Psychometric Design and Validation of the Urban Mobility Experiences Scale

Abstract

Urban mobility plays a key role in territorial equity, access to services, and population well-being, as unfavorable mobility experiences are associated with stress and physical and mental deterioration. However, in Latin American and border cities, validated instruments for comprehensively assessing these experiences remain scarce. This study aimed to design and evaluate the psychometric properties of the Urban Mobility Experiences Scale [UMES]. A cross-sectional study was conducted with 423 adults from Nogales, Sonora, Mexico, selected through convenience sampling. The initial UMES consisted of 24 items distributed across five conceptual dimensions. Content validity was assessed by nine experts using Aiken’s V coefficient, while construct validity was examined through exploratory factor analysis with principal axis factoring and PROMAX rotation. Data adequacy was verified using the Kaiser–Meyer–Olkin index and Bartlett’s test of sphericity. Internal consistency was estimated using McDonald’s Omega. All items demonstrated adequate content validity (V ≥ 0.80). Five factors were identified, explaining 53.6% of the total variance, with factor loadings above 0.40. Reliability was acceptable across all dimensions (ω ≥ 0.70), and overall internal consistency was high (ω = 0.912). The UMES is a valid and reliable instrument for assessing urban mobility experiences in intermediate and border cities and may inform evidence-based policies promoting equity, sustainability, and urban well-being.

1. Introduction

Urban mobility is understood as the set of movements of people and goods within cities, regardless of the mode used—walking, cycling, public transportation, or private automobile—and constitutes an essential element for ensuring inclusive and sustainable transportation systems [1]. Beyond its functional dimension, mobility plays a strategic role in the social and economic structure of cities by facilitating equitable access to basic services, reducing territorial inequalities, strengthening social cohesion, and promoting urban integration [2]. For this reason, mobility is currently recognized as a determinant of health, influencing both quality of life and the overall well-being of the population, including its physical and mental dimensions [3].

Moreover, recent findings in the literature indicate that unfavorable travel experiences are associated with higher levels of self-perceived stress, fatigue, emotional discomfort, and diminished well-being [4]. This perspective has guided the study of urban mobility toward a model that integrates subjective, affective, and cognitive factors relevant to people’s daily lives from a public health standpoint, rather than focusing solely on the traditional paradigm centered on infrastructure and the technical functioning of urban mobility systems [5]. Consequently, mobility experiences represent a broad field of study for analyzing people’s movements, as they encompass dimensions such as accessibility, safety, comfort, time efficiency, and the emotional state experienced during travel.

There is global evidence of studies conducted under the experiential approach to urban mobility that assessed the quality of urban transport and identified various attributes that influenced the users’ overall experience, travel satisfaction, and modal choice. These attributes include comfort, punctuality, perceived safety, accessibility, and waiting conditions [6,7,8]. Likewise, it has been reported that limited infrastructure, territorial inequality, service variability, and prolonged travel times generate more physically and psychologically demanding experiences, negatively affecting the individual and collective well-being of the population [9,10,11].

In Mexico, the urbanization process has accelerated. Official projections estimating an 83% urban population by 2030 present challenges related to accessibility, road safety, public transportation coverage [12], and territorial management. Research in intermediate cities such as Colima and Hermosillo has documented factors that reduce citizen satisfaction and degrade mobility experiences, including persistent deficiencies in transport quality, service regularity, and waiting times [13,14]. Similarly, reports from Toluca, Monterrey, and the Mexico City Metropolitan Area describe metropolitan zones with complex urban dynamics characterized by congestion, territorial fragmentation, and high mobility demand. These conditions prolong travel times and generate circumstances that negatively impact the users’ travel experiences [15,16,17,18]. Studies in both intermediate cities and large metropolitan areas highlight persistent structural challenges related to accessibility, operational efficiency, and safety, which in turn affect the cognitive and affective dimensions of contemporary urban mobility in the country.

This national landscape underscores the urgent need for tools capable of comprehensively evaluating urban mobility experiences, incorporating both structural conditions and users’ subjective perceptions. Such instruments must undergo psychometric evaluation, including exploratory factor analysis (EFA) and reliability coefficients beyond Cronbach’s alpha—such as McDonald’s Omega—which provides greater precision in models with heterogeneous factor loadings [19,20]. Despite this, no validated instruments currently assess urban mobility experiences comprehensively, as existing scales focus only on specific constructs such as satisfaction, perceived accessibility, or service quality, most of which were developed in European and Asian contexts, limiting their applicability in Latin American and border cities.

