Introduction

Enhancing the transportation system will enable people to access crucial services and social capital, including public health, employment opportunities, housing, education, and welfare, thereby improving quality of life (QoL). The Sustainable Development Goals (SDGs) concept primarily involves development that positively impacts both urban areas and QoL, affording city residents access to improved services, living conditions, and greater comfort (United Nations Development Programme 2022). To tackle Thailand’s transportation-related quality of life (QoL) issues, the Ministry of Transport has instituted a policy aimed at proactively develo** a comprehensive public transport system. This initiative encompasses the supervision of services to ensure convenience, safety, punctuality, and reasonable pricing. In recognizing the significance of these factors in propelling the country’s infrastructure towards a sustainable future, it is imperative that the wheel, rail, and water transportation systems are accessible to all and introduce confidence in their passengers, primarily through a transport-centered approach (Hansen 1959; Ferreira and Batey 2007). Increasing the comfortability of travel is an integral part of the index indicating the QoL of a country (Ministry of Transport 2018). However, at present, Thailand’s transportation system faces significant challenges that cannot fully meet the needs of users. The reason for this is that Thailand is still reliant on the main road-based transport system while the majority of commuters primarily enjoy private cars. Consequently, the volume of people using road transport exceeds the capacity of the road, resulting in long-term congestion problems. Without the development and improvement of other forms of transport networks connecting each mode of transport for efficiency, these problems are likely to become more severe in the future (The parliamentary budget office 2021).

Furthermore, the change in urban conditions caused by the development policy framework, urban behavior, and the lack of maintenance in the city’s infrastructure will result in severe consequences for the city such as traffic congestion. According to pollution statistics from the World Air Quality Index Project (2021), Bangkok reached the red level in 2021, indicating a health impact for up to 12 days in a year, which represents 3.29 percent of the time. Moreover, issues related to accessibility and accidents in Thailand contribute to the highest road fatality rates in Southeast Asia and Asia (World Health Organization 2018). Traffic congestion and pollution in Bangkok affect the residents both directly and indirectly. Traffic congestion has a direct negative impact on stress, while air pollution similarly affects the physical health of Bangkok residents. This view is supported by the findings of Litman (2024) and Iamtrakul et al. (2024), which indicated that transportation planning adversely affected the physical and mental well-being of the urban population. Furthermore, according to Litman (2024) and Singleton and Clifton (2014) better safety, mobility, pollution, accessibility, etc., improved QoL. Therefore, this study is essential as it aims to analyze the factors influencing the quality of life (QoL) in travel for sustainable mobility among residents of Bangkok. It identifies eight key factors to indicate QoL in travel by addressing the critical need to understand and enhance the transportation experience for the megacity’s inhabitants.

Literature review

Quality of life (QoL)

Since the 1960s, QoL has been a commonly discussed research topic (Campbell et al. 1976; Sarmiento et al. 2010). It is a complex concept that encompasses multiple dimensions and can be described as the extent to which individuals’ needs and values are met (Diener 1995; Diener et al. 1999). As a multidimensional concept, QoL is associated with an individual’s subjective assessment of their life circumstances. It considers the influence of the culture and value systems within their social contest, along with their aspirations, anticipations, and benchmarks (WHOQOL 1998). QoL encompasses both physical well-being and mental state, as recognized by Group (1998). In summarizing the various approaches to defining QoL, Felce and Perry (1995) proposed that it can be conceptualized as a combination of objectively assessable life circumstances. QoL implies an individual’s perception of their life conditions as a whole and the degree to which they are satisfied with them (Brown et al. 1989; Das 2008). Cummins (1992) conceptualized QoL as an objective evaluation of a person’s life circumstances and their subjective evaluation of satisfaction, determined by the significance they place on each sphere. Comparing different places when measuring QoL is often disregarded since their QoL and livability are inherently subjective. However, the opinions and preferences of people from different communities toward the elements of livability are likely to vary (Myers 1987). The measurement of general QoL and specific domains may include health, neighborhood, friendships, housework, leisure life, environment, housing situation, and job (Campbell et al. 1976).

