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Praveen S. and Rajat Rastogi

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MODE CHOICE MODELS DEFINING TRAVEL TO LEISURE DESTINATIONS Praveen S Post Graduate Student Department of Civil Engineering, Indian Institute of Technology Roorkee Roorkee-247 667, Uttarakhand, India E-mail: [email protected] Rajat Rastogi (Corresponding Author) Assistant Professor Department of Civil Engineering, Indian Institute of Technology Roorkee Roorkee-247 667, Uttarakhand, India E-mail: [email protected]

Word Count: Text = 4439 Tables = 5 x 250 = 1250 Total count = 5689 Final Submission Date: October 10, 2012

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Abstract: This paper presents the travel behaviour of domestic tourists regarding their choice of travel mode and destination value. Decision making process is based on the utility maximization hypothesis which assumes that an individual’s choice is a reflection of underlying preferences for each of the available alternatives and that individual selects the alternative with the highest preference or utility. The data required for the study is collected from six study cities having different leisure values. The study cities Delhi and Hyderabad have historical heritage value, Kochi and Goa are known for their waterfronts and Tirupati and Haridwar have a religious character. Personal interviews were conducted to collect the relevant information using a selfadministered questionnaire at the place of leisure. The variables considered are household characteristics like household income, vehicle ownership, and number of aged or children in a family; trip characteristics like travel time and travel cost; and behavioral intents measured on importance rating scale for variables related to social aspects, destination values, travel and miscellaneous aspects. Output of factor analysis in the form of behavioral latent factors was included in the logit model. The results would be helpful in understanding and predicting the individual decisions so as to forecast the leisure travel demand. This would also aid in improving the destination aspects and travel mode availability, thus aiding in optimum allocation of resources. Key words: Mode choice, Destination Value, logit model, Leisure travel, India.

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1. INTRODUCTION Travel behaviour study is a vital link in the overall transportation planning and this requires understanding of the behaviour of travellers with respect to their choice of destination to travel, mode of travel as well as the time of travel. The choice of tourism destinations and mode choice behaviour of an individual depends upon the convenience of tourism transportation as it acts as a tourist’s flow medium and is always taken as an influential element for the tourists on their choices and decisions. Various researchers have studied the decision making process of leisure travellers. Richards and Ben-Akiva (1974) and Timmermans (1996) developed disaggregate simultaneous destination and mode choice model for shopping trips. Multi-Nomial Logit (MNL) form of the model was used. McIntosh and Goeldner (1990) defined travel destinations as those which include national parks, amusement parks, historical sites, cultural resources, shopping facilities, natural scenery, entertainment facilities, recreational facilities, lodging facilities, food and beverages establishment and the people and culture of different travel destinations and artificially developed sites which are the ‘mainsprings’ that drive people to travel. McClaskie (1986) claimed that there are three categories of factors that shape the individual‘s decision to participate in recreation and travel cities. They are mainly: familiarity factors, person-community factors and barrier factors. Familiarity factors represent recreational behaviour based on familiar activities. Person-Community factors relate to social reference groups. Barrier factors are age, sex, level of income, type of occupation, level of education and availability of leisure time. All of these have bearing on the individual’s destination-choice and travel behaviour. Crompton (1992) investigated the decisions regarding the final destination choice by carrying out an elimination process on different choice sets. According to him, there are three stages involved in the selection process. He found that situational constraints (cost, time, etc.) are important in the decision making process. After the decision is made, an evaluation can be made about the decision. Seddighi et al. (2002) proposed a methodological framework within which the impact of characteristics of a tourism product on foreign travel can be captured and analysed. Results indicated that the inclusion of product characteristics/attributes in tourism analysis appeared to strongly contribute towards the better understanding of travel choice behaviour. Um and Crompton (1990) and Johansson et al. (2006) considered the effect of attitude on mode and destination choice. According to their study, people’s perceptions of a destination may be influenced by external inputs and internal inputs. External inputs refer to various sources of information such as significance, symbolic and social stimuli such as mass media, from friends, relatives, etc. and from past experience. Internal inputs refer to traveller’s socio demographics, values and motive and attitudes. Aktas et al. (2006) suggested that the destination with stronger positive images will have a higher probability of being included and chosen in the process of decision-making. They concluded that by linking the drivers of satisfaction with the image of the destination that is portrayed, it is possible to focus on the key attributes that will ensure that the destination can meet or exceed the visitors’ expectations and therefore, ensure their return. Pozsgay and Bhatt (2001), Kozak (2001) and Simma et al. (2002) analysed the destination choice for recreational trips, MNL models were developed using home-based recreational trips data. The results indicated that first-timers were more likely to switch to other destinations than repeaters. More mature a destination looked more repeat tourists it had and greater was the stated intention score, which indicated its potential for future visits. Jonnalagadda et al. (2004) developed tour based destination and mode choice model based on Nested logit (NL)

