Gender differences in commute time and accessibility in Sofia, Bulgaria: a study using 3D geovisualisation

Authors


  • The information, practices and views in this article are those of the author(s) and do not necessarily reflect the opinion of the Royal Geographical Society (with IBG).

Abstract

Much research on human mobility patterns and accessibility to date has been conducted largely in Western European and North American countries, where the private vehicle is the main means for commuting. As a result, most studies focused largely on car-based mobility (automobility) and accessibility, and relatively little is known about countries in other regions of the world. Based on an activity-travel dataset collected in Sofia, Bulgaria and using 3D geovisualisation, this study attempts to fill this gap through examining gender differences in commute time and potential access to urban opportunities. The results reveal important gender differences in commute time and individual accessibility. Among the surveyed participants, women tend to spend more time on their commute trips and have more restrictive spatial reach to urban opportunities compared with men, largely as a result of their reliance on public transit as their primary mode of transport. Further, women have lower accessibility compared with men who used the same travel mode. This case study adds important new knowledge about a geographical area that has been under-studied by Anglophone geographers. It also shows that GIS-based geovisualisation and analysis are powerful tools for uncovering gender differences in the geographical distribution of commute time, which conventional quantitative methods cannot reveal.

Introduction

Advances in geospatial methods and improved means for collecting detailed space–time data in the past two decades have opened many new opportunities for studying and understanding human mobility. The increased availability of individual activity-travel data and the functionalities of contemporary GIS software make it possible to represent and analyse human spatial behaviour in unprecedented ways. The use of GIS methods also allows the incorporation of large amounts of geographical data that are essential for any meaningful analysis of human mobility (Kwan 2000, 2004b; Shen et al. 2013). Similarly, accessibility has been studied through various analytical perspectives and has also benefited enormously from the advances in geospatial methods and readily available GIS data of urban areas (e.g. Kwan 1998 2013; Weber and Kwan 2002; Neutens et al. 2010; Wang 2012). However, much of this recent research on human mobility and accessibility has been conducted largely in countries where the private vehicle is the main means for commuting and performing out-of-home activities for most people (e.g. Western European and North American countries). As a result, most studies to date focused largely on car-based mobility (automobility) and accessibility, and relatively little is known about countries in other regions of the world.

Public transit, being dependent on schedules and predetermined routes and stops, is a highly complex phenomenon. Researchers from different domains have analysed transit systems along with their social, economic and environmental impact for serviced territories (Donaghy et al. 2005). Increasing use of public transit and reducing auto trips are often considered a significant step towards more sustainable urban environments (e.g. Newman and Kenworthy 1999; Crane and Scweitzer 2003; Hanson 2010). These sustainable transport strategies are often associated with the notion of new urbanism, which promotes the use of public transit through urban design (e.g. mixed land use, and more compact and walkable neighbourhoods served by transit). Transit-oriented development thus has become an important element of the sustainable development planning process (Deakin 2001). While the growing ridership of public transit observed during the past 15 years in Europe and North America indicates the increasing role of transit, for most urban areas it is still far from a truly convenient means for accessing urban opportunities compared with driving. Differences in the accessibility provided by cars and public transit have significant social implications, as disadvantaged social groups and people who cannot afford to own (or have no access to) cars often have no choice but to use public transit, and thus became captive of a less effective mode of transport in their daily lives (Rutherford and Wekerle 1988).

This study focuses on transit use and accessibility in a city in Central and Eastern Europe (CEE). It examines gender differences in commute trips and potential access to urban opportunities in Sofia, Bulgaria. It investigates the differential gender effects of the modal shift from public to private transport as the city went through various transitional reforms since the early 1990s. The study analyses the multimodal accessibility and travel time of the public transit system (e.g. bus, subway and tram) in the study area using a GIS-based 3D geovisualisation method. Because public transit services follow specific schedules and have predetermined routes and stops, we developed a method to represent, visualise and analyse transit-based travel time and accessibility within a GIS using data collected from 80 individuals in the study area. The results show that transit users spent more time on their trips and experience lower levels of accessibility to urban opportunities compared with car users. Women have lower accessibility compared with men who used the same travel mode. While these empirical findings are not entirely surprising, this case study adds important new knowledge about a geographical area that has been under-studied by Anglophone geographers and others. The study also shows that GIS-based geovisualisation and analysis are helpful for uncovering the social implications of the significant modal shift in CEE countries in the past two decades or so.