In conclusion, as outlined by Lee and Sener (2016), the concept of Quality of Life (QoL), which encompasses elements of psychology, philosophy, and health, can be delineated into four distinct categories. The first category involves objective evaluation which relates to the life perceptions of individuals (Bowling et al. 2003; Diener 1995; Farquhar 1995; Sirgy et al. 2006). The second category is subjective measurement which often referred to as subjective well-being (Diener 2000; Felce and Perry 1995; Hofstede 1984). The third is a combination of objective and subjective determination (Atkinson 2013; Diener and Biswas-Diener 2003; Ferkany 2012; Nelson et al. 2007). The fourth category encompasses domain-specific factors including housing, work-related impacts, neighborhood features, and transportation (Kyle and Dunn 2008; Drobnič et al. 2010; Sirgy and Cornwell 2002; Furie and Desai 2012). Quality of life in transportation (QoLT) has gained increasing attention among researchers and policymakers due to its acknowledged significance in public health and various other domains.

Quality of life relating to transportation (QoLT)

A correlation exists between access to transportation and human social interaction, such as employment, education, shop**, as well as sports and recreation. In modern society, transportation is considered to be fundamental to economic activity (Rodrigue et al. 2017). According to Ferrans (1996) and Moons et al. (2006), QoL can be perceived as well-being, happiness, satisfaction with life, and living standards, all of which are frequently used interchangeably (Diener et al. 1985). Transportation has emerged as an important urban element that is still not entirely understood as an element of QoL. The impact of transportation or its various components on QoL and life satisfaction can differ, as indicated by Hamer and Chida (2008), Michalos and Zumbo (1999) and Forkenbrock (2004). Based on the theoretical framework, studies concerning quality of life in relation to transportation (QoLT) have predominantly focused on subjective well-being measures to enhance transport mobility, they have not yet gathered sufficient attention for understanding the association between them (Chidambaram 2022). The concept of subjective well-being primarily comprises two views. There are the eudaimonic view which encompasses the cognitive dimension involving respondents’ evaluations of their satisfaction, and the hedonic view which focuses on individuals’ feelings about life and experiences (Singleton 2019; Chen et al. 2019; De Vos et al. 2019). Previous studies tend to use different variables. For instance, Joewono and Kubota (2007) used nine variables which comprises of availability, accessibility, reliability, fare, comfort, users’ service, information, safety and environmental impact. On the other hand, Schneider et al. (2013) applied seven primary variables which includes access, safety, design, mobility, environment, maintenance and transparency. In their study, Mattson et al. (2021) focused on seven key transportation characteristics, namely transit quality, road quality, low traffic congestion, traffic safety, bicycle competence, and the affordability of available transport.

In addition, the sustainability of the transportation system can be assessed by analyzing both the current and anticipated positive and negative aspects and externalities of traffic and transport. Several efforts have been made to compile indicators that take into account congestion delay, diversity, commuting speed and quality of transportation choice within a community, accessibility of activities (for both drivers and non-drivers), and the percentage of household’s expenditures allocated to transportation (Litman 2003; Gilbert and Tanguay 2000; Gudmundsson 2001). Hence, the measurement factors concerning quality of life in transportation (QoLT) encompass various dimensions based on the subjective well-being view which relate to the perceived level of travel satisfaction. These dimensions include accessibility, comfort, safety, quality of service, among others.

Structural equation modelling and quality of life in travel

When it comes to techniques for measuring and evaluating travel-related quality of life, numerous studies employ various analytical methods. In addition to regression models, which are commonly used to explore the strength of influence of multiple independent variables on a dependent variable (Cheng 2001), Structural Equation Modeling (SEM) stands out as an extension of the regression model. SEM offers the advantage of handling multiple dependent variables simultaneously (Nilashi et al. 2022; Singleton 2019). For example, Wang & Gao (2024). Olsson et al. (2020) demonstrated that excellent public transport services can enhance a person’s overall travel satisfaction. According to Ettema et al. (2010), daily travel satisfaction is associated with QoL. As part of the study by Flanagan (1978), participants were asked various questions about previous life events that they found highly satisfying or detrimental to their QoL.