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formulation. In the model, the linkage was done by incorporating the mode choice utility log sum in the destination choice models. Koppelman and Bhat (2006) indicated that in a typical travel situation the modes of transportation depend upon factors such as availability of mode, travel cost, travel time, vehicle ownership etc. Lucas et al. (2007) studied the travel behaviour of older people. These models suggested that being elderly and/or retired had a negative effect towards drive, passenger and walk as compared with transit. Bekhor et al. (2008) studied the destination choice problem in transportation planning process. Their study mainly focussed on the similarity among alternatives. Most of the models had MNL form. They suggested that the zones should be grouped based on the land use characteristics. Eymann et al. (1997) did a study on the micro econometric analysis of spatial choice in a cross-section. They developed NL models to analyse the determinants of individual choice among destinations and vacation activities. The boundaries of the preferred choice were found to be determined by language borders, topographical characteristics, climate and distance from home. Hong et al. (2006) explained travellers’ decision-making process by investigating the roles of categorization of destinations, affective image and constraints in forming the choice set. Constraints were classified into two, namely antecedent and structural constraints. Antecedent constraints include intra and inter personal constraints. Their results showed that respondents categorized them into four types, namely mountainous, coastal, historic and exotic. The results supported the effectiveness of the categorization concept and sequential process in the destination choice process. The relative influences of the constraints over the intention to visit each destination were estimated by direct elasticity. Yagi and Mohammadian (2008) developed a work tour mode and destination choice. Dataset obtained from survey provided daily travel patterns and household socio-demographic characteristics. Newman et al. (2010) explored the relationship between mode and destination choice in an integrated nested choice model. They stated that ordering of choices should be reversed from the usual sequence of destination choice preceding mode choice. Such leisure studies in domestic sector have not been carried out in general and especially in India. Such a study would be useful for tourism managers in evolving strategies for improved visitation. The results obtained may be useful in understanding and predicting individual’s destination choice and mode choice decisions as well as in forecasting demand with respect to improvement in destination attributes etc. in the context of domestic leisure travel. India is taken as a reference area for the study. 1.1 Leisure Travel Scenario The trend of domestic with respect to international visits indicates a rising pattern over the years. This was observed to be 58 for India and 38 for China. This implies that citizens of India are more likely to go for a leisure trip at a leisure location inside the country, rather than outside the country. This makes India the most visited domestic tourist destination in the world. Table 1 gives relative statistics of domestic and outbound international travel in selected countries of the world. Table 2 gives state wise domestic tourist visits in India between 2005 and 2009. It can be noted that in general all the states in India have shown increase in domestic visits in four years.

