Analysing gender equality in Barcelona through (spatiotemporal) segmentation

ScienceDirect Available online at www.sciencedirect.com Transportation Research Procedia 82 (2025) 530–546 2352-1465 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 16th World Conference on Transport Research 10.1016/j.trpro.2024.12.058 Keywords: Travel behaviour;fragmentation analysis; gender;life course, activity participation 1. Introduction The spatial and temporal distribution of housing and activities in the urban environment determine how people move to reach places in a timely and secure manner. However, other issues define how we get access different services, * Corresponding author. E-mail address: [email protected] 16th World Conference on Transport Research (WCTR 2023) Analysing gender equality in Barcelona through (spatiotemporal) segmentation Lídia Monteroa, Lucía Mejía-Dorantesb*, and Jaume Barcelóa aDepartment of Statistics and Operations Research Campus Nord, Universitat Politècnica de Catalunya (UPC) Barcelona Spain 08034 bConsultant. Karlsruhe, Germany, 76185 Abstract Activity participation is influenced by many factors, such as the ones related to the built environment, but also to individual attributes. Herein we explore sequences of daily activities and travel employing techniques from the sequencing of events in the life course of individuals. Studying sequences of daily episodes (each activity and each trip) allows us to study the entire trajectory of a person’s activity during a day while at the same time considering the number of activities, order of activities in a day, and their durations jointly. We applied this method to a sample of residents in the Metropolitan Area of Barcelona (RMB) in 2018, 2019, and 2020 Travel Surveys. We have focused on fragmentation analysis in activity participation, especially concerning gender, age, activity, and transportation mode. As expected, the survey from 2020 deserves a particular approach since activity patterns vary compared to surveys before the COVID-19 spread outbreak. In this respect, active transport shows to be particularly important that year. Overall, the results show that activity participation cannot be disentangled from gender, individual life-course, and the built environment. © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 16th World Conference on Transport Research Lídia Montero et al. / Transportation Research Procedia 82 (2025) 530–546 531 for example, the transport systems available in the area, along with the topographic and weather characteristics of the site. Furthermore, the intrinsic attributes of the person who needs to access different places, such as the socioeconomic level, profession, gender, disabilities, family status, religion, ethnicity, among others, play a key role in shaping mobility choices. In this regard, different studies observe that lifestyles differ notably in terms of gender, which influences mobility patterns (EIGE, 2019; Soto Villagrán and Mejía Dorantes, 2022). Studies show that working schedules depend on gender (Hyde et al., 2020). This is translated into women having more complicated commuter patterns than men, based on shorter, chained trips (Cresswell and Uteng, 2008; EIGE, 2022),and preferring certain transport modes (Cubells et al., 2020; Hanson, 2010). Even if after the COVID-19 pandemic outbreak, we have seen a remarkable increase in ICTs in daily activities, as many statistics show in Europe (Eurostat, 2020)and in our specific case study (Instituto Nacional de Estadística, 2021). However, as Fana et al. (2020) observe, generally people that work from home (WfH) tend to be high-skilled workers with better wages and contract conditions. Notwithstanding this increase in ICT in daily activities, many activities that need to be conducted physically, which cannot be transferred to virtual services: visiting schools, doctors, and escorting activities. Furthermore, other activities are preferably carried out personally, for example those that make us feel part of a community or society, that contribute to people’s well-being. In all these cases, citizens depend on the transport alternatives available. In this study, we carry out a sequence analysis to measure the fragmentation in activity participation in a Transport Oriented Development (TOD) area to discuss the similarities or differences between results. This paper aims to gain a better understanding of the spatiotemporal patterns and travel behavior in Barcelona, analyze gendered mobility patterns, and discuss which characteristics play a significant role in shaping mobility. Our approach is based on sequence analysis, making use of three recent and consecutive mobility surveys in the Metropolitan Region of Barcelona (RMB). This paper is divided into 7 sections and organized as follows. After this introduction, we present the literature review. Afterward, we present the case study, datasets, and methodological approach. Section 5 presents the results. Finally, in the last section we present the discussion,main conclusions and implications of this study. 