Research on the Thermal Comfort Experience of Metro Passengers Under Sustainable Transportation: Theory of Stimulus-Organism-Response Integration with a Technology Acceptance Model

Abstract

(1) Background: Metro is an important part of urban transportation, carrying huge passenger volume every day. With improvements in people’s living standards, passengers’ demand for a comfortable Metro experience is increasing. In the context of urban development, maintaining a good thermal comfort level of Metro cars is not only conducive to providing a comfortable and healthy environment for passengers, but also has great significance for reducing energy consumption and sustainable urban transportation development. This study provides empirical evidence for Metro design and operation strategies, aiming at creating a safer and more comfortable passenger experience. (2) Methods: By combining passengers’ comfort perception (cognitive value of thermal environment) and rideability perception (confidence in thermal comfort control), this study established a correlation model between thermal comfort and passenger unsafe behavior, namely the integration of SOR (Stimulus-Organism-Response) and TAM (Technology Acceptance Model). This study used methods such as field surveys, structural equation modeling, and reliability and validity analyses to investigate the impact of Metro thermal comfort on passenger behavior safety. (3) Results: This study found that the Metro thermal environment, including temperature, humidity, and airflow velocity, significantly affects passengers’ comfort perception and behavior choices. (4) Conclusions: Passengers may exhibit avoidance behavior in uncomfortable thermal environments, leading to uneven distribution of people in the train car and increasing safety risks. Improving Metro thermal environments can effectively enhance passengers’ perceived comfort and reduce unsafe behavior motivation, which is of great significance for safe Metro operations.

1. Introduction

With the acceleration of urbanization and improvements in living standards, passengers’ expectations for Metro travel extend beyond basic safety and convenience. A comfortable travel environment is increasingly becoming a focal point of passenger attention [1]. Among various factors, the thermal environment within train cars significantly influences passenger comfort [2]. Enhancing the thermal environment and air quality in train cars necessitates comprehensive regulation of both the air conditioning systems and mechanical ventilation systems [3]. Consequently, energy consumption associated with these environmental control systems in Metro stations and train cars is substantial, accounting for approximately 50% of total energy usage [4].

From the perspective of long-term development, the thermal comfort of the Metro system is closely related to the overall sustainable development of the city. Optimizing the thermal comfort of the Metro can promote the development of sustainable transportation. Sustainable transport refers to all modes of transport with low environmental impact, including walking, cycling, public transport, and green vehicles [5]. One of the goals of sustainable transport systems is to reduce dependence on fossil fuels and improve the efficiency of public transport systems [6]. The good thermal comfort environment of Metro improves the travel experience of passengers and attracts more citizens to choose Metro as a travel mode, thus reducing the use of private cars and reducing traffic congestion and air pollution. Therefore, creating a good thermal environment and air quality of Metro cars is not only conducive to providing a comfortable and healthy environment for passengers, but also to reducing Metro energy consumption through reasonable management strategies and to improving urban traffic conditions and promoting green travel and sustainable development of the city [7].

The International Organization for Standardization (ISO 7730 [8]) defines “thermal comfort” as an individual’s degree of satisfaction with their thermal environment; this reflects a subjective psychological state. The assessment of thermal comfort encompasses several indicators that integrate both environmental factors—such as temperature, humidity, and wind speed—and individual behavioral aspects, including clothing choices and activity levels. While current research on thermal comfort predominantly focuses on indoor environments found in ground-level buildings such as offices, residences, and laboratories [9], there is an urgent need to conduct similar studies considering global constraints on land resources alongside accelerated development of underground spaces. This necessity becomes particularly salient at heavily utilized daily transit hubs like Metro stations where understanding thermal comfort dynamics could greatly enhance travelers’ experiences. In areas such as train carriages where passengers congregate closely together, ensuring optimal conditions for thermal comfort directly impacts their everyday commuting experience.

The metro station, as a quintessential example of underground space, presents a closed natural environment that isolates it from the outdoor surroundings and limits external intervention. This situation complicates the achievement of natural ventilation. Furthermore, another characteristic of underground buildings is their elevated relative humidity. These features considerably diminish passengers’ thermal comfort acceptability [10]. Simultaneously, the metro station serves as a transient space for passengers who are in motion and make brief stops. The experience of using the Metro constitutes a short-term thermal encounter [11]. Due to fluctuating environmental conditions and ongoing interactions between passengers and their physical surroundings [12], variations in spatial conditions can influence passengers’ perceptions of the thermal environment [13]. Uncomfortable thermal conditions may lead to avoidance behaviors among passengers, resulting in an uneven distribution of passenger density within train cars. This misdistribution can subsequently heighten conflicts among passengers in densely populated areas during boarding and disembarking processes, thereby presenting potential safety risks [14].

Within the metro system, passenger behavior plays a crucial role in ensuring safe operations throughout the entire network. Their conduct within train cars and responses during emergencies are directly associated with both the operational integrity of Metro trains and overall passenger safety. Unsafe passenger behavior is defined as actions that may precipitate collisions while utilizing the Metro service, such as movement within train compartments, localized crowding instances inside cars, or exhibiting anxious or irritable emotional states [15]. While previous research has highlighted the significance of adhering to safe passenger behavior practices [16], there are limited investigations into understanding how unsafe behaviors among passengers develop specifically within contexts characterized by Metro-induced thermal discomfort.

Currently, traditional PMV (Predicted Mean Vote) thermal comfort research primarily emphasizes the independent measurement of six key parameters: air temperature, air humidity, air velocity, average radiation, clothing thermal resistance, and metabolic rate [17]. However, there is a notable deficiency in understanding changes in user behavior—such as predictive behaviors of Metro passengers—which may render previous approaches inadequate for anticipating passenger attitudes toward their current thermal sensations or the motivations behind their behaviors in response to thermal stimuli.

In this study, we posit that passengers’ unsafe behavior on the Metro is associated with their level of thermal comfort. To validate this hypothesis, we integrated the PMV thermal comfort index with the SOR (Stimulus-Organism-Response) model and the TAM (Technology Acceptance Model) framework. We conducted a field survey involving 403 passengers on Line 1 of the Changsha Metro to analyze how thermal comfort influences safety behaviors among passengers. This analysis encompassed various points, including temperature, humidity, wind speed, perceived rideability, comfort levels, motivation factors, and decision-making processes.

We employed structural equation models and other analytical methods to assess the impact of Metro thermal comfort on passenger safety behavior. Specifically, our investigation focused on whether enhancements in thermal comfort conditions led to improved perceptions of ride comfort, whether these heightened perceptions subsequently influence motivations for engaging in unsafe behaviors, and whether there exists a discernible connection between unsafe passenger conduct and underlying motivational factors as well as decision-making processes.

The organization of this paper is structured as follows. The first section on Introduction addresses the significance of thermal comfort in Metro systems and underscores the necessity of investigating passengers’ unsafe behaviors. Subsequently, the second section on Related Research presents an overview of the current status regarding Metro thermal comfort, passenger behavior, and pertinent reference models. The third section, dedicated to methodology, develops measurement scales derived from the existing literature to operationalize the constructs in the model. The fourth section focuses on Questionnaire Design and Pre-testing; it involves designing a survey questionnaire for preliminary testing purposes. The fifth section details the implementation of an offline questionnaire survey conducted with passengers on Line 1 of Changsha Metro in Hunan Province, China, followed by data analysis. In the sixth section, the Discussion highlights an examination of the survey results. Finally, in the seventh section, entitled Conclusion, key findings from this study are summarized.

2. Related Research

2.1. Metro Thermal Comfort

Due to the unique environmental characteristics and the increasing number of users, thermal comfort in underground spaces has emerged as an increasingly significant research topic [18]. In Metro systems, thermal comfort pertains to the level of comfort associated with the thermal environment experienced within Metro stations and train cars. Typically, Metro systems maintain a relatively constant temperature within train cars to ensure passenger comfort. Over the past two decades, numerous studies have evaluated thermal comfort in Metro stations [19,20]. Factors influencing thermal comfort in these systems include temperature, humidity, air velocity, external ambient temperature, and passenger density within the train cars.