Quality of life relating to transportation (QoLT) for sustainable urban environments

Cities provide a variety of opportunities for people through transportation means that enable mobility to support social and economic activities, especially accessibility to jobs and education, particularly for low-income and disadvantaged groups. However, cities need to adapt to the growing demands of the daily lives and lifestyle changes of their inhabitants since the population of those living in cities is anticipated to hit around 2.5 billion by 2050 (United Nations 2022). Due to this rapid growth, many cities are progressively addressing some of these QoL challenges such as inequality, overcrowding, environmental problems, etc. (Osoro 1991). These challenges also include transportation and environmental issues, which has been becoming increasingly serious. Energy-related carbon emissions, traffic congestion, and pollutant emissions significantly contribute to global warming and people’s health problems (Miskolczi et al. 2021; Wang and Sun 2022; Afrin and Yodo 2020; Tang et al. 2020). In 2020, the World Bank Group reported that the issues surrounding transportation and the environment in urban areas were having an adverse impact on sustainability, health, the economy, and QoL (The World bank group 2020). Therefore, sustainable city development requires a decisive measure capable of minimizing these adverse effects on the global environment and socio-economic aspects. Sustainable cities can not only provide significant benefits to the human beings living within them but also help to mitigate further intensive pressure on the environment (Silva et al. 2018). Sustainable transport is related to the sustainability of cities, so its objectives must be integrated and concretized into the goal of decreasing the detrimental impacts of urban transportation and environmental problems on the ecosystem, human health, etc. One of the key factors involved in creating a sustainable city is the adaptability of strategies toward transportation accessibility. Therefore, ensuring inclusiveness among various aspects of socioeconomic development and human behaviors in different societal contexts can lead to urban transport system improvement. This could also bring desirable consequences in terms of reducing reliance on private vehicles while simultaneously mitigating the pressure of environmental impacts on the transportation sector.

Data and methodology

The analysis focuses on examining the factors influencing Quality of Life (QoL) during travel within the Sukhumvit district of Bangkok, Thailand through the lens of subjective well-being.

Study area and sampling

In this study, Sukhumvit area located among three districts of Bangkok, Thailand were examined which are Watthana, Khlong Toei, and Phra Khanong (refer to Fig. 1). This representative area is considered as one of the prime locations in the Bangkok metropolitan area, the capital of the country, which is characterized by its lively and bustling atmosphere. Situated along Sukhumvit road, it serves as a prominent hub for shop**, dining, and nightlife. Noteworthy attractions in the area include expansive shop** malls and luxurious hotels. Accordingly, this area exemplifies the modernity, density, and diversity of Bangkok, offering comprehensive transportation options (road, rail, and water-based) and excellent connectivity through the Bangkok Mass Transit System (BTS Skytrain). When considering the statistics of commuter usage of public transportation in Bangkok, it was found that commuters predominantly utilize public transportation via buses, accounting for 38 percent, followed closely by the BTS (Skytrain) at 37 percent, and the MRT (Subway) at 20 percent, with other modes representing the remaining proportion (Traffic and Transportation Department 2023). The presence of the Metropolitan Rapid Transit (MRT) and Airport Rail Link facilitates convenient navigation throughout the expansive area connected by Sukhumvit road and extending to other parts of the city. Together, the study area covers three districts: Phra Khanong District, which covers an area of 13.06 sq. km. and has a population of 85,696 people (Phra Khanong District Office 2024); Watthana District, which encompasses 12.57 sq. km. and supports a population of 85,334 people (Vadhana 2024); and Khlong Toei District, which occupies 12.32 sq. km. and is inhabited by 93,193 people (Khlongtoei District Office 2024).

Fig. 1
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Study area: Sukhumvit District of Bangkok, Thailand

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Data on Quality of Life in Transportation (QoLT) were gathered through a questionnaire designed to quantify users’ perceptions of life satisfaction. The collected data were then analyzed using Structural Equation Modeling (SEM). Hence, it was imperative to establish an appropriate sample size. Existing evidence suggests that even relatively small sample sizes can suffice for simple SEM models (Marsh and Hau 1999). Nevertheless, 200 samples or more is considered to be adequate for SEM (Lei and Wu 2007; Kline 2005, 2011; Csizér and Kormos 2009; Boomsma and Hoogland 2001). The required sample size is influenced by several factors, including data normality, model complexity, and missing data patterns as noted by Jackson (2003) and Kline (2011). Bentler and Chou (1987) suggested the size of the sample is generally suggested to be at least five times the number of free parameters in the model. However, some researchers suggest that the ratio of sample size to free parameters should ideally be as high as 10 to 1 (Raykov and Marcoulides 2006; Eckes and Grotjahn 2006), or even 20 to 1 (Tanaka 1987; Kline 2011). Consequently, the sample size for this study was determined by multiplying 38 by at least 10 times, resulting in 380 samples (38 sub-variables and 9 main variables). Thus, the sampling frame comprised 500 residents of the Sukhumvit district who served as input data representatives.

