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Competition, Integration, or Complementation? Exploring Dock-Based Bike-Sharing in New York City

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The bike-sharing system has advanced urban transportation by solving “the last mile problem,” enabling riders to better connect to public transit. There has been a paucity of knowledge, however, regarding the relationship between bike-sharing and public transit. In this article, we solicit one year of bike trip data comprising approximately 17 million trips from Citi Bike, the largest dock-based bike-sharing system in New York City. Then, we derive six bike usage clusters based on three clustering variables: the start trips, end trips, and station empty status. Finally, we propose three relationships between bike-sharing and public transit: competition, integration, and complementation. The result demonstrates that bike-sharing can largely compete with public transit in New York City. A significant portion of bike-sharing trips are more time-intensive than their public transit alternatives. The article concludes that this competition exists due to riders’ preferences for lower costs and flexibility over savings in travel time, which helps to improve transportation equity for socioeconomically disadvantaged populations. Thus, in New York City, bike-sharing primarily fulfills the need for lowcost and flexible travel rather than solving “the last mile problem.” This revelation provides new insights into the roles of bike-sharing in urban transportation.
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Competition, integration, or complementation? Exploring the role of dock-based
bike-sharing in New York City
Yunhe Cui1, Xiang Chen1,*, Xurui Chen2, Chuanrong Zhang1
1. Department of Geography, University of Connecticut, Storrs, CT 06269, USA
2. Vipshop, Guangzhou, Guangdong 510220, China
*Corresponding author: Xiang Chen
Email: xiang.chen@uconn.edu
ORCiD: https://orcid.org/0000-0002-5045-9253
Acknowledgments
We thank Dr. Caiwen Ding for assisting with the usage of Google Map API and Dr. Ran
Xu for assisting with the statistical analysis.
Author Biographies
YUNHE CUI is a Ph.D. student in the Department of Geography at the University of
Connecticut, Storrs, CT, 06269. E-mail: yunhe.cui@uconn.edu. Her research interests
include human mobility, shared mobility services, and urban transportation.
XIANG CHEN is an Assistant Professor in the Department of Geography at University
of Connecticut, Storrs, CT 06279. E-mail: xiang.chen@uconn.edu. His research is
focused on GIScience, food access, and community health.
XURUI CHEN is a data scientist at Vipshop, Guangzhou, Guangdong 510220, China. E-
mail:sherryairui@gmail.com. Her work is focused on consumer behavior analysis.
CHUANRONG ZHANG is a Professor in the Department of Geography at the University
of Connecticut, Storrs, CT, 06269. E-mail: cindy.zhang@uconn.edu. Her research
interests are in GIS, geostatistics, and their applications to natural resource management
and environmental evaluation.
Competition, Integration, or Complementation? Exploring Dock-based Bike-
sharing in New York City
Abstract
The bike-sharing system has advanced urban transportation by solving the last
mile problem,” enabling riders to better connect to public transit. However, there has
been a paucity of knowledge regarding the relationship between bike-sharing and public
transit. In this article, we solicit one year of bike trip data comprising approximately 17
million trips from Citi Bike, the largest dock-based bike-sharing system in New York
City. Then, we derive six bike usage clusters based on three clustering variables: the start
trips, end trips, and station empty status. Finally, we propose three relationships between
bike-sharing and public transit: competition, integration, and complementation. The result
demonstrates that bike-sharing can largely compete with public transit in New York City.
A significant portion of bike-sharing trips is more time-intensive than their public transit
alternatives. The article concludes that this competition exists due to riders’ preferences
for lower costs and flexibility over savings in travel time, which helps to improve
transportation equity for socioeconomically disadvantaged populations. Thus, in New
York City, bike-sharing primarily fulfills the need for low-cost and flexible travel rather
than solving “the last mile problem.” This revelation sheds new insights into the roles of
bike-sharing in urban transportation.
Keywords: bike-sharing, public transit, K-means clustering, mobility, last mile problem
1 Introduction
One emerging trend in modern transportation is the sharing economy. While
traditional modes of mass transit (e.g., buses, subways) enable the sharing of mobility
services by lowering individual travel costs, they are unable to fulfill the need for short-
distance travel between the transit station and the traveler’s origin/destination, which is
generally known as “the last mile problem” (Liu et al., 2012; Moorthy et al., 2017;
Shaheen & Chan, 2016). Recent urban and transportation planning initiatives have taken
strides to promote shared mobility services, such as bike-sharing. Bike-sharing
accommodates individual travel by enabling more flexibility in mode-switching while
alleviating environmental impacts from automobiles, such as traffic congestion and air
pollution. It has gained considerable popularity in world megacities, including Beijing,
Paris, and New York City (NYC), where the public transit infrastructure has reached its
full capacity for expansion (DeMaio, 2009; Liu et al., 2012).
