Structural Equation Modeling of Rider Wellbeing for Sustainable Transportation Planning of the Dubai Metro

Abstract

Public transportation systems in modern cities are transitioning from infrastructure- and technology-centric models to human-centered development. One emerging focus area is rider wellbeing, which integrates physical, emotional, and psychological dimensions of transit experiences. This study investigates rider wellbeing in the Dubai Metro system, leveraging a large-scale survey of 1409 users and analyzing the data using Generalized Structured Component Analysis (GSCA). The research identifies three latent constructs—Service Efficiency and Accessibility (SEA), Physical Environment and Passenger Comfort (PEPC), and Service Operations and Assurance (SOA)—as key determinants of rider wellbeing. The final model demonstrated strong fit (FIT = 0.639; AFIT = 0.621) and established a structural equation: Wellbeing = 0.216(SEA) + 0.513(SOA) + 0.318(PEPC) + ε. Findings reveal the need to prioritize speed, comfort, connectivity, and digital communication enhancements. Sustainable transportation planning is dependent on public transportation being not just available but also perceived as dependable, comfortable, and convenient to use. This study connects metro service characteristics to rider wellbeing and provides evidence to help guide service goals that promote rider retention and social sustainability. The study is unique in that it presents a latent-variable model that evaluates service features collectively (rather than individually) and converts them into interpretable planning levers using Dubai Metro survey data. By improving metro users’ experiences, the framework contributes to the sustainable mobility paradigm by enabling cities to maintain and expand public transportation use, an enabling solution for lowering vehicle dependency and associated negative impacts. This paradigm also benefits the environment by reducing emissions, increasing air quality, and promoting sustainable urban ecosystems. The proposed framework offers actionable insights for improving metro planning in Dubai and contributes broadly to global public transit development. Incorporating wellbeing into transportation planning supports smart city goals, enhances rider satisfaction, and fosters sustainable urban mobility.

1. Introduction

Urban mobility in Dubai has seen rapid and transformative growth over the past two decades, evolving from a car-dependent city into a global leader in public transportation infrastructure. Among the most significant milestones in this transformation is the Dubai Metro, launched in 2009. As the world’s longest fully automated, driverless metro network, the Dubai Metro spans over 75 km and serves as a backbone of mobility in the city, catering to millions of passengers annually [1,2]. It forms a key part of Dubai’s Smart City vision, which aims to create efficient, sustainable, and technologically advanced urban systems.

The sustainability challenge of urban mobility spans beyond technological advancement and network growth. Sustainable mobility is increasingly being defined as a transition away from narrowly optimizing movement efficiency and toward delivering broader environmental, social, and economic results, such as decreased externalities, greater accessibility, and improved quality of life [3]. In this context, public transportation is strategically vital, but its environmental benefits are not immediately recognized. They are dependent on whether users perceive the system as dependable, safe, economical, and responsive to daily requirements, because repeated unpleasant experiences may decrease ridership and impair the system’s ability to serve as a viable alternative to private car dependency [4].

Historically, transportation planning in Dubai and many other global cities has focused on infrastructure-centric metrics—such as network expansion, operational efficiency, and technological innovation. However, a growing body of research and practice has highlighted the limitations of this approach. As cities strive to become more livable and equitable, there is an increasing need to shift from infrastructure-centric models to rider-centric planning that prioritizes users’ lived experiences [5].

This research makes an integral contribution to sustainability. First, it strengthens a social sustainability lens by defining rider wellbeing as a measurable outcome of transportation performance, capturing not only functional adequacy but also comfort, assurance, and perceived value during travel, all of which are increasingly recognized as policy-relevant outcomes in the travel-wellbeing literature [6]. Second, the paradigm promotes sustainability-informed decision-making by identifying practical variables (such as affordability, coverage, connection, and reliability) that can increase long-term public transportation use. Although this study does not directly estimate environmental impacts, it clarifies an enabling mechanism commonly assumed in sustainable mobility policy: improving lived experience and perceived value can support retaining and encourage greater reliance on public transportation, allowing for broader sustainability gains [3,4].

In addition to its practical importance, this study makes a scholarly contribution by operationalizing rider wellbeing as a planning-relevant outcome and verifying a theory-informed latent structure that combines various service indicators into a small collection of interpretable constructs. Rather than assessing service features in isolation, the framework depicts how efficiency/accessibility, physical comfort, and operational certainty interact to determine total wellbeing, offering an empirical foundation for hypothesis testing and comparing service priorities across settings.

In this context, wellbeing has emerged as a critical concept in transportation planning, emphasizing not just service delivery but how commuting experiences affect individuals’ physical, emotional, and psychological [7]. Wellbeing in transit encompasses comfort, safety, accessibility, dignity, and satisfaction.

This study aims to develop a validated, evidence-based framework for integrating rider wellbeing into metro transportation planning, using the Dubai Metro as a case study. It answers the following research questions: What are the key factors affecting rider wellbeing in metro systems? How can these factors be grouped into theoretical constructs? How do these constructs interact to shape wellbeing? What strategies can improve rider experiences? How can this model inform broader transit planning?

The remaining sections of the paper are organized as follows: Section 2 discusses the theoretical and empirical background on rider wellbeing and service quality; Section 3 describes the survey design, sampling approach, measures, and GSCA modeling procedure; Section 4 presents the results; Section 5 discusses the implications for sustainable transport planning in Dubai and elsewhere; and Section 6 concludes with limitations and future research directions.

2. Literature Review

2.1. Conceptualization of Wellbeing

The concept of wellbeing has been defined and operationalized through various global lenses. The World Health Organization [8] defines wellbeing as a state in which individuals realize their abilities, can cope with normal stresses, and contribute productively to society. The Organization for Economic Co-operation and Development [9] extends this definition to include personal security, environmental quality, social support, and subjective life satisfaction. These multidimensional definitions provide a foundation for evaluating wellbeing beyond material indicators, emphasizing lived experience and emotional outcomes.

In transportation, wellbeing includes physical, emotional, and psychological aspects of travel. Bouck et al. [7] argue that the quality of the commute directly influences mental health, productivity, and social participation. Almardood and Maghelal [10] highlight that public transport systems should be evaluated not only for their efficiency but also for their ability to promote dignity, safety, and comfort. Negative experiences such as overcrowding, delays, or unclear information can lead to stress and dissatisfaction, while well-designed systems foster inclusion, autonomy, and reduced anxiety [11].

To measure service performance, models like SERVQUAL and RATER have been widely adopted. Originally developed for commercial services, SERVQUAL assesses five key dimensions: reliability, assurance, tangibles, empathy, and responsiveness [12]. Transit researchers have adapted these models to assess public transport quality, focusing on accessibility, punctuality, cleanliness, and user interaction [13,14]. However, these models often fail to capture the deeper emotional and psychological impacts associated with transit use.

2.2. Rider Wellbeing and Sustainable Urban Mobility

Sustainability in Transportation refers to the planning and operation of transportation systems in ways that meet contemporary travel requirements without jeopardizing future generations’ ability to meet their own transportation needs, while balancing environmental preservation, social equality, and economic viability. In practice, sustainable urban mobility spans three interconnected areas: environmental sustainability (reducing emissions, energy use, noise, and congestion through cleaner modes and efficient networks), social sustainability (equitable access, affordability, safety, inclusion, and improved user experience and well-being), and economic sustainability (dependable and efficient movement that supports productivity, reduces travel-time and operating costs, and ensures financial resilience [1,2,3].

Sustainable transportation is typically defined as mobility that promotes long-term environmental responsibility, social inclusiveness, and economic viability. Within this framework, sustainable mobility changes the focus away from optimizing movement efficiency and toward improving accessibility, decreasing adverse environmental impacts, and promoting quality-of-life outcomes [3]. Public transportation is frequently positioned as a cornerstone of sustainable cities; however, its ability to achieve sustainability outcomes is dependent not only on infrastructure provision, but also on whether the system is perceived as usable, safe, affordable, and psychologically acceptable in everyday life.

Importantly, wellbeing can be viewed as a behavioral entryway to broader environmental advantages. Evidence examining the factors that influence public transportation interest, particularly among car users, consistently identifies reliability, regularity, comfort, safety, and convenience as critical quality factors [4]. As a result, wellbeing can be viewed as an intermediate outcome that links service performance to sustained use and, possibly, to the broader sustainable mobility goals emphasized by the sustainable mobility paradigm [3]. The current work contributes to this trend by developing a structured latent-variable model that connects service qualities to overall rider wellbeing in a metro context.

