Available online at: https://www.sciencedirect.com/science/article/abs/pii/S0965856420306479
Bikeshare systems are rapidly becoming more prevalent in metro and micropolitan areas around the world. However, low-income populations, people of color, and transit-dependent households are not highly representative of the bikeshare user’s profile. Some bikeshare programs in the United States, such as Divvy in Chicago, try to address this equity problem by promoting their systems among low-income communities. Currently, there is limited research estimating bikeshare ridership in these communities and quantitatively analyzing the impacts of financial barriers in disadvantaged areas at the station level. This research fills this gap by analyzing the current utilization of bikeshare systems among disadvantaged populations. The study develops a Negative Binomial regression model to estimate bikeshare ridership using data from Chicago’s bikeshare system. The results show that bikeshare stations in disadvantaged communities generate around two-thirds of the average annual trips across all stations. The employment rate plays an important role in increasing bikeshare ridership, especially for disadvantaged areas. Additionally, the research found that the proportion of trips by annual members is significantly lower in disadvantaged communities than in other areas. However, interestingly, residents in disadvantaged communities tend to make longer bikeshare trips once they are annual members. Their dependence on bikeshare systems may result from accessibility improvement (e.g., work commute by bikeshare). Based on all the findings, we discuss planning implications for more socially inclusive and equitable bikeshare systems.
Key words: Bikeshare system, Disadvantaged communities, Negative binomial, Employment rate