How Underlying Attitudes Affect the Well-Being of Travelling Pilgrims—A Case Study from Lhasa, China

Abstract

This study used structural equation modelling to analyse the relationship between the attitudes of a pilgrim group and their well-being when travelling. Using market segmentation theory, the travel market of the pilgrim group was segmented, and the travel preferences of different sub-markets were separated according to each pilgrim’s subjective feelings of travel quality. The results show that travel emotional value, travel expectancy perception, and perception of fairness impact the travel well-being of pilgrims. K-means clustering was used to segment the travellers into markets and to propose strategies to improve the travel well-being of travellers. To meet the attitudes of various people, this analysis was based on different travel sub-markets. The results show that, to improve travel well-being, the preferences of female and elderly groups should become the focus to ensure better comfort and convenience, moderate safety, and reliability. For younger age groups, the emotional value of the travel process should be considered, which may help to improve their well-being.

1. Introduction

With the growing development of the transportation infrastructure, travel well-being has gradually become an important reference indicator for policy formulation in the transportation field. Exploring travel well-being can help expand researchers’ understanding of the irrational factors of travel behaviour. Experiences reported in the USA indicate that research on travel well-being can effectively guide the planning of transportation systems and the formulation of transportation policies, also providing a positive and important contribution to the creation of a people-centred transportation system [1]. At the same time, the implementation of effective travel guidance strategies and measures to enhance the well-being of travellers is also an important research topic.

The structure of the components of travel well-being has been discussed extensively by many scholars. Andrews and Withey have argued that travel well-being includes the cognitive assessment of the travel process and the experience of emotions [2]. Veenhoven further defined the well-being of travellers as a holistic assessment of the quality of travel by an individual based on self-preferences [3]. This holistic assessment, in turn, consists of an emotional experience and a cognitive, subjective assessment, where affective experience is based on the theory of hedonism [4]. However, cognitive assessment is defined as the perceived difference between expectations and reality. The smaller the difference, the more pronounced the perception of satisfaction, and conversely, the more pronounced the perception of deprivation. Diener further divided the emotional experience and concluded that travel well-being consists of three components: positive effect, negative effect, and life satisfaction [5].

Travel well-being directly reflects people’s satisfaction with transport services and the cumulative effect of psychological emotions during long-term travel. It is a new extension of travel behaviour research in the field of transport research. Compared with the utility maximisation theory of classical economics, research on travel well-being can better capture the true preferences of individuals (according to their travel choices) from a psychological perspective. Research on travel well-being is at the forefront of international research, and research findings are important for assessing the rationality and effectiveness of transport policies.

However, the implications of travel well-being research far exceed that of the assessments of the rationality and effectiveness of transport policies. The literature on the specific form of pilgrimage travel is relatively sparse. More than tourism for recreational and leisure purposes, the changes in mental activity and spiritual pursuits during pilgrimage travel may be key factors affecting the well-being of pilgrims partaking on a given trip [6]. Therefore, for this study, we researched travelling pilgrims; thus, pilgrimage groups will serve as the research context for this paper. In this study, the factors influencing travel well-being during the pilgrimage process are explored through structural equation modelling (SEM). Based on the traditional model of the influencing factors of travel well-being, three subjective conditional variables were introduced, and SEM was used to construct a model of how travel well-being influences the pilgrimage group. Quantitative analysis of the impact of travel well-being on pilgrims can help to accurately understand the transport choices and decision-making behaviour of travellers. There are cities where pilgrimaging has been preserved, such as Lhasa, Mecca, Jerusalem, and Lumbini. The findings of this study will help to suggest strategies that balance the travel needs of various groups of people and enhance the well-being of travellers.

2. Literature Review

The framework of factors influencing travel well-being as a special domain of subjective well-being is built on the framework of factors influencing subjective well-being. Early scholars in this field mainly used non-set-count models to explore the impact of the objective factors of travel on the travel well-being of urban residents [7]. While this type of research has made an important contribution to the improvement of transport infrastructure, the understanding of travel well-being is mostly based on a retrospective evaluation of the quality of travel. It is not possible to visualise the causal relationship between the well-being of travel activities and variables. Research has shown that attitudes, values, and perceptions of travel equity are important factors influencing travel well-being [8,9,10]. For example, an excessively long travel time will increase the negative mood of travellers and reduce the respondents’ evaluation of the quality of travel [11,12]. Complex trips can reduce the predictability and reliability of trips, leading to increased stress and negatively impacting travel well-being [13].