2.3. Public Transit and Rider Choice: An Examination of Decision-Making Factors

An efficient, comfortable, and reliable public transit system is essential for sustainable and equitable urban mobility in Dubai’s rapidly growing metropolitan regions [5]. The country’s public transportation infrastructure has seen massive investments in recent years across networks like Dubai Metro, bus fleets, and inter-city rail links. Nevertheless, Almardood and Maghelal [10] show that improving infrastructure provisions alone is not enough–transit systems should perform well on the quality aspects that are important to keep satisfied, loyal riders choosing transit over private vehicles. Identifying exactly what factors are crucial in influencing positive rider experiences in the context of Dubai becomes important to design relevant improvements. This segment focuses on the multi-dimensional aspects that have been extensively highlighted in local research, such as those conducted by Fang et al. [15] and Sun et al. [16], as the factors determining transit satisfaction, loyalty, and modal choice in the country. Qualitative opinion analysis and data-driven statistical methods both offer perspectives on rider prioritization. Though findings differ slightly across some Emirati cities, infrastructure reliability, accessibility, coverage, cooling, cleanliness, seating availability, and seamless connectivity are the main critical expectations. Improvements in targeting on all these levels can significantly increase the competitiveness of transit [17].

Digital services and real-time information engagement are becoming increasingly important in public transportation experiences because they eliminate uncertainty and perceived effort when traveling. The availability, clarity, and consistency of information across channels (such as station signage, announcements, timetables, and mobile applications) in metro systems can have an impact on navigation, perceived reliability, and comfort, particularly for non-native speakers and first-time users. Enhancing real-time communication and multilingual information support is thus a key approach for improving rider experience and sustaining public transportation use [18,19].

Recent empirical research has progressively shown links between public transportation experiences and travel-related wellbeing, while also emphasizing the importance of digital information systems in altering perceived control and stress during transit use. Mode-comparison studies reveal that travel satisfaction and affect varied by mode of transportation, with public transport passengers frequently expressing lower satisfaction and less positive affect than walkers/cyclists, and that enhancing transit efficiency can improve commuter wellbeing [20,21]. Real-time transportation information, such as app-based arrival estimates and service updates, can minimize wait time and uncertainty at stops while increasing perceived accessibility [18]. Evidence from train disruption scenarios suggests that correct real-time information might reduce negative psychological reactions (e.g., annoyance) associated with delays, whereas imprecise information may cause them [22]. Furthermore, real-time crowding data has emerged as a practical digital intervention that can assist riders in avoiding packed cars (by opting to wait for a less-crowded departure), so promoting comfort and perceived safety during peak or interrupted situations [23].

2.4. Developing Wellbeing Constructs

Rider wellbeing is defined as a multidimensional outcome that includes not only functional service performance, but also perceived comfort, certainty, and the general acceptability of the journey in everyday life. To express these dimensions in a clear and understandable manner, the explanatory variables are divided into three latent constructs: Service Efficiency and Accessibility (SEA), Physical Environment and Passenger Comfort (PEPC), and Service Operations and Assurance. This structure is consistent with how metro users perceive service quality as an integrated system—where access and usability enable the trip, the physical environment shapes comfort and stress, and operational reliability and service assurance foster confidence and trust—thus supporting a coherent latent-variable framework for sustainable transportation planning [6,7,8,9].

To address this, recent studies have introduced latent constructs that better reflect the complexity of rider experiences. Service efficiency and accessibility (SEA) includes aspects like speed, affordability, and ease of navigation [24]. Physical environment and passenger comfort (PEPC) pertains to cleanliness, temperature, and ergonomic design [25]. Service operations and assurance (SOA) focuses on interpersonal interactions, punctuality, privacy, and network coverage.

To explore the interrelationships between these constructs, researchers have employed structural equation modeling (SEM), a statistical method that allows for the simultaneous analysis of multiple variables and their causal pathways. SEM has been used to model transit user behavior and satisfaction in various settings [26,27]. However, due to its reliance on large samples and normality assumptions, new approaches like Generalized Structured Component Analysis (GSCA) have gained popularity. GSCA is particularly effective for component-based modeling in small to medium samples and does not require multivariate normality [28,29].

Despite global advances in transit wellbeing research, the Middle East remains underrepresented. In Dubai, studies have focused on satisfaction and service quality [25], but few have developed comprehensive frameworks that quantify and model wellbeing. Given Dubai’s diverse population, extreme climate, and strategic mobility goals, there is a need for localized, data-driven models that align with its smart city ambitions. This study addresses this gap by applying GSCA to model rider wellbeing in the Dubai Metro.

Overall, prior studies have provided valuable evidence on metro service quality, satisfaction, and ridership intentions; however, they often examine service attributes in isolation and do not explicitly represent the rider experience as an integrated system of latent perceptions linked to wellbeing as an outcome relevant to sustainable mobility. This study contributes by (i) defining rider wellbeing as an overall journey-acceptability outcome for planning, (ii) organizing service perceptions into three theoretically grounded latent dimensions (SEA, PEPC, and SOA), and (iii) estimating their simultaneous relationships using GSCA rather than studying factors individually. The resulting framework offers a practical way to prioritize improvements that can support sustained public transport use by strengthening the quality and reliability of the user experience in a high-demand metro context.

2.5. Public Transport Use During COVID-19 and the Post-Pandemic Era

According to a recent study, public transportation interruptions and perceived health concerns during COVID-19 had an impact on both access and user wellbeing, whereas the post-pandemic period changed expectations around safety, information transparency, and confidence in shared mobility. According to qualitative and empirical research, service reliability, crowding conditions, and communication about disruptions or health-related measures can all influence perceived wellbeing and willingness to continue using transit, highlighting the importance of reassurance and real-time information in rider experience management [30,31,32,33]. This evidence confirms the necessity of integrating wellbeing into sustainable transport planning and gives a modern context for viewing wellbeing as a multidimensional result of service quality judgments.

The COVID-19 pandemic significantly altered public transportation use by introducing both operational interruption and increased perceived health risk, with direct repercussions for rider wellbeing and faith in shared mobility. Evidence from Metro Manila shows that public transportation disruption during the pandemic impacted access to essential services (including healthcare) and was closely related to wellbeing outcomes, demonstrating how system reliability under crisis conditions can become a wellbeing determinant rather than just a performance attribute [30]. As systems began to recover, research in Thessaloniki found that predicted post-pandemic public transportation utilization is dependent on how users reassess risk, trust, and the acceptability of shared travel settings [31].

Post-COVID behavioural evidence from Xi’an suggests that travel satisfaction and travel wellbeing have different relationships with travel choice, highlighting the importance of treating “wellbeing” as a distinct planning-relevant outcome in public transport evaluation [32]. More recent research indicates that the epidemic has affected passengers’ dependency on public transportation, emphasizing that regaining ridership necessitates addressing the elements that rebuild trust and perceived acceptability of metro use [33]. Collectively, this literature supports the relevance of wellbeing-oriented transit planning by emphasizing the importance of perceived safety, service continuity, and trust-building measures in sustaining public transportation use during interruptions and in the post-pandemic era [30,31,32,33].

2.6. Bridging Wellbeing Theory and Transportation Planning

Wellbeing is well established in public health and social science, but its application in transportation planning remains conceptually broad and inconsistent—sometimes as a momentary travel affect and satisfaction, and other times as a broader life-evaluation outcome that travel can influence [6,7]. This conceptual spread results in a “implementation gap” for transit decision-making because a highly multidimensional and subjective notion is difficult to operationalize as a service performance metric alongside common indicators such as reliability, accessibility, and perceived quality [4,5]. To bridge this gap, the current study adopts a transport-specific definition by treating rider wellbeing as an overall journey acceptability evaluation (i.e., whether the metro experience is dependable, comfortable, and usable enough to integrate into daily routines), consistent with widely used wellbeing framing in applied policy. To address this gap, the current study uses a transport-specific definition, assessing rider wellbeing as an overall journey-acceptability evaluation (i.e., whether the metro experience is dependable, comfortable, and usable enough to integrate into daily routines), which is consistent with widely used wellbeing framing in applied policy contexts [8,9]. This refinement retains human-centered wellbeing while simultaneously making it quantitative, allowing for monitoring and improvement.

Building on this operationalization, the study advances a latent-variable translation from “wellbeing” to actionable planning levers by organizing rider perceptions into three theoretically grounded service dimensions—SEA (service efficiency/accessibility), PEPC (physical environment/comfort), and SOA (operations/assurance)—and estimating their concurrent relationships with wellbeing using component-based SEM (GSCA) [26,27,28,29]. This approach encourages a shift from evaluating isolated service attributes to diagnosing an integrated experience system, while explicitly incorporating contemporary service expectations that influence perceived control and confidence—particularly digital and real-time information functions such as disruption updates and crowding/queue management, which have been shown to shape rail experience and satisfaction [18,19,22,23]. In this context, wellbeing becomes a practical consequence for sustainable mobility planning (i.e., encouraging retention, loyalty, and mode choice through experience quality). Consistent with data associating transit satisfaction/emotions and commute wellbeing to overall mobility outcomes [20,26].

