DOI: 10.25338/b8x064
title: Dock‐based and dockless bikesharing systems: analysis of equitable access for disadvantaged communities

description: Dockless bikeshare systems show potential for replacing traditional
 dock-based systems, primarily by offering greater flexibility for bike
 returns. However, many cities in the US currently regulate the maximum
 number of bikes a dockless system can deploy due to bicycle management
 issues. Despite inventory management challenges, dockless systems offer
 two main advantages over dock-based systems: a lower (sometimes zero)
 membership fee, and being free-range (or, at least free-range within
 designated service areas). Moreover, these two advantages may help to
 solve existing access barriers for disadvantaged populations. To date,
 much of the research on micro-mobility options has focused on addressing
 equity issues in dock-based systems. We have limited knowledge of whether,
 and the extent to which dockless systems might help mitigate barriers to
 bikeshare for disadvantaged populations. Using San Francisco and Los
 Angeles as case studies, because both cities have both dock-based and
 dockless systems running concurrently, we quantify bikeshare service
 levels for communities of concern (CoCs) by analyzing the spatial
 distribution of service areas, available bikes and bike idle times, trip
 data, and rebalancing among the dock-based and dockless systems. We find
 that dockless systems can provide greater availability of bikes for CoCs
 than for other communities, attracting more trip demand in these
 communities because of a larger service area and frequent bike rebalancing
 practices. More importantly, we notice that the existence of electric
 bikes helps mitigate the bikeshare usage gap between CoCs and other
 tracts. Besides the data analyses for bikeshare trips, we also study the
 spatial distribution of online suggested station locations and find that
 the participants’ desired destinations for work/school purposes have not
 been covered to the same extent in CoCs as in other communities. Our
 results provide policy insights to local municipalities on how to properly
 regulate and develop dockless bikeshare systems to improve mobility
 equity.
description: There are three sets of data collected for this study: 1)
 communities of concern; 2) bikeshare data; 3) online suggestion data. We
 introduce the collection process for three datasets separately.
 <strong>Communities of concern</strong>: There is not
 a unified definition of disadvantaged populations or underserved
 communities across different regions in the US. Our previous study defines
 disadvantaged communities based on income, percentage of minority
 populations, and vehicle ownership (Qian and Niemeier 2019). In San
 Francisco, the MTC provides its own definition of disadvantaged
 communities for the transportation field. In a recent report evaluating
 dockless bikeshare in San Francisco, the SFMTA uses the MTC definition
 when conducting equity analyses (MTC 2018). The MTC terms and identifies
 disadvantaged populations as “Communities of Concern (CoCs)” based on the
 2012-2016 American Community Survey (ACS) 5-year tract-level
 data. In Los
 Angeles, since different areas have their customized disadvantaged
 populations, we adopt a different definition of CoCs in Los Angeles, which
 is provided by the Southern California Association of Governments (SCAG)
 from its Plan Performance Environmental Justice Technical Report. In the
 report, SCAG investigated all Census Designated Places (CDPs) and City of
 Los Angeles Community Planning Areas (CPAs) and selected regions that fall
 in the top 33% of all communities in SCAG region for having the highest
 concentration of minority populations and low-income households (Southern
 California Association of Governments 2020). A person is classified as
 “minority” if the individual self-identified as one of the minority groups
 in the census (Table 4). SCAG performed poverty classification according
 to the income guidelines outlined by the U.S. Department of Health
 & Human Services. <strong>Bikeshare
 data</strong>: The analyses require data from both bikeshare
 systems. However, as would be true in cities across the U.S., the data
 from the two bikeshare systems in San Francisco/Los Angeles are not in the
 same format. Currently, Motivate and B-cycle operate most of the
 dock-based bikeshare systems in the US. Among all of these dock-based
 systems, Motivate operates Citi Bike (New York), Divvy (Chicago), Capital
 Bike Share (Washington DC), Ford GoBike (Bay Area), Biki (Honolulu), and
 Bluebikes (Greater Boston) which together contributed over 80% of all
 dock-based bikeshare trips in 2018 (NACTO 2019). All of the dock-based
 bikeshare systems operated by Motivate and B-cycle provide trip data,
 including information about trip start day and time, end day and time,
 start station, end station, bike id, and rider type (annual member or day
 pass user). For the members’ trips, the database also includes the riders’
 gender and year of birth. However, the operators do not provide
 information on bike availability or rebalancing activities. We also found
 that these limitations exist for all dock-based bikeshare systems in the
 US because, currently, all operators only provide trip-based data, not
 operational data. On the other hand, there is no
 trip data for dockless bikeshare systems; companies have not shared the
 data in this form because of privacy concerns or commercial advantages.