Primarily, appropriateness of temperature is a key determinant affecting passengers’ thermal comfort. Sui et al. conducted a study assessing summer thermal environments at Xi’an Metro Station in China and found that operating the air conditioning system at recommended temperatures often fails to meet most passengers’ needs for thermal comfort [21]. Passengers are likely to experience temporary relative discomfort unless platform temperatures are maintained at least 1 °C lower than concourse temperatures. This underscores the fact that effective temperature regulation is essential for enhancing passengers’ overall thermal experience. Zhou et al. also discovered that sudden fluctuations in temperature significantly influence passengers’ perceptions of warmth or coolness [22]. In winter months, stark contrasts between outdoor and indoor temperatures lead to noticeable discomfort when individuals transition between air-conditioned Metro stations and outside conditions. Additionally, Cho et al. identified that uneven distribution of temperatures across Metro train cars contributes to feelings of discomfort among passengers [23]; therefore, it is crucial to minimize temperature differentials within train compartments to enhance riders’ experiences regarding their overall thermal comfort.

Humidity can significantly influence the environmental perception of Metro passengers and may pose potential health risks. Abbaspour et al. conducted a field study evaluating thermal comfort in Metro stations and train cars in Tehran, revealing that the relative humidity within Tehran’s Metro system was relatively low (approximately below 30%). This deficiency in humidity contributed to discomfort among passengers, manifesting as irritation of the nose, eyes, and throat [24]. Furthermore, elevated humidity levels can intensify passengers’ discomfort related to heat, particularly when the relative humidity exceeds 70% [25]. Consequently, maintaining an optimal range of humidity within train cabins is essential for ensuring passenger comfort and safeguarding their health.

The ventilation system of the Metro serves as a primary means to optimize the complex physical environment within Metro cars, governing airspeed to regulate temperature, humidity, and overall air quality [26]. Additionally, effective ventilation can significantly dilute and eliminate pollutants present in the cars, thereby enhancing passenger comfort and health levels [27]. Research conducted by Ye et al. indicated that the majority of Metro passengers are particularly concerned about indoor air quality. Over 50% of respondents expressed a desire for increased fresh air intake, highlighting a pronounced demand among Metro users for improved ventilation in their traveling environments [19]. Furthermore, Yang et al. identified that narrow passageways within trains are subject to the piston effect and can experience high wind speeds; strong airflow conditions may contribute to discomfort among passengers [28].

In terms of innovative research methodologies focused on thermal comfort for Metro passengers, Pan et al. introduced a dynamic approach specifically designed for Beijing Metro Line 8 that aims to assess passenger thermal comfort levels [12]. Their findings revealed that temperature fluctuations within Metro stations and trains are influenced by both external environmental factors and internal air conditioning systems, while passengers’ thermal perceptions vary according to their locations within these spaces. Although certain aspects of the thermal environment encountered in Metros are deemed acceptable to some extent, there remains significant potential for enhancement.

Currently, an established foundation exists regarding research into thermal comfort within Metros coupled with evaluation methodologies; however, further exploration into passenger thermal comfort from multifaceted perspectives is essential. This includes comprehensively evaluating passenger attitudes and behaviors in order to develop more holistic assessment models aimed at improving both comfort levels and health outcomes for riders.

2.2. Passenger Behavior

The rapid and efficient development of Metro systems introduces new challenges and requirements for ensuring their reliable and safe operation. The occurrence of Metro safety incidents can primarily be attributed to three categories of factors [15]: (1) mechanical equipment and subsystems (such as safety doors, train operational systems, etc.); (2) the surrounding environment (including facilities, other passengers, Metro personnel, etc.); and (3) human-related factors (such as passenger behavior). Unsafe behaviors exhibited by metro passengers—referred to as MPB (Metro Passenger Behavior)—can significantly contribute to an increase in accident rates.

Within Metro operations, a lack of awareness regarding passenger safety coupled with inadequate traffic safety training results in accidents predominantly arising from unsafe passenger behaviors. This category not only accounts for the highest incidence rate, but also indicates that enhancing passenger conduct could substantially improve both the overall safety and emergency response capabilities of the entire Metro system [16]. Common forms of unsafe MPB include unsteady standing while trains are in motion, excessive density of passengers within confined spaces, consumption of food onboard, engaging in fights or altercations, and carrying hazardous items.

Schneider et al. examined the relationship between passengers’ perceived crowd density and their feelings of safety, identifying the following negative correlation: as passenger density increases, the sense of safety among passengers tends to decrease. Furthermore, unfavorable perceptions related to discomfort arising from crowded conditions can trigger unsafe behaviors. This discomfort is recognized as one of the contributing factors leading to such behaviors [14]. Chen et al. highlighted that the standard for standing passenger density in Metro cars significantly affects passenger congestion during operation, which subsequently influences both spatial comfort and safety for passengers [29]. Lu et al. assessed the severity of incidents caused by various types of unsafe behaviors and found that crowding behavior poses an exceptionally high-risk level [16].

In research conducted by Berkovich et al., examining passengers’ choice of seating positions revealed that when given options, individuals are more inclined to select seats they believe best meet their needs, potentially due to feelings of increased safety or comfort associated with those choices [30].

Based on the aforementioned research, it is evident that enhancing passenger behavior can not only directly reduce the incidence of accidents, but also improve the emergency response capabilities of the Metro system. Consequently, Metro system administrators should implement measures to encourage passengers to adopt safer behaviors, thereby augmenting the overall safety and emergency response capacity of the entire Metro network.

Thermal comfort is one of the important factors affecting passenger behavior. In Metro and other public transportation systems, thermal environment directly affects passengers’ comfort and satisfaction. Studies have shown that the thermal environment affects the thermal balance of the human body by affecting the heat exchange between the human body and the surrounding environment, and thus affects the behavior of passengers [31]. For example, when the temperature in the carriage is too high or too low, passengers may feel uncomfortable, which affects their behavior, such as reducing the time spent in the carriage or choosing other means of transportation. In addition, the driver’s thermal comfort is also closely related to his driving performance. In one study, Malaysian researchers found a correlation between bus drivers’ thermal comfort and their driving performance [32].

2.3. Research Model

SOR is a “Stimulus-Organism-Response” theoretical model proposed by Mehrabian and Russel in 1974 in the field of environmental psychology. Environmental factors can stimulate people’s emotions and cognition and make people react to approaches or escape [33]. This model is often used to explain and predict an individual’s behavior in a given situation. The model mainly involves three variables: Stimulus, Organism, and Response.

The SOR theory posits that the relationship between external stimuli (S) and individual responses (R) is neither direct nor mechanical, as the behavioral subject is an organism endowed with cognitive processes and emotional experiences, which confer subjective agency. When confronted with external stimuli, individuals do not merely react, but instead engage in active decision-making processes. Throughout this engagement, specific psychological activities may be activated, subsequently influencing their internal state; this indicates that the internal condition of the organism plays a pivotal role. Thus, the theory positions the organism (O) as a mediating link between external stimuli (S) and individual responses (R), reflecting internal transformations such as perception, attitude, and motivation.

In recent years, an increasing number of scholars have integrated the SOR theoretical mechanism into safety behavior research. This approach begins with the identification of external stimuli (S) that may influence safety behavior. Such stimuli can encompass hazardous factors in the physical environment, organizational policies, or social and cultural contexts, among others. An unsafe work environment can prompt employees to engage in risky behaviors [34]. Turning to the organism component (O), this pertains to how individuals respond to external stimuli. It encompasses cognitive evaluations, emotional responses, and motivational states of individuals. When employees perceive elevated safety risks within their workplace, they may experience feelings of anxiety or concern; such emotional states can significantly impact their behavioral choices [35]. Lastly, the response element (R) refers to an individual’s ultimate behavioral reaction to these stimuli. In the context of safety behavior studies, this typically relates to specific actions taken by employees regarding safety measures, such as adhering to established safety protocols or reporting potential hazards.

The application of the Stimulus-Organism-Response (SOR) theory to the examination of safety behavior can facilitate a systematic understanding and prediction of its occurrence. By investigating how various types of external stimuli influence individual psychological states and behavioral responses, this approach can provide theoretical foundations for developing effective safety management strategies.

TAM (Technology Acceptance Model) is a framework developed to explain and predict individuals’ acceptance of new technology. Within this domain, perceived usefulness and perceived ease of use are identified as fundamental determinants influencing users’ acceptance of technological innovations [36]. Perceived usefulness (PU) refers to the belief that utilizing a particular technology can enhance an individual’s work efficiency or overall quality of life. Conversely, perceived ease of use (PEOU) pertains to the simplicity associated with learning and operating that technology.

