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Transportation Research Procedia 25C (2017) 2412–2431 www.elsevier.com/locate/procedia

World World Conference Conference on on Transport Transport Research Research -- WCTR WCTR 2016 2016 Shanghai. Shanghai. 10-15 10-15 July July 2016 2016

Intercity Intercity travel travel analysis analysis for for aa university university township township with with emphasis emphasis on on air air travel travel a a b, Mariam Mariam Thomas Thomasa,, Aditya Aditya V V Sohoni Sohonia,, K KV V Krishna Krishna Rao Raob,** a a

Masters Student, Civil Engineering Department, IIT Bombay, Mumbai 400 076, India Masters Student, Civil Engineering Department, IIT Bombay, Mumbai 400 076, India b bProfessor, Civil Engineering Department, IIT Bombay, Mumbai 400 076, India Professor, Civil Engineering Department, IIT Bombay, Mumbai 400 076, India

Abstract Abstract Intercity travel is crucial to the economic development of any nation with air travel demand modeling being a key component in transportation Intercity travel is crucial to the economic development of any nation with air travel demand modeling being a key component in transportation planning. For this study, the university township of IIT Bombay in Mumbai, India is considered to capture the travel choice behaviour of its planning. For this study, the university township of IIT Bombay in Mumbai, India is considered to capture the travel choice behaviour of its residents comprising of students, faculty and staff, when traveling to other cities. The prime areas of analysis are determining trip generations residents comprising of students, faculty and staff, when traveling to other cities. The prime areas of analysis are determining trip generations based on socioeconomic characteristics of the travelers using regression analysis, understanding mode choice by looking into travel decisions based on socioeconomic characteristics of the travelers using regression analysis, understanding mode choice by looking into travel decisions made as a function of both mode and income characteristics using utility theory, and to decipher the dimension of airport choice when a new made as a function of both mode and income characteristics using utility theory, and to decipher the dimension of airport choice when a new airport comes into play along with an existing airport using utility theory, all of these based on responses of the travelers to Revealed Preference airport comes into play along with an existing airport using utility theory, all of these based on responses of the travelers to Revealed Preference (RP) and Stated Preference (SP) questionnaires, with the latter based on the response of 1% of the population. Extensive sample set of 7 % of the (RP) and Stated Preference (SP) questionnaires, with the latter based on the response of 1% of the population. Extensive sample set of 7 % of the university population is considered with two questionnaires, one an RP approach focusing on outgoing trips in 2014 and the other, an SP university population is considered with two questionnaires, one an RP approach focusing on outgoing trips in 2014 and the other, an SP approach to model airport choice. Interviewees are divided into five categories, three for students based on monthly stipend and the rest for approach to model airport choice. Interviewees are divided into five categories, three for students based on monthly stipend and the rest for faculty and staff, with trips being differentiated purpose-wise. Number of annual trips by air and other modes is estimated for the sample set with faculty and2 staff, with trips being differentiated purpose-wise. Number of annual trips by air and other modes is estimated for the sample set with adjusted R2 and p-values giving simplified trip generation equations. Choice of main modes like flight and train and ground access modes of auto adjusted R and p-values giving simplified trip generation equations. Choice of2 main modes like flight and train and ground access modes of auto and taxi, is deciphered using Multinomial Logit modeling with McFadden R2, p-values and prediction-success tables being evaluation criteria. and taxi, is deciphered using Multinomial Logit modeling with McFadden R , p-values and prediction-success tables being evaluation criteria. Conditional probability of airport choice is calculated using a balanced and orthogonal fractional factorial design of 9 options with airfare, flight Conditional probability of airport choice is calculated using a balanced and orthogonal fractional factorial design of 9 options with airfare, flight frequency, airport access time and airport delay, each being three-level indicators. A sound theoretical framework to dissect the intricacies of frequency, airport access time and airport delay, each being three-level indicators. A sound theoretical framework to dissect the intricacies of intercity travel choice with special importance to air travel has been constructed here with the results demonstrating that travel behaviour is not intercity travel choice with special importance to air travel has been constructed here with the results demonstrating that travel behaviour is not an independent entity but a function of mode and socioeconomic characteristics. Individuals whose households have higher socioeconomic an independent entity but a function of mode and socioeconomic characteristics. Individuals whose households have higher socioeconomic conditions fly more frequently and choose comfortable modes of travel, in general. Airfare and flight frequency are found to be vital attributes conditions fly more frequently and choose comfortable modes of travel, in general. Airfare and flight frequency are found to be vital attributes for a potential airport choice study. This study provides flexibility in application to any region under similar principles to understand the nuances for a potential airport choice study. This study provides flexibility in application to any region under similar principles to understand the nuances of travel behaviour better. of travel behaviour better. © 2017 2017 The byby Elsevier B.V. © TheAuthors. Authors.Published Published Elsevier B.V. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. Peer-review under responsibility WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. Keywords: Intercity travel; Air travel demand modeling; Travel choice behaviour; Trip generation; Mode choice; Airport choice; Revealed preference; Stated Keywords: Intercity travel; Air travel demand modeling; Travel choice behaviour; Trip generation; Mode choice; Airport choice; Revealed preference; Stated preference preference

