Sustainable Public Transportation
The UESI Sustainable Public Transportation (SPT) category assesses the accessibility of a city’s public transportation system to commuters, by measuring how close public transport stops are to a city’s residents. We score cities on two indicators:
- Proximity to Public Transit (PPT): the proximity of a public transportation stop to where people live in an urban neighborhood. This indicator is represented as the mean distance required for residents to reach a public transit stop in each neighborhood.1
- Public Transportation Coverage (PTC): the ratio of area within walking distance to a public transportation stop for each neighborhood. PTC measures the ratio of neighborhood area within walking distance to a transit stop. Transportation planning guidelines frequently define walking distance as quarter-mile or 400-meter increments 2 3 4and assume that people will walk farther to access transit of higher speeds and greater reliability, regardless of the mode. However, we use mode here as a proxy for more general qualities of urban transportation. The PTC ratio is based on buffers with a radius of 420 meters (approximately 0.25 miles) for bus stops and 1.2 kilometers (approximately 0.75 miles) for train stops, to reflect this difference. 5
Description
Public transportation infrastructure (e.g., rail, mass transit, and bus) enables people to move across a landscape, connects residential areas with work and recreational opportunities, and is a central aspect of urban and regional planning. Well-developed public transportation systems have the potential to provide economic, environmental, and social benefits to the cities they serve7. Within cities, accessible public transport has the potential to reduce greenhouse gas (GHG) emissions as an alternative to personal automobile use, which, in 2024, accounted for 16% of US GHG emissions8. Public transport also contributes to social inclusion in communities9. Public transport, being the most affordable form of motorized transportation, is heavily relied upon by lower-income populations10. Accessible public transportation connects populations within a city, providing access to essential services, such as schools, grocery stores, health facilities, job sites, and recreational facilities, allowing all populations to participate in society11. Public transportation also encourages more active lifestyles for residents, providing tangible public health benefits to their cities12. But for these benefits to be realized, residents must be convinced to shift from private to public transportation, which can only happen on the condition that public transport is readily accessible and a viable mode of travel to residents.13 14Recognizing the importance of public transportation for both environmental and social inclusion goals, many city governments have set goals for public transportation availability.
Transportation in the Sustainable Development Goals
Transportation is a cross-cutting issue that relates to seven SDGs15, each suggesting transportation-relevant indicators. SDG 11 Target 11.2 calls for sustainable urban development for all, particularly for “vulnerable situations, women, children, persons with disabilities and older persons” (see Box 3, Why Isn’t Public Transit Gender Neutral?, for a discussion of barriers to public transit access according to gender). The single indicator for Target 11.2, SDG Indicator 11.2.1, refers to the “proportion of population that has convenient access to public transport, by sex, age and persons with disabilities.”16
As mentioned above, transportation is one of the largest contributors to urban GHGs globally. Increasing the share of trips taken by public transportation, instead of through individual automobile travel, can lower GHG emissions per passenger mile. For example, the average single occupancy vehicle in the United States emits 0.964 pounds of carbon dioxide per passenger mile, whereas light rail and buses emit 0.30 and 0.643 pounds, respectively.17 To address transportation’s large share of urban emissions, dense mixed-use developments surrounding public transit are a goal for urban planning and development. SDG 12 also suggests ending fossil fuel subsidies used to lower the price of gasoline and other fossil fuels in some countries, which would greatly increase the price of car travel and likely lead to changes in urban development patterns. Seto et al. (2011) showed that fuel prices are a strong indicator of the rate of urban expansion globally, hinting at the systemic nature of the relationship between transportation and urbanization. 18 Other SDGs (SDG 7 on sustainable energy generation and provision, SDG 8 on decent work opportunities19 and economic growth, SDG 9 on resilient infrastructure) rely on changes in transportation infrastructure and patterns of use, although the transportation sector is not specifically mentioned in the targets or indicators.
Measure Sustainable Public Transportation Access
Leveraging open-source global land cover data from ESA WorldCover and OpenStreetMap, which contains public transport stops and their types, we measure transportation access at the neighborhood scale across cities globally. See the table below for the definitions of the public transportation stops.20
Table 1. Definitions of Transit Queried from OpenStreetMap
| Transit type | OpenStreetMap definition |
| Bus | A form of public transport that operates mainly on the road network |
| Train (rail/mainline) | Full sized passenger or freight trains in the standard gauge for the country or state |
| Subway | A city passenger rail service running mostly grade-separated (defined as a method of aligning a junction of two or more surface transport axes at different heights so that they will not disrupt the traffic flow on other transit routes when they cross each other) |
| Tram | City-based rail systems with one/two carriage vehicles, which often share roads with cars |
| Metro | A rapid transit train system |
| Light Rail | A higher-standard tram system, normally in its own right-of-way |
To evaluate cities’ SPT accessibility, we calculate the Proximity to Public Transit (PPT) and Public Transportation Coverage (PTC) indicators. The PPT indicator calculated as the mean distance to the nearest transit stop from a hundred randomly generated residents was computed. The urban areas from ESA WorldCover 2021 are extracted for each city. These urban areas were used as a land cover mask when generating the 100 random points to simulate where a city’s real population may be located. Figure 4 below shows the 100 simulated residents and the urban areas in one of the neighborhoods in Beijing, China.
