Gender gap generators for bike share ridership: Evidence from Citi Bike system in New York City

Journal of Transport Geography 76 (2019) 1–9

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Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo

Gender gap generators for bike share ridership: Evidence from Citi Bike system in New York City

T

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Kailai Wang , Gulsah Akar City and Regional Planning, Knowlton School of Architecture, The Ohio State University, Columbus, OH, United States of America

A R T I C LE I N FO

A B S T R A C T

Keywords: Bike share Gender gap Environmental correlates Fractional logit model

Bike-sharing is one of the rapidly growing transport services around the world. This study aims to identify the factors that affect gender differences in bike share ridership. Using data from New York City's Citi Bike Share system, we investigate the environmental correlates of bike share usage for males and females. We also model the influences of bicycle facilities, land use factors, and public transit services on the share of trip arrivals made by females. The results suggest that the environmental correlates of bike share usage for males and females are broadly similar. However, the estimated magnitudes suggest that our variables of interest may influence males and females differently. For example, installing more bicycle racks are positively associated with bike share ridership for both genders. We further find that this factor affects women more than men. Specifically, a 1% increase in the number of bicycle racks is correlated with a 1.18% increment in the share of trips arrivals made by women. The findings can be used to assess the effectiveness of future infrastructure investments aimed at minimizing the gender gap in bike share usage. The findings also offer valuable insights into the ways of increasing the overall bike share ridership, thereby promoting local bicycling culture.

1. Introduction As a green and innovative transport mode, bike sharing is becoming increasingly popular in cities around the world (O'Brien, Cheshire & Batty, 2014; Fishman, 2016). Bike share programs bring in a variety of socioeconomic and environmental benefits, including reduced traffic congestion, air and noise pollution, and energy consumption (DeMaio, 2009; Shaheen et al., 2010). Bike share programs also benefit users from multiple perspectives, such as convenience, improved access to public transit nodes, and increased physical activity and health (Fishman, 2016). However, bike share users are disproportionately young, male, white, highly educated, and from higher income groups (Fishman, 2016, Howland et al., 2017; McNeil et al., 2018). This study focuses on gender gap in bike share use. Women are referred to as the “indicator species” for bicycle-friendly environments due to their risk aversion (Baker, 2009). A higher percentage of female bike share users may indicate a more comfortable environment for bicycling (Baker, 2009; Emond et al., 2009; Garrard et al., 2008; Pucher, Dill, & Handy, 2010). A sustainable transport system requires providing equitable mobility across the gender line (Hanson, 2010). This study aims to explore the impacts of spatial characteristics of bike share stations on gender disparity in bike share usage. Empirical data reveal that, though gender gap may vary across bike ⁎

sharing programs, bike share users are disproportionately male (Buehler, 2011; Fishman et al., 2014; Goodman and Cheshire, 2014; Kaufman et al., 2015). Goodman and Cheshire (2014) show that, among all trips made by registered users of the London bike share programs (BSP), < 20% are made by women. A study of Australia's bikeshare members has found that women account for 23% and 40% of annual members in Melbourne and Brisbane, respectively (Fishman et al., 2014). Buehler (2011) report that approximately two thirds of the annual members of the Capital Bikeshare (CaBi) system in Washington DC are men. In New York City (NYC), 77.7% percent of member rides are made by men (Kaufman et al., 2015). The shares of trips made by women range between 14% and 41% depending on the locations of the bike share stations. These studies also find that female bike share users prefer riding on bicycle facilities, avoid heavy traffic and are less likely to use bike share for their commute trips (Goodman and Cheshire, 2014; Kaufman et al., 2015). The spatial characteristics of the surrounding environments at bike share stations, in terms of bike share and bicycling facilities, land use and built environment characteristics, public transit services, may influence the gender disparity in bike share usage. Assessing these influences can provide useful insights for planners and practitioners while promoting the equity and inclusiveness of bike share programs. To date, empirical work has extensively analyzed the effects of

Corresponding author. E-mail addresses: [email protected] (K. Wang), [email protected] (G. Akar).

https://doi.org/10.1016/j.jtrangeo.2019.02.003 Received 21 August 2018; Received in revised form 19 December 2018; Accepted 16 February 2019 0966-6923/ © 2019 Elsevier Ltd. All rights reserved.

