Gender Differences in Public Transport Use in a Car-Dependent European Region: Evidence from Andalusia (Spain)

Gender Differences in Public Transport Use in a Car-Dependent European Region: Evidence from Andalusia (Spain) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gender Differences in Public Transport Use in a Car-Dependent European Region: Evidence from Andalusia (Spain) María Isabel Olmo-Sanchez, Elvira Maeso-González This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8648400/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Gender differences in public transport use are key to understanding mobility inequalities in highly car-dependent urban contexts. This study investigates the determinants of public transport choice using gender-disaggregated binary logistic regression models, drawing on 6,032 motorized trips in a southern European region. The results show that the determinants of public transport use differ by gender, not as a single uniform effect but through differences in the relevance and intensity of explanatory factors. While age, educational attainment, and driving license availability are significant in both models, women’s modal choice is associated with a broader set of variables, particularly those related to household organization (living with a partner and household income) and trip purpose. Among men, by contrast, public transport use is more closely linked to individual and territorial characteristics; notably, residing in dispersed-settlement areas reduces the likelihood of use only in the male model. Overall, these findings suggest that the role of urban form varies with the functional organization of everyday mobility, with domestic constraints and trip purposes playing a stronger role in shaping women’s public transport demand. From a methodological perspective, the study shows that gender-disaggregated models allow us to identify explanatory structures that may be partially or totally hidden in aggregate approaches, providing relevant evidence for the analysis of equity in transportation and informing targeted planning measures to improve service fit with gendered travel needs across different urban forms. Gender Public transport Modal choice Care-related mobility Binary logit models Car dependency 1. Introduction The use of public transport shows persistent gender differences in urban mobility, especially in contexts characterized by high car dependency. The literature has consistently shown that access to and effective use of transport systems are not socially neutral, but are conditioned by structural factors such as income, territory, life cycle, and, particularly relevant, gender [ 1 , 2 , 3 ]. From a gender perspective, numerous studies have documented that women develop more fragmented and complex mobility patterns, associated with a greater diversity of daily activities and domestic and care responsibilities [ 4 , 5 ]. Compared to men, women make a greater number of daily trips, generally over shorter distances and for a greater variety of reasons [ 6 , 7 , 8 ]. These differences are not solely due to individual preferences, but are related to structural constraints linked to the social organization of time, the unequal distribution of family responsibilities, and differentiated access to mobility resources [ 4 , 9 ]. From a methodological point of view, although discrete choice models have been widely used to analyze modal behavior, gender is usually incorporated only as a control variable in joint models. This approach may obscure relevant structural differences in the modal choice processes between women and men [ 2 , 3 ]. Some recent studies have made progress in estimating gender-differentiated models, but they tend to focus on subjective perceptions of travel or time valuations rather than on structural determinants of everyday mobility, such as household organization, territorial context, or access to mobility resources [ 10 ]. In this framework, gender should be understood not as an additional individual characteristic, but as a structuring principle of everyday mobility and modal choice, which systematically conditions the opportunities and constraints faced by women and men. This approach is particularly relevant in southern Europe, where persistent high dependence on cars is combined with metropolitan areas structured around an urban core-periphery pattern defined by strong historical centrality and functionally dependent peripheral development, characterized by large urban cores and extensive low-density peripheries [ 11 ], as well as a still marked gender division of tasks [ 5 , 12 ]. In this context, the urban regions of Andalusia (southern Spain) provide a particularly relevant case study for analyzing the determinants of public transport choice from a gender perspective. Andalusia is heavily dependent on private vehicles, has a heterogeneous public transport offering, and combines compact urban centers with dispersed residential areas, features shared by many metropolitan systems in a southern European context. The objective of this article is to analyze the factors that influence the choice of public transport in a southern European region from a gender perspective, by estimating separate binary logistic regression models for women and men. The study addresses the following research questions: (1) What sociodemographic, family, territorial, and displacement-related factors influence the probability of using public transport among women and men? (2) To what extent do the relevance and magnitude of these factors differ according to gender? (3) What structural differences can be observed in the overall configuration of modal choice patterns between women and men in urban contexts characterized by high car dependency? The article makes a twofold contribution. From a methodological point of view, it demonstrates that the estimation of gender-disaggregated models allows for the identification of differentiated explanatory structures that remain partially hidden in aggregate approaches. From an empirical perspective, it provides evidence for a southern European context characterized by high car dependency and urban core-periphery structure, which is still underrepresented in the international literature on modal choice and equity in transportation. The article is structured as follows. Section 2 reviews the literature on mobility, gender, and modal choice. Section 3 presents the case study, data, and methodology. Section 4 presents the results of the estimated models. Section 5 discusses the main findings. Section 6 develops the public policy implications. Section 7 addresses the limitations of the study and suggests future lines of research. Finally, section 8 summarizes the main conclusions. 2. Literature review The literature on gender and mobility has consistently documented that the travel patterns of women and men differ systematically, an inequality that is largely explained by care mobility, a concept developed by Sánchez de Madariaga [ 5 ] to refer to travel associated with domestic tasks, caregiving, and family responsibilities. In general terms, women tend to make more fragmented and chained trips, while men concentrate on more linear journeys associated with paid employment and car use. These patterns are corroborated in recent studies that identify greater fragmentation and multitasking in women's journeys, as well as more intensive use of public transport and active modes, especially in metropolitan contexts [ 13 , 14 ]. This evidence reinforces the idea that everyday mobility is largely conditioned by time and organizational constraints that affect women and men differently. In southern Europe, these inequalities are particularly pronounced due to the persistence of traditional caregiving roles, lower car availability among women, and more dispersed urban structures, which reduce the competitiveness of public transport. Empirical evidence in Spain confirms these patterns, showing that women make more care-related trips, travel shorter distances, and are more sensitive to family variables and modal availability [ 6 , 7 , 8 ]. Zucchini [ 15 ] points out that, in Mediterranean countries, the lower availability of cars, the organization of care, and the dispersed urban structure generate more complex patterns of female mobility with a greater chain of trips, limiting women's modal autonomy. The OTLE report (2024) reinforces this diagnosis, confirming the persistence of gender gaps in everyday mobility, especially in the use of public transport. Contemporary approaches to transport equity show that mobility systems distribute accessibility and opportunities unevenly, placing certain social groups at a disadvantage [ 2 , 3 ]. This effect is particularly visible in women and people without access to a car, who are penalized by the limitations of public transport in dispersed metropolitan environments, marked by deficits in coverage, frequency, and reliability [ 11 , 13 ]. These gaps are exacerbated in territorial contexts with high car dependency and dispersed urban patterns. Low residential density weakens the competitiveness of public transport and reinforces the centrality of private vehicles, restricting accessibility throughout the metropolitan area [ 11 , 16 , 17 ]. Added to this territorial pattern is a persistent division of labor by gender, which conditions access to cars, time management, and the configuration of daily commutes, especially in relation to care-related mobility [ 12 , 13 ]. Despite the extensive literature on gender inequalities in mobility, evidences based on gender-disaggregated modal choice models remain limited in contexts with high car dependency. Logit models have been widely used to analyze modal choice and estimate the probability of using different modes of transport based on individual, territorial, and trip characteristics [ 18 , 19 , 20 ]. In recent years, these models have been applied to study patterns of public transport use in various contexts [ 21 , 22 ]. However, most of these studies incorporate gender only as a control variable within a single model, which prevents the identification of structural differences between women and men in the determinants of modal choice. Some recent studies have made progress in estimating gender-differentiated modal choice models; however, these approaches have focused mainly on subjective perceptions of travel, individual attitudes, or time valuations, rather than on structural determinants of everyday mobility, such as household organization, territorial structure, or access to mobility resources [ 10 , 13 ]. In this context, a significant methodological gap persists in recent literature: the scarcity of separately estimated modal choice models for women and men that allow for the identification of structural differences in the determinants of public transport use versus car use. This study contributes to filling this gap through a gender-disaggregated econometric analysis applied to a southern European context. 