1. Introduction 1. Introduction Travel demand modeling has become a crucial part of any transportation planning system in today’s world due to the Travel demand modeling has become a crucial part of any transportation planning system in today’s world due to the increasing need for travel, with intercity travel having become vital for the economic development of any country. When studying increasing need for travel, with intercity travel having become vital for the economic development of any country. When studying

* Corresponding author. Tel.: +91-22-2576 7305; fax: +91-22-2576 7302. * Corresponding author. Tel.: +91-22-2576 7305; fax: +91-22-2576 7302. E-mail address: [email protected] E-mail address: [email protected] 2214-241X © 2017 The Authors. Published by Elsevier B.V. 2214-241X © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY.

2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. 10.1016/j.trpro.2017.05.249

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the travel making behaviour of intercity travelers anywhere, the understanding of models which capture their decision making thought processes is vital. How a traveler chooses a particular mode from a set of available alternatives is the sole focus of utility theory. The university township of the Indian Institute of Technology, Bombay (IITB) in Mumbai, India has been considered for this study in order to dissect the travel making behaviour of its inhabitants primarily comprising of students, faculty and staff members. The complete analysis encompasses the responses of 781 individuals for the Revealed Preference (RP) study and 100 air travelers for the separate Stated Preference (SP) study. The aims of this study are threefold. Using the RP approach, intercity trip generation equations for both air and other modes of travel relating to vital socioeconomic characteristics of the travelers using regression analysis, as well as mode choice utility equations for both main modes of flight and train as well as ground access modes of taxi and auto using income characteristics of the travelers along with Level of Service (LOS) attributes of the modes such as waiting time, travel time and travel cost using utility theory, have been analyzed. Airport choice utility functions for the upcoming Navi Mumbai International Airport (NMIA) when competition with the existing Chhatrapati Shivaji International Airport (CSIA) would occur has also been derived based on the SP study. The primary findings from this study emphasize the importance of income when considering trip generation analysis, especially household disposable income which is set apart for travel purposes. Individuals having higher socioeconomic status were seen to choose more comfortable and expensive options for both main as well as ground access modes of travel. The influencing variables of primary importance when looking into the dimension of airport choice were determined to be airfare and flight frequency which exactly echoes the findings from literature. This study therefore helped to decipher the intricacies behind travel making decisions of travelers for intercity trips at the level of a university township such as IITB. 2. Literature review A detailed review of the literature available in the areas of trip generation modeling, main mode and ground access mode choices as well as the dimension of airport choice yielded a thorough understanding about the mechanics behind travel making decisions of potential travelers and this has been presented in this section. As per Intriligator (1983), econometric models are the most sophisticated and complex methods utilized in airport and aviation demand modeling to understand underlying relationships between causal variables and the variable on which the effect occurs on. Trend models cannot take care of economic, social or market forces that come into play in a real life estimation for which econometric models are a necessity. To understand the variables which affect aviation demand and to investigate the reasons for these relationships to happen, mathematical techniques are often found most suitable to study the correlation between the dependent and explanatory variables. Trip generation is the first stage of travel demand modeling which is used to predict the number of trips produced and attracted to each zone. Air passenger surveys provide information about the air travel trip ends in the past. A thorough comprehension of where these trips begin and end in a region must be done with an air passenger trip generation model according to Ashiabor (2007). Estimating passenger demand at airports started off in the early 1950s using the gravity model, wherein forecasts between airports or city pairs were made as stated by Harvey (1951). Jacobsen (1970) made significant efforts in the development of airport-specific forecasting models which formed a relation between the dependent variable of generated trips at an airport with two independent variables of total income per capita for the airport catchment area and the average airfare per distance on all routes in the States. Haney (1975) made efforts to analyze airport specific demand for the St. Louis Airport in the United States with the model having the dependent variable of total annual traffic at the airport and independent variables being the socioeconomic factors in the metropolitan catchment area of the airport. Regression analysis was used for both the above mentioned studies. The first step in any engineering project is planning. In transportation, planning is especially important because transportation systems are among the most expensive to build or modify. Investment in transportation improvements is based on the understanding of future demand and to achieve this, an understanding of trip making behavior is essential. According to AlAhmadi (2006), understanding the behavior of the trip maker will provide the model builder with the most likely variables for inclusion in the model. Disaggregate modeling is preferred to aggregate ones because the former focuses its analysis at the level of an individual behavioral unit which in turn allows for the consideration of factors that influence travel behavior of individuals and for the maximum use of available data. In utility theory, every alternative that is presented as choices before an individual is assumed to have a measure of attractiveness or utility associated with it and the decision maker is assumed to choose that alternative which yields the highest utility to him or her. Utilities are expressed as a sum of the measured attractiveness and a random term, where the measured attractiveness is a function of the alternative as well as the decision maker’s characteristics. Of the model structures available to determine mode choice, the most common ones are the Multinomial Logit (MNL) model and the Nested Logit (NL) model. The former assumes the IIA property, where alternatives are assumed to be independent of each other. However, it is generally not used when a combination of modes with similar characteristics is there in which case the NL model helps to group these into nests for analysis. In this study, only the MNL model has been considered.