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TABLE 1 Ratio of Domestic to Outbound International Travel in Selected Countries Ratio of domestic to outbound international Sl. No. Country travel 1 India 58.00 2 China 38.00 3 USA 19.00 4 Japan 15.15 5 Australia 14.90 6 Canada 11.47 7 Peru 9.90 8 New Zealand 9.60 9 Russia 3.57 10 France 3.50 11 Poland 3.39 12 Italy 3.00 13 Indonesia 2.70 14 Greece 2.50 15 Spain 2.18 16 Germany 1.53 17 United Kingdom 1.51 18 Portugal 1.12 19 Netherlands 1.02 20 Belgium 0.96 21 Austria 0.42 (Source: Eijgelaar et al., 2008) 2.0 METHODOLOGY A survey questionnaire was designed to collect information from the tourists who were contacted at the place of tourist attraction in the six selected cities. The cities were selected based on their dominant value which attracts the tourists and the accessibility defined by connectivity through different travel modes. City of Delhi and Hyderabad were selected due to their historical and cultural heritage value; Haridwar and Tirupati represented religious/pilgrimage value; and Goa and Kochi had internationally known waterfront value. Thus three destination values were considered. Sample size was calculated giving due consideration to the percentage visitors to help top ranking state in India and taking 95% confidence level. This indicated that a size of 300 would be sufficient. It was decided to collect information from 300 tourists in each of the selected city, thus making a total sample size of 1800. Only those respondents were contacted who were visitors to the selected city in which data were collected and who stayed in the city for at least overnight. The survey questionnaire was formulated in sections to collect specific information on different aspects. Data collected in questionnaire included information regarding the trip, travel modes, household and personal information of the travellers. The type of data collected is briefly discussed below.

Praveen S. and Rajat Rastogi


TABLE 2 State wise Domestic Tourist Visits in India States/UTs

156 157 158 159 160 161







% share in 2009 (Rank) 24.2 (01) 0.1 (30) 0.6 (18) 0.0 (31) 2.4 (11) 0.1 (21) 0.1 (26) 0.1 (27) 0.1 (24) 0.3 (20) 0.3 (19) 2.4 (10) 1.0 (17) 1.7 (12) 1.4 (13) 1.2 (15) 5.0 (04) 1.2 (14) 0.0 (35) 3.6 (07) 3.7 (06) 0.0 (32) 0.1 (23) 0.0 (33) 0.0 (34) 1.1 (16) 0.1 (28) 0.1 (22) 3.9 (05) 0.1 (25) 17.8 (03) 0.0 (29) 20.7 (2) 3.4 (8) 3.2 (9) 100.0

Andhra Pradesh 93529554 111715376 127933333 132684906 157490000 Arunachal Pradesh 50560 80137 91100 149292 195000 Assam 2467652 3268657 3436833 3617306 3851000 A. & N. Islands 30225 118580 136015 123914 142000 Bihar 8687220 7774732 10352887 11889611 15686000 Chandigarh 614176 704531 928159 908569 915000 Chhattisgarh 324495 363759 414322 442910 512000 Dadra/N. Haveli 526142 478000 473489 505380 507000 Daman & Diu 394914 420628 446490 465033 563000 Delhi ** 2061782 2237130 2388330 2132970 2041000 Goa 1965343 2098654 2208986 2020416 2127000 Gujarat 9457486 11936957 13477316 15505264 15910000 Haryana 5913394 6019927 6252945 5973123 6408000 Himachal Pradesh 6936840 7671902 8481988 9372697 11037000 Jammu & Kashmir 7239481 7646274 7915271 7638977 9235000 Jharkhand 2042723 2138685 4906394 6030028 7610000 Karnataka 30470316 36195907 37825953 12797937 32702000 Kerala 5946423 6271724 6642941 7591250 7789000 Lakshadweep 6908 22941 16642 1571 7000 Madhya Pradesh 7090952 11062640 13894500 22088927 23106000 Maharashtra * 14329667 16880348 19226716 20553360 23739000 Manipur 94299 116984 101484 112151 124000 Meghalaya 375901 401529 457685 549936 591000 Mizoram 44715 50987 43161 55924 57000 Nagaland 17470 15850 22085 46513 21000 Orissa 4632976 5239896 5944890 6358445 6892000 Punjab 431036 353907 368593 509428 457000 Pondicherry 574011 652735 798528 827799 851000 Rajasthan 18787298 23483287 25920529 28358918 25559000 Sikkim 251744 292486 329075 368451 548000 Tamil Nadu 43213128 58340008 70254972 98285121 115756000 Tripura 216330 230645 244795 245438 318000 Uttar Pradesh 95440947 105549478 116244008 124843242 134832000 Uttarakhand 14215570 16666525 19803280 20546323 21935000 West Bengal 13566911 15808371 18580669 19314440 20529000 India 391948589 462310177 526564364 562915569 650039000 Note: * Estimated using all India growth. ** Estimated using tourist visits figures of sample hotels furnished by state Govt. Source: Ministry of Tourism, Govt. of India. (Website:, 8 August 2011)