Figure 1: Generation of simulated residents within only urban areas in a neighborhood in Beijing for PPT calculation. Data source: public transport stop: OpenStreetMap, Urban areas: ESA WorldCover 2021.
To calculate the PTC indicator, we placed a buffer of 420 meters around each bus stop and 1.2 kilometers around each intra-city rail stop. We then dissolved these buffers to create a GIS layer designating the area proximate to a transit stop. The proximate area was compared to the neighborhood’s area to calculate the percentage of a neighborhood covered by public transit. Figure 2 below shows the locations of transit stops as points on a map of one of the neighborhoods in Beijing. The green shading indicates areas within the walkshed of transit stops, and the pink shading indicates the city boundaries.
Figure 2. An illustration of the Public Transit Coverage layer in Beijing.
Results
Public transit coverage in the UESI cities (Figure 3) ranges from near universal coverage in neighborhoods of Tokyo (JPN), Porto (PRT), Paris (FRA), and Athens (GRC), to less than 10 percent in Shinyanga (TZA), and Zunyi (CHN).
When evaluating PPT for different types of transportation, 118 cities have a mean PPT of less than the typical walking distance to bus stops (420 meters). Notable examples of the top-performing cities include Barcelona (ESP), Lisbon (PRT) and Paris (FRA). Conversely, 71 cities have an average distance to any transit stop greater than 1.2 kilometers, which exceeds the maximum distance a resident is generally willing to walk to a metro or railway stop. Cities such as Shinyanga (TZA), Qinhuangdao (CHN), and Zunyi (CHN) face significant challenges due to their extended distances to public transit. These long distances pose substantial barriers to public transportation use.
The majority of the top ten cities are situated in developed countries, some of which are well-known for their transit systems. Part of the gap between developed and developing countries may be due to poor data coverage or a reliance on informal bus and taxi services in developing countries.
Figure 3A. Public transportation coverage of top and bottom 10 cities. The numbers in the circle is the median value, while the error bars represent the standard deviation of respective values differences across neighborhoods of the city.
Figure 3B. Average distance to public transportation of top and bottom 10 cities. The numbers in the circle is the median value, while the error bars represent the standard deviation of respective values differences across neighborhoods of the city.
The distributions of these two transportation variables vary across different regions (Figure 4). Europe & Central Asia has the shortest average proximity to public transit at 0.367 kilometers, closely followed by the Middle East & North Africa at 0.348 kilometers. Sub-Saharan Africa has the longest average distance at 0.835 kilometers, while South Asia also has a relatively large average distance of 0.8 kilometers. The most significant variance in proximity is observed in East Asia & Pacific. In terms of transportation coverage, the Middle East & North Africa leads with 82% of neighborhoods served by public transit, followed by Europe & Central Asia at 79%. Sub-Saharan Africa again lags behind, with the lowest coverage at 30%, while East Asia & Pacific also shows room for improvement with 51% coverage. The variance in coverage is pronounced, particularly between the well-served regions like the Middle East & North Africa and the under-served Sub-Saharan Africa.
Figure 4 Distribution of proximity to public transit and public transportation coverage across regions.
Key equity considerations
Using the approach described in the Equity and Social Inclusion Indicator Profile, an analysis of the distributional equity of income and Distance to Public Transit was performed. The results show that a significant number of UESI cities have low-income inequality (i.e. high Gini values in Q3, Q4) (see Figure 9). In Shijiazhuang (CHN), Wuhan, (CHN), Catania, (ITA), and Phnom Penh (KHM), the burden of lack of proximity to public transit is falling more heavily on lower-income populations (Q1). The inequities are most severe in Atlanta (USA), Tehran(IRN), and Sao Paulo (BRA), as determined in their position in the lower left-hand quadrant (Q3) of the equity typology plot (Figure 5).
Some cities, however, have managed to achieve near-perfect equality with respect to distance to public transit - the most notable being Vienna (AUT) Austria, Brussels (BEL),and Tirana (ALB) which has the lowest Environmental Concentration Index (ECI) value of nearly 0, although they still fail to have an equitable income distribution. Several cities are located in the upper right-hand quadrant (low Gini, positive ECI), which suggests that cities like Zunyi (CHN), Kyoto (JPN), and Rosario(ARG) are burdening wealthier populations with greater distances to public transit. This result may also reflect a preference for wealthier populations living in these cities to live farther away from public transit stops.
