100,0% 165 100,0% AGE Low (<30) 20 46,5% 20 27,8% 17 43,6% 6 54,5% 63 38,4% Medium (30-45) 13 30,2% 26 36,1% 14 35,9% 3 27,3% 56 34,1% High (>45) 10 23,3% 26 36,1% 8 20,5% 2 18,2% 45 27,4% Total 43 100,0% 72 100,0% 39 100,0% 11 100,0% 165 100,0% INCOME (€/month) Low (<1000) 20 46,5% 19 26,4% 17 43,6% 6 54,5% 62 37,8% Medium (1000-1500) 9 20,9% 27 37,5% 16 41,0% 2 18,2% 54 32,9% High (>1500) 14 32,6% 26 36,1% 6 15,4% 3 27,3% 48 29,3% Total 43 100,0% 72 100,0% 39 100,0% 11 100,0% 165 100,0% OCCUPATION Employed full time 22 51,2% 43 59,7% 16 41,0% 3 27,3% 84 50,9% Self-employed worker 3 7,0% 10 13,9% 6 15,4% 1 9,1% 20 12,1% Student 6 14,0% 9 12,5% 9 23,1% 3 27,3% 27 16,4% Student-worker 3 7,0% 2 2,8% 4 10,3% 1 9,1% 10 6,1% Retired or pensioner 3 7,0% 2 2,8% 2 5,1% 7 4,2% Unemployed 2 4,7% 1 1,4% 3 1,8% Housewife 1 2,3% 1 1,4% 1 9,1% 3 1,8% Other 3 7,0% 4 5,6% 2 5,1% 2 18,2% 11 6,7% Total 43 100,0% 72 100,0% 39 100,0% 11 100,0% 165 100,0%
5 We have administered 188 interviews with 78 interviews directly in the 4 airports and 110 in the airports’ catchments areas. After preliminary data quality tests we discarded 23 interviews and used only 165 in total. The main characteristics of the four airports are reported in table 2.
Table 2 – Main characteristics of Ancona, Rimini, Forlì and Bologna airports as perceived by sample members.
Ancona Bologna Forlì Rimini Count % Count % Count % Count % BOOKING Travel agency 28 65,1% 51 70,8% 14 35,9% 2 18,2% Internet 15 34,9% 18 25,0% 25 64,1% 8 72,7% Other 3 4,2% 1 9,1% Total 43 100,0% 72 100,0% 39 100,0% 11 100,0% TRIP PURPOSE Business 10 23,3% 13 18,1% 8 20,5% 1 9,1% Leisure 29 67,4% 59 81,9% 30 76,9% 10 90,9% Other 4 9,3% 1 2,6% Total 43 100,0% 72 100,0% 39 100,0% 11 100,0% TYPE OF CARRIERS Low-cost 23 53,5% 17 23,6% 39 100,0% 10 90,9% Regular 20 46,5% 55 76,4% 1 9,1% Total 43 100,0% 72 100,0% 39 100,0% 11 100,0% CONNECTIVITY Low 12 7,3% 11 6,7% 34 20,6% 10 6,1% Medium 106 64,2% 57 34,5% 94 57,0% 91 55,2% High 47 28,5% 97 58,8% 37 22,4% 64 38,8% Total 165 100,0% 165 100,0% 165 100,0% 165 100,0% WAITING TIME Low 81 49,1% 36 21,8% 89 53,9% 70 42,4% Medium 80 48,5% 101 61,2% 72 43,6% 88 53,3% High 4 2,4% 28 17,0% 4 2,4% 7 4,2% Total 165 100,0% 165 100,0% 165 100,0% 165 100,0% TYPE OF PARKING Small & Payment 80 48,5% 25 15,2% 109 66,1% 112 67,9% Large & Payment 54 32,7% 134 81,2% 26 15,8% 24 14,5% Small & Free 18 10,9% 1 ,6% 22 13,3% 19 11,5% Large & Free 13 7,9% 5 3,0% 8 4,8% 10 6,1% Total 165 100,0% 165 100,0% 165 100,0% 165 100,0% FLIGHT FREQUENCY One per day 60 36,4% 7 4,2% 96 58,2% 86 52,1% Two per day 90 54,5% 54 32,7% 60 36,4% 69 41,8% Three per day 15 9,1% 104 63,0% 9 5,5% 10 6,1% Total 165 100,0% 165 100,0% 165 100,0% 165 100,0% London 16,3% Paris 11,1% Dublin 28,2% Vienna 36,4% Paris 9,3% NewYork 8,3% Catania 28,2% Stuttgart 27,3% MAIN DESTINATION Rome 9,3% London 5,6% London 15,4% London 9,1% Catania 9,3% Olbia 5,6% Palermo 10,3% Catania 9,1% Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev ADVANCE BOOKING (day) 37,07 24,283 27,23 17,719 27,44 14,623 41,82 23,481 ACCESS TIME (minute) 61,74 45,249 116,94 60,576 73,97 47,019 49,09 27,370 ACCESS COST (€) 14,40 10,475 29,35 14,377 19,64 28,260 16,55 8,904
6 In table 3 we provide evidence of the rating of the airlines, both regular and low-cost, that received at least 10 evaluations. Interestingly enough there is no substantial differentiation between the two types of carriers overall (6,85 for regular and 6,82 for low-cost) notwithstanding some variance in the single company evaluation.