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2.1 Trip Data It included trip frequency, reasons behind making leisure trip, trip sponsor (self, office or Leave Travel Concession – LTC), trip package type (group package or individual family package), budget of the trip and group members (family, friends, relatives, colleagues) etc. 2.2 Travel Data The travel information collected from respondents included, type of mode and service availed, travel cost, travel time, number of persons accompanying, additional mode if used, travel time and travel cost by that mode, reasons behind choosing the main mode, deficiencies in existing travel conditions, if any and mode used for local travel. 2.3 Household Data Household data of travellers included number of family members, family demographics (number of male, elderly and children), monthly household income, vehicle ownership and languages known. 2.4 Perception based Data The travellers were requested to rate the importance of the social factors like budget available in hand for the trip and number of aged or children in a family, and travel factors like direct connectivity, availability of affordable travel modes, comfort and convenience of travel, distance from home and safety during travel. The data were collected using face-to-face personal interview method through paper format or personal digital assistant (PDA) format. 3.0 METHOD OF ANALYSIS – LOGIT MODEL Logit-based models have been widely used for travel demand analysis. NL model was first proposed by Ben-Akiva (1973 and 1974).These are the most widely used models and are an extension of the MNL models. It represents important deviations from the IIA (Independence of irrelevant alternatives) property but retains most of the computational advantages of the MNL model. The NL model is characterized by grouping (or nesting) subsets of alternatives that are more similar to each other with respect to excluded characteristics (Ben-akiva and Lerman 1987). Alternatives in a common nest exhibit a higher degree of similarity and competitiveness than alternatives in different nests. Best fit nested logit structure are formulated after carrying out various trials say related to destination choice at upper level and mode choice at lower level or vice-versa. Complex nest structures can be developed which offer substantial flexibility in representing differential competitiveness between pairs of alternatives. 4.0 MODEL SPECIFICATIONS The data was collected from various tourist destinations in India. Based on this data, the models were developed. Travel time (tt) and travel cost (tc) were used as generic variables as they appear in utility equations for all the modes. Different trials were taken by using the various variables to reach at the best solution. Different variables which were found to be influencing the mode and destination choice were considered as the alternate specific variables. The variables were eliminated if found non-significant or illogical. The variables eliminated were tried for

Praveen S. and Rajat Rastogi

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other alternatives also and checked. Various significant variables considered in the model development are listed in Table 3. TABLE 3 Significant variables considered in the model development Sr. no Variables used Description 1 Travel time Time taken from origin to destination (in Hrs) by main mode. It is generic variable. 2 Travel cost Money spent on travel (in Rs.) per person for main travel mode. It is generic variable. 3 Car ownership (CO) Total no. of cars owned by a household (no’s) 4 Service (ser) Type of service availed, aligned from highest service class to lowest service class possible across the alternate travel modes, say Business class in Air travel is coded as 1 Economy class in Air travel is coded as 2 I A/C in Train travel is coded as 3 II A/C in Train travel is coded as 4 III A/C in Train travel is coded as 5 CC in Train and bus travel is coded as 6 II SL in Train travel is coded as 7 AC service in Bus or Car is coded as 8 Deluxe bus is coded as 9 Express bus is coded as 10 Non A/C service in Bus or Car is coded as 11 5 Budget of the trip (in Rs) It is the amount kept for the trip including travel, boarding and lodging, local sightseeing etc. 6 Direct connectivity It is the average value of the importance rating given to this factor. 7 Affordable modes It is the average value of the importance rating given to the factor. 8 Comfort & convenience It is the average value of the importance rating given to the factor. 9 Group size It is the size of the group travelling together and known to each other. It may comprise of family members, relatives or friends. 11 No of aged in a family This variable refers to the number of elderly people in the family of the respondents who are greater than or equal to 60 years of age. Its effect is also taken in terms of importance ratings. 12 No of children in a family This variable refers to the number of children in the family of the respondent who are below 17 years of age. It is also measured on importance rating score. 13 Income of the household (in Rs) The monthly household income were