Based on satisfaction theory, service quality research has asserted that satisfaction with the same product varies with sociolect-demographic characteristics. This theory also applies to well-being research in the field of travel, as different people have different expectations of what constitutes travel well-being. The gap between actual travel experiences and the expectations of different groups of travellers may provide a better explanation of travel well-being than absolute travel indicators. Therefore, travel well-being needs to consider the impact of sociolect-economic and demographic factors. Higgins argued that men are more likely to be satisfied with their commute compared to women [14]. Novaco et al. similarly found that women felt particularly stressed during long-distance commuting, and this stress spilled over into their work and home lives [15]. Susilo and Cats applied an age perspective and found that younger travellers were less satisfied when travelling on foot compared to older travellers [16]. Compared with younger age groups, older age groups have more positive feelings about modes of travel other than cycling [17].

Direct evidence suggests that active leisure and social activities during travel lead to higher levels of travel well-being than passive travel activities. Bergstad surveyed 1330 Swedish travellers and found that, when traveling passively, the correlation between satisfaction with active outings and travel well-being was stronger than the correlation between travel satisfaction and travel well-being [18]. De Vos provided further evidence showing that not all activities occur at the travel destination and that travel activities that are associated with high well-being can also take place while travelling to the destination [19]. Pilgrimage travel, for example, has been used since the birth of religion, may be one of the oldest travel-activities, and is still growing today, forming a sizeable industry worth USD 18 billion per year [20,21]. Therefore, it is necessary to study the travel behaviour of pilgrim groups; however, the currently available literature still has its deficiencies.

SEM is a flexible linear parametric multivariate statistical modelling technique that integrates the concepts and methods of path analysis and validated factor analysis while adding new enhancements in statistical analysis techniques. Golob presented a joint model of attitudes and behaviours to explain the differences in mode choices and attitudes across the population of San Diego, CA, USA [22]. Using Melbourne, Australia, as their research area, Currie quantified the link between a lack of transport resources and social exclusion [23]. Further, the role of the psychological factors of travel in mediating the phenomenon of social exclusion and the well-being of travellers was also identified. Baek examined the structural relationship between pilgrimage travel, meaning of life, quality of life, and maturity of faith through SEM to explore the impact of changes in psychological factors during pilgrimage travel on both the meaning of life and travel well-being [24].

The present paper aims to contribute to the literature by discussing and elucidating three main aspects: (i) How latent attitudes affect travel well-being in different types of trips requires further research. Therefore, the primary research content of this paper is the influence of latent attitudes on travel well-being in the context of pilgrimage travel.

(ii) The factors affecting travel well-being are not limited to travel efficiency. Under certain conditions, a slow mode of travel is more likely to hold positive emotional value for the traveller than a fast mode of travel [25]. In this paper, potential attitudes are divided into three dimensions: perceived travel expectancy, travel equity, and the emotional value of travel. The impact of all three on travel well-being is explored.

(iii) In this paper, pilgrim groups are segmented according to different measures that are proposed based on the characteristics of travelling groups in different markets. This approach meets the subjective preferences of travellers and thus improves the well-being of the pilgrimage population.

3. Methodology

This paper builds on previous research to establish new potential attitude variables. The central premise behind this is that potential attitude is a direct factor that affects travel well-being in an inaccessible way, while the factor of perceived quality of travel only has a direct effect on the potential attitude of the traveller. In addition, to ensure the reliability of results, the influence of the heterogeneity of the travelling population on travel well-being was also considered. K-means clustering was employed to segment the travel behaviour characteristics of the pilgrim group, analyse the travel preferences of different travellers, and provide suggestions for improving the travel well-being of pilgrims. The specific steps can be described as follows:

Step 1: Dimensional calibration between travel well-being and underlying attitudes.

Step 1.1: Theoretical assumptions. Hypotheses are proposed regarding the causal relationships between latent variables.

Step 1.2: Extraction of variables. Based on the literature, the range of factors that influence travel well-being are quantified, and the specific meaning of latent variables is identified. Latent variables are constructs that cannot be directly measured, such as motivation, attitudes, beliefs, satisfaction, and stress. Latent variables can be measured indirectly by a set of observed variables or indicators.

Step 2: Constructing measurement models.

