Tài liệu Airline choice for domestic flights in vietnam application of multinomial logit model

UNIVERSITY OF ECONOMICS ERASMUS UNVERSITY ROTTERDAM HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM –THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS AIRLINE CHOICE FOR DOMESTIC FLIGHTS IN VIETNAM: APPLICATION OF MULTINOMIAL LOGIT MODEL BY TRAN PHUOC THO MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, December 2016 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS AIRLINE CHOICE FOR DOMESTIC FLIGHTS IN VIETNAM: APPLICATION OF MULTINOMIAL LOGIT MODEL A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By TRAN PHUOC THO Academic Supervisor: TRUONG DANG THUY HO CHI MINH CITY, December 2016 ACKNOWLEDGEMENT First of all, I would like to express my gratitude supervisor Dr. Truong Dang Thuy of the Vietnam – The Netherlands Programme (VNP) at Ho Chi Minh City University of Economics for his patience, enthusiasm, and immense knowledge. He not only guided me to the right direction but also continuously supported in overcoming a lot of obstabcles in my research. Second, I would like to thank all of the respondents for spending their time to answer the questions in my survey. They contribute significantly in collecting data for my study. Without their participation, I am sure that the survey could not be conducted successfully. Finally, my sincere thanks also go to my family and my friends for encouraging me throughout two years of study as wel as throughout the process of researching and writing this thesis. Thank you. Tran Phuoc Tho December, 2016 I ABBREVIATIONS RUM Random Utility Model SP Stataed Preference RP Revealed Preference VNA Vietnam Airline VJ Vietjet Air BL Jetstar Pacific LCC Low cost carrier II ABSTRACT In 2015, Vietnam witnessed the booming of airline industry. The participation of low cost carriers makes the airline market more and more competitive. Understanding the behavior of passengers is essential for any carriers to make their strategic policies. This study employs the multinomial logit model with the data of 122 respondents to investigate the impacts of characteristics of passengers as well as attributes of airlines on the airline choice. The characteristics of passengers include age, gender, marital status, education, and income whereas the attributes of airlines consist of price, number of flights of airlines, punctuality, comfort of seat space, and quality of check in service. A stated preference survey is conducted online from 16th to 23rd of October 2016 to collect the data of 122 respondents, who used to travel by air at least one time before. They are required to finish three tasks. The first task is providing their information, such as age, gender, marital status, education, and income. The second one is evaluating about the quality of services of the three airlines, including Vietnam Airline, Vietjet, and Jetstar. The final part is hypothetical scenarios of fifteen domestic routes given along with the prices of airlines for the respondents to choose one of the three airlines. Jetstar is chosen as the base outcome, the results of multinomial logit model suggest that characteristics of airlines have relationships with the ratios of probability of chosing Vietnam Airline or Vietjet over probability of chosing Jetstar, except for the satisfaction of customers about staff at the check in counter. When comparing one airline and the based airline (Jetstar), the attributes of the third airline is also necessary to be taken into consideration. In general, a good judgment of service of an airline makes the odds ratios of that airline and the base increased. In contrast, a good evaluation of the based carrier or of the other airline makes the odds ratios declined. Besides that, income has positive association with probability of choice Vietnam Airline and Vietjet but negative relation with Jetstar, holding other variables constantly. III TABLE OF CONTENTS Contents ACKNOWLEDGEMENT ............................................................................................................................. I ABSTRACT.................................................................................................................................................. B TABLE OF CONTENTS:............................................................................................................................ IV LIST OF TABLES ....................................................................................................................................... VI LIST OF FIGURES .................................................................................................................................... VII INTRODUCTION ........................................................................................................................................ 1 1.1. Problem statement ......................................................................................................................... 1 a. Overview of airline industry ......................................................................................................... 1 b. Airline industry in Vietnam .......................................................................................................... 1 1.2. Research objectives ....................................................................................................................... 