Unlike usual urban mobility assessments, which mainly focus on the availability of infrastructure, the choice of transport modes, or objective travel indicators, the concept of urban mobility experience emphasizes the subjective, lived experience of users while navigating urban space. This perspective includes perceptual and contextual dimensions such as safety, comfort, accessibility, and environmental interaction, which are often ignored or fragmented in existing instruments. Current mobility scales tend to address isolated components rather than capturing the integrated experience of moving through a complex urban environment, especially in border-city contexts. In this context, the Urban Mobility Experience Scale (UMES) is presented as a context-specific, multidimensional instrument designed to assess how individuals experience urban mobility in cross-border and socio-spatially asymmetric settings.

Although several instruments evaluate specific aspects of mobility—such as satisfaction, accessibility, or transport service quality—comprehensive psychometrically validated tools focusing on experiential mobility in Latin American border cities remain relatively scarce. A central argument of this research is that understanding mobility as an experiential construct provides more comprehensive and practical insights for urban planning and public health interventions than traditional mobility indicators alone. In response to this contextual and methodological gap, the present study advances the field through the development and psychometric validation of a multidimensional instrument designed to capture the complexity of urban mobility experiences in a border-city environment. Therefore, the aim of this study was to develop and psychometrically evaluate the Urban Mobility Experiences Scale (UMES) within a Latin American border-city context.

2. Materials and Methods

2.1. Study Design

A methodological, cross-sectional study was conducted for the design, validation, and psychometric analysis of the Urban Mobility Experiences Scale (UMES). The process was carried out in two complementary phases: the first corresponded to content validation based on theoretical review and expert judgment, while the second consisted of exploratory factor analysis (EFA) and the estimation of internal reliability.

2.2. Study Population

The study was conducted along Plutarco Elías Calles Avenue in Nogales, Sonora, a border town in northern Mexico [21]. Plutarco Elías Calles Avenue is one of the city’s main urban corridors, connecting residential areas with commercial areas, public services, and border-related economic activities, and serves as the main access road to the international crossing area. Due to its strategic location, heterogeneous land use and complex traffic dynamics, the avenue concentrates large flows of pedestrians, vehicles, and public transport during the day, making it a critical environment for observing different urban mobility experiences. Therefore, this area was chosen as a representative urban axis for daily intercity mobility. According to the National Institute of Statistics and Geography [INEGI), based on the 2020 census, the adult population of Nogales reached 179,873 inhabitants in 2020 [22].

2.3. Inclusion and Exclusion Criteria

The study included adults (≥18 years), residents of Nogales, and regular users of the avenue—whether traveling by foot, bicycle, car, or public transportation—who agreed to participate voluntarily and provided informed consent. No questionnaires were excluded from the final analysis, as all collected instruments met the predefined completeness and internal consistency criteria. No restrictions were established based on physical, sensory, or cognitive conditions; when required, assistance was provided for reading and understanding the instrument, ensuring inclusive and autonomous participation.

2.4. Sample Size and Sampling Technique

Sample size was determined according to the psychometric criteria for exploration factor analysis, which recommend between 5 and 10 participants per item [23,24]. Given that the initial version of the UMES consisted of 24 items, a minimum of 240 participants was estimated. The final sample comprised 423 individuals, exceeding the sample adequacy standards and contributing to greater factor stability. Sampling was conducted through non-probabilistic convenience sampling, based on the accessibility and willingness of avenue users during the data collection period.

2.5. Instrument Development

The UMES was developed through a theoretical and empirical review of the literature on urban mobility, public transportation quality, road safety, environmental psychology, and urban well-being. This process allowed for the identification of five conceptual dimensions associated with urban mobility experiences: Accessibility and Connectivity (AC), Quality and Comfort of Public Transportation (QC), Mobility Safety (MS), Travel Time and Efficiency (TTE), and Sustainability and Urban Environment (SUE). The initial instrument included 24 items formulated as value-based statements, answered on a five-point Likert scale (1 = Never to 5 = Always), where higher scores indicate more frequent favorable urban mobility experiences.

2.6. Data Collection

The instrument was administered in person at strategic points along Plutarco Elías Calles Avenue between July and September 2025. Surveys were conducted during peak hours (7:00–11:00 and 16:00–19:00) to capture a diverse range of users. Data collection was carried out by previously trained surveyors, ensuring standardized procedures and adherence to ethical principles of confidentiality and respect.