As the Technology Acceptance Model advanced, TAM2 and TAM3 emerged, offering further expansion and depth to the original framework. TAM2 significantly bolstered the model’s capacity to elucidate user acceptance behavior by integrating social influence factors alongside cognitive tool processes such as subjective norms, voluntariness, image, task relevance, output quality, result visibility, and perceived ease of use [37]. The incorporation of these elements enabled TAM2 to more comprehensively encapsulate the multifaceted factors affecting users’ acceptance of technology, thereby enhancing its adaptability and predictive capabilities.

TAM3 further enriched this discourse by integrating critical components such as trust and perceived risk [38]. Trust functions as a mechanism for mitigating vulnerability between individuals, while perceived risk directly influences user acceptance decisions regarding technology. For instance, research indicates that perceived risk moderates the impact of subjective norms on both perceived usefulness and intentions toward adoption in studies related to electronic services [39]. Furthermore, TAM3 also examined attitudes towards using electronic wallets along with other technologies within specific contexts—such as during the COVID-19 pandemic—which arise from an intricate interplay among perceived usefulness, perceived ease of use, and perceived risk [40].

We can also observe the application and implications of the Technology Acceptance Model (TAM) within the transportation sector. For instance, a study focused on taxi services revealed that user satisfaction, alongside the variables outlined in TAM, constitutes a significant factor that can enhance our understanding of technology acceptance [41]. This finding suggests that in the transportation domain, apart from fundamental technology acceptance factors, additional elements such as service quality must be taken into account to more comprehensively predict and improve user behavior.

These extensions and elaborations underscore the robust adaptability of TAM, allowing for it to maintain its relevance and effectiveness in an ever-evolving technological landscape.

The “Stimulus” in SOR model was clearly defined as the factors related to thermal comfort in the Metro environment, mainly including temperature, humidity, and air velocity and other physical environment variables that could be directly measured. The “Organism” section focuses on the integrated cognitive, emotional, and psychological responses of passengers to these thermal Comfort stimuli, which are embodied in the subjective perception of rideability and comfort. And “Response” covers all kinds of behavioral decisions and actual actions of passengers in the Metro carriage, such as seat selection, movement in the carriage, and getting off the train in advance.

At the same time, for the TAM, we have carried out targeted adaptation and integration. The concept of “Perceived Usefulness” in TAM is transformed into “Perceived Rideability” in line with the Metro context. The overall evaluation and acceptance of Metro as a mode of transportation based on thermal comfort experience are emphasized. “Perceived Ease of Use” corresponds to “Perceived Comfort”, highlighting the passenger’s subjective perception of the comfort of the thermal environment in the Metro car on the convenience of using Metro services.

3. Method

The Stimulus-Organism-Response (SOR) framework can elucidate and anticipate individual behavior in specific contexts. Given that Metro usage is a routine activity, passengers’ perceptions of their ride experience significantly influence their behavioral intentions. To better articulate the pathway relationship between thermal comfort stimuli in Metros and the resultant individual responses from passengers, we employed an integrated model combining the SOR framework and the Technology Acceptance Model (TAM).

In the TAM framework, the primary emphasis is on emerging technologies, wherein perceived usability and perceived usefulness constitute its core components. In the context of public transportation, we substitute perceived usefulness (PU) with perceived rideability, a functional dimension pertinent to Metro travel. If passengers assess the Metro car environment as acceptable, they are more likely to opt for this mode of transport. Moreover, we replace perceived ease of use (PEOU) with perceived comfort, indicating how effortless it feels to utilize the service based on internal environmental conditions within the car. When passengers deem both the vehicle environment and thermal comfort satisfactory, they are more inclined to repeat their journey or exhibit reduced motivation for altering their travel behavior. The theoretical model underpinning these arguments is illustrated in Figure 1.

Thus, we propose a central argument positing that thermal comfort conditions within Metros correlate with the following unsafe behaviors displayed by passengers:

Can improvements in thermal comfort conditions enhance passenger perceptions of rideability and comfort?

Does an increase in perceptions regarding rideability and comfort lead to diminished motivations for unsafe behaviors among passengers?

Is there a definitive link between unsafe passenger behaviors and their underlying motivational factors, as well as decision-making processes related to those behaviors?

We will elaborate further on the hypothesized model. This study developed an integrated model combining the Stimulus-Organism-Response (SOR) framework and the Technology Acceptance Model (TAM). In this model, the relationships among seven key variables provide a reference point for constructing a new conceptual framework, informed by the integration of nine distinct hypotheses.

• Temperature:

The temperature within Metro cars significantly influences passengers’ comfort and overall travel experience. Temperature is a crucial factor affecting passenger satisfaction; thus, effective management of the environmental conditions within the car is essential for enhancing passenger comfort. Both excessively high and low temperatures can lead to dissatisfaction among passengers, adversely impacting their travel experiences [42].

Based on this review, this study posits that air temperature has a profound effect on passengers’ perception of thermal comfort in Metros. Consequently, the following hypotheses are proposed:

H1: 

Air temperature significantly impacts passengers’ perceived rideability.

H2: 

Air temperature significantly affects passengers’ perceived comfort.

• Humidity:

Humidity constitutes one of the primary factors influencing overall passenger comfort. In environments characterized by high humidity levels, individuals often perceive increased warmth [25]. Suboptimal humidity conditions can cause discomfort or ill feelings among passengers [24]. Furthermore, humidity is intricately linked to air quality, which consequently impacts passenger health and safety [43].

Based on this review, this study suggests that air humidity considerably influences passengers’ thermal comfort in Metro systems. Hence, we propose the following hypotheses:

H3: 

Air humidity has a significant impact on passengers’ perceived rideability.

H4: 

Air humidity substantially affects passengers’ perceived comfort.

• Air Velocity:

By regulating the intensity of airflow velocity, it is feasible to control various parameters such as air temperature, humidity, and overall air quality within the cabin [26]. Furthermore, appropriate airflow management can effectively address issues related to odor and pollutant accumulation in the cabin environment, thereby enhancing the comfort and health standards for passengers [27].

Based on this review, this study posits that airflow velocity significantly influences passengers’ thermal comfort in Metro systems. Consequently, the following hypotheses are proposed:

H5: 

Airflow velocity has a significant impact on passengers’ perceived rideability.

H6: 

Airflow velocity has a significant impact on passengers’ perceived comfort.

• Perceived Rideability:

Thermal comfort is a critical factor that influences passengers’ choices when selecting Metro cars. Research indicates that passengers possess subjective perceptions of physical variables, such as temperature and airflow, which directly impact their decisions [44,45]. Moreover, passengers may experience discomfort in crowded vehicles and are likely to prefer those offering more space for movement [46]. Opting for cars with a lower seat occupancy rate increases their likelihood of securing a seat [47]. This demonstrates that thermal comfort, available movement space, and levels of crowding significantly affect passengers’ motivation to choose a particular vehicle.

Based on this review, the present study posits that perceived rideability plays a crucial role in shaping passengers’ experiences and feelings regarding Metros. Hence, the following hypothesis is proposed:

H7: 

Perceived rideability has a significant impact on passengers’ behavioral motivation.

• Perceived Comfort:

The perception of comfort among Metro passengers is a multifaceted issue that encompasses various elements, including the interior environment of both stations and trains, passenger behavior, as well as psychological and physiological responses. The train’s interior environment plays a crucial role in influencing passenger comfort. Specific factors such as passengers’ perceptions regarding thermal conditions, air quality, lighting, and noise levels are particularly significant [11,48]. It has been demonstrated that passengers’ perceived comfort directly affects their overall satisfaction with public transportation services. Research indicates that when passengers experience elevated levels of comfort, they are more inclined to express satisfaction with the service provided [49,50]. Furthermore, perceived safety and service quality emerge as additional key factors that impact user satisfaction within public transportation systems [51].

Based on this review, the present study posits that perceived comfort significantly influences passengers’ experiences and behavioral decisions while using Metros. Consequently, we propose the following hypothesis:

H8: 

Perceived comfort has a significant impact on passengers’ motivation to act.

• Motive:

Motivation constitutes a multifaceted psychological process that encompasses various layers of influence, from physiological needs to psychological necessities as well as social and cultural requirements. It includes external stimuli, social norms, subjective norms, and value systems. Within the context of public transportation, passengers’ motivational drivers are intricate; they can be effectively influenced by alterations in attitudes, subjective norms, and perceived behavioral control, ultimately affecting both their intentions and actual behaviors [52]. Additionally, changes in intentions can enhance the likelihood of adopting new behaviors; this suggests that establishing specific action plans can significantly elevate the probability of executing desired actions [53].