Data collection

The QoLT questionnaire consisted of two sections and was designed to obtain commuters’ personal data including their background and perceptions toward QoL, the first of which centered on their socioeconomic characteristics. The second part of the questionnaire, the data collection from respondents were presented with 38 questions to evaluate their level of QoLT. The set of questions comprised of 9 variables: (1) accessibility (X1) (8 sub-variables), (2) design (X2) (3 sub-variables), (3) safety (X3) (6 sub-variables), (4) cost (X4) (3 sub-variables), (5) environment (X5) (4 sub-variables), (6) mobility (X6) (9 sub-variables), (7) information (X7) (3 sub-variables), (8) satisfaction toward QoLT (X8) (1 sub-variable), and (9) satisfaction toward QoL (X9) (1 sub-variable) as presented in Table 1. Two steps were involved in develo** the questionnaires prior to data collection. Firstly, the questions were designed, and a preliminary survey was then undertaken to assess the respondents’ understanding of the questions. This process ensures that the designed questions can be clearly understood and answered directly according to the intended purpose. Secondly, an improved questionnaire was developed following the respondents’ comments from the pilot survey prior to preparing the final questionnaire.

Table 1 Variables of analysis
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The offline survey was conducted at transit stations, stops, or terminals within the study area. A grid-based approach utilizing 500 by 500 square meter sections was employed to distribute the questionnaire. Face-to-face interviews were conducted to gauge the perceptions and satisfaction of Sukhumvit district residents regarding transportation in terms of both QoLT and overall QoL. Data collection took place over a two-month period from December 2021 to January 2022. This timeframe was selected to avoid data collection during end-of-year activities, with questionnaire administration scheduled for early December and late January. To ensure comprehensive coverage of various transportation modes (personal car, motorcycle, para transportation, public transportation, and pedestrian/bicycle).

Data analysis

Quality of QoLT was measured using the rating scale for life satisfaction based on a 7-point system, commonly used by the World Health Organization, ranging from very dissatisfied (1) to very satisfied (7) (WHOQOL 1998; Schneider et al. 2013; Andrews and Crandall 1976). The following question was constructed to determine life satisfaction: “How satisfied are you with the following parts of each factor in the area?”. The descriptive statistics were applied to understand the respondent characteristics before proceeding with the analysis of the factors affecting QoLT in Sukhumvit District, Bangkok, to achieve sustainable urban mobility. These factors were assessed using SEM (as depicted in Figs. 2 and 3).

Fig. 2
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Conceptual model

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Fig. 3
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Framework of the Study

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SEM has been used in several research fields and generalized studies (Joreskog 1973; Wiley 1973), recently gaining popularity in transportation assessment, particularly when analyzing the satisfaction of customers toward transport services (Stuart et al. 2000; Githui et al. 2010; Hadiuzzaman et al. 2017), perception of the transport service quality (Rahman et al. 2016; Rahman 2021; Sharma et al. 2020; Tiglao et al. 2020), etc. After importing the data into the model to commence the analysis process, the fitness of the model was considered. The guidelines for determining the model fitness for SEM can be verified by fit indices to investigate how well a model fits the sample data, allowing the one with a superior fit to be taken into consideration. When develo** the final model, it is important to take into account the statistical fit of the model and the required number of parameters to attain the desired level of fit. This category consists of alkaline information criterion (AIC) (Hooper et al. 2008; Preacher and Merkle 2012; Anderson and Gerbing 1984), Chi-squared test, Root Mean Square Residual (RMR), and Root Mean Square Error of Approximation (RMSEA) (Preacher and Coffman 2006; Browne and Cudeck 1993, Hair et al. 2010). The comparative fit index (CFI) is most commonly used in this category (Bagozzi and Yi 1988).

Results

Characteristics of respondents

This study surveyed a sample size of 500 individuals out of a population of 255,725. The appropriateness and adequacy of the sample size were assessed based on criteria ensuring suitability for inclusion in statistical analysis. The analysis results of the factors influencing QoLT for sustainable urban mobility in Sukhumvit District, Bangkok, obtained from the questionnaires are presented in Table 2. The analysis results show the attributes of 500 respondents aged more than 18 years (271 males, 213 females, and 16 individuals with other gender identities). More than 86% of the respondents identified as Buddhists, while 60% held a bachelor’s degree. Additionally, 44% reported an average personal income ranging between 289–720 USD per month, followed by 721–1,152 USD, and 1,153–1,584 USD, respectively. In terms of vehicle ownership, it can be observed that most respondents’ own cars as their mode of transportation (40%), followed by motorcycles (37%), and bicycles (23%). The majority of commuters preferred to carry out their daily commute in their vehicles since they were more comfortable, safe, convenient, and saved time.