2.7. Data Analysis

2.7.1. Content Validity

Content validity was examined through the assessment of a panel of nine experts in urban planning, public health, and research methodology, selected for their professional experience and their trajectory in instrument design and validation. Each item was evaluated for clarity (1 = completely unclear; 5 = completely clear) and relevance (1 = completely irrelevant; 5 = completely relevant). Aiken’s V coefficient was employed as the primary indicator of agreement among evaluators, with values of V ≥ 0.80 considered satisfactory evidence of content validity [25]. Aiken’s V, a widely recognized and robust index for estimating inter-rater concordance, ranges from 0 to 1, with higher values indicating greater consistency [26,27]. The coefficient was calculated using the following expression:

$$V={\displaystyle \frac{\sum s}{n\left(c-1\right)}}$$

The term s represents the difference between the score assigned by each judge to the item [r] and the minimum possible score on the scale [lo]. The variable n corresponds to the total number of judges, while c denotes the number of response categories included in the rating scale.

To improve the accuracy of the evaluation and adequately account for variability in expert judgment, confidence intervals for Aiken’s V were calculated according to procedures recommended in the literature [28,29]. A lower confidence limit of ≥0.70 was established as the minimum acceptable threshold. The lower (L) and upper (U) bounds of the confidence interval were derived using the following formulas:

$$L={\displaystyle \frac{2nkV+z^2-z\sqrt{4nkV\left(1-V\right)+z^2}}{2\left([nk+z^2]\right)}}$$

In the case of the confidence interval calculations, L denotes the lower limit and U the upper limit of the interval. The parameter z refers to the critical value of the standard normal distribution, and V corresponds to the content validity coefficient [Aiken’s V]. The variable n represents the number of participating judges, and k indicates the number of scale values minus one [k = c − 1].

$$U={\displaystyle \frac{2nkV+z^2+z\sqrt{4nkV\left(1-V\right)+z^2}}{2\left([nk+z^2]\right)}}$$

This methodological approach ensured rigorous psychometric verification by identifying items with strong evaluator agreement, as well as those requiring refinement prior to conducting the factor analysis.

2.7.2. Exploratory Factor Analysis

Analyses were conducted using IBM SPSS Statistics version 30 under an institutional license. The adequacy of the correlation matrix was assessed through the Kaiser–Meyer–Olkin [KMO] index and Bartlett’s test of sphericity, with values of KMO > 0.80 and p < 0.001 considered appropriate [20,23]. Communalities were examined, accepting values > 0.30, although values ≥ 0.25 were deemed admissible when factor loadings were high and the sample size was ≥400, consistent with the literature [24]. Exploratory factor analysis [EFA] was performed using principal axis factoring, suitable when multivariate normality is not assumed, and the PROMAX oblique rotation, given the expected correlation among factors [20,23].

2.7.3. Internal Reliability

The reliability of each dimension and the overall instrument was estimated using McDonald’s Omega coefficient (ω), which is considered more appropriate than Cronbach’s alpha in models with heterogeneous factor loadings [19]. A value between 0.70 and 0.90 was adopted as the acceptable range [30].

2.8. Ethical Considerations

The study adhered to international ethical principles for research involving human participants. The protocol was reviewed and approved by the corresponding Institutional Research Committee. All participants were informed about the study’s objectives and provided written informed consent before completing the questionnaire.

3. Results

3.1. Population Sociodemographic Profile

Regarding the sociodemographic characteristics of the participants (n = 423), the largest age group was between 36 and 45 years old (26.5%), followed by those aged 26 to 35 (21.5%). In terms of gender distribution, 56.3% were male and 43.7% were female. Regarding marital status, 40.2% of the sample reported being married, while 32.9% were single. In relation to educational attainment, the majority of the participants had completed middle school (42.1%), followed by elementary school (23.9%) and high school (21.3%). Finally, regarding their professional activity, a significant majority of the population was employed (66.2%), with homemakers representing the second largest group at 13.5% (

Table 1. Sociodemographic profile.

Variable	Frequency	Percentage (%)
Age
15–25	68	16.1
26–35	91	21.5
36–45	112	26.5
46–55	76	18.0
56–65	48	11.3
66–75	21	5.0
76–85	7	1.7
Sex
Male	238	56.3
Female	185	43.7
Marital Status
Single	139	32.9
Married	170	40.2
Domestic Partnership	79	18.7
Divorced	13	3.1
Widowed	22	5.2
Education Level
Elementary School	101	23.9
Middle School	178	42.1
Technical Degree	29	6.9
High School	90	21.3
Bachelor’s Degree	23	5.4
Graduate Degree	2	0.5
Occupation
Homemaker	57	13.5
Unemployed	28	6.6
Employed	280	66.2
Retired	33	7.8
Student	19	4.5
Not employed	6	1.4

).

3.2. Content Validity

Content validity was determined based on the evaluation of nine experts.