The measurement model establishes a functional relationship between the “perceived quality of travel”, “potential attitudes”, and observed indicators, such as the relationship between indicators (e.g., the time to access the services of transport resources and the time to the destination and time saved along the way). The relationship can be expressed by the following equations:

$$x={\Lambda }_x\xi +\delta$$

(1)

$$y={\Lambda }_y\eta +\epsilon$$

(2)

In the above, the symbols $x$ and $y$ represent vectors containing exogenous and endogenous indicators. The matrices ${\Lambda }_x$ and ${\Lambda }_y$ represent the relationships between exogenous latent variables and exogenous indicators and the relationships between endogenous latent variables and endogenous indicators, respectively. The vectors ξ and η are composed of exogenous latent variables and endogenous latent variables, while δ and ε represent the error terms associated with their respective variables. When selecting indicators of fitness for structural equations, different indicators need to be selected based on different research purposes.

Step 3: Constructing a travel happiness impact model.

Step 3.1: Constructing the structural model. Based on the causal relationship among variables set by the theoretical hypothesis, the SEM is constructed in the form of a path diagram or a set of equations, along with the definition of variables. The relationship between different latent variables, such as the relationship between the influence of potential attitudes on the travel well-being of the pilgrim group, was inscribed. We use path coefficients to measure the strength of these relationships. The equation is shown in the following:

$$\eta ={\rm B}\eta +\Gamma \xi +\zeta$$

(3)

where $\eta$ denotes the vector composed of endogenous latent variables, $\xi$ denotes the vector composed of exogenous latent variables, and ${\rm B}$ denotes the relationship between endogenous latent variables. $\Gamma$ denotes the relationship of exogenous latent variables to endogenous latent variables. $\zeta$ denotes the vector composed of residual terms of the internal equation, reflecting the unexplained part of the latent variable $\eta$ in the internal equation.

Step 3.2: Model fit. The residuals of the model-implied covariance matrix are minimised with respect to the sample covariance matrix.

Step 3.3: Model evaluation refers to the evaluation of the fit between the hypothetical model and the collected data. This includes an evaluation of the overall model fit and an evaluation of the structural fit, which is further divided into evaluations of the measurement model and theoretical model. The measurement model evaluates whether indicator variables can effectively reflect latent variables based on the reliability and validity tests of the latent variables.

Step 4: Strategies to enhance the well-being of the pilgrim population on their travels.

Step 4.1: Based on the data collected from the n respondents in this paper, the original data array is shown in Equation (4) by using p clustering of attitude indicators.

$$X=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \cdots & x_{1p}\\ x_{21}& x_{22}& \cdots & x_{2p}\\ ⋮& ⋮& \ddots & ⋮\\ x_{n1}& x_{n2}& \cdots & x_{np}\end{array}\right]$$

(4)

Step 4.2: Randomly select k data as the centres of the initial test classes. The centre of each class is $C_k^0=\left\{c_1^{(0)},c_2^{(0)},\cdots ,c_k^{(0)}\right\}$, where k represents the number of classes. A better value of k can be selected empirically.

Step 4.3: The distance of each value is calculated from the centre of this class using the Euclidean distance. The squared error and the local minimum form the basis for the calculation.

$$d_{ij}={\left({\displaystyle \sum _{a=1}^p{\left(x_{ia}-x_{ja}\right)}^2}\right)}^{1/2}$$

(5)

In Equation (5), $d_{ij}$ represents the Euclidean distance of the data $X_i$ and $X_j$.

$D(X_i-C_k^0)$ denotes the Euclidean distance between a sample in $X_i$ and a class to the cluster centre $C_k^0$. The objective function for a class can then be expressed according to Equation (6).

$$I={\displaystyle \sum _{a=1}^{j}D(X_i-C_k^0)}$$

(6)

Step 4.4: Calculate K new clustering centres.

$$D_t={\displaystyle \sum _{h=1}^{k}I}$$

(7)

Step 4.5: The clustering result is obtained when the maximum number of iterations has been reached; otherwise, go back to Step 4.3 and continue to calculate the new cluster centres.

Step 4.6: The principles of cluster analysis are three-fold: (1) that the clusters themselves should be as compact as possible; (2) that the different clusters should be as separate as possible; and, more importantly, (3) that the resulting clusters are reasonable and meaningful. Cheng, L has numerically divided the clustering centres of the travelling population [26].

Step 4.7: Based on the results obtained from Step 1 and Step 2, an analysis was carried out to provide recommendations for improving the well-being of the pilgrim population on their journey.