Table 2 Socioeconomic profiles of 500 respondents in the study area
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Quality of life related to transportation (QoLT)

Distribution of respondents’ answers

This section focuses on life satisfaction in QoLT and to evaluate their QoLT. The respondents were presented with a total number of 38 questions. The list of questions comprised of 9 variables: (1) accessibility (8 sub-variables), (2) design (3 sub-variables), (3) safety (6 sub-variables), (4) cost (3 sub-variables), (5) environment (4 sub-variables), (6) mobility (9 sub-variables), (7) information (3 sub-variables), (8) satisfaction toward the QoL in terms of travel (1 sub-variable), and (9) satisfaction toward the QoL (1 sub-variable) by means of a 7-point Likert scale as presented in Table 3.

Table 3 Reliability and validity testing of quality of life related to transportation (QoLT)
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As can be observed, in terms of accessibility, the respondents were most satisfied with their access to motorcycle taxis (XA5 = 4.10), followed by access to paratransit (XA3 = 3.97), and the adequacy of BTS/MRT seats (XA6 = 3.93). From the design perspective, the respondents expressed the most satisfaction with the appropriateness of the label location (XD2 = 3.94), followed by label and sign clarity (XD1 = 3.83), and lighting (XD3 = 3.78), respectively. Furthermore, when considering the safety issue, respondents were most satisfied with traveling by car (XS4 = 4.11), followed by taxi (XS5 = 4.00), and van (XS6 = 3.97). From the cost perspective, respondents were found to be most satisfied with the appropriateness of train fare (XC2 = 3.98), followed by paratransit (XC3 = 3.71), and mass rapid transit (XC1 = 3.70). In terms of protecting the environment, respondents were most satisfied with the lack of light pollution (XE3 = 3.67), followed by air/dust pollution (XE2 = 3.37), and traffic noise pollution (XE1 = 3.32), respectively. From the mobility perspective, respondents were most satisfied with the vacation travel time (XM4 = 4.13), followed by the travel time to shop** (XM3 = 4.10), and school/university (XM5 = 3.95). In terms of information provision, respondents were most satisfied with bus travel schedules (XI2 = 3.90), followed by safety information in the case of an accident (XI3 = 3.83), and parking lot information (XI1 = 3.82).

The accuracy of the measurement procedure was tested for reliability by using Cronbach’s Alpha which presents as the most widely used method for the determination whether the value exceeds the acceptable threshold of 0.6 (Byrne 2010). The overall Cronbach’s Alpha value was 0.979 for all items (38 items in total) with each main variable demonstrating high reliability of accessibility (0.944 for 8 items), design (0.870 for 3 items), safety (0.931 for 6 items), cost (0.853 for 3 items), environment (0.911 for 4 items), mobility (0.954 for 9 items), information (0.868 for 3 items), and quality of life (0.846 for 2 items).The overall Cronbach’s Alpha value indicates that the questions used in the analysis were suitable for the model. Moreover, all factor loadings exceeding 0.6 demonstrate acceptable indicator reliability for exploratory studies while a Composite Reliability (CR) greater than 0.7 indicates good internal consistency reliability (Mandhani et al. 2020). To determine the univariate normality, the result of the distribution of each observed variable was examined for skewness and kurtosis. A skewness absolute index greater than + / − 3.0 is considered an extreme value (Chou and Bentler 1990). Kurtosis is a measure that indicates the degree of peakedness and the heaviness of the tails in a distribution. An absolute value higher than ± 10.0 indicates a problem (Kline 2005). When examining for multicollinearity, the variables were found to be strongly correlated, indicating redundancy and raising concerns in SEM. The detection of multicollinearity generally involves assessing bivariate correlations, which ideally should not exceed 0.85 (Kline 2005). Table 4 presents the results of bivariate correlations which indicate correlation values ranging from 0.22 to 0.82 and all below 0.85, thus suggesting estimation potential.