Journal of Transport Geography 76 (2019) 1–9

K. Wang and G. Akar

Understanding the demographics of bike share users has become a common focus among researchers and transport practitioners. Policy interest in promoting bike share usage needs to accommodate older adults, females, people of color, and lower-income residents (Buehler, 2011; Fishman, 2016; Howland et al., 2017; McNeil et al., 2018; Wang et al., 2018). For example, McNeil et al. (2018) report many residents in lower-income communities, both people of color and lower-income residents, regard bike share as a positive addition for their neighborhoods, and most of them are interested in trying bike share. However, cost, concern about liability for the bicycles, lack of information of bike share programs are viewed as three major deterrents preventing them from bike sharing. Given a specific focus on the subscribers of Citi Bike system in New York City, Wang et al. (2018) find the environmental correlates of bike-sharing are heterogenous across different age cohorts. Planners and transport practitioners who know the needs of different demographic groups could invest more efficiently in transport infrastructure for the targeted groups. Still, it is much unclear what accounts for the discrepancy in bike share usage between men and women.

bicycle facilities, built environments, public transit services, and temporal and weather factors on the station-level bike share ridership (Buck and Buehler, 2012; El-Assi et al., 2017; Faghih-Imani et al., 2014; Faghih-Imani and Eluru, 2015; Faghih-Imani and Eluru, 2016; Fournier et al., 2017; Gebhart and Noland, 2014; Lin et al., 2018; Noland et al., 2016; Rixey, 2013; Tran et al., 2015; Wang et al., 2015; Wang et al., 2018). The results show that locating more bike share stations within areas with compact development patterns or near recreational destinations would maximize the overall ridership. To our knowledge, no one has specifically explored the extent to which the above-mentioned factors influence the bike share usage of men and women differently. This study provides a multivariate analysis to disentangle the true effect of each of the factors on gender gap in bike share usage. Male riders generate more bike share trips for commuting than female riders (Goodman and Cheshire, 2014; Kaufman et al., 2015), and thus hypothetically they may be more likely to integrate bike share with public transit services. Female riders are more risk averse and prefer separated bicycle facilities as compared to their male counterparts (Akar et al., 2013; Emond et al., 2009; Handy, 2011; Heesch et al., 2012; Krizek et al., 2005). Installing more off-street bicycle facilities may diminish the gender gap, and efficiently promote female ridership. The findings of this study offer valuable insights into accommodating more female bike share users through efficient infrastructure investments. This paper is organized as follows. The next section reviews the literature on bike share demand and gender gap in bicycle ridership. Section 3 presents the descriptive statistics of gender differences in bike share usage from multiple dimensions. In Section 4, the methodology and model structures are introduced. Then, we present the model estimations with policy implications. The paper concludes with important findings, recommendations, and limitations of this study.

2.2. Gender differences in bicycling choice Over the last two decades, numerous scholars have critically analyzed the gender gap in bicycling choice (Abasahl et al., 2018; Akar and Clifton, 2009; Akar et al., 2013; Emond et al., 2009; Ermagun and Levinson, 2016; Ermagun and Samimi, 2015; Garrard et al., 2008; Guliani et al., 2015; Handy, 2011; Heesch et al., 2012; Krizek et al., 2005; McDonald, 2012; Trapp et al., 2011). Using data from the 1977, 1983, 1990, 1995, 2001, and 2009 US National Household Travel Surveys, McDonald (2012) reveals that boys between 8 and 13 years old bicycle to school 2–3 times more than girls in the U.S. The author suggests that the gap may be due to the lower levels of independent mobility of girls than boys. Other studies focusing on schoolchildren suggest that parents' confidence in their children's bicycling skills and parental safety perception of neighborhood environments play important roles in encouraging children to bicycle to school (Guliani et al., 2015; Trapp et al., 2011). Some researchers argue that as children become older and go to college, the gender gap is expected to diminish (Ermagun and Levinson, 2016; Ermagun and Samimi, 2015), however, women are still less likely to bicycle than men (Abasahl et al., 2018; Akar and Clifton, 2009; Akar et al., 2013; Emond et al., 2009; Handy, 2011; Heesch et al., 2012; Krizek et al., 2005). Akar and Clifton (2009) conducted research on a target population of faculty, staff, and students at the University of Maryland. They found that safety and travel time were major concerns while making commute mode choices. As compared to men, women were less likely to use bicycle for commuting. The authors pointed that planning efforts such as installing bicycle routes and formulating proper traffic regulations in and around campus areas may promote bicycling. Other empirical studies suggest that the safety perceptions make women bicycle less than men (Emond et al., 2009; Handy, 2011; Heesch et al., 2012). For example, Akar et al. (2013) conducted a study at the Ohio State University to further explore the factors affecting females' bicycling decisions. The study found different risk perceptions between men and women in similar environments. In a study conducted in Australia, Garrard et al. (2008) show that female bicyclists in Melbourne prefer to use separate bicycle routes over shared paths. Literature on gender differences in bicycling have not been limited to safety concerns. Using trip-level bike share data from Nanjing in China, Zhao et al. (2015) find that the trip chains of female bicyclists involve more stops than their male counterparts. Other studies reveal that women make more bike trips for shopping and recreational purposes, and household responsibilities (Emond et al., 2009; Handy, 2011; Krizek et al., 2005). This paper brings together the growing literature on bike sharing and the established work on gender differences in bicycling choice. Using data from New York's Citi Bike System, we offer a detailed exploration of how bicycle facilities, land use and built environment