4. Data Sources and Variable Determination

Located on the roof of the world, Lhasa still preserves the millennia-old tradition of pilgrimage, which has evolved from a traditional religious ritual to a social custom. As an important pilgrimage destination, Lhasa has developed a unique highland travel culture. During his studies in Lhasa, one author conducted long-term observation and research on the lifestyle of local residents. In 2021, a questionnaire survey was conducted on Tibetan pilgrims in Lhasa using a random sampling method. The travel patterns, travel characteristics, and subjective feelings during the travel process of pilgrims of different ages and different social identities were collected. Both electronic/online questionnaires and paper questionnaires were used because of differences in education, language, and the age of respondents. For pilgrims under 60 years of age, questionnaires were used; for those over 60 years old, interviews were conducted to maximise accuracy. Of the 500 questionnaires administered to pilgrims, 466 valid responses were received. The results provide data support and a theoretical basis for the formulation of transport policies in other areas with pilgrimage customs.

The survey conducted for this study assessed two main components: the attitude data of travel and travel well-being data. One of the measures of travel well-being differs from that of travel satisfaction. Current multi-modal multi-stage trips involve too much variability in terms of trip or vehicle attributes. This means that respondents need to integrate their subjective perceptions of potentially different levels of travel stages into an overall feeling of travel. Therefore, the travel stage is a more appropriate measure of potential attitudes. However, the measure of travel well-being incorporates both cognitive-level judgments and the cumulative effect of emotions. To consider the different research contexts and purposes, for this study, data on the travel well-being of the pilgrim group were collected throughout the day at the same time as the survey in order to prepare the data for the subsequent study [6].

To accurately portray the impact of multidimensional travel-influencing factors on the travel well-being of the pilgrim group, in this study, additional factors were used. The factors of travel safety (TS), travel time saving (TTS), travel reliability (TR), convenience of travel (COT), and travel comfort (TC) have been considered in traditional studies on travel behaviour. In addition to these, this paper also introduces three potential attitude variables: travel expectancy attitude (TEA), travel emotional value (TEV), and perceived travel fairness (PTF). The specific contents of the questionnaire are shown in

Table 1. Hypothetical factors affecting the well-being of the pilgrim population during the trip.

Latent Variables	Indicators
TS	TS1: The planning of travel routes ensures the safety of travellers.
	TS2: The mode of travel used is safe.
	TS3: The infrastructure is in place to ensure that travellers can carry out their activities in an orderly manner.
TTS	TTS1: Short time to access transport resource services.
	TTS2: Short time to destination.
	TTS3: Short time spent on congested roads.
TR	TR1: Ability to control the travel time.
	TR2: Ability to know exactly how long it will take to reach the destination.
	TR3: Ability to arrive at the destination on time.
COT	COT1: Easy access to the different modes of travel.
	COT2: Access to different modes of transport.
	COT3: Fewer transfers required to reach destination.
TC	TC1: Ability to travel in a quieter environment.
	TC2: A spacious and uncrowded travel environment during travel.
TEV	TEV1: Stability and comfort during the trip, as expected.
	TEV2: Transport infrastructure provides understanding and respect for the travelling public during the journey.
	TEV3: Reduces fatigue and negative emotions during travel.
TEA	TEA1: Satisfied with travel patterns and optimistic about the future of travel.
	TEA2: Consider their travel situation to be in an advantageous position to complete their trip as planned.
PTF	PTF1: There are enough ways to travel to ensure that most residents can make the pilgrimage daily.
	PTF2: Ensuring equal access to travel opportunities for the community.
	PTF3: Transportation resources are inclusive and tailored to the individual.

Note: TS indicate travel safety, TTS indicate travel time saving, TR indicate travel reliability, COT indicate convenience of travel, TC indicate travel comfort, TEV indicate travel emotional value, TEA indicate travel expectancy attitude, and PTF indicate perceived travel fairness.

.

Travel emotional value includes both positive and negative emotional values. The provision of positive emotional values can bring about good feelings in people. Morris noted that emotional value is difficult to quantify as a potential cost of travel and that travellers may be affected by the travel environment in a negative way that reduces their well-being [27]. The study of Hennessy and Wiesenthal also demonstrated that, in a high-congestion travel environment, non-professional drivers show higher levels of stress, frustration, aggression, anger, and other negative emotions [28]. Thus, the long-term stability of travel well-being largely depends on the positive emotional value the mode of travel can provide.

