The valid survey responses comprised 41.8% male and 58.2% female participants (see Table 1). Age distribution revealed: 5.5% under 18 years, 32.7% aged 18–26, 23.0% aged 27–36, 18.2% aged 36–44, 13.3% aged 46–60, and 7.3% over 60 years. Frequency analysis indicated 30.91% of respondents used public transportation daily, 27.27% 2–3 times weekly, and 41.82% 3–4 times monthly. This demographic profile is consistent with target sample requirements, ensuring the data’s validity for supporting research conclusions.
The first section of the questionnaire revealed five critical safety service function demands prioritized through participant selections: (1) ensuring hygienic in-vehicle environments, (2) providing safety facilities, (3) conducting station entry checks, (4) delivering safety technology solutions, and (5) maintaining safe passenger spacing.
The second section of the questionnaire specifies functional requirements indicators (see Table 2), where respondents selected answers from a five-point response scale – “I like it that way,” “It must be that way,” “I am neutral,” “I can live with it that way,” and “I dislike it that way” – for each functional and dysfunctional of questions under these design elements (Gérson 2007). These responses were categorized into distinct quality attributes using the standardized Kano evaluation table (see Table 3).
Following the original model’s classification methodology, the predominant attribute determines the requirement classification (see Table 4). For instance, SA8 demonstrated a predominant indifferent attribute (59.4%), indicating it represents an indifferent quality. However, the online survey format may introduce response bias due to participants’ casual engagement, potentially inflating indifferent attribute proportion for multiple service elements. This phenomenon does not negate the service’s importance but necessitates comparative analysis of secondary attribute distributions. Notably, SA10 and SA12 exhibited minimal disparity between attractive and indifferent attribute proportions.
To enhance the classification accuracy of service design requirements, the Better-Worse coefficients are implemented for prioritization. This analytical framework calculates two indices: Satisfaction Index (SI) and Dissatisfaction Index (DSI). SI is a positive value (0 < SI ≤ 1) quantifying satisfaction enhancement when a service requirement is implemented, where values approaching 1 indicate stronger positive impacts. DSI is a negative value (−1 ≤ DSI < 0) measuring satisfaction deterioration when a service feature is omitted, with values approaching −1 denoting greater negative effects.
The quadrant diagram visualizes these coefficients through absolute DSI values plotted against SI values. Service requirements positioned in higher absolute value quadrants (excluding indifferent qualities) warrant prioritized implementation. The computational formulas are as follows:
$${\rm{B}}{\rm{etter}}({\rm{SI}})=\frac{A+O}{A+O+M+I}$$
$${\rm{W}}{\rm{orse}}({\rm{DSI}})=\left(\frac{M+O}{A+O+M+I}\right)(-1)$$
The analysis integrating Kano model classification and Better-Worse coefficient evaluation (see Fig. 1) identified distinct service quality categories: SA3, SA4, SA6, and SA9 were classified as Attractive qualities, SA5 and SA1 as One-dimensional qualities, while SA7, SA8, SA12, SA14, and SA15 exhibited consistent Indifferent quality characteristics. Classification discrepancies emerged in five service elements (SA1, SA2, SA10, SA11, SA16). SA1 and SA16 were classified from the previous Indifferent qualities to Must-be qualities, SA2 and SA11 were classified from the previous Attractive qualities to One-dimensional qualities, and SA10 was classified from the previous Attractive quality to Indifferent quality. Based on the preliminary inference of the author and colleagues, it is believed that SA1 can be classified as a Must-be quality, SA11 as a One-dimensional quality, and SA2, SA10, and SA16 remain unchanged in their original quality categories (see Table 5).
Quadrant diagram of Better-Worse coefficients.
SA1 (disinfection status display) was deemed institutionally mandatory despite passenger-perceived non-essentiality, where information absence caused negligible dissatisfaction but outdated data provoked negative responses. SA11’s (advanced air filtration systems) transition from Attractive to One-dimensional quality reflected the normalization of air filtration systems in public transit, converting technological novelty into baseline expectations. SA2 (rapid temperature screening at entry) maintained Attractive status as conventional temperature screening delays remained within acceptable tolerance thresholds. SA10 (temporary medical assistance stations) maintained Attractive quality because professionally equipped medical facilities are not widely implemented in current transit station practices, and their introduction can significantly enhance passenger satisfaction. SA16’s (passenger capacity control) Indifferent designation resulted from operational impracticalities in passenger density control coupled with marginal safety perception impacts.