3. Case study, data, and methodology The urban regions of Andalusia constitute a complex metropolitan system, with a high frequency of interurban travel and strong interdependence between residential areas and centers of activity. From the supply side, road transport predominates and public transport provision is uneven. Collective mobility is mainly based on urban and interurban bus networks, while rail modes are limited and localized in a few cities, such as Seville, Malaga, and Granada, through commuter and light rail services [ 23 , 24 , 25 ]. This configuration restricts accessibility and reinforces the use of private vehicles, especially in peripheral and low-density municipalities [ 11 ]. The combination of residential dispersion, functional polycentrism, and high motorization accentuates the influence of territorial factors and modal availability on transport choice, making Andalusia a representative case of metropolitan systems in southern Europe. The empirical analysis is based on the Survey of Mobility in the Urban Regions of Andalusia [ 26 ], the main source available with representative microdata on daily mobility in this area. Although compiled in 2011, its quality, coverage, and level of disaggregation allow for a rigorous analysis of the determinants of modal choice in a broad and heterogeneous context [ 10 ]. In addition, the factors considered respond to structural conditions that are relatively stable over time, which supports the use of the 2011 data [ 11 , 13 , 14 ]. After cleaning the database, the final sample consisted of 6,032 motorized trips reported on weekdays by individuals aged 16 years and over. Analyses were conducted using the survey expansion weights to account for the sampling design and ensure population representativeness. This sample size allows the separate estimation of gender-disaggregated models for women and men. Descriptive gender differences in modal shares, trip purposes, and key spatio-temporal indicators for the same survey and study area have been reported in previous work [ 7 ]. Building on that descriptive evidence, the present study focuses on identifying gender-differentiated determinants of public transport use through multivariate binary logit models. The dependent variable was defined as a dichotomous choice between public transport (urban and interurban bus, metro, tram, and commuter rail) and private motorized transport (car and motorcycle). Active modes (walking and cycling) were excluded, as they respond to different logics and typically require specific analytical approaches [ 10 , 21 ]. The explanatory variables were selected based on their relevance in the literature and their availability in the survey, and were grouped into four blocks: (1) individual sociodemographic variables (age, educational level, nationality, and employment status); (2) household variables (living with a partner, household income, and household size); (3) territorial variables (municipality size and settlement type); and (4) travel characteristics (driving license, trip purpose, distance, and travel time). This structure facilitates interpretation and captures key dimensions from a gender perspective. Two binary logistic regression models were estimated, a common technique for analyzing discrete modal choice decisions, recently applied in studies on public transport and private vehicles in different contexts [ 10 , 21 , 27 ]. In order to identify structural differences by gender, separate models were estimated for women and men, rather than a joint model with interactions. This strategy avoids assuming structural homogeneity and facilitates comparison from an equity perspective. The models achieved Nagelkerke pseudo R² values of 0.48 in the male model and 0.53 in the female model. Model fit was assessed using several complementary indicators. In both models, omnibus tests confirmed a significant improvement over the null specification, while pseudo-R² measures and the Hosmer–Lemeshow test indicated good calibration. 4. Results The results of the models obtained show that the determinants factors of public transport choice differ between men and women, both in the magnitude and structure of the effects (Tables 1 and 2 ). Both models exhibit good fit and calibration, providing a solid basis for their interpretation. Although there are factors common to both models, the separate estimation by gender allows for the identification of differentiated patterns that would be partially hidden in a joint analysis. 4.1. Results of the male model In the male model (Table 1 ), the probability of using public transport is mainly conditioned by sociodemographic, territorial, and travel-related factors. Age shows a positive effect from the age of 50 onwards, with statistically significant increases in the 50–64 and over-65 age groups, while the intermediate cohorts (30–49 years) show no significant effects. Educational level is also relevant, with men with secondary and university education being more likely to use public transport. Nationality and unemployment status do not show significant effects, although being economically inactive is associated with a higher probability of use. Household variables have limited explanatory power. Living with a partner and household size are not significant, while family income only reduces the likelihood of using public transport at higher income levels. The territorial context is clearly a determining factor. Living in medium-sized or small municipalities significantly reduces the likelihood of using public transport compared to large municipalities, as does living in sparsely populated areas. Among the variables specific to travel, the availability of a driving license emerges as the most influential factor, significantly reducing the probability of using public transport. Travel for study purposes significantly increases this probability, while domestic and leisure reasons do not have statistically significant effects. Finally, greater distance reduces the probability of using public transport, while longer travel times increase it. Table 1 Binary logit model results for public transport choice among men Variable Odds ratio (OR) 95% confidence interval Sig. Block 1. Individual sociodemographic variables: age, educational level, nationality, and employment status Reference category: Age 16–29 Age 30–39 1.37 [0.52–3.56] Age 40–49 1.23 [0.47–3.21] Age 50–64 2.41 [1.04–5.58] * Age ≥ 65 4.77 [1.70–13.36] ** Reference category: primary education Secondary education 2.15 [1.09–4.26] * University education 3.62 [1.54–8.49] ** Reference category: non-Spanish Spanish nationality 1.50 [0.28–8.06] Reference category: employed Unemployed 0.79 [0.30–2.06] Inactive 2.53 [1.01–6.33] * Block 2. Household variables: living with a partner, household income, and household size Reference category: not living with a partner Living with a partner 0.68 [0.37–1.24] Reference category: €2,700 0.47 [0.23–1.00] * Household size 0.95 [0.79–1.14] Block 3. Territorial and residential environment variables: municipality size and type of settlement Reference category: large municipality Medium municipality 0.30 [0.17–0.55] *** Small municipality 0.32 [0.18–0.59] *** Reference category: compact settlement Intermediate settlement 0.62 [0.29–1.32] Dispersed settlement 0.32 [0.13–0.77] * Block 4. Travel variables: driving license, trip purpose, distance, and travel time Reference category: no driving license Driving license 0.08 [0.04–0.14] *** Reference category: work Trip purpose: education 5.09 [1.85–14.03] ** Trip purpose: domestic 0.51 [0.23–1.11] Trip purpose: leisure 1.22 [0.53–2.79] Trip distance (km) 0.94 [0.92–0.96] *** Travel time (min) 1.07 [1.06–1.09] *** Note: Statistically significant coefficients are indicated as follows: *** p < 0.001, ** p < 0.01, * p < 0.05 4.2. Results of the female model In the female model (Table 2 ), the probability of using public transport is conditioned by a broader set of factors, with generally stronger associations than those observed in the male model. Age has a positive and greater effect from the age of 50 onwards. With regard to educational level, both secondary education and, especially, university education significantly increase the probability of using public transport. Nationality and employment status are not significant. Household variables show a clear influence. Living with a partner significantly reduces the probability of using public transport. Middle-income households are less likely to use public transport than lower-income households. Household size is not significant. The territorial context continues to be relevant: living in medium-sized or small municipalities significantly reduces the likelihood of using public transport, while the type of settlement does not show statistically significant effects. Among the travel variables, the availability of a driving license is once again one of the most influential determinants. Travel for study significantly increases the probability of using public transport, while domestic and leisure travel reduces it. Public transport use is higher for shorter distances and longer travel times. Table 2 Binary logit model results for public transport choice among women Variable Odds ratio (OR) 95% confidence interval Sig. Block 1. Individual sociodemographic variables: age, educational level, nationality, and employment status Reference category: age 16–29 Age 30–39 1.28 [0.71–2.33] Age 40–49 1.65 [0.92–2.95] Age 50–64 3.77 [2.04–6.96] *** Age ≥ 65 5.50 [2.50–12.08] *** Reference category: primary education Secondary education 2.06 [1.22–3.49] ** University education 4.64 [2.42–8.88] *** Reference category: non-Spanish Spanish nationality 0.89 [0.43–1.84] Reference category: employed Unemployed 0.71 [0.38–1.32] Inactive 0.99 [0.57–1.71] Block 2. Household variables: living with a partner, household income, and household size Reference category: not living with partner Living with partner 0.41 [0.27–0.60] *** Reference category: €2,700 0.64 [0.34–1.17] Household size 1.08 [0.93–1.25] Block 3. Territorial and residential environment variables: municipality size and type of settlement Reference category: large municipality Medium municipality 0.33 [0.21–0.53] *** Small municipality 0.37 [0.24–0.57] *** Reference category: compact settlement Intermediate settlement 1.10 [0.63–1.91] Dispersed settlement 0.66 [0.35–1.24] Block 4. Travel variables: driving license, trip purpose, distance, and travel time Reference category: no driving license Driving license 0.13 [0.09–0.20] *** Reference category: work Trip purpose: education 5.19 [2.51–10.74] *** Trip purpose: domestic 0.44 [0.27–0.72] ** Trip purpose: leisure 0.47 [0.24–0.90] * Trip distance (km) 0.92 [0.90–0.94] *** Travel time (min) 1.08 [1.07–1.10] *** Note: Statistically significant coefficients are indicated as follows: *** p < 0.001, ** p < 0.01, * p < 0.05. Separate models were estimated for men and women in order to capture gender-specific determinants of public transport use. Although we report odds ratios, we emphasize practical relevance (direction and relative strength of associations), given the nonlinearity of logit models. 4.3. Comparison between models The comparison between the two models (Table 3 ) shows differences in the significance and magnitude of the determinants of modal choice by gender. In both genders, the probability of using public transport increases with age, especially after the age of 50, although the effect is more intense in the female model. Educational level significantly increases the probability of public transport use in both models, with higher effects at secondary and, especially, university levels, and with a greater magnitude in the female model. Employment status is only statistically significant in the male model, where inactivity is associated with a higher probability of public transport use. In relation to household variables, living with a partner significantly reduces the probability of using public transport only in the female model. Likewise, income level has a significant negative effect in the female model in the middle-income brackets, while in the male model this effect is only observed in the highest income bracket. Territorial variables indicate that living in medium-sized or small municipalities significantly reduces the probability of using public transport for both genders, while living in dispersed settlements has a significant negative effect only in the male model. The availability of a driving license significantly reduces the probability of using public transport in both models, with a slightly more intense effect on the male model. In terms of the reason for travel, trips for educational purposes significantly increase the probability of using public transport for both genders, while trips for domestic and leisure purposes reduce this probability mainly in the female model. Finally, in both models, a longer travel distance reduces the probability of using public transport, while a longer travel time increases it, with slightly stronger effects in the female model. Table 3 Comparative results of modal choice models by gender Variable Men Odds ratio (OR) Sig. Women Odds ratio (OR) Sig. Block 1. Individual sociodemographic variables: age, educational level, nationality, and employment status Reference category: age 16–29 Age 30–39 1.37 1.28 Age 40–49 1.23 1.65 Age 50–64 2.41 * 3.77 *** Age ≥ 65 4.77 ** 5.50 *** Reference category: primary education Secondary education 2.15 * 2.06 ** University education 3.62 ** 4.64 *** Reference category: non-Spanish Spanish nationality 1.50 0.89 Reference category: employed Unemployed 0.79 0.71 Inactive 2.53 * 0.99 Block 2. Household variables: living with a partner, household income, and household size Reference category: not living with partner Living with partner 0.68 0.41 *** Reference category: €2,700 0.47 * 0.64 Household size 0.95 1.08 Block 3. Territorial and residential environment variables: municipality size and type of settlement Reference category: large municipality Medium municipality 0.30 *** 0.33 *** Small municipality 0.32 *** 0.37 *** Reference category: compact settlement Intermediate settlement 0.62 1.10 Dispersed settlement 0.32 * 0.66 Block 4. Travel variables: driving license, trip purpose, distance, and travel time Reference category: no driving license Driving license 0.08 *** 0.13 *** Reference category: work Trip purpose: education 5.09 ** 5.19 *** Trip purpose: domestic 0.51 0.44 ** Trip purpose: leisure 1.22 0.47 * Trip distance (km) 0.94 *** 0.92 *** Travel time (min) 1.07 *** 1.08 *** Note: Statistically significant coefficients are indicated as follows: *** p < 0.001, ** p < 0.01, * p < 0.05 5. Discussion The results provide clear evidence that the choice of public transport in urban regions of Andalusia is conditioned by gender, not only in terms of the magnitude of the observed effects, but also through differentiated explanatory structures. The estimation of binary logit models separated by gender allows us to identify patterns that would be partially or totally hidden in aggregate specifications, which supports the methodological relevance of disaggregated approaches and their consistency with the literature on equity and spatial justice in transportation [ 2 , 3 , 13 ]. The greater number and diversity of significant variables in the female model indicates that women's mobility is more intensely conditioned by social, family, and functional constraints, while male modal choice is explained by a more limited set of individual and territorial factors. These results reinforce the interpretation of gender not as an additional individual characteristic, but as a structuring principle of everyday mobility, which systematically shapes opportunities and constraints [ 4 , 5 , 13 ], and challenge approaches that incorporate gender solely as a control variable in modal choice models [ 2 , 3 ]. From a sociodemographic perspective, age emerges as a relevant determinant in both models, with a marked increase in the probability of public transport use from the age of 50 onwards, especially in the case of women. This pattern is consistent with evidence linking aging with reduced car availability and greater dependence on collective modes, as well as with gender differences in the organization of daily mobility throughout the life cycle [ 3 , 13 ]. Similarly, educational attainment significantly increases the probability of public transport use in both genders, with a clear gradient and higher effects at secondary and, especially, university levels, and with greater intensity in the female model. These results are consistent with the literature that associates educational capital with more diversified mobility patterns and a greater ability to interact with public transport systems [ 6 , 13 ]. In contrast, employment status is only statistically significant in the male model, where inactivity is associated with a higher probability of public transport use, pointing to gender-differentiated relationships between employment, daily routines, and modal choice [ 12 ]. Variables related to household organization introduce some of the clearest gender differences. In the case of women, living with a partner and/or belonging to households with intermediate income levels significantly reduces the probability of using public transport, while these variables are not relevant in the male model. The absence of an effect of household size in both models suggests that it is not the number of members that determines mobility, but rather the way in which daily responsibilities are organized and mobility resources are distributed, particularly effective access to a car. The fact that household variables are only significant in the female model indicates that women's modal choice is more closely linked to the domestic context and the actual availability of mobility resources, in line with the literature on care mobility and inequality in access to private vehicles [ 5 , 8 , 13 ]. From a territorial perspective, residence in medium-sized and small municipalities significantly reduces the probability of public transport use for both genders, reflecting the structural role of supply and accessibility in smaller contexts. However, residence in areas of dispersed settlement only has a significant negative effect in the male model. This asymmetry suggests that, in the case of women, modal choice is less conditioned by urban morphology itself and more by the functional organization of daily trips, which leads them to maintain a certain level of public transport use even in dispersed residential contexts. This result is consistent with studies showing that the effects of territorial structure on modal choice manifest themselves differently by gender depending on the patterns of chaining and daily organization of mobility [ 11 , 13 , 28 ]. The characteristics of travel synthesize and reinforce these structural differences. The availability of a driving license is the most influential determinant of modal choice in both models, significantly reducing the probability of public transport use for both men and women, although with a more intense effect in the male model. This result confirms the persistence of a strong dependence on cars in the urban regions analyzed, in line with evidence for metropolitan contexts characterized by dispersed urbanization and fragmented public transport supply [ 11 , 29 ]. In terms of the reason for travel, trips for study significantly increase the probability of public transport use in both genders, while trips for domestic and leisure reasons reduce this probability only in the female model. This pattern highlights the lower compatibility of public transport with non-routine, chained trips subject to greater time constraints, which are characteristic of women's daily mobility, as pointed out in the literature on care mobility [ 5 , 6 ]. Finally, distance and travel time have consistent but asymmetric effects. Greater distance reduces the probability of public transport use for both genders, with a more intense penalty in the female model, suggesting that women are more sensitive to the spatial constraints associated with daily organization and time pressure. Conversely, longer travel times increase the probability of using public transport in both models, with a slightly higher effect in the case of women, especially in congested metropolitan contexts. Taken together, these results reinforce the interpretation of modal choice as a response to structural constraints of time and space and underscore the centrality of time as a key dimension in the analysis of mobility from a gender perspective [ 4 , 9 , 13 ]. 6. Public policy implications The results indicate that modal choice in highly car-dependent metropolitan contexts operates through structural determinants that differ by gender, challenging “neutral” policies and supporting the systematic integration of a gender perspective in planning and service design. [ 2 , 3 ] First, the strong role of driving license availability confirms the centrality of the car as a mobility resource. Restrictive measures should be paired with measurable improvements in public transport performance, particularly given women’s greater exposure to non-work and chained trips. [ 3 , 11 ] KPIs: door-to-door travel-time competitiveness, off-peak frequency, and reliability (on-time performance). Second, interventions should prioritize medium-sized and small municipalities and dispersed settlements, where public transport access is structurally limited. High-frequency metropolitan corridors, semi-direct services, and equity-oriented demand-responsive transport (DRT) as a feeder can reduce spatially driven modal gaps. [ 11 , 28 ] KPIs: coverage within walking distance of frequent services, time to key destinations, and DRT response time/acceptance rates. Third, women’s lower propensity to use public transport for domestic and leisure trips suggests a service–demand mismatch for care-related mobility. Policies should improve functional fit by strengthening direct access to everyday facilities and reducing waiting and transfer penalties; fare and information tools can further reduce coordination burdens. [ 5 , 6 ] KPIs: transfers per domestic/leisure trip, off-peak waiting time, and gender-disaggregated public transport share for these purposes. Finally, time-related constraints reinforce travel time and reliability as strategic levers. Increasing competitiveness through bus/HOV lanes, signal priority, express services, or stop rationalization is essential to ease daily time pressure and support public transport use. [ 9 , 13 ] KPIs: commercial speed, headway regularity, and travel-time variability (e.g., 95th percentile travel time). 7. Study limitations and future research directions This study has some limitations that should be considered when interpreting the results. First, the analysis is based on data from the 2011 Mobility Survey in the Urban Regions of Andalusia. Although this is the most comprehensive and detailed source available with representative microdata for this territorial area, its age could raise questions about the current relevance of the patterns observed. However, the main determinants analyzed (availability of mobility resources, household organization, territorial structure, and time constraints) respond to structural factors that have shown high stability in European contexts, even in recent scenarios of transport system transformation [ 10 , 11 , 13 , 14 ]. In this sense, the results should be interpreted not as a snapshot of the current situation, but as evidence of persistent mechanisms that continue to condition modal choice. Second, since the unit of analysis is the trip rather than the individual, some degree of within-respondent correlation may persist when multiple trips are reported by the same person. Nevertheless, the aim of the study is not individual-level prediction but the identification of gender-differentiated explanatory structures, for which the trip-based approach remains appropriate. Third, the approach adopted focuses on objective variables available in the survey and does not incorporate attitudinal or perceptual dimensions, such as the perception of safety, service reliability, comfort, or subjective time constraints. Although the exclusion of these variables does not invalidate the results obtained, their inclusion would allow for a deeper understanding of the mechanisms underlying modal choice and how they interact with the identified structural determinants, especially from a gender perspective. Furthermore, the analysis is limited to motorized travel, excluding active modes, which respond to specific logics and are highly relevant to women's daily mobility. Future research could integrate active mobility through complementary methodological approaches that allow for a more complete understanding of the complexity of travel patterns and modal strategies, particularly in compact urban contexts and short-distance travel. Looking ahead to future lines of research, it would be desirable to expand this analysis using more recent mobility surveys that offer comparable microdata, not so much to question the validity of the structural determinants identified, but to analyze possible changes in their intensity and interaction with new conditions of supply, regulation, and territorial organization. Likewise, the use of longitudinal designs would allow us to examine how gender inequalities in modal choice evolve over the life cycle and in different socioeconomic contexts. Finally, combining quantitative and qualitative approaches could provide a deeper understanding of everyday mobility decisions, especially with regard to time management, travel chains, and the adaptive strategies developed by women and men in response to transportation system constraints. Such mixed approaches would contribute to enriching the analysis of equity in mobility and to more accurately guiding the design of gender-sensitive transport policies. 