Table 3 – Rating of airlines that received at least 10 evaluations
Airlines Type of carrier N Mean Std. Dev. Minimum Maximum RYANAIR Low-cost 89 7,3708 1,07007 4,00 10,00 MERIDIANA Low-cost 37 7,1622 1,14294 4,00 9,00 EUROFLY Low-cost 20 7,0000 1,02598 5,00 9,00 EASYJET Low-cost 26 6,96 ,824 5 9 MYAIR.COM Low-cost 10 6,8000 2,57337 1,00 10,00 AIR ONE Low-cost 41 6,7805 1,62037 1,00 10,00 AIR DOLOMITI Low-cost 16 6,6875 1,81544 1,00 9,00 WINDJET Low-cost 31 6,6129 1,54224 1,00 9,00 EVOLAVIA Low-cost 15 6,0000 2,32993 1,00 9,00 TOT 6,82 LUFTHANSA Regular 61 8,1967 1,06175 5,00 10,00 KLM Regular 25 8,0400 1,05987 5,00 10,00 BRITISH AIRWAYS Regular 56 7,59 1,411 1 10 AIR FRANCE Regular 53 7,47 1,683 1 10 IBERIA Regular 15 6,4000 ,91026 4,00 8,00 ALITALIA Regular 112 6,37 1,811 1 10 EGYPTAIR Regular 17 5,4118 2,06334 1,00 9,00 ROYALAIR Regular 10 5,3000 2,00278 3,00 8,00 TOT 6,85
The econometric results of the reference model are presented in table 41.
Table 4 – Reference model
|Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | ACC_M -.01119585 .04679966 -.239 .8109 ACC_H -.13551853 .06026543 -2.249 .0245 I .52715097 .06371802 8.273 .0000 LC .77119945 .06343673 12.157 .0000 CO_M .00318047 .04412346 .072 .9425 CO_H .32656349 .04287811 7.616 .0000 WT_M .12899954 .04390260 2.938 .0033 WT_H -.48534582 .05156461 -9.412 .0000 P_LP -.01705385 .05205472 -.328 .7432 P_SF .04411371 .05169615 .853 .3935 P_LF .24784490 .04899820 5.058 .0000 FR_TWO .08420825 .04296774 1.960 .0500 FR_THREE .21475597 .04272197 5.027 .0000 K_LC -2.70822126 .43519493 -6.223 .0000 K_CO_L -.55922656 .28157206 -1.986 .0470 K_WT_H -1.10157746 .30358218 -3.629 .0003 K_P_SP -.49262558 .36206917 -1.361 .1736 K_FR_ONE -1.84944740 .56196313 -3.291 .0010 Log -likelihood -1540.339 R-sqrd .15824 RsqAdj .15440
1 All the models estimated in this paper are MNL. Estimation of more flexible models will be carried out in the future. The intent of this paper is to individuate the most appropriate MNL model. In fact, as Hensher et al. (2005, p. 481), suggest, the analyst should “… devote a considerable amount of time to exploratory data analysis using MNL, typically over 50% of the allocated model estimation time”.