Modal shift in Central and Eastern European countries: purpose and context of study

The context for this study is the significant modal shift from public to private transport in CEE countries in the post-communist/socialist era. These countries include Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovenia, Slovakia and several others. Before the transition to market economies, cities in these countries offered frequent multimodal public transit services (e.g. bus, tram and railway) at very low fares. These mass transit systems were state owned and heavily subsidised by their state or municipal governments. Urban residents largely relied on public transport as the main means of commuting and performing their daily activities (Suchorzewski 2005). The share of public transit use of all daily motorised trips made by residents in the major cities of these CEE countries was about 85% in the early 2000s (Suchorzewski 2005).

Ownership and use of private vehicles in these countries remained low until economic and administrative reforms began around the late 1980s and early 1990s. As part of the reform measures, public transport systems were privatised and commercial road transport and private vehicle markets were deregulated. These changes resulted in considerable rise in public transit fares, service cutbacks, rapid increase in the ownership and use of private cars, and a significant shift from public to private transport modes in these countries – developments that also happened in North American and Western European countries previously (Pucher 1995). In Poland, for instance, the share of urban public transport declined from 93% in the late 1970s to about 60% in the early 2000s (Suchorzewski 2005). The rapid increase in car use in turn led to significant deterioration in traffic conditions and environmental problems in the major cities in CEE.

Since these changes affect the daily lives of men and women differently, their social implications call for careful investigation. Decades of research on gender differences in travel behaviour and mobility patterns in North American and Western European countries has shown that men on average travel further and use cars more often than women (e.g. Hanson and Hanson 1980; Pickup 1984; Johnston-Anumonwo 1992; Schwanen and Dijst 2003; Polk 2004; Rosenbloom 2006; Crane 2007). Further, women tend to make shorter work trips, use public transit more, make more household-serving trips (especially shopping trips and child chauffeuring), and drive fewer miles per year than men (e.g. Hanson and Johnston 1985; Wachs 1987; Blumen and Kellerman 1990; Hanson and Pratt 1991; Johnston-Anumonwo 1997; Schwanen 2007). These gender differences in mobility and activity patterns were strongly associated with women's increasing participation in paid employment, their gender roles in the domestic sphere, and their needs to ‘juggle’ their employment and household responsibilities (Hanson and Hanson 1980; Rutherford and Wekerle 1988; McLafferty and Preston 1991; Kwan 1999 2000; Schwanen et al. 2008, 2014; Scheiner and Holz-Rau 2012).

While gendered mobility and gender differences in activity-travel patterns have been examined for decades in North American and Western European countries (e.g. Hanson and Hanson 1980; Hanson and Johnston 1985; Solá and Vilhelmson 2012), gender differences in travel behaviour are rarely studied in CEE countries. In typical travel studies in these countries, gender is rarely used as an explanatory variable even when several other categories of travellers are considered (Suchorzewski 2005). Further, very few studies on human mobility patterns, transit use or accessibility in CEE countries have been published in English to date. As a result, little is known about gender differences in trip patterns and activity-travel behaviour in CEE countries outside these countries themselves. This study thus seeks to contribute to filling this gap through examining gender differences in commute trips and potential access to urban opportunities in Sofia, Bulgaria. It focuses on the differential gender effects of the modal shift from public to private transport as the city went through various transitional reforms since the early 1990s.