Table 2. Content validity by expert judgment: Aiken’s V coefficient and confidence intervals for the criterion of item clarity in the UMES.

Item	Criterion	Aiken’s V	Lower Limit	Upper Limit
AC1	Clarity	0.972	0.858	0.995
AC2	Clarity	0.833	0.681	0.921
AC3	Clarity	0.833	0.681	0.921
AC4	Clarity	0.833	0.681	0.921
QC5	Clarity	0.889	0.747	0.956
QC6	Clarity	0.833	0.681	0.921
QC7	Clarity	0.917	0.782	0.971
QC8	Clarity	0.806	0.650	0.902
QC9	Clarity	0.861	0.713	0.939
MS10	Clarity	0.972	0.858	0.995
MS11	Clarity	0.889	0.747	0.956
MS12	Clarity	0.889	0.747	0.956
MS13	Clarity	0.944	0.819	0.985
MS14	Clarity	0.944	0.819	0.985
MS15	Clarity	0.917	0.782	0.971
MS16	Clarity	0.889	0.747	0.956
TTE17	Clarity	0.889	0.747	0.956
TTE18	Clarity	0.917	0.782	0.971
TTE19	Clarity	0.861	0.713	0.939
SUE20	Clarity	0.889	0.747	0.956
SUE21	Clarity	0.889	0.747	0.956
SUE22	Clarity	0.917	0.782	0.971
SUE23	Clarity	0.889	0.747	0.956
SUE24	Clarity	0.917	0.782	0.971

Note. Aiken’s V assesses expert agreement on item relevance. Values of V ≥ 0.80 are acceptable; lower confidence limits ≥ 0.70 indicate adequate content validity. AC: Accessibility and Connectivity, QC: Quality and Comfort of Public Transportation, MS: Mobility Safety, TTE: Travel Time and Efficiency, SUE: Sustainability and Urban Environment.

presents the Aiken’s V coefficient values for the clarity criterion. The lowest value corresponded to item 6 (V = 0.806), while items 1 and 10 obtained the highest scores (V = 0.972). All V values exceeded the ≥0.80 threshold established as acceptable evidence of content validity, although some lower confidence interval limits were slightly below 0.70, without compromising the overall consistency of the evaluated criterion.

Table 3. Content validity by expert judgment: Aiken’s V coefficient and confidence intervals for the criterion of item relevance in the UMES.

Item	Criterion	Aiken’s V	Lower Limit	Upper Limit
AC1	Relevance	0.889	0.747	0.956
AC2	Relevance	0.917	0.782	0.971
AC3	Relevance	0.889	0.747	0.956
AC4	Relevance	0.917	0.782	0.971
QC5	Relevance	0.917	0.782	0.971
QC6	Relevance	0.833	0.681	0.921
QC7	Relevance	0.917	0.782	0.971
QC8	Relevance	0.889	0.747	0.956
QC9	Relevance	0.889	0.747	0.956
MS10	Relevance	0.944	0.819	0.985
MS11	Relevance	0.917	0.782	0.971
MS12	Relevance	0.917	0.782	0.971
MS13	Relevance	0.944	0.819	0.985
MS14	Relevance	0.944	0.819	0.985
MS15	Relevance	0.861	0.713	0.939
MS16	Relevance	0.889	0.747	0.956
TTE17	Relevance	0.833	0.681	0.921
TTE18	Relevance	0.917	0.782	0.971
TTE19	Relevance	0.861	0.713	0.939
SUE20	Relevance	0.944	0.819	0.985
SUE21	Relevance	0.861	0.713	0.939
SUE22	Relevance	0.861	0.713	0.939
SUE23	Relevance	0.889	0.747	0.956
SUE24	Relevance	0.889	0.747	0.956

Note. Aiken’s V assesses expert agreement on item relevance. Values of V ≥ 0.80 are acceptable; lower confidence limits ≥ 0.70 indicate adequate content validity. AC: Accessibility and Connectivity, QC: Quality and Comfort of Public Transportation, MS: Mobility Safety, TTE: Travel Time and Efficiency, SUE: Sustainability and Urban Environment.

presents Aiken’s coefficient values for the relevance criterion. All items surpassed the recommended threshold of V ≥ 0.80, indicating an adequate level of agreement among experts. The lower bounds of the confidence intervals remained close to the reference value of 0.70, with items 6 and 17 showing the lowest values [V = 0.681]. Taken together, the findings support that the UMES demonstrates satisfactory content validity for both clarity and relevance.