Figure 5. Distance to Public Transit Equity typology quadrant plot. The plot considers the Income Gini and Distance to Transit Concentration Index to define four quadrants. The Income Gini Values represent the distribution of wealth across the population and range in value from 0 to 1. A Gini value of zero indicates a perfectly equal distribution of income across the population, while a high Gini value (out of a maximum of 1) suggests a highly unequal distribution of wealth. The Environmental Concentration Index (ECI) measures the variation of Distance to Public Transit in response to income. Positive ECI values indicate that the environmental burden is allocated on the wealthier citizens, while a negative ECI indicates that the environmental burden is allocated on the poorer citizens. The color of the dots represents if the city is either in the top or bottom 10 of the overall transportation score. See the Equity and Social Inclusion Profile for a more detailed description of this plot.
The equity curves shown in Figure 6 provide further insights into the distribution of PPT indicator and Income in the UESI cities. Cities exhibiting nearly equitable distributions of the distance to public transit can be identified as those with an orange line that follows the dotted diagonal line (the line of perfect equity). These include Amsterdam (NLD), Bangalore (IND), Berlin (DEU), Copenhagen (DNK), Johannesburg (ZAF), Montreal (CAN), Paris (FRA), Sao Paulo (BRA), Sapporo (JPN), Seoul (KOR), Singapore (SGP), and Vancouver (CAN). The transportation curves of the following cities suggest that the environmental burden is allocated more heavily to lower-income earners: Beijing (CHN) and Manila (PHL). The transportation curves for Atlanta (USA), and Los Angeles (USA) suggest the opposite; i.e., that the burden of greater distance to public transit is more heavily allocated to higher-income earners. It is important to note that these curves do not indicate whether the Distance to Public Transportation for a given city is large or small. They simply indicate whether the distance to public transportation remains relatively constant across the population.
Figure 6. Environmental and income distribution curves for selected UESI cities. These plots show the concentration distributions of distance to public transit (e.g., the concentration curve in blue) and income (e.g., the Lorenz curve in red) throughout neighborhoods in cities. Deviations from the dotted line (e.g., the line of perfect equity) illustrate cities that are less equitable in their distribution of distance to public transit. Concentration curves above the line of equity indicate the environmental burden is more heavily allocated to those with less income; concentration curves below the line of equity indicate that the environmental burden is more heavily allocated to those with greater income. See the Equity and Social Inclusion Indicator Profile for a more detailed description of this plot.
Discussion
Access to public transit is a critical measure of urban environmental sustainability, although it is a challenging indicator to assess consistently across cities. The UESI’s two sustainable public transit indicators, Proximity to Public Transport and Public Transportation Coverage, provide a first step for comparing how cities are perform. When interpreting these indicators, however, it is important to note that they likely overestimate the real distance of a neighborhood to transit stops, since we expect population density to be higher closer to transit stops but have no way to account for this variability in the indicators. As well, the relationship between population density and the viability of public transit, while commonly used, varies between rapidly urbanizing and developed cities, and it is not clear whether transit stops are a leading or lagging indicator for population density.22 Despite these limitations, the spatial access to public transportation explored by these indicators remains a crucial element of equitable access to public transportation in UESI cities.
Data Limitation
Our transportation data originates from OpenStreetMap (OSM), an open-source database that relies on citizens to contribute data. While OSM is a valuable resource, it lacks systematic and globally consistent updates, and the information available may be outdated or incomplete.23 For instance, cities in the US like Evansville, IL and Paterson, NJ were lacking public transportation data, as well as many Chinese cities in general.24 Thus, the transportation results may be affected by this data scarcity.
Quality of service & other forms of accessibility
As previously mentioned, a key assumption underpinning the SPT indicators is urban residents’ use of these options. Simple proximity to public transit does not translate into use. Contributing to public transit’s use is the quality of service - an indicator that we currently do not evaluate in the UESI but could be incorporated into future iterations. For example, this analysis does not cover aspects such as the frequency of buses or trains, reliability of service, connectivity between different public transport services, and information availability, such as route information, or transport arrival timings, or commute times of riders. These elements are also important factors for a more robust assessment of a city’s public transport system since they contribute to the overall quality of public transportation service, which can also affect commuters’ willingness to use the public transportation system. Commuters may be more willing to walk further for public transport they deem as high quality, a variable affecting accessibility that spatial distance does not quantify. 25
Additionally, the data used in calculating the SPT does not consider the accessibility of public transport to marginalized communities, which the UN sustainable transportation goals highlight as a key requirement in developing sustainable transportation systems.26 Persons with disabilities, the elderly, poorer populations, women, and children, may require additional accessibility requirements to suit their transportation needs. Measures like ‘pink transport’ or women-only public transport, wheelchair-accessible vehicles, and subsidized or free transportation are all accessibility measures that benefit these communities and have been implemented in cities around the world, but remain unaccounted for in the SPT due to the lack of data.