2.7. Novelty of the Research

The fundamental contribution of this study is the development of a transportable explanatory framework that converts broad wellbeing concepts into a concrete, transit-operational structure. In contrast to solely algorithmic proposals, this work is new on three levels. It operationalizes rider wellbeing as a concrete “journey-acceptability” outcome, moving beyond abstract life-evaluation constructs to provide a metric that can be directly applied to transit planning. Second, rather than considering service attributes in isolation, the study proposes an integrated latent structure consisting of three interconnected service pillars—SEA, PEPC, and SOA—to capture the joint cognitive appraisal of metro transit. Finally, the framework bridges the theoretical-practical gap by mapping measurement items to concrete operational levers, resulting in a scalable tool for benchmarking, longitudinal monitoring, and sustainability-oriented decision-making across several metro systems.

3. Materials and Methods

3.1. Survey Design and Data Collection

This study adopted a cross-sectional survey, with quantitative emphasis to develop a significant model of rider wellbeing. The primary focus was placed on a structured quantitative survey administered to 1409 Dubai Metro riders. The survey which focused on 15 of the 55 metro stations that are estimated to be in high demand, was carried out in July and August of 2024. covering Sunday through Saturday from 7:00 a.m. to 10:00 p.m. The wide variety of survey measurements made it accessible to record the whole range of user-generated trips from and to these metro stations, including age, gender, weekday and weekend travel reasons, peak and off-peak travel times, and more.

The station selection was purposefully chosen to highlight where the Dubai Metro experience is most important for both riders and operators. Rather than distributing the survey evenly across all stations, data collection focused on 15 stations identified as high-demand, ensuring that the sample captured the everyday conditions where service pressures are typically highest—such as peak-hour crowding, platform and carriage congestion, queueing at gates and ticketing points, and the complexity of transfers and wayfinding. This method ensures that the wellbeing framework is informed by the “real-world” friction points that have the greatest impact on rider stress, uncertainty, and perceived service reliability. At the same time, focusing on high-volume stations can be considered a sensible design decision: riders in these situations may be subjected to more frequent discomfort and operational problems, exacerbating negative sentiments compared to quieter stops. To ensure openness, the findings should be understood largely as typical of high-use network settings, with future study expanding coverage to lower-demand stations to explore how wellbeing determinants vary over a wider range of station contexts.

Because the study was conducted in July and August, the findings on comfort and the physical environment should be viewed in the context of Dubai’s high summer heat. In such cases, thermal comfort and climate-controlled surroundings might become primary predictors of perceived acceptability and wellbeing rather than secondary amenities, emphasizing the necessity of PEPC-related treatments in hot-weather cities [34].

It took about 12 to 15 min on average to complete the survey, which was available in English. About 72% of those contacted decided to take part in the survey. The method employed was non-probability convenience sampling. The Dubai Roads and Transport Authority (RTA) approved and supervised the study, and a qualified RTA field team handled data gathering due to privacy concerns [2].

In practice, this station-intercept strategy entailed addressing riders in publicly accessible station areas and inviting voluntary participation, which is useful when a complete passenger sample frame is not available. To reduce time-of-day and weekday bias, data was collected during operation hours and throughout the week, capturing both peak and off-peak conditions as well as a variety of travel purposes. For this purpose, data were collected from metro users using an online questionnaire administered via SurveyMonkey (San Mateo, CA, USA). The sample size obtained was deemed acceptable for the study’s latent-variable modeling objectives because it significantly exceeded standard SEM-oriented guidelines for stable estimates relative to the number of observed indicators and structural routes, allowing for robust inference in the GSCA procedure. The findings are interpreted with appropriate caution in terms of generalizability, and they are intended to be strengthened in future work by benchmarking or probability sampling.

Using Cochran’s technique and conservative parameters—a 95% confidence level (Z = 1.96), a 0.05 margin of error, and an expected proportion of 0.5—the theoretical minimum required sample size was found to be 385. The actual data collection in the Metro area provided 1409 respondents, which was greatly above the minimum criteria and confirmed the sample’s statistical adequacy for the study.

The research followed the proposed workflow shown in Figure 1. The instrument was developed based on a comprehensive review of the literature and validated through a pilot study. It included Likert-scale items ranging from 1 (strongly disagree) to 5 (strongly agree), assessing perceptions of service quality, comfort, accessibility, and overall wellbeing. Surveys were distributed both online and in-person at key metro stations across Dubai. The resulting sample reflected the city’s diverse commuter demographics.

The survey questions covered different dimensions of the experience with transit, such as affordability, speed of the service, ease of the service, availability of information, facilities, cleanliness, comfort, professionalism of staff, privacy, punctuality, connectivity, clarity of ticketing, and coverage. There was also a direct measure of the overall wellbeing of the rider as the dependent variable.

Structure of the survey: Service Efficiency and Accessibility (SEA) had 16 questions, Physical Environment and Passenger Comfort (PEPC) had 8 questions, Service Operations and Assurance (SOA) had 22 questions, Demographics and Trip Purpose had 9 questions, and Metro Journey Details had 8 questions. “Overall, how would you rate your metro trip experience, considering your overall satisfaction and general impression?” was the question used to collect the dependent variable.

Rider wellbeing is considered the model result and is operationalized through this global evaluation item, which captures the respondent’s whole trip experience in terms of satisfaction and general impression. This single-item global measure is utilized as the dependent variable to quantify how the three latent service constructs shape the lived travel experience together [6,8,9]. Rider wellbeing is defined in the paper’s framework as the overall experience and general acceptance of the metro travel, which reflects the rider’s overall assessment of the trip rather than a specific service feature. A direct global evaluation item, such as “Overall, how would you rate your metro trip experience, considering your overall satisfaction and general impression?” is used to operationalize wellbeing as the dependent (outcome) variable in the paper.

3.2. Methodological Framework

To ensure integrity, the analysis followed a step-by-step process to improve reproducibility and ensure alignment with the planning application. First, the survey instrument was created from existing literature and modified through a pilot validation to guarantee item clarity and concept coverage and to collect rider assessments of metro service aspects that transit operators may affect, as well as an overall wellbeing outcome indicating the acceptability of the route experience. Second, the dataset was screened and prepared, which included data cleansing and validation to ensure completeness and suitability for latent modeling, before being summarized using descriptive statistics and frequency analysis to profile respondent characteristics and travel patterns. Third, measurement consistency was evaluated using internal consistency checks, and correlation analysis was utilized to investigate the initial strength and direction of correlations between particular service items and rider wellbeing, which informed the subsequent modeling stage. The measurement specification was then divided into three interconnected latent pillars (SEA, PEPC, and SOA) in accordance with service-quality theory and the thesis-based construct rationale, to indicate how riders structure perceptions rather than using indicators as independent predictors. Fourth, GSCA Pro (Version 1.2.1.0; developed by Heungsun Hwang, Gyeongcheol Cho, In-Hyun Baek, and Hosung Choo.) was used to estimate both the measurement component (connecting observed indicators to latent constructs) and the structural component (hypothetical links between constructs and rider wellbeing). The GSCA model was estimated using specialized software with an alternating least squares optimization procedure, supported by bootstrapping to evaluate parameter stability. Model adequacy was assessed using established global fit indices, alongside interpretation of indicator weights/loadings and structural path coefficients. Finally, the results were interpreted as planning factors (e.g., operational assurance, information quality, and comfort conditions), thereby connecting the statistical model to actionable priorities for the Dubai Metro and other high-demand metro environments.

The structural modeling approach was selected as Generalized Structured Component Analysis (GSCA), which is a component-based SEM technique that combines path analysis and latent variable modeling by defining latent variables as weighted composites of observed indicators. This makes GSCA a good fit for prediction-oriented research and practical survey situations, and it is especially useful when rigorous distributional assumptions are not desired. This supports decision-relevant interpretation in planning contexts. In this study, GSCA provides an adequate framework to analyze the complicated interactions among different service aspects and overall rider wellbeing within a single cohesive latent-variable model, while estimating both measurement and structural components. The metrics were chosen to highlight service components that can be addressed by operational management, personnel practices, information systems, station environment interventions, and network integration regulations. In this way, the modeling technique is both explanatory and diagnostic: each latent pillar summarizes a set of service perceptions that can be tracked over time and converted into improvement initiatives, thereby assisting wellbeing- and sustainability-oriented planning decisions.

3.3. Ethics & Consent

The Dubai Roads and Transport Authority (RTA)—Strategic Planning Department —authorized the survey to be conducted on metro property with Riders (Request Code: #RF231180; Approval Date: 19 February 2024). The goal, methods, and voluntary nature of the study were described to participants before participation, and informed consent was acquired. Respondents were not obliged to answer any question and may stay anonymous. The Declaration of Helsinki’s ethical guidelines were followed in the course of this study. Responses were anonymized, safely stored, and only reported in aggregate to maintain confidentiality.