2. Literature review This section starts with a summary of the research efforts on bike share demand, followed by the recent studies on gender differences in bicycling choice. 2.1. Bike share demand Empirical evidence suggests that providing better provisions of bike sharing infrastructure (number of stations and capacity) and bicycle facilities (access to bike lanes) may increase the overall ridership (Buck and Buehler, 2012; Faghih-Imani et al., 2014; Faghih-Imani and Eluru, 2015; Wang et al., 2015). Land use and built environment characteristics, such as population, job, retail and food services densities, and proximity to central business districts, have significant impacts on the bike share usage (El-Assi et al., 2017; Faghih-Imani and Eluru, 2015; Noland et al., 2016; Wang et al., 2015). In general, placing bike share stations in areas with higher densities, mixed land uses, and adequate commercial facilities may attract more riders. The relationships between bike share usage and public transport facilities are mixed. Some studies point that bike sharing stations near metro systems and regional train systems are more likely to be chosen as destinations by long-term members (Faghih-Imani and Eluru, 2015; Fournier et al., 2017; Tran et al., 2015). Other studies report a substitution effect between public transit facilities and bike share usage (Gebhart and Noland, 2014). Also, temporal characteristics and weather conditions significantly influence bike share ridership (El-Assi et al., 2017; Gebhart and Noland, 2014; Wang et al., 2018). These research efforts provide great assistance for coordinating existing systems and installing new programs as they point out the ways of promoting the overall ridership. One potential issue is that these studies aggregate the overall bike share ridership at the station-level, and therefore are highly likely to ignore the differences among different demographic groups, such as men and women, multiple age cohorts, and different income groups. Since a large proportion of bike share users are males, the needs of females may go unnoticed. 2

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factors, and public transit services affect the bike share ridership for men and women differently. The findings of this study can be used to assess the efficacy of upcoming policy interventions on reducing the gender gap in bike share usage. The findings also provide insights into coordinating existing systems, designing new programs and promoting the overall usage of bike share. These efforts are expected to have longterm effects on the future landscape of urban mobility.

Table 1 The average demand per day in 2016 by month. Daily trip productions January February March April May June July August September October November December

3. Data and method 3.1. Data processing We construct the research dataset using multiple sources. We obtain the bike share ridership data from Citi Bike website.1 Citi Bike system in New York City (NYC) is the largest bike share program in the U.S. Existing studies have investigated various factors associated with this system's bike share ridership (Faghih-Imani and Eluru, 2016; Lin et al., 2018; Noland et al., 2016; Wang et al., 2018). Following these studies, we download the data on bicycle facilities, built environment and land use characteristics, and public transit services from NYC Open Data.2 As this study focuses on exploring the influences of spatial features at the station-level, we choose the month of September as the study period. This is because September is the busiest season in 2016 and provides a bikeable environment with few days of adverse weather conditions. Table 1 shows the Citi Bike system's average daily demand by month in 2016. Fig. 1 shows the map of the study area and the locations of the bike share stations. There are 598 active stations located in Manhattan, Brooklyn, and Queens. Citi Bike trip data includes information on trip origin and destination stations, start and end times of trips, user types (member/nonmember) and user demographics (age and gender). Since only annual subscribers provide demographic information (age and gender), we remove trips generated by non-subscribers from our final trip dataset. In September 2016, 85.7% of total bike share trips were made by annual subscribers. We summarize the system hourly trip attractions by males and females, respectively (as shown in Table 2). The number of bike share trips by men is almost 2 times higher than that of women. American Community Survey (ACS) data of 2016 shows that among the entire New York City population over 18 years of age, 52.3% are females. The results reveal bike share ridership is strongly skewed by gender. This study focuses on analyzing trip arrivals. This is because bike share users are more likely to return bicycles to stations near their destinations. For those who are using the bike-share system for their first mile, then their intermediate destination would be the transit stop. Therefore, they would return bicycles to stations that are in close proximity to public transit nodes (i.e., bus stops and subway entrances). The spatial characteristics of trip attraction stations may be highly correlated with bike share riders' trip purposes, intermediate destinations or final destination activities. We use a 500-m buffer to capture the spatial characteristics for each active bike share station. Distances up to 400 to 500 m have been regarded as walkable distances (Atash, 1994; Aultman-Hall et al., 1997; Krizek, 2003; McCormack et al., 2008; Pikora et al., 2003). We take 500-m as the service radius of bike share stations covering bike share users' potential destinations. Using smaller buffers may fail to capture the effects of some relevant built environment features. For example, we expect that bike share stations with adequate bicycle facilities within 500 m may attract more bike share trips. The descriptive statistics for these bike share stations are reported in Table 2. Fig. 2 displays how we measure the numbers of bus stops, subway entrances and the area of green space for a selected Citi Bike station. The data for population density and job density are provided by 1 2

18,870 19,340 29,675 33,771 39,108 48,677 44,512 50,239 54,951 50,763 39,474 26,021

EPA's Smart Location Database (SLD) at the Census Block level.3 EPA calculates the population and job densities using 2010 US Census U.S. and Census Longitudinal Employer-Household Dynamics (LEHD) of 2010. In our study, population density and job density are measured as the number of residents per square mile and the number of jobs per square mile, respectively. The weather data we use comes from the Central Park station from National Climatic Data Center. The meteorological attributes used, such as temperature, humidity, and wind speed, are measured per hour (total 720 h). In addition, we create seven dummy variables to capture the temporal variations: AM (7:00–10:00), Midday (10:00–16:00), PM (16:00–20:00), Evening (20:00–24:00), Night (0:00–7:00), Weekday and Weekend.

