cabi.bib

@online{cabisite,
  title = {How {{Metro DC}}’s {{Bikeshare System Works}} | {{Capital Bikeshare}}},
  url = {https://capitalbikeshare.com/how-it-works},
  urldate = {2023-06-19},
  abstract = {Capital Bikeshare is a fun and affordable way to get around the DC area. Get access to 5,000 bikes and 700+ stations when you buy a pass or become a member.},
  langid = {american},
  file = {C\:\\Users\\maxli\\Zotero\\storage\\CN7ZEYBQ\\how-it-works.html}
}

@article{dehdariebrahimiUsingGISbasedSpatial2022,
  title = {Using a {{GIS-based}} Spatial Approach to Determine the Optimal Locations of Bikeshare Stations: {{The}} Case of {{Washington D}}.{{C}}},
  shorttitle = {Using a {{GIS-based}} Spatial Approach to Determine the Optimal Locations of Bikeshare Stations},
  author = {Dehdari Ebrahimi, Zhila and Momenitabar, Mohsen and Nasri, Arefeh A. and Mattson, Jeremy},
  date = {2022-10-01},
  journaltitle = {Transport Policy},
  shortjournal = {Transport Policy},
  volume = {127},
  pages = {48--60},
  issn = {0967-070X},
  doi = {10.1016/j.tranpol.2022.08.008},
  url = {https://www.sciencedirect.com/science/article/pii/S0967070X22002190},
  urldate = {2023-06-19},
  abstract = {Today, the number of cities implementing bike-share programs is remarkably increasing. One of the critical elements of implementing a successful bike-share program is integrating it with other transportation modes such as bus and metro and extending its coverage in high-density residential and employment areas to encourage more demand. In this study, we utilize a GIS-based method to visualize the spatial distribution of bike-share stations using the location-allocation problem, using the Capital bike-share program in Washington D.C. metropolitan areas as a study area. We chose Washington D.C. as it was one of the first cities in the United States that launched a bike-share program and currently has one of the largest bike-share systems across the country. The location-allocation problem, including Target Market Share (TMS) and Maximize Coverage and Minimize Facility (MCMF), is considered to analyze the accessibility of promoting transit modes with bike-share systems across the District of Columbia. Location-allocation models are investigated to determine the potential bike station locations accessible to the maximum population and within a 300-m buffer around public transit stations (e.g., bus, metro). The results show that the bike-share system in Washington D.C. is more accessible for transit users as an access/egress mode. At the same time, in areas farther away from downtown D.C., docking stations are more distanced apart and offer less coverage, especially in residential-only areas. Finally, our methodology can potentially be utilized for optimal station location allocation in any other city where maximum exposure is required.},
  langid = {english},
  keywords = {Bike-share program,GIS,Location-allocation model,Public transit,Washington D.C},
  file = {C\:\\Users\\maxli\\Zotero\\storage\\5KLB8VNI\\S0967070X22002190.html}
}

@article{fishmanBikeshareReviewRecent2016,
  title = {Bikeshare: {{A Review}} of {{Recent Literature}}},
  shorttitle = {Bikeshare},
  author = {Fishman, Elliot},
  date = {2016-01-02},
  journaltitle = {Transport Reviews},
  volume = {36},
  number = {1},
  pages = {92--113},
  publisher = {{Routledge}},
  issn = {0144-1647},
  doi = {10.1080/01441647.2015.1033036},
  url = {https://doi.org/10.1080/01441647.2015.1033036},
  urldate = {2023-06-19},
  abstract = {The number of cities offering bikeshare has increased rapidly, from just a handful in the late 1990s to over 800 currently. This paper provides a review of recent bikeshare literature. Several themes have begun to emerge from studies examining bikeshare. Convenience is the major motivator for bikeshare use. Financial savings has been found to motivate those on a low income and the distance one lives from a docking station is an important predictor for bikeshare membership. In a range of countries, it has been found that just under 50\% of bikeshare members use the system less than once a month. Men use bikeshare more than women, but the imbalance is not as dramatic as private bike riding (at least in low cycling countries). Commuting is the most common trip purpose for annual members. Users are less likely than private cyclists to wear helmets, but in countries with mandatory helmet legislation, usage levels have suffered. Bikeshare users appear less likely to be injured than private bike riders. Future directions include integration with e-bikes, GPS (global positioning system), dockless systems and improved public transport integration. Greater research is required to quantify the impacts of bikeshare, in terms of mode choice, emissions, congestion and health.},
  keywords = {bicycle,bikeshare,environment,safety,transport and society},
  annotation = {\_eprint: https://doi.org/10.1080/01441647.2015.1033036}
}