Based on this review, we posit that motivation critically impacts behavioral decision-making processes within Metro systems. Accordingly, we propose the following hypothesis:

H9: 

Motivation has a significant impact on behavioral decision-making.

• Behavioral Decision:

The process of passenger behavioral decision-making is complex, comprising multiple dimensions and layers that encompass technical acceptance, psychological factors, personal attitudes, and social influences. Psychological factors are pivotal in shaping passengers’ behavioral decisions. For instance, passengers’ attitudes towards and perceptions of public transportation significantly influence their behavioral intentions while also impacting their satisfaction levels and loyalty to specific modes of transport [54].

4. Questionnaire Design and Pre-Testing

4.1. Questionnaire Design

Based on existing research, appropriate measurement dimensions were developed to align with the characteristics of Metro thermal comfort and passenger behavior. Specifically, seven dimensions were identified: temperature, humidity, air velocity, perceived rideability, perceived comfort, motivation, and behavioral decision-making. For each dimension, from three to five corresponding measurement items were established. A five-point Likert scale (“strongly disagree”, “disagree”, “neutral”, “agree”, “strongly agree”) was employed in the questionnaire design to enhance clarity and ease of completion for respondents, thereby improving both data collection efficiency and quality [55].

In formulating the survey questionnaire, we relied on established methodologies and guidelines pertinent to each dimension as outlined in the current literature. Notably, we incorporated measurement items highlighted by Yang (2022) [28] regarding thermal comfort indicators and those related to motivation and behavioral decision-making as detailed by Lu (2023) [16]. The focus of the questionnaire development was on crafting clear and unbiased questions that would allow for the respondents to understand each item easily without requiring any external assistance.

Furthermore, limiting multiple-choice question (MCQ) options to a range of 3–5 can prolong response time while not significantly compromising validity, reliability, or item discrimination [56]. Additionally, it has been shown that the number of items displayed per screen influences participation rates and completion rates among respondents. Evidence suggests that presenting multiple items on a single screen increases non-response rates and adversely affects participants’ evaluations of the survey layout. Consequently, designing a questionnaire with an emphasis on 3–5 questions per section may yield clearer evaluations from respondents while potentially enhancing their satisfaction levels and leading to higher completion rates.

The content of the questionnaire needs to reflect the psychological and behavioral reactions of passengers under the influence of uncomfortable thermal environment, so the design of the questionnaire content needs to be considered, especially in the aspects of motivation and behavioral decision-making. The following is the explanation of the design of motivation and behavioral decision-making in the questionnaire content.

The measurement items in the Motive dimension are designed to capture the internal impulses and intentions of passengers caused by thermal discomfort. For example, when passengers feel uncomfortable with the heat inside the car (MO1), this motivation to leave the car may prompt them to make unsafe behaviors such as sudden rise and move around during the train running. The motivation to undress (MO2) may influence the behavior of the passengers themselves and may cause distress to the passengers around them. The emotional state of feeling upset (MO3) can affect a passenger’s attention and behavioral stability, increasing the risk of unsafe behavior.

The Behavioral Decision dimension focuses on the observation and choice behavior of passengers in the carriage. Passenger observation of comfortable position (BD1), crowded section (BD2), and empty seat (BD3) reflects their concern for their own riding environment and the need to adjust. However, if you pay too much attention to these and move frequently during the train operation, it may lead to unsafe behaviors such as hitting others or losing your own balance. Choosing to take the subway when there are fewer people (BD4) and choosing the location according to the situation of the crowd getting off (BD5)—although it is the decision of passengers to pursue a more comfortable ride experience—may lead to crowd gathering and crowding during rush hour and other situations, increasing the possibility of unsafe behaviors such as pushing and stampeding.

The questionnaire items utilized in this study are summarized in

Table 1. Dimensions of the questionnaire and measurement items.

Dimensions	Names	Measurement Items	Reference Sources
Temperature	TE1	The heating/air conditioning in the current car is insufficient.	(Yang et al., 2022) [28]
	TE2	The temperature in the current car is cold/stuffy.
	TE3	A certain part of my body (head/hand/foot) is stuffy or cold.
	TE4	The temperature situation in the current car is not satisfactory.
Humidity	HU1	The air humidity situation in the current car is uncomfortable.	(Yang et al., 2022) [28]
	HU2	The air humidity situation in the current car needs to be adjusted.
	HU3	The air humidity situation in the current car makes me feel uncomfortable.
	HU4	The air humidity situation in the current car is not satisfactory.
Air Velocity	AV1	The constant wind feeling in the car for a long time will make me feel uncomfortable.	(Yang et al., 2022) [28]
	AV2	The wind sensation is obvious during the Metro operation.
	AV3	There is a wind blowing continuously in my current position.
	AV4	The wind sensation felt at different positions in the car is different.
Perceived Rideability	PR1	When the thermal environment in the car is not comfortable, I will choose to take the next Metro.	(Yang et al., 2022) [28]
	PR2	I will consider the acceptability of the thermal environment in the car to choose whether to take this car.
	PR3	I am very satisfied with the facilities and environment in this car.
	PR4	I think this Metro is suitable for long-term travel.
Perceived Comfort	PC1	I think the weather outside will not affect the comfort of the car’s interior.	(Yang et al., 2022) [28]
	PC2	I think I will feel uncomfortable when there are too many passengers in the car.
	PC3	I think I will feel comfortable after getting on the Metro compared to waiting.
	PC4	I think I can keep a good mood even if I take the Metro for a long time.
Motive	MO1	The uncomfortable thermal feeling in the car will make me want to leave the car quickly.	(Lu et al., 2023) [16]
	MO2	The uncomfortable thermal feeling in the car will make me want to take off some clothes.
	MO3	The uncomfortable thermal feeling in the car will make me feel restless and uneasy.
Behavioral Decision	BD1	I will observe possible comfortable positions.	(Lu et al., 2023)) [16]
	BD2	I will observe crowded parts of the car.
	BD3	I will observe where there are empty seats in the current car.
	BD4	I would rather choose to take the Metro when there are fewer people.
	BD5	The movement of large crowds of people getting off the Metro at the station will affect my choice of location.

.

4.2. Pre-Testing

4.2.1. Content Validity

Content validity refers to the extent to which the items in the scale reflect the overall measurement structure. The measurement content includes the relevance, comprehensiveness, and comprehensibility of the item and the measured structure [57]. Content validity is a key aspect of developing and evaluating measurement tools such as questionnaires or scales. It ensures that the content of the instrument covers what it is supposed to measure accurately. The process of evaluating the validity of content involves several steps, including defining the features to be measured, selecting items that represent all aspects of those features, and obtaining expert judgment about the relevance and clarity of those items [58]. In addition, the Content Validity Index (CVI) was evaluated by the expert group, and the item values (I-CVI) and the overall Scale (S-CVI) were calculated without deleting or adding item contents. The standard used was 0.8 [59].

In this study, we aimed to ensure both face and content validity for our questionnaire while enhancing its clarity and coverage within this survey context. To achieve this aim, we recruited ten expert participants from Central South University in China across two fields—product design and thermal comfort research—including four faculty members specializing in product design, one educator focusing on thermal comfort research direction, along with five PhD students, yielding a total cohort of ten participants. All experts possess substantial user experience alongside familiarity with existing usability scales as well as relevant knowledge within thermal comfort research domains. Each participating expert was asked to independently evaluate each item (including response options) using a four-point scale reflecting their perceived level of consistency (relevance) relative to corresponding structural attributes of the scale itself. The rating criteria included “very consistent”, “consistent”, “inconsistent”, and “very inconsistent”. These experts subsequently provided valuable feedback concerning both relevance and comprehensiveness pertaining to those items evaluated.

4.2.2. Surface Validity Index

The surface validity index mainly evaluates the clarity and readability of the items and the format of the questionnaire. It reflects the target user’s understanding of the wording of items and options. The Surface Effectiveness Index (FVI) is evaluated using target users [60]. It counts each item in the Scale (I-FVI) and the Overall Scale (S-FVI). We asked participants to rate the document item.

We selected ten ordinary users and asked them to independently rate the comprehensibility of the project and options on the following dimensions: “Very clear, clear, not clear, very unclear”. We asked participants to read each item and question aloud and to address any difficulties in understanding it. Participants offered their opinions on items they did not understand and offered their own explanations. The statements of each item were modified based on feedback, such as semantic optimization, and the description of the degree of feeling involved was refined from the relatively vague “feeling uncomfortable” to specific degree descriptions such as discomfort in local locations, so that participants could more accurately express their feelings. Secondly, adjust the logical order of items to ensure that the order of questions is in line with the thinking logic of passengers. For example, when asked about the relationship between thermal comfort feelings and behavioral decisions, they are arranged in a logical order from general feelings (such as feelings about the temperature in the car) to specific behavioral tendencies (such as whether seat selection is changed because of the thermal environment). The participants were then re-rated to check the revised version.