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Mode choice is an essential step in transportation planning. Prediction of intercity mode choice is vital to balance spending by government on the improvement of transportation systems. Each mode must be given due consideration based on the number of people who use it and to design it appropriately without any under-design or over-design issues of any of its components according to Miskeen and Rahmat (2012). Can (2013) examined how the characteristics of domestic tourists and travel mode attributes influenced mode choice in Nha Trang, Vietnam by applying the Multinomial Probit (MNP) model and inferred that travel time, travel cost to income ratio, mode quality variables and income were the key elements in explaining mode choice. Roman et al. (2014) estimated discrete choice models for public transport services in the Madrid-Barcelona Corridor for air transport, high speed rail (HSR) and bus. When looking at ground access modes towards the airport, most literature showed the importance of access time as opposed to access cost. Psaraki and Abacoumkin (2002) forecasted the share of each transport mode used by airport passengers in order to estimate capacity requirements for access related facilities at the new Athens International Airport. Gupta et al. (2008) stated from their study of the New York City Metropolitan Area that the primary usage of car as an access mode occurs due to the level of importance placed on time saving. Alhussein (2011) modeled access mode characteristics of travelers to King Khaled International Airport in Saudi Arabia using a binary logit model wherein the importance of private cars as an access mode was discovered. When studying the factors which influence airport choice in a region, several variables were found to be of vital importance, the most important of which were airfare and flight frequency. Bradley (1998) found airfare to be one among the major factors affecting airport choice at the larger special scale of the European Union. All things being equal, passengers tend to prefer flying from an airport in which they can obtain a less expensive airfare for their trip according to Parrella (2013). Mason (2000) discovered airfare and flight frequency to be among the top ranked factors in the airport selection of business travelers in the London MAR. Ashford and Benchman (1987) found flight frequency to be of critical importance affecting the potential competitiveness of an airport in the U. K. Furuichi and Koppelman (1994) also found departure frequency of flights from an airport to be an important consideration in any airport choice in Japan. 3. Detailing of questionnaires A revealed as well as stated preference approach was primarily the methodology in this study. 3.1. A Revealed Preference (RP) approach The RP questionnaire was detailed through face-to-face interviews with 7 % of the college population which were conducted from January 2015 onwards in order to get the travel information of 2014 with trips originating from IITB only being considered. There were primarily 3 sections in it with the fourth section used only for the initial pilot study. 3.1.1. Part A- Household information All basic household characteristics of the travelers were collected in this section and some of these were used as socioeconomic variables in explaining travel behaviour. House ownership type: Category-wise division providing house ownership information. Built-up area: Category-wise division providing built-up area of the house in square feet. Household size: Total number of male and female members in the household. Car ownership: Number of cars owned by the household. Household income: Household income per month in Indian Rupees (INR) Disposable income: Income left over after all household expenses are taken care of (in INR). Kalic et al. (2012) found that as the disposable income increases, people were seen to spend more money to travel by air. • Number of working members: Number of employed members in the household. • • • • • •