Fuzzy Analytic Hierarchy Process (FAHP) for calculating passenger demand and service design element weights
In traditional Analytic Hierarchy Process (AHP), pairwise comparisons are conducted for each hierarchical level of customer satisfaction objectives. Saaty (1980) proposed a 9-point scale in The Analytic Hierarchy Process, where numerical values (1–9) represent relative importance: 1 (equally important), 3 (slightly more important), 5 (significantly more important), 7 (strongly more important), and 9 (extremely more important), with intermediate values (2, 4, 6, 8) denoting gradations between these judgments. However, customer demands often involve ambiguous linguistic descriptions, and human assessments of qualitative attributes are inherently subjective and imprecise. Thus, traditional AHP is insufficient for accurately capturing customer demands and determining their weights.
To calculate precise weights for passenger demands and service design elements, this study employs Fuzzy AHP (FAHP), converting linguistic evaluations of passenger demands into triangular fuzzy numbers (TFNs) to construct pairwise comparison matrices (see Supplementary Appendix A). TFNs are categorized into symmetric/asymmetric scales and wide/narrow ranges. To minimize zero or identical weights for service design elements, a 5-level asymmetric scale with high-precision decimal retention is adopted, where ALI means very low, VSLI means relatively low, SLI means low, WLI means slightly low, EI means equal, WMI means slightly high, SMI means high, VSMI means relatively high, and AMI means very high (Isaai et al. 2011). Data for this phase were collected through expert interviews. On May 12, 2021, five public transportation experts were interviewed. Individual decision weights were assumed homogeneous. To mitigate inconsistencies caused by cognitive differences, extreme values (maximum/minimum) were excluded, and the mean by selecting two of the three remaining data sets (mode) was calculated.
For example, if an interviewee considers “ensuring hygienic in-vehicle environments (\({{PR}}_{1}\))” more important than “providing safety facilities (\({{PR}}_{2}\))” with a “Relatively High (VSMI)” rating, the corresponding TFN is \({{PR}}_{12}=\left(2,5/2,3\right)\), while the reciprocal is \({{PR}}_{21}=(1/3,2/5,1/2)\). For n interviewees, data filtering and averaging are applied within the same category. Similarly, fuzzy judgment matrices are computed for each level of the hierarchical structure of passenger safety demands (see Supplementary Appendix A).
Consistency validation is required for pairwise comparison matrices to ensure non-contradictory judgments. In practice, human judgments may lack perfect consistency. Thus, matrices are deemed acceptable if their consistency ratio (CR) falls within a specified range. The consistency index (CI) and CR are calculated as:
$${CI}=\left({\lambda }_{\max }-n\right)/\left(n-1\right)$$
$${CR}={CI}/{{RI}}_{(n)}$$
where \({\lambda }_{\max }\) is the maximum eigenvalue, \(n\) is the matrix dimension, and \({{RI}}_{(n)}\) is the random index dependent on \(n\) (see Supplementary Appendix A). A CR < 0.1 indicates acceptable consistency.
A triangular fuzzy number, denoted \(\widetilde{A}=\left(l,m,u\right)\), can be defuzzified into a crisp value:
$${A}{{\_}}{crisp}=(4m+l+n)/6$$
Taking the comparison matrix \({G\_}{crisp}\) as an example (see Supplementary Appendix A), through computation, the maximum eigenvalue of the matrix is obtained: \({{\rm{\lambda }}}_{\max }=5.1770\). Given the matrix dimension \({\rm{n}}=5\) and the random index \({{\rm{RI}}}_{({\rm{n}})}=1.11\), the consistency index and consistency ratio of the matrix can be calculated as :
$${CI}=\left({\lambda }_{\max }-n\right)/\left(n-1\right)=(5.1770-5)/(5-1)\approx 0.0443$$
$${CR}={CI}/{{RI}}_{\left(n\right)}=\left(\frac{0.0443}{1.11}\right)\approx 0.0399 < 0.1$$
All comparison matrices exhibited CR values below 10% (see Table 6), confirming acceptable consistency.