Table 2. Results of surface/content validity measurement.

Item Number	I-CVI	I-FVI
Instructions	NA	1.0
Item 1	0.9	0.9
Item 2	1.0	1.0
Item 3	1.0	1.0
Item 4	1.0	0.8
Item 5	1.0	1.0
Item 6	1.0	1.0
Item 7	1.0	0.8
Item 8	1.0	1.0
Item 9	1.0	0.9
Item 10	0.9	0.9
Item 11	1.0	1.0
Item 12	1.0	0.8
Item 13	1.0	1.0
Item 14	1.0	1.0
Item 15	0.9	0.9
Item 16	1.0	1.0
Item 17	1.0	0.9
Item 18	1.0	1.0
Item 19	1.0	1.0
Item 20	0.9	1.0
Item 21	1.0	1.0
Item 22	1.0	1.0
Item 23	1.0	1.0
Item 24	1.0	0.9
Item 25	1.0	0.9
Item 26	0.9	1.0
Item 27	1.0	1.0
Item 28	0.9	0.9
Instruction	NA	1.0
Item options	NA	1.0
	S-CVI = 0.978	S-FVI = 0.954

shows the measurement results of the questionnaire after the questionnaire content was adjusted based on the previous scores.

5. Survey

5.1. Participants

In terms of gender distribution, male participants represented 51.6% of the sample, while female participants constituted 48.4%. Regarding age demographics, the majority of participants were aged between 18 and 60 years old, comprising 73.5% of the total population surveyed. With respect to travel habits, a significant portion of participants reported using the Metro from four to eight times per week, accounting for 53.1%. In terms of duration spent on Metro journeys, most respondents indicated their travel time ranged from 31 to 60 min (50.9%), followed by those who traveled for durations between 0 and 30 (≤30) min (32.5%). The statistical results are presented in

Table 3. Sample feature description.

Variable	Option	Frequency	Percentage
Gender	Man	208	51.6%
	Woman	195	48.4%
Age	Under 18 years old	63	15.6%
	18–35 (≤35) years old	186	46.2%
	36–60 years old	110	27.3%
	Over 60 years old	44	10.9%
The number of times you take the Metro per week	0–3 times	85	21.1%
	4–8 times	214	53.1%
	9–12 times	57	14.1%
	12 times or more	47	11.7%
The average duration of each Metro ride	0–30 (≤30) min	131	32.5%
	31–60 min	205	50.9%
	Over 60 min	67	16.6%

.

5.2. Testing Process

This study will recruit participants on Line 1 of the Changsha Metro. We will administer questionnaires through on-site recruitment methods. The questionnaires are designed to be anonymous and do not solicit sensitive personal information. We are committed to ensuring the protection of participants’ privacy. We will provide a clear explanation regarding the purpose of the study, expected time commitments, and compensation for participation. Upon agreement from the participants, the research assistant will distribute the experimental questionnaire and offer guidance on accurate completion. After participants finalize their responses, we will collect and verify all data obtained. Compensation will be dispensed according to our prior agreement, either via electronic transfer or cash payment. Confirmation of receipt of payment will be provided alongside our sincere appreciation for their involvement in this research endeavor.

5.3. Test Environment and Materials

This study intends to recruit and survey participants primarily at Line 1 of the Changsha Metro in Hunan Province. The data collection will occur from 22 December 2023 to 4 April 2024, during the morning and evening peak hours on Line 1. These periods are characterized by a significant volume of passenger traffic within the Metro system, thereby facilitating the recruitment of an adequate number of participants with a diverse demographic composition. Four key stations along Line 1 have been selected as distribution points for the questionnaires: Hou jia tang Station, Nan men kou Station, Huang xing Square Station, and Wuyi Square Station. All these stations are situated in bustling areas of Changsha City that experience high passenger volumes and various types of passengers. This strategic selection enhances our ability to collect more representative data. In the process of on-site recruitment, both electronic and paper-based questionnaires were utilized. Participants had the option to choose their preferred format for completing the questionnaire. For those opting for the electronic version, we provided a QR code for them to scan in order to fill out the questionnaire online using their mobile phones.

5.4. Result

A total of 423 complete questionnaires were collected. After excluding respondents who did not pass the logic test and those who completed the questionnaire in under 60 s, we retained a final sample of 403 questionnaires. The data along with paper questionnaire samples were then imported into Wenjuanxing, a survey platform, for subsequent sample analysis.

5.4.1. Normality Test Results

The normality test of each measurement item were tested by skewness and kurtosis. In the analysis of data distribution characteristics, kurtosis and skewness are two important indexes. Kurtosis is a measure of the concentration of concentrated extreme values. In statistics, the expected value of kurtosis is 3, which means that the data distribution has the same tail thickness as the normal distribution. If the kurtosis is greater than 3, it means that the data distribution has a thicker tail, that is, more extreme values. If the kurtosis is less than 3, it means that the data distribution has a thinner tail, i.e., fewer extreme values [61]. Skewness refers to the degree of symmetry, or more specifically, the lack of symmetry. If this value is between −0.5 and 0.5, the distribution of the value is almost symmetric. If it is between 0.5 and 1, the data are positively skewed. Moderate skewness was found in other studies [62]. As can be seen from the analysis results in

Table 4. Results of descriptive statistics and normality tests for measurement items across different dimensions.

Dimensions	Measurement Items	M (Mean)	SD (Standard Deviation)	Skewness	Kurtosis	Overall M	Overall SD
Temperature	WD1	2.53	1.246	0.572	−0.757	2.4423	1.05268
	WD2	2.39	1.291	0.527	−0.964
	WD3	2.35	1.168	0.674	−0.219
	WD4	2.5	1.341	0.565	−0.882
Humidity	SD1	2.39	1.157	0.566	−0.622	2.3728	0.96943
	SD2	2.38	1.196	0.611	−0.612
	SD3	2.37	1.201	0.608	−0.537
	SD4	2.35	1.193	0.565	−0.707
Air Velocity	LS1	2.47	1.179	0.412	−0.921	2.4795	1.09331
	LS2	2.44	1.231	0.42	−1
	LS3	2.58	1.444	0.52	−1.114
	LS4	2.43	1.185	0.453	−0.859
Perceived Rideability	KC1	2.49	1.224	0.461	−0.971	2.4752	1.1133
	KC2	2.36	1.249	0.426	−1.12
	KC3	2.63	1.452	0.525	−1.102
	KC4	2.42	1.17	0.492	−0.911
Perceived Comfort	SS1	2.62	1.277	0.324	−1.062	2.495	1.0608
	SS2	2.55	1.321	0.512	−0.932
	SS3	2.34	1.149	0.436	−0.742
	SS4	2.47	1.297	0.469	−0.992
Motive	DJ1	2.73	1.297	0.367	−0.938	2.6667	1.10279
	DJ2	2.57	1.328	0.433	−0.996
	DJ3	2.7	1.318	0.45	−0.915
Behavioral Decision	JC1	2.21	1.203	0.824	−0.13	2.2943	1.04916
	JC2	2.31	1.26	0.822	−0.358
	JC3	2.42	1.355	0.781	−0.53
	JC4	2.27	1.257	0.715	−0.611
	JC5	2.26	1.327	0.816	−0.521

, the absolute values of skewness and kurtosis coefficients of each measurement item in this study are within the standard range, so it can be shown that the data of each measurement item meet the approximate normal distribution.

5.4.2. Questionnaire Reliability Test

In this analysis, the results of reliability analysis are shown in

Table 5. Results of questionnaire reliability analysis.

Variable	Cronbach’s α	Number of Items
Temperature	0.854	4
Humidity	0.834	4
Air Velocity	0.887	4
Perceived Rideability	0.894	4
Perceived Comfort	0.861	4
Motive	0.79	3
Behavioral Decision	0.877	5
Overall Reliability	0.921	28

, and the coefficient of reliability for the overall and each secondary dimension of the Metro thermal comfort response behavior scale is within the range of 0.7–1, indicating that the scales used in this study have good internal consistency.