The bike-sharing system has undergone four generations of evolution (DeMaio,
2009). The prototype bike-sharing system, the White Bikes, debuted in Amsterdam on 28
July 1965. The first generation system adopted an unregulated business model, where
bikes were left unlocked and restricted within a bounded area (Shaheen et al., 2010). In
the early 1990s, a second generation of bike-sharing was developed through an
integration with a coin deposit station, where the station served as a hub for payment,
return, and storage of bikes. However, these two generations suffered from rampant bike
theft and vandalism, primarily due to the lack of administration and unrestricted bike
usage. As the coin deposit system began to wane, a third generation, the automated dock-
based system (hereafter, “dock-based system”), emerged. The dock-based system
monitored bikers’ identities and travel histories via an electronic payment system, such as
magnetic stripe cards and cell phones, to offset the risk of bike misuse. After its initial
local success, the dock-based system successively expanded to about 125 world cities as
of 2010 (Shaheen et al., 2010) and remains active in world megacities. At the same time,
a fourth generation, the dockless system, was developed by embedding bikes with smart
locks and Global Positioning System (GPS) tracking features. While China currently
boasts the world’s largest dockless bike market, to date, only a few U.S. cities (i.e.,
Seattle and Washington) have adopted the dockless system.
Location-based bike usage and tracking data derived from the dock-based and
dockless systems enable the further exploration of bike usage. These spatiotemporal
analyses generally split into three groups of studies. The first group explored how the
physical and built environments impact bike usage. These environmental variables
included weather and seasonality (Kim, 2018; Martinez, 2017; Miranda-Moreno &
Nosal, 2011; Nankervis, 1999; Sears et al., 2012), land use and infrastructure (Habib et
al., 2014; Kim et al., 2012; Lu et al., 2018; Pucher et al., 2010; Schoner & Levinson,
2014), and public transportation (Fishman et al., 2014; Guo et al., 2017; Zhang & Zhang,
2018). The second group explored the improvement of system efficiency. As the supply
and demand of the bikes did not align spatially, optimization models were employed to
improve the placement of the docking stations (García-Palomares et al., 2012; Lin et al.,
2013; Martinez et al., 2012) or dynamically rebalance bikes (Chiariotti et al., 2018;
Ghosh & Varakantham, 2017; Legros, 2019). The third group explored the environmental
impacts of bike-sharing, such as reducing carbon footprints and air pollution. It was
found that bike-sharing as a sustainable transportation mode can have potential
environmental benefits by reducing energy use, carbon dioxide emissions, and harmful
air pollutants (e.g., nitrogen oxide), under the premise that bike-sharing ridership was
sourced from private automobile or public transit (Chen et al., 2022; Li et al., 2021;
Zhang & Mi, 2018). On the other hand, the operation of the system raised environmental
concerns due to the carbon footprint generated from bike rebalancing (D’Almeida et al.,
2021; Luo, 2019; Luo et al., 2020). More severely, with the bike-share market
diminishing in some cities, oversupplied bikes were abandoned, piled up, and dismantled,
generating non-disposable “bike litter” and thus substantial environmental problems
(Chen, 2019).
One underexplored area in the literature is how bike-sharing intertwines with the
public transit system to solve “the last mile problem.” This relationship can be built on
the theory of spatial interaction in transportation geographytransportation services can
either compete with or complement each other depending on the spatial interaction
between the supply and demand, further driven by the distance decay (Ullman & Boyce,
1980). These notions, known as complementarity and competition, can be extended to the
discussion of bike-sharing. For example, it was unclear if bike-sharing was diverting
urban commuters from public transit or supplementing the unfulfilled travel demand.
Although numerous studies attempted to demystify the synergy between bike-sharing and
public transit, the findings were rather inconsistent. For example, a case study in
Washington D.C. revealed that a 10 percent increase in bike trips generated a 2.8 percent
increase in public transit ridership (Ma et al., 2015). Singleton and Clifton (2014) noted
that bike-sharing and public transit had both competition and integration. Another study
in four U.S. cities showed that the bike-sharing system was largely integrated with the
public transit system (Kong et al., 2020). However, a separate study across major U.S.
cities highlighted that bike-sharing can negatively impact bus ridership (Graehler et al.,
2019). On a regional scale and relevant to this article, the relationship between bike-
sharing and public transit in NYC has not been well elucidated. For example, Campbell
and Brakewood (2017) found a significant decrease in bus ridership as a result of
expanding the bike-sharing system in NYC. However, Ashraf et al (2021) found a 2.3
percent increase in subway ridership when there was a 10 percent increase in bike-
sharing trips in NYC.
While it is relatively challenging to rationalize these inconsistencies, one path
forward is to explore the long-term spatiotemporal bike usage patterns. Existing studies
employed either primary trip data for a limited period (Kong et al., 2020; Saberi et al.,
2018) or secondary bike usage or summary data (Cheng et al., 2018; Shaheen et al.,
2010). Also, the majority of these studies were focused on the statistical analysis of the
trip data while overlooking the spatiotemporal dynamics. To fill the void, the article
solicits one-year trip data from Citi Bike and then reveals the three different roles (i.e.,
competition, integration, and complementation) that bike-sharing plays in relation to
public transit. These revelations will provide new insights into the roles of bike-sharing in
urban transportation.