3.4. Construct Operationalization

Rider wellbeing was operationalized using three latent constructs defined as:

• Service Efficiency & Accessibility (SEA): This construct represents the functional aspects of metro service that enable riders to efficiently access and utilize the system [35]. It encompasses four key indicators:
  • Affordability: The perceived value for money of metro fares
  • Speed of service: The timeliness and rapidity of metro operations
  • Ease of service: The user-friendliness and convenience of using the metro
  • Information availability: The accessibility and clarity of service information
• Physical Environment & Passenger Comfort (PEPC): This construct captures the tangible environmental conditions that riders experience during their metro journey [36]. It includes three indicators:
  • Facilities: The availability and quality of metro facilities and amenities
  • Cleanliness: The hygiene and maintenance standards within the metro system
  • Comfort: The physical comfort experienced during the metro journey
• Service Operations & Assurance (SOA): This construct represents the operational reliability and service assurance aspects of the metro experience [37]. It encompasses six indicators:
  • Staff professionalism: The conduct and competence of metro personnel
  • Privacy: The sense of personal space and privacy during the journey
  • Punctuality: The adherence to scheduled departure and arrival times
  • Connectivity: The integration and network coverage of the metro system
  • Ticketing clarity: The ease of understanding and using the ticketing system
  • Coverage: The geographic reach and accessibility of the metro network

The dependent variable in the model is rider wellbeing, which represents the overall positive experience and satisfaction derived from using the metro system.

To analyze the relationships among these constructs, the study employed Generalized Structured Component Analysis (GSCA) as shown in Figure 2. GSCA is a component-based structural equation modeling technique particularly suited for moderate sample sizes and non-normal data. It allows for simultaneous estimation of measurement and structural models and is more flexible than traditional covariance-based SEM.

The latent structure of Service Efficiency and Accessibility (SEA), Physical Environment and Passenger Comfort (PEPC), and Service Operations and Assurance (SOA) was adopted because public-transport service quality is consistently treated as a multidimensional rider evaluation, where service attributes are cognitively grouped into distinct but related domains: (i) functional accessibility and ease-of-use (e.g., affordability, speed/efficiency, informational support, and overall During construct setup, different groupings/structures of the service qualities were also investigated, but the SEA-PEPC-SOA organization gave the most cohesive and stable mapping of observed indicators. As a result, SEA is defined to capture the functional “can I use it easily and efficiently?” dimension (affordability, speed of service, ease of service, and information availability) [13,38], PEPC captures the tangible and sensory travel environment (facilities, cleanliness, and comfort) [39], and SOA captures the “can I trust the system and feel assured while using it?” facet (staff professionalism, privacy, punctuality, connectivity, ticketing clarity, and network coverage), allowing for an interpretable explanation of overall rider wellbeing.

3.5. Data Analysis

The data analysis followed a two-step process: first, estimating indicator weights and loadings (measurement model), and second, estimating path coefficients between constructs (structural model). The analysis was conducted using GSCA Pro (Version 1.2.1.0) software. Model fitness indicators confirmed the model’s significance, with FIT = 0.639, AFIT = 0.621, and GFI = 0.970.

4. Results

4.1. Descriptive Statistics

The demographic analysis using IBM SPSS Statistics (Version 29.0.2.0; IBM Corp., Armonk, NY, USA), reveals that the metro ridership is predominantly composed of Asian nationals (72.8%), followed by Expat Arabs (17.0%), Westerners (4.4%), and other nationalities (3.8%), with UAE nationals representing only 0.5% of the sample as shown in Figure 3. This distribution reflects Dubai’s diverse expatriate population and suggests that public transportation, particularly the metro, is heavily utilized by the expatriate workforce.

The residency status data indicates that 90.4% of respondents are Dubai residents, with smaller proportions from other emirates (3.3% from Sharjah) and visitors (5.0%). This concentration of Dubai residents aligns with the metro’s primary role as an urban transit system serving local commuting needs rather than tourism or inter-emirate travel.

Gender distribution shows a significant male majority (87.2%) compared to female riders (12.8%). This pronounced gender imbalance may reflect broader workforce demographics in Dubai, cultural factors affecting transportation choices, or potential barriers to female ridership that merit further investigation.

Age distribution data shown in Figure 4 indicates that young adults dominate metro ridership, with 59.5% of respondents aged 21–30 and 31.3% aged 31–40. Only 8.3% of riders were over 40 years old, suggesting the metro particularly appeals to younger commuters, possibly due to its modernity, technological integration, or alignment with the commuting patterns of younger workers.

The age categories were created using regularly used demographic reporting brackets, which are comparable with how official data and large-sample transportation surveys normally divide populations into interpretable life-stage groupings. The brackets differentiate between early adulthood, mid-career working ages, and older cohorts, which is analytically important because perceptions of service quality, risk, comfort, and information utilization might vary significantly by life stage. Using these standard age bands increases interpretability, allows for comparison with official demographic summaries, and ensures appropriate counts within each category for consistent descriptive reporting and subgroup checks.

The educational background of metro riders in Figure 5 reveals a highly educated user base, with 47.6% holding bachelor’s degrees and 17.2% holding master’s degrees. This educational profile suggests that the metro serves a significant proportion of professional and skilled workers in Dubai. Usage frequency data demonstrates that metro ridership is characterized by regular commuters, with 74.3% using it more than once per day and 17.5% using it once daily. This high frequency of use underscores the metro’s importance as a daily transportation solution rather than an occasional option.

Further analysis of metro riders’ income was conducted as part of the study; the majority (69.1%) felt that this was sensitive information and preferred not to reveal it, while riders with salaries under AED 10,000 represented 27.5%, as seen in Figure 6. The remaining 3.4% reported ≥AED 10,000 monthly. The income ranges were divided into broad bands to represent significantly distinct socioeconomic strata in a fashion that is easy to plan and broadly consistent with census-style reporting processes. This method avoids over-fragmenting respondents into narrow income intervals, which can result in sparse categories and unstable comparisons, while also capturing significant differences in affordability sensitivity and perceived value of service qualities. The chosen bands thus allow for a clearer interpretation of the sample profile and facilitate policy-relevant discussion (e.g., how rider experience and wellbeing may differ between lower- and higher-income segments), with the intention that future studies can compare these groupings to published national or city-level demographic distributions when available.

According to a trip purpose analysis shown in Figure 7, over 50% of riders go to work, demonstrating the metro’s vital significance as a means of transportation for Dubai’s citizens. At the same time, 20% of all journeys are for commercial purposes, another ~20% were home-related trips. This adds up to 40%, suggesting that metro is a significant mode of transportation outside of the typical work trip. The remaining 10% fluctuates depending on other activities including dining, shopping, attending school, and tourism.

4.2. Statistical Analysis for Satisfaction Factors

The analysis began with data cleaning and validation, followed by descriptive statistics to understand the central tendencies, distributions, and variability of responses across all survey items. This included computing means, standard deviations, skewness, and kurtosis values to assess the normality of distributions. Frequency analyses were conducted to examine the demographic characteristics of respondents and their patterns of transit use. The reliability of the measurement scales was assessed using Cronbach’s alpha, with values of 0.908 for the metro survey, indicating excellent internal consistency in the dataset.

The results begin with descriptive statistics for each individual service attribute. Overall, the Dubai Metro received high scores across most indicators, with the average self-reported wellbeing score reaching 4.52 out of 5. The highest rated attributes were connectivity (mean = 4.54), staff professionalism (mean = 4.42), and privacy (mean = 4.35). On the other hand, speed of service (mean = 3.61), ease of service (mean = 3.71), and information availability (mean = 3.84) were rated comparatively lower, suggesting specific areas that could be targeted for improvement. The demographic profile of the participants. The survey captured a wide cross-section of Dubai’s mobile population, i.e., students, professionals, and tourists.

This sample shows a high level of rider wellbeing (mean ≈ 4.5/5). The main factors influencing wellbeing are identified by an analysis of answers across important service dimensions, which also reveals areas where focused improvements are most likely to have the biggest impact. These findings translate city-level policy into specific, doable priorities for planning and investment throughout the metro network, in line with the Dubai Urban Plan 2040, which places a high priority on wellbeing and people-centered transportation.

The correlation matrix reveals significant relationships between service attributes and rider wellbeing. All 13 service attributes show statistically significant positive correlations with rider wellbeing (p < 0.01). The strongest correlations with rider wellbeing are observed for connectivity (r = 0.816), staff professionalism (r = 0.757), and punctuality (r = 0.700). These strong associations suggest that a well-connected network, professional staff interactions, and reliable service are particularly important determinants of rider wellbeing in the metro system.