7 The reference model includes the cut-offs on all five explanatory variables to take in due consideration the distinction between compensatory and non-compensatory decisions concerning airport choice (Swait, 2001). The overall explanatory power of the model (adj R- sqrd=0,154) is acceptable and is higher than that of the model (not reported) not considering the cut-offs effects (adj R-sqrd=0,129) that in turn is greater than the adj R-sqrd (0,112) of the model (not reported) not explicitly considering the role of inertia, a dummy variable related to the last actual airport choice. All the signs and the estimated values of the coefficients (ßs) are in line with expectations recalling that effects coding was used2. Given that all the variables used are categorical in nature we can compare the various ßs estimated and rate the variables according to their relative impact on choice. Low-cost (LC), describing the effect of the presence of a low-cost carrier in a given choice set, has the strongest impact among the variables describing airport’s attributes, followed by Inertia (I) and Waiting Time High (WT_H). The most important variable overall, however, is the cut-off for low-cost (K_LC). The signs for the cut-offs are all negative as expected and statistically significant with the only exceptions of Parking Small & Payment (K_P_SP). It is remarkable that among the five statistically significant cut-offs, three (K_LC, K_FR_ONE, K_WT_H) have a coefficient greater than all the estimated ßs for airport’s attributes. Both access time and connectivity are statistically significant only when they are high (ACC_H, CO_H). Waiting time has a negative impact on choice only when it becomes high (WT_H=-0,48) in fact a medium waiting time has a positive impact on utility (WT_M=0,12) even if smaller than that of a low waiting time (WT_L=o,36). Flight frequency parameters are both statistically significant and the greatest impact on utility is produced by the increase of flight frequency from 2 to 3 flights a day (FR_THREE=0,21). Finally, as it is for parking, it is interesting to notice that this variable has a significant impact on choice only when parking area is large and free (P_LF=0,24). Table 5 shows the results of the most extended model estimated including socioeconomic variables, namely income and age3. After testing for discrete and continuous, we have chosen to consider the segmentation variables discrete and divided them in three classes4: low (always the reference), medium, high (INM, INH and AGM, AGH). The explanatory power of the model substantially increases (adj R-sqrd=0,184) and interesting information are revealed. Out of the 66 variables used 19 are statistically significant. The variables with the
2 When adopting effects coding instead of assigning 0 to the base level across the n-1 new variables, where n is the number of the levels for each attribute, the base level is coded -1 across each of the above newly created variables. Effects coding has the same advantage of dummy coding since it allows for the estimation of non- linear effects in the attribute levels but avoids the shortcoming of perfectly confounding the base attribute level with the grand mean of the utility function. 3 We have also tested a model interacting the explanatory variables with gender (female, male) and discovered (Wald test for linear restrictions) that the additional parameters included were not statistically different from zero. The results reported in the paper only describe the effects of age and income interactions. 4 Data variable classification in three categories is based on the respective frequency distributions with the intent of having an homogeneous presence in each class.
8 greatest influence are, respectively, LC which, in this case, represents the impact that the presence of a low cost carrier has within the choice set of an agent characterised by Age Low (AGL) and Income Low (INL), followed by Low Cost Income High (LC_INH) and Low Cost Age High (LC_AGH). The sign and relative weight of all the significant variables is as expected with the only dubious interpretation of the relative weight of Connectivity High Income High (COH_INH=0,199) which, even if positive, is smaller than the coefficient for Connectivity High Income Low (COH_INL=0,468). There are the same four statistically significant cut-offs. The cut-off for the Low Cost (K_LC) is still the most powerful explanatory variable followed by the cut-off for frequency equal to one (K_FR_ONE) but , in this case, the ß for LC is the third most important variable.