8. Conclusions This study analyzes the choice of public transport in urban regions of Andalusia from a gender perspective by estimating separate binary logistic regression models for women and men. The results show, first, that gender is significantly associated with the probability of using public transport, as the models estimated for women and men show clear differences both in the factors that are significant and in the magnitude of their effects. In relation to the factors that influence this probability, the results indicate that for both genders, sociodemographic variables such as age and educational level increase the probability of using public transport, while the availability of a driving license reduces it significantly. However, the relevance of other determinants differs substantially by gender. In the case of women, the choice of mode of transport is conditioned by a broader set of factors, including variables related to household organization, available economic resources, and reasons for travel. This greater sensitivity to family and functional constraints reflects greater exposure to restrictions related to time management, the sequencing of activities, and effective access to mobility resources. In contrast, in the male model, the probability of using public transport appears to be more closely associated with individual and territorial factors and less influenced by the domestic context. The territorial analysis shows gender differences in the role of the residential environment. Although living in medium-sized and small municipalities reduces the use of public transport in both models, residential dispersion only has a significant negative effect among men and is not significant among women. This result suggests that the influence of urban form on modal choice is not uniform, but is conditioned by the functional organization of daily trips; in the case of women, this mediation could contribute to sustaining public transport use even in dispersed residential contexts. Likewise, the differences observed in sensitivity to distance and travel time reinforce the interpretation of modal choice as a response to structural constraints of time and space, which are particularly relevant in women's daily mobility. From a methodological perspective, the results show that treating gender solely as a control variable in aggregate models can obscure relevant mechanisms of inequality. Estimating gender-disaggregated models allows for the identification of differentiated explanatory structures and provides a more solid analytical basis for understanding modal choice in metropolitan contexts with high car dependency. Overall, the article contributes to a more accurate understanding of gender inequalities in urban mobility and reinforces the need to place gender at the center of transportation system analysis and planning. Declarations Funding No funding was received for this study. Author Contribution Author Contributions StatementE.M-G. and M.I.O-S. contributed equally to the conception, design, data analysis, and writing of this manuscript. Both authors reviewed and approved the final version. Acknowledgement Funding for open access charge: Universidad de Málaga / CBUA Data Availability The data supporting the results of this study are secondary data obtained from the Institute of Statistics and Cartography of Andalusia (IECA). The analysis is based on the Social Survey 2011: Mobility in Urban Regions of Andalusia. The anonymized microdata are publicly available and can be downloaded from the official IECA repository at:https://www.juntadeandalucia.es/institutodeestadisticaycartografia/dega/encuesta-social-2011-movilidad-en-las-regiones-urbanas-de-andalucia References Ng, W. S., & Acker, A. (2018). Understanding urban travel behavior by gender for efficient and equitable transport policies. International Transport Forum (ITF) Discussion Paper 2018-01. Martens, K., Bastiaanssen, J., & Lucas, K. (2019). Measuring transport equity: Key components, framings and metrics. In Measuring transport equity (pp. 13–36). Elsevier. https://doi.org/10.1016/B978-0-12-814818-1.00002-0 Litman, T. M. (2022). Evaluating transportation equity: Guidance for incorporating distributional impacts in transport planning. Institute of Transportation Engineers. ITE Journal , 92 (4), 43–49. Hanson, S. (2010). Gender and mobility: New approaches for informing sustainability. Gender Place & Culture , 17 (1), 5–23. https://doi.org/10.1080/09663690903498225 Sánchez de Madariaga, I. S. (2009). Housing, mobility and urbanistic for equality within diversity: Cities, gender, and dependence. Ciudad y Territorio Estudios Territoriales , 41 (161-2), 581–597. https://recyt.fecyt.es/index.php/CyTET/article/view/75953 Miralles-Guasch, C., Martínez Melo, M., & Marquet, O. (2016). A gender analysis of everyday mobility in urban and rural territories: from challenges to sustainability. Gender Place & Culture , 23 (3), 398–417. https://doi.org/10.1080/0966369X.2015.1013448 Olmo-Sánchez, M. I., & Maeso-González, E. (2014). Travel patterns, regarding different activities: work, studies, household responsibilities, and leisure. Transportation Research Procedia , 3 , 119–128. https://doi.org/10.1016/j.trpro.2014.10.097 Olmo-Sánchez, M. I., & Maeso-González, E. (2016). Gender differences in commuting behavior: Women's greater sensitivity. Transportation Research Procedia , 18 , 66–72. https://doi.org/10.1016/j.trpro.2016.12.009 Turner, J., & Grieco, M. (2000). Gender and Time Poverty: The Neglected Social Policy Implications of Gendered Time, Transport and Travel. Time & Society , 9 (1), 129–136. https://doi.org/10.1177/0961463X00009001007 Pourhashem, G., Malichova, E., Piscova, T., & Tatiana, K. (2022). Gender difference in perception of value of travel time and travel mode choice behavior in eight European countries. Sustainability , 14 (16), 10426. https://doi.org/10.3390/su141610426 Sierra Muñoz, J., Duboz, L., Pucci, P., & Ciuffo, B. (2024). Why do we rely on cars? Car dependence assessment and dimensions from a systematic literature review. European Transport Research Review , 16 (1), 17. https://doi.org/10.1186/s12544-024-00639-z Uteng, T. P. (2021). Gender gaps in urban mobility and transport planning. In Advances in transport policy and planning (Vol. 8, pp. 33–69). Academic Press. https://doi.org/10.1016/bs.atpp.2021.07.004 Chidambaram, B., & Scheiner, J. (2023). The gender dimensions of travel time use in Germany. European Transport Research Review , 15 , 1. https://doi.org/10.1186/s12544-023-00574-5 Lejsková, P., Pojkarová, K., Kudláčková, N., Becková, H., & Čubranić-Dobrodolac, M. (2023). Gender differences in transport behavior patterns. LOGI: Scientific Journal on Transport and Logistics , 14 (1), 329–340. https://doi.org/10.2478/logi-2023-0030 Zucchini, E. (2015). Gender and transport: analysis of care mobility as a starting point for building a broader knowledge base of mobility patterns. The case of Madrid (Doctoral dissertation, Architecture). https://doi.org/10.20868/UPM.thesis.39914 Prieto-Curiel, R., & Barroso, F. (2025). The growing dominance of cars in suburban areas. Transportation Research Interdisciplinary Perspectives , 32 , 101559. https://doi.org/10.1016/j.trip.2025.101559 Rosati, R. M. (2025). Urban sprawl and routing: A comparative study on 156 European cities. Landscape and Urban Planning , 253 , 105205. https://doi.org/10.1016/j.landurbplan.2024.105205 Ben-Akiva, M., & Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand . MIT Press. de Ortúzar, J. D., & Willumsen, L. (2011). Modelling Transport (4th ed.). Wiley. Train, K. E. (2009). Discrete choice methods with simulation . Cambridge University Press. Oliveira, M. L., & Lima, J. P. (2023). A multinomial logistic regression model for public transportation use in a medium-sized Brazilian city. Production , 33 , e20230027. https://doi.org/10.1590/0103-6513.20230027 Tikouk, J., & Boubkr, A. A. (2025). Gender differences in transportation mode choice for health trips in Morocco: a multinomial logistic regression approach. Advances in Transportation Studies , 66. https://doi.org/10.53136/979122181935923 Vega, P., & Roman, M. (2011). Mobility Patterns in Public Transport in Andalusia . Regional Ministry of Public Works and Housing. Directorate-General for Transport. PITMA (2021). Andalusia Transport and Mobility Infrastructure Plan 2021–2030 Junta de Andalucía. https://www.juntadeandalucia.es/organismos/fomentoarticulaciondelterritorioyvivienda/areas/infraestructuras-movilidad/pitma.html OTLE (2024). Perspectiva de Género en el Transporte y la Movilidad. Observatory of Transport and Logistics in Spain. Ministry of Transport and Sustainable Mobility. https://otle.transportes.gob.es/informes-anuales-monograficos/perspectiva-genero IECA (2011). Social Survey 2011: Mobility in Urban Regions of Andalusia. Institute of Statistics and Cartography of Andalusia. https://www.juntadeandalucia.es/institutodeestadisticaycartografia/dega/encuesta-social-2011-movilidad-en-las-regiones-urbanas-de-andalucia Vassallo Magro, J. M., Tarriño Ortiz, J., Gómez Sánchez, J., & Soria Lara, J. A. (2021). Impact on acceptability and modal split of measures to improve air quality in central Madrid. In: R-Evolucionando el transporte, 1591–1627. https://doi.org/10.36443/9788418465123 Rodríguez Moya, J. M., & García Palomares, J. C. (2012). Gender diversity in everyday mobility in the Community of Madrid. Bulletin of the Association of Spanish Geographers , 58 , 105–132. https://doi.org/10.21138/bage.2061 Dell’Olio, L., Ibeas, A., & Cecin, P. (2011). The quality of service desired by public transport users. Transport Policy , 18 (1), 217–227. https://doi.org/10.1016/j.tranpol.2010.08.005 Alfaro, E., Marin, C., & Useche, S. A. (2025). Mind the Gap! Gender differences in the predictors of public transport usage intention. Transportation Research Part F: Traffic Psychology and Behaviour , 111 , 453–466. https://doi.org/10.1016/j.trf.2025.03.013 Montero, L., Mejía-Dorantes, L., & Barceló, J. (2023). The role of life course and gender in mobility patterns: a spatiotemporal sequence analysis in Barcelona. European Transport Research Review , 15 (1), 44. https://doi.org/10.1186/s12544-023-00621-1 Pani, A., Sahu, P., & Mishra, S. (2023). Gender disparities in multimodal travel attitudes, behavior, and satisfaction. Transportation Research Part D: Transport and Environment , 123 , 103917. https://doi.org/10.1016/j.trd.2023.103917 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8648400","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594993782,"identity":"c10df781-c375-4e8c-83d8-ac56f8c5c4ee","order_by":0,"name":"María Isabel Olmo-Sanchez","email":"","orcid":"","institution":"University of Malaga","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Isabel","lastName":"Olmo-Sanchez","suffix":""},{"id":594993783,"identity":"04a9e31d-21e9-4d52-8940-cb700766158b","order_by":1,"name":"Elvira Maeso-González","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAo0lEQVRIiWNgGAWjYPACG9K1pJGu5TAJavnFDj98+KPifOLa9gOMD38Qo0VydpqxMc+Z24nbziQwG/MQo8XgdoKZNGMbUMuBBDZpohxmfzv9+8+fbecSt51/wP6TKIcZSOeYMfC2HUjcdiOBjYEoh0nczimW5jmTbLztxsNmaaK08M9O3/jxR4Wd7LbzyQc/EuUwJMDYQKKGUTAKRsEoGAU4AQAQajTO/d6rbgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Malaga","correspondingAuthor":true,"prefix":"","firstName":"Elvira","middleName":"","lastName":"Maeso-González","suffix":""}],"badges":[],"createdAt":"2026-01-20 11:18:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8648400/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8648400/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108181299,"identity":"3c6f5197-3445-4950-9733-38ac60de2c83","added_by":"auto","created_at":"2026-04-30 08:58:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":518048,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8648400/v1/4d3fda35-23b5-4d23-ae0f-8753cd278f6c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender Differences in Public Transport Use in a Car-Dependent European Region: Evidence from Andalusia (Spain)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe use of public transport shows persistent gender differences in urban mobility, especially in contexts characterized by high car dependency. The literature has consistently shown that access to and effective use of transport systems are not socially neutral, but are conditioned by structural factors such as income, territory, life cycle, and, particularly relevant, gender [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a gender perspective, numerous studies have documented that women develop more fragmented and complex mobility patterns, associated with a greater diversity of daily activities and domestic and care responsibilities [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Compared to men, women make a greater number of daily trips, generally over shorter distances and for a greater variety of reasons [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These differences are not solely due to individual preferences, but are related to structural constraints linked to the social organization of time, the unequal distribution of family