To evaluate the hierarchical passenger safety demands, fuzzy number comparison principles and extent analysis (Chang 1996) were applied. Extent analysis calculates the synthetic extent value \({{\rm{D}}}_{{\rm{i}}}\) for each object relative to goals. For the \({\rm{i}}\)-th object, \({{\rm{D}}}_{{\rm{i}}}\) is defined as:
$${D}_{i}=\mathop{\sum }\limits_{j}^{m}{A}_{{gi}}^{j}\bigotimes {\left[\mathop{\sum }\limits_{i}^{n}\mathop{\sum }\limits_{j}^{m}{A}_{{gi}}^{j}\right]}^{-1}.$$
(1)
For the \(k\)-th level, the synthetic extent value \({D}_{i}^{k}\) of the \(i\)-th element is:
$${D}_{i}^{k}=\mathop{\sum }\limits_{j=1}^{n}{a}_{{ij}}^{k}\bigotimes {\left(\mathop{\sum }\limits_{i=1}^{n}\mathop{\sum }\limits_{j=1}^{n}{a}_{{ij}}^{k}\right)}^{-1},i=1,2,\ldots ,n$$
(2)
For element \({D}_{{{PR}}_{1}}\):
$${D}_{{{PR}}_{1}}=\mathop{\sum }\limits_{j=1}^{5}{a}_{1j}\bigotimes {\left[\mathop{\sum }\limits_{i=1}^{5}\mathop{\sum }\limits_{j=1}^{5}{a}_{{ij}}\right]}^{-1}=(0.09,0.19,0.36)$$
Similar calculations yield synthetic extent values for all hierarchical elements:
$${D}_{{{PR}}_{1}}=(0.088,0.191,0.365)$$
$${D}_{{{PR}}_{2}}=(0.113,0.234,0.459)$$
$${D}_{{{PR}}_{3}}=(0.121,0.232,0.486)$$
$${D}_{{{PR}}_{4}}=(0.099,0.197,0.436)$$
$${D}_{{{PR}}_{5}}=(0.083,0.152,0.277)$$
Definition 1: For two TFNs \({A}_{1}\) and \({A}_{2}\), the possibility degree \({A}_{1}\ge {A}_{2}\) is defined as: \({\rm{V}}\left({A}_{1}\ge {A}_{2}\right)={\sup }_{x\ge y}[\min ({{\mu }_{A}}_{1}\left(x\right),{{\mu }_{A}}_{2}\left(y\right))]\).
Theorem 1: If \({A}_{1}\,\)= \(\left({l}_{1},{m}_{1},{u}_{1}\right)\) and \({A}_{2}\) = \(\left({l}_{2},{m}_{2},{u}_{2}\right)\):
1. A sufficiently necessary condition for \({\rm{V}}\left({A}_{1}\ge {A}_{2}\right)=1\) is \({m}_{1}\ge {m}_{2}\).
2. If \({m}_{1}\le {m}_{2}\), then let \({\rm{V}}\left({A}_{1}\ge {A}_{2}\right)={\rm{hgt}}({A}_{1}\cap {A}_{2})\), where \(d\) is the intersection point: \({\rm{V}}\left({A}_{1}\ge {A}_{2}\right)=\mu \left(d\right)=\left\{\begin{array}{c}\frac{{{\rm{l}}}_{2}-{{\rm{u}}}_{1}}{\left({{\rm{m}}}_{1}-{{\rm{u}}}_{1}\right)-({{\rm{m}}}_{2}-{{\rm{l}}}_{2})}\\ 0,{\rm{other}},\end{array}\right.,{{\rm{l}}}_{2}\le {{\rm{u}}}_{1}\)
Definition 2: The possibility degree of a fuzzy number \(A\) exceeding \(k\) fuzzy numbers \({A}_{i}(i=\mathrm{1,2},\ldots ,k)\) is:
$$V\left(A\ge {A}_{1},{A}_{2},\ldots ,{A}_{k}\right)=\min V\left(A\ge {A}_{i}\right),i=1,2,\ldots ,k$$
Let \(d\left. ({p}_{i}^{k}\right)=\min V({S}_{i}^{k}\ge {S}_{j}^{k})\), where \({p}_{i}^{k}\) is the \(i\)-th element at the \(k\)-th level. The weight vector \({W}_{k}^{{\prime} }\) is:
$${W}_{k}^{{\prime} }={(d\left({p}_{1}^{k}\right),d\left({p}_{2}^{k}\right),\ldots ,d\left({p}_{n}^{k}\right))}^{T}$$
Normalized weights \({W}_{k}\) become:
$${W}_{k}={(w\left({p}_{1}^{k}\right),w\left({p}_{2}^{k}\right),\ldots ,w\left({p}_{n}^{k}\right))}^{T}$$