3 1.3. Research questions ........................................................................................................................ 4 1.4. Scope of the thesis ........................................................................................................................ 4 1.5. Structure of thesis ......................................................................................................................... 4 LITERATURE REVIEW ............................................................................................................................. 5 2.1 Theoretical review ....................................................................................................................... 5 a. Random Utility Model (RUM) ..................................................................................................... 5 b. Reveal Preference & Stated Preference survey ............................................................................. 7 2.2. Empirical review ........................................................................................................................... 8 RESEARCH METHODOLOGY ................................................................................................................ 13 3.1. Stated preference method ................................................................................................................. 13 3.2. Questionnaire and survey process .................................................................................................... 14 3.3. Attributes of airlines ........................................................................................................................ 16 3.4. Model specification .......................................................................................................................... 18 DATA & EMPIRICAL RESULTS............................................................................................................. 23 4.1. Data .................................................................................................................................................. 23 4.2. Empirical results .............................................................................................................................. 31 a. Controlling variables ................................................................................................................... 35 b. Attributes of airline ..................................................................................................................... 37 IV c. Effect of different routes ............................................................................................................. 38 CONCLUSION ........................................................................................................................................... 41 REFERENCES .............................................................................................................................................. i APPENDIX ................................................................................................................................................... v V LIST OF TABLES Table 3.1. Summary of hypothetical scenarios in survey: ................................................. 15 Table 3.2. Attributes of airline: .......................................................................................... 17 Table 3.3. Prices and numbers of flights by routes of carriers .......................................... 20 Table 3.4. Description of variables: ................................................................................... 21 Table 4.1. Social demographic characteristics ................................................................... 27 Table 4.2. Estimation results of multinomial logit model ................................................. 32 VI LIST OF FIGURES Figure 3.1. The screen of the online survey ................................................................................ 16 Figure 4.1. Airline Choice for Destinations................................................................................ 24 Figure 4.2. Frequency Of Income ............................................................................................... 25 Figure 4.3. Willingness to pay for routes.................................................................................... 26 Figure 4.4. Check-In Service Evaluation .................................................................................... 28 Figure 4.5. Cabin Crew Service Evaluation ............................................................................... 28 Figure 4.6. Food & Drink Onboard Evaluation ......................................................................... 29 Figure 4.7. Inflight Seat Space Evaluation ................................................................................. 29 Figure 4.8. On-time Performance Evaluation ............................................................................ 30 Figure 4.9. Schedules Delay Evaluation ..................................................................................... 30 Figure 4.10. Predicted probability of airline choice and income ............................................... 