2. Literature review A sequence is a list of events and actions performed in an ordered manner. As Abbott and Forrest (Abbott and Forrest, 1986)describe, a sequence dataset may describe two different patterns. One may describe activities that occur only once and the other may describe activities that occur several times in a certain sequence. Whereas the first idea may be solved with permutation, the second analyzes recurrent events. Many authors in the sociology field have studied life course events such as marriage, childbearing, and employment through activity sequence analysis (Elzinga and Liefbroer, 2007a; Giele and Elder, 1998). This analysis has been recently applied to mobility behaviour (Ahmed et al., 2021; Shi et al., 2022). In this respect, Bhat and Pnjari (Bhat and Pinjari, 2007)observe that sequences of activities and the daily transitioning from one activity to another as well as the amount of time spent in each activity represent an important direction of travel behavior analysis. The analysis of activities’ sequences and travel is critical in formulating econometric models embedded in activitybased daily simulations of household activity-travel patterns for large-scale travel demand analysis as many authors observe (Bhat et al., 2013; Burchell et al., 2020; Paleti et al., 2017; Rasouli and Timmermans, 2014). Authors, such as Studer and Ritschard (Studer and Ritschard, 2016), identify the following important intertwined characteristics: •Experienced states: the distinct alternatives present in the sequence •Distribution: the total time or state distribution within a sequence •Timing: the moment in which each state appears •Duration: the period of time in the different successive states •Sequencing: the order in which the distinct successive states take place 532 Lídia Montero et al. / Transportation Research Procedia 82 (2025) 530–546 On the other hand, the fragmentation concept has been many times addressed (Alexander et al., 2010; Couclelis, 2006; Hubers et al., 2008; E. McBride et al., 2020). They define the fragmentation of travel and activities as the sequence of many short trips that take place during the daily schedule of a person. Whereas temporal fragmentation is related to the different times that activities are carried out, spatial fragmentation is related to the locations where activities are performed. Together with other activities and movements that occur in a larger frametime, they build up a string of activities with different durations and purposes. This string may have different complexities depending on different extrinsic and intrinsic characteristics of the individual and the urban environment which shape mobility behaviour. Furthermore, despite not explaining how and why individuals engage in activity-travel fragmentation, the classification of activity-travel fragmentation into clusters makes it possible to understand different groups. For instance, it allows us, to establish the relationship with the socioeconomic characteristics of the segments. Some authors, such as McBride et al. (2020), have pointed out the need to understand gender roles that it is considered to be related to time allocation to activities and thus, activity-travel. Additionally, some researchers observe gender-differentiated patterns in segmentation analyses. For example, Leszczyc and Timmermans (2002) analyze the Dutch diary and concluded that gender and age are important determinants of moving from one activity type to another. Burchell et al., (2020) analyze the gender differences in the segmentation of workplace patterns using the 2015 European Working Conditions Survey (EWCS, 2015), which presents information before the pandemic. The authors observe that there are clear differences when analyzing gender, for example, women were more likely than men to work at the employer’s offices. On the other hand, von Behren et al. (2020) carried out an image-based clustering analysis of the individuals’ pattern segmentation using the German Mobility Panel of activity (BMDV, 2020). The authors identify two clusters with children in the household, one is predominantly characterised by women and part-time workers. In the next sections, we apply this theoretic approach to analyze the gendered mobility patterns in the Metropolitan Region of Barcelona. To our knowledge, this is the first attempt to apply this technique in the Spanish context, furthermore by making use of a longitudinal dataset. 