Moderate correlations are observed for cleanliness (r = 0.694), comfort (r = 0.670), and facilities (r = 0.630), highlighting the importance of physical environment factors in shaping the overall transit experience. The correlation patterns also reveal interesting interrelationships among service attributes. For instance, affordability shows strong correlations with speed of service (r = 0.789) and ease of service (r = 0.710), suggesting that perceptions of value for money are closely tied to service efficiency. Similarly, staff professionalism strongly correlates with connectivity (r = 0.839), indicating that human factors and network functionality may be perceived as interrelated aspects of service quality.

Bartlett’s test of sphericity and the Kaiser-Meyer-Olkin (KMO) index were used to evaluate the factorability of the variables. The KMO calculation yielded a respectable overall result of 0.886, showing a good fit for latent construct modeling. However, Bartlett’s test was significant, as shown by the results (x² = 13,372, p < 0.001), which indicates the variables are sufficiently intercorrelated to confirm the use of the GSCA latent construct in this research.

The results showed a high condition index of 82.4 with a substantial variance in proportion for both privacy (0.57) and Ticketing clarity (0.91), supporting the analysis for multicollinearity and possibly suggesting some dependency of these parameters. All constructs, however, indicate outcomes that fall under the standard criteria. Additionally, the GSCA’s latent construct will tackle any multicollinearity issues.

Because wellbeing is measured on an ordinal Likert scale, a Mann–Whitney U test (two-tailed) was utilized to compare ratings between male and female riders. The findings revealed a minor but statistically significant difference (U = 127,170.5, Z = 2.23, p = 0.025), with males having a higher mean rank (731.01) than females (664.59), indicating a slightly larger tendency for males to report higher wellbeing categories. However, the practical significance of this difference was negligible (r = 0.059), and both groups had the same median rating of “Happy”.

In addition to the gender-based subgroup analysis, Mann–Whitney U test was used to determine whether overall wellbeing ratings varied by age. To ensure acceptable sample sizes, the survey age variable was divided into two categories: ≤30 years (Under 21 and 21–30) and >30 years (31–40 and above). The test found a statistically significant but small difference in overall wellbeing between the two age groups (U = 217,030.5, Z = −4.53, p < 0.001). Riders over 30 had a higher mean rank (778.40) than riders under 30 (686.25). However, the impact size was minor (r = 0.119), and the median assessment was “Happy” in both groups. While age-related changes can be seen in the distribution of responses, central wellbeing evaluations are generally consistent across age groups.

4.3. Construct Analysis

The construct SEA was evaluated across four indicators.

Table 1. SEA—Service Efficiency and Accessibility.

Indicator	Mean Score	Weight	Loading
Speed of Service	3.61	0.316	0.925
Ease of Service	3.71	0.277	0.875
Affordability	4.12	0.258	0.867
Information Availability	3.84	0.296	0.818

presents the mean scores, indicator weights, and loadings.

While SEA demonstrated a moderate mean score, it had a meaningful structural impact on rider wellbeing, with a direct path coefficient of 0.216. Speed of service had the highest impact despite receiving the lowest mean score, indicating it is a critical area for operational enhancement.

Table 2. PEPC—Physical Environment and Passenger Comfort.

Indicator	Mean Score	Weight	Loading
Comfort	4.13	0.427	0.875
Cleanliness	4.21	0.417	0.810
Facilities	4.06	0.331	0.873

outlines PEPC, which was found to be the second most influential on overall wellbeing.

PEPC showed a strong path coefficient of 0.318 to rider wellbeing. Cleanliness and comfort had the highest loadings, suggesting that environmental quality is an essential contributor to positive transit experiences. Riders particularly valued temperature control, noise levels, and seating conditions. The SOA construct was the most influential in the model, as shown in

Table 3. SOA—Service Operations and Assurance.

Indicator	Mean Score	Weight	Loading
Connectivity	4.54	0.248	0.904
Staff Professionalism	4.42	0.222	0.899
Punctuality	4.29	0.199	0.673
Privacy	4.35	0.204	0.750
Ticketing Clarity	3.74	0.182	0.764
Network Coverage	3.88	0.211	0.710

.

SOA had the highest direct effect on wellbeing with a path coefficient of 0.513. Connectivity and staff professionalism were the strongest contributors, indicating that both system integration and interpersonal service play a crucial role in shaping user satisfaction.

4.4. Interconstruct Relationships

The model also revealed significant interrelationships between constructs. The relationship between PEPC and SOA (coefficient = 0.746) was particularly strong, suggesting that improvements in the physical environment lead to better perceptions of operational service attributes. These results are presented in

Table 4. Interconstruct path coefficients.

From → To	Path Coefficient
SEA → PEPC	0.247
SEA → SOA	0.231
PEPC → SOA	0.746
SEA → Wellbeing	0.216
PEPC → Wellbeing	0.318
SOA → Wellbeing	0.513

.

4.5. Model Fit Assessment

The estimated metro GSCA model demonstrated satisfactory fit to the data across distinct fit indices as shown in

Table 5. Comprehensive fit indices for metro GSCA model.

Fit Index	Value	Threshold	Interpretation
FIT	0.639	Higher is better	63.9% of total variance explained
AFIT	0.621	Higher is better	Maintains explanatory power after adjustment for complexity
GFI	0.970	>0.90	Excellent model fit
SRMR	0.091	<0.08	Slightly above the 0.08 guideline; acceptable given other indices.

:

4.6. Composite Structural Equation for Metro Rider Wellbeing

Based on the GSCA modeling, the final structural equation for rider wellbeing is expressed as:

Rider Wellbeing = 0.216(SEA) + 0.513(SOA) + 0.318(PEPC)+ ε

(1)

This equation indicates that while all three constructs matter, the greatest returns on enhancing overall wellbeing may come from improvements in operations and assurance, followed by investments in environmental comfort.

Also, it quantifies the direct effects of each latent construct on Rider Wellbeing. Service Operations & Assurance has the strongest direct effect (coefficient = 0.513), followed by Physical Environment & Passenger Comfort (coefficient = 0.318) and Service Efficiency & Accessibility (coefficient = 0.216). The term ε₃ represents the residual variance in Rider Wellbeing not explained by the three latent constructs.

By substituting the measurement equations into the structural equation for Rider Wellbeing, a composite equation can be derived that expresses Rider Wellbeing directly in terms of all observed variables:

Rider Wellbeing = 0.216 × (0.258 × Affordability + 0.316 × Speed of Service + 0.277 × Ease of Service + 0.296 × Information Availability) + 0.318 × (0.331 × Facilities + 0.417 × Cleanliness + 0.427 × Comfort) + 0.513 × (0.222 × Staff Professionalism + 0.204 × Privacy + 0.199 × Punctuality + 0.248 × Connectivity + 0.182 × Ticketing Clarity + 0.211 × Coverage) + ε

(2)

This expanded equation can be simplified to express the total effect of each observed variable on Rider Wellbeing:

Rider Wellbeing = 0.056 × Affordability + 0.068 × Speed of Service + 0.060 × Ease of Service + 0.064 × Information Availability + 0.105 × Facilities + 0.133 × Cleanliness + 0.136 × Comfort + 0.114 × Staff Professionalism + 0.105 × Privacy + 0.102 × Punctuality + 0.127 × Connectivity + 0.093 × Ticketing Clarity + 0.108 × Coverage + ε

(3)

This final equation reveals that Comfort (total effect = 0.136) and Cleanliness (total effect = 0.133) have the strongest total effects on Rider Wellbeing in the metro context, followed by Connectivity (total effect = 0.127) and Staff Professionalism (total effect = 0.114).

For multicollinearity, The inter-construct correlations show that some latent dimensions move together (e.g., the high SEA-SOA association, r ≈ 0.773), as expected in an integrated metro system where efficient access and operational assurance are jointly experienced (for example, affordability is strongly correlated with speed of service, r = 0.789, and staff professionalism is strongly correlated with connectivity, r = 0.839). Rather than treating this overlap as a fault, it was evaluated whether the shared variance was significant enough to jeopardize the structural results’ stability or interpretability. Collinearity diagnostics revealed a high condition index (82.4), with the highest concentration of shared variation found at the indicator level for privacy (variance proportion = 0.57) and ticketing clarity (variance proportion = 0.91). These specific operational factors appeared to be dependent on one another, but the remaining indicators and constructs met acceptable interpretation criteria. In this context, the GSCA component-based estimation is appropriate because it models constructs as latent composites and estimates their effects concurrently, which helps to accommodate correlated predictors and allows path coefficients to be interpreted as incremental (unique) contributions of each service dimension to wellbeing after accounting for overlap with the other dimensions. As a result, even with a strong SEA-SOA correlation, the model remains useful for prioritization since it distinguishes what is common among service evaluations from what each dimension uniquely explains in rider wellbeing [28,29].