5.4.3. Questionnaire-Based Factor Analysis

First, the confirmatory factor analysis (CFA) model was employed to assess the fit of the Metro thermal comfort response behavior scale. According to the results of the model fit test presented in

Table 6. CFA model fit test.

Indicator	Reference Standard	Measured Result
CMIN/DF	1–3 is excellent, 3–5 is good	1.683
RMSEA	<0.05 is excellent, <0.08 is good	0.041
IFI	>0.9 is excellent, >0.8 is good	0.963
TLI	>0.9 is excellent, >0.8 is good	0.957
CFI	>0.9 is excellent, >0.8 is good	0.962

, the CMIN/DF (chi-square degrees of freedom ratio) is 1.683, which lies within the acceptable range of 1–3. The RMSEA (Root Mean Square Error of Approximation) recorded a value of 0.041, indicating an excellent fit as it falls below the threshold of 0.05. Furthermore, the indices for IFI (Incremental Fit Index), TLI (Tucker–Lewis Index), and CFI (Comparative Fit Index) all achieved commendable scores of 0.9 or higher [63]. Therefore, based on these analytical outcomes, we can conclude that the CFA model for the Metro thermal comfort response behavior scale demonstrates good fit quality, as illustrated in Figure 2.

Under the premise that the CFA model of the Metro thermal comfort response behavior scale demonstrates a satisfactory fit, we will further assess the convergent validity Average Variance Extracted (AVE) and composite reliability (CR) of each dimension within the scale. The AVE value and CR are crucial metrics for conducting convergent validity analysis. Generally, an AVE value of 0.5 or greater, alongside a CR value of 0.7 or above, indicates a strong level of convergent validity. We computed both the square root of the AVE values and the CR values for each factor. According to the analysis results presented in

Table 7. Convergent validity and composite reliability test of the questionnaire dimensions.

			Estimate	AVE	CR
TE1	<---	Temperature	0.743	0.5947	0.8541
TE2	<---	Temperature	0.836
TE3	<---	Temperature	0.723
TE4	<---	Temperature	0.778
HU1	<---	Humidity	0.717	0.5573	0.8341
HU2	<---	Humidity	0.722
HU3	<---	Humidity	0.788
HU4	<---	Humidity	0.757
AV1	<---	Air Velocity	0.821	0.6702	0.8904
AV2	<---	Air Velocity	0.805
AV3	<---	Air Velocity	0.805
AV4	<---	Air Velocity	0.843
PR1	<---	Perceived Rideability	0.824	0.6841	0.8965
PR2	<---	Perceived Rideability	0.812
PR3	<---	Perceived Rideability	0.843
PR4	<---	Perceived Rideability	0.829
PC1	<---	Perceived Comfort	0.763	0.6102	0.8622
PC2	<---	Perceived Comfort	0.807
PC3	<---	Perceived Comfort	0.751
PC4	<---	Perceived Comfort	0.802
BD1	<---	Behavioral Decision	0.723	0.5897	0.8777
BD2	<---	Behavioral Decision	0.769
BD3	<---	Behavioral Decision	0.741
BD4	<---	Behavioral Decision	0.782
BD5	<---	Behavioral Decision	0.821
MO1	<---	Motive	0.73	0.5567	0.7902
MO2	<---	Motive	0.763
MO3	<---	Motive	0.745

, it is evident that in validating the Metro thermal comfort response behavior scale utilized in this study, all dimensions achieved AVE values exceeding 0.5 and CR values surpassing 0.7. This finding suggests that every dimension possesses robust convergent validity and composite reliability.

In the assessment of discriminant validity for the scale, as illustrated by the analysis results presented in

Table 8. Outcomes of the differential validity assessment for each dimension of the questionnaire.

Variable	Temperature	Humidity	Air Velocity	Perceived Rideability	Perceived Comfort	Motive	Behavioral Decision
Temperature	0.5947
Humidity	0.431	0.5573
Air Velocity	0.328	0.483	0.6702
Perceived Rideability	0.345	0.409	0.349	0.6841
Perceived Comfort	0.425	0.491	0.406	0.34	0.6102
Motive	−0.364	−0.485	−0.266	−0.301	−0.298	0.5567
Behavioral Decision	−0.517	−0.62	−0.44	−0.398	−0.499	0.505	0.5897
The square root of AVE	0.771	0.747	0.819	0.827	0.781	0.746	0.768

, the standardized correlation coefficients between each dimension and their corresponding Average Variance Extracted (AVE) values are all found to be less than the square root of those AVE values. This finding indicates that the dimensions exhibit strong discriminant validity.

5.4.4. Correlation Analysis

In this analysis, Pearson correlation analysis was used to conduct exploratory analysis of the correlation between various variables. As can be seen from the analysis results (see

Table 9. The results of Pearson’s correlation analysis between different dimensions.

Dimension	Temperature	Humidity	Air Velocity	Perceived Rideability	Perceived Comfort	Motive	Behavioral Decision
Temperature	1
Humidity	0.357 **	1
Air Velocity	0.290 **	0.412 **	1
Perceived rideability	0.300 **	0.355 **	0.313 **	1
Perceived comfort	0.366 **	0.415 **	0.349 **	0.301 **	1
Motive	0.302 **	0.394 **	0.220 **	0.253 **	0.248 **	1
Behavioral Decision	0.442 **	0.524 **	0.381 **	0.351 **	0.435 **	0.418 **	1

** p < 0.01.

), according to the results of the correlation coefficient, it can be seen that the correlation coefficient r among all variables is greater than 0, and the correlation score varies from 0 to 0.5. Closer to 0.5 indicates a more significant positive correlation. Therefore, the synthesis can show that there is a significant correlation between each variable in this analysis.

5.4.5. Structural Equation Modeling

Structural equation modeling (SEM) is a robust analytical technique that simultaneously addresses the relationships between observed variables and latent constructs, evaluates both direct and indirect effects among multidimensional variables, and facilitates the verification of discriminant and convergent validity [64]. In this study, we investigate the relationship between Metro thermal comfort and passenger unsafe behavior, which encompasses multiple causal pathways. This includes latent variables such as perceived comfort, rideability, and motivation, as well as observed indicators like temperature, humidity, and air velocity. SEM allows for a comprehensive analysis of the intricate interactions among these factors and elucidates how thermal comfort influences behavioral decisions through psychological response mechanisms in passengers. Therefore, SEM serves as an ideal methodological tool for testing our research hypotheses.

Initially, we assessed the model fit of the SEM representing the Metro thermal comfort response behavior scale. As detailed in

Table 10. Validation of the SEM for Metrorail passenger thermal comfort and behavioral responses.

Indicator	Reference Standard	Measured Result
CMIN/DF	1–3 is excellent, 3–5 is good	1.941
RMSEA	<0.05 is excellent, <0.08 is good	0.048
IFI	>0.9 is excellent, >0.8 is good	0.947
TLI	>0.9 is excellent, >0.8 is good	0.94
CFI	>0.9 is excellent, >0.8 is good	0.947

, results from model fit tests indicate that CMIN/DF (chi-square to degrees of freedom ratio) equals 1.941, well within the acceptable range of 1–3. The RMSEA (root mean square error of approximation) is found to be 0.048; this value lies within an excellent threshold of less than 0.05. Moreover, all tested indices, including IFI (Incremental Fit Index), TLI (Tucker–Lewis Index), and CFI (Comparative Fit Index), met or exceeded excellent criteria with values above 0.9. Thus, based on these analytical outcomes, it can be concluded that the SEM model concerning Metro thermal comfort response behavior exhibits favorable fitness characteristics.

Through the final structural equation model (SEM) analysis, as presented in

Table 11. Results of path relationship hypothesis testing for SEM model of Metro passengers’ thermal comfort and behavioral responses.