Table 5 – Socioeconomic interactions
|Variable | Coefficient| St. Error|b/St.Er.|P[|Z|>z]| |Variable | Coefficient| St. Error|b/St.Er.|P[|Z|>z]| ------+------+------+------+------+ +------+------+------+------+------+ ACC_M -.06614968 .08989746 -.736 .4618 ACM_AGM -.09205524 .13009636 -.708 .4792 ACC_H -.04789160 .11254514 -.426 .6704 ACM_AGH -.28276627 .14283961 -1.980 .0477 I .56780906 .06739089 8.426 .0000 ACH_AGM .20958662 .16326017 1.284 .1992 LC 1.61741041 .12970447 12.470 .0000 ACH_AGH .14114955 .18042723 .782 .4340 C O_M -.01212193 .08183013 -.148 .8822 LC_AGM -.62607791 .17178063 -3.645 .0003 CO_ H .46886292 .07896195 5.938 .0000 LC_AGH -.79019613 .19080507 -4.141 .0000 WT _M .00810639 .08180260 .099 .9211 COM_AGM .06036539 .11564146 .522 .6017 WT _H -.37992271 .09012079 -4.216 .0000 COM_AGH -.05703957 .13166527 -.433 .6649 P _LP .04255677 .09507324 .448 .6544 COH_AGM -.11293503 .11350491 -.995 .3197 P _SF .07842474 .09486500 .827 .4084 COH_AGH .03444433 .12833092 .268 .7884 P_LF .20449922 .09326226 2.193 .0283 WTM_AGM .15433519 .11539412 1.337 .1811 FR_TWO -.03093935 .08129422 -.381 .7035 WTM_AGH .06657406 .13175969 .505 .6134 FR_T HREE .24913793 .08038865 3.099 .0019 WTH_AGM -.23027049 .13407796 -1.717 .0859 ACM_INM .29948461 .12735736 2.352 .0187 WTH_AGH -.19836057 .15046045 -1.318 .1874 ACM_INH .17991760 .14839568 1.212 .2254 PLP_AGM -.09992362 .13781719 -.725 .4684 ACH_INM -.38837005 .15842216 -2.451 .0142 PLP_AGH -.19926621 .15768223 -1.264 .2063 ACH_INH -.27838490 .18444229 -1.509 .1312 PSF_AGM .24500340 .13647774 1.795 .0726 LC_INM -.42267690 .17242078 -2.451 .0142 PSF_AGH .11263789 .15635801 .720 .4713 LC_INH -.84094597 .18917082 -4.445 .0000 PLF_AGM .06793556 .13114149 .518 .6044 COM_INM .06838751 .11564571 .591 .5543 PLF_AGH .14905796 .14716994 1.013 .3111 COM_INH -.01818816 .13121143 -.139 .8898 FTWO_AGM .02749679 .11437436 .240 .8100 COH_INM -.06090579 .11299246 -.539 .5899 FTWO_AGH .07416222 .12928846 .574 .5662 COH_INH -.26902165 .12830190 -2.097 .0360 FTHREE_AGM -.03025900 .11386585 -.266 .7904 WTM_INM .09866060 .11526692 .856 .3920 FTHREE_AGH -.12112124 .12986791 -.933 .3510 WTM_INH .08357845 .13033809 .641 .5214 K_LC -2.41769845 .46125298 -5.242 .0000 WTH_INM -.11513203 .13315782 -.865 .3872 K_CO_L -.66570863 .29008634 -2.295 .0217 WTH_INH .112 41196 .14966794 .751 .4526 K_WT_H -1.20653062 .31229328 -3.863 .0001 PLP_INM -.01964589 .13868544 -.142 .8874 K_P_SP -.67941343 .37915491 -1.792 .0731 PLP_INH .12287100 .15498855 .793 .4279 K_FR_ONE -1.68249346 .56041339 -3.002 .0027 PSF_INM -.08997699 .13505840 -.666 .5053 PSF_INH -.41283216 .15691537 -2.631 .0085 PLF_INM .03965436 .13093473 .303 .7620 PLF_INH -.05936358 .14691094 -.404 .6862 FTWO _INM .13810700 .11450682 1.206 .2278 FTWO _INH .14960498 .12894093 1.160 .2459 FT HREE_INM .00530113 .11421454 .046 .9630 FT HREE_INH .09955740 .12866639 .774 .4391
Log likelihood function -1468.259 Rsqrd .1976 3 RsqAdj .18403
In table 6 we present the segmentation of the sample according to the motivation of the last flight made by respondents. We had 32 business trips and 128 leisure with a residual of 5 people declaring other motivations for their last trip. The ßs of the two models cannot be directly compared since the two models are non-nested5; however, one can fruitfully contrast the two rankings of the variables explaining their relative importance in the two groups. The
5 Recall that in order to compare the ßs of the two non-nested models one could use a nested-logit “trick” as suggested by Ben-Akiva and Morikawa (1990), Bradley and Daly (1991) and also performed in Marcucci and Gatta (2007). This problem is in fact similar to that derived from merging SP and RP data.