35 Figure 4.11. Predicted probability of airline choice and age..................................................... 36 VII CHAPTER 1 INTRODUCTION 1.1. Problem statement a. Overview of airline industry In 2015, the world’s aviation industry achieved the highest net profit in history, 33 billion dollars. It is nearly double when compared to a net profit of 17.4 billion dollars in 2014. Particularly, the aviation industry in Asia Pacific obtained net profit of more than 5.8 billion dollars. In addition, region of Asia Pacific accounted for 31% of global passengers, while Europe and North America is 30% and 26%, respectively. It is noted that low cost carrier has transported over 950 million passengers, approximately 28% of those who are scheduled passengers (IATA report, 2016). According to The International Air Transport Association (IATA), number of air travelers is forecasted to increase nearly double, from 3.8 billion in 2016 to 7.2 billion in 2035. IATA also announces the five fastest growing markets that have the most additional passengers per year for over the next 20 years, including China, US, India, Indonesia, and Vietnam. In detail, Vietnam may have 112 million new passengers for a total of 150 million. Moreover, IATA also stated that Vietnam is one of the seven countries which have fastest growth in aviation industry. Besides that, Vietnam Government pays much attention to infrastructure which is one of the most critical components of air transport sector. Vietnam’s planning is to have 26 airports by 2020; particularly Long Thanh International Airport will be ready by 2020. b. Airline industry in Vietnam The Vietnam airline industry, which was administered by Ministry of Transport and Civil Aviation Authority of Vietnam, has witnessed rapid growth in 2015 compared to the figures in 2014. The whole market served 40.1 million of passengers and transported 771 thousand tons of cargo. In particular, transportation of domestic carriers is 31.1 million passengers, increased by 21%. This positive sign with the falling of crude oil price of 30% in 2015 are stimulus for airline carriers to continue reducing fares in order to meet the demand of transportation of passengers. 1 It could be said that airline industry in Vietnam has a potential market due to many reasons. First, population of Vietnam is more than 90 million. Thus, demand of traveling is very high. Moreover, in the recent years, income of Vietnamese is increasing so that the demand of transit of people is also higher day by day. People have many options to choose means of transports not only faster but also safer. Although there are some disasters of airline in 2014 in the world, it seems that traveling by air is the safest way. According to IATA Safety Report, there were 12 fatal accidents in the total of 73 accidents, which caused to 641 fatalities on over the world in 2014. This is not a high proportion when comparing to about 33 billion passengers in 2014 (IATA Annual Review 2015). Moreover, air travel helps people save much time. For examples, it takes about two days to transit by train from Ho Chi Minh City to Ha Noi while only two hours by air. Finally, thanks to internet, e-commerce is more and more popular. People can stay at home, and buy tickets with the cheap price at the time of promotion of carriers. In 1956, the Government established the Vietnam Civil Aviation Department. At that time, there were only five aircrafts to serve some domestic flights. In 1993, Vietnam Airlines was set up as a national carrier. Until 1995, by gathering 20 aviation enterprises, Vietnam Airlines Corporation was born and the airline itself is the core business. Now, Vietnam Airlines is operating an extensive network of domestic and international services to Southeast and North Asia, Europe and Australia. In July 2016, ANA Holding Inc became a strategic shareholder after purchasing of an 8.77% stake. Vietnam Airlines claimed that, under the restructure plan, it will keep on to divest the shareholding of state to 75%. Skytrax, organization of the leading airline and airport rating of the world, certified that Vietnam Airlines is a 4-star airline. Vietjet Air, an international low cost carrier, was the first privately owned airline in Vietnam. Although Vietjet Air was approved to operate in November 2007, it launched the first flight in December 2011, with only 3 aircrafts. Up to 2015, Vietjet had 29 aircrafts with 28 domestic routes and 12 international routes. As planning of Vietjet in 2016, it will have 42 aircrafts to meet the demand of travel and open more 3 domestic and 5 international fleets. Another airline is Jetstar Pacific Airlines JSC. This airline was founded in 1990 as Pacific Airlines and commenced operations in 1991 with charter cargo services under control of Vietnam Airlines Corporation. In 2005, it began to operate in passenger service. In 2007, Qantas Airway Limited bought a portion of Pacific Airlines’ shares and changed it as model of low cost 2 carrier. It officially became a part of Jetstar network in 2008, named Jetstar Pacific. In 2012, Vietnam Airlines purchased a 70% stake, so up to now Qantas is having only 30% stake in the company. Vietnam Air Services Company (VASCO) is one of a subsidiary of Vietnam Airlines Corporation. From 2004 to now, VASCO has transported passengers from Tan Son Nhat Airport to Southern airport such as Ca