Path Relationship			Estimate	S.E.	C.R.	p
Perceived rideability	<---	Temperature	0.189	0.058	3.167	0.002
Perceived comfort	<---	Temperature	0.25	0.058	4.287	***
Perceived rideability	<---	Humidity	0.263	0.075	3.896	***
Perceived comfort	<---	Humidity	0.329	0.075	4.968	***
Perceived rideability	<---	Air Velocity	0.168	0.062	2.771	0.006
Perceived comfort	<---	Air Velocity	0.184	0.06	3.145	0.002
Motive	<---	Perceived rideability	−0.231	0.058	−3.802	***
Motive	<---	Perceived comfort	−0.254	0.058	−4.082	***
Behavioral Decision	<---	Motive	0.325	0.052	5.513	***
Behavioral Decision	<---	Perceived rideability	−0.194	0.043	−3.767	***
Behavioral Decision	<---	Perceived comfort	−0.368	0.047	−6.521	***

*** p < 0.001.

, regarding the path hypothesis test results and the SEM outcomes depicted in Figure 3, we established a significant correlation between Metro passengers’ thermal comfort and their behavioral responses, thereby validating our research hypotheses. Specifically, temperature and humidity comfort demonstrated notable positive effects on perceived rideability (β = 0.19 and β = 0.26) and perceived comfort (β = 0.25 and β = 0.33), respectively. In contrast, air velocity exerted a comparatively smaller influence on both perceived rideability and perceived comfort (β = 0.17 and β = 0.18), yet these effects remained statistically significant. These findings bolster the validity of hypotheses H1–H6.

Moreover, the analysis results indicate that Metro passengers’ perceived rideability and perceived comfort are negatively correlated with motivation (β = −0.23 and β = −0.25), further corroborating the accuracy of hypotheses H7 and H8. The significant positive effect of motivation on passengers’ behavioral decisions (β = 0.33) affirms hypothesis H9’s validity, suggesting that a high level of motivation encourages passengers to take actions to adapt or modify their thermal environment.

It is noteworthy that all path coefficients within the model achieved significance at p < 0.01, reflecting a strong explanatory capacity for the model while confirming how thermal comfort influences passengers’ perceptions, motivations, and ultimately their behavioral decisions.

Through the above analysis, we not only validated the effectiveness of each hypothesis, but also revealed the specific pathways by which thermal comfort affects passengers’ behavioral decisions through their perceptions and motivations.

6. Discussion

6.1. Thermal Comfort Factors in Metro and Passenger Perception

The results of the model analysis regarding the three factors influencing Metro thermal comfort—temperature, humidity, and air velocity—are largely consistent with the proposed hypotheses. All three factors exhibit positive correlations with perceived rideability and perceived thermal comfort. Among these, humidity demonstrates the most significant impact on perceived rideability (β = 0.26), followed by temperature (β = 0.19) and air velocity (β = 0.17). In terms of perceived thermal comfort, humidity again has the greatest influence (β = 0.33), succeeded by temperature (β = 0.25), while air velocity contributes least to this aspect (β = 0.18). These findings align with conclusions drawn from previous studies.

First and foremost, an increase in humidity within a high-temperature environment compromises the body’s capacity to regulate temperature through sweat evaporation, potentially exacerbating heat stress [65]. Research conducted by Fang et al. indicates that, under conditions of thermal equilibrium, individuals’ subjective evaluations of environmental comfort and acceptability fluctuate with rising levels of relative humidity [66]. This phenomenon is particularly pronounced in tropical and subtropical regions where both humidity and temperatures are generally elevated. The investigation by Zuo et al. found that as temperature and humidity rise, individuals’ perception of air quality deteriorates [43], underscoring the significance of humidity as a critical factor influencing passengers’ perceptions. Nevertheless, some studies suggest that in certain contexts, the impact of temperature within Metro carriages may have a more substantial effect on passenger perception than humidity [22]. We speculate that these discrepancies among research findings could be attributed to differences in the climatic conditions prevalent during the experiments conducted. Changsha, located in Hunan province, experiences a subtropical monsoon climate characterized by abundant rainfall coinciding with warm weather during this season. From late March to mid-May, cold and warm air frequently converge in southern China resulting in persistent overcast conditions accompanied by reduced sunlight [67]. Consequently, there is typically a marked increase in precipitation from spring to the early summer months in Changsha. Rainfall serves as one of the key determinants directly influencing atmospheric humidity levels. When rainfall surpasses the soil and vegetation’s absorption capacity, excess water manifests as surface runoff or subterranean infiltration, thereby elevating moisture content within the atmosphere [68]. Moreover, it is noteworthy that seasonal changes also affect air humidity levels at Metro stations. Pan et al. conducted a study examining the dynamic thermal comfort of passengers in Beijing Metros during the summer months. Their findings revealed that fluctuations in relative humidity within Metro trains exceeded those observed for temperature, with relative humidity levels being notably higher [12].

In different environments, variations in humidity also significantly influence individuals’ perceptions. Byber et al.’s research indicates that moderate increases in humidity within office settings can substantially diminish employees’ sensations of dryness in their skin and mucous membranes, alleviate symptoms associated with allergies and asthma, as well as enhance employees’ perceptions of indoor air quality [69]. This underscores the idea that appropriate increases in humidity are advantageous for improving employee comfort, particularly in relatively dry conditions.

However, in certain instances, an increase in humidity may not significantly influence an individual’s physiological responses or perception of thermal comfort [62]. This phenomenon may be attributed to the individual’s capacity for adaptation and the specific environmental conditions present. Individual differences also play a crucial role; various studies have demonstrated substantial variability in people’s subjective perceptions regarding changes in humidity [70]. These variations may be linked to factors such as an individual’s physiological characteristics, health status, and adaptability to different environments. Moreover, individuals hailing from diverse climatic regions may exhibit notable disparities in their physiological and psychological responses to humidity. For instance, those who have resided for extended periods in tropical or subtropical areas might possess heightened non-evaporative cooling abilities as well as more effective mechanisms for sweat-induced cooling [71].

Furthermore, modeling results indicate that temperature and airflow velocity are critical variables that impact passengers’ perceived rideability and thermal comfort. In summary, although humidity significantly affects passengers’ thermal comfort within this study context, this effect is not governed by a singular factor, but is instead influenced by an interplay of temperature, airflow dynamics, and individual variabilities, among others. Therefore, enhancing people’s thermal comfort necessitates a comprehensive consideration of these factors along with the implementation of corresponding measures aimed at optimizing environmental conditions.

6.2. Perceived Rideability, Perceived Comfort, and Motive

The analysis results of the model align closely with the proposed hypotheses. Both perceived sensory comfort and overall perceived comfort exhibit a negative correlation with passenger behavioral motivation.

Motivation, as a psychological factor that drives individual behavior, can be classified into intrinsic motivation and extrinsic motivation [72]. Intrinsic motivation pertains to the sense of satisfaction and achievement individuals experience from the behavior itself, whereas extrinsic motivation involves individuals pursuing external rewards or avoiding potential punishments [73]. These two types of motivation not only independently influence behavior, but may also interact synergistically to jointly determine an individual’s choices and decisions [74].

From a broader perspective on consumer behavior, the level of environmental comfort perception significantly affects customer satisfaction. For instance, factors such as a pleasant atmosphere, clean surroundings, and convenient locations all contribute to enhancing customer experience. The physical environment plays a crucial role in shaping customers’ overall satisfaction, which directly influences their behavioral intentions, such as whether they will make a purchase or express a desire to return. Consequently, by enhancing accommodation quality and environmental comfort levels, it is possible to increase customer satisfaction; this improvement can indirectly foster positive behavioral motivations among consumers.

However, within the context of Metro travel, passengers’ perceptions of thermal comfort inside train cars exhibit a negative correlation with their motivation to take action. More specifically, as passengers perceive an increase in rideability and comfort of the train car, their motivation to take action diminishes. This phenomenon arises because, in this particular context, “motivation” refers explicitly to the behavioral drive stimulated by an uncomfortable environment within the train car. Behavioral change typically entails a transition from one state to another, which generally necessitates overcoming certain forms of resistance or challenges. When passengers experience extreme comfort in their surroundings, they may lack the impetus to alter the existing status quo. This observation aligns with the identified negative correlation; as one variable—such as perceived rideability or comfort—increases, there is a corresponding decrease in the other variable (behavioral motivation).

Passengers actively adapt to the thermal environment of train cars through various means, including behavioral, physiological, and psychological responses [75]. From the perspective of passenger behavior in Metros, external motivators may include discomfort stemming from the thermal environment, social norms that either endorse or penalize specific behaviors, and the anticipation of rewards or punishments. In contrast, internal motivations arise from passengers’ desire for personal comfort by seeking a more suitable thermal environment. When passengers perceive a high degree of alignment with their surroundings, their evaluation of the environment becomes increasingly positive, often influenced by physical factors such as temperature, ventilation, feelings of security, and esthetic qualities.