Policy intentions
When aggregating neighborhood transportation coverage scores into the city-wise Proximity to Public Transit (CPPT) indicator, individual neighborhood scores are weighted according to their respective population density, in relation to the city average. This weighting rewards cities whose public transportation is concentrated in higher density areas since this means more commuters have access to this public transportation. However, it is challenging to ascertain whether areas with higher population density benefit from extensive public transportation coverage due to effective transportation planning, or if these areas have higher population density because they already feature extensive public transportation coverage. Proximity to public transit is a key determinant of the desirability of housing and business locations, and at the same time, urban planners target densely populated areas for transit service, and it can be difficult to determine which came first. Since the UESI seeks to provide intra-city level insights on city transportation performance for city practitioners, policymakers, and scholars, it is prudent to understand the drivers and results of transportation accessibility, and whether there are policies in certain cities and how they may affect it. However, current data is not sufficient to support this analysis, and we are seeking more information and data for future studies.
Across city comparisons
Due to historical, cultural, and various other factors, the way neighborhoods are defined varies significantly among cities around the world. For instance, neighborhoods in Beijing tend to be much larger, encompassing large areas of rural spaces, while densely built cities like New York have smaller, denser, and predominantly urban neighborhoods, as illustrated in the figure below. The basic unit of analysis, the neighborhood, is not uniform between cities, making it difficult to draw direct comparisons across them. Cities with larger neighborhoods that encompass rural areas do not require as intense public transportation coverage, which results in them receiving a lower score than cities with only urban neighborhoods with high population density. This issue is especially relevant to Chinese cities, which often have larger neighborhoods that include non-urban areas. Additionally, as mentioned earlier, the absence of transportation data from OSM results in these cities receiving lower transportation scores compared to others.
Figure 8. Landcover composition comparison between New York City and Beijing.
By acknowledging these limitations and discussing their potential impact on our findings, we aim to provide a clear and transparent understanding of our results, as well as highlight potential approaches that future iterations of UESI could adopt. Due to these limitations, when calculating the aggregated UESI weighted score for all cities, we downweight the transportation indicators compared to other indicator categories to take into consideration the fact that some cities might be unfairly penalized based on the way they define their neighborhood boundaries.
Future work could further explore intersections between transit and pollution. Higher-density areas may have higher levels of air pollution due to traffic congestion -- leading to a negative externality associated with a possible urban sustainability solution. This aspect may complicate the basic premise that walking to public transportation is a net health benefit. While effective public transit should reduce congestion and improve the capacity and efficiency of the overall transit system, cars and trucks do commonly use the same areas as pedestrians accessing public transit.
Box 1. An alternative dataset for measuring public transit
Given that the population data collected for use throughout the UESI are disaggregated only to the neighborhood level, we explored an alternative method of capturing the variation of population density within neighborhoods. While the indicator used in the chapter relies on neighborhood census data, the alternate method utilizes a gridded data set based on census population estimates gathered from around the world, which is calculated by Center for International Earth Sciences Information Network (CIESIN, 2017). The UESI indicators rely on self-reported government census data for neighborhood population, which consists of a single population value for each neighborhood. Using the neighborhood level census data to measure access to transit stops assumes that the population is evenly spread within the neighborhood, which is often not the case (See, among others, Robert Cervero, 2007; Seto et al., 2014). 27 28As described elsewhere, using the average population could penalize planning that provides higher levels of public transportation service to more dense neighborhoods; thus, the indicator should ideally account for variation in population density within a neighborhood.
Many of the cities included in the UESI do not make disaggregated census data publicly available, however, which poses a particular challenge for this indicator because it is highly spatial in nature. One alternative we explored for this indicator is to draw on globally available data from the Global Rural-Urban Mapping Project (GRUMP) version 4, published by the NASA Socioeconomic Data and Applications Center and hosted by the CIESIN at Columbia University. The resolution of the GRUMP dataset, however, is larger than some smaller neighborhoods in the selected cities (see Figure 2 below, and recall Figure 1 displaying the wide range of neighborhood sizes). Because of the variation in neighborhood size, this method results in non-trivial software errors when calculating the indicator based on GRUMP population data. The highlighted polygon is a small neighborhood in Bangalore that illustrates the possibility for error when a neighborhood only partially intersects a few cells from the GRUMP data set, each with a widely different population estimate. Absent a more disaggregated dataset, the UESI indicator is designed to estimate the number of people in the analysis area (city or neighborhood) that live within the transit walkshed based on the ratio of the area within the walkshed and the total area.