Based on Definition 1 and Definition 2, the weight vector \({{\rm{W}}}_{{\rm{G}}}^{{\prime} }\) for the category level of the hierarchy can be calculated using the following equation:
$$\begin{array}{lll}\displaystyle{W}_{G}^{{\prime} }&=&{(d\left({{PR}}_{1}\right),d\left({{PR}}_{2}\right),d\left({{PR}}_{3}\right),d\left({{PR}}_{4}\right),d\left({{PR}}_{5}\right))}^{T}\\&=&(0.854,0.978,1.00,0.897,0.661)\end{array}$$
Normalized weights \({{\rm{W}}}_{{\rm{G}}}\) become:
$$\begin{array}{ll}\displaystyle{W}_{G}:&\displaystyle\left({W}_{{{PR}}_{1}},{W}_{{{PR}}_{2}},{W}_{{{PR}}_{3}},{W}_{{{PR}}_{4}},{W}_{{{PR}}_{5}}\right)\\&=\displaystyle\left(0.194,0.223,0.228,0.204,0.151\right)\end{array}$$
Following a similar approach, the weight vectors \({{\rm{W}}}_{{{\rm{PR}}}_{{\rm{i}}}}\) and \({{\rm{W}}}_{{{\rm{SA}}}_{{\rm{j}}}}\) can be computed, where \({\rm{i}}=1-5\), \({\rm{j}}=1-16\).
Therefore, the total weight of passenger demand (see Table 7) can be derived by using the following equation:
$${{TW}}_{{{SA}}_{1}}={W}_{{{PR}}_{1}}* {W}_{{{SA}}_{1}},{{TW}}_{{{SA}}_{5}}={W}_{{{PR}}_{1}}* {W}_{{{SA}}_{5}};$$
$${{TW}}_{{{SA}}_{4}}={W}_{{{PR}}_{2}}* {W}_{{{SA}}_{4}},{{TW}}_{{{SA}}_{6}}={W}_{{{PR}}_{2}}* {W}_{{{SA}}_{6}},{{TW}}_{{{SA}}_{7}}={W}_{{{PR}}_{2}}* {W}_{{{SA}}_{7}},$$
$${{TW}}_{{{SA}}_{11}}={W}_{{{PR}}_{2}}* {W}_{{{SA}}_{11}},{{TW}}_{{{SA}}_{13}}={W}_{{{PR}}_{2}}* {W}_{{{SA}}_{13}};$$
$${{TW}}_{{{SA}}_{2}}={W}_{{{PR}}_{3}}* {W}_{{{SA}}_{2}},{{TW}}_{{{SA}}_{3}}={W}_{{{PR}}_{3}}* {W}_{{{SA}}_{3}};$$
$${{TW}}_{{{SA}}_{8}}={W}_{{{PR}}_{4}}* {W}_{{{SA}}_{8}},{{TW}}_{{{SA}}_{9}}={W}_{{{PR}}_{4}}* {W}_{{{SA}}_{9}},{{TW}}_{{{SA}}_{10}}={W}_{{{PR}}_{4}}* {W}_{{{SA}}_{10}},$$
$${{TW}}_{{{SA}}_{12}}={W}_{{{PR}}_{4}}* {W}_{{{SA}}_{12}};$$
$${{TW}}_{{{SA}}_{14}}={W}_{{{PR}}_{5}}* {W}_{{{SA}}_{14}},{{TW}}_{{{SA}}_{15}}={W}_{{{PR}}_{5}}* {W}_{{{SA}}_{15}},{{TW}}_{{{SA}}_{16}}={W}_{{{PR}}_{5}}* {W}_{{{SA}}_{16}}.$$
UTAUT2 research model and hypothesis
The essential service design elements for public transportation safety systems were identified through preliminary research, focusing on hygiene management during infectious disease outbreaks. Key measures include deploying sufficient sanitation staff to increase cleaning frequency in stations and vehicles, alongside automated facilities such as robotic sweepers and disinfection spray systems. Meanwhile, health screening need to integrate long-range infrared temperature detection and combine health code verification with transit passes to minimize close contact between passengers and staff. Real-time disinfection status should be displayed via interactive dashboards at high-traffic hubs, while temporary medical stations and on-site sales of protective equipment (e.g., disinfectants, air filters) be established to enhance emergency preparedness.