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categorised into seven groups covering low, middle and high income groups. Income less than Rs.10000 is coded 1, Rs.10001-Rs.20000 is coded 2, Rs.20001-Rs.35000 is coded 3, Rs.35001-Rs.50000 is coded 4, Rs.50001-Rs.75000 is coded 5, Rs.75001-1 lakh is coded 6, and Income greater than 1 lakh is coded 7. 14 Safety It is the average value of the importance rating given to the variable. 15 Distance from home It is also measured on an importance rating scale. Importance ratings: 1) Extremely unimportant; (2) Not important; (3) Immaterial; (4) Important; (5) Extremely important 5.0 SALIENT FEATURES OF THE STUDY Salient feature of the data are given in Table 4. The predominance of middle income group (MIG) indicated the increased participation of the middle income group in leisure activities. This also pointed out towards the availability of disposable income for leisure activities among the middle income group. The travellers were found mainly spending their own resources on leisure travel and activities as compared to different sponsorships that may be available to them for the trip. Majority of the travellers owned a car or two-wheeler which also indicated their financial prowess. Travellers were found visiting different destinations in a group size of up to 6 in majority of the cases and the composition of the group was basically by members of their family. The average budget kept for waterfront destinations was quite high as compared to the budget kept for other two types of the destinations. It was comparable for historical and religious destinations. Relatively more time was kept by the travellers when visiting waterfront destination and lower if it is religious destination. The average distance travelled by the travellers to destinations in south India was quite less as compared to the destinations in north India. Train was found to be the most preferred travel mode. Most of the travellers did self planning of the trip instead of through a travel agent.

Praveen S. and Rajat Rastogi



TABLE 4 Salient features of the leisure data S. No


1 2

Final sample size Household Income (Rs.) a) < 10000 b) 10000-20000 c) 20001-35000 d) 35001-50000 e) 50000-75000 f) 75001-100000 g) >100000 Vehicle Ownership (Nos.) a) Only Car b) Only Two-wheeler c) Both Car and Two wheeler d) No Car or Two-wheeler Tourist Group Size (Nos.) a) 1-2 b) 3-4 c) 5-6 d) 7-8 e) >8 Ave. Budget kept for the trip (in Rs.) Ave. Time kept for the trip (days) Ave. Travel Distance (km) Type of Planning (%) a) Self Planned b) Travel Agency c) Unplanned Modal Shares (%) a) Air b) Train c) Bus d) Hired Car e) Self Car