responsibilities, and differentiated access to mobility resources [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a methodological point of view, although discrete choice models have been widely used to analyze modal behavior, gender is usually incorporated only as a control variable in joint models. This approach may obscure relevant structural differences in the modal choice processes between women and men [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Some recent studies have made progress in estimating gender-differentiated models, but they tend to focus on subjective perceptions of travel or time valuations rather than on structural determinants of everyday mobility, such as household organization, territorial context, or access to mobility resources [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this framework, gender should be understood not as an additional individual characteristic, but as a structuring principle of everyday mobility and modal choice, which systematically conditions the opportunities and constraints faced by women and men. This approach is particularly relevant in southern Europe, where persistent high dependence on cars is combined with metropolitan areas structured around an urban core-periphery pattern defined by strong historical centrality and functionally dependent peripheral development, characterized by large urban cores and extensive low-density peripheries [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], as well as a still marked gender division of tasks [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, the urban regions of Andalusia (southern Spain) provide a particularly relevant case study for analyzing the determinants of public transport choice from a gender perspective. Andalusia is heavily dependent on private vehicles, has a heterogeneous public transport offering, and combines compact urban centers with dispersed residential areas, features shared by many metropolitan systems in a southern European context.\u003c/p\u003e \u003cp\u003eThe objective of this article is to analyze the factors that influence the choice of public transport in a southern European region from a gender perspective, by estimating separate binary logistic regression models for women and men.\u003c/p\u003e \u003cp\u003eThe study addresses the following research questions: (1) What sociodemographic, family, territorial, and displacement-related factors influence the probability of using public transport among women and men? (2) To what extent do the relevance and magnitude of these factors differ according to gender? (3) What structural differences can be observed in the overall configuration of modal choice patterns between women and men in urban contexts characterized by high car dependency?\u003c/p\u003e \u003cp\u003eThe article makes a twofold contribution. From a methodological point of view, it demonstrates that the estimation of gender-disaggregated models allows for the identification of differentiated explanatory structures that remain partially hidden in aggregate approaches. From an empirical perspective, it provides evidence for a southern European context characterized by high car dependency and urban core-periphery structure, which is still underrepresented in the international literature on modal choice and equity in transportation.\u003c/p\u003e \u003cp\u003eThe article is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the literature on mobility, gender, and modal choice. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the case study, data, and methodology. Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of the estimated models. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e5\u003c/span\u003e discusses the main findings. Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e6\u003c/span\u003e develops the public policy implications. Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e7\u003c/span\u003e addresses the limitations of the study and suggests future lines of research. Finally, section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e8\u003c/span\u003e summarizes the main conclusions.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eThe literature on gender and mobility has consistently documented that the travel patterns of women and men differ systematically, an inequality that is largely explained by care mobility, a concept developed by S\u0026aacute;nchez de Madariaga [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] to refer to travel associated with domestic tasks, caregiving, and family responsibilities. In general terms, women tend to make more fragmented and chained trips, while men concentrate on more linear journeys associated with paid employment and car use. These patterns are corroborated in recent studies that identify greater fragmentation and multitasking in women's journeys, as well as more intensive use of public transport and active modes, especially in metropolitan contexts [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This evidence reinforces the idea that everyday mobility is largely conditioned by time and organizational constraints that affect women and men differently.\u003c/p\u003e \u003cp\u003eIn southern Europe, these inequalities are particularly pronounced due to the persistence of traditional caregiving roles, lower car availability among women, and more dispersed urban structures, which reduce the competitiveness of public transport. Empirical evidence in Spain confirms these patterns, showing that women make more care-related trips, travel shorter distances, and are more sensitive to family variables and modal availability [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Zucchini [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] points out that, in Mediterranean countries, the lower availability of cars, the organization of care, and the dispersed urban structure generate more complex patterns of female mobility with a greater chain of trips, limiting women's modal autonomy. The OTLE report (2024) reinforces this diagnosis, confirming the persistence of gender gaps in everyday mobility, especially in the use of public transport.\u003c/p\u003e \u003cp\u003eContemporary approaches to transport equity show that mobility systems distribute accessibility and opportunities unevenly, placing certain social groups at a disadvantage [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This effect is particularly visible in women and people without access to a car, who are penalized by the limitations of public transport in dispersed metropolitan environments, marked by deficits in coverage, frequency, and reliability [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These gaps are exacerbated in territorial contexts with high car dependency and dispersed urban patterns. Low residential density weakens the competitiveness of public transport and reinforces the centrality of private vehicles, restricting accessibility throughout the metropolitan area [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Added to this territorial pattern is a persistent division of labor by gender, which conditions access to cars, time management, and the configuration of daily commutes, especially in relation to care-related mobility [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the extensive literature on gender inequalities in mobility, evidences based on gender-disaggregated modal choice models remain limited in contexts with high car dependency. Logit models have been widely used to analyze modal choice and estimate the probability of using different modes of transport based on individual, territorial, and trip characteristics [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In recent years, these models have been applied to study patterns of public transport use in various contexts [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, most of these studies incorporate gender only as a control variable within a single model, which prevents the identification of structural differences between women and men in the determinants of modal choice. Some recent studies have made progress in estimating gender-differentiated modal choice models; however, these approaches have focused mainly on subjective perceptions of travel, individual attitudes, or time valuations, rather than on structural determinants of everyday mobility, such as household organization, territorial structure, or access to mobility resources [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, a significant methodological gap persists in recent literature: the scarcity of separately estimated modal choice models for women and men that allow for the identification of structural differences in the determinants of public transport use versus car use. This study contributes to filling this gap through a gender-disaggregated econometric analysis applied to a southern European context.\u003c/p\u003e"},{"header":"3. Case study, data, and methodology","content":"\u003cp\u003eThe urban regions of Andalusia constitute a complex metropolitan system, with a high frequency of interurban travel and strong interdependence between residential areas and centers of activity. From the supply side, road transport predominates and public transport provision is uneven. Collective mobility is mainly based on urban and interurban bus networks, while rail modes are limited and localized in a few cities, such as Seville, Malaga, and Granada, through commuter and light rail services [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This configuration restricts accessibility and reinforces the use of private vehicles, especially in peripheral and low-density municipalities [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The combination of residential dispersion, functional polycentrism, and high motorization accentuates the influence of territorial factors and modal availability on transport choice, making Andalusia a representative case of metropolitan systems in southern Europe.\u003c/p\u003e \u003cp\u003eThe empirical analysis is based on the Survey of Mobility in the Urban Regions of Andalusia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the main source available with representative microdata on daily mobility in this area. Although compiled in 2011, its quality, coverage, and level of disaggregation allow for a rigorous analysis of the determinants of modal choice in a broad and heterogeneous context [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, the factors considered respond to structural conditions that are relatively stable over time, which supports the use of the 2011 data [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter cleaning the database, the final sample consisted of 6,032 motorized trips reported on weekdays by individuals aged 16 years and over. Analyses were conducted using the survey expansion weights to account for the sampling design and ensure population representativeness. This sample size allows the separate estimation of gender-disaggregated models for women and men.\u003c/p\u003e \u003cp\u003eDescriptive gender differences in modal shares, trip purposes, and key spatio-temporal indicators for the same survey and study area have been reported in previous work [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Building on that descriptive evidence, the present study focuses on identifying gender-differentiated determinants of public transport use through multivariate binary logit models.\u003c/p\u003e \u003cp\u003eThe dependent variable was defined as a dichotomous choice between public transport (urban and interurban bus, metro, tram, and commuter rail) and private motorized transport (car and motorcycle). Active modes (walking and cycling) were excluded, as they respond to different logics and typically require specific analytical approaches [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe explanatory variables were selected based on their relevance in the literature and their availability in the survey, and were grouped into four blocks: (1) individual sociodemographic variables (age, educational level, nationality, and employment status); (2) household variables (living with a partner, household income, and household size); (3) territorial variables (municipality size and settlement type); and (4) travel characteristics (driving license, trip purpose, distance, and travel time). This structure facilitates interpretation and captures key dimensions from a gender perspective.