3.3. Exploratory Factor Analysis (EFA)

The EFA was conducted using the principal axis factoring extraction method with PROMAX oblique rotation, under the assumption of correlation among construct dimensions. The adequacy of the correlation matrix was optimal, with a Kaiser–Meyer–Olkin (KMO) index of 0.898 and a significant Bartlett’s test of sphericity [p < 0.001], confirming the suitability of the factor analysis (

Table 4. Exploratory factor analysis (EFA) factor loadings and communalities of the UMES using PROMAX oblique rotation.

Items	Factor					Communality
	1	2	3	4	5
MS10	0.883					0.508
QC9	0.809					0.562
QC8	0.731					0.526
MS11	0.614					0.474
QC6	0.556					0.506
QC7	0.546					0.527
TTE19	0.493					0.495
QC5	0.482					0.469
MS16	0.432					0.435
MS15	0.390					0.270
SUE22		0.842				0.685
SUE24		0.807				0.679
SUE23		0.799				0.648
SUE21		0.744				0.527
SUE20		0.454				0.405
AC2			0.857			0.809
AC1			0.839			0.607
AC3			0.666			0.641
AC4			0.423			0.407
MS13				0.706		0.510
MS14				0.566		0.443
MS12				0.548		0.288
TTE18					0.800	0.655
TTE17					0.732	0.712
Omega (ω)	0.835	0.853	0.835	0.752	0.784	0.912

).

Communalities ranged from 0.270 (“Sidewalks are designed to allow safe pedestrian movement”, MS15) to 0.809 (“Public transportation routes allow easy access to key areas such as schools, hospitals, or workplaces”, AC2), indicating that most items presented an adequate proportion of explained variance. Overall, the factorial solution retained 53.567% of the common variance explained by the extracted factors. All item loadings exceeded the recommended threshold of 0.400, except item MS15, with a load of 0.390. The resulting factor structure and the corresponding adjustments are described below.

3.3.1. Factor 1: Quality and Comfort of Public Transportation

The five original items of this dimension grouped coherently under the first factor. The order of factor loadings was as follows: “Pedestrian and cycling infrastructure is adequate, accessible, and safe” (QC9), “Public transportation offers sufficient space to travel, even during peak hours” (QC8), “Noise levels inside public transportation allow for a comfortable trip” (QC6), “Public transportation stops and stations are safe and comfortable to wait in” (QC7), and “Public transportation vehicles are clean, in good condition, and allow for comfortable travel” (QC5). Item MS16, “Waiting times for public transportation are reasonable and consistent”, originally belonging to another dimension, exhibited a higher loading on this factor. After reviewing its semantic content, it was deemed congruent with attributes of service quality and comfort; therefore, it was conceptually reassigned to this dimension.

3.3.2. Factor 2: Sustainability and Urban Environment

The five proposed items integrated consistently under this factor. The loading pattern organized the items as follows: “Urban mobility strategies include specific actions to reduce air pollution, such as clean transportation or restrictions on motorized vehicles” (SUE22); “Green areas and pedestrian spaces are part of urban mobility routes as a strategy to reduce environmental impact” (SUE24); “Initiatives have been implemented to promote the use of electric vehicles, clean public transportation, or other ecological mobility alternatives” (SUE23); “Sustainable mobility options—such as bike lanes and adequate sidewalks—have been implemented to facilitate non-motorized movement” (SUE21); and “Available means of transportation generate less pollution because they use cleaner technologies” (SUE20), supporting the conceptual stability of this dimension.

3.3.3. Factor 3: Accessibility and Connectivity

The four items grouped appropriately, with a loading pattern that ordered them as follows: “Public transportation routes allow easy access to key areas such as schools, hospitals, or workplaces” (AC2); “There are diverse transportation options—such as public transit, bicycles, or cars—that are easily accessible” (AC1); “The location of public transportation stops and stations facilitates access from residential or frequently visited areas” (AC3); and “Road and transportation infrastructure allows for easy movement for pedestrians, cyclists, and other users” (AC4). This result aligns with the theoretical structure established for the dimension.

3.3.4. Factor 4: Mobility Safety

Three items showed strong loadings on this factor: “Pedestrian crossings and traffic lights ensure pedestrian safety” (MS13); “Road signage is clear and helps reduce risks in urban mobility” (MS14); and “Traffic is regulated, and road safety rules are followed” (MS12). However, items MS10, MS11, and MS15 loaded on another factor, as their wording did not support clear conceptual alignment. Therefore, these items were reformulated to improve semantic precision and correspondence with the mobility safety dimension, resulting in the following: “I feel safe moving around the city, both during the day and at night” (MS10); “Lighting and public space design contribute to safe mobility” (MS11); and “Sidewalks and pedestrian crossings facilitate safe walking without risk of accidents” (MS15).