This example highlights the need in future iterations to develop a more standardized method of identifying comparable urban units of analysis.
Figure A. The GRUMP data set for population in Bangalore with the neighborhoods overlaid show that these spatially-derived population density estimates may be too coarse in resolution at the neighborhood scale. While the GRUMP data shows promise for future iterations, it is not viable to estimate the population density within the transit walksheds. (Source: CIESIN, 2017.)
Box 2: Cycling Towards Sustainability: The Case of Washington DC’s Capital Bikeshare
Across the globe, the transportation sector has been at the forefront of the sharing economy revolution, which is based on loaning, rather than ownership, of goods.29 One feature of the revolution has been the expansion of bike sharing, which are short-term and often self-serviced bike rentals.
Adopting bike sharing into cities' public transport systems can result in economic, environmental and social benefits. As an emission-free mode of transport, bicycles do not add to local air pollution or greenhouse gas emissions.30 They create less noise pollution than mechanical vehicles and can reduce congestion on streets during peak hours.31 Usage of these services also encourages active mobility, which is associated with various health benefits, including a reduced risk of type 2 diabetes.32 Literature has shown that in the U.S., the health benefits of bike sharing save up to 36 million USD annually- mainly from the increase in physical activity they bring.33
Bike-sharing programs are particularly suited to complementing public transport systems through their use to and from public transport stops, commonly referred to as first- and last-mile trips.34 In this instance, bike docking stations are installed near high-traffic transport spots so that riders can switch from bike to public transit, and vice versa. Their use for such trips makes them an efficient way to expand the spatial and temporal coverage of traditional public transit systems, which may not be able to cover areas or times of the day where ridership is low. 35Bike docking stations are relatively cheap to install, and can operate 24/7 with minimal increase in operational costs.
Since 2008, Washington DC’s Department of transportation has been operating a bike-sharing program under Capital Bikeshare, which provides bicycles for public usage at a fee. Using an app, users can loan bikes from docking stations located around the city and return them to another station at the end of their trip. To explore the benefits and limitations of bike-sharing programs, we explored 2023 Capital Bikeshare usage data, as well as Washington DC spatial data provided by Opendata DC. We found that in Washington DC, bike-sharing stations increased the amount of urban areas walkable to public transportation, also known as public transportation walksheds, by 30%. Due to this increase, of the 78.4km2 of urban areas in the city, 6,660 hectares -- nearly 38 percent of the total land area of Washington, D.C., - now lie within bike sharing or public transportation walksheds.
Figure A. Distribution of bike stations, buses, and metro walksheds overlaid over Washington DC’s urban areas.
To maximize walkshed coverage, bike-sharing stations should be located in the city’s periphery, which is less likely to have walkable access to public transportation. Currently, the bulk of Washington DC’s bike stations are clustered around the district’s center, where there already exists a high density of bus and metro services (Figure A). However, to maximize walkshed coverage, more bike-sharing stations should be built in the city’s peripheral areas, which are less likely to have walkable access to public transportation. Out of the 66.6 km2 of urban space within a bike-sharing or public transportation walkshed, 41.7 km2 is covered by both bike-sharing and public transportation, representing a significant overlap in transportation options. While this overlap is necessary to facilitate bike sharing for first- and last-mile transit, greater coverage of the district’s urban spaces could be achieved if bike stations were also spread out towards the district’s peripheral urban areas that are not yet within public transportation or bike-sharing walksheds. The heavy usage of Washington DC’s bike stations in the city center (Figure B) disputes literature from other cities suggesting that bike-sharing usage per station tends to be more concentrated in areas with lower public transportation access. 36This pattern means that new stations installed in these peripheral areas could be built with lower capacities to match the lower demand.
Figure B. Distribution of bike-sharing station usage rates
Apart from expanding public transportation walksheds, bike sharing can be a good first- and last-mile option because they shorten the walking time otherwise needed to get to stations or reduce the inconvenience of having to search for car parking spaces around stations.37 A previous study done in Washington DC found that the city’s most often used bike-sharing stations were located close to metro stations, suggesting that commuters were using bike-sharing for first and last-mile trips.38 The same study also argued that an increase in bike-sharing usage is positively associated with an increase in metro usage,39 and other literature has shown that this positive association is strongest with short to medium length trips, or trips under 15 mins.40 41 Our analysis found that in Washington DC, the bulk of bike sharing trips were short trips lasting between 6 - 19 minutes (25th and 75th percentile) (Figure C). This suggests that in the city, bike sharing is indeed used as a first- and last-mile option, and has been successful in complementing public transport systems by increasing station accessibility.