\u003c/p\u003e \u003cp\u003eTwo binary logistic regression models were estimated, a common technique for analyzing discrete modal choice decisions, recently applied in studies on public transport and private vehicles in different contexts [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn order to identify structural differences by gender, separate models were estimated for women and men, rather than a joint model with interactions. This strategy avoids assuming structural homogeneity and facilitates comparison from an equity perspective. The models achieved Nagelkerke pseudo R\u0026sup2; values of 0.48 in the male model and 0.53 in the female model.\u003c/p\u003e \u003cp\u003eModel fit was assessed using several complementary indicators. In both models, omnibus tests confirmed a significant improvement over the null specification, while pseudo-R\u0026sup2; measures and the Hosmer\u0026ndash;Lemeshow test indicated good calibration.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe results of the models obtained show that the determinants factors of public transport choice differ between men and women, both in the magnitude and structure of the effects (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both models exhibit good fit and calibration, providing a solid basis for their interpretation. Although there are factors common to both models, the separate estimation by gender allows for the identification of differentiated patterns that would be partially hidden in a joint analysis.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Results of the male model\u003c/h2\u003e \u003cp\u003eIn the male model (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the probability of using public transport is mainly conditioned by sociodemographic, territorial, and travel-related factors.\u003c/p\u003e \u003cp\u003eAge shows a positive effect from the age of 50 onwards, with statistically significant increases in the 50\u0026ndash;64 and over-65 age groups, while the intermediate cohorts (30\u0026ndash;49 years) show no significant effects. Educational level is also relevant, with men with secondary and university education being more likely to use public transport. Nationality and unemployment status do not show significant effects, although being economically inactive is associated with a higher probability of use.\u003c/p\u003e \u003cp\u003eHousehold variables have limited explanatory power. Living with a partner and household size are not significant, while family income only reduces the likelihood of using public transport at higher income levels.\u003c/p\u003e \u003cp\u003eThe territorial context is clearly a determining factor. Living in medium-sized or small municipalities significantly reduces the likelihood of using public transport compared to large municipalities, as does living in sparsely populated areas.\u003c/p\u003e \u003cp\u003eAmong the variables specific to travel, the availability of a driving license emerges as the most influential factor, significantly reducing the probability of using public transport. Travel for study purposes significantly increases this probability, while domestic and leisure reasons do not have statistically significant effects. Finally, greater distance reduces the probability of using public transport, while longer travel times increase it.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary logit model results for public transport choice among men\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% confidence interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBlock 1. Individual sociodemographic variables: age, educational level, nationality, and employment status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: Age 16\u0026ndash;29\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.52\u0026ndash;3.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.47\u0026ndash;3.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 50\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.04\u0026ndash;5.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.70\u0026ndash;13.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: primary education\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.09\u0026ndash;4.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.54\u0026ndash;8.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: non-Spanish\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpanish nationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.28\u0026ndash;8.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: employed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.30\u0026ndash;2.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.01\u0026ndash;6.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlock 2. Household variables: living with a partner, household income, and household size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: not living with a partner\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.37\u0026ndash;1.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: \u0026lt; \u0026euro;1,100\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome \u0026euro;1,101\u0026ndash;1,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.33\u0026ndash;1.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome \u0026euro;1,801\u0026ndash;2,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.29\u0026ndash;1.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome \u0026gt; \u0026euro;2,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.23\u0026ndash;1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.79\u0026ndash;1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlock 3. Territorial and residential environment variables: municipality size and type of settlement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: large municipality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium municipality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.17\u0026ndash;0.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall municipality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.18\u0026ndash;0.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: compact settlement\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate settlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.29\u0026ndash;1.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDispersed settlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.13\u0026ndash;0.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlock 4. Travel variables: driving license, trip purpose, distance, and travel time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: no driving license\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDriving license\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.04\u0026ndash;0.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: work\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip purpose: education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.85\u0026ndash;14.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip purpose: domestic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.23\u0026ndash;1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip purpose: leisure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.53\u0026ndash;2.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip distance (km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.92\u0026ndash;0.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTravel time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.06\u0026ndash;1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Statistically significant coefficients are indicated as follows: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Results of the female model\u003c/h2\u003e \u003cp\u003eIn the female model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the probability of using public transport is conditioned by a broader set of factors, with generally stronger associations than those observed in the male model.\u003c/p\u003e \u003cp\u003eAge has a positive and greater effect from the age of 50 onwards. With regard to educational level, both secondary education and, especially, university education significantly increase the probability of using public transport. Nationality and employment status are not significant.\u003c/p\u003e \u003cp\u003eHousehold variables show a clear influence. Living with a partner significantly reduces the probability of using public transport. Middle-income households are less likely to use public transport than lower-income households. Household size is not significant.\u003c/p\u003e \u003cp\u003eThe territorial context continues to be relevant: living in medium-sized or small municipalities significantly reduces the likelihood of using public transport, while the type of settlement does not show statistically significant effects.\u003c/p\u003e \u003cp\u003eAmong the travel variables, the availability of a driving license is once again one of the most influential determinants. Travel for study significantly increases the probability of using public transport, while domestic and leisure travel reduces it.\u003c/p\u003e \u003cp\u003ePublic transport use is higher for shorter distances and longer travel times.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary logit model results for public transport choice among women\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% confidence interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBlock 1. Individual sociodemographic variables: age, educational level, nationality, and employment status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: age 16\u0026ndash;29\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.71\u0026ndash;2.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.92\u0026ndash;2.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 50\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[2.04\u0026ndash;6.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[2.50\u0026ndash;12.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: primary education\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.22\u0026ndash;3.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[2.42\u0026ndash;8.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: non-Spanish\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpanish nationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.43\u0026ndash;1.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: employed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.38\u0026ndash;1.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.57\u0026ndash;1.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlock 2. Household variables: living with a partner, household income, and household size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: not living with partner\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.27\u0026ndash;0.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: \u0026lt; \u0026euro;1,100\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome \u0026euro;1,101\u0026ndash;1,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.41\u0026ndash;0.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome \u0026euro;1,801\u0026ndash;2,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.29\u0026ndash;0.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome \u0026gt; \u0026euro;2,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.34\u0026ndash;1.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.93\u0026ndash;1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlock 3. Territorial and residential environment variables: municipality size and type of settlement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: large municipality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium municipality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.21\u0026ndash;0.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall municipality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.24\u0026ndash;0.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: compact settlement\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate settlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.63\u0026ndash;1.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDispersed settlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.35\u0026ndash;1.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlock 4. Travel variables: driving license, trip purpose, distance, and travel time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: no driving license\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDriving license\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.09\u0026ndash;0.