3.2. Bivariate statistical analysis To begin the analysis, we show the bivariate relationships between our explanatory variables and station-level bike share ridership for both genders. We calculate the total trip attractions at each station during the whole month of September for men and women, respectively. We then conduct Pearson correlation analyses between our explanatory variables and each gender's ridership. The estimated coefficients and the significance levels are reported in Table 2. The signs of the correlation coefficients are consistent across men and women, however, not all coefficients are statistically significant for both groups. Also, the scales of the estimated coefficients indicate that the effects of our explanatory variables on trip attractions may vary across men and women. We find that the length of off-street bike lanes, the number of benches, and the area of recreation space are statistically significant and positively associated with females' bike share ridership. On the other hand, these factors do not significantly associate with the outcomes of males. The results are consistent with previous studies (Akar et al., 2013; Emond et al., 2009; Handy, 2011; Heesch et al., 2012; Krizek et al., 2005; Zhao et al., 2015). That is, females are more sensitive to being close to bicycle infrastructure, more likely to use bicycles for non-commuting trips, and have longer trip chains. It is also worth mentioning that the number of bus stops and the number of subway entrances are positively associated with trip attractions of both genders. The scales of the estimated coefficients reflect males are more likely to link public transit services and bike-sharing. One potential issue of the bivariate analysis presented above is that it fails to capture the true effect of a specific factor on the outcome variable while controlling for other factors. In our case, it is necessary to account for weather and temporal effects. Table 3 provides the proportions of system trip attractions in different weather and temporal conditions for both genders. We also

https://www.citibikenyc.com/system-data http://opendata.cityofnewyork.us/

3

3

https://www.epa.gov/smartgrowth/smart-location-mapping#SLD

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Fig. 1. Study area and the location of bike share stations.

three models:

calculate the share of female ridership in these conditions. For example, the percentage of trip arrivals made by females from 00:00 to 07:00 am is calculated as:

(i) A negative binomial model explaining the factors associated with trip attractions of women (ii) A negative binomial model explaining the factors associated with trip attractions of men (iii) A fractional logit model investigating the extent each factor contributes to the share of trips made by women.

The share of female arrivals female arrivals from 00: 00 to 07: 00 = total number of trip arrivals made by both genders from 00: 00 to 07: 00 Although trip arrival distributions are almost identical for men and women, we observe some differences. For example, female ridership consists of nearly one-fifth of total system usage at night (0:00–7:00), which is less than other times of day. Women contribute to 30% of the system trip attractions on weekends, which is higher than that on weekdays (Table 4).

The trip attraction data consist of hourly trip counts at each bike share station. As expected, some stations are used much more frequently than others. The data are not normally distributed and thus models cannot be estimated using ordinary least squares (OLS) regression. For the first two models explaining the trip attractions of women and men, we employ a negative binomial modelling approach, which is a generalization of the Poisson model that does not assume the mean of the data be equal to the variance. The negative binomial model adds an error term to account for unobserved heterogeneity and allow for variance greater than the mean – overdispersion (Washington et al.,

3.3. Modelling approach The main goal of this research is to understand how bicycle facilities, land use and built environment characteristics, and public transit services contribute to the gender gap in bike share usage. We estimate 4

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share usage using a fractional logit model. In addition, a fractional logit model is capable of handling the extreme values of 0 and 1 without having to manipulate the data. The model structure is written as follows (Papke and Wooldridge, 1996, 2008):

Table 2 Descriptive summary of sample characteristics. Variable a

System hourly trip attractions by men System hourly trip attractions by womena Bike share and bicycling facilities (598 stations) Number of docks at station in 500-m buffer Number of Citi Bike stations in 500-m buffer Bike route length (kilometers) in 500-m buffer Off-road bike route length (kilometers) in 500-m buffer Number of bicycle racks in 500-m buffer Number of benches in 500-m buffer Land use and built environment characteristics (598 stations) Number of intersections in 500-m buffer Sidewalk length (kilometers) in 500-m buffer Population density at census block level (people per m2) × 1000 Job density at census block level (jobs per m2) × 1000 Distance to the nearest university (kilometers) Area of green space in 500-m buffer (kilometers2) Area of recreation space in 500-m buffer (kilometers2) Public transit services (598 stations) Number of bus stops in 500-m buffer Number of subway entrances in 500-m buffer Temporal units and weather (720 h) Night (00:00–7:00) AM (7:00–10:00) Midday (10:00–16:00) PM (16:00–20:00) Evening (20:00–24:00) Weekend Weekday Sunny Cloudy Rain Temperature (°F) Humidity (%) Wind speed (MPH)

Mean

Std. dev.