@article{gehrkeBikeshareStationArea2019,
  title = {A Bikeshare Station Area Typology to Forecast the Station-Level Ridership of System Expansion},
  author = {Gehrke, Steven R. and Welch, Timothy F.},
  date = {2019},
  journaltitle = {Journal of Transport and Land Use},
  volume = {12},
  number = {1},
  eprint = {26911265},
  eprinttype = {jstor},
  pages = {221--235},
  publisher = {{Journal of Transport and Land Use}},
  issn = {1938-7849},
  abstract = {The continuous introduction and expansion of docked bikeshare systems with publicly available origin-destination data have opened exciting avenues for bikeshare research. In response, a flux of recent studies has examined the sociodemographic determinants and safety or natural environment deterrents of system ridership. An increasing abundance of disaggregate spatial data has also spurred recent calls for research aimed at extending the utility of these contextual data to model bikeshare demand and trip patterns. As planners and operators seek to expand bikeshare services into underserved areas, a need exists to provide a data-driven understanding of the spatial dynamics of bikeshare use. This study of the Washington, DC, metro region's Capital Bikeshare (CaBi) program answers this call by performing a latent class cluster analysis to identify five bikeshare station area types based on variation in a set of land development pattern, urban design, and transportation infrastructure features. This typology is integrated into a planning application exploring the potential for system expansion into nearby jurisdictions and forecasting the associated trip-making potential between existing and proposed station locations.},
  file = {G\:\\My Drive\\Ebook Library\\zotero\\Gehrke_Welch_2019_A bikeshare station area typology to forecast the station-level ridership of.pdf}
}

@online{ImpactsCOVID19Pandemic,
  title = {Impacts of the {{COVID-19 Pandemic}} on {{Bikeshare Usage}} by {{Rider Membership Status Across Selected U}}.{{S}}. {{Cities}} - {{Tung Vo}}, {{Natalia Barbour}}, {{Lori Palaio}}, {{Michael Maness}}, 2023},
  url = {https://journals.sagepub.com/doi/full/10.1177/03611981221131542},
  urldate = {2023-06-19},
  file = {C\:\\Users\\maxli\\Zotero\\storage\\G9Q7AFL9\\03611981221131542.html}
}