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: work\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip purpose: education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[2.51\u0026ndash;10.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip purpose: domestic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.27\u0026ndash;0.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip purpose: leisure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.24\u0026ndash;0.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip distance (km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.90\u0026ndash;0.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTravel time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.07\u0026ndash;1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Statistically significant coefficients are indicated as follows: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSeparate models were estimated for men and women in order to capture gender-specific determinants of public transport use. Although we report odds ratios, we emphasize practical relevance (direction and relative strength of associations), given the nonlinearity of logit models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Comparison between models\u003c/h2\u003e \u003cp\u003eThe comparison between the two models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) shows differences in the significance and magnitude of the determinants of modal choice by gender. In both genders, the probability of using public transport increases with age, especially after the age of 50, although the effect is more intense in the female model.\u003c/p\u003e \u003cp\u003eEducational level significantly increases the probability of public transport use in both models, with higher effects at secondary and, especially, university levels, and with a greater magnitude in the female model. Employment status is only statistically significant in the male model, where inactivity is associated with a higher probability of public transport use.\u003c/p\u003e \u003cp\u003eIn relation to household variables, living with a partner significantly reduces the probability of using public transport only in the female model. Likewise, income level has a significant negative effect in the female model in the middle-income brackets, while in the male model this effect is only observed in the highest income bracket.\u003c/p\u003e \u003cp\u003eTerritorial variables indicate that living in medium-sized or small municipalities significantly reduces the probability of using public transport for both genders, while living in dispersed settlements has a significant negative effect only in the male model.\u003c/p\u003e \u003cp\u003eThe availability of a driving license significantly reduces the probability of using public transport in both models, with a slightly more intense effect on the male model. In terms of the reason for travel, trips for educational purposes significantly increase the probability of using public transport for both genders, while trips for domestic and leisure purposes reduce this probability mainly in the female model.\u003c/p\u003e \u003cp\u003eFinally, in both models, a longer travel distance reduces the probability of using public transport, while a longer travel time increases it, with slightly stronger effects in the female model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative results of modal choice models by gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen Odds ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWomen Odds ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eBlock 1. Individual sociodemographic variables: age, educational level, nationality, and employment status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: age 16\u0026ndash;29\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 50\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: primary education\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: non-Spanish\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpanish nationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: employed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlock 2. Household variables: living with a partner, household income, and household size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: not living with partner\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: \u0026lt; \u0026euro;1,100\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome \u0026euro;1,101\u0026ndash;1,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome \u0026euro;1,801\u0026ndash;2,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome \u0026gt; \u0026euro;2,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlock 3. Territorial and residential environment variables: municipality size and type of settlement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: large municipality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium municipality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall municipality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: compact settlement\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate settlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDispersed settlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlock 4. Travel variables: driving license, trip purpose, distance, and travel time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: no driving license\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDriving license\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReference category: work\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip purpose: education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip purpose: domestic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip purpose: leisure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip distance (km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTravel time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Statistically significant coefficients are indicated as follows: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe results provide clear evidence that the choice of public transport in urban regions of Andalusia is conditioned by gender, not only in terms of the magnitude of the observed effects, but also through differentiated explanatory structures. The estimation of binary logit models separated by gender allows us to identify patterns that would be partially or totally hidden in aggregate specifications, which supports the methodological relevance of disaggregated approaches and their consistency with the literature on equity and spatial justice in transportation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The greater number and diversity of significant variables in the female model indicates that women's mobility is more intensely conditioned by social, family, and functional constraints, while male modal choice is explained by a more limited set of individual and territorial factors. These results reinforce the interpretation of gender not as an additional individual characteristic, but as a structuring principle of everyday mobility, which systematically shapes opportunities and constraints [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and challenge approaches that incorporate gender solely as a control variable in modal choice models [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a sociodemographic perspective, age emerges as a relevant determinant in both models, with a marked increase in the probability of public transport use from the age of 50 onwards, especially in the case of women. This pattern is consistent with evidence linking aging with reduced car availability and greater dependence on collective modes, as well as with gender differences in the organization of daily mobility throughout the life cycle [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Similarly, educational attainment significantly increases the probability of public transport use in both genders, with a clear gradient and higher effects at secondary and, especially, university levels, and with greater intensity in the female model. These results are consistent with the literature that associates educational capital with more diversified mobility patterns and a greater ability to interact with public transport systems [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In contrast, employment status is only statistically significant in the male model, where inactivity is associated with a higher probability of public transport use, pointing to gender-differentiated relationships between employment, daily routines, and modal choice [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVariables related to household organization introduce some of the clearest gender differences. In the case of women, living with a partner and/or belonging to households with intermediate income levels significantly reduces the probability of using public transport, while these variables are not relevant in the male model. The absence of an effect of household size in both models suggests that it is not the number of members that determines mobility, but rather the way in which daily responsibilities are organized and mobility resources are distributed, particularly effective access to a car. The fact that household variables are only significant in the female model indicates that women's modal choice is more closely linked to the domestic context and the actual availability of mobility resources, in line with the literature on care mobility and inequality in access to private vehicles [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a territorial perspective, residence in medium-sized and small municipalities significantly reduces the probability of public transport use for both genders, reflecting the structural role of supply and accessibility in smaller contexts. However, residence in areas of dispersed settlement only has a significant negative effect in the male model. This asymmetry suggests that, in the case of women, modal choice is less conditioned by urban morphology itself and more by the functional organization of daily trips, which leads them to maintain a certain level of public transport use even in dispersed residential contexts. This result is consistent with studies showing that the effects of territorial structure on modal choice manifest themselves differently by gender depending on the patterns of chaining and daily organization of mobility [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe characteristics of travel synthesize and reinforce these structural differences. The availability of a driving license is the most influential determinant of modal choice in both models, significantly reducing the probability of public transport use for both men and women, although with a more intense effect in the male model. This result confirms the persistence of a strong dependence on cars in the urban regions analyzed, in line with evidence for metropolitan contexts characterized by dispersed urbanization and fragmented public transport supply [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In terms of the reason for travel, trips for study significantly increase the probability of public transport use in both genders, while trips for domestic and leisure reasons reduce this probability only in the female model. This pattern highlights the lower compatibility of public transport with non-routine, chained trips subject to greater time constraints, which are characteristic of women's daily mobility, as pointed out in the literature on care mobility [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, distance and travel time have consistent but asymmetric effects. Greater distance reduces the probability of public transport use for both genders, with a more intense penalty in the female model, suggesting that women are more sensitive to the spatial constraints associated with daily organization and time pressure. Conversely, longer travel times increase the probability of using public transport in both models, with a slightly higher effect in the case of women, especially in congested metropolitan contexts. Taken together, these results reinforce the interpretation of modal choice as a response to structural constraints of time and space and underscore the centrality of time as a key dimension in the analysis of mobility from a gender perspective [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e"},{"header":"6. Public policy implications","content":"\u003cp\u003eThe results indicate that modal choice in highly car-dependent metropolitan contexts operates through structural determinants that differ by gender, challenging \u0026ldquo;neutral\u0026rdquo; policies and supporting the systematic integration of a gender perspective in planning and service design. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFirst, the strong role of driving license availability confirms the centrality of the car as a mobility resource. Restrictive measures should be paired with measurable improvements in public transport performance, particularly given women\u0026rsquo;s greater exposure to non-work and chained trips. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] KPIs: door-to-door travel-time competitiveness, off-peak frequency, and reliability (on-time performance).\u003c/p\u003e \u003cp\u003eSecond, interventions should prioritize medium-sized and small municipalities and dispersed settlements, where public transport access is structurally limited. High-frequency metropolitan corridors, semi-direct services, and equity-oriented demand-responsive transport (DRT) as a feeder can reduce spatially driven modal gaps. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] KPIs: coverage within walking distance of frequent services, time to key destinations, and DRT response time/acceptance rates.\u003c/p\u003e \u003cp\u003eThird, women\u0026rsquo;s lower propensity to use public transport for domestic and leisure trips suggests a service\u0026ndash;demand mismatch for care-related mobility. Policies should improve functional fit by strengthening direct access to everyday facilities and reducing waiting and transfer penalties; fare and information tools can further reduce coordination burdens. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] KPIs: transfers per domestic/leisure trip, off-peak waiting time, and gender-disaggregated public transport share for these purposes.\u003c/p\u003e \u003cp\u003eFinally, time-related constraints reinforce travel time and reliability as strategic levers. Increasing competitiveness through bus/HOV lanes, signal priority, express services, or stop rationalization is essential to ease daily time pressure and support public transport use. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] KPIs: commercial speed, headway regularity, and travel-time variability (e.g., 95th percentile travel time).\u003c/p\u003e"},{"header":"7. Study limitations and future research directions","content":"\u003cp\u003eThis study has some limitations that should be considered when interpreting the results. First, the analysis is based on data from the 2011 Mobility Survey in the Urban Regions of Andalusia. Although this is the most comprehensive and detailed source available with representative microdata for this territorial area, its age could raise questions about the current relevance of the patterns observed. However, the main determinants analyzed (availability of mobility resources, household organization, territorial structure, and time constraints) respond to structural factors that have shown high stability in European contexts, even in recent scenarios of transport system transformation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this sense, the results should be interpreted not as a snapshot of the current situation, but as evidence of persistent mechanisms that continue to condition modal choice.\u003c/p\u003e \u003cp\u003eSecond, since the unit of analysis is the trip rather than the individual, some degree of within-respondent correlation may persist when multiple trips are reported by the same person. Nevertheless, the aim of the study is not individual-level prediction but the identification of gender-differentiated explanatory structures, for which the trip-based approach remains appropriate.\u003c/p\u003e \u003cp\u003eThird, the approach adopted focuses on objective variables available in the survey and does not incorporate attitudinal or perceptual dimensions, such as the perception of safety, service reliability, comfort, or subjective time constraints. Although the exclusion of these variables does not invalidate the results obtained, their inclusion would allow for a deeper understanding of the mechanisms underlying modal choice and how they interact with the identified structural determinants, especially from a gender perspective.\u003c/p\u003e \u003cp\u003eFurthermore, the analysis is limited to motorized travel, excluding active modes, which respond to specific logics and are highly relevant to women's daily mobility. Future research could integrate active mobility through complementary methodological approaches that allow for a more complete understanding of the complexity of travel patterns and modal strategies, particularly in compact urban contexts and short-distance travel.\u003c/p\u003e \u003cp\u003eLooking ahead to future lines of research, it would be desirable to expand this analysis using more recent mobility surveys that offer comparable microdata, not so much to question the validity of the structural determinants identified, but to analyze possible changes in their intensity and interaction with new conditions of supply, regulation, and territorial organization. Likewise, the use of longitudinal designs would allow us to examine how gender inequalities in modal choice evolve over the life cycle and in different socioeconomic contexts.\u003c/p\u003e \u003cp\u003eFinally, combining quantitative and qualitative approaches could provide a deeper understanding of everyday mobility decisions, especially with regard to time management, travel chains, and the adaptive strategies developed by women and men in response to transportation system constraints. Such mixed approaches would contribute to enriching the analysis of equity in mobility and to more accurately guiding the design of gender-sensitive transport policies.\u003c/p\u003e"},{"header":"8. Conclusions","content":"\u003cp\u003eThis study analyzes the choice of public transport in urban regions of Andalusia from a gender perspective by estimating separate binary logistic regression models for women and men. The results show, first, that gender is significantly associated with the probability of using public transport, as the models estimated for women and men show clear differences both in the factors that are significant and in the magnitude of their effects.\u003c/p\u003e \u003cp\u003eIn relation to the factors that influence this probability, the results indicate that for both genders, sociodemographic variables such as age and educational level increase the probability of using public transport, while the availability of a driving license reduces it significantly. However, the relevance of other determinants differs substantially by gender. In the case of women, the choice of mode of transport is conditioned by a broader set of factors, including variables related to household organization, available economic resources, and reasons for travel. This greater sensitivity to family and functional constraints reflects greater exposure to restrictions related to time management, the sequencing of activities, and effective access to mobility resources. In contrast, in the male model, the probability of using public transport appears to be more closely associated with individual and territorial factors and less influenced by the domestic context.\u003c/p\u003e \u003cp\u003eThe territorial analysis shows gender differences in the role of the residential environment. Although living in medium-sized and small municipalities reduces the use of public transport in both models, residential dispersion only has a significant negative effect among men and is not significant among women. This result suggests that the influence of urban form on modal choice is not uniform, but is conditioned by the functional organization of daily trips; in the case of women, this mediation could contribute to sustaining public transport use even in dispersed residential contexts.\u003c/p\u003e \u003cp\u003eLikewise, the differences observed in sensitivity to distance and travel time reinforce the interpretation of modal choice as a response to structural constraints of time and space, which are particularly relevant in women's daily mobility.\u003c/p\u003e \u003cp\u003eFrom a methodological perspective, the results show that treating gender solely as a control variable in aggregate models can obscure relevant mechanisms of inequality. Estimating gender-disaggregated models allows for the identification of differentiated explanatory structures and provides a more solid analytical basis for understanding modal choice in metropolitan contexts with high car dependency. Overall, the article contributes to a more accurate understanding of gender inequalities in urban mobility and reinforces the need to place gender at the center of transportation system analysis and planning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions StatementE.M-G. and M.I.O-S. contributed equally to the conception, design, data analysis, and writing of this manuscript. Both authors reviewed and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eFunding for open access charge: Universidad de M\u0026aacute;laga / CBUA\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the results of this study are secondary data obtained from the Institute of Statistics and Cartography of Andalusia (IECA). 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Gender disparities in multimodal travel attitudes, behavior, and satisfaction. \u003cem\u003eTransportation Research Part D: Transport and Environment\u003c/em\u003e, \u003cem\u003e123\u003c/em\u003e, 103917. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.trd.2023.103917\u003c/span\u003e\u003cspan address=\"10.1016/j.trd.2023.103917\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gender, Public transport, Modal choice, Care-related mobility, Binary logit models, Car dependency","lastPublishedDoi":"10.21203/rs.3.rs-8648400/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8648400/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGender differences in public transport use are key to understanding mobility inequalities in highly car-dependent urban contexts. This study investigates the determinants of public transport choice using gender-disaggregated binary logistic regression models, drawing on 6,032 motorized trips in a southern European region. The results show that the determinants of public transport use differ by gender, not as a single uniform effect but through differences in the relevance and intensity of explanatory factors. While age, educational attainment, and driving license availability are significant in both models, women\u0026rsquo;s modal choice is associated with a broader set of variables, particularly those related to household organization (living with a partner and household income) and trip purpose. Among men, by contrast, public transport use is more closely linked to individual and territorial characteristics; notably, residing in dispersed-settlement areas reduces the likelihood of use only in the male model. Overall, these findings suggest that the role of urban form varies with the functional organization of everyday mobility, with domestic constraints and trip purposes playing a stronger role in shaping women\u0026rsquo;s public transport demand. From a methodological perspective, the study shows that gender-disaggregated models allow us to identify explanatory structures that may be partially or totally hidden in aggregate approaches, providing relevant evidence for the analysis of equity in transportation and informing targeted planning measures to improve service fit with gendered travel needs across different urban forms.\u003c/p\u003e","manuscriptTitle":"Gender Differences in Public Transport Use in a Car-Dependent European Region: Evidence from Andalusia (Spain)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 16:45:51","doi":"10.21203/rs.3.rs-8648400/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6f4b16c9-b32d-41d5-a7c1-e1efca34bc68","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T13:16:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 16:45:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8648400","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8648400","identity":"rs-8648400","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}