4.7. Practical Significance of the Framework

From a practical standpoint, the GSCA framework offers transportation planners and policymakers a structured approach to understanding and enhancing rider wellbeing. Quantifying the relative contributions of different service dimensions, it helps prioritize investments and interventions that will yield the greatest improvements in rider experiences. Moreover, the framework’s acknowledgement of both direct and indirect pathways of influence provides a more nuanced understanding of how service improvements in one area might cascade through the system to enhance overall rider wellbeing. This systems perspective is particularly valuable in complex transit environments like Dubai, where multiple service dimensions must be orchestrated effectively to create positive rider experiences. In summary, the GSCA framework for rider wellbeing represents a sophisticated analytical tool that combines theoretical rigor with practical utility, providing a solid foundation for hypothesis testing and development of evidence-based strategies to enhance transit experiences in Dubai.

4.8. Metro Model Hypothesis Testing

The correlation and GSCA analysis for the metro model generated statistical evidence to test 19 hypotheses concerning the relationships between individual service attributes, latent constructs, and rider wellbeing. Based on the path coefficients, standard errors, and 95% confidence intervals provided in the analysis output, each hypothesis can be systematically evaluated as shown in

Table 6. Hypothesis Testing Results for Individual Attributes (H1–H13).

Hypothesis	Correlation with Wellbeing	GSCA Weight (SE, 95% CI)	GSCA Loading (SE, 95% CI)	Effect Type	Significance	Final Result
H1: Affordability has a significant effect on rider wellbeing.	r = 0.344, p < 0.001	0.258, SE = 0.005, CI [0.247–0.267]	0.867, SE = 0.005, CI [0.857–0.876]	Direct (via SEA)	Significant	Supported
H2: Speed of service has a significant effect on rider wellbeing.	r = 0.470, p < 0.001	0.316, SE = 0.005, CI [0.306–0.326]	0.925, SE = 0.004, CI [0.919–0.933]	Indirect (via SEA)	Significant (no direct path)	Supported Indirectly
H3: Ease of service has a significant effect on rider wellbeing.	r = 0.387, p < 0.001	0.277, SE = 0.004, CI [0.269–0.285]	0.875, SE = 0.007, CI [0.864–0.886]	Indirect (weak)	Significant (weak effect)	Weak Support
H4: Information availability has a significant effect on rider wellbeing.	r = 0.551, p < 0.001	0.296, SE = 0.004, CI [0.289–0.303]	0.818, SE = 0.014, CI [0.793–0.850]	Direct (via SEA)	Significant	Supported
H5: The quality of facilities has a significant effect on rider wellbeing.	r = 0.630, p < 0.001	0.331, SE = 0.008, CI [0.315–0.347]	0.873, SE = 0.007, CI [0.859–0.885]	Direct (via PEPC)	Significant	Supported
H6: Cleanliness has a significant effect on rider wellbeing.	r = 0.694, p < 0.001	0.417, SE = 0.007, CI [0.403–0.430]	0.810, SE = 0.009, CI [0.787–0.827]	Direct (via PEPC)	Significant	Supported
H7: Comfort has a significant effect on rider wellbeing.	r = 0.670, p < 0.001	0.427, SE = 0.008, CI [0.413–0.442]	0.875, SE = 0.006, CI [0.864–0.888]	Direct (via PEPC)	Significant	Supported
H8: Staff professionalism has a significant effect on rider wellbeing.	r = 0.757, p < 0.001	0.222, SE = 0.008, CI [0.210–0.240]	0.899, SE = 0.005, CI [0.889–0.909]	Direct (via SOA)	Significant	Supported
H9: Privacy has a significant effect on rider wellbeing.	r = 0.577, p < 0.001	0.204, SE = 0.005, CI [0.194–0.215]	0.750, SE = 0.011, CI [0.727–0.772]	Indirect (weak, via SOA)	Significant (no direct path)	Supported Indirectly
H10: Punctuality has a significant effect on rider wellbeing.	r = 0.700, p < 0.001	0.199, SE = 0.007, CI [0.183–0.213]	0.673, SE = 0.015, CI [0.643–0.701]	Indirect (via SOA)	Significant (no direct path)	Supported Indirectly
H11: Connectivity has a significant effect on rider wellbeing.	r = 0.816, p < 0.001	0.248, SE = 0.009, CI [0.225–0.264]	0.904, SE = 0.006, CI [0.894–0.915]	Indirect (strong, via SOA)	Significant	Supported Indirectly
H12: Ticketing clarity has a significant effect on rider wellbeing.	r = 0.592, p < 0.001	0.182, SE = 0.006, CI [0.169–0.192]	0.764, SE = 0.012, CI [0.741–0.787]	Indirect (via SOA)	Significant	Supported Indirectly
H13: Coverage has a significant effect on rider wellbeing.	r = 0.580, p < 0.001	0.211, SE = 0.005, CI [0.201–0.221]	0.710, SE = 0.016, CI [0.679–0.742]	Indirect (via SOA)	Significant	Supported Indirectly

for hypotheses 1–13. It is noteworthy that several attributes exhibited limited support for the wellbeing model while also demonstrating statistically significant correlation results when examined with riders’ wellbeing. Despite this outcome, it is advised to keep these characteristics in the proposed model since they conceptually explain how the model measures different aspects of the riders’ psychology.

The analysis mostly used GSCA weights to test hypotheses H1–H13, which evaluate the impact of specific service qualities on bus rider wellbeing. Together with standard errors, confidence intervals, and significance values, weights show how much each observed variable contributed to the development of its latent construct. As a result, weights offer the statistical foundation for assessing whether an attribute’s construct significantly enhances rider wellbeing. In order to ensure that attributes accurately reflect their constructs, factor loadings are utilized as well to demonstrate measurement validity. Thus, loadings enhance construct validity, while weights validate significance.

SEM path coefficients (β values) were used in the study for hypotheses H14–H19, which investigate the links between latent factors and how they affect rider wellbeing. Path coefficients illustrate the structural linkages in the model by quantifying the strength and direction of effect between constructs. Path coefficients function at the construct level and directly test the theoretical paths outlined in the model, in contrast to weights, which function at the attribute level. The hierarchical structure of the framework is reflected in hypothesis testing in response to this methodological distinction: individual attributes are used to form constructs through statistically significant weights, and these constructs then have statistically significant paths that impact wellbeing. Thus, the application of path coefficients for construct-level hypotheses and weights for attribute-level hypotheses offers a rigorous and theoretically consistent approach.

Table 7. Hypothesis Testing of Construct Relationships with Rider Wellbeing (H14–H19).

Hypothesis	SEM Path Coefficient (β, SE, p-Value)	Effect Type	Significance	Final Result
H14: Service efficiency & accessibility have a significant effect on physical environment & passenger comfort.	β = 0.247, SE = 0.026, p < 0.001	Direct	Significant	Supported
H15: Service efficiency & accessibility have a significant effect on service operations & assurance.	β = 0.231, SE = 0.014, p < 0.001	Direct	Significant	Supported
H16: Physical environment & passenger comfort have a significant effect on service operations & assurance.	β = 0.746, SE = 0.014, p < 0.001	Direct	Significant	Strongly Supported
H17: Service efficiency & accessibility have a significant effect on metro rider wellbeing.	β = 0.216, SE = 0.015, p < 0.001	Direct	Significant	Supported
H18: Physical environment & passenger comfort have a significant effect on metro rider wellbeing.	β = 0.318, SE = 0.027, p < 0.001	Direct	Significant	Supported
H19: Service operations & assurance have a significant effect on metro rider wellbeing.	β = 0.513, SE = 0.031, p < 0.001	Direct	Significant	Strongly Supported

shows the SEM Construct Significance and Hypothesis Results for each of the three constructs—SEA, SOA, and PEPC—which indicate that they are strongly connected with wellbeing on one side and with each other on the other side.

5. Discussion

5.1. Key Findings and Their Implications

The findings of this study confirm that rider wellbeing in the Dubai Metro is shaped by a combination of service efficiency, environmental comfort, and operational assurance. Among the three latent constructs, SOA emerged as the strongest predictor of wellbeing, with a path coefficient of 0.513. This aligns with previous studies that emphasize the importance of reliability, safety, and staff behavior in public transit systems [27,40]. The high loading for connectivity (0.904) suggests that passengers value seamless and comprehensive access to destinations, which is consistent with latent findings on the role of network integration in rider satisfaction [41].

The greater impact of SOA might be seen as an assurance mechanism: operational dependability, network integration, and professional service contacts reduce uncertainty and stress during everyday travel, which can have a disproportionate impact on overall wellbeing. In a high-frequency commuting scenario, riders may value system confidence (punctuality, connectedness, and dependable procedures) as much as, if not more than, efficiency-related features [4,13,14].