In this discussion context, it can be concluded that when passengers believe their environment is highly conducive to comfort, they develop an attachment to it; consequently, their psychological state tends to become more relaxed and pleasant. Under such circumstances, passengers’ reactions to environmental changes may diminish—for instance, emotional fluctuations or behavioral adjustments are less likely—thus fostering calmness and rationality while promoting safe conduct. Conversely, exposure to extreme thermal conditions such as excessive heat or cold can lead to heightened agitation and unease among passengers; this distress may escalate impetuses towards unsafe behaviors.

6.3. Motive and Behavioral Decisions

Motivation plays a crucial role in influencing passengers’ behavioral decision-making (β = 0.33). The interplay between motivation and behavior is multidimensional and complex. Within the Metro environment, this relationship is not only directly affected by thermal comfort conditions within train cars, but may also be influenced by various other factors such as established behavioral norms and individual attitudes.

Behavioral norms are defined as the universal standards and rules accepted by societies or groups, which can significantly shape individual choices. In the context of Metros, passengers often modify their behaviors based on prevailing social norms—for instance, yielding seats to those in need during peak times or adhering to the “first in, last out” rule when boarding. Management strategies aimed at enhancing Metro operation safety—such as increasing safety education for passengers, refining station design to mitigate crime opportunities, and improving staff performance—are critically important for reducing unsafe behaviors and accidents. Research has demonstrated that inappropriate conduct within Metro cars, including eating or littering, is subject to influence from the social norms upheld by fellow passengers [76]. This underscores that the attitudes and expectations individuals hold regarding others’ behaviors can substantially impact their own actions through social norm dynamics.

An individual’s attitude is a persistent evaluation of a particular object or situation that significantly influences their behavioral tendencies. Given that Metro passengers are less impacted by vehicular traffic compared to general road users, the risk of serious collisions remains low. However, passengers often underestimate the actual dangers associated with certain behaviors, engaging in actions they deem acceptable, such as leaning against the door of a moving train, boarding or alighting from the train after the closing bell has sounded, and crossing in front of rapidly moving train cars [15]. During peak hours, passengers exhibit an increased motivation to violate riding rules compared to non-peak periods. This tendency can be attributed to how crowded environments affect sensory experiences and emotions, motivating individuals to prioritize time-saving measures or seek relief from discomfort.

Interestingly, insights gained from on-site interviews indicate that older individuals tend to be more cognizant of the potential safety consequences of their actions when contrasted with younger groups. The older cohort displays an awareness of their physical limitations; for instance, according to feedback from elder respondents, even if they experience discomfort due to heat within a train car, they are less inclined to cluster together or move about excessively [77]. Consequently, it can be inferred that the behavioral decision-making processes across different demographic groups are not only influenced by external environmental stimuli, but also heavily shaped by individual physical conditions and cognitive capacities.

Therefore, to gain a comprehensive understanding of the behavioral responses of passengers in the Metro environment, it is essential to consider not only physical factors such as thermal comfort, but also social psychological factors including behavioral norms and individual attitudes. These elements collectively influence an individual’s decision-making process regarding behavior and ultimately determine their final choices as passengers.

6.4. Thermal Comfort in Metro and Behavioral Decisions

According to the findings of the model research, thermal comfort exerts a significant indirect influence on passengers’ behavioral decision-making. Specifically, passengers may modify their seating arrangements, clothing choices, or movement patterns in response to varying thermal environments in order to achieve a more comfortable body temperature [78]. These behavioral adaptations are directed towards better acclimatization to the thermal conditions within the train car, demonstrating that passengers actively undertake measures to enhance their thermal comfort when confronted with uncomfortable thermal settings.

Other studies investigating passenger thermal comfort in transportation vehicles have reached similar conclusions. For instance, passengers in airplane cabins actively adjust their behavior to maintain thermal comfort in response to variations in the thermal environment [75]. Research indicates that passengers exhibit different behavioral responses when confronted with air temperatures of 22 °C, 20 °C, and 26 °C during winter boarding; these responses may include utilizing air conditioning outlets or adding or removing clothing. This observation suggests that passengers possess awareness and the ability to adapt their behavior according to differing thermal environments. Furthermore, Liu et al.’s study demonstrates that a passenger’s seat position influences their perceived thermal comfort. Depending on their seating choice, variables such as skin temperature and thermal sensation may vary significantly [79]. Consequently, it is likely that passengers select more comfortable seat positions based on their individual thermal sensations. Additionally, passenger thermal comfort within bus cabin environments is influenced by airflow conditions [80]. Research has revealed the notable impact of varying airflow speeds on passenger thermal comfort levels. Therefore, it is reasonable to expect that passengers might proactively adjust their seating arrangements or employ ventilation systems to modify the airflow surrounding them for enhanced thermal satisfaction.

In summary, thermal comfort does indeed affect passengers’ behavioral decisions, such as changing positions, adjusting clothing, and moving around. These behavioral changes are made to adapt to different thermal environments in order to achieve a more comfortable subjective temperature. Therefore, understanding and predicting passengers’ behaviors in this regard is crucial for designing more humane and comfortable transportation environments.

Moreover, another topic is introduced for discussion, namely the impact of thermal comfort on passengers’ willingness to travel. In different modes of transportation, passengers’ thermal comfort perception has a significant impact on their behavioral choices. From the perspective of high-speed rail and trains, although high-speed train seats are considered more comfortable, airplanes are still chosen by some passengers due to their short flight time [81]. This indicates that in time-sensitive situations, even if the level of comfort is lower, passengers may choose airplanes due to other factors (such as time).

In the context of selecting urban transportation modes, factors such as the degree of crowding and the reliability of public transportation significantly influence passengers’ satisfaction and their willingness to utilize these services [49]. Overcrowded or unreliable transit options can lead to increased stress among passengers, consequently diminishing their overall commuting satisfaction. This indicates that when choosing a mode of transportation, passengers tend to consider the entirety of their journey rather than merely focusing on their starting point and final destination.

7. Conclusions

This paper discusses the close relationship between thermal comfort and passengers’ unsafe behavior, and verifies the importance of improving thermal environment to enhance passengers’ behavioral safety and overall comfort. By integrating SOR (Stimulus-Organism-Response) and TAM (Technology Acceptance Model), we built a more comprehensive analytical framework. It is clearly stated that improving thermal comfort in Metro carriages—especially by regulating key parameters such as temperature, humidity, and air velocity—can significantly reduce passenger avoidance behaviors caused by thermal discomfort, thereby reducing safety hazards. Specific conclusions are as follows:

• Humidity, temperature, and air velocity within Metro cars substantially affect passengers’ perceptions of rideability and comfort.
• Passengers’ perceptions of rideability and comfort are pivotal determinants influencing their behavioral choices, highlighting the importance of considering individual thermal comfort in both Metro design and operational practices.
• With the increase in passengers’ awareness of environmental rideability and comfort perception, the motivation of passengers to make adjustments also decreases.
• The motivation exhibited by passengers plays a crucial role in shaping their behavioral decisions.

8. Limitations and Future Prospects

8.1. Variables and Sample Limitations

In addition to thermal comfort conditions, factors such as the operating peak hours arrangement, line layout, and urban population distribution patterns all play a crucial role in determining the number of passengers in the train car. For example, during peak hours in the morning and evening, even if the thermal comfort environment is good, due to the concentration of commuting demand, the train car may still be in a high-density state. In our study, we focused only on the direct relationship between thermal comfort and passenger behavior and did not fully consider the comprehensive impact of these external factors on crowd density, which is a major limitation of our study.

Our study is based solely on data collected from Line 1 of the Changsha Metro during a specific time period (from 22 December 2023 to 4 April 2024). This means that our research findings may not be applicable to all metro systems or other time periods. Different metro lines may have different passenger age structures, travel habits, and surrounding environments, which may result in different expressions of the impact of thermal comfort on crowd density. For example, some tourist lines or lines connecting specific functional areas (such as commercial areas or industrial zones) may have unique passenger flow and behavior patterns, and our research conclusions may not apply to these situations.

8.2. Future Research Prospects

Despite its limitations, our study is the first attempt to apply the SOR and TAM to the study of the relationship between thermal comfort and passenger behavior in Metros, providing a theoretical framework and empirical basis for subsequent research. We propose that future research can build on our findings by expanding the scope of study, adopting more diverse data collection methods, covering different Metro systems and longer time periods, to further explore the comprehensive impact of various factors on Metro passenger density and passenger behavior. At the same time, we hope that the research results will draw the attention of Metro operation and management departments to thermal comfort factors and encourage them to consider thermal environment optimization when formulating operation strategies. Although the impact on passenger density may be limited, it can still contribute to enhancing the overall passenger experience and safety.