Travel expectancy attitude is the assessment of the positive or negative aspects of travel behaviour that is undertaken by the traveling individual. Abou-Zeid noted that most prior research has only focused on motivation itself, neglecting the formation of motivation and its subsequent outcomes in the dynamic process of behaviour [29]. Exploring the effects that travel mode, purpose, and distance have on traveller satisfaction showed that perceived expectations better explained travel preferences than objective attributes [30,31].

Fairness is essentially defined as the balance between the distribution of benefits and the payment of costs. The perception of travel fairness is a subjective judgement by the traveller regarding whether their travel entitlements and cost payments are in equilibrium. Perceptions of travel fairness are influenced by both egalitarianism and social inclusion [32,33,34]. When conflicts and contradictions arise between the travel rights of different groups, travel equity should be a dynamic and coordinated process. A higher perception of equity relies heavily on the rational allocation of transport resources [35,36].

The most typical scales include the Satisfaction of Travel Scale (STS) designed by Ettema et al. in the Netherlands, which consists of nine items [37,38]. The Satisfaction with Daily Travel Scale, designed by Bergstad [18], consists of five items. The STS scale was developed from the Swedish Core Affect Scale and applied to measuring satisfaction with daily travel. This scale combines both cognitive and emotional ratings and consists of three components: (1) cognitive ratings of trip quality, (2) ratings of trip emotions from stress to ease, and (3) ratings of trip emotions from boredom to excitement. De Vos et al. tested the reliability of the STS scale using data on leisure trips in the city of Ghent, Belgium [17].

To measure the travel well-being of the travelling population, this paper uses nine declarative statements based on the STS scale to measure the travel well-being of the pilgrimage population. The results are shown in

Table 2. Pilgrim group travel well-being scale.

Latent Variables	Indicators
Emotional dimension	TWB1: Overall, a sense of well-being experienced from the pilgrimage.
	TWB2: The process and the goal of a pilgrimage is full of meaning.
	TWB3: Inner peace and relaxation during the pilgrimage.
	TWB4: Active and interested in the pilgrimage.
	TWB5: Optimistic about the pilgrimage travel process.
	TWB6: Pilgrimage trips are an important part of life, and the respondents feel confident in their ability to complete these activities.
Cognitive level	TWB7: The trip was better than initially expected.
	TWB8: Enjoying good travel conditions during the trip.
	TWB9: The overall travel for the pilgrimage was very smooth.

. Respondents were asked to answer the questions using a scale ranging from 0 to 5, with 0 indicating complete disagreement and 5 indicating complete agreement.

The following hypotheses are proposed in relation to previous publications on the relationship between pilgrimage experiences and travel, as well as in relation to the findings and variable analyses of studies on the factors that influence travel well-being. The hypotheses are shown in

Table 3. Path hypothesis explanation.

Hypothetical Path	Hypothesis Description
H1	Travel security has a positive impact on emotional values.
H2	Travel security has a positive impact on expected attitudes.
H3	Travel security has a positive impact on perceptions of fairness.
H4	Time-saving travel has a positive impact on emotional value.
H5	Time-saving travel has a positive impact on expected attitudes.
H6	Time-saving has a positive impact on perceptions of fairness.
H7	Convenience of travel has a positive impact on perceptions of fairness.
H8	Convenience of travel has a positive impact on emotional value.
H9	Convenience of travel has a positive impact on expected attitudes.
H10	Travel reliability has a positive impact on perceptions of fairness.
H11	Travel reliability has a positive impact on expected attitudes.
H12	Travel reliability has a positive impact on emotional value.
H13	Travel comfort has a positive impact on perceptions of fairness.
H14	Travel comfort has a positive impact on expected attitudes.
H15	Travel comfort has a positive impact on emotional value.
H16	Perceived travel fairness has a positive impact on expected attitudes.
H17	Emotional value has a positive impact on expected attitudes.
H18	Perception of fairness has a positive impact on travel well-being.
H19	Expected attitudes has a positive impact on travel well-being.
H20	Emotional value has a positive impact on travel well-being.

.

5. Results

5.1. Factor Analysis

Confirmatory factor analysis (CFA) tests the hypothesis of the relationship between measured and potential variables. It is commonly used to assess the reliability of potential variables and to test the structure of factors under specific theoretical assumptions. Nine latent variables were subjected to CFA analysis: travel safety, time savings during travel, travel comfort, travel reliability, convenience of travel, travel expectancy attitude, travel emotional value, perceived fairness, and travel well-being. There were observed variables with standardised factor loadings of less than 0.5, which needed to be adjusted. Items with factor loadings of less than 0.5 were removed, and the remaining variables are shown in

Table 4. Component reliability and convergent validity.