As past studies have shown, GIS-based 3D geovisualisation is a particularly powerful tool for exploring the mobility patterns of different social groups (e.g. Kwan 2000 2004b; Ren and Kwan 2007; Yu and Shaw 2008; Lee and Kwan 2011; Wang et al. 2012; Shen et al. 2013). Individual movement in space–time is a complex trajectory with many interacting dimensions, including the location, timing, duration, sequence, as well as type and purpose of activities and/or trips. This characteristic of activity-travel patterns has made the simultaneous analysis of its many dimensions difficult (Burnett and Hanson 1982). Further, no conventional method is capable of incorporating the large amount of geospatial data necessary to contextualise and interpret these patterns adequately in their specific geographical contexts (Kwan 2000). 3D geovisualisation enables researchers to identify distinctive characteristics of the mobility patterns of different social groups that may be very difficult or impossible to observe using other methods (Kwan 2002). It is thus particularly useful for studying gender differences in commute trips and potential accessibility to urban opportunities in the city of Sofia1.

While previous studies have used 3D geovisualisation to examine the effect of social difference on individual mobility, they focused largely on car-based activity-travel patterns and accessibility. Analysing transit-based mobility patterns poses specific challenges, since transit use not only involves transit networks and the associated spatiotemporal distribution of services (e.g. bus schedules), it often involves the use of different travel modes (e.g. subway, bus and tram), including walking to and from public transit stations. Few studies to date have explored this kind of multimodal transit-based mobility patterns or accessibility using 3D geovisualisation [e.g. Shen et al. (2013) examined the intra-personal day-to-day variability of commuting behaviour using a 7-day GPS dataset and 3D geovisualisation]. Further, while GIS-based studies of public transit services have been conducted from various perspectives to examine their effectiveness and quality [e.g. Gleason (1975) on bus stop allocation optimisation, and Hsiao et al. (1997), Biba et al. (2010) and Foda and Osman (2010) on pedestrian access to transit], how to represent the complex characteristics of a multimodal public transit system remains a challenge for transport geographers (Kwan et al. 2003). This study also seeks to address these challenges in the study of transit-based mobility patterns. Further, through examining different characteristics of commute trips and accessibility of car and transit users in the city of Sofia using 3D geovisualisation, the study intends to enhance our understanding of human mobility in an EEC country.

Study area

The study area for this research is Sofia, the capital city of Bulgaria (Figure 1). It was chosen for reasons of data availability and our expectation that there are significant gender differences in accessibility in the city. Sofia is a compact monocentric city with an area of 492 km2 and 1.5 million inhabitants. It is the largest city in Bulgaria and the 15th largest city in the EU. Most commercial and cultural activities (e.g. major universities, cultural institutions and businesses) in Bulgaria are concentrated in Sofia. The unemployment rate in the city is lower than in other parts of Bulgaria. According to the 2011 census, Sofia's population was made up of 96.4% ethnic Bulgarians. Among minority populations, about 1.6% were officially identified as Roma, 0.6% as Turkish and 1.4% belonged to other ethnic groups or did not self-declare.

Figure 1.

The study area: Sofia, Bulgaria

Sofia and its surrounding areas expanded rapidly and became the most heavily industrialised region of Bulgaria during the post-World War II era of industrialisation under socialism. The city experienced significant influx of workers from other parts of the country. In 1989 Bulgaria began to transform from a socialist state to a market-based economy. Sofia has since then witnessed tremendous and unrestrained development in high-density business districts and residential neighbourhoods, which continues to the present day. As ownership of private vehicle grew rapidly in the 1990s, traffic condition in Sofia began to deteriorate and is an increasingly pressing problem in recent years. It has resulted in persistently lower travel speeds and more serious congestion, accompanied by all the associated negative social, economic and environmental consequences.

The deteriorating traffic conditions in Sofia originated largely in its history of urban planning and regulation of the private vehicle market. Planning during the centrally planned regime prior to 1989 had ignored the traffic implications of different land-use mix and the rapid expansion of private vehicles as a means for transportation in the post-socialist period. Instead of creating mixed land-use zones and bringing jobs and housing closer together, it created high-density, mono-function commercial and residential zones in Sofia. As a result the majority of the population in the city is now concentrated in neighbourhoods with over seven/eight-story residential buildings. The functionally specialised land-use zones and the spatial separation between the commercial and residential zones led to the need for long commuting and dramatic increase in traffic.