9 different number of observations for the two groups might explain the disparity in the number of statistically significant variables for the two samples (9 for business and 12 for leisure).
Table 6 – Business-Leisure Segmentation
BUSINESS LEISURE ------+------+------+------+------+ +------+------+------+------+------+ |Variable | Coefficient| St . Error|b/St.Er.|P[|Z|>z]| |Variable | Coefficient| St. Error|b/St.Er.|P[|Z|>z]| ------+------+------+------+------+ +------+------+------+------+------+ ACC_M -.08725250 .11700646 -.746 .4558 ACC_M .01189453 .05257131 .226 .8210 ACC_H -.04381738 .14842516 -.295 .7678 ACC_H -.20609822 .06994337 -2.947 .0032 I .35938533 .15119984 2.377 .0175 I .57469990 .07291093 7.882 .0000 LC .28958742 .14240318 2.034 .0420 LC .85436358 .07276131 11.742 .0000 CO_M -.10819845 .10202135 -1.061 .2889 CO_M .03171853 .05014764 .633 .5271 CO_H .36316131 .09787079 3.711 .0002 CO_H .31587880 .04896782 6.451 .0000 WT_M .15529623 .09901999 1.568 .1168 WT_M .11000196 .05037963 2.183 .0290 WT_H -.34559922 .11513072 -3.002 .0027 WT_H -.50559747 .05905199 -8.562 .0000 P_LP .12750210 .11399654 1.118 .2634 P_LP -.04364581 .05984372 -.729 .4658 P_SF -.12572599 .12448136 -1.010 .3125 P_SF .06350663 .05867241 1.082 .2791 P_LF .30395218 .10950149 2.776 .0055 P_LF .24672360 .05614955 4.394 .0000 FR_TWO .25725373 .09869530 2.607 .0091 FR_TWO .02958401 .04930979 .600 .5485 FR_THREE .27085482 .09716079 2.788 .0053 FR_THREE .22069326 .04888169 4.515 .0000 K_LC -2.32344659 .65994015 -3.521 .0004 K_LC -2.76397532 .58762944 -4.704 .0000 K_CO_L .32471254 .54824680 .592 .55 37 K_CO_L -.83342373 .34017086 -2.450 .0143 K_WT_H -.95806684 .53502462 -1.791 .0733 K_WT_H -1.20071946 .36951869 -3.249 .0012 K_P_SP -.86159484 .64285978 -1.340 .1802 K_P_SP -.22700822 .43962523 -.516 .6056 K_FR_ONE -1.61759473 .79072220 -2.046 .0408 K_FR_ONE -1.64871294 .79765990 -2.067 .0387
Log likelihood funct ion -299.8374 Log likelihood function -1180.989 Rsqrd .15513 RsqAdj .13485 Rsqrd .16806 RsqAdj .16316
This effect is also evident when comparing the explanatory power of each model (adj R- sqrd=0,134 for business and 0,163 for leisure). The ranking of the relative importance for the statistically significant variables for the two models is reported in table 7
Table 7 - Variables Ranking: Business and Leisure
Ranking Business Leisure Variables 1 K_LC K_LC 2 K_FR_ONE K_FR_ONE 3 CO_H K_WT_H 4 I LC 5 WT_H K_CO_L 6 P_LF I 7 LC WT_H 8 FR_THREE CO_H 9 FR_TWO P_LF 10 FR_THREE
It is interesting to note that for both trip purposes we have respectively the same two most important variables: low- cost and frequency cut-offs. Inertia is more important for business trips (4th) while less so for leisure (6th) suggesting that airport “loyalty” is greater for business trips while people travelling for leisure tend to give less importance to previous choices. On the other hand the reverse is true for low-cost, representing the impact of the presence of a low cost carrier in a choice profile. In this case our results show that this variable is more important for leisure (4th) than for business (7th). Another comparison worthy of note is for Connectivity High (CO_H) 3rd for business and only 8th for leisure.