3.3.5. Factor 5: Travel Time and Efficiency

In this factor, two items grouped according to their loadings: “Daily commutes have a sufficient duration to complete activities without losing time” (TE17]) and “Public transportation routes help reduce waiting and travel times” (TE18). However, one item (TTE19) loaded onto the quality and comfort dimension (QC), so it was reformulated as: “Travel times are generally predictable and efficient, without excessive delays throughout the day” (TTE19), to align conceptually with the travel time and efficiency dimension, given that its content relates to the predictability and regularity of travel durations.

3.4. Internal Reliability

Reliability was estimated using McDonald’s Omega coefficient (ω). As shown in Table 4, all dimensions exceeded the recommended threshold of 0.70, indicating an adequate level of internal consistency. The dimension with the lowest reliability was Mobility Safety (MS) (ω = 0.752), whereas the highest corresponded to Sustainability and Urban Environment (SUE) (ω = 0.853). The overall instrument demonstrated satisfactory global reliability (ω = 0.912) (

Table 5. Initial proposed version of the Urban Mobility Experiences Scale (UMES).

Dimension	Code	Item
Accessibility and Connectivity (AC)	AC1	There are various transportation options—such as public transit, bicycles, or cars—that are easily accessible.
Accessibility and Connectivity (AC)	AC2	Public transportation routes allow easy access to key areas such as schools, hospitals, or workplaces.
Accessibility and Connectivity (AC)	AC3	The location of public transport stops and stations facilitates access from residential or frequently visited areas.
Accessibility and Connectivity (AC)	AC4	Transport and road infrastructure allow easy mobility for pedestrians, cyclists, and other users.
Quality and Comfort of Public Transport (QC)	QC5	Public transport vehicles are clean, in good condition, and comfortable for traveling.
Quality and Comfort of Public Transport (QC)	QC6	Noise levels inside public transportation allow for a comfortable trip.
Quality and Comfort of Public Transport (QC)	QC7	Public transport stops and stations are safe and comfortable for waiting.
Quality and Comfort of Public Transport (QC)	QC8	There is enough space to travel in public transportation, even during crowded times.
Quality and Comfort of Public Transport (QC)	QC9	Pedestrian and cyclist infrastructure is adequate, accessible, and safe.
Mobility Safety (MS)	MS10	Traveling—even at night—is perceived as a safe experience.
Mobility Safety (MS)	MS11	Good lighting and mobility spaces enhance the perception of safety.
Mobility Safety (MS)	MS12	Traffic is regulated and road safety rules are respected.
Mobility Safety (MS)	MS13	Pedestrian crossings and traffic lights ensure pedestrian safety.
Mobility Safety (MS)	MS14	Road signage is clear and helps reduce risks in urban mobility.
Mobility Safety (MS)	MS15	Sidewalks are designed to allow safe pedestrian movement.
Mobility Safety (MS)	MS16	Waiting times for public transportation are reasonable and consistent.
Travel Time and Efficiency (TTE)	TTE17	Daily commuting time is sufficient to complete activities without losing time unnecessarily.
Travel Time and Efficiency (TTE)	TTE18	Public transport routes help reduce waiting time and travel time.
Travel Time and Efficiency (TTE)	TTE19	Travel times are consistent, without major variations across different times of the day.
Sustainability and Urban Environment (SUE)	SUE20	Available transportation modes generate less pollution because they use cleaner technologies.
Sustainability and Urban Environment (SUE)	SUE21	Sustainable mobility options—such as bike lanes and adequate sidewalks—have been implemented to support non-motorized movement.
Sustainability and Urban Environment (SUE)	SUE22	Urban mobility strategies include actions to reduce air pollution, such as clean transportation or limiting motorized vehicle use.
Sustainability and Urban Environment (SUE)	SUE23	Measures have been promoted to encourage the use of electric vehicles, clean public transport, or other ecological mobility options.
Sustainability and Urban Environment (SUE)	SUE24	Green areas and pedestrian spaces are incorporated into mobility routes as part of strategies to reduce environmental impact.

and

Table 6. Final validated version of the Urban Mobility Experiences Scale (UMES).