2 Methodology
2.1 Study area
NYC is the most populated U.S. city, occupying a land area of 302 square miles
across five county-level boroughs: Bronx, Brooklyn, Manhattan, Queens, and Staten
Island. Because of the high cost of living in Manhattan, roughly one-third of daily
commutes occur across boroughs (City of New York, 2017). Unlike typical car-oriented
U.S. cities, less than 10 percent of trips are made via private automobiles due to existing
public transit-oriented infrastructure and policy (New York City Department of Motor
Vehicles, 2018). Thus, commuters in NYC are highly reliant on the Metropolitan
Transportation Authority (MTA), known as the nation’s largest comprehensive public
transit system, which includes buses, metro, and commuter rails. As the sharing economy
booms, bike-sharing has gained popularity. In NYC, 35 percent of residents reported the
use of ride-sharing services, including bike-sharing (New York City Department of
Transportation, 2018). As the dominant dock-based system in the Greater New York City
region, Citi Bike offers a service coverage in the urban core (i.e., Manhattan, Northwest
Brooklyn, West Queens, and Jersey City) with more than 13,000 bikes, over 850 stations,
and has exceeded 100 million completed trips since its inception (Citi Bike, 2020).
2.2 Data
We retrieved our data from open access data repositories and by web crawling.
First, we collected one-year bike trip data containing about 17 million trips in 2018
within the five NYC boroughs from Citi Bike’s system data website (Citi Bike, 2018b).
Each trip entry included the start time, end time, start docking station ID, end docking
station ID, and trip duration. Only trips in the range of 1 minute to 3 hours were retained,
as trips outside of this time duration were atypical. We then consolidated the number of
start trips and end trips for each of the 768 docking stations at a timestamp of 1 hour (see
the next section for details). Second, one important yet missing variable for analysis
involves the empty status of the docking station, as this status indicates the unavailability
of bike usage at the station level. Since this variable was not provided by Citi Bike, we
scraped docking stations’ real-time empty status from the Citi Bike usage map (Citi Bike,
2018a) every 10 minutes for 14 consecutive days in December 2018. These datasets were
processed by Python codes in Jupyter Notebook. Figure 1 shows the map of docking
stations overlaid with public transit stations.
Figure 1. Citi Bike docking stations and public transit stations in NYC. The metro
stations include both subway stations and rail stations.
2.3 Methods
Citi Bike does not release the path information related to an individual trip.
However, this data restriction can be overcome by analyzing the bike usage pattern at
docking stations, which is a method employed by other dock-based bike-sharing studies
(Faghih-Imani et al., 2014; Faghih-Imani et al., 2017; Li et al., 2015; O'Mahony &
Shmoys, 2015; Rixey, 2013). In this study, we employed the K-means clustering method
to further delve into the bike usage patterns at docking stations.
The K-means clustering method is an unsupervised learning algorithm that
divides observations into multiple subgroups (aka clusters) based on predefined
clustering variables (MacQueen, 1967). Mathematically, the K-means clustering method
partitions n observations into k clusters { 󰇞 with corresponding centers
{ 󰇞, where the objective is to minimize the clustering variables’ within-cluster
deviance in terms of the sum of squared errors (SSE), as shown in Equation (1) (Arthur &
Vassilvitskii, 2006). The algorithm and its extensions have been widely employed to
identify the bike usage patterns in related bike-sharing studies (Feng et al., 2017; Ma et
al., 2019; Xu et al., 2013).
 

 (1)
where is the set of points p in cluster i (i = 1 to k), and  is the centroid of cluster .
To implement the algorithm, we employed three sets of clustering variablesthe
hourly start trips within the t-th hour of the day at station i (), the hourly end trips
within the t-th hour of the day at station i (), and the hourly empty station count within
the t-th hour of the day at station i (), as shown in Equations (2) through (4). Since
each set of variables was on an hourly basis for 24 hours of a day, there were a total of 72
clustering variables, as shown in Equation (5).
 


 (2)
 


 (3)
 


 (4)
         (5)
Notation:
: Number of bike trips starting from station i within the t-th hour of the d-th day;
: Number of bike trips ending at station i within the t-th hour of the d-th day;
: Number of empty counts (at every 10 minutes) at station i within the t-th hour of
the d-th day when the station has no docked bikes;
: Observation period (in days).  = 365 for  and ;  = 14 for 
in this study.
We implemented the algorithm with Si,t (Start), Ti,t (End), and Ei,t (Empty) as the
clustering variables by using the Python package sklearn (Pedregosa et al., 2011).
Choosing the k value or the number of clusters is key to algorithmic implementation. To
this end, we employed the elbow method (Thorndike, 1953) to estimate the k value. The
elbow method plots the SSE against the k value and identifies the optimal value when the
SSE declines the most. We identified that both k = 2 and k = 6 were optimal; however,
using k = 2 cannot distinguish meaningful usage patterns among clusters. Accordingly,
we chose k = 6. Also, we performed a mixed-design analysis of variance (ANOVA) with
the Tukey test as the post hoc test to compare the usage patterns among clusters. The p-
value of the test was < 0.05, meaning that the difference was statistically significant.