10 Table 8 reports the results obtained by segmenting the sample according to the type of air carrier used in the last trip made. We had 76 interviewees using a regular carrier and 89 a low- cost carrier for their last trip. The considerations previously expressed for the comparability of the ßs for the two models (business, leisure) also apply in this case. For the low-cost segment we have 8 statistically significant variables with an adj R-sqrd of 0,163 while 10 variables with 0,154 adj R-sqrd for the regular one. In order to estimate the low-cost model we had to eliminate the cut-off for LC since there were no such occurrences in our sample.
Table 8 - Regular-Low Cost Segmentation
LOW -COST TRAVELLERS REGULAR TRAVELLERS ------+------+------+------+------+ +------+------+------+------+------+ |Variable | Coefficient| St . Error|b/St.Er.|P[|Z|>z]| |Variable | Coefficient| St. Error|b/St.Er.|P[|Z|>z]| ------+------+------+------+------+ +------+------+------+------+------+ ACC_M .03569003 .06368808 .560 .5752 ACC_M -.06920392 .06994983 -.989 .3225 ACC_H -.10499691 .08174983 -1.284 .1990 ACC_H -.16360940 .09114603 -1.795 .0726 I .63687351 .08635219 7.375 .0000 I .42699526 .09693525 4.405 .0000 LC 1.00313789 .08851851 1 1.333 .0000 LC .51355411 .09324886 5.507 .0000 CO_M -.00152631 .06009337 -.025 .9797 CO_M .01477401 .06586394 .224 .8225 CO_H .34104808 .05808404 5.872 .0000 CO_H .31368508 .06454498 4.860 .0000 WT_M .06006179 .06017484 .998 .3182 WT_M .22765555 .06570111 3.465 .0005 WT_H -.41619702 .06709010 -6.204 .0000 WT_H -.59694502 .08250593 -7.235 .0000 P_LP -.00200868 .07064865 -.028 .9773 P_LP -.02259595 .07772512 -.291 .7713 P_SF .04981475 .07098541 .702 .4828 P_SF .03865982 .07647883 .505 .6132 P_LF .16507043 .06839101 2.414 .0158 P_LF .34968326 .07150790 4.890 .0000 FR_TWO .07787996 .05899382 1.320 .1868 FR_TWO .09669595 .06334519 1.526 .1269 FR_THREE .23467729 .05864055 4.002 .0001 FR_THREE .19637406 .06339108 3.098 .0019 K_CO_L -.88046397 1.14842131 -.767 .4433 K_LC -2.45864059 .44168328 -5.567 .0000 K_WT_H -1.44893549 .66337908 -2.184 .0289 K_CO_L -.52068852 .30100351 -1.730 .0837 K_P_SP -1.04051112 .87430367 -1.190 .2340 K_WT_H -.89842556 .34959316 -2.570 .0102 K_FR_ONE -1.51149467 .76751652 -1.969 .0489 K_P_SP -.30340143 .40730408 -.745 .4563 K_FR_ONE -2.07853588 .79067595 -2.629 .0086
og likelihood function -819.0613 Log likelihood function -705.6828 Rsqrd .17019 RsqAdj .16353 Rsqrd .16276 RsqAdj .15441
When segmenting the sample for low-cost and regular, the ranking of the statistically significant variables (Table 9) is, under certain aspects, more homogeneous than in the leisure-business segmentation. In fact, for both groups we have 3 cut-offs as the most important variables. When scaling the low-cost ranking for K_LC, we find frequency and waiting time cut-offs at the top. Similar sequences are given by the LC\I, and CO_H\FR_THREE. In this case it is interesting to note that inertia is not very important for both groups (5th low-cost and 6th regular) testifying that this is not an important element for both sub-samples.