1446.27 508.15

1218.99 438.37

32.41 6.58 3.89 0.25 143.27 4.15

10.21 2.45 1.74 0.45 107.23 3.37

76.58 27.57 26.90

31.20 4.37 23.03

45.95 1.50 0.08 0.01

80.66 0.90 0.12 0.01

34.07 12.17

18.16 13.37

29.2% 12.5% 25.0% 16.7% 16.7% 26.7% 73.3% 60.0% 32.4% 5.8% 71.22 64.88 4.69

logit:E(Y | X ) =

where Y is the share of female ridership, X represents our explanatory variables, and β are the estimated coefficients. The estimated parameters of both the negative binomial models and the fractional logit model cannot be interpreted as linear marginal effects. Therefore, we calculate the average elasticity effects (AEEs) for the policy-related variables in all models. These variables refer to bicycle and bike share infrastructure, land use and built environment characteristics, and public transit facilities. An elasticity effect illustrates the percentage change in our dependent variable resulting from a 1% change in an input variable, while holding others constant. For the two models explaining the trip attractions of women and men, the elasticity effects show the percentage changes in the number of station-level trip arrivals that are caused by a 1% increase in a given variable. As shown in Table 5, a 1% increase in the number of docking points at bike share stations increases hourly trip attractions of men by 1.01% on average. In the third model, the elasticity effect describes the change in the proportion of trips made by females resulting from a 1% change of the independent variable. For example, a 1% increase in the length of offstreet bike routes around bike share stations increases the share of female trip arrivals by 0.42% on average. 4. Results and discussion We report the model results in Table 5. The first two models are negative binomial models with hourly trip arrivals of males and females at station-levels. The third model is a fractional logit model with the share of hourly female ridership at station-level as the dependent variable. When developing the models, we removed those explanatory variables that were not statistically significant at the 90% level from the final models. The correlates of bike share usage for men and women are very similar. However, the results of the proportion model suggest that our variables of interest may affect male and female riders differently. We find that most factors have significant effects on gender gap in bike share ridership. As a side note, we have also estimated models based on trip productions. The results are broadly similar to our reported attraction models. We therefore focus our discussion on the results of the attraction models as shown in Table 5.

7.70 16.99 3.54

a

Notes: Trip attractions refer to attraction trip ends at any station. System hourly trip attractions by each gender = the total number of trip attractions by each gender in September/720 h; 720 h = 30 days × 24 h.

2010). Our third model focuses on the share of female bike-share trips. Modelling the number of female bike share usage may not exactly show whether the spatial features of bike share stations are female-friendly. Some bike share stations are in high demand for both males and females as they are located in high-density areas with mixed land uses, and commercial facilities (Faghih-Imani and Eluru, 2015; Wang et al., 2015; Noland et al., 2016; El-Assi et al., 2017). Although the number of female trip ends may increase at these stations due to locational factors, the number of male trips may increase even more. Therefore, looking solely at trip numbers may hinder the existence of and determinants for gender gap. To explore the factors associated with gender gap, we set the share of female ridership at each bike share station in each hour as the outcome variable in our third model, as follows:

Y=

exp(X β) ,0≤Y≤1 1 + exp(X β)

4.1. Bicycle and bike share facilities The number of bike docks at a bike share station is statistically significant and positively associated with trip attractions for both men and women. This may be due to the fact that system operators install more docking points at popular stations as compared to unpopular stations. As expected, increasing the length of bike lanes may promote the bike share usage among both genders. We find that both the number of docking points and the length of bike lanes do not significantly influence the share of trip arrivals made by women. The length of off-road bike lanes is positively associated with bike share trip attractions for both genders. Moreover, this factor tends to have a positive impact on the share of female riders. A 1% increase in the length of off-road bike lanes around bike share stations is correlated with a 0.46% increase in the proportion of trip arrivals taken by females. The result is expected because female riders value off-street bicycle facilities more as compared to their male counterparts (Akar et al., 2013; Emond et al., 2009; Handy, 2011; Heesch et al., 2012; Krizek et al., 2005). The number of benches is negatively associated with the bike share usage of both genders. This result may seem surprising at first, but it is

number of trip arrivals of females total trip arrivals made by both genders

This outcome variable builds on the previous studies investigating the determinants of gender gap in bicycling choice (Akar et al., 2013; Baker, 2009; Emond et al., 2009; Garrard et al., 2008; Pucher et al., 2010). These studies suggest women are more sensitive to risks associated with bicycling. Females strongly prefer bike paths and cycle tracks that are physically separated from motor vehicle traffic (Akar et al., 2013; Emond et al., 2009). Cities with more protected bicycle facilities are more likely to have higher rates of female bicyclists (Garrard et al., 2008). Given the fractional nature of the outcome, we link our explanatory variables with the proportion of women's bike 5