3 Clustered bike usage patterns
By applying the K-means clustering algorithm, we derived six docking station
types with unique bike usage patterns. The spatial distribution of the clustered stations is
shown in Figure 2, while the temporal patterns are shown in Figure 3 with normalized
temporal indices. For comparison, Figure 3 also includes the hourly vehicle traffic
volume (NYC OpenData, 2020). Table 1 summarizes the spatiotemporal patterns of bike
usage for each station type.
Figure 2. Spatial distribution of six types of docking stations.
Figure 3. Daily bike usage patterns by station type (curves) and the hourly vehicle traffic
volume (bars).
The different types of docking stations reveal distinct bike usage patterns (Figure
3), which are related to the neighborhoods in which they are primarily located (Figure 2).
First, Type-3 stations, which have the largest amount (n = 310, 40.4 percent), are
principally distributed in low- to medium-density residential areas, such as Harlem and
Astoria (Labels 9 and 10 in Figure 2). Type-5 stations, with the second-highest number (n
= 216, 28.1 percent), are primarily located in high-density residential areas and mixed
commercial-residential areas, such as Chinatown (Label 3 in Figure 2). Because these
two station types generally serve residential areas, their temporal patterns are relatively
similar, with more Start trips in the morning hours (7:0012:00) and more End trips in
the evening and night hours (17:0023:00). Second, for Type-1 and Type-6 stations, there
are more End trips than Start trips during the morning rush hours (around 8:00); however,
during the afternoon rush hours (16:0018:00), their usage patterns are the opposite
(Start > End). The spatial distribution further reveals that these two station types are
typically located in commercial districts, such as Times Square (Label 5 in Figure 2).
Third, Empty counts are caused by the cascading effect of more Start trips than End trips.
For example, when there are Start > End trips in the afternoon rush hours, there will be a
spike in Empty counts during the morning of the following day (Type-1 and Type-6).
Similarly, when there are Start > End trips in the morning, Empty counts will increase at
noon (Types-2, 3, 4, and 5).
Table 1. Spatiotemporal patterns of bike usage by station type.
Type
Bike usage pattern
Major neighborhoods*
Major land use
Cumulative daily
Start**
Cumulative daily End**
1
Morning: Start << End
Evening: Start >> End
Empty spikes in the midnight and
morning rush hours
5, 6
Commercial
96
97
2
Morning: Start > End
Evening: Start < End
Empty spikes at noon and before the
midnight
2, 3, 4
Mixed residential
158
158
3
MorningStart > End
Evening: Start < End
Empty spikes at noon
8, 9, 10, 11, 13, 15, 16
Residential (low to
medium density)
17
17
4
MorningStart > End
Evening: Start < End
Empty spikes in the morning rush hours
and at noon
1, 2, 3, 4, 7
Commercial (near
transit)
101
102
5
MorningStart > End
Evening: Start < End
Empty is slightly high at noon and
relatively stable throughout the day
1, 3, 7, 8, 12, 14, 16
Residential (high
density); mixed
residential and
commercial
48
47
6
Morning: Start < End
Evening: Start > End
Empty spikes in the midnight and
morning rush hours
2, 5
Commercial
195
197
*. Refer to Figure 2 for the neighborhood.
**. The index is the cumulative count over 24 hours, averaged among the stations of the same type.
4 Relationships with public transit
Based on the clustered spatiotemporal patterns, we further examine the
relationships between the dock-based system and the public transit system (i.e., MTA)
based on three criteria: bike trip duration, proximity to a public transit station, and bike
trip length. The first criterion compares the duration of a bike trip to its alternative public
transit trip. The second criterion identifies if the start or end of a bike trip is near a public
transit station. Specifically, we consider 100 meters to be a competitive distance for
mode-switching. The third criterion is based on the bike trip length, which can largely
influence travelers’ mode choices (Scheiner, 2010). It was found that most bike-sharing
trips are under two miles (Hochmair, 2015; Lee et al., 2016; Taylor & Mahmassani,
1996; Zuo et al., 2018). Thus, we consider two miles, which corresponds to 14.45
minutes based on Citi Bike users’ average riding speed (i.e., 8.3 miles per hour)
(Schneider, 2016), to be the threshold for the preference of bike usage. Based on these
three criteria, we propose three different relationships between bike-sharing and public
transit, as below and in Figure 4.
Competition: A bike trip is competing with public transit if the trip can be
completed by its public transit alternative in a shorter time. The competition trip signifies
that travelers may prefer bike-sharing even if it is more time-costly and energy-intensive
than public transit.
Integration: A bike trip is considered integrated with the public transit system if
(1) the trip is less time-costly than its public transit alternative, (2) either the origin or
destination of the trip is within the 100-m buffer of a public transit station, and (3) the trip
is under two miles. The integration trip is a short-distance trip that can facilitate mode-
switching from and to the public transit system.