To address urgent public health challenges, an autonomous intelligent medical public transit system was designed as a multifunctional mobile smart space. This system incorporates shared, interconnected, and data-driven functionalities, adapting dynamically to real-time demands. In medical mode, it autonomously detects symptoms (e.g., fever) using AI-powered sensors, with data visualized on holographic screens. Virtual physicians conduct preliminary consultations to triage cases and reduce anxiety, while 5G networks enable remote diagnostics and telemedicine, connecting patients with nationwide medical expertise. Meanwhile, the system operates flexibly across multiple modes: during energy replenishment, vehicles undergo overnight disinfection and charging; in logistics mode, interiors are reconfigured for medical supply transport; and in commute mode, autonomous navigation optimizes route efficiency using real-time congestion updates.
The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework was applied to analyze factors influencing user behavioral intentions toward autonomous intelligent medical public transit systems. The flowchart synthesizes user-centric analysis (Kano Model), quantitative prioritization (FAHP), and technology-adoption theory (UTAUT2) to optimize resource allocation for health-oriented travel services(see Fig. 2).
Flowchart of health-oriented travel services optimisation.
Performance expectancy
Performance expectancy (PE) refers to how strongly individuals believe that utilizing a given innovation (e.g.The use of self-sufficient intelligent medical public transport systems) is anticipated to significantly improve performance, particularly concerning their users’ confidence in travel safety. The concept corresponds with elements from five separate theoretical frameworks, encompassing aspects such as perceived value (TAM, TAM2, C-TAM-TPB), external incentives (MM), compatibility with job responsibilities (MPCU), comparative benefits (IDT), and anticipated outcomes (SCT). It has been identified as a significant indicator of users’ willingness to accept new technologies (Madigan et al. 2017).
Compared to traditional transportation tools, autonomous medical transit systems offer improved safety, convenience, and efficiency. Consequently, the hypothesis can be expressed as:
H1. Performance expectancy positively influences users’ behavioral intention to adopt autonomous intelligent medical public transit systems.
Effort expectancy
Effort expectancy (EE) refers to the perceived ease of using a technology, defined as “the degree of ease associated with consumers’ use of the technology” (Venkatesh et al. 2012). It relates to factors such as perceived ease of use (TAM, TAM2), complexity (MPCU), and ease of use (IDT). Effort expectancy has been shown to significantly influence acceptance in transportation studies (Choi and Ji 2015; Madigan et al. 2016). For autonomous medical transit systems, complexity may arise when passengers interact with mobile applications. While some users may find this interaction negligible, others might perceive it as burdensome, negatively impacting adoption intentions. Thus, the hypothesis is formulated as:
H2. Effort expectancy positively influences users’ behavioral intention to adopt autonomous intelligent medical public transit systems.
Social influence
Social influence (SI) pertains to the degree to which individuals believe that key figures in their lives, such as peers and mentors, expect them to utilize a specific technology (e.g., relatives and acquaintances). This relates to elements such as social expectations (TRA, TAM2, TPB, DTPB, C-TAM-TPB), interpersonal influences (MPCU), and perceived status (IDT). It can be described as “the extent to which individuals believe that significant others think they ought to utilize a specific technology” (Venkatesh et al. 2012). It was established that social pressures significantly influence the acceptance of transportation systems (Adell et al. 2011; Kervick et al. 2015). When considering the implementation of self-driving medical transportation systems, individuals are inclined to seek advice from their social connections prior to making a decision. Consequently, the hypothesis is established as follows:
H3. Social influence positively influences users’ behavioral intention to adopt autonomous intelligent medical public transit systems.