Latent Variable	Observation Variables	Standard Load	Cronbach’s α	CR	AVE
TS	TS1	0.846	0.771	0.8643	0.6801
	TS2	0.849
	TS3	0.777
TTS	TTS1	0.879	0.858	0.9325	0.8224
	TTS2	0.991
	TTS3	0.844
TC	TC1	0.812	0.758	0.7959	0.661
	TC2	0.814
TR	TR1	0.746	0.787	0.7359	0.4829
	TR2	0.710
	TR3	0.623
COT	COT1	0.602	0.735	0.6977	0.4371
	COT2	0.624
	COT3	0.748
TEA	TEA1	0.799	0.710	0.7537	0.605
	TEA2	0.756
TEV	TEV1	0.736	0.720	0.8111	0.5891
	TEV2	0.805
	TEV3	0.760
PTF	PTF1	0.712	0.755	0.7989	0.5706
	PTF2	0.733
	PTF3	0.817
TWB	TWB1	0.742	0.809	0.8848	0.4625
	TWB2	0.672
	TWB3	0.748
	TWB4	0.553
	TWB5	0.677
	TWB6	0.637
	TWB7	0.755
	TWB8	0.681
	TWB9	0.630

Note: TS indicate travel safety, TTS indicate travel time saving, TR indicate travel reliability, COT indicate convenience of travel, TC indicate travel comfort, TEV indicate travel emotional value, TEA indicate travel expectancy attitude, PTF indicate perceived travel fairness, and TWB indicate travel well-being.

. The corrected standardised factor loadings were all above 0.5, meeting acceptability criteria [39].

Component reliability (CR) is the composition of the reliability of all observed variables, where values greater than 0.7 indicate that the indicator has a high level of internal consistency. Average variance extracted (AVE) represents the explanatory power of the variance of observed variables on latent variables. A high AVE indicates the high convergent validity of the latent variable, and its standard value should be greater than 0.5 [40]. Table 4 shows that the CR values of each variable and Cronbach’s alpha are all greater than 0.7, while the convergent validity within each latent variable is good overall. The discriminant validity indicator tests whether the overall correlation between the latent variable and corresponding multiple observed variables is greater than the correlation between latent variable and latent variable.

Table 5. Different validity tests.

	TS	TTS	TC	TR	COT	TEA	TEV	PTF	TWB
TS	0.825
TTS	−0.071	0.906
TC	0.05	0.007	0.507
TR	0.063	0.021	0.028	0.695
COT	0.012	0.039	0.257	0.004	0.813
TEA	0.029	0.051	0.032	0.116	0.094	0.778
TEV	0.04	0.033	0.244	0.029	0.118	0.001	0.768
PTF	0.052	0.026	0.225	0.048	0.227	0.024	0.099	0.755
TWB	0.031	0.034	0.191	0.061	0.177	0.069	0.147	0.200	0.680

Note: TS indicate travel safety, TTS indicate travel time saving, TR indicate travel reliability, COT indicate convenience of travel, TC indicate travel comfort, TEV indicate travel emotional value, TEA indicate travel expectancy attitude, PTF indicate perceived travel fairness, and TWB indicate travel well-being.

shows that there is good discriminant validity between variables and that the overall discriminant validity between constructs is acceptable from an overall perspective.

5.2. Model Results

The results of the CFA-based analysis indicated that the data are suitable for analysis using structural equations. A structural path analysis model (as shown in Figure 1) was constructed using the software AMOS 23.

When selecting fitness indicators for structural equations, different studies have selected different indicators. Among them, chi-square, degrees of freedom, the ratio of chi-square to the degrees of freedom, the goodness-of-fit index, the modified goodness-of-fit index, the canonical fit index, the comparative fit index, the root mean square of approximation error, and the root mean square residual are the most frequently reported indicators in scholarly studies using fitness metrics [41]. Therefore, these indicators were also used as fitness indicators for the structural model in this paper. The results show that the modified goodness-of-fit index and the goodness-of-fit index were 0.853 and 0.883, respectively, which does not meet the recommended range of indicators. However, the other indicators met the fitness requirements. The SEM and the actual data were found to fit well.

SEM was developed to test the proposed hypotheses, and the results are shown in

Table 6. Normalised path coefficients.