Complementation: A bike trip is considered complementary to the public transit
system if it is beyond the abovementioned scenarios. The complementation trip is less
time-costly than its public transit alternative. However, such a trip is either too far away
from a public transit station or a long-distance trip, making its trip purpose unlikely to be
mode-switching.
Figure 4. Flowchart of trip classification.
To derive the alternative public transit time for each bike trip, we employed the
Directions application programming interface (API) by Google Maps. Specifically, for
each bike trip, we derived the alternative transit time based on the same trip origin and
destination. It is worth noting that the transit time derived by the Directions API also
includes walking time from/to the public transit stations and during the transfer, which is
a realistic measure of the transit time. Then, we derived the type of each bike trip by
building spatial queries. Finally, the three types of bike trips were further aggregated on
the station level based on their trip origin, as shown in Table 2.
Table 2. Trip composition by station type.
Competition
Integration
Complementation
Station
type
Trip composition for the same station type (%)
1
63.06
9.92
27.02
2
60.60
8.03
31.37
3
63.28
10.93
25.79
4
60.66
9.75
29.59
5
61.73
10.15
28.11
6
60.69
8.59
30.72
All types
61.47
9.50
29.03
5 Discussion
Table 2 shows that the dominant bike trips (61.47 percent) are competition trips,
followed by complementation (29.03 percent) and integration (9.50 percent). The trip
composition does not vary substantially by station type. Since competition trips are more
time costly than their alternative trips that could be otherwise completed by public transit,
the result suggests that bike-sharing can largely compete with public transit and attract
potential public transit users. One evidence refers to the trips starting at Type-3 stations,
where the degree of competition slightly intensifies (63.28 percent). Type-3 stations are
primarily distributed in low- to medium-density residential areas in Queens and
Brooklyn, and these areas have adequate coverage of bus stations but limited coverage of
metro stations. Since the bus schedule is considerably less frequent, travelers in these
areas may have a stronger preference for bike-sharing due to its more flexible nature,
eventually making bike-sharing more competitive.
The study reveals the unique mobility roles of bike-sharing in NYC. While past
studies argued that bike-sharing aims to solve “the last mile problem” (Liu et al., 2012;
Moorthy et al., 2017; Shaheen & Chan, 2016), it is not the case in NYC. According to the
results, the integration trips, which are short-distance trips starting or ending near a public
transit station, are considerably limited (9.5 percent). The result signifies that most users
do not adopt bike-sharing for connecting to public transit or for the convenience of mode-
switching. This result can be further explained by Citi Bike’s site planning strategy.
Specifically, Citi Bike employed a community-based participatory model in the initial
planning phaseNew Yorkers were asked to propose candidate docking station sites by
placing pins on a map, and the sites receiving high preference were further refined by
professionals to ensure an equitable spatial distribution (e.g., one within each 1000 square
feet grid) (New York City Department of Transportation, 2013). This participatory
model, which emphasized community members’ travel demands instead of proximity to
public transit, is a possible cause of the low integration.
Revealing the mixed relationships between bike-sharing and public transit is
important, as it helps to demystify the trip purpose of bike-sharing, which is not
necessarily to minimize travel time. In addition to flexibility, bike-sharing has a financial
competitive edge, especially for frequent riders. For example, a monthly pass for
unlimited Citi Bike use is only $15 on average, which is less than one-eighth the cost of a
monthly metro pass in NYC (Citi Bike, 2022; Metropolitan Transportation Authority,
2022). Also, various community programs have been implemented to further reduce bike
fares for socioeconomically disadvantaged commuters, such as those living in public
housing units or receiving Supplemental Nutrition Assistance Program (SNAP) benefits
(Citi Bike, 2021). Therefore, the existence of competition could mean a certain degree of
transportation equity in the city (Lee et al., 2017), as bike-sharing provides a low-cost
commuting option for those experiencing financial hardships.
Nevertheless, the study has limitations. First, the study is focused on daily bike
usage patterns and does not reveal the temporality over different days of a week. Future
research could examine the variation between weekdays and weekends to reveal the
temporal nuances in bike usage, as indicated by Kong et al. (2020). Second, the study
only examines the dock-based system. Dockless bikes (e.g., JUMP and Lime), which
have limited coverage in NYC, are ignored. Dockless systems provide more flexibility to
accommodate mode switching but offer less certainty about bike availability (Chen et al.,
2020). Exploring and comparing the different roles of dock-based and dockless systems
could be another promising research direction. Third, the study has scraped the empty
status of bike stations in a two-week period due to the unavailability of data. Future
research should consider the seasonality of bike usage patterns by seeking alternative
datasets or extrapolation methods. Finally, the study ignores individuals’ travel behaviors
(e.g., trip purpose, mode preference, path selection). This omission, stemming from the
unavailability of traveler data, is a fundamental limitation of large mobility data.
Therefore, to further elucidate bike-sharing behaviors and contexts, it is essential to
integrate mobility data mining with conventional datasets, such as structured travel
surveys (Chen et al., 2021).