Figure C. Distribution of trip durations
Bike sharing as a form of transportation comes with its own set of limitations. Bike transit exposes users to various weather conditions directly, which can affect the number and duration of ridership usage. Uncomfortable weather conditions like rainy or snowy conditions, as well as overly hot or cold temperatures, can result in a temporary drop in demand for bike-sharing services.42 In Washington DC, bike-sharing usage peaks when temperatures are between 70 - 80 F, while the usage rate falls off when the temperature is outside of this ideal range (Figure D.). This trend indicates that bike-sharing usage is highly seasonal. In Washington DC, January, the coldest month, sees bike sharing usage drop by 51% as compared to October, the busiest month. In other cities, bike-sharing programs sometimes cease operations during winter months due to low utilization rates.43 The viability of bike sharing to complement traditional public transport systems thus also fluctuates depending on seasonal climatic conditions. Cities that intend to integrate bike sharing into their public transport systems should thus consider other improvements to the system, like extended bus lines, to compensate for the drop in bike sharing usage during months where commuters are less able to cycle.
Figure 4: Distribution of bike-sharing rides per day by time of year and daily temperature
Furthermore, bike-sharing services tend to be built around a “one size fits all” design approach, which results in bicycles that are not suitable for use by everyone.44 People with physical or visual disabilities,45 or parents traveling with young children,46 may not be able to effectively use traditional bike-sharing services. While Capital Bikeshare provides both manual and electric bikes, these bikes are not suited for use by the aforementioned populations. These users may require bikes with alternative designs, like cargo or wheelchair bikes, to suit their traveling needs, or may be incapable of independent cycling.47 48 Therefore, the accessibility advantages of bike-sharing services are restricted to individuals who are able to use the bicycles offered by these programs. One key tenet of UN SDGs for developing sustainable transportation is “leaving no one behind,” which calls for ensuring that all populations, including historically marginalized communities, have equal access to transportation. This principle means that cities intending to integrate bike sharing into their public transport systems must pass additional measures to provide the mobility-impaired with alternative forms of transport, like door-to-door on-demand rides.49
Box 3. Why isn’t transport gender neutral?
Our analysis of public transit access equity assumes urban dwellers will take advantage of public transportation within walksheds. In reality, this scenario is seldom the case; many factors, such as safety on public transit, can lower transit use. In particular, women face more barriers to public transport access. This problem has huge social development and civil rights implications as limited access to public transport restricts women from jobs and learning opportunities, and prevents them from actively participating in social activities.
Sexual Harassment and Access to Public Transport
Both women and men experience sexual harassment on public transport, though harassment survivors are primarily women. Sexual harassment can take a variety of different forms, that can range from verbal and physical contact.50 The FIA Foundation and CAF Latin American Development Bank report “Ella Se Mueve Segura- She Moves Safely,” examines women’s security on public transport in three Latin American cities (see Figure E). The report finds that 72 percent of Buenos Aires female respondents and 58 percent of male respondents feel insecure on public transport due to harassment.51
Figure E. Summaries of experienced and observed harassment in Buenos Aires, Quito, and Santiago, as reported by men (in gray) and women (in purple). Across all three cities, women experienced and witnessed higher levels of harassment. The highest disparity between harassment between men and women was found in Buenos Aires (FIA Foundation & CAF Latin American Development Bank, 2017).
In China, a report on the state of public harassment in Shenzhen's public transport shows 42 percent of women have experienced sexual harassment. The probability of experiencing harassment is 10 percent higher among students and young people.52 Their higher risk demonstrates that the phenomena of sexual harassment depends on a combination of different factors, including gender, age and class. These reported figures, however, may underestimate the true values, as most survivors choose to remain silent.53
The experience of sexual harassment can affect physical and mental health. The insecurity of public transport has caused women riders to abandon public transport, thereby restricting their job and learning opportunities, and preventing them from actively participating in social activities. Losing the ability to use public spaces as a result of sexual harassment is a civil rights violation, and sexual harassment represents a threat to social development.30
Professional Participation and Decision Making Power
Women are less represented in transportation occupations such as bus, train, or truck drivers and construction workers. This underrepresentation is partially linked to the gender stereotypes that exist in many cultures and to gender discrimination in hiring and working environments. In addition, the transportation sector has long been a male-dominated sector. There are fewer women employed compared to men, and women seldom serve as high-level decision-makers. Transportation policies have long neglected the needs of women, due to their absence from the planning and decision-making processes.54
Addressing Transport Gender Inequity with Data
Data collection can support a shift towards greater gender equity in public transit access and use. There is a huge data gap in addressing gender inequity in transportation. Without data, we cannot formulate transportation policies that take into account the needs of women. For starters, we do not know where and how women use public transportation. Research shows that in Buenos Aires, women’s travel needs are different from men’s, including traveling more frequently and for different purposes (such as picking up their children).55 Cities should collect more gender-specific data to help develop more gender-inclusive transportation policies and create safer public spaces. Crowdsourcing is one option for gathering such data. The Safecity project (http://safecity.in) in India allows survivors to locate and share their stories through mobile phones.56 This valuable data can not only increase public awareness of sexual harassment, but also advance the formulation and legislation of anti-sexual harassment policies.