Mau, Con Dao, Rach Gia, Can Tho and many other routes. Besides of service flight, VASCO also plays a role as a multi functioning airline and providing maintenance service for private aircrafts. In summary, there are four domestic carriers are operating in Vietnam at present, including Vietnam Airlines, Vietjet, Jetstar, and VASCO. In the past, there were another two airlines used to operate: Indochina Airlines and Air Mekong. Due to difficulty in finances, Indochina Airlines claimed to stop all of the flights after one year in operation in 2009. Similarly, because of loss in business, Air Mekong had to halt commercial flights in 2013. Until January in 2015, it is officially revoked by The Ministry of Transport. There are many literatures about the theory of customer behavior and empirical studies about airline choice of passengers. The annual report of IATA (The International Air Transport Association) in 2015 shows the answers of the passengers with the question “What is the first reason for choosing an airline?” It is found that nonstop flight (15%) and lowest fare (14%) are the reasons why customers choose an airline while recommended by travel agent and in-flight service is just accounted for 4% and 3%, respectively. However, in Vietnam, airline industry has just been booming in the recent years so there are not many researches focus on this topic. Knowing the preference of passengers is necessary for both aviation firms and foreign investors. It helps not only the three carriers have policies that are suitable for Vietnamese people but also investors in evaluate the airline market to make decision in investing or not. 1.2. Research objectives This study uses stated preference survey and employs the multinomial logit model to identify the factors that have impacts on airline choice of passengers. These factors include the characteristics of both airline and air travelers. This study is expected to provide information on 3 factors affecting the choice of passengers, and thus provide information for carriers in identifying their target market segments and efficiently improving their services. 1.3. Research questions There are two questions are proposed. First, what are attributes of airlines that giving impacts on travelers in deciding which airline to fly? Second, what are demographic factors of air travelers that have influence on their airline choice? 1.4. Scope of the thesis Although there are four carriers in Vietnam airline market, this research examines the airline choice of three carriers, including Vietnam Airline (VNA), Vietjet (VJ), and Jetstar (BL). VASCO is excluded from the choice set since VASCO just operate in the Southest with short flight, for example from Sai Gon to Ca Mau, Rach Gia, Con Dao. Moreover, the main business of VASCO is providing maintenance service for aircrafts, not transporting passengers. Therefore, the market share of VASCO is very small so the elimination of VASCO is not a severe problem. 1.5. Structure of thesis The rest of the study includes four chapters. Chapter 2 reviews not only the theory of random utility, stated preference and reveal preference data but also the empirical study of choice model in airline industry. The third chapter presents methodology research with description of questionnaire, process of survey, and empirical model. Chapter 4 describes in detail the data collected from the survey and gives the results of model. Finally, chapter 5 concludes main results and limitations of the study. 4 CHAPTER 2 LITERATURE REVIEW This chapter first introduces the economic literature of individual choice, which is the foundation for empirical studies in analyzing choices of economic agents, including air travellers. The chapter then provides a review of empirical studies that analyzed choice of passengers among carriers. Based on these reviews, a model is set up to analyze the choices of air travelers among the three airlines: Vietnam Airlines, Vietjet, and Jetstar. 2.1 Theoretical review a. Random Utility Model (RUM) Random Utility Model is commonly used to represent individual choice behavior. Thurstone (1927) first introduced a law of comparative judgment and originally developed the terms of psychological stimuli, which leads to the result of binary probit model now. This is a model of whether the respondents could get the different level of stimulus. The stimuli concept was further developed as utility by Marschak (1960). The random utility model implies that the decision maker may know the utility of each choice alternative but the researcher may not know it fully. Therefore, it is necessary to take uncertainty into account. This leads to the result that the model of utility consists of two parts, deterministic and random components. Deterministic components could be observed and interpreted by the analyst while random components are unknown. There are four main causes of uncertainty that Manski (1977) identified, including measurement errors, the use of proxy variables, unobserved of attributes of the choicer and unobserved attributes of the alternatives. Discrete choice models are based on the random utility theory and other assumptions. It is assumed that the decision-makers choose among a finite choice set, which are collectively exhaustive and mutually exclusive alternatives and they select the alternative that brings the highest utility. With