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Fig. 2. Sample station's measurements.

actually quite intuitive. The number of benches may act as an important proxy for walkable environments (Cervero and Duncan, 2003; Pikora et al., 2003; Saelens and Handy, 2008). One would expect to see more benches in areas with high pedestrian volumes, and this may increase bicyclists' perceived risks of pedestrian – bicycle conflicts. Thus, bike share users may become unwilling to return bicycles to those stations surrounded by benches. Although the overall effect of existence of benches is negative on trip numbers for both genders, the estimate of our proportion model suggests this effect is less for women. The estimated results suggest that installing more bicycle racks may increase the overall bike share ridership. Moreover, we find the number of bicycle racks exerts a statistical significant and positive influence on the share of trip arrivals made by women. Though those racks cannot be used for Citi Bike bicycles, the areas with more bicycle racks generally have more bicycle activities (Noland et al., 2016) and women may feel more comfortable around other bicyclists. The estimated margin shows that a 1% increase in the number of bicycle racks around bike share stations may lead to a 1.18% increase in the proportion of trips made by female riders. Among all our variables related to bicycle facilities, this factor has the largest effect on minimizing the gender gap in bike share usage.

Table 3 Pearson correlations between bike share station attributes and trip arrivals.

Bike share and bicycling facilities Number of docks at station in 500-m buffer Number of Citi Bike stations in 500-m buffer Bike route length (kilometers) in 500-m buffer Off-road bike route length (kilometers) in 500-m buffer Number of bicycle racks in 500-m buffer Number of benches in 500-m buffer Land use and built environment characteristics Number of intersections in 500-m buffer Sidewalk length (kilometers) in 500-m buffer Population density at census block level (people per m2) Job density at census block level (jobs per m2) Distance to the nearest university (kilometers) Area of green space in 500-m buffer (kilometers2) Area of recreation space in 500-m buffer (kilometers2) Public transit services Number of bus stops in 500-m buffer Number of subway entrances in 500-m buffer ⁎⁎

Male

Female

0.55⁎⁎ 0.31⁎⁎ 0.25⁎⁎ 0.08 0.38⁎⁎ 0.04

0.46⁎⁎ 0.27⁎⁎ 0.28⁎⁎ 0.16⁎⁎ 0.47⁎⁎ 0.10⁎⁎

0.03 0.03 0.04⁎⁎ 0.19⁎⁎ −0.46⁎⁎ −0.16⁎⁎ 0.03

0.04 0.03 0.05⁎⁎ 0.10⁎⁎ −0.37⁎⁎ −0.10⁎⁎ 0.11⁎⁎

0.36⁎⁎ 0.41⁎⁎

0.21⁎⁎ 0.28⁎⁎

Significant at the 95% level.

6

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Since female riders are more likely to use bicycles for non-utilitarian purposes (Emond et al., 2009; Handy, 2011; Krizek et al., 2005), they may regard local parks as recreation destinations. Thus, increasing the area of green space may promote the share of female trip arrivals. The area of recreational land uses is positively associated with bike share usage for both genders. This factor also performs as a significant and negative predictor for the share of trip arrivals made by female riders. Future research could explore the influences of different types of recreation land uses, such as playing fields, indoor gyms and waterfront spaces.

Table 4 System trip attractions under different temporal and weather conditions.

Time periods 00:00–07:00 07:00–10:00 10:00–16:00 16:00–20:00 20:00–24:00 Total Weekday and weekend Weekday Weekend Total Sunny, cloudy, and rainy Sunny Cloudy Rainy Total Temperature ranges Below 60 °F 60 °F–70 °F 70 °F–80 °F Above 80 °F Total

Time units

Malea

Femalea

The share of female arrivalsb

210 90 180 120 120

5.3% 19.9% 28.9% 33.9% 11.8% 100%

3.7% 20.3% 31.7% 33.5% 10.9% 100%

19.6% 26.3% 27.8% 25.7% 24.5%

528 192

79.9% 20.1% 100%

75.4% 24.6% 100%

24.9% 30.1%

432 233 42

64.9% 32.6% 2.6% 100%

67.0% 31.1% 2.0% 100%

26.5% 25.0% 21.1%

52 291 269 108

2.7% 36.7% 40.6% 20.1% 100%

2.1% 35.8% 41.2% 20.9% 100%

21.9% 25.6% 26.3% 26.7%

4.3. Public transit services The relationships between public transit services and our outcomes show that males and females are likely to have different purposes for bike share usage. The number of bus stops is statistically significant and positively related to males' trip attractions. Both the number of bus stops and the number of subway entrances are negative predictors of the share of female trips. These results are not surprising. Male users are more likely to generate bike share trips for commuting, and thereby they may integrate bike share usage with their public transit services more often. To further check our assumptions, we examine the relationships between trip arrivals and the number of public transit nodes during rush hours on weekdays by looking at the Pearson correlation coefficients. The results are shown in Table 6. The scales of the estimates suggest that, during weekday rush hours, both indicators of transit access (number of bus stops and number of subway entrances) have larger effects on males' trip arrivals. The results are consistent with our multivariate analyses.

a System trip attractions under a certain condition by each gender during the whole month. b The share of female arrivals = female trip arrivals/(female trip arrivals + male trip arrivals).