6 Conclusions
The bike-sharing system plays a critical role in the urban system by enhancing
intra-city travel connectivity and accessibility. This article analyzes the spatiotemporal
patterns of Citi Bike, the largest dock-based bike-sharing system in NYC. Further, it
proposes three different relationships between bike-sharing and public transit:
competition, integration, and complementation. The result reveals a primary competition
role that bike-sharing plays in relation to public transit. The existence of such competition
can benefit socioeconomically disadvantaged commuters, eventually facilitating a certain
degree of transportation equity.
Relevant to the study is the emergence of the COVID-19 pandemic, as it
drastically changed how people commute daily. The health concerns about the virus
spreading through public transit pushed more people to adopt bike-sharing. The increase
in bike-sharing usage was seen in upper Manhattan, Astoria, and part of Brooklyn, where
the inhabitants are mostly working class, who may not be able to work remotely during
the pandemic (Pase et al., 2020). Given this growing reliance on bike-sharing, it is hoped
that this study could offer new insights into the strategic expansion of the bike-sharing
system to further bolster social equity in transportation planning.
References
Arthur, D., & Vassilvitskii, S. (2006). k-means++: The advantages of careful seeding.
Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete
Algorithms, New Orleans, Louisiana, USA.
Ashraf, M. T., Hossen, M. A., Dey, K., El-Dabaja, S., Aljeri, M., & Naik, B. (2021).
Impacts of bike sharing program on subway ridership in New York City.
Transportation Research Record, 2675(9), 924-934.
Campbell, K. B., & Brakewood, C. (2017). Sharing riders: How bikesharing impacts bus
ridership in New York City. Transportation Research Part A: Policy and
Practice, 100, 264-282.
Chen, R. (2019). “Bike litter” and obligations of the platform operators: Lessons from
China's dockless sharing bikes. Computer Law & Security Review, 35(5), 105317.
Chen, X., Zhang, A., Wang, H., Gallaher, A., & Zhu, X. (2021). Compliance and
containment in social distancing: Mathematical modeling of COVID-19 across
townships. International Journal of Geographical Information Science, 35(3),
446-465.
Chen, Y., Zhang, Y., Coffman, D. M., & Mi, Z. (2022). An environmental benefit
analysis of bike sharing in New York City. Cities, 121, 103475.
Chen, Z., van Lierop, D., & Ettema, D. (2020). Dockless bike-sharing systems: what are
the implications? Transport Reviews, 40(3), 333-353.
Cheng, P., Hu, J., Yang, Z., Shu, Y., & Chen, J. (2018). Utilization-aware trip advisor in
bike-sharing systems based on user behavior analysis. IEEE Transactions on
Knowledge and Data Engineering, 31(9), 1822-1835.
Chiariotti, F., Pielli, C., Zanella, A., & Zorzi, M. (2018). A dynamic approach to
rebalancing bike-sharing systems. Sensors, 18(2), 512.
Citi Bike. (2018a). City Bike station map. https://member.citibikenyc.com/map. (last
accessed 11 March 2022).
———. (2018b). City Bike system data. https://www.citibikenyc.com/system-data. (last
accessed 11 March 2022).
———. (2020). About Citi Bike. https://www.citibikenyc.com/about. (last accessed 11
March 2022).
———. (2021). Community programs. https://ride.citibikenyc.com/community-
programs. (last accessed 11 March 2022).
———. (2022). Pricing. https://citibikenyc.com/pricing. (last accessed 11 March 2022).
City of New York. (2017). The ins and outs of NYC commuting.
https://www1.nyc.gov/assets/planning/download/pdf/planning-level/housing-
economy/nyc-ins-and-out-of-commuting.pdf. (last accessed 11 March 2022).
D’Almeida, L., Rye, T., & Pomponi, F. (2021). Emissions assessment of bike sharing
schemes: The case of Just Eat Cycles in Edinburgh, UK. Sustainable Cities and
Society, 71, 103012.
DeMaio, P. (2009). Bike-sharing: History, impacts, models of provision, and future.
Journal of Public Transportation, 12(4), 3.
Faghih-Imani, A., Eluru, N., El-Geneidy, A. M., Rabbat, M., & Haq, U. (2014). How
land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing
system (BIXI) in Montreal. Journal of Transport Geography, 41, 306-314.
Faghih-Imani, A., Hampshire, R., Marla, L., & Eluru, N. (2017). An empirical analysis of
bike sharing usage and rebalancing: Evidence from Barcelona and Seville.
Transportation Research Part A: Policy and Practice, 97, 177-191.
Feng, Y., Affonso, R. C., & Zolghadri, M. (2017). Analysis of bike sharing system by
clustering: the Vélib’case. IFAC-PapersOnLine, 50(1), 12422-12427.
Fishman, E., Washington, S., Haworth, N., & Mazzei, A. (2014). Barriers to bikesharing:
an analysis from Melbourne and Brisbane. Journal of Transport Geography, 41,
325-337.
García-Palomares, J. C., Gutiérrez, J., & Latorre, M. (2012). Optimizing the location of
stations in bike-sharing programs: A GIS approach. Applied Geography, 35(1-2),
235-246.