3.1.2. Part B- Person information This section pertained to personal information consisting of the socioeconomic characteristics of the traveler individually. It is important to note that the personal income for students was that obtained as a stipend from the college for their research work. • • • • •

Occupation: Determines which category of travelers the person falls under, whether student or faculty or staff. Age: Indicates the age of the person. Sex: Indicates whether the traveler is male or female. Personal Income: Monthly fellowship provided to the person (if any in INR). Personal Disposable Income: Remainder of monthly fellowship which can be used for travel purposes (if any in INR).

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3.1.3. Part C- Trip information The actual trip information of every journey made in 2014 by a traveler has been collected in this section with the purpose of the trip divided into four sets with the first two having significantly lesser carry-on luggage. These were as follows: • • • •

Employer Business: Travel for official reasons being funded externally. Personal Business: Travel for personal reasons financed by one’s own self. Social: Travel purpose being to attend a function or an inauguration. Leisure: Travel purpose being for relaxation, generally to home or for a holiday.

3.1.3.1. Modes of travel Modes were divided into two categories for this study as main modes and ground access modes. Main modes are those enabling intercity travel and ground access modes facilitate intra-city travel to and from the airport. For this study, main modes of flights and trains and ground access modes of taxi and auto were looked into with all time and cost aspects of the journey being recorded. For the main mode journeys, the trip was considered until the final train station or destination airport starting from IITB. 3.1.3.2. Travel characteristics When considering different modes, there were some points of paramount importance which a potential traveler gives weightage to which are explained here: • Waiting time: Waiting time refers to the time period for which the passenger waits for the mode for which he or she is interested in. A shorter waiting period makes the mode more desirable. • Travel time: Travel time refers to the period of time for which the traveler is in the mode and moving towards his destination, from the point wherein he got onto the mode. Increased travel time, especially if there is another mode offering a lesser travel time, would make the former mode less desirable, forcing people to go elsewhere for their travel needs. • Travel cost: Travel cost is a key factor which helps to determine the possibility of a person choosing a particular mode. Even if the travel time is very less for a mode, all persons wishing to travel to the same destination cannot afford to travel by it, perhaps due to their socio-economic conditions or due to their priorities with respect to the travel purpose. Thus, travel cost helps to determine passengers willing to pay for an expensive mode for better LOS attributes. 3.1.3.3. Frequency of travel The frequency of travel was noted by asking about the number of air trips made (if any) along with trips by any other mode other than air, in 2014. This helped in classification of passengers into high and low frequency travelers, both by air as well as by other modes and it formed the dependent variable in the trip generation analysis in this study. 3.1.4. Part D- A ranking of airport and airline choice attributes This section consisted of determining factors which were ranked most important to an air passenger when choosing between airports and airlines in a Multi Airport Region (MAR). Isolation of these variables when looking into access to the airport in terms of distance and cost, airport characteristics as well as airline features was done. Ten factors were taken up to reinstate their relevance and individual importance in any airport choice which will be explained in detail in the upcoming sections. 3.2. A Stated Preference (SP) approach Stated preference (SP) surveys are designed to obtain information reflecting conditions that are not currently observable by referring to the stated responses of the respondents about hypothetical choices. They are highly preferred because of their flexibility, by incorporating a wide range of airport LOS attributes for the purpose of strategic planning. According to Bradley and Kroes (1992), SP games which are properly designed can help to avoid the multi-collinearity problem and allow researchers to examine the effect of changes in closely related variables or attributes. There are many features of both airports as well as airlines that affect passengers in their choices. For this study, a set of ten variables which were likely to be of consideration was taken into account and has been shown below. • Access time: Access time measures the travel time between the traveler’s initial origin and the departure airport as per Ishii, Jun, and Dender (2009), with the zone of origin here being IIT Bombay. In contrast to many studies, it was found by Ndoh et al. (1990) that access time appeared to be the most significant airport LOS attribute. In this study, it was ranked significantly high on the 1 to 10 scale.