4.2. Land use and built environment characteristics 4.4. Temporal and weather effects Number of street intersections is positively associated with female trip attractions as well as the share of female trip arrivals. The results are not surprising because female riders are more concerned about road traffic (Akar et al., 2013; Emond et al., 2009; Handy, 2011; Garrard et al., 2008). Higher intersection densities may reduce the vehicle volume and speed, thereby promoting the safety perceptions of female riders. Increasing street connectivity by 1% induces a 1.27% increment in the share of trips taken by females. Job density is statistically significant and positively related to both genders' bike share trip attractions. This is because job density can serve as a proxy for compact urban environments. Installing bike share stations in compact urban environments may attract more riders (El-Assi et al., 2017; Wang et al., 2015). Moreover, we find that job density is negatively associated with the share of trip arrivals made by female riders. This is probably because male users generate more bike share trips for commuting as compared to their female counterparts (Goodman and Cheshire, 2014). As the distance to the nearest university campus becomes longer, the station-level usage decreases for both genders. The results are consistent with previous research (Wang et al., 2018). That is, university students and employees use the bike share system more often than others. Interestingly, the distance to the nearest university campus is positively associated with the proportion of female trip arrivals. This may be related to the gender and bicycle trip ratios at a university versus urban settings in NYC, however, we do not have the data to validate this hypothesis. In our case, green space mainly represents the total size of local parks. Increasing the area of green space around bike share stations may significantly reduce the bike share usage among male riders. We find that the area of green space around bike share stations is positively associated with the share of trips made by women. These results are reasonable. Local parks may act as physical barriers, lengthening the distances of commuting trips. It is possible that male riders generate more bike share trips for commuting (Goodman and Cheshire, 2014).

This study controls for the effects of temporal and weather factors that are known to affect bike share ridership. In our count models, the signs of these factors are as expected and consistent with previous studies (El-Assi et al., 2017; Faghih-Imani and Eluru, 2016; Gebhart and Noland, 2014; Wang et al., 2018). We find that the proportions of trip arrivals of women vary under different weather and temperature conditions. The overall results imply that both genders are less likely to use bike share systems in adverse meteorological conditions. The shares of female ridership are higher during favorable weather conditions. The estimated results suggest that both genders have more bike share trips on weekdays, and the shares of female ridership on weekdays are less than that of weekends. The count models show both genders generate more bike share trips during morning and afternoon peaks (07:00–10:00 and 16:00–20:00) than midday (10:00–16:00). We further find that the proportions of trip arrivals made by women are larger on morning peaks and smaller on afternoon peaks as compared to midday. With respect to the weather conditions, our results reveal that both genders make more bike share trips on sunny days. The shares of female trip arrivals are larger on sunny days as compared to cloudy days. While rain decreases the use of bike share system overall, it increases the gender gap in bike share use. As expected, higher temperatures encourage more bike share usage among both genders. The results also reveal that the female bike-share proportion decreases when the temperature drops below 60 °F. 5. Conclusion Bike-sharing is one of the rapidly growing transportation services around the world. A better understanding of the determinants of bike share usage may help planners and policy makers promote the effectiveness of policy interventions and infrastructure investments aimed at encouraging bicycling. Using the open data from New York's Citi Bike system, this study contributes to the existing research efforts on exploring how bicycle infrastructure, built environment characteristics, 7

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Table 5 Results for negative binomial models and fractional logit model. Males

Bicycle and bike share infrastructure Number of docks at station Bike route length Off-road bike route length Number of benches Number of bicycle racks Land use and built environment characteristics Intersection density Job density Distance to the nearest university Area of green space Area of recreation space Public transit facilities Number of bus stops Number of subway entrances Temporal and weather factors Weekday (base case: weekend) Time periods (base case: 10:00–16:00) Night AM PM Evening Weather (base case: cloudy) Sunny Rainy Temperature range (base case: below 60 °F) 60 °F–70 °F 70 °F–80 °F Above 80 °F Constant Dispersion – ln(alpha) Model fitness Initial log likelihood Final log likelihood Degree of freedoms AIC BIC Observations a

Females a

The share of trip arrivals made by females a

Coef.