Box 4: Measuring transit accessibility in Singapore
The accessibility of urban amenities (e.g., shops, restaurants, schools, and workplaces) and public transportation is often identified as an important step toward improving sustainable urban transportation. At the neighborhood scale, mixed use development patterns place more urban amenities and sites of employment within walking distance to residential land uses and public transportation stops, potentially reducing demand for long distance travel. Transit-oriented design (commonly referred to as TOD) can encourage the use of public transportation or non-motorized travel over personal motorized vehicles. Transit accessibility can therefore lead to decreased demand for energy-intensive modes of transportation, meaning that in addition to being an effective planning instrument, the degree to which a city’s transit is accessible to amenities can be an indicator of urban sustainability. This measure of access to urban amenities is not currently used in the UESI, but is an important aspect to consider when evaluating urban transit. The method and case study below offer insight into a possible approach for quantifying a city’s transit accessibility.
Operationalizing Transit Accessibility
In accordance with the above description, transit accessibility should be (1) positively proportional to a neighborhood’s population density, (2) characterized by mixed use development, and (3) sensitive to the number of amenities within the neighborhood. Finally, (4) amenities farther from the station should contribute less to that station’s score. Based on these criteria, a transit station’s accessibility score can be quantified with the following equation:
where D is the population density of the neighborhood in which the station is located;
A is the total number of amenities within the 500m buffer; and
da is the distance between an amenity and the station.
Accessibility, however, is also a property of cities as a whole. Cities should score higher on the index if their transit system is more accessible. Thus a city’s score can be calculated with the following equation:
where N is the number of metro stations in the city;
Cn is the accessibility score for station n; and
Dn is the distance between station n and the central business district, which accounts for the centrality of the station. 57
Application of Transit Accessibility to Singapore’s Metro System
Using the operational definition described above, accessibility scores were calculated for metro stops in Singapore, shown in Figure 3. Data on station and amenity locations were obtained from the OpenStreetMap platform and population density figures were based on Singapore’s 2010 subzone census.
Figure E. Transit accessibility score results across Singapore for neighborhoods surrounding MRT stops (darker colors correspond to higher scores).
The highest scoring nodes in Singapore are generally clustered around Marina Bay, part of the city center located near the island’s southeastern coast. The highly scoring nodes located on the west of the island are part of the Jurong East planning area, which Singapore’s Urban Redevelopment Authority identified as a future center of urban growth set to eventually become the city’s “second Central Business District.”58 Though the plans for this area are quite long term, some development has already occurred, as reflected in the neighborhood scores. This method of calculating transit accessibility, then, reflects some important characteristics of Singapore’s urban development.
Footnotes
Further work is needed to integrate the proximity of transit stops to work opportunities, as residences and work opportunities make up two ends of the daily commute.
El-Geneidy, A. et al. (2014). New evidence on walking distances to transit stops: identifying redundancies and gaps using variable service areas. Transportation, 41(1): 193-210.
The algorithm used here calculates the circular buffer, rather than the network buffer. Oliver, Schuurman, and Hall (2007) suggest that network buffers will generate significantly different results from the circular buffer when calculating walkability based on land use. While their focus is slightly different, assessing the “walkability” of a neighborhood based on the land uses surrounding pedestrian paths, proximity to public transportation values will also differ if based on the road network rather than the circular buffer. These values would be more in line with an indicator such as accessibility as described in Box 2.
Daniels, R. & Mulley, C. (2013). Explaining walking distance to public transport: The dominance of public transport supply. The Journal of Transport and Land Use, 5(2): 5-20.
These walking distances are the same as those employed by the DVRPC’s Equity Through Transit mapping project. Delaware Valley Regional Planning Commission. Equity Through Transit Map Kit. Retrieved from: https://dvrpcgis.maps.arcgis.com/apps/MapSeries/index.html?appid=06eab792a06044f89b5b7fadeef660ba.
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Saif, M. A., Zefreh, M. M., & Torok, A. (2019). Public Transport Accessibility: A Literature Review. Periodica Polytechnica Transportation Engineering, 47(1), Article 1. https://doi.org/10.3311/PPtr.12072
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Transportation work in the informal sector of transportation - the dominant mode of some cities - can be anything but decent. See Rizzo’s (2017) Taken for a Ride: Grounding Neoliberalism, Precarious Labour, and Public Transport in an African Metropolis for a critical discussion of the transportation sector in Dar es Salaam.
To access data from OpenStreetMap, the Overpass Turbo client was used. The query was written in the XML query format, as incomplete data were observed when using the Overpass QL format. The query included six forms of transit listed in Table 1.