 However, they provide information through the General Bikeshare Feed
 Specification (GBFS), which is an open data standard for bikeshare. The
 GBFS provides real-time bike information (including bike id, location,
 battery level, and service status), and the number of available bikes in
 available hubs in a city. Unfortunately, the standard does not provide the
 bike id when the bike is at a hub. Additionally, the real-time bike data
 does not include any user data. Currently, many cities have required
 dockless bikeshare companies (e.g., JUMP, Bird, Lime, Lyft, Skip, Spin) to
 share real-time data in GBFS format. If a dockless bikeshare company
 provides data as required, it will be information in this format and
 available through an application programming interface (API). We developed
 a web-scraping (web data extraction) tool for the systematic and
 continuous collection of the real-time information from GBFS (e.g., JUMP
 Bike). Despite its limitations, the GBFS is very useful, and we developed
 a robust framework based on reasonable assumptions to infer other
 bikeshare data (e.g., bike availability and rebalancing operations) to
 support our analyses. For the study, we use historical
 bikeshare trip data provided by Ford GoBike; their database includes all
 bikeshare trips between 2013 and 2019. JUMP Bike (dockless), as mentioned,
 does not directly provide historical bikeshare trip data, thus, we use the
 web-scraping tool to gather minute-by-minute data from January to March
 2019. We use this three-month sample data because, by March 2019, JUMP
 Bike had already been operating for over one year; thus, users were
 familiar with the service. Moreover, although there are some declines in
 bike ridership in San Francisco at the end of the year, ridership (based
 on data from bike counters) does not significantly fluctuate throughout
 the year (T. Winters 2017). For example, during 2018 the average number of
 bike counts (at the available bike counters) between January and March was
 15,385 per month, which is 93% of the overall monthly average of 16,533.
 We found similar trends when analyzing the monthly trip numbers for Ford
 GoBike in San Francisco. The average number of monthly dock-based
 bikeshare trips between January and March was 210,598 in 2019, which is 3%
 above the average monthly usage (204,063 trips). As these findings were an
 indication that the three-month sample collected was representative, the
 analyses compare both systems within this period (January to March of
 2019). <strong>Online suggestion
 data</strong>: The bikeshare operation companies (Baywheels in San
 Francisco or Metro Bike Share in Los Angeles) develop public online
 portals where users can suggest potential bikeshare station locations and
 comment on existing ones (Metro Bike Share 2021; Baywheels 2021). In this
 portal, users placed a dot (suggested location) on the maps of San
 Francisco and Los Angeles, providing a detailed description of their
 reasons (e.g., home, work/school, shopping, and fun) for choosing the
 locations, and their home zip codes. We applied the web scraping
 technology to download those suggestion data from these online portals. In
 San Francisco, we collected the historical information in the portal until
 the end of 2020. However, the data contained a number of duplicate
 locations by the same users and test data, which were removed. In the end,
 there is a total of 721 records of new bikeshare station suggestions
 available in San Francisco. In Los Angeles, after removing duplicated
 data, there are 2,354 suggestions of new bikeshare stations.
description: Please refer to the data method section