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• Access cost: Access cost is the cost of accessing the airport from the origin of the passenger. Access cost was seen to be given the lowest ranking amongst those considered, perhaps due to the range of mode choices available in reaching the airport. • Airport ambience: Airport ambience comprises all the general features of the airport including all of its facilities, amenities and general architectural aesthetics. Graham (2009) felt that the passenger areas must be designed in such a way as to increase commercial revenues of airports, by looking into retail designing based on their preferences. However, in this study, majority of the interviewed people did not give it a significant ranking. • Airport user charges: Airport charge is the amount which is paid by the airlines to airport operators which includes landing and parking charges, fuel throughput and use of common terminals. This amount is taken care of in the airfare of the passenger as an airport user charge and refers to all the components of the cost of the trip excepting the airfare and thus includes development fee, passenger service fee, user development fee, government service tax and convenience fee. Even though airfare was considered vital, the user charge component was not as important, leading to a low ranking. This could be because the difference in user charge between the airports may not be significantly high, as compared to the airfare between the same. • Airport delay: Airport delay refers to the excess time in processing passengers and baggage either due to lack of personnel or due to the high level of activity in the terminal building. It is important to note that this delay includes the time from which the passenger steps onto the departure kerb of the airport to the time where he reaches his or her gate and does not include flight delays as that is something which may not always be in the control of the airport authority. This was seen to be ranked at a significant level by the air passengers leading to a sufficiently high ranking on the overall scale. • Airfare: Airfare is the cost of flying from an origin to another destination via air through airports, which could be more than two in the case of transit flights. This factor was seen to be the single most important factor in any airline choice of a potential passenger, as can be seen from the ranking levels in the next section. • Flight frequency: Flight frequency is measured as the average number of flights per hour during peak or off-peak hours, per airline–airport combination as per Ishii, Jun, and Dender (2009). Flight frequency is attributed to be a service quality which is desirable. An increased frequency provides a wider range of departing time options. This indirectly cuts down on the costs of deviations from the preferred travel schedule of the traveler. From the survey information, business passengers were seen to give more importance to this factor than social and leisure passengers. This factor was ranked very high on the overall scale. • On-time performance of the airline: This factor explains how well the airline maintains its schedule in terms of on-time departures. Certain airlines are known for their tardiness and delay in departing and this makes it an unattractive option for a passenger. This was also seen to be moderately relevant in affecting airline choice. • Service reliability of airline: This factor goes to show the reliability in terms of consistent performance of the airline under consideration. Though the choice of an airline is affected by certain factors such as fare, comfort etc., the features of the airline in terms of its good performance is also a key factor that affects utility or attractiveness of the airline according to Jung and Yoo (2014). However, its ranking was seen to be not significant enough to take into future study. • In-flight service: In-flight service is an important aspect of airline operations, The total in-flight experience includes several components ranging from in-flight entertainment and food to cabin seats and the customer service from cabin staff. However, the overall ranking of this variable was seen at a significantly high level for this study. 3.3. Pilot survey results A pilot survey was conducted in October 2014 with 100 RP surveys and 100 SP surveys. From the RP study, it was seen that certain variables held more importance to a traveler than others and thus, the questionnaire was changed for the main study to make it more clear and user-friendly. For the SP study, the ten variables shown above were ranked with respect to passenger preference as shown in Table 1. Of the 100 interviewed, 68 were air travelers who were asked to rank from 1 to 10 (with 1 being the highest level and 10, the lowest), the level of importance which they assigned to each variable. The maximum number of people who assigned a particular rank to a variable was determined and arranged in descending order from which the top four were selected as the primary influencing variables for the airport choice study. Table 1 Ranking of influencing variables Rank