6 7 8 11


Historical Heritage Destinations Delhi Hyderabad 271 283

Waterfront Destination Goa Kochi 283 280

Religious Destination Tirupati Haridwar 275 297

2.0% 6.0% 35.0% 31.0% 15.0% 9.0% 2.0%

0.0% 6.0% 56.0% 34.0% 3.0% 1.0% 0.0%

2.0% 8.0% 22.0% 33.0% 22.0% 10.0% 3.0%

5.0% 24.0% 15.0% 14.0% 12.0% 17.0% 13.0%

0.0% 10.0% 53.0% 33.0% 2.0% 2.0% 0.0%

3.0% 13.0% 30.0% 30.0% 16.0% 5.0% 3.0%

9.0% 16.0% 72.0% 3.0%

5.0% 14.0% 80.0% 1.0%

2.0% 10.0% 88.0% 0.0%

12.0% 29.0% 51.0% 8.0%

0.0% 19.0% 47.0% 34.0%

10.0% 32.0% 50.0% 8.0%

42.0% 33.0% 10.0% 6.0% 9.0% 15425 2.2 1027.34

22.0% 51.0% 23.0% 2.0% 2.0% 10427.9 1.8 641.524

21.0% 35.0% 23.0% 8.0% 13.0% 20732.47 2.4 1153.98

17.0% 49.0% 23.0% 4.0% 7.0% 14331.9 2.4 476.31

16.0% 59.0% 22.0% 2.0% 1.0% 10076.0 1.9 572.60

29.0% 36.0% 23.0% 5.0% 7.0% 14008.44 1.6 1039.30

68.0 11.0 21.0

65.0 2.0 33.0

67.0 23.0 10.0

57.0 10.0 33.0

73.0 3.0 24.0

86.0 12.0 2.0

12 77 6.0 1.0 4.0

2.0 68.0 21.0 6.0 3.0

11.0 56.0 13.0 11.0 9.0

5.0 45.0 14.0 15.0 21.0

1.0 70.0 21.0 4.0 4.0

9.0 70.0 13.0 2.0 6.0

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6.0 DEVELOPMENT OF LEISURE TRAVEL MODEL The three types of destinations were taken together. Out of many trials, NL could not explain the decision making process as logsum parameter(s) were not found to be significant, as well as logical. Therefore, MNL model was considered to be the best fit model which explains the decision making process. It indicates that the alternatives are competing at the root level and not at the level of dominant value of the destination like historical, religious and waterfront. This may be due to the different nature of the destinations and the difference in likely purpose with which such destinations are selected for leisure visit. It was observed that when the data was segregated into the mentioned three destination values, the NL model with competing destinations originating from root and travel modes originating from destination node was found to be statistically significant. These results are not reported in this paper. The statistics of MNL model are given in Table 5. Table 5 Statistics of best fit MNL Model Variable Coefficient Estimates Travel Time -0.555E-03 (-4.1) Travel Cost -0.3438E-05 (-2.2) Household income 0.9834 (6.6) Service -0.80E-02 (-4.1) Importance factor for aged -0.609 (-2.3) Car ownership 1.402 (3.2) Service -0.82E-01 (-4.1) Importance factors for -0.875 (-5.1) connectivity Group size -0.584 (-2.6) Importance factor for distance -0.73E-05 (-2.2) Household Income 1.024 (-2.0) Service -0.121 (-5.7) Importance factors for children -0.58 (-3.1) Importance factors for budget 0.7182 (2.7) Car ownership 0.945 (2.7) Household income 1.47 (5.4) Importance factors for comfort -0.2916 (-5.9) and convenience Group size -0.25 (-1.8) Importance factors for 0.52E-01 (0.3) affordability Importance factors for safety -0.40E-01 (-0.2)* Importance factors for -0.1832 (-4.5) connectivity Importance factors for -0.3954 (-9.4) affordability Car ownership -1.37 (-9.7) Household Income -0.384 (-7.7) Importance factors for -0.118 (-3.2)

Relevance of Variables Generic Generic Air at Delhi Train at Delhi Bus at Delhi Hired car at Delhi Train at Hyderabad Bus at Hyderabad Hired car at Hyderabad Self Driven Car at Hyderabad Air at Goa Train at Goa Bus at Goa Hired Car at Goa Self Driven Car at Goa Air at Kochi Train at Kochi Bus at Kochi Hired Car at Kochi Self Driven Car at Kochi Train at Tirupati Bus at Tirupati Car Category at Tirupati Air at Haridwar Train at Haridwar

Praveen S. and Rajat Rastogi

affordability Importance factors for children Importance factors for aged Constants Beta 110 Beta 130 Beta 140

-0.1066 (-8.8) -1.626 (-8.9) -6.15 (-7.7) -0.99 (-1.2) -4.82 (-6.7)