3. Case Study We apply this theory in the Metropolitan Region of Barcelona (RMB). It is divided into 36 municipalities in the Metropolitan Area of Barcelona (AMB), which accounts for more than 3.2 Million inhabitants. It has a well-scattered public transportation network with more than 200 bus lines, 10 metro lines, 15 railways lines, and two tramway lines. More than 9 million trips are made every day. The rest of the RMB area consists of 164 municipalities and 1,848,514 inhabitants. A detailed travel demand modelling is regularly conducted in the AMB subarea .A case of special interest is the Primary Crown of the Metropolitan area that includes the 18 most populated municipalities. More information on the Metropolitan Region of Barcelona may be found in (Mejía-Dorantes et al., 2021). 4. Methodology and Data Description The entire daily sequences of activities and travel patterns are quantified by four indicators, defined in McBride et al. (2019, 2020). •Normalized Entropy, which explores the variety in daily schedules •Turbulence, which shows the complexity in daily schedules, and •Complexity, a normalized [0,1] score based on entropy, and different sequences of the individual’s schedule •Travel Time Ratio (TTR): the total travel time in a day divided by the sum of the total time outside the home plus the total travel time in a day As explained by McBride et al. (2020), these summary indicators are correlated to each other. They quantify daily activity-travel patterns for each individual in a numerical way. Lídia Montero et al. / Transportation Research Procedia 82 (2025) 530–546 533 The statistical analysis is carried out by the TraMineR package in R (Alexis Gabadinho et al., 2011). It has been widely used for the analysis of biographical longitudinal data in social sciences, but other approaches have been also described. 4.1. Methodology A sequence is defined as a series of time points at which a subject can move from one discrete ‘‘state’’ to another. People with many states in their daily schedule have fragmented schedules. In this research we used sequence analysis to statistically analyze the fragmentation of respondents’ days using a minute-by-minute time series, in which every minute of the day contains a specific state for each person in the study. These “states” are the places which individuals visit during their diary day. Activities initially considered are: home (H); work (W); casual (C) for not frequently visited places; other (O) for frequently visited places that are not the working place; and travel (T). Among the techniques in the travel behavior field that can be used to measure the duration of activities and transition rates from one activity to the next, we make use of entropy, turbulence and complexity. Entropy is the proportion of total time spent in each state and the number of state transitions is not taken into account (A Gabadinho et al., 2011): ℎ(𝑥𝑥)=ℎ(𝜋𝜋1…𝜋𝜋𝑆𝑆)=−∑𝜋𝜋𝑖𝑖𝑙𝑙𝑙𝑙𝑙𝑙(𝜋𝜋𝑖𝑖) 𝑆𝑆 𝑖𝑖=1 (1) Where 𝜋𝜋𝑖𝑖is the proportion of occurrences of the ith state in the considered sequence. S is the number of potential states, and 𝒙𝒙is the sequence defined from minute to minute day activities. The proportion of minutes allocated to each state during a day defines the entropy indicator. For this measure the number changes of state is irrelevant. If a person has no state change during the entire day, his/her entropy would be 0. In contrast, having several states makes entropy to increase. The range of possible values depends on the number of states and the maximum is allocated at sequences showing equal amount of time in each state. In our case maximum entropy is 1.61. A normalized entropy score is often used consisting on dividing entropy into maximum entropy, thus a 0 to 1 range is obtained. The sequence turbulence is a measure proposed by Elzinga and Liefbroer (2007b) for measuring schedule complexity in daily activies. It is based on sequence permanence and uses the number of distinct subsequences that can be extracted from the distinct state sequence and the variance of consecutive time points spent in a distinct sta te. For a sequence 𝒙𝒙, the formula for 𝑇𝑇(𝒙𝒙)turbulence, as stated by McBride et al. (2019) as follows: 𝑇𝑇(𝑥𝑥)=𝑙𝑙𝑙𝑙𝑙𝑙2(𝜙𝜙(𝑥𝑥)𝑠𝑠𝑚𝑚𝑚𝑚𝑚𝑚 2+1 𝑠𝑠2+1 ) (2) Where 𝜙𝜙(𝒙𝒙)is the of distinct subsequences that can be extracted from the distinct state sequence accounting on time precedence. 