Influencing variable

1

Airfare

2

Flight frequency

3

Access time

4

Airport delay

5

On-time performance of airline

6

Service reliability of airline

7

In-flight service

8

Airport user charges

9

Airport ambience

10

Access cost

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4. Revealed Preference (RP) methodology The RP surveys were analyzed in two ways, with the first one being to determine trip generation equations using regression analysis and the second one being to determine mode choice equations using utility theory. The SP survey was analyzed entirely using utility theory itself in order to decipher the dimension of airport choice. The fundamentals of these two concepts have been taken up below. 4.1. Regression analysis Regression analysis is a statistical tool for the investigation of relationships between variables (independent and dependent) wherein the effect of one variable on another is ascertained looking into the statistical significance of the independent variables by various goodness-of-fit statistics. Multiple linear regression can incorporate the effect of a large number of explanatory variables, with no constraints on the mathematics side in terms of visualization. Microsoft Excel has been used in this study for the regression analysis. 4.1.1. Variables Several variables were identified from this study for regression analysis. The sample set was first of all pooled together and analyzed after which it was segregated based on monthly income into the 5 categories of Students without Fellowship (SWOF), Students with Fellowship Level 1 (SWF1), Students with Fellowship Level 2 (SWF2), Faculty (F) and Staff (S). The dependent variables were primarily the number of external Air trips and Other trips made by individuals out of IITB in 2014. Other trips include trips by all modes apart from air which were mostly train journeys with a few bus trips as well. Total trips have also been considered as an overall dependent variable for the whole sample set which is simply the sum of the Air and Other trips. There were eight independent variables which were taken into consideration as given below. These have been used in the entire sample set equations as well as for the five groups mentioned and have been added to each equation as a single variable first looking into goodness-of-fit statistics following which the next variable was added and checked. The equations which explained trip generation in the most simple manner was chosen by looking into statistical significance. However, the third and fourth variables (PI and PDI) are not taken into account for SWOF, as these students do not have fellowship. • • • • • • • •

Household Income (HHI) Disposable Income (DI) Personal Income (PI) Personal Disposable Income (PDI) Car Ownership (Car) Built-up Area (BUArea) Household Size (HHSize) Number of Working Members in Household (NoWorkrs)

4.1.2. Regression modeling with intercept A true regression equation almost always has the presence of a constant which is also called its intercept. In a multiple regression equation, the constant represents the value that would be predicted for the dependent variable if all the independent variables were simultaneously equal to zero, which is a situation which may not be physically or economically feasible. The constant is generally used in the equation regardless of its statistical significance. There are primarily two reasons for this with one being that the presence of the constant ensures that the mean of the residuals will be exactly zero which provides an unbiased sample and the second being that the regression line will provide the best fit to the available data which may only be linear locally. All of the equations in this study have been formed taking into account the intercept value. 4.1.3. Interpreting regression statistics The following goodness-of-fit statistics have been considered for the analysis: • Correlation coefficient: The correlation coefficient varies from -1 to +1. A good correlation between the dependent and independent variables is a necessity when modeling a regression equation. However, a good correlation amongst the independent variables is undesirable because highly correlated independent variables would explain the same part of the variation in the dependent variable. • R2: R2 is called the ‘coefficient of determination’. It ranges from 0 to 1 and is a fraction of the variance in the two variables that is considered “shared”. It quantifies how well a model fits the data. However the issue with R2 is that an addition of

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independent variables to the regression equation always increases the R2 value. Hence, if only considering R2 value, the model with the most parameters would be selected always which may not be a true representation of the actual case. • Adjusted R2: The adjusted R2 value helps to compare with equations having different number of independent variables. This value would increase with addition of a variable only if that variable is truly causing an effect on the dependent variable. Hence, this value tells the percentage of variation explained by only those independent variables that truly affect the dependent variables. This is a factor of great importance in this study. • p-value: The p-value for each term tests the null hypothesis that the coefficient is equal to zero, implying it has no effect. A low p-value (