Dimension	Code	Item
Accessibility and Connectivity (AC)	AC1	There are various transportation options—such as public transit, bicycles, or cars—that are easily accessible.
Accessibility and Connectivity (AC)	AC2	Public transportation routes allow easy access to key areas such as schools, hospitals, or workplaces.
Accessibility and Connectivity (AC)	AC3	The location of public transport stops and stations facilitates access from residential or frequently visited areas.
Accessibility and Connectivity (AC)	AC4	Transport and road infrastructure allow easy mobility for pedestrians, cyclists, and other users.
Quality and Comfort of Public Transport (QC)	QC5	Public transport vehicles are clean, in good condition, and comfortable for traveling.
Quality and Comfort of Public Transport (QC)	QC6	Noise levels inside public transportation allow for a comfortable trip.
Quality and Comfort of Public Transport (QC)	QC7	Public transport stops and stations are safe and comfortable for waiting.
Quality and Comfort of Public Transport (QC)	QC8	There is enough space to travel in public transportation, even during crowded times.
Quality and Comfort of Public Transport (QC)	QC9	Pedestrian and cyclist infrastructure is adequate, accessible, and safe.
Quality and Comfort of Public Transport (QC)	QC10	Waiting times for public transportation are reasonable and consistent.
Mobility Safety (MS)	MS11	I feel safe moving around the city, both during the day and at night.
Mobility Safety (MS)	MS12	Lighting and the design of public spaces contribute to safe mobility.
Mobility Safety (MS)	MS13	Traffic is well regulated and allows safe circulation.
Mobility Safety (MS)	MS14	Pedestrian crossings and traffic lights promote pedestrian safety.
Mobility Safety (MS)	MS15	Road signage promotes safe mobility.
Mobility Safety (MS)	MS16	Sidewalks and pedestrian crossings facilitate safe walking without risk of accidents.
Travel Time and Efficiency (TTE)	TTE17	Daily travel times are adequate and efficient, allowing better use of time.
Travel Time and Efficiency (TTE)	TTE18	Public transportation routes reduce waiting and travel times.
Travel Time and Efficiency (TTE)	TTE19	Travel times tend to be predictable and efficient, without excessive delays throughout the day.
Sustainability and Urban Environment (SUE)	SUE20	Available transportation modes generate less pollution because they use cleaner technologies.
Sustainability and Urban Environment (SUE)	SUE21	Sustainable mobility options—such as bike lanes and adequate sidewalks—have been implemented to support non-motorized movement.
Sustainability and Urban Environment (SUE)	SUE22	Urban mobility strategies include actions to reduce air pollution, such as clean transportation or limiting motorized vehicle use.
Sustainability and Urban Environment (SUE)	SUE23	Measures have been promoted to encourage the use of electric vehicles, clean public transport, or other ecological mobility options.
Sustainability and Urban Environment (SUE)	SUE24	Green areas and pedestrian spaces are incorporated into mobility routes as part of strategies to reduce environmental impact.

).

4. Discussion

This study aimed to design and evaluate the psychometric properties of the Urban Mobility Experience Scale (UMES), an instrument that measures favorable or unfavorable conditions experienced by users during their daily travel. The findings indicate that the UMES can be considered a valid and reliable tool for use in complex urban contexts characterized by cross-border flows, irregular topography, and pronounced socio-spatial heterogeneity, such as Nogales, Sonora [21].

The Aiken’s V coefficients confirmed a high level of consensus among experts, with values exceeding 0.80 across all items. These results are consistent with methodological criteria reported in the literature on content validity [25,26,27,28,29,30] and with recent studies on instrument development assessing mobility, accessibility, and urban services [31,32,33,34]. Although some lower confidence limits slightly fell below 0.70, they remained sufficiently close to the suggested benchmark to be considered acceptable within current psychometric standards [24]. This pattern aligns with studies showing that semantic variability among evaluators may lead to minor discrepancies without compromising an instrument’s conceptual coherence [6,7,11].

The exploratory factor analysis supported the UMES’s initial theoretical structure, identifying five coherent dimensions that adequately represent the central components of urban mobility experiences. Statistical adequacy indicators confirmed that the correlation matrix was appropriate for the identification of latent patterns, enabling the extraction of a stable and conceptually consistent factorial structure.

The reassignment of item MS16 to the quality and comfort of public transport dimension was consistent with both statistical evidence and semantic meaning, a behavior also observed in prior studies where attributes such as comfort, waiting time, and environmental conditions converge within global assessments of transportation services [8,10,15,16]. Likewise, the reformulation of item TTE19 improved its conceptual alignment with the travel time and efficiency dimension, in agreement with the literature emphasizing predictability and temporal stability as essential components of travel experiences and urban well-being [4,9,10,31]. The reformulation of items MS10, MS11, and MS15 enhanced their semantic clarity and strengthened their conceptual fit within the mobility safety dimension.