@article{kavitiTravelBehaviorPrice2019,
  title = {Travel Behavior and Price Preferences of Bikesharing Members and Casual Users: {{A Capital Bikeshare}} Perspective},
  shorttitle = {Travel Behavior and Price Preferences of Bikesharing Members and Casual Users},
  author = {Kaviti, Shruthi and Venigalla, Mohan M. and Lucas, Kimberly},
  date = {2019-04-01},
  journaltitle = {Travel Behaviour and Society},
  shortjournal = {Travel Behaviour and Society},
  volume = {15},
  pages = {133--145},
  issn = {2214-367X},
  doi = {10.1016/j.tbs.2019.02.004},
  url = {https://www.sciencedirect.com/science/article/pii/S2214367X18302096},
  urldate = {2023-06-19},
  abstract = {Even though casual users of bikeshare account for a large share of ridership and revenue at public bikeshare systems in North America, very little is known about the characteristics and preferences of casual users and how they compare to registered members. The primary objectives of the study include identifying the similarities and differences between members and casual users along demographics, usage and indicated preferences; and examining and modeling pricing preferences of bikeshare users. An intercept survey was conducted to obtain demographic information, bikeshare usage and various preferences of Capital Bikeshare users in the metro Washington DC area. The survey data was validated against the data from an existing member survey with large sample using goodness of fit tests. Survey participants reported that single trip fare (STF) and annual membership paid at once as their preferred pricing options and a combination of STF, 24-hour pass, and annual membership with monthly installments as their favorable pricing model. Logistic regression findings indicate that, when compared to casual users, registered members are more likely to be White, earn more and reside in the D.C. area. Casual users make fewer bikeshare trips and are less sensitive to the service (station density) compared to members. Gender, age and income distribution do not appear to influence casual fare product choice. Results from this study are useful in policy-making, planning and operations for bikeshare systems.},
  langid = {english},
  keywords = {Bikeshare,Bikesharing,Casual users,Pricing preferences,Registered members},
  file = {C\:\\Users\\maxli\\Zotero\\storage\\GZ9V38PV\\S2214367X18302096.html}
}

@article{maEstimatingImpactsCapital2019,
  title = {Estimating the {{Impacts}} of {{Capital Bikeshare}} on {{Metrorail Ridership}} in the {{Washington Metropolitan Area}}},
  author = {Ma, Ting and Knaap, Gerrit-Jan},
  date = {2019-07-01},
  journaltitle = {Transportation Research Record},
  volume = {2673},
  number = {7},
  pages = {371--379},
  publisher = {{SAGE Publications Inc}},
  issn = {0361-1981},
  doi = {10.1177/0361198119849407},
  url = {https://doi.org/10.1177/0361198119849407},
  urldate = {2023-06-19},
  abstract = {Bikeshare programs have transformed the urban transportation landscape. However, their impacts on rail transit have not been fully examined. Some researchers find shared bikes help reduce the first-mile/last-mile gaps and boost rail transit ridership, although others see bikeshare as a competitor for riders. Previous studies have mostly relied on surveys of bikeshare program users as the data source, and few have addressed this question using more rigorous methods. In this paper, the authors take the Washington metropolitan area as an example and use statistical methods to quantify the impact of the bikeshare program on rail transit ridership. Using detailed ridership data between 2010 and 2015, they break down Metrorail ridership by type (entries vs. exits) and time of the day (AM peak vs. PM peak) to analyze how Capital Bikeshare (CaBi) interacts with Metrorail. Furthermore, Metrorail stations are categorized into core stations and peripheral stations to examine the impacts of CaBi in different built environments. Regression results show that the impacts of CaBi vary by Metrorail station location. For core Metrorail stations, CaBi docking stations within ¼-mile of a Metrorail station reduce rail ridership in all measures. In particular, CaBi would reduce the number of AM-peak exits by 4,738 per station per month. However, CaBi complements Metrorail in peripheral neighborhoods. Having CaBi installed nearby would increase monthly Metrorail ridership by 1,175 AM-peak exits, 1,417 PM-peak entries, 2,284 AM-peak entries, and 2,422 PM-peak exits. Based on the findings, the authors suggest a collaboration between Metrorail and CaBi to add more bikeshare stations within ¼-mile of peripheral Metrorail stations to increase the ridership of both systems.},
  langid = {english}
}

@online{SystemData,
  title = {System {{Data}}},
  url = {http://ride.capitalbikeshare.com/system-data},
  urldate = {2023-06-19},
  abstract = {Developers, engineers, statisticians and academics can find and download data on Capital Bikeshare membership, ridership and trip histories.},
  langid = {english},
  organization = {{Capital Bikeshare}},
  file = {C\:\\Users\\maxli\\Zotero\\storage\\9MYZ39F9\\system-data.html}
}