𝑠𝑠2is the variance for the state duration 𝑠𝑠𝑚𝑚𝑚𝑚𝑚𝑚 2is the maximum variance to be given based on the duration of the sequence and it is computed as 𝑠𝑠𝑚𝑚𝑚𝑚𝑚𝑚 2=(𝑛𝑛−1)(1−𝑡𝑡)2, where n-1 is the number of transitions in the sequence and 𝑡𝑡is the sequence duration divided by the number of distinct states in the sequence. 𝐶𝐶(𝒙𝒙)=√(𝑛𝑛𝑡𝑡(𝒙𝒙) (𝑙𝑙(𝒙𝒙)−1)ℎ(𝒙𝒙) ℎ𝑚𝑚𝑚𝑚𝑚𝑚) (3) 534 Lídia Montero et al. / Transportation Research Procedia 82 (2025) 530–546 Complexity C(x) includes 𝑛𝑛𝑛𝑛(𝒙𝒙) as the number of distinct transitions within a sequence, 𝑙𝑙(𝒙𝒙) is the length of the sequence, ℎ(𝒙𝒙) is the entropy indicator and ℎ 𝑚𝑚𝑚𝑚𝑚𝑚is the maximum entropy in the sample. This indicator will have a value between 0 and 1, with zero corresponding to entropy zero and no transitions (e.g., staying at a single place for the entire day of observation). The travel time ratio (TTR), defined by McBride et al. (2020) as a compact indicator that represents the trade-offs of people between travel and activity time. In this paper, TTR is defined as ‘the total travel time in a day divided by the sum of the total time at home plus the total travel time in a day’. Thus, TTR ranges from 0.5 (no trip-makers) to 1.0 (whole day out of home). To achieve a better understanding of the behaviour of the turbulence and its dependency on individual features like gender, age group, activity, handicapped status,WfH possibility, day of the week and macrozone in the area of Barcelona, along with different descriptive variables including the percentage of households over 100m2, number of bus stops, number of metro, tram and train stops, a linear model of their logarithms has been applied to the 2018-2020 subsample of trip makers. Later we compare all sequences with each other to address sequence dissimilarity and compute pairwise mean fragmentation indicators by year using Tukey’s Honestly Significant Difference (HSD) Test (Tukey et al., 1984). Finally, we use a clustering technique to group sequences of activities with similar dissimilarity scores. The final number of clusters is optimized to represent the data using a criterion of within-group similarity and across groups dissimilarity. 4.2. Data description The methodology described in Section 4.1 has been applied to the data from EMEF (Weekday Mobility Survey, in Catalan) surveys of 2018, 2019, and 2020 to define the sequences of visited places by a person during a day jointly with the duration of activities at each place and the travel times to reach these places. They are traditional mobility surveys that analyze the mobility of residents in the Metropolitan Region of Barcelona (RMB) for individuals aged 16 and over. The spatial granularity is at the municipality level, but as Barcelona is divided into ten districts, it leads to a total of 296 macro-zones, where only 45 of them are in the AMB area. The EMEF 2020 survey was launched during the Fall of 2020 when some mobility restrictions and the prevalence of some online activities were still present due to the COVID-19 situation. The EMEF2020 deserves a particular analysis since activity patterns are expected to vary compared to the years before the COVID-19 outbreak. The data collected for each journey refers to trips for the day before: origin and destination (macro-zones), purpose, mode (a very detailed list of possibilities), travel start time and duration (min), vehicle use, parking use, etc. The sample units are individual residents, not households. The sample size for each year after removing the category “professional drivers” are: 9,930; 9,934; and 10,024, respectively for 2018 to 2020 in the AMB area. The total trips are 36,368; 37,463; and, 30,591, respectively for 2018 to 2020 in the RMB area. After filtering (professional travellers, residential area missings, etc.), the final number of residents included in the total sample was found to be 26,860. We also make use of demographic and land use data.This information is defined with the same spatial granularity defined by the EMEF surveys. It consists on population segmented by gender (only men and women), age group (5 groups), education level, educational places, services, land use, residential morphology, average per capita rent, and number of stops in the public network. 