Path	Path Coefficient	p	Standardisation	CR	Hypotheses
TS→TEV	0.119	0.208	0.102	1.259	H1: Accepted
TS→TEA	0.085	0.488	0.078	0.693	H2: Rejected
TS→PTF	0.183	***	0.173	2.176	H3: Accepted
TTS→TEV	0.07	0.134	0.087	1.497	H4: Rejected
TTS→TEA	0.119	**	0.158	2.009	H5: Accepted
TTS→PTF	0.067	**	0.091	1.787	H6: Accepted
COT→PTF	0.543	***	0.566	6.619	H7: Accepted
COT→TEV	0.096	0.254	0.092	1.189	H8: Rejected
COT→TEA	0.607	***	0.616	3.904	H9: Accepted
TR→PTF	0.114	***	0.131	1.884	H10: Accepted
TR→TEA	0.329	***	0.370	2.848	H11: Accepted
TR→TEV	0.086	0.225	0.091	1.222	H12: Rejected
TC→PTF	0.130	***	0.188	2.653	H13: Accepted
TC→TEA	0.045	0.635	0.063	0.545	H14: Rejected
TC→TEV	0.324	***	0.431	5.026	H15: Accepted
PTF→TEA	0.546	***	0.531	−3.527	H16: Accepted
TEV→TEA	0.096	0.361	0.101	−1.003	H17: Rejected
PTF→TWB	0.556	***	0.553	7.687	H18: Accepted
TEA→TWB	0.239	***	0.244	3.249	H19: Accepted
TEV→TWB	0.237	***	0.257	4.272	H20: Accepted

Note: TS indicate travel safety, TTS indicate travel time saving, TR indicate travel reliability, COT indicate convenience of travel, TC indicate travel comfort, TEV indicate travel emotional value, TEA indicate travel expectancy attitude, PTF indicate perceived travel fairness, TWB indicate travel well-being, and CR indicate component reliability. *** p < 0.001, ** p < 0.01.

. Travel safety, time savings during travel, travel convenience, travel reliability, and travel comfort all significantly impact fairness perception, with impact coefficients of 0.173, 0.991, 0.566, 0.131, and 0.188, respectively. Time savings during travel, travel convenience, and travel reliability significantly impact the expected attitude, with impact coefficients of 0.158, 0.616, and 0.370, respectively. Travel comfort significantly impacts emotional value, with an impact coefficient of 0.431. The coefficients of influence of the potential variables on travel well-being were 0.553 for perceived travel fairness, 0.257 for perceived emotional value of travel, and 0.244 for perceived travel expectancy. This result shows that potential attitudes are an important factor in travel well-being. Perceived service quality is an important condition that leads to changes in intrinsic factors. To achieve a high level of travel well-being, both the subjective feelings of the traveller and the objective attributes of the environment should be considered.

5.3. Travel Market Segmentation Results

The model shows that travel perception variables are the subjective reflections of travellers regarding their travel environment. Therefore, five perception variables in the travel process (i.e., safety, time saving, comfort needs, travel reliability, and convenience) were selected as clustering conditions to classify travel groups with similar class attitudes. A comparison of clustering indicators showed that a k of 4 resulted in the most desirable clustering results, optimally describing the differences between clusters. The clustering centres for each sub-market are shown in

Table 7. Variable characteristics of market segments.

Attitudinal Factor	C1	C2	C3	C4
Perceived safety	3.09 £	3.27 £	3.08 £	2.93 £
Time saving perception	3.42 £	2.01	1.95	3.66 *
Comfort perception	2.06	3.98 *	2.66 £	3.7 *
Reliability perception	3.4 £	3.15 £	3.54 £	3.41 £
Perceived convenience	3.02 £	4.01 *	3.31 £	3.82 *

Note: * and ^£ denote intervals where cluster centres are at high and medium levels, respectively. Values indicate clustering centres in one dimension of the attitude factor: high-level (>3.5), medium-level (2.6–3.5), and low-level (<2.6).

.

The attributes of the attitudinal factors within each cluster can be summarised as follows: (i) Cluster 1 (C1) is a group that experiences medium satisfaction with time savings and the reliability, convenience, and safety of the current travel environment; (ii) the members of Cluster 2 (C2) experience high satisfaction with the comfort and convenience of the current travel environment and medium satisfaction with safety and reliability; (iii) the members of Cluster 3 (C3) experience medium satisfaction with comfort, reliability, convenience, and safety; (iv) the members of Cluster (C4) are highly satisfied with time savings, comfort, and convenience and moderately satisfied with safety and reliability.