Another reason for the increased congestion is the deregulated market of private vehicles in Bulgaria, which now allows families to buy cheap second-hand cars from other EU countries (while they had to wait for 10 or more years to qualify to buy a car a decade ago). Indeed the largest market of second-hand cars in Eastern Europe is now located in Gorulbiane, Sofia, which had over 50 000 cars on sale prior to the financial crisis of 2007–10. Affordability of vehicles and gas as well as a low-occupancy rate has led to a dramatic increase in the number of private vehicles, which use infrastructure that was not at all planned for that great number of cars today. Private vehicle ownership in the city has grown rapidly in the 1990s. More than 1 000 000 cars were registered in Sofia after 2002 and the city has the fourth highest number of cars per capita in the EU (0.55 vehicles per person).

However, Sofia has a well developed and an extensive public transit system with bus, tram, trolleybus and subway lines running in all areas of the city. The Sofia Metro subway system began operation in 1998, which now has two lines and 27 stations. The construction of a third line is planned to start in 2014. This line will complete the proposed subway system of three lines with a total of 63 stations. As shown in Figure 2, a significant share of individuals (37.0 %) in Sofia use public transit as the primary means of commuting, while car users form the second largest group (23.3 %) of commuters. Surveys show significant differences in the gender and age distribution of commuters based on the main transport mode they use: the typical transit user is a woman (68%), aged between 45 and 65, while the majority of car users are men (64%) and in the age group 25–45 (Mott MacDonald 2009).

Figure 2.

Distribution of commuters by transport mode in Sofia

Source: Sofia Transit Usage Survey, July 2009

Little is known to date about the experiences of different population subgroups or different groups of transit users in Sofia. While Kotsev (2007) found significant disparities in accessibility between different parts of Sofia using place-based accessibility measures, there is no research that explores variations in individual accessibility in the study area to date. Kotsev (2007) observed that the different accessibility experiences of different population subgroups in the city are largely related to the current geographical distribution of population and characteristics of the infrastructure of the monocentric city. The gravity-based study by Kotsev (2007) was facilitated by the availability of data, but it could not identify the effects of finer social difference (such as gender and other socio-demographic characteristics) on individual accessibility. To fill this gap, this study examines gender differences in commute trips and potential access to urban opportunities in the city.

Data and method

This study uses and analyses various types of data that were integrated into a single GIS database. The primary individual activity-travel data, with detailed locational and address information necessary for the performance of time-geographic analysis and geovisualisation, were collected in 2011 from a stratified random sample of 80 subjects in the study area. These participants were selected based on stratification by gender and age, with a focus on even geographical coverage over the entire study area2. The sample consists of individuals working full time with a representative and equal mix of gender and primary transport mode. Socio-demographic characteristics of the individuals in the sample (Table 1) are similar to the characteristics of the commuters in the study area (compared with other surveys with much larger sample sizes for the study area; these other surveys were conducted largely for planning purposes and thus lack information on the space–time distribution of individual activities and trips). Overall, 63.4% of the women in the sample used public transit instead of cars as their primary mode of commuting, while 69.2% of the men in the sample used cars as their primary commuting mode (Table 1).

Table 1. Socio-demographic characteristics of sampled population
Sample characteristicNumber of respondents
TotalMenWomen
Gender803842
Age   
18–30351520
30–4520137
45–65251015
Education   
Secondary or lower24159
Higher562333
Income   
Low311318
Medium341717
High1587
Commuting mode   
Public transit381226
Car422715

Several GIS data layers were also used in the study. The first of these is a detailed street network layer that includes information on impedances and directional restrictions. This digital street network allows for the performance of cost-weighted routing that takes into account the effect of distance or travel time for car users. Another GIS data layer, provided by the Center for Urban Mobility in Sofia, is the routes and stations of the public transit system in the study area. It covers all public transit modes and includes a total of 2655 stations and 4442 links. On this layer, transit stations are represented as points with various attributes, such as frequency of service and duration of waiting between services. Other GIS data layers used in the study include layers of public facilities and social opportunities that serve a large number of inhabitants in the city (e.g. healthcare facilities, retail outlets and schools). These layers were created through geocoding using information available on the Sofia Municipality government website. The reason for using this web resource is to ensure that the information contained in these GIS layers is accurate and up-to-date (since it is updated regularly by the local government officials).