every alternative, the deterministic factors of utility are stated as a function of attributes, for example a linear function. The probability of selecting an alternative of an individual is the outcome of the choice model. Besides that, random components are also the key 5 factors. The difference of assumptions of the distribution of the error terms causes many forms of choice models. According to Train (2009), the main models include logit, GEV, probit and mixed logit model. First, logit model is assumed that the error terms is iid extreme value. The term of iid means independent, identically distributed (Train, 2009). It is assumed that the unobserved factors are not correlated and have the same variance with alternatives. This assumption, on the one hand, is restrictive, on the other hand, makes the choice probability have a very convenient form. This convenience makes the logit model used popularly; however, in some situations, the assumption of un-correlation over alternatives could be not appropriated. The sequences of choices over time are also derived under the independence assumption. This means that each choice does not depend on the others. Thanks to the convenient form, most of the researchers utilize this model to examine many aspects of air choice behavior. (Escobari & Mellado (2014); Warburg (2005); Yoo & Ashford (1996)) Second, to avoid the assumption of independence in logit model, generalized extreme value models or GEV which imply a generalization of the distribution of extreme value were developed (Train, 2009). The generalization allows the relationship of unobserved factors and alternative. It could be seen as a special case of logit model when this correlation does not exist. The less or more flexibility of the correlations depend on the kinds of GEV model. For instance, a comparatively simple GEV classifies the alternatives in many groups, called nests. The unobserved factors are assumed to have the same correlation with alternatives in the same nest but no correlation with ones in the others nests. Hess (2008) employs nested logit model to establish model of air travel behavior. Pels et al (2001) also use nested logit model to describe the passenger concerning in airports and airlines. Third, probit can deal with three limitation of logit model. Train (2009) shows the restrictions of logit model, including not representing random taste variation, IIA property and correlation between unobserved components and alternatives. However, probit model assumes that errors terms are normally distributed. Therefore, the only limitation of probit model is that, in some cases, unobserved factors may not have normal distribution. 6 Finally, mixed logit permit the unobserved factors to have any distribution. In this model, unobserved factors could be divided in two parts. One part includes all of the heteroskedasticity and correlation while the other part is iid extreme value. It is noted that the first part could obey any distribution, not excluding non-normal distribution. Adler et al (2005) apply mixed logit model to develop itinerary choice model. The research of Warburg (2005) employs both of multinomial logit model and mixed logit to understand the flight choice behavior of passengers. In reality, there are many other discrete choice models specified for specific purposes by researchers. These models are often established by incorporate the concepts of other models. For example, a mixed probit could be obtained by breaking down the observed components as in mixed logit, yet, the second part is normal distributed in lieu of extreme value distributed. By acknowledging the motivation and derivation of these models, researchers are able to determine the model that is suitable for a specific situation to achieve the goals of their studies. b. Reveal Preference & Stated Preference survey There are two main kinds of surveys which are conducted to analyze the behavior of customers, including revealed preference (RP) and stated preference (SP) survey. RP data provide information about the preferences in a real choice environment. This brings the primary advantage of RP data, actual behavior of respondent. However, it is difficult to do trade-off analysis with RP data (Bhat & Sardesai, 2004). Moreover, for new alternatives introduced in the new market, it could not handle the models with RP data (Whitaker et al, 2005). According to Yoo and Ashford (1996), there are three practical limitations of RP data. First, it is not enough variation for some interesting variables to calibrate a statistical model. Second, researchers face to difficulty with estimating model that reflects the trade-off ratios due to the correlations of explanatory variables. Finally, to calibrate statistical models, it is necessary to carried out very large surveys to obtain enough observations. Therefore, not many researchers employ this method of survey in modeling choice behavior of customers. Carrier (2008) use RP data of a booking data so that the study does not include the non-booked travel alternatives, such as income, purpose of travel,…Escobari and Mellado (2014) collect data from the online travel agency and use posted priced and the changes of inventory to explain the demand of flights. 