5. Results A first analysis was performed by simplifying the kind of activities that people carry out, or places that people visit. The activities initially considered are home (H); work (W); casual (C) for not frequently visited places; other (O) for frequently visited places that are not the working place; and travel (T). The next Fig. 1shows as an example some sequences of activities and durations for the 3 first elements of our sample and some defined scores. The first two patterns belong to male persons coming from a place out of Barcelona, who live in the first district of Barcelona city. They have neither a car, nor a motorbike.The third pattern belongs to a student who normally uses Lídia Montero et al. / Transportation Research Procedia 82 (2025) 530–546 535 public transport or walks. The activity set is defined as the activity alphabet and, herein, for illustration purposes, set up as casual (C), home (H), other (O), travel (T), and work (W). Fig. 1. Graphical representation of daily-travel pattern for 3 units of the working sample. For ilustration purposes, the activity distribution across the day in a subset of the working sample is shown in Fig. 2. An interpretation of this figure indicates at 13:00h, 3% of the sample units show a recurrent activity, 32% stays at home, 14% are involved in other activity, 9% is traveling, and the rest, 58%, is working. Fig. 2. Activity Distribution by time. Fig. 3shows normalized entropy, turbulence, and complexity distributions useful for the generation of synthetic 536 Lídia Montero et al. / Transportation Research Procedia 82 (2025) 530–546 populations (anonymized population that is representative of real population numbers), where some questions arise. For example, why has the normalized entropy a maximum about 0.3-0.4 corresponding to daily pattern (H-T-W-T-HT-O-T-H 450-10-740-10-110-20-280-40), and the 0 value corresponds to non-trip-makers, the same high bar at zero is seen for turbulence and complexity histograms. Fig. 3Normalized entropy, Turbulence and Complexity distributions. EMEF 2018 to 2020 data. To achieve a better understanding of the behaviour of the turbulence and its dependency on the individual features, a linear model of their logarithms has been applied to the 2018-2020 EMEF subsample of trip makers. These individual characteristics include gender, age group, activity, disability status, WfH possibility (with regards to year 2020), day of the week, and macro-zone along with descriptive variables such as percentage of households over 100 m2, number of bus stops, number of metros, tram, and train stations. The marginal effect of the variables having significant neteffects is shown in Fig. 4The results show that the fragmentation of female out-of-home activities is significantly greater than those for men in the 30-44 age group. A remarkable effect of year 2020 on the logarithm of the turbulence is seen once controlled by the other considered factors. Fig. 4. Turbulence transformation marginal effect for 2018-2020 EMEF (only trip makers). Lídia Montero et al. / Transportation Research Procedia 82 (2025) 530–546 537 Furthermore, Fig. 5shows the logarithm of the turbulence according to gender, age group, and year, once education, birthplace, disability status, and WfH possibilities are accounted for in the model. Women in the 30-44 age group show the highest turbulence in any year; while men turbulences seem stable between age 16 to 64 groups in 2018 and 2019, the youngest groups, either men’s or women are affected by a severe decrease in fragmentation in 2020. In general terms, the 2020 turbulences are reduced after the COVID-19 breakout. Fig. 5. Marginal effect of the turbulence transformation with regards to year, gender, and age-group interaction in the total 2018-2020 EMEF dataset (only trip makers) after controlling by education, origin country, handicapped and WfH effects. 538 Lídia Montero et al. / Transportation Research Procedia 82 (2025) 530–546 Fig. 6. Normalized Entropy marginal effects (upper figure) and Travel Time Rate (TTR, lower figure) for gender and age-group and year interaction in 2018-2020 EMEF subsample of trip-makers after controlling by year, education, activity, country of origin, disability, and workingfrom-home effects. Given the nature of the results and the no justification of any normality hypothesis, further analysis to get insights on their behaviour were conducted resorting to nonparametric statistical methods, as for example Kruskal-Wallis test for homogeneity in means across groups. The Tukey HSD test has been employed to address pairwise comparisons between means in groups. Considering the (normalized) entropy indicator, the gross effect of gender is significant according to KruskalWallis non-parametric test for means depending on year at 95% confidence level. The male entropy is greater than the female entropy being the highest value at the youngest age group (see Fig. 6). 