To represent participants' activity data in the GIS database, all individual activity locations were geocoded and verified through visual analysis of satellite imagery of the study area. These activity locations, as origins and destinations of trips, were geocoded in order to allow for the performance of network analyses and geovisualisation. In addition, this was performed in order to identify the exact location of the buildings to be used for finding the pedestrian route between a particular origin/destination location and the transit station used for a particular trip.

Travel routes that link individual activity locations (as trip origins and destinations) were identified with different methods depending on whether the trip was accomplished by using a car or by public transit. For car-based trips, a shortest path algorithm was used to find the least-cost routes between the origin and destination locations. Because data on congested travel time for the study area were not available from any source, we used free-flow travel time to compute the time needed for traversing these shortest routes for the car users in the sample (12 km/h for one-way streets, 15 km/h for two-way streets and 35 km/h for boulevards). While this could lead to an underestimation of actual travel time compared with using congested travel time (and a large difference between car-based and transit-based accessibility), we cross-validated the estimated travel time with the perceived travel time provided by respondents of the survey. Since there is only small difference between trip duration derived with the estimated shortest route and free-flow travel time and the perceived trip duration of surveyed respondents, the method used in the study to derive the travel routes between a particular pair of activity locations and car-based travel time yielded realistic results.

For transit-based trips, an online route planner created and supported by the Center for Urban Mobility in Sofia (http://www.tix.bg/bg/Sofia/) was used to identify the best route and travel time between a particular pair of origin and destination. All transit-based journeys were divided into separate segments based on the transport modes used (e.g. bus, tram and subway). A transit-based journey may consist of several segments and takes into account walking from the origin building to the origin stations, walking from the destination station to the destination building, as well as walking between transfer stations.

Since travel routes are linear features in a GIS, additional processing was performed in order to visualise the trip data of the survey subjects using 3D GIS. Each trip along a transit or car route was transformed into a set of points based on a temporal sampling of 1 min – which means that a point was extracted from the current location of the subject along the route every 1 min as the person traversed the travel route and as the journey unfolds in time. These points represent the locations along the journey sampled 1 min apart, beginning from the origin to the destination and taking into account the speeds of different travel modes or segments (e.g. 5 km/h for walking and 40 km/h for subway). For the trips made by the 80 study participants, a total of 4404 points were extracted. After these points were extracted, a non-parametric density estimation method called kernel estimation, as described in Silverman (1986), Gatrell (1994) and Kwan (2000), was used to generate density surfaces that represent the geographical distribution of journey times for a trip or a group of trips made by selected survey participants. The method was implemented through covering the study area by a grid structure with 2079 rows and 1598 columns of cells (and thus with a total of 3 322 242 cells with a pixel size of 10 m). Various density surfaces (e.g. for women or men participants) representing the geographical distribution of journal times were originally created with this grid structure and then visualised in 3D scenes using ArcScene of ArcGIS.

Figure 3 shows the density surface generated with this method based on the points sampled along the travel route of a single multimodal commute trip of one transit user. The journey proceeded from the lower-left peak to the top-right portion of the figure. The travel route consisted of several segments, including walking to a bus stop, waiting at the bus stop, bus transit, waiting at a subway station, subway transit, and walking from the destination subway station to the workplace. Points sampled along the route, shown as yellow dots in the figure, are unevenly distributed – as travel speeds for different transport modes vary significantly (e.g. points are sparsely located when travel speeds are higher). The two peaks in the figure represent locations where the subject was waiting at a bus stop and a subway station respectively (and thus was not moving over space).

Figure 3.