Table 4 Bivariate correlations
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Considering the differences in the average quality of life related to travel among different travel mode groups, the study has divided travel patterns into three main groups: 1) personal vehicle travelers, 2) non-motorized travelers, and 3) public transit travelers, as shown in Table 5. Analysis revealed that the environment and safety factors are statistically significant at p < 0.001. Moreover, the design and mobility factors are statistically significant at p < 0.05, while accessibility and cost are statistically significant at p < 0.1.

Table 5 Comparison of Quality of Life Related to Transportation (QoLT) between motorized and non-motorized commuters
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However, the information factor is not statistically significant. The data reveals that various modes of travel exhibit different average quality of life related to transportation systems, particularly concerning safety and environmental concerns. This discrepancy arises from the inherent characteristics of different travel patterns which highlight the vulnerabilities associated with each mode. Among these, non-motorized group travelers are the most susceptible as they lack protective modes of travel such as walking or bicycling.

Structural equation model and model fit assessment

The hypothetical model undertook multiple verification and refinement iterations to achieve a coherent interpretation. The model fit indices indicate satisfactory results, including CMIN/DF at 2.99 (below the critical value of 3), CFI at 0.93 (above the critical value of 0.90), RMSEA at 0.06 (below the critical value of 0.08), and NFI at 0.90 (meeting the critical value of > 0.90). To summarize, the proposed model exhibits satisfactory levels of reliability, validity, and predictive relevance. The analysis results on the factors influencing Quality of Life (QoL) during travel in Sukhumvit District, Bangkok, with respect to sustainable urban mobility, are depicted in Fig. 4. The hypotheses for Quality of Life in Transportation (QoLT) in this study revolved around transportation factors, encompassing accessibility, design, safety, cost, environment, mobility, and information. The following four policy variables yielded favorable outcomes: (1) accessibility (p-value 0.001), (2) cost (p-value 0.05), (3) environment (p-value 0.05), and (4) information (p-value 0.05).

Fig. 4
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Results of SEM analysis

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Discussion

This study aims to analyze the factors influencing the Quality of Life (QoL) in travel for sustainable mobility. The focus lies on examining the significant economic district of Bangkok, namely the Sukhumvit area, which epitomizes an area characterized by the potential for urban development, featuring a concentration and diversity of activities, both economic and social. Importantly, there is infrastructure and services related to the transportation system that offer a variety of options for commuters, including both public and semi-public transportation.

Additionally, the area boasts a population exceeding two hundred thousand people (within the study area). In this study, efforts were made to collect attitude data from the sample to ensure representativeness, including obtaining an appropriate number of samples for analysis. Additionally, the study aimed to distribute the survey data points across the area using a grid placement method. The results indicated that representative data were collected through grids, covering 43 percent of the total number of grid representatives. Thus, the sample in this study has the potential to be representative of studies within that context.

The results emphasize the significance of factors impacting the quality of life during travel. Accessibility emerges as the primary driver of overall QoL and is directly linked to each dimension of Quality of Life in Transportation (QoLT). According to Pardo (2012), accessibility strategies and methods are essential for achieving sustainable urban transportation. QoLT is a composite of several dimensions, such as accessibility, mobility, affordability, convenience, efficiency, social equity, safety, security, low carbon emissions, comfort, and friendliness toward commuters and the environment (Pardo 2012). Accessibility planning is considered crucial to the optimization of environmental stability and improving the QoL of urban populations (Coppola and Papa 2013; Iamtrakul et al. 2024). The suggested structure of this research has the potential to assist in the recognition of four different aspects of QoLT, namely physical, mental, social, and economic well-being. The transportation system is influenced by three main elements, namely accessibility, the built environment, and motorized traffic (Lee and Sener 2016). The concept of accessibility refers to a person’s mobility or capacity to move freely and conveniently toward their intended destination, providing them with greater opportunities. As a result, accessibility has emerged as a crucial contributory factor in QoL improvement (Fonda et al. 2001; Metz 2000). According to prior research, reduced mobility tends to lead to less accessibility, which in turn negatively impacts the opportunity for society to participate while also reducing social interaction, resulting in less QoL (Ling and Mannion 1995; Webster et al. 2002). Enhancing accessibility necessitates a compromise among diverse land use, transport options, opportunities at various times, and the demands and capabilities of individuals (Rode et al. 2019).