Facilitating conditions
Facilitating conditions pertain to the beliefs held by individuals regarding whether the supporting organizational and technical infrastructures are adequate for utilizing a system. This concept is associated with perceived behavioral control as outlined in the theory of planned behavior (TPB), supportive circumstances (MPCU), and compatibility (IDT), which is characterized as “the awareness that individuals have regarding the resources and assistance accessible for engaging in a behavior” (Venkatesh et al. 2012). The conditions that facilitate adoption play a crucial role in studies of technology within the transportation sector (Madigan et al. 2017; Motak et al. 2017). Individuals in autonomous medical transit systems who have limited access to essential resources (such as information, support lines, and community assistance) are likely to demonstrate lower intentions to adopt such technologies. Consequently, the following hypothesis is proposed:
H4. Facilitating conditions positively influence users’ behavioral intention to adopt autonomous intelligent medical public transit systems.
Hedonic motivation
Hedonic motivation (HM) is defined as “the fun or pleasure derived from using a technology” (Venkatesh et al. 2012). Though underexplored in UTAUT2, this construct is influential in transportation adoption research (Madigan et al. 2017). Users who perceive interactions with autonomous medical transit systems as enjoyable are more likely to adopt them. Thus, the hypothesis is formulated as:
H5. Hedonic motivation positively influences users’ behavioral intention to adopt autonomous intelligent medical public transit systems.
Price sensitivity
The UTAUT2 framework’s concept of “price value” has been adjusted to reflect “price sensitivity” owing to insufficient data on the extensive deployment of services. Price sensitivity can be understood as “the way in which consumers respond to pricing and alterations in prices” (Goldsmith et al. 2005). While prior studies suggest its relevance in technology adoption (Tsai and LaRose 2015), it remains underexplored in UTAUT2. Thus, the hypothesis is formulated as:
H6. Price sensitivity negatively influences users’ behavioral intention to adopt autonomous intelligent medical public transit systems.
Perceived risk
The concept of perceived risk (PS), understood as “the likelihood of experiencing a loss while trying to achieve a target outcome” (Featherman and Pavlou 2003), plays a significant role in the acceptance of autonomous technologies. Factors like safety issues or technological malfunctions, which are frequently disregarded in conventional frameworks (Koenig-Lewis et al. 2015), hold particular importance in the context of self-driving cars. Consequently, the hypothesis can be articulated as:
H7. Perceived risk negatively influences users’ behavioral intention to adopt autonomous intelligent medical public transit systems.
Linking Kano-FAHP results to UTAUT2 constructs
To ensure the UTAUT2 model accurately reflected passenger priorities identified through Kano-FAHP analysis, we systematically mapped the key service attributes to corresponding acceptance factors. For instance, high-priority features like timely sanitation (SA₅, weight = 0.133) and contactless entry (SA₄, Attractive quality) directly informed Performance Expectancy (e.g., “This system reduces infection risk through frequent cleaning”) and Effort Expectancy (e.g., “QR code boarding is easy to use”). Similarly, Perceived Risk items incorporated emergency equipment (SA₁₃) and air filtration (SA₁₁), while Hedonic Motivation accounted for user satisfaction with PPE vending machines (SA₉). Pilot testing confirmed these adaptations improved item clarity, with 85% of participants agreeing survey questions matched their transit safety concerns. This integration ensured UTAUT2 constructs measured acceptance drivers most relevant to pandemic-era transit design.
Scale design
Following established quantitative technology acceptance research, validated scales were utilized for survey design. The final questionnaire consisted of four sections: a cover page, demographic characteristics (e.g., age, gender, education level), an informational sheet detailing the autonomous intelligent medical public transit system (e.g., advantages, vehicle features), and core measurement items adapted from prior studies. Drawing on structural equation modeling (SEM) literature (Dawes 2002; Brown 2011), a 7-point Likert scale was adopted for 29 items across eight latent variables.
In order to preemptively tackle any potential challenges before formal data collection began, the research team carried out a preliminary test of the survey instrument. A total of 51 responses from the pilot study were gathered and examined. Following the analysis, three elements that demonstrated inadequate reliability, validity concerns, or minimal factor loadings were excluded. Minor wording revisions were made to enhance clarity and reduce complexity, incorporating feedback from participants. Subsequently, the finalized Chinese questionnaire was created as an online version on the Wenjuanxing platform. The specific questionnaire items and their sources are provided in Supplementary Appendix B.
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