Ghosh, S., & Varakantham, P. (2017). Incentivizing the use of bike trailers for dynamic
repositioning in bike sharing systems. Proceedings of the International
Conference on Automated Planning and Scheduling, 27(1), 373-381.
Graehler, M., Mucci, R. A., & Erhardt, G. D. (2019). Understanding the recent transit
ridership decline in major US cities: Service cuts or emerging modes. 98th Annual
Meeting of the Transportation Research Board, Washington, DC, USA.
Guo, Y., Zhou, J., Wu, Y., & Li, Z. (2017). Identifying the factors affecting bike-sharing
usage and degree of satisfaction in Ningbo, China. PloS One, 12(9), e0185100.
Habib, K. N., Mann, J., Mahmoud, M., & Weiss, A. (2014). Synopsis of bicycle demand
in the City of Toronto: Investigating the effects of perception, consciousness and
comfortability on the purpose of biking and bike ownership. Transportation
Research Part A: Policy and Practice, 70, 67-80.
Hochmair, H. H. (2015). Assessment of bicycle service areas around transit stations.
International Journal of Sustainable Transportation, 9(1), 15-29.
Kim, D., Shin, H., Im, H., & Park, J. (2012). Factors influencing travel behaviors in
bikesharing. Transportation Research Board 91st Annual Meeting, Washington,
DC, USA.
Kim, K. (2018). Investigation on the effects of weather and calendar events on bike-
sharing according to the trip patterns of bike rentals of stations. Journal of
Transport Geography, 66, 309-320.
Kong, H., Jin, S. T., & Sui, D. Z. (2020). Deciphering the relationship between
bikesharing and public transit: Modal substitution, integration, and
complementation. Transportation Research Part D: Transport and Environment,
85, 102392.
Lee, J., Choi, K., & Leem, Y. (2016). Bicycle-based transit-oriented development as an
alternative to overcome the criticisms of the conventional transit-oriented
development. International Journal of Sustainable Transportation, 10(10), 975-
984.
Lee, R. J., Sener, I. N., & Jones, S. N. (2017). Understanding the role of equity in active
transportation planning in the United States. Transport Reviews, 37(2), 211-226.
Legros, B. (2019). Dynamic repositioning strategy in a bike-sharing system; how to
prioritize and how to rebalance a bike station. European Journal of Operational
Research, 272(2), 740-753.
Li, A., Gao, K., Zhao, P., Qu, X., & Axhausen, K. W. (2021). High-resolution assessment
of environmental benefits of dockless bike-sharing systems based on transaction
data. Journal of Cleaner Production, 296, 126423.
Li, Y., Zheng, Y., Zhang, H., & Chen, L. (2015). Traffic prediction in a bike-sharing
system. Proceedings of the 23rd SIGSPATIAL International Conference on
Advances in Geographic Information Systems, Seattle, Washington, USA.
Lin, J.-R., Yang, T.-H., & Chang, Y.-C. (2013). A hub location inventory model for
bicycle sharing system design: Formulation and solution. Computers & Industrial
Engineering, 65(1), 77-86.
Liu, Z., Jia, X., & Cheng, W. (2012). Solving the last mile problem: Ensure the success
of public bicycle system in Beijing. Procedia-Social and Behavioral Sciences, 43,
73-78.
Lu, W., Scott, D. M., & Dalumpines, R. (2018). Understanding bike share cyclist route
choice using GPS data: Comparing dominant routes and shortest paths. Journal of
Transport Geography, 71, 172-181.
Luo, H. (2019). Environmental Impact Assessment and Improved Design of Bike Sharing
System from the Life Cycle Perspective, Master’s Thesis, Purdue University
Graduate School, West Lafayette, IN, USA.
Luo, H., Zhao, F., Chen, W.-Q., & Cai, H. (2020). Optimizing bike sharing systems from
the life cycle greenhouse gas emissions perspective. Transportation Research
Part C: Emerging Technologies, 117, 102705.
Ma, T., Liu, C., & Erdoğan, S. (2015). Bicycle sharing and public transit: Does Capital
Bikeshare affect Metrorail ridership in Washington, DC? Transportation
Research Record, 2534(1), 1-9.
Ma, X., Cao, R., & Jin, Y. (2019). Spatiotemporal clustering analysis of bicycle sharing
system with data mining approach. Information, 10(5), 163.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate
observations. Proceedings of the fifth Berkeley symposium on mathematical
statistics and probability.
Martinez, L. M., Caetano, L., Eiró, T., & Cruz, F. (2012). An optimisation algorithm to
establish the location of stations of a mixed fleet biking system: An application to
the city of Lisbon. Procedia-Social and Behavioral Sciences, 54, 513-524.
Martinez, M. (2017). The impact weather has on NYC Citi Bike share company activity.
Journal of Environmental and Resource Economics at Colby, 4(1), 12.
Metropolitan Transportation Authority. (2022). Fares. https://new.mta.info/fares. (last
accessed 11 March 2022).
Miranda-Moreno, L. F., & Nosal, T. (2011). Weather or not to cycle: Temporal trends
and impact of weather on cycling in an urban environment. Transportation
Research Record, 2247(1), 42-52.