Beta 230 Beta 240

0.708 (1.6) -0.205 (-0.2)*

Beta 250

-0.990 (-1.3)

Beta 310 Beta 330 Beta 340 Beta 350

-6.47 (-7.5) -1.6 (-6.0) -5.29 (-4.5) -3.67 (-7.1)

Beta 410 Beta 430 Beta 440 Beta 450

-9.8 (-5.9) -0.85 (-1.8) -2.25 (-3.2) -1.55 (-1.7)*

Beta 520 Beta 530

-3.2 (-2.2) -4.6 (-1.9)

Beta 610 Beta 630 Beta 640

-3.5 (-2.0) -2.8 (-3.2) -3.2 (-2.0)

Model L(0) L(c)

244 245


Bus at Haridwar Self Driven Car at Haridwar Air specific constant at Delhi Bus specific constant at Delhi Car category specific constant at Delhi Bus specific constant at Hyderabad Hired Car specific constant at Hyderabad Self Driven Car specific constant at Hyderabad Air specific constant at Goa Bus Specific constant at Goa Hired Car specific constant at Goa Self Driven Car specific constant at Goa Air specific constant at Kochi Bus specific constant at Kochi Hired Car specific constant at Kochi Self Driven Car specific constant at Kochi Bus specific constant at Tirupati Car category specific constant at Tirupati Air specific constant at Haridwar Bus specific constant at Haridwar Self Driven Car specific constant at Haridwar

Structural Parameters MNL -4548.27 Likelihood value with zero coefficients -3951.55 L(c): Likelihood value with constant coefficients -4548.27 -3832.30 Likelihood value at convergence 0.157 Rho-squared statistics 0.0302 Rho-squared statistics 161.43 -

Initial Likelihood L(θ) ρ 2 with respect to zero ρ 2 with respect to constant Subjective value of Travel Time (Rs/Hr) Sample Size *Not significant at 95 % confidence level


Values in parentheses are t- statistics

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The effects of variables on the use of a travel mode to different destinations were found to be varying. The use of air mode was found to be influenced by household income of the respondents. Positive effect was observed for a visit to Delhi, Goa and Kochi, whereas, it was negative for visit to Haridwar. This must be due to an indirect air connectivity of the city through either Delhi or Dehradun. Travellers were found to be inclined to use train with a better service level if travelling to Delhi, Hyderabad and Goa. In case of travel to Kochi, Tirupati and Haridwar, the travel by train was influenced by the importance attributed by the traveller to comfort and convenience, connectivity and affordability respectively. More is the importance attributed to these variables lesser is the probability of use of train to these destinations. The importance attributes to different variables was found influencing the use of bus mode for travel to destinations under discussion. It was observed that if the importance attributed is high then the probability of use of bus by travellers would be lower. The influencing variables were presence of aged in a family, direct connectivity of the destination, presence of children in a family, group size, and affordability of travel modes. Influence of variables on use of hired car could not be ascertained for travellers visiting Delhi and Haridwar. In the case of travel to Hyderabad, the higher group size was found influencing the use of hired car negatively. For travel to Goa and Kochi, its use was found increasing with an increase in the importance attributed to budget and affordability respectively. Travellers visiting Tirupati would prefer the use of own car above hired car if they own one. Travellers would like to drive to Delhi and Goa if they own a car, but they would not like to drive to Hyderabad, Kochi or Haridwar if they placed higher importance to travel distance to the city, safety during travel and presence of aged in a family respectively. The goodness of the model was found to be good with a reasonable rho-square. The subjective value of time was found to be quite high at Rs. 161.43 per hour spent at leisure. This indicates that travellers place much more value to the time spent at leisure may be because it provides time to regenerate their spent energies on regular activities. It also provides them time to enjoy it with family and friends which is becoming scares now-a-days due to activity schedules. The subjective value of time was also found to be much higher than the subjective value of time being observed during commuting within a city. It was usually observed to be below Rs. 50 per hour. The relative values indicate that travellers would like to go on a leisure trip to maximize their output out of time and monetary resources spent. Next section now outlines the conclusions drawn from the analysis presented in this paper. 7.0 CONCLUSIONS The study presented in this paper discusses the travel behaviour of the tourists who are contacted in different tourist attraction cities across India. For comparison purposes, the cities were classified by the dominant value they represent or are known for that, namely historical and heritage, waterfront and religious. Certain differences could be made in the socioeconomic profile of the travellers who visited different set of cities. The proportion of high income travellers was found to be more at waterfront destinations like Goa and Kochi, whereas, middle income group travellers were dominating at other two types of the destinations. More than 70% of the travellers visiting Delhi, Hyderabad and Goa had car and two-wheeler both whereas for rest of the cities it was around 50% only. Groups of bigger size were found travelling to waterfront destinations specifically and to Delhi and Haridwar otherwise. The average group