Table 9 - Variables Ranking: Regular – Low Cost
Ranking Low-Cost Regular Variables 1 K_FR_ONE K_LC 2 K_WT_H K_FR_ONE 3 K_P_SP K_WT_H 4 LC WT_H 5 I LC 6 WT_H I 7 CO_H P_LF 8 FR_THREE CO_H 9 P_LF WT_M 10 FR_THREE
11 With the intent of providing valuable information to policy makers and to airport managers we have estimated, on the base of the labelled SP choice experiments conducted, a model with alternative specific constants (ASC) and variables for the 4 airports considered. All the reported variables are significant and have been selected after performing a log- likelihood ratio test for the unrestricted versions of the model. The model reported in table 10 is parsimonious and has a satisfactory explanatory power. It was estimated with the intent of discovering if the sample interviewed had different preference structures when considering the various airports. Following this rationale the variable specific parameters were forced to be the same (generic) when their difference could not be proved statistically different from zero.
Table 10 - Model with airport-specific constants/variables
Variables Ancona Bologna Forlì Rimini ACC_M -0,4894 - -0,2270 - (-3,24) (-2,61) ACC_H -0,8414 - - - (-3,87) I 0,4964 0,7501 0,7501 0,4964 (4,31) (7,14) (7,14) (4,31) LC 1,5064 1,5064 1,5064 1,2886 (13,45) (13,45) (13,45) (8,92) CO_H 0,3404 0,3404 0,3404 0,3404 (8,60) (8,60) (8,60) (8,60) WT_M - 0,3768 - - (3,70) WT_H -0,3965 -0,7788 -0,3965 -0,3965 (-8,33) (-6,65) (-8,33) (-8,33) P_LP - - 0,2106 - (2,08) P_LF 0,3292 0,3292 - 0,3292 (6,23) (6,23) (6,23) FR_TWO 0,2362 - - - (2,65) FR_THREE - 0,4315 0,4315 0,2180 (6,96) (6,96) (2,56) K_LC -2,3426 -2,3426 -2,3426 -2,3426 (-5,25) (-5,25) (-5,25) (-5,25) K_CO_L - -1,6655 - - (-2,18) K_WT_H -1,2954 - - -1,2954 (-3,21) (-3,21) LC_INM - -0,7038 - - (-3,43) LC_INH -0,5708 -1,3368 -0,5708 -0,5708 (-3,62) (-5,39) (-3,62) (-3,62) LC_AGM -0,6706 - -0,6706 -0,6706 (-4,51) (-4,51) (-4,51) LC_AGH -0,8056 -0,8056 -0,8056 -0,8056 (-4,95) (-4,95) (-4,95) (-4,95) ASC - -0,5932 -0,4995 - (-4,28) (-4,07)
(t-values in brackets) Log-likelihood -1540.339 R-sqrd .15824 RsqAdj .15440
12 Access time is relevant only for Ancona and Forlì with a negative impact on utility. Inertia has a positive impact and results more important for Bologna and Forlì. Once more, excluding the cut-offs, LC proves the most important variable and has the same effect for Ancona, Bologna and Forlì while it has a relatively smaller impact for Rimini. There is no difference in connectivity impacts for all airports considered. The preference structure for waiting time is different for Bologna with respect to the other airports. In fact while WT_M has still a positive impact and only with WT_H we have a value of -0,77, for all other airports we have a negative value of WT_H equal to -0,39 showing a greater sensitivity to waiting time when considering Bologna. Parking Large and Free (P_LF) is equally relevant for all airports but Forlì where Parking Large and Payment (P_LP) is significant with a positive impact on utility showing that, in this case, even if not for free a large parking is appreciated. Bologna and Forlì are equally influenced by a three flight a day frequency (FR_THREE) whereas Rimini results less influenced by this variable and Ancona, on the other hand, is already influenced by a two flight a day frequency (FR_TWO) showing a greater sensibility towards this attribute. K_LC has the same and strong influence for all airports while K_CO_L is only relevant for Bologna and K_WT_H is relevant for Ancona and Forlì. For all interactions variables only those connected to low-cost are significant. In line with what was shown earlier-on we detect that with growing income and age there is, in general, a progressive decay of the positive impact of low-cost on the utility of the sampled agents. These considerations can be useful in characterising potential customer segments.