A multimodal density surface of a single transit user

Analysis and results

We first examine the average daily travel time (in min) of the 80 survey participants with respect to gender and primary travel mode. As shown in Table 2, car users spent less time on travelling than transit users when undertaking their daily activities. The average daily travel time for car users in the sample is 56 min, while that for transit users is 78 min. This car versus transit difference in average daily travel time is also observable within each gender group. The largest difference in daily travel time is between male car users (57 min) and female transit users (80 min). In terms of gender difference, the daily travel time for women (72 min) is longer than that of men (61 min). This is largely due to the fact that more women in the sample used transit instead of car as their primary travel mode (see Table 1). Indeed, there is much smaller gender difference within each of the travel modes. For instance, male and female car users had almost the same daily travel time (57 min versus 56 min).

We now turn to examine the density surfaces that represent the geographical distribution of journey time of survey participants. We performed interactive 3D geovisualisation of these surfaces using the ArcScene module of ArcGIS. As previous research has shown, 3D geovisualisation is useful for identifying distinctive human mobility patterns and differentiating the experiences of different population subgroups (e.g. Kwan 2000 2002 2004b; Ren and Kwan 2007; Lee and Kwan 2011; Shen et al. 2013). It is a helpful first step towards the analysis of potential inequalities among individuals and can provide useful results to inform later analyses that use sophisticated quantitative methods or models (e.g., structural equation models). It therefore can play an important role in any meaningful examination of accessibility in space–time. Since the largest difference in travel time is between male car users and female transit users (Table 2), the following analyses focus mainly on these two gender-travel mode subgroups in order to highlight the stark contrast between them.

Table 2. Distribution of the 80 surveyed participants by gender and primary transport mode
GenderTransitCarBoth travel modes
Number of respondentsAverage daily travel time (min)Number of respondentsAverage daily travel time (min)Number of respondentsAverage daily travel time (min)
Men127426573861
Women278015564272
Both genders (all subjects)397841568065

Figures 4 and 5 represent the geographical distributions of travel time for the women in the sample who commute using public transit and the men in the sample who commute by private vehicles. Commute trips in both directions (i.e. travel to work and from work) were included to generate these two surfaces. The home locations of the sampled participants are geographically dispersed in the densely populated residential areas where the majority of Sofia's population resides (Figure 1). Their work locations are concentrated largely in three areas, which also reflect the geographical distribution of job locations in the study area: the city centre, Business Park Sofia in the Mladost neighbourhood, and alongside Tsarigradsko Shose Boulevard in a huge area of newly built office buildings. The two figures capture the geographical distribution of journey time for the dominant transport mode of the two gender groups, where women rely mainly on public transit for their journey to work and men mainly use private vehicles to travel to work (see Table 2). Since these two figures use the same vertical scale (which represents the density of sampled points along subjects' travel routes), the higher peaks for the women who use public transit to travel to work compared with men mean that they spent more time en route as they commuted to or from work (Figure 4).

Figure 4.

Distribution of travel time of women with transit-based commute trips (looking northwest toward downtown Sofia)

Figure 5.

Distribution of travel time of men with car-based commute trips (looking northwest toward downtown Sofia)

As shown in Figures 4 and 5, a considerable portion of both transit and car-based commute trips pass through the city centre as a result of the monocentric structure of Sofia. The peaks in Figure 4 coincide with transit stops along the women's commute trips in both directions. The majority of them can easily be identified by an observer with knowledge of the study area as major transit stops, where transit users waited to continue their journey and/or to change their transport mode. This 3D scene effectively reveals that a significant portion of the time taken on transit-based commute trips was spent when waiting at transit stops, which was perhaps due to the poor synchronisation of schedules within the transit system in Sofia. The roads which were most traversed by the transit system as shown in Figure 4 represent the transport backbone of the city: Tsarigradsko Shose Boulevard, the route of the subway system, as well as other major roads (Alexander Malinov Boulevard, Botevgradsko Shose Boulevard, and Yanko Sakazov Boulevard) that provide access to major destinations in the study area.