Numerous studies present collective QoL improvements using sustainable urban transport with enhanced overall accessibility and mobility. It is noteworthy that the creation of sustainable transportation plans, coupled with QoL evaluation, is vital for the effective formulation of transportation strategies and urban progress. Sustainable transportation plans can enhance the QoL for people both in the present and the future (Steg and Gifford 2005). Urban transport represents an individual burden for economic consideration, especially for families with household cost issues. The cost can be broken down into essential and optional trips, with the choice of transportation mode, whether of higher or lower quality, being affected (Iamtrakul et al. 2024). Furthermore, environmental issues (e.g., air quality/air pollution, noise, etc.) significantly influence the QoLT framework due to the effects of pollution on health or public health (Lee and Sener 2016). A transportation system that offers easily accessible, dependable, and reasonably priced services contributes to the well-being and overall satisfaction of people in various ways. It provides convenient access to work, social connections, and health-promoting amenities such as educational institutions, parks, libraries, and healthcare facilities (The Health Foundation 2021).

Hence, it is crucial to enhance the potential for develo** transportation systems that enhance quality of life, not solely focusing on physical accessibility or location. It is imperative to also consider access based on affordability and the provision of quality services, along with the connectivity of the system. Simply having transportation service points in close proximity does not guarantee access; if users are unable to afford or access the specific services they require, they cannot integrate their trips with activities that contribute to enhancing their standard of living. The study results highlight the significance of factors that influence the enhancement of quality of life associated with travel, particularly those related to accessibility, affordability, and the environment. Consequently, policy implementation should prioritize measures aimed at improving access, both in terms of providing accessible infrastructure and service systems, while also addressing the affordability concerns of consumers or travelers. To encourage decision-making in transitioning from personal vehicles to various forms of public transport, which can positively impact resolving traffic issues such as congestion, road accidents, and pollution. Additionally, it is worth noting that the data utilized in this study was collected during the outbreak of the coronavirus disease 2019 (COVID-19), which could potentially influence the attitudes of the sample group. Nevertheless, the lockdown policy of the country involved a comprehensive shutdown of numerous businesses which was effectively proved since March 2020. Subsequently, the government’s strategy transitioned towards the gradual relaxation of lockdown measures by aiming to facilitate the populace in adapting to living safely with the virus. This shift occurred in September 2021, as announced by the Center for COVID-19 Situation Administration (CCSA). Hence, during data collection, this research aimed to prioritize the perceptions and travel experiences of the sample group during normal times. Nevertheless, assessing quality of life involves considering two main perspectives which include both objective and subjective. Particularly, the subjective perspective reflects an individual’s characteristics through satisfaction, perception, and various feelings. This complexity results in a diverse range of factors involved in measuring quality of life in travel. Consequently, future studies should aim to integrate a comprehensive assessment encompassing individuals’ basic characteristics, attitudes, as well as the physical components of cities and transportation systems.

Conclusion

The analysis of factors affecting QoLT in Sukhumvit district, Bangkok, could be considered as a pilot case in achieving sustainable urban mobility. The policy variables related to transportation, e.g., accessibility, mobility, cost, design, safety, environment, and information are crucial in determining QoL in terms of travel. A total of 500 sample residents across Bangkok, Thailand, namely Watthana, Khlong Toei, and Phra Khanong participated in this study. The gathering data were collected through face-to-face questionnaires covering the target area, distributed to cover the various choices of urban transportation modes available to commuters (personal car, motorcycle, para transportation, public transportation, bicycle, and pedestrian). The perceptions of individuals who commute daily were assessed by using Structural Equation Modeling (SEM) to measure their Quality of Life in Transportation (QoLT). SEM is one of the most widely used tools for assessing and exploring the internal relationships of quality of life in travel, as it is enable the indication both direct and indirect relationships within this specific domain.

The findings reveal QoLT to be a multi-faceted concept, with four distinct categories having a statistically significant impact on QoLT. The key variables consist of (1) accessibility (p-value 0.001), (2) cost (p-value 0.05), (3) environment (p-value 0.05), and (4) information (p-value 0.05). Remarkably, accessibility was discovered as the major factor with the most influence on QoLT which in turn directly influences QoL. Finally, improving the transportation planning and policy in terms of QoLT would ensure inclusive access for residents in any target urban area to basic services, thereby enhancing social capital (e.g., public health, job, housing, etc.). This could ultimately lead to an improvement in the QoL of residents in the city.