5. POLICY SIMULATIONS
The preliminary analysis conducted in this paper provides some interesting suggestions concerning the various airports and indications for policy intervention. The base case scenario is defined by multiplying the appropriate ßs of table 10 by the single agents’ evaluation of each attribute (deterministic portion of the utility function), subsequently using these values to estimate (MNL) the choice probabilities of each agent for any given airport and then averaging the results to calculate the overall choice probabilities. Once again we would like to stress that the results provided should not be considered as market shares given that SP data were used. Table 11 reports, for each airport, the ranking of the 4 policies with the greatest impact on airports’ choice probabilities. The results were calculated by varying for each attribute, with a significant ß, the perception level bring it to the most favourable level for the airport. Table 11 provides important information concerning the priority that should be assigned to each policy given its potential impact.
13 Table 11 - Base case scenario and simulations
Ancona Bologna Forlì Rimini BASE SCENARIO Choice Probabilities 20,9% 30,6% 22,3% 26,2% SIMULATIONS 1st best Parking +8,0% Parking +7,2% Flight freq. +8,4% Parking +11,1% 2nd best Connectivity +4,2% Connectivity +3,0% Connectivity +5,3% Flight freq. +9,3% 3rd best Waiting time +3,2% Low-cost +2,9% Parking +5,1% Waiting time +4,8% 4th best Flight freq. +2,9% Waiting time +2,8% Access time +4,3% Connectivity +4,2%
6. FUTURE RESEARCH PLANS
The preliminary results obtained and described in this paper can be extended and improved by implementing the following steps in the future: - reconstruct the revealed preference (RP) database, that is the last actual airport choice made, already available and use it to estimate the alternative specific constants in order to forecast market shares; - increase the number of interviews after developing a detailed and consistent sampling strategy; - test different data acquisition methods: postal and web based (all the present interviews were conducted on a face-to-face basis using lap top computers); - some shortcomings encountered in the sample stratification process suggest that the acquisition of more details related to the characteristics of the last trip made and those of the not chosen options within the agents’ choice set might prove of great interest. (e.g.: final trip destination, type of aircraft, trip length, on board services, frequent flyer programs, plane fare, alternative ground modes evaluated and their relative cost, booking methods, etc.); - acquire detailed information concerning mode trip choice to the airport (availability, cost, number of interconnections, trip length, etc.); - implement a RP and SP data merging; - estimate more flexible models with respect to agents’ and airports’ heterogeneity (NL, ML, PCMNL, CNL) - acquire more details concerning the air carrier and their pricing policies (seat availability and dimensions, future development plans, discount rates and special offers, etc.).
14 7. SUMMARY AND CONCLUSIONS
This paper provides evidence concerning the most important determinants of origin airport choice in a multi-airport region. The variables determining airport choice probabilities were selected by conducting a focus group and coupling this evidence with suggestions derived by previous studies. The novelty of this paper has also to do with the specific multi-airport region in central Italy studied that has never been examined before using this methodology and with the specific intent of providing relevant information both to policy makers and airport managers. The former aspect is crucial for research since a diversification of the area of study is important in order to validate results obtained within different institutional, regulatory and geographical contexts that so much influence demand characteristics in this sector. The present study made use of SP data only and adopted a classical sample segmentation approach while implementing the concept of cut-offs so to explicitly distinguish between compensatory and non-compensatory decision. The results obtained suggest that the distinction introduced is relevant in this research field. We concentrate on origin airport and departing passengers given that data concerning arriving passengers are not available and connecting passengers issues are not relevant in our case. The main results achieved so far relate to: a) the relevance of cut-offs playing an important role in decision making; b) the presence of statistically significant alternative specific variables that can be usefully employed in simulation exercises; c) significant socioeconomic segmentations for age and income giving interesting suggestions for demand segmentation purposes. Future methodological developments of this paper will look into: RP data estimates and RP-SP data merging procedures, test different data acquisition methods, implement more flexible treatments of heterogeneity.
ACKNOWLEDGMENTS
The authors want to thank Eva Valeri for her precious help in administering the questionnaire.
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