p-value

AEEsa

0.079 0.008 0.000

0.000 0.002 0.000

0.42% 0.68% 1.18%

0.001 −0.541 0.090 0.401 −1.432

0.006 0.001 0.000 0.000 0.017

1.27% −0.50% 2.48% 0.60% −0.27%

−0.001 −0.003

0.025 0.005

−0.99% −0.70%

0.000

−0.120

0.000

−2.241 0.256 0.470 −0.623

0.000 0.000 0.000 0.000

−0.484 0.117 −0.079 −0.236

0.000 0.000 0.000 0.000

0.000 0.000

0.116 −0.668

0.000 0.000

0.042 −0.123

0.000 0.000

0.000 0.000 0.000 0.000 0.000

0.679 0.633 0.654 −2.422 −0.242

0.000 0.000 0.000 0.000 0.000

0.153 0.161 0.180 −1.195

0.000 0.000 0.000 0.000

Coef.

p-value

AEEs

Coef.

p-value

AEEs

0.031 0.054 0.392 −0.023 0.003

0.000 0.001 0.000 0.002 0.000

1.01% 0.21% 0.10% −0.10% 0.46%

0.027 0.042 0.469 −0.015 0.004

0.000 0.010 0.000 0.047 0.000

0.86% 0.16% 0.12% −0.06% 0.54%

1.217 −0.286 −0.875 7.517

0.003 0.000 0.000 0.000

0.06% −0.43% −0.07% 0.07%

0.001 0.713 −0.214

0.068 0.029 0.000

0.11% 0.03% −0.32%

8.215

0.000

0.08%

0.005

0.004

0.20%

0.268

0.000

0.106

−1.809 0.200 0.588 −0.407

0.000 0.000 0.000 0.000

0.079 −0.539 0.489 0.438 0.463 −1.430 −0.095 −840,746.6 −725,669.9 22 1,451,384.0 1,451,625.0 430,560

−529,162.1 −447,783.3 21 895,608.5 895,838.9 430,560

−148,067.4 −146,361.6 21 292,765.2 292,984.8 256,654

AEEs refer to the average elasticity effects.

related to the share of female bike share trips. These significant relationships imply that our explanatory variables may influence the bike share usage of men and women differently. A particular example is that extending the length of off-street bike routes could significantly promote the bike share ridership for both genders. This factor is also a significant and positive predictor of the share of trip arrivals made by women, contributing to minimizing the gender gap in bike share usage. We find that the length of off-street bike routes, number of benches and bicycle racks, intersection density, and green space are significantly and positively associated with the proportion of bike share trips made by women. The results are important. Although it is possible to promote the overall quality of bike share services, planners and practitioners who understand the factors associated with gender differences in bike share usage could invest efficiently and effectively in transport infrastructure to reduce this disparity. Our findings suggest that female bike share riders are more sensitive to traffic conditions and are less likely to make bike share trips for commuting as compared to their male counterparts. Both the number of bus stops and the number of subway entrances around bike share stations are negative predictors of the proportion of female bike share trips. The results imply that men may be linking bike share usage with public transit services more often than women. Future intervention programs aimed at integrating emerging disruptive mobility services into traditional public transit systems should carefully consider the potential female users' adoption patterns. This study is not without limitations. First, the data used in this

Table 6 Pearson correlations between the number of public transit services and trip arrivals during the rush hours on weekdays (07:00–10:00 and 16:00–20:00).a

Hourly trip arrivals by males Hourly trip arrivals by females

Number of bus stops

Number of subway entrances

0.29⁎⁎ 0.17⁎⁎

0.32⁎⁎ 0.21⁎⁎

⁎⁎

Significant at the 95% level. The bivariate relationships above are statistically significant at the 95% level. However, it is reasonable that these relationships may become statistically not significant when we control for the effects of other factors. a

and public transit facilities affect bike share usage for males and females differently. Specifically, we focus on identifying the factors that influence the gender gap in bike share ridership. The results not only confirm some of the findings from previous studies, but also add new insights into the environmental correlates of the gender gap in bike share usage. The descriptive statistics show that more than two thirds of the bike share trips were made by men, which indicates bike share ridership is strongly skewed by gender. The results of bivariate statistical and multiple regression analyses reveal that most of our variables significantly influence the bike share usage of men and women in the same direction. We also find that these explanatory variables are significantly 8

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K. Wang and G. Akar

study does not include any individual attitudinal factors. We cannot exactly compare the underlying motivations and barriers to bike share usage between men and women. Despite this limitation, this study makes full use of the available spatially detailed data from New York City. Future research could design stated preference surveys to achieve a better understanding of how attitudinal factors contribute to the gender gap in bike share usage. The second major limitation comes from the lack of socio-demographic data on non-subscribers of Citi Bike system. This lack of information may skew our data in unknown ways. Future research should focus on both members and casual users pending socio-demographic data availability. This study analyzes gender gap in bike share usage using data from New York City. Gender differences in the built environmental correlates of bike share ridership may be different in other urban settings. Even though the results of this study may not be directly transferable, the analytic methods discussed in this research are applicable elsewhere.

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