5.1. Sequence analysis with a new alphabet of activities Aredefined set of activities based on the types of places individuals visit during their diary day were used for further analyses: Home (H), Work (W), School (only for students, S), Casual (for not frequently visited places, C), and Others (frequently visited places that are not the working place, O). Furthermore, the alphabet should and must be extended and refined since activities like Escorting/Accompanying (A) can be considered, as well as the differentiation of activities by travel mode since their data are available. Travel activities are divided into TW for active modes (walking, cycling), TC for private transport (car, van, motorcycle), TP for public transport (bus, metro, tram, and train), and TM for other transport modes, such as van and truck. An alphabet of 10 activities is considered. Either trip makers or non-trip makers are included and a sequence of 1440 minutes (or more) with each minute classified as a category of the alphabet. Fig. 7shows the refined daily activity pattern distribution across years. The 2020 state activity pattern distribution shows an overrepresentation of whole day home activities. The percentages of activity distribution across the day for men and women and by year are shown in Table 1. Work activities out of home are greater for men than women, school activity is similar in both cases. On the other hand, home activity in 2020 (73.5%) increased by almost 13 points compared to previous years, while school and work activities out of home are clearly reduced. The percentage of private transport use is severely affected by residential area (analysed here as crowns), showing that in non-central crowns there is an increase in private transport activity (3.4%) compared to Barcelona city (1.5%); consequently, public transport in the central crown as Barcelona city shows 2.8% incidence, while external crowns lie between 1.2-1.4% incidence. The spatial effects must be addressed in activity analysis (Table 1). Lídia Montero et al. / Transportation Research Procedia 82 (2025) 530–546 545 In this paper an activity analysis across days in the RMB area has been addressed. Focusing on fragmentation indicators and the potential connection to modal preferences in one of the greatest European cities such as Barcelona is notably relevant. Our research analysis is focused in the Metropolitan Area of Barcelona, where the spatial and temporal behaviour of transport demand is rather different from other case studies using the same methodology in terms of the underlying socioeconomic reality, the transport-oriented development (TOD) which can be seen through the rich public transport network, the urban structure, and different activity alternatives and services. A sequence analysis is used in this paper to measure fragmentation in activity participation and travel. Studying sequences of daily activities (each activity at a place and each trip) includes the entire trajectory of a person’s activity during a day while jointly considering the number of activities and trips, their ordering, and their durations. The complexity of the data resulting from fragmentation in that case led us to resort it to make use of an OD dimensionality reduction and clustering techniques to conduct the analysis. The clustering analysis shows clear differences between clusters overrepresented by women and by men. Furthermore, as it has already been highlighted in Table 1, activity distributions by year, gender and geographic residential area show significative differences, as for instance the work activity proportion in men is higher than in women. Furthermore, the percentage of private transport use is notably influenced by residential area, showing that in non-central crowns there is an increase of private transport activity compared to Barcelona city. On the other side, public transport in the central crown as Barcelona city shows a higher incidence, compared to the external crowns. This study makes evident, that it is impossible to disentangle the role of sustainable transport with the built environment and dwelling location. This study has also revealed some behaviors that to be properly understood require a deeper analysis, and likely more detailed data not available in this case. 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