7 In contrast, in SP survey, the hypothetical scenarios are designed to understand the stated responses of the interviewers. Thus, SP data could reduce the limitation of RP data. According to Collins et al. (2012), with SP data, it is possible to reproduce the output of behavior, such as willingness to pay. In addition, by conducting SP survey, it is able to explore the choice behavior of consumers regarding the alternatives that do not exist. Nevertheless, SP data has limitation that the respondents may be uninterested or careless in a survey, or may express their own opinions about the context of survey rather than give information about a new product usage (Warburg, 2006). Besides that, decision making in hypothetical situation easily leads to the result of bias because people may not do as what they say. In practical, most of the researchers use SP survey for modeling choice behavior. Adler et al (2005) do SP survey to analysis trade-offs in air itinerary choice while Collins et al (2012) use the interactive stated choice survey to investigate the behavior of air travelers. Wen and Lai (2010) and Proussaloglou and Koppelman (1999) also use SP data to examine air carrier choice of passengers. In general, due to the full complement of RP and SP data, there are estimation techniques to be developed to combine these data sources to deal with limitation of each type of data. It is suggested that the most effective way is to use both of method. RP is useful for forecasting demand or realistic purposes while SP is useful for system planning purpose (Yoo & Ashford, 1996). Similarly, to present model of itinerary choice, Atasoy and Bierlaire (2012) use mixed dataset of RP and SP. The mixed data enable the study to succeed in estimating elasticity of price in demand model. 2.2. Empirical review There are several studies that examine all the different aspects of airline choice behavior. For instances, the researches of Basar and Bhat (2004), Hess and Polak (2005), and Pathomsiri and Haghani (2005) investigate the airport choice in multi-airport regions. Besides that, some papers focus on not only airport choice but also other aspects of travel. Ndoh et al. (1990) study airport choice and route choice of passengers whereas Furiuchi and Koppelman (1994) examine the passengers’ destination choice and airport choice. In addition, there are a few studies pay attention to air traveler choice rather than airport choice, such as the research of Chin (2002), Algers and Beser (2001), Proussaloglou and Koppelman (1999), and Yoo and Ashford (1996). 8 The multinomial logit model of choice is utilized in most of the studies mentioned above. Other studies, such as Ndoh et al. (1990), Furiuchi and Koppelman (1994), and Pels et al. (2001) use the nested logit model to estimate the multidimensional and spatial choices of air travelers. However, the papers that attempt to consider the issues of behavior or effects in air travel choices employ the mixed multinomial logit model (Hess & Polak, 2005; Pathomsiri & Haghani, 2005). Moreno (2006) uses the multinomial logit model to address airline choice for domestic flights in São Paulo. There were 1,923 passengers interviewed at the departing lounges of São PauloGuarulhos International Airport (GRU) and São Paulo-Congonhas Airport (CGH). It is believed that airline choice is the result of the tradeoff due passengers have to face with flight cost, flight frequency, and performance of airline. Thus, three types of variables are tested. First, variables associated with cost are the lowest and highest fare. The second type of variables is those associated with flight frequency, including the existence of connections or stops, travel period, and the day of the week. Finally, age of airline is used to be proxy of performance of airline. This study finds that the lowest fare is the best explained variable of airline choice. Besides that, senior passengers seem to pay more attention to airline age than junior passengers. In the same way, Nason (1981) conducts a stated preference survey to ask respondents to make a choice of airline among a list of airlines. By employing multinomial logit model, the research considers airline choice as a function of attributes of airline service as well as characteristics of passengers. With revealed preference survey, Prossaloglou and Koppelman (1995) examine airline choice of passengers who depart from Dallas and Chicago in the US. In multinomial logit model, independent variables are schedule convenience, reliability, fares, city pair presence, market presence, and frequently flyer program of membership. The results show that the attractiveness of carriers and its market share are positively associated with program of frequently flyer. Similarly, Nako (1992) explores the choice of airlines of business travelers as a function of the frequently flyer program of airlines. It is concluded that frequently flyer programs affect positively on demand of airline. Similarly, Prossaloglou and Koppelman (1999) investigate the passengers’ choice of airline, flight, and fare class by using logit model. The authors consider that air travelers are rational decision makers, who tend to choose the alternative brings the highest utility. The explanatory variables include fare class, fare price, presence of carrier market, service quality, frequent flyer participation of travelers, and flight schedules. Moreover, 9 the study use separate models to estimate for different groups, such as business and leisure