However, travel time peaks for the male car users, as shown in Figure 5, are less pronounced compared with the peaks of female transit users. They are largely found at the Sofia roundabout, Nikola Mushanov Boulevard, Vardar Boulevard, Dragan Tsankov Boulevard and other major roads, which are shared by both car and transit lines. Overall, the spatial distribution of travel time for men has two distinctive features. Near the urban core and downtown area, it is geographically more concentrated (largely around the city centre and alongside Tsarigradsko Shoes Boulevard) compared with the pattern of the women. Second, in more peripheral areas, the spatial distribution of travel time for men who used private vehicles shows a wider areal coverage than the women who used public transit (as indicated by the minor undulations near the left, bottom and top edges of the area in Figure 5). Since both groups of participants have similar residential and work locations, this suggests that men spent more commuting time in only a few areas of the city (e.g. the more congested areas), while they generally spent less time on their commute trips and covered much wider areas compared with the female participants.

To identify differences in the spatial distribution of commute time between the female transit riders and male car users, two ‘difference surfaces’ were generated by using the map algebraic operator ‘minus’. To generate the first difference surface, the value in a cell in the output grid was obtained by subtracting the value of the corresponding cell in the surface for men from the value of the corresponding cell in the surface for women (brown surface in Figure 6). To generate the second difference surface, the value in a cell in the output grid was obtained by subtracting the value of the corresponding cell in the surface for women from the value of the corresponding cell in the surface for men (blue surface in Figure 6). The resulting difference surfaces are shown in Figure 6, where density peaks in brown indicate locations where female transit users spent more time than male car users, and density peaks in blue indicate locations where male car users spent more time than female transit users. The overall pattern suggests that women spent most of their travel time along transit routes and their reach for other locations in the study area is rather limited. The biggest difference between travel time for men and women is observed within the city centre, where surveyed transit users who were predominantly women spent considerably more time than car users in the sample who were predominantly men (despite the congested streets in downtown Sofia).

Figure 6.

Difference in commute time distribution between car and transit users (looking northwest toward downtown Sofia)

These observations corroborate the geographical patterns revealed in Figures 4 and 5, which show that men who were car users in the sample have much wider spatial reach compared with the women in the sample. This suggests higher levels of potential accessibility to urban opportunities for the men in the sample. The results also indicate that the female group not only faced more space–time constraints – they needed to spend more time on their commute trips and thus had less time for other activities in their daily lives – but also had limited access to public services because they predominantly used public transit.

This visual analysis of the travel time surfaces reveals important differences in travel time distribution and potential spatial reach between male car users and female transit users in the study area. To further examine the difference in potential accessibility between these two groups, we evaluated the level of potential accessibility for each participant in the sample based on the number of urban opportunities in the study area within reach given a person's primary transport mode (the potential spatial reach area) and a threshold distance. First, we constructed an individual-based pedestrian potential spatial reach area (PSRA) around each transit stop or station along the transit route of each transit user in the sample using a 400 m network buffer (about 0.25 mile and traversable by walking for approximately 5 min), which is the maximum distance an individual is willing to walk around transit stations (Hsiao et al. 1997). Similarly, a car-based potential spatial reach area (PSRA) was constructed for each car user in the sample using a 5 min network buffer along their daily commute routes, which was assumed to be the maximum distance an individual is willing to drive around his or her typical daily commute routes (Figure 7). Then we constructed a GIS layer for the 774 urban opportunities in the study area. These opportunities include various publicly available opportunities, such as schools, universities, banks, department stores, drugstores, healthcare facilities and public institutions. Finally, potential accessibility for each person in the sample was estimated by counting the opportunities that are located within his/her potential spatial reach area(s) based on the typical travel mode used. Analysis was performed on the sampled participants based on two primary transport modes (car versus transit) and two gender categories (men versus women; see Table 3).

Figure 7.