The correlation analysis reveals strong relationships between several service attributes as well as higher-level constructs (for example, the strong SEA-SOA association), as would be expected in an integrated transit service where riders experience efficiency/access and operational assurance simultaneously [28]. To ensure that this shared variance did not distort the structural estimates, the research explicitly examined multicollinearity diagnostics during model preparation; the results revealed a high condition index and significant shared variance for specific indicators (e.g., privacy and ticketing clarity), implying some indicator-level dependency, while overall diagnostics remained within acceptable modeling criteria [13,27,37].

Given this context, structural paths are interpreted as incremental effects (i.e., each construct’s unique contribution after accounting for overlap), and estimate stability is ensured via the GSCA estimation technique and bootstrapped inference built into the workflow. In other words, the strong SEA-SOA link is recognized as a substantial element of the service system (interdependence), rather than being ignored, and the model definition is utilized to distinguish between what is shared and what uniquely describes wellbeing.

A very low direct path from PEPC to wellbeing can be understood as physical-environment perceptions having minimal incremental explanatory power after operational/service-assurance and efficiency/access variables are taken into account [27,37,39]. In practice, this can happen when PEPC attributes (cleanliness, comfort, and facilities) are relatively high and homogeneous across riders, becoming “baseline expectations” rather than differentiators, so their variation contributes less to differences in overall wellbeing than constructs related to reliability, responsiveness, and system functioning. This view is consistent with the model behavior observed in the larger framework, where SEA-SOA links can be substantial but PEPC’s direct effect on wellbeing can be small in some cases. The coefficient does not imply that PEPC is irrelevant; it may still function indirectly (e.g., by changing perceptions of operational certainty) and remain critical under “stress conditions” (crowding, disruptions, bad weather), where physical comfort and perceived hygiene can become more essential. As a result, the debate might point out that PEPC’s influence may be context- and condition-dependent, and future research can explore situational moderators (peak vs. off-peak, disruption times, seasonal heat) to determine when physical-environmental elements become more important [22,34].

From model results to interventions. To position the model into effect, each latent pillar was interpreted as a set of actionable interventions rather than a simply statistical concept. For example, SEA can inform actions that reduce friction and uncertainty in access and information; PEPC supports station and in-vehicle environment improvements that maintain comfort and perceived hygiene; and SOA supports interventions that improve reliability cues, staff responsiveness, and assurance during routine operations and disruptions. This translation stage emphasizes how the methodology contributes to the application context and ensures that findings are founded in operationally actionable components.

The findings are presented with a planning perspective in mind. The measurement results demonstrate how observable service items combine to form the three latent service pillars, whereas the structural outputs quantify the extent to which each pillar contributes to total rider wellbeing through travel acceptability. Because the pillars are interconnected in real systems (for example, operational certainty frequently correlates with perceived efficiency and information clarity), the model should be viewed as a system-level explanation rather than a collection of isolated single-factor effects. As a result, the findings are utilized to determine which planning levers have the greatest influence on wellbeing during the survey period, as well as to prioritize enhancements that promote long-term metro use.

5.2. Implications for Sustainable Urban Mobility

From a sustainability standpoint, SOA’s dominance and the significant contributions of PEPC and SEA highlight the tangible factors through which metro services may assist sustainable urban mobility. Sustainable mobility is dependent on public transportation being not only available but also consistently acceptable and dependable in daily life; by demonstrating that operational assurance and network integration have the greatest wellbeing effect, the study highlights the service conditions most likely to sustain ridership and strengthen public transport’s role as an alternative to private car travel [3,4].

These findings can be interpreted through a triple-bottom-line lens: (i) social sustainability through improved access, comfort, safety, and perceived fairness; (ii) economic sustainability through increased reliability and reduced friction in daily travel; and (iii) environmental sustainability through conditions that can encourage sustained ridership and reduce car dependency when combined with supportive land-use and first/last-mile policies.

The GSCA findings reveal that the three drivers of rider wellbeing can also be interpreted as practical sustainability actions, with coefficients indicating which ones are most important. The strongest influence comes from operational factors under Service Operations and Assurance (SOA) (β = 0.513), followed by Physical Environment and Passenger Comfort (PEPC) (β = 0.318) and Service Efficiency and Accessibility (SEA) (β = 0.216). Overall, the model performs well (FIT = 0.639; AFIT = 0.621). When viewed through a sustainability lens, SEA directly addresses social sustainability because affordability, ease of use, and access to information shape whether the metro is effectively feasible for different income groups and travel needs, ultimately affecting fairness in access to jobs, services, and daily opportunities [42,43].

SOA, as the most influential factor, reflects the “backbone” of a sustainable system in practice: reliability, punctuality, connectivity, coverage, and assurance decide whether people can reliably plan their trip around the metro [4,13]. Taken together, the findings indicate that sustainability in metro services is more than just infrastructure delivery; it is also about constantly offering a service experience that feels dependable, comfortable, and accessible in daily life [3,6].

A similar trend explains how long-term ridership might provide indirect sustainability advantages. Because SOA has the greatest effect (0.513), boosting dependability and network integration is the most effective way to increase wellbeing, and wellbeing is essential for retaining riders and establishing regular use [6,26]. This is important for environmental results since public transportation only contributes to lower congestion and emissions when it is dependable and well-connected enough that individuals do not feel compelled to use their own cars [3,4,38]. PEPC improves this pathway by ensuring that the experience remains comfortable enough for ongoing use, especially during peak periods or in bad weather situations where discomfort can quickly send riders away [25,44]. PEPC also directly contributes to sustainability by improving the daily acceptability of public transportation—cleanliness, comfort, seating availability, and overall station/vehicle conditions reduce stress and discomfort, making regular metro use more inclusive and feasible for a broader range of riders (e.g., older adults, families, and those with longer journeys).

SEA promotes the same sustainable route by lowering practical barriers—cost, complexity, and information gaps—that frequently discourage continuous use, especially among occasional riders or limited groups. In terms of planning, the coefficients indicate a clear prioritization: strengthening SOA results in the greatest gains in wellbeing and retention, while PEPC and SEA provide the necessary conditions to keep the system comfortable and equitably accessible—together enabling sustainable urban mobility outcomes even when environmental impacts are not directly measured in this study [13,42].

5.3. Actionable Recommendations for Dubai Metro

Staff professionalism and punctuality were also significant contributors to SOA, showing that customer service and human interaction shape trust in transit systems. This implies that investment in service training, multilingual communication, and punctuality monitoring systems could substantially enhance user experiences [45].

PEPC had the second-highest influence on wellbeing (coefficient = 0.318). Indicators such as cleanliness and comfort received high mean scores and strong loadings. These results support studies by [25], which identify physical conditions—such as seating, temperature, and noise control—as primary factors affecting psychological comfort during transit use. Dubai Metro performs well in this area, but ergonomic upgrades, improved ventilation, and sensory-friendly features could further improve experiences, particularly for elderly and neurodiverse passengers [26].

Service efficiency and accessibility (SEA), while the least influential construct (coefficient = 0.216), still plays a foundational role in shaping perceptions of service quality. Speed of service and ease of use were rated relatively lower, indicating potential deficiencies. These findings are in line with Cao and Cao [24], who identified transit speed and access simplicity as critical to reducing commuting stress. Enhancing train frequency during peak hours and improving signage and ticketing systems—especially for non-native speakers—could address these issues [2,39].

The interconstruct relationships reveal a clear pattern of systemic interdependence. SEA significantly influences PEPC (path coefficient = 0.247) and SOA (0.231), suggesting that efficiency improvements can indirectly enhance comfort and operational perception. The strong path from PEPC to SOA (0.746) indicates that a clean, comfortable environment fosters a sense of safety and trust in the system. This supports the holistic transit models proposed by Ramos et al. [5], where infrastructure, service, and perception are viewed as interconnected components.

These findings suggest that a piecemeal approach to transit improvement is insufficient. Instead, planners should adopt a systemic framework in which changes to one aspect of the service ripple through others. For example, improving real-time information systems may reduce confusion (SEA), which enhances comfort and emotional stability (PEPC), and in turn leads to better perception of operational reliability.

The model also aligns with Dubai’s strategic goals, including the Dubai Urban Plan 2040 and the UAE Centennial 2071, both of which emphasize human wellbeing, smart mobility, and sustainability [46]. By prioritizing rider wellbeing, transit agencies can meet not only performance targets but also broader social indicators such as public health, equity, and environmental stewardship [8,47].

5.4. Contribution to Broader Research

This study contributes to the growing body of literature advocating for a shift from functional to human-centered transit planning. Traditional models have prioritized technical performance, but new research—including this study—emphasizes the role of emotional, psychological, and social dimensions in shaping the transit experience [38,48]. The model developed here demonstrates that wellbeing is not merely a subjective sentiment but a measurable and actionable outcome. The framework can be applied beyond Dubai. Cities in Asia, Africa, and Latin America—many of which face similar demographic diversity and rapid urbanization—can benefit from this model [1]. It is particularly relevant for metro systems aiming to increase ridership and develop equitable access to public services [10,49]. The findings also support the UN Sustainable Development Goals, particularly Goal 11: Sustainable Cities and Communities.