Moorthy, A., De Kleine, R., Keoleian, G., Good, J., & Lewis, G. (2017). Shared
autonomous vehicles as a sustainable solution to the last mile problem: A case
study of Ann Arbor-Detroit area. SAE International Journal of Passenger Cars-
Electronic and Electrical Systems, 10(2017-01-1276), 328-336.
Nankervis, M. (1999). The effect of weather and climate on bicycle commuting.
Transportation Research Part A: Policy and Practice, 33(6), 417-431.
New York City Department of Motor Vehicles. (2018). Vehicle registrations in force.
https://dmv.ny.gov/statistic/2018reginforce-web.pdf. (last accessed 11 March
2022).
New York City Department of Transportation. (2013). NYC bike share: Designed by New
Yorkers. http://www.nyc.gov/html/dot/downloads/pdf/bike-share-outreach-
report.pdf. (last accessed 11 March 2022).
———. (2018). New York City Mobility Report.
http://www.nyc.gov/html/dot/downloads/pdf/mobility-report-2018-screen-
optimized.pdf. (last accessed 11 March 2022).
NYC OpenData. (2020). Traffic volume counts (2014-2019).
https://data.cityofnewyork.us/Transportation/Traffic-Volume-Counts-2014-2019-
/ertz-hr4r. (last accessed 11 March 2022).
O'Mahony, E., & Shmoys, D. (2015). Data analysis and optimization for (citi) bike
sharing. Proceedings of the AAAI Conference on Artificial Intelligence.
Pase, F., Chiariotti, F., Zanella, A., & Zorzi, M. (2020). Bike sharing and urban mobility
in a post-pandemic world. IEEE Access, 8, 187291-187306.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel,
M., Prettenhofer, P., Weiss, R., & Dubourg, V. (2011). Scikit-learn: Machine
learning in Python. Journal of machine Learning research, 12, 2825-2830.
Pucher, J., Dill, J., & Handy, S. (2010). Infrastructure, programs, and policies to increase
bicycling: an international review. Preventive Medicine, 50, S106-S125.
Rixey, R. A. (2013). Station-level forecasting of bikesharing ridership: Station network
effects in three US systems. Transportation Research Record, 2387(1), 46-55.
Saberi, M., Ghamami, M., Gu, Y., Shojaei, M. H. S., & Fishman, E. (2018).
Understanding the impacts of a public transit disruption on bicycle sharing
mobility patterns: A case of Tube strike in London. Journal of Transport
Geography, 66, 154-166.
Scheiner, J. (2010). Interrelations between travel mode choice and trip distance: Trends
in Germany 19762002. Journal of Transport Geography, 18(1), 75-84.
Schneider, T. W. (2016). A tale of twenty-two million citi bike rides: Analyzing the NYC
bike share system. https://toddwschneider.com/posts/a-tale-of-twenty-two-
million-citi-bikes-analyzing-the-nyc-bike-share-system/. (last accessed 11 March
2022).
Schoner, J. E., & Levinson, D. M. (2014). The missing link: Bicycle infrastructure
networks and ridership in 74 US cities. Transportation, 41(6), 1187-1204.
Sears, J., Flynn, B. S., Aultman-Hall, L., & Dana, G. S. (2012). To bike or not to bike:
seasonal factors for bicycle commuting. Transportation Research Record,
2314(1), 105-111.
Shaheen, S., & Chan, N. (2016). Mobility and the sharing economy: Potential to facilitate
the first-and last-mile public transit connections. Built Environment, 42(4), 573-
588.
Shaheen, S. A., Guzman, S., & Zhang, H. (2010). Bikesharing in Europe, the Americas,
and Asia: Past, present, and future. Transportation Research Record, 2143(1),
159-167.
Singleton, P. A., & Clifton, K. J. (2014). Exploring synergy in bicycle and transit use:
Empirical evidence at two scales. Transportation Research Record, 2417(1), 92-
102.
Taylor, D., & Mahmassani, H. (1996). Analysis of stated preferences for intermodal
bicycle-transit interfaces. Transportation Research Record, 1556(1), 86-95.
Thorndike, R. L. (1953). Who belongs in the family? Psychometrika, 18(4), 267-276.
Ullman, E. L., & Boyce, R. R. (1980). Geography as Spatial Interaction. University of
Washington Press.
Xu, H., Ying, J., Lin, F., & Yuan, Y. (2013). Station segmentation with an improved K-
means algorithm for Hangzhou public bicycle system. Journal of Software, 8(9),
2289-2296.
Zhang, Y., & Mi, Z. (2018). Environmental benefits of bike sharing: A big data-based
analysis. Applied Energy, 220, 296-301.
Zhang, Y., & Zhang, Y. (2018). Associations between public transit usage and
bikesharing behaviors in the United States. Sustainability, 10(6), 1868.
Zuo, T., Wei, H., & Rohne, A. (2018). Determining transit service coverage by non-
motorized accessibility to transit: Case study of applying GPS data in Cincinnati
metropolitan area. Journal of Transport Geography, 67, 1-11.
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