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Saif, M. A., Zefreh, M. M., & Torok, A. (2019). Public Transport Accessibility: A Literature Review. Periodica Polytechnica Transportation Engineering, 47(1), Article 1. https://doi.org/10.3311/PPtr.12072
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Brown, V., Barr, A., Scheurer, J., Magnus, A., Zapata-Diomedi, B., & Bentley, R. (2019). Better transport accessibility, better health: A health economic impact assessment study for Melbourne, Australia. International Journal of Behavioral Nutrition and Physical Activity, 16(1), 89. https://doi.org/10.1186/s12966-019-0853-y
Clockston, R. L. M., & Rojas-Rueda, D. (2021). Health impacts of bike-sharing systems in the U.S. Environmental Research, 202, 111709. https://doi.org/10.1016/j.envres.2021.111709
Radzimski, A., & Dzięcielski, M. (2021). Exploring the relationship between bike-sharing and public transport in Poznań, Poland. Transportation Research Part A: Policy and Practice, 145, 189–202. https://doi.org/10.1016/j.tra.2021.01.003
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Radzimski, A., & Dzięcielski, M. (2021). Exploring the relationship between bike-sharing and public transport in Poznań, Poland. Transportation Research Part A: Policy and Practice, 145, 189–202. https://doi.org/10.1016/j.tra.2021.01.003
Radzimski, A., & Dzięcielski, M. (2021). Exploring the relationship between bike-sharing and public transport in Poznań, Poland. Transportation Research Part A: Policy and Practice, 145, 189–202. https://doi.org/10.1016/j.tra.2021.01.003
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Radzimski, A., & Dzięcielski, M. (2021). Exploring the relationship between bike-sharing and public transport in Poznań, Poland. Transportation Research Part A: Policy and Practice, 145, 189–202. https://doi.org/10.1016/j.tra.2021.01.003
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Eren, E., & Uz, V. E. (2020). A review on bike-sharing: The factors affecting bike-sharing demand. Sustainable Cities and Society, 54, 101882. https://doi.org/10.1016/j.scs.2019.101882
Breengaard, M. H., Henriksson, M., & Wallsten, A. (2021). Smart and Inclusive Bicycling? Non-users’ Experience of Bike-Sharing Schemes in Scandinavia. In H. Krömker (Ed.), HCI in Mobility, Transport, and Automotive Systems (pp. 529–548). Springer International Publishing. https://doi.org/10.1007/978-3-030-78358-7_37
Goralzik, A., König, A., Alčiauskaitė, L., & Hatzakis, T. (2022). Shared mobility services: An accessibility assessment from the perspective of people with disabilities. European Transport Research Review, 14(1), 34. https://doi.org/10.1186/s12544-022-00559-w
Breengaard, M. H., Henriksson, M., & Wallsten, A. (2021). Smart and Inclusive Bicycling? Non-users’ Experience of Bike-Sharing Schemes in Scandinavia. In H. Krömker (Ed.), HCI in Mobility, Transport, and Automotive Systems (pp. 529–548). Springer International Publishing. https://doi.org/10.1007/978-3-030-78358-7_37
Breengaard, M. H., Henriksson, M., & Wallsten, A. (2021). Smart and Inclusive Bicycling? Non-users’ Experience of Bike-Sharing Schemes in Scandinavia. In H. Krömker (Ed.), HCI in Mobility, Transport, and Automotive Systems (pp. 529–548). Springer International Publishing. https://doi.org/10.1007/978-3-030-78358-7_37
Goralzik, A., König, A., Alčiauskaitė, L., & Hatzakis, T. (2022). Shared mobility services: An accessibility assessment from the perspective of people with disabilities. European Transport Research Review, 14(1), 34. https://doi.org/10.1186/s12544-022-00559-w
Goralzik, A., König, A., Alčiauskaitė, L., & Hatzakis, T. (2022). Shared mobility services: An accessibility assessment from the perspective of people with disabilities. European Transport Research Review, 14(1), 34. https://doi.org/10.1186/s12544-022-00559-w
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D’Silva, E. (2018, January). Why Transport is not Gender Neutral. Presented at the 2018 Transforming Transportation Conference, Washington, D.C.
D’Silva, E. (2018, January). Why Transport is not Gender Neutral. Presented at the 2018 Transforming Transportation Conference, Washington, D.C.
FIA Foundation, & CAF Latin American Development Bank. (2017). Ella Se Mueve Segura- She Moves Safel (FIA Foundation Research Series No. Paper 10). Retrieved from: https://www.fiafoundation.org/media/461162/ella-se-mueve-segura-she-moves-safely.pdf
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To account for polycentricity, this index could be modified to consider the distance between a neighborhood and the highest scoring neighborhood within some radius.
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