By enhancing safety, accessibility, and user satisfaction, transit systems contribute to social inclusivity and lower environmental impact [9,50]. Moreover, the model aligns with smart city principles by encouraging the use of real-time data, user feedback, and predictive analytics to improve service delivery [36]. Transit agencies can embed wellbeing indicators into performance dashboards, conduct regular user surveys, and use GIS tools to visualize disparities in service [51]. In policy terms, this study recommends that metro authorities adopt wellbeing as a Key Performance Indicator (KPI), alongside traditional metrics such as frequency and coverage. By piloting the model in other cities or transit modes, agencies can validate and adapt it to local contexts. The ultimate goal is to create transportation systems that are not only efficient but also dignified, inclusive, and emotionally supportive.

6. Conclusions

6.1. Summary of Findings

This study developed and validated a data-driven framework for integrating rider wellbeing into metro transportation planning, using the Dubai Metro as a case study. Through a large-scale survey of 1409 riders and the application of Generalized Structured Component Analysis, the research identified three latent constructs—SEA, PEPC, and SOA—as the foundational dimensions of rider wellbeing. The final model demonstrated strong fitness values and produced a structural equation:

Rider Wellbeing = 0.216(SEA) + 0.513(SOA) + 0.318(PEPC) + ε.

This equation confirms that while all constructs contribute meaningfully to wellbeing, operational assurance has the greatest influence. The findings also revealed significant interdependencies between constructs, suggesting that improvements in one area can positively affect others. For example, enhancing the physical environment can improve perceptions of service operations.

Further analysis of the metro findings reveals several nuanced implications that merit consideration. The psychosocial dynamics of metro ridership in Dubai demonstrate what might be termed a “cascading wellbeing effect,” wherein improvements in physical environment attributes create psychological comfort that enhances perceptions across all service dimensions. This phenomenon aligns with environmental psychology theories suggesting that physical surroundings significantly influence cognitive and emotional states [52]. The relatively weak direct effect of SEA (coefficient = 0.216) compared to other constructs challenges conventional transit planning paradigms that often prioritize efficiency metrics over experiential factors. As Ettema et al. [53] argue, this finding supports a growing recognition that subjective wellbeing in transit contexts extends beyond utilitarian considerations to encompass hedonic and eudaimonic dimensions of experience.

The strong correlation between connectivity and rider wellbeing (r = 0.816) suggests that metro users conceptualize the system not merely as a collection of stations and trains but as an integrated mobility network that facilitates their broader life activities. This perspective resonates with Mobility-as-a-Service (MaaS) frameworks [54] that emphasize seamless integration across transportation modes and urban spaces. The metro’s high ratings for connectivity (M = 4.54) reflect Dubai’s successful implementation of transit-oriented development principles that position metro stations as nexus points within the urban fabric. The comparative strength of physical environment factors further suggests that Dubai’s investment in architecturally distinctive, climate-controlled stations has created a competitive advantage relative to other transit modes—a crucial consideration in a region where extreme climate conditions can significantly impact modal choice.

When interpreting these findings through cultural and contextual lenses, it becomes apparent that Dubai’s metro system functions not merely as transportation infrastructure but also as a socio-spatial environment that shapes public perceptions of the city’s modernity and global status. The emphasis on staff professionalism may partially reflect cultural expectations regarding service interactions in a society where hospitality traditions remain influential despite rapid urbanization. The relatively low importance of affordability (total effect = 0.056) compared to experiential factors suggests that metro riders may conceptualize the service as a premium mobility option justified by its experiential quality—a perspective that aligns with Dubai’s broader positioning as a luxury destination. These contextual nuances underscore the importance of culturally sensitive approaches to transit planning that recognize the symbolic and experiential dimensions of mobility beyond mere functional utility.

This conclusion is significant from a sustainability standpoint because sustainable urban mobility is dependent on public transportation being consistently dependable and acceptable in everyday life: when reliability and network integration are strong, users are more likely to remain loyal to the metro and use it more frequently, reducing reliance on private cars and supporting lower congestion and transport-related environmental burdens. At the same time, the contribution of affordability and accessibility positions wellbeing as a social sustainability outcome, because these factors affect equitable access to opportunities and whether diverse population groups can participate in city life without disproportionate cost, stress, or exclusion.

Positioning rider wellbeing as an explicit performance outcome significantly contributes to Dubai’s transition to people-centered and sustainable urban development. The Dubai Urban Plan 2040 and the UAE’s long-term vision (UAE Centennial 2071) emphasize livability, smart mobility, and sustainable growth—goals that cannot be met solely through infrastructure expansion if users do not consistently perceive public transportation as reliable, comfortable, and usable in daily life. This study adds value by defining wellbeing as a measurable, planning-relevant variable and by modeling the transit experience as an integrated system rather than studying service factors separately. In practice, that systems view is important: when many service components move together (operations, comfort, and accessibility), advances in one area can reinforce views in others, boosting overall trust in the metro and promoting long-term ridership. Over time, retaining and expanding ridership is a key enabling pathway for sustainability because it helps reduce car dependence and the congestion and emissions impacts that come with it—making wellbeing-informed service planning a practical lever for aligning metro investment and operational priorities with Dubai’s strategic sustainability agenda.

6.2. Research Limitation

This study includes limitations that must be addressed before interpreting and applying the results. First, the sample has unequal demographic representation (e.g., a large male majority), which may limit the confidence with which the results may be applied to all rider groups and may under-represent opinions that differ by gender, age, or nationality. Second, the analysis focuses primarily on service-related perceptions and does not explicitly measure other subjective influences such as momentary emotions, psychological state, or social interaction during travel, all of which may contribute to wellbeing and influence the relative importance of the modeled service dimensions.

Third, the evidence is limited to a single context (Dubai Metro) and time period, and the model is not directly compared to other cities or modes of transportation; thus, transferability should be approached with caution until the latent structure and relationships are validated across multiple systems and multimodal settings. Fourth, the data were collected during a specific period of time, and the estimated relationships may reflect the service conditions and seasonal context of that period rather than stable long-term patterns; repeated measurements across different seasons and operational conditions would help assess the temporal stability of the model. Lastly, since the model was generated using cross-sectional survey data collected throughout the survey period, the results should be viewed as typical relationships under general service conditions rather than a definitive description of rider wellbeing during disruption periods. Rider wellbeing may be particularly sensitive in “stress” situations like service disruptions, severe delays, or peak crowding, where uncertainty, congestion, and a loss of perceived control can exacerbate bad sensations. Under these conditions, the relative relevance of the service dimensions may shift—for example, operations and assurance and real-time communication may become more relevant, whereas comfort perceptions may deteriorate more severely in congested locations. Future research should use repeated measurements across operating circumstances to determine the stability of the latent correlations. These limitations suggest that the findings are best suited as an evidence-based framework for prioritizing service improvements in similar metro contexts, while future research should improve generalizability through more balanced sampling, the inclusion of richer psycho-social measures, and comparative and longitudinal studies across modes and locations.

6.3. Future Research

This study on rider wellbeing in Dubai’s public transportation system opens several avenues for future research that could extend and deepen our understanding of transit experiences. A logical next step would be conducting longitudinal research to track how rider wellbeing fluctuates across different seasons, particularly examining how extreme summer temperatures in Dubai might alter the relative importance of physical environment factors. Additionally, future studies could explore potential moderating variables such as trip purpose (commuting vs. leisure), trip length, and time of day, which may significantly influence rider expectations and experiences. The substantial gender imbalance in our sample (87.2% male in the metro sample) highlights the need for targeted research on female riders’ experiences, potentially uncovering gender-specific determinants of wellbeing that could inform more inclusive transit design. The relatively weak showing of affordability as a direct predictor of wellbeing in both models warrants further investigation, perhaps through discrete choice experiments that could better isolate price sensitivity among different rider segments.

Future research may also examine the experience of transferring between modes of transportation, since intermodal connections are an important but understudied component of integrated transportation networks [55].

Further research could also benefit from methodological expansions. Qualitative approaches, including in-depth interviews and observational studies, could complement our quantitative findings by uncovering the underlying reasoning behind rider preferences and behaviors. The integration of objective service metrics (e.g., actual wait times, crowding levels, and temperature readings) with subjective wellbeing measures could provide a more comprehensive understanding of the relationship between service delivery and rider experiences [44]. Future studies might also explore the transfer experience between transportation modes, as intermodal connections represent a critical yet understudied element of integrated transportation networks. Finally, comparative research across multiple cities with similar multimodal systems could help distinguish universal determinants of rider wellbeing from context-specific factors, contributing to a more generalizable theory of transit experiences that could guide global best practices in transportation planning and operations.