passengers. These models are based on stated preference data, which is collected by a two-tier survey. First, initial data involving in the characteristics of passengers, such as previous trip, purpose of trip, address, membership of frequent flyer are collected via mail survey. Second, a sample of mail survey respondents is chosen randomly to be interviewed by phone. The questionnaire is designed to simulate the search of individual for air travel options and their selection among alternatives like during a real process of booking air tickets. The results suggest that behavior of leisure and business travelers are significant different. Leisure travelers are more price-sensitive but less time-sensitive than business travelers. Furthermore, businessmen pay more attention to frequent flyer programs and they are also willing to pay more to fly with their most preferred airlines. In contrast, Pels et al. (2001) also use separate models for business and leisure traveler but the results suggest that the difference between two groups is very small. The authors utilize the nested logit model to examine the preferences of passengers in concerning airports and airlines. In this research, the nests defined by airports as well as the nest defined by airlines are considered in detail. This empirical study use data of the survey in San Francisco Bay Area in 1995. Furthermore, it is implied that an airline has two types of competitors: ones operate in the same airport and the others operate in other airports because access time to the airport are significant for both leisure and business travelers. Besides that, Warburg (2005) says that it is valuable to understand the passengers’ flight choice behavior and predict air travel demand. The study helps the carriers give appropriate pricing policy and predict air travel demand in new routes. In 2001, Warburg conducted stated preference survey which consists of 119 business and 521 non-business passengers. The respondents were passengers who reported their most recent domestic flight. They had to make 10 binary choices between the actual flight and hypothesis flight, which was 10 itinerary alternatives with the same departure and arrival place. Therefore, Warburg (2005) claims that there is not existence of universal choice set. This could be explained that travelers have different flight itineraries so there is ability of different choice set for each passenger. The study employs both multinomial logit model and mixed logit model to examine the behavior of two groups of passengers: business and non-business travelers. Similarly to the results of Prossaloglou and 10 Koppelman (1999), in multinomial logit model, the business people seem to be more sensitive to time while non-business ones are more sensitive to fare and men are more sensitive to fare than women. Moreover, the study of Yoo and Ashford (1996) investigates the flight choice behavior of Korean. The respondents were who had long distance international air trips, which took more than 10 hours air journey time. By employing logit model for both RP and SP data, the researchers also want to do comparative analysis of RP and SP survey. Surveys were conducted at the passenger terminal of Kimpo International Airport in Seoul, in Oct 1993 for RP Survey and in August 1994 for SP survey. Total number of samples was equal in RP and SP data. The research gives the result that passengers paid more for Korean airline than foreign airline and Korean residents paid more than foreign residents. Likely, Escobari and Mellado (2014) estimate the demand of international flights by using a unique dataset with information of flight choices, prices, and characteristics of non-booked flights. The data collected from the online agency “expedia.com”, consist of 317 flights from 6 carriers between 19 and 24 Dec, from New York to Toronto and vice versa. The prices and inventory changes for the flights departed from 19 to 24 Dec, 2008 were recorded. The study focuses on one way and non-stop flights. The findings show that if the price increases 10% in 100 seat aircraft, the quantity demand decrease by 7.7 seats. For revealed preference survey, Ukpere et al. (2012) investigate the determinants of airline choice making in the Nigerian domestic air transport. With the questionnaire follows Likert scale of ranking, data are collected to obtain both socio-economic characteristics and attributes of airline. The socio-economic characteristics include sex, age, marital status whereas the airline attributes consist of comfort, on-board service, fare, frequency, behavior of crew, and power of monopoly. These determinants could have effects on passengers in choosing airlines at the selected airports. By using the nested logit model, the findings show that all of these variables are significant, that means they effect on making decision of customers. The authors also recommend that airline should charge competitive fares and make their products distinct from others to attract more air travelers. In constrast, Adler et al. (2005) do stated preference survey on the internet in 2003 to collect the detail information of about 600 individuals who have just paid for domestic air trip. The aim of this study is to understand the tradeoffs that an individual faces to when choosing itinerary choices. The characteristics of itineraries in the survey include 11