Regarding internal consistency, McDonald’s Omega values exceeded the recommended 0.70 threshold across all dimensions, confirming the instrument’s robustness. The highest reliability was observed in the sustainability and urban environment dimension, consistent with studies highlighting the conceptual coherence of items associated with environmental perception [5,11]. The lowest reliability, found in the mobility safety dimension, aligns with the literature reporting high interindividual variability in the perception of urban risk [14,17,18]. The global reliability value [ω = 0.912] supports the conclusion that the UMES is a psychometrically sound instrument.

Comparisons with previously developed tools—such as the Satisfaction with Travel Scale [6], the Perceived Accessibility Scale [7], and the Passenger Satisfaction Scale [8]—revealed three relevant contributions of the UMES. First, it evaluates real mobility experiences rather than solely perceived satisfaction, as conducted by most existing scales. Second, it integrates five domains encompassing essential components of urban well-being, such as operational efficiency and sustainability, consistent with trends in healthy mobility [2,5,13]. Third, it was contextualized in a mid-sized border city whose socio-spatial dynamics differ substantially from the European and Asian contexts in which most current instruments have been developed [6,7,8,9,10,11]. This approach aligns with recent efforts advocating for robust, culturally appropriate tools tailored to the diversity of contemporary urban realities [31]. Consequently, the methodological and conceptual contributions presented here strengthen the perspective of providing an instrument capable of capturing the complexity of mobility experiences in heterogeneous urban environments, consistent with recent publications on urban health [32,33,34]. Additionally, the findings support the inclusion of the dimensions of Public Transport Quality and Comfort (QC) and Travel Time and Efficiency (TTE), which reinforce the instrument’s utility for characterizing urban mobility experiences in similar contexts, particularly in mid-sized border cities.

Among the recent research, this has highlighted the restorative potential of urban environments to reduce stress and promote well-being through everyday interactions such as walking, leisure, and recreational mobility. Evidence from neuro-urbanism research suggests that restorative effects are not limited to urban parks or green spaces but can also occur in well-designed urban streets that combine comfort, safety, accessibility and active use. From this perspective, the experience of urban mobility is a key pathway through which individuals engage in a potentially restorative environment in their daily routines. However, despite the growing interest in regenerative urban environments, there is a lack of validated instruments that capture these dimensions of mobility experiences in everyday urban contexts. This study addresses this gap by developing and psychometrically evaluating the UMES.

Among the strengths of the study are the use of robust psychometric procedures, including exploratory factor analysis, McDonald’s Omega coefficient, multidisciplinary expert participation, and adequate sample size. Also, this study contributes to advance global knowledge by exploring a specific city with a border, which can help it to be replicated among similar cities.

Regarding the limitations, the use of convenience sampling and the application of the instrument in a single urban corridor may restrict the generalizability of the findings. Additionally, the absence of confirmatory factor analysis represents a structural limitation. Given the exploratory scope of this initial validation, confirmatory procedures are recommended in future studies to evaluate model stability across independent samples. Furthermore, the cross-sectional study design—based on data collected at a single point in time—does not allow for temporal comparisons. In addition, as the data were self-reported, potential biases associated with subjective perception cannot be ruled out.

Future research should incorporate confirmatory factor analyses and apply the scale in comparable urban contexts to strengthen its external validity. Linking the scale with objective mobility indicators—such as actual travel times, environmental noise exposure, and pollution levels—may further clarify the relationship between environmental conditions and daily travel experiences. Additionally, combining the UMES with subjective indicators, including perceived stress or evaluations of road safety, could provide a more comprehensive understanding of urban mobility experiences. Such approaches may be particularly relevant in mid-sized border cities in northern Mexico, where territorial configuration and socio-spatial dynamics shape distinctive mobility patterns.

5. Conclusions

The Urban Mobility Experiences Scale (UMES) demonstrated strong psychometric performance, with solid content validity, a coherent five-factor structure, and adequate internal reliability across all dimensions. These findings indicate that the UMES is a reliable and comprehensive instrument for assessing key dimensions of urban mobility experiences, including quality, safety, accessibility, environmental conditions, and efficiency of daily travel, particularly in intermediate and border-city contexts. The exploratory factor analysis showed excellent sampling adequacy and a stable factorial solution, while content validity and reliability indicators confirmed the overall consistency of the instrument.

The application of the UMES may support evidence-based decision-making and inform the development of urban mobility policies aimed at promoting equity, sustainability, and urban well-being. Future research should further validate the scale through confirmatory factor analysis and explore its relationship with objective mobility indicators and urban health outcomes to strengthen its external validity.

Overall, these findings position the UMES as a promising tool for advancing research on urban mobility experiences and supporting evidence-informed planning in diverse urban settings.