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size was found between 4 and 5, which indicates towards higher propensity of a family than relatives or friends. The budget kept for travel to Goa was found to be exceptionally high even though the average numbers of days kept for the trip were more or less similar across destinations. This needs consideration from government side (tourism department) as well as from hospitality industry at Goa. Bringing the level of expenditure down would surely stimulate travellers from other income groups to consider Goa as their next destination for leisure. Another point of observation was the average travel distance between home city and the destination city. It was clear that travellers in south India are travelling lesser distances as compared to north India. This has regional implication. It probably indicates towards potential tourist flows within region or closely adjoining areas to the destinations in south India as compared to the north India. It also has economic implication in terms of generation of revenues through the use of travel modes or spending powers of travellers. There is a need to bring the destinations in south India more vigorously on the tourist map and to initiate tourism policies for the same. One more point of contention for the tourism industry is higher reliance of the travellers on self than on travel agents for planning their trip. It might be due to either nonavailability of trust worthy and experienced travel agents in wide spread area on the country, or previously held bad experience with a travel agent or relatively higher charges demanded by the travel agents than the expected, or deficiency in the information level available to the travellers. These need to be rectified only than the employment generation in the tourism industry can become viable as well as feasible. On part of travel, higher reliance on train for travel to all the destinations irrespective of the destination value shows that travellers rely more on railways than other modes of travel. Even though air connectivity was available, the travellers were not found using air mode. It is due to the higher prices of tickets when travelling by air, especially the surcharges levied by the government. Similarly, there is a need to improve the bus facility types to destinations of tourism importance. At present, even Volvo line buses are not operated on most of the routes. Introduction of highly improved bus services will improve the re-visitation to such destinations. The development of choice model has indicated that the choice of a travel mode is not only governed by the primary variables like travel time and cost, but also by the socioeconomic characteristics of the traveller and their attitude towards social and travel related variables. Socioeconomic characteristics influencing the mode choice are found to be household income and car ownership. Household income not only affects the choice of a mode but it also indirectly affects the choice of a service a traveller may wish to avail in the chosen travel mode. Car ownership is found to induce self-driving. Social variable influencing the leisure travel is ‘group size’. It indicates that leisure travel is assumed to be a group activity, which may consist of the members from same family or a mix of many correlated or uncorrelated families. The attitudes were measured on an importance scale. Here also clear categorisation appears in the form of social and travel related variables. Social attitudinal variables identified were presence of aged and children in a family or budget kept for the travel. Travel related attitudinal variables found influencing were direct connectivity, affordability, safety during travel, comfort and convenience, and distance from home city. The study presented in this paper has clearly highlighted that the potential of a leisure destination can be improved not only by upgrading the hospitality industry related to tourism but it also requires proper consideration to specific socioeconomic, travel and attitudinal variables. Leisure activity is different than regular commuting activity in a city in a sense that traveller

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desires higher satisfaction when travelling for leisure. This satisfaction is an outcome of selfattitude. Therefore, for the proper development of tourism industry and leisure destination, the infrastructure, hospitality facilities, socioeconomic and attitudinal aspects should be given due importance.

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