Table 8. Travel in segmented markets.

	C1 (142/466)	C2 (132/466)	C3 (118/466)	C4 (74/466)
Gender
Male	69	76	39	41
Female	72	55	78	32
Percentage of women	51.1%	42%	66.7%	43.8%
Age
Under 34 years old	36	47	21	18
35–54 years old	37	40	29	32
55–64 years old	38	32	50	24
Over 65 years old	31	13	18	0
Percentage of people over 65 years old	24.8%	0.1%	11.1%	0%
Disposable income
<CNY 2000	22	30	18	18
CNY 2000–5000	66	50	60	30
CNY 5000–7000	20	38	15	23
>CNY 7000	34	14	25	3
Percentage of population with disposable income less than CNY 2000	15.5%	22.7%	15.3%	24.3%

demonstrates the differences in travel preferences between different sociolect demographic groups. These data suggest further strategies to enhance the travel well-being of the pilgrim population.

Table 8 shows that the group clustered in C4 has the highest proportion of people with a disposable income of less than CNY 2000. Further, C3 has the highest proportion of women, and C1 has the highest proportion of people over 65 years of age. The perceived quality of travel comfort is the lowest in C1. This indicates that the elderly are more concerned with comfort in their transport environment, which matches the gradual deterioration of their physical functions. The proportion of young people under the age of 34 is higher in C3 and C2 than in the other sub-markets. These groups are more satisfied with the convenience, comfort, and reliability of travel, but they are less satisfied with the time saving aspect.

6. Conclusions

This study used pilgrimage groups as study participants to explore the factors influencing travel well-being during pilgrimages. The data were obtained from several surveys on the travel characteristics of pilgrim groups (organised by Tibet University in 2021). The travel characteristics of the Tibetan pilgrimage population were examined. The relationship between the influence of underlying attitudes on the travel well-being of pilgrims was identified, and interesting research results were generated. In this section, suggestions for how to improve the travel well-being of pilgrims are provided.

Pilgrimage groups consisting of older people usually require a high level of comfort for travel. The provision of seats and rest areas on the pilgrimage will enable the pilgrims to regain their strength effectively when they are tired. Furthermore, the frequency of transport (i.e., the bus schedule) should be increased to prevent overcrowded conditions.

Pilgrim groups with strict travel time requirements are usually less satisfied with time saving. For this group, prioritising bus travel should be emphasised, the load on the roads should be reduced, the capacity of the transport network should be increased (by expanding the size of routes), and the increasing emotional value of travel for the residents should be met.

An energetic young group of pilgrims is generally satisfied with safety, convenience, reliability, and comfort. For this group, attempts can be made to improve the flexibility of public transport or to increase their sense of responsibility for low-carbon travel. Further measures can ensure that pedestrian corridors are neat and tidy, that the riding environment is hygienic, and that a good environment for walking trips is provided.

Pilgrim groups who are concerned about their safety usually have high requirements for safety and reliability of travel. Therefore, when planning road networks and designing road infrastructure, additional non-motorised lanes should be reasonably provided and safely connected with motorised lanes to lessen urban congestion and improve both reliability and safety.

It is recommended that the main basis for the transport policy should be the perceived fairness of travel, and reasonable requirements should be imposed on the convenience, comfort, and reliability of the travel environment. The travel preferences of different groups of people should be considered, and transport resources should be reasonably allocated to improve the well-being of pilgrims. Adhering to the “people-oriented” principle, special policies can be introduced for blind and disabled people, as the elderly, the young, the sick, and the disabled may encounter difficulties when travelling. In addition, new and convenient modes of travel should be encouraged. The development of shared economy transport models should be regulated not only to reduce travel times for travellers but also to promote the effective allocation of transport resources and improve travel efficiency. Examples of this include ridesharing, shared bicycles, and carsharing, which are all “enablers” of efficient and convenient travel.

This study examines pilgrimage travel within a city-based context, focussing on the impact of anticipated attitudes, emotional values, and perceived fairness on travel happiness. Inevitably, travel happiness is constrained by factors such as travel length and the spiritual dimension of effort. By acknowledging these potential limitations and areas for future investigation, we encourage researchers to conduct work that delves deeper into the complex relationship between effort and journey quality in the context of pilgrimages. This will contribute towards achieving a more comprehensive understanding of the factors that influence customer satisfaction in this unique and meaningful travel experience.