A car-based potential spatial reach area along a daily commute route

The results in Table 3 show a notable difference in individual accessibility to urban opportunities between car and transit users: car users have higher levels of accessibility than transit users. Car users can on average reach 57.1 urban opportunities in the study area while transit users can on average reach only 34.2 opportunities. This car versus transit difference is also observable for each gender group. Male car users in the sample can reach 60.4 urban opportunities while male transit users can reach 41.0 opportunities. However, female car users in the sample can reach 52.7 urban opportunities while female transit users can reach 31.3 opportunities. Thus, women have lower accessibility compared with men who used the same travel mode. As shown in Table 3, the largest difference in accessibility to urban opportunities is between male car users (60.4 opportunities) and female transit users (31.3 opportunities). Since the overall car versus transit difference in accessibility (57.1 − 34.2 = 22.9) is greater than the overall men versus women difference in accessibility (54.2 − 42.0 = 12.2), travel mode seems to have a greater effect than gender on a person's level of accessibility in the study. In light of the considerable amount of time needed for public transit to go from a specific origin to a specific destination in the study area, these results suggest that the low levels of accessibility experienced by the women in the sample were primarily due to their limited access to private vehicles.

Table 3. Potential accessibility to services by transport mode and gender
GenderTransitCarBoth travel modes
Number of respondentsAverage number of facilities within researchNumber of respondentsAverage number of facilities within researchNumber of respondentsAverage number of facilities within research
Men1241.02660.43854.2
Women2731.31552.74242.0
Both genders (all subjects)3934.24157.18048.1

Conclusion

Much research on human mobility and accessibility to date has been conducted in Western European and North American countries, where the private vehicle is the main means for commuting and performing out-of-home activities for most people. As a result, most studies focused largely on car-based mobility (automobility) and accessibility, and relatively little is known about countries in other regions of the world. Gender differences in travel behaviour are rarely studied in Central and Eastern European countries and few such studies have been published in English to date. Based on an activity-travel dataset collected in Sofia and using 3D geovisualisation, this study attempts to fill this gap through examining gender differences in commute trips and potential access to urban opportunities.

The results reveal important gender differences in the distribution of commute time and accessibility. Among the surveyed participants, women have lower accessibility compared with men who used the same travel mode. They tend to spend more time on their daily travel and commute trips and have more restrictive spatial reach to urban opportunities in the study area compared with men, largely as a result of their reliance on public transit as their primary mode of transport. While these findings are not entirely surprising, the study adds important new knowledge about a geographical area that has been under-studied by Anglophone geographers. It shows that mobility in Central and Eastern European countries can also be gendered, not unlike the situations in Western European and North American countries.

However, addressing these gender differences in mobility and accessibility to urban opportunities is highly challenging. Increasing the mobility of women through increasing their access to or use of private vehicles may worsen traffic congestion. Encouraging men to use public transit may reduce their mobility or accessibility. Suitable policy measures should thus be carefully examined in future research. For instance, for women in two-worker or multiple-worker households with a private vehicle, can their mobility be increased through sharing the car in the household for some of their trips? For women in households without a private vehicle, can their mobility be improved through participating in car-pooling (or multiple-occupant) programmes that enable them to commute by other households' private vehicles? Both of these measures would not significantly increase the number of private vehicles on the road. Their feasibility and effectiveness should be investigated in future research.

Acknowledgements

We thank the reviewers for their helpful comments. We are grateful to the experts of the Center for Urban Mobility in Sofia for providing the transit data. Alexander Kotsev would like to express his gratitude to Bob Begg, Anton Popov, Stoyan Nedkov, and the team of the Bulgarian Fulbright office for making possible his visit to the Ohio State University Department of Geography, where a significant portion of this research was conducted. This research was also supported by a grant (11 High-tech G11) from the Architecture and Urban Development Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government.

Notes

  1. 1

    While many had questioned the use of GIS methods in research on the geographies of social difference, there were attempts by feminist geographers to address these concerns through transcending the binarism between critical/feminist perspectives and GIS/quantitative methods (e.g. Kwan 2002 2004a; McLafferty 2002; Pavlovskaya 2002; Schuurman 2002). The use of 3D geovisualisation to study gender differences in commute time and accessibility in this research is informed by this new understanding of the relationships between critical/feminist geographies and GIS methods.

  2. 2

    Note that the study's small sample size precludes examination of socioeconomically marginalised subgroups that have small shares of the population (e.g. Roma who comprise only 1.6% of the population of Sofia).

Ancillary