Bikeshare Ridership Research Articles

Facilitators and barriers to bikeshare use among users and non-users in a socioeconomically diverse urban population.

Public bike sharing programs are becoming increasingly popular worldwide. While there is a growing body of literature exploring participation and facilitators among bike share users, little is known about the views of people who have not enrolled in bikeshare programs and how they differ from current users. This knowledge is critical to expand bikeshare ridership, particularly among low-income populations who typically have lower participation levels.We developed a cross-sectional survey to assess perceived barriers and facilitators to bikeshare use among users and non-users of the Bluebikes bikeshare program in Boston, Massachusetts. Survey respondents were recruited from lower-income Boston neighborhoods via flyers, social media, Craigslist, and in-person between June 12–July 31, 2019. A total of 512 people completed the survey (277 bikeshare users and 235 non-users).Bikeshare users in our sample differed significantly from non-users with respect to age, sex, and race. Barriers and facilitators of bikeshare use were largely similar between users and non-users, as well as among users stratified by household income. The most frequently cited barriers included: safety concerns, lack of a helmet, proximity to stations, trouble with renting/returning a bike, and weather. The main facilitators included: convenience, proximity to stations, environmental benefits, economic benefits, fun, and health benefits. Salience of many of the most frequent barriers and facilitators increased with frequency of ridership.Barriers identified by users and non-users of bikeshare programs suggest key areas of program improvements and/or areas of focus for future recruitment efforts. Likewise, potential facilitators noted by non-users may represent key marketing opportunities for bikeshare programs that are seeking to expand in socioeconomically diverse urban settings.

Bikesharing, equity, and disadvantaged communities: A case study in Chicago

Abstract 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.

Toward sustainable travel: An analysis of campus bikeshare use

In this study, we use University of Maryland's (UMD) bikeshare ridership data along with historical weather data, elevation, and transit service location data to analyze bikeshare trip patterns and explore the various factors influencing demand for the system across the UMD campus. We analyzed the spatial, temporal, and environmental factors influencing trips within a 19-month period to shed some light on how the bikeshare system is being used across campus and in its surroundings and to determine the most important factors shaping the demand. Results show that, similar to the city-wide bikeshare systems, demand for campus bikeshare is mostly influenced by weather, time of day, day of the week, month of the year, and accessibility to transit and various other destinations. However, unlike the city-wide bikeshare systems, there is not a concentration of trips within peak hours, as trips are scattered throughout the day. This is probably due to the flexible working schedules of bikeshare users on-campus (i.e., students and faculty), as opposed to the users of city-wide systems. Additionally, results indicate a higher on-campus usage of the system within the proximity of the transit hubs with a median trip duration of 6.8 min which supports the complementary relationship between bikeshare system and conventional transit systems.

A spatial-temporal gender and land use analysis of bikeshare ridership: The case study of Baltimore City

Bikeshare programs are growing in popularity across the United States as cities aim to provide an alternative mode of transportation that addresses last-mile needs while promoting recreation and tourism. In fall 2016, Baltimore Bikeshare System (BBS) launched with 20 stations. Using trip data for over 17,000 trips from October to May, this study explores the temporal and spatial patterns of bikeshare use in the city. The current study has two main goals: first, exploring the influence of socio-demographic factors on travel patterns of Baltimore's bikeshare ridership, and second, evaluating possible relationships between gender and land use in terms of origin/destination stations of their trips. The methodology of this study was based on statistical analysis including ANOVA, and binary and multinomial logistic regression models. The results of the study include several findings. First, the travel pattern of riders differs according to the status of their memberships, the day of week, and the land use of where they started and ended their trip. Second, there is a large gender difference among BBS membership users, and males are the dominate users. Finally, females tended to have more recreational trips and start and end their trips from/to the same station when compared to male riders.

Built Environment Factors Affecting Bike Sharing Ridership: Data-Driven Approach for Multiple Cities

Identification of factors influencing ridership is necessary for policy-making, as well as, when examining transferability and aspects of performance and reliability. In this work, a data-driven method is formulated to correlate arrivals and departures of station-based bike sharing systems with built environment factors in multiple cities. Ridership data from stations of multiple cities are pooled in one data set regardless of their geographic boundaries. The method bundles the collection, analysis, and processing of data, as well as, the model’s estimation using statistical and machine learning techniques. The method was applied on a national level in six cities in Germany, and also on an international level in three cities in Europe and North America. The results suggest that the model’s performance did not depend on clustering cities by size but by the relative daily distribution of the rentals. Selected statistically significant factors were identified to vary temporally (e.g., nightclubs were significant during the night). The most influencing variables were related to the city population, distance to city center, leisure-related establishments, and transport-related infrastructure. This data-driven method can help as a support decision-making tool to implement or expand bike sharing systems.

The influence of campus characteristics, temporal factors, and weather events on campuses-related daily bike-share trips

Recently, there has been an increase in bike-sharing programs on university campuses. However, there is scarce literature on the campus characteristics that are most favorable for these programs. This study evaluates the impacts of campus characteristics, temporal factors, and weather events on daily bike-sharing trips within U.S. university campuses. The study used data collected between 2014 and 2018 from 177 stations located within 25 campuses. With an average of 38 trips per day, a high variability in the number of daily trips was observed, with a maximum of 708 and a minimum of zero. Both traditional Negative Binomial (NB) and Mixed Effect Negative Binomial (MENB) models were applied, and their performances were compared based on a set of criteria. The MENB performed better than NB. The MENB model results revealed that among campus related factors, number of faculty/staffs, number of full-time students, private universities, campus size, and Latino and Asian student dominance are positively associated with an increase in trips per day, whereas number of part-time students, distance from campus to central business district, and students' ages are negatively associated. The temporal factors depicted that weekends, public holidays, time between semesters, and summer semester were associated with decreases in number of daily trips, while years in operation and fall semesters were positively associated with daily ridership. Severe weather events resulted in less bike-sharing, and average daily temperature was positively associated with number of trips. Campus bike-sharing planners can utilize these findings for estimating daily bike-sharing ridership on campuses.

Bike-Share Systems: Accessibility and Availability

The cities of Paris, London, Chicago, and New York (among many others) have set up bike-share systems to facilitate the use of bicycles for urban commuting. This paper estimates the impact of two facets of system performance on bike-share ridership: accessibility (how far the user must walk to reach stations) and bike-availability (the likelihood of finding a bicycle). We obtain these estimates from a structural demand model for ridership estimated using data from the Vélib’ system in Paris. We find that every additional meter of walking to a station decreases a user’s likelihood of using a bike from that station by 0.194% (±0.0693%), and an even more significant reduction at higher distances (>300 m). These estimates imply that almost 80% of bike-share usage comes from areas within 300 m of stations, highlighting the need for dense station networks. We find that a 10% increase in bike-availability would increase ridership by 12.211% (±1.097%), three-fourths of which comes from fewer abandonments and the rest of which comes from increased user interest. We illustrate the use of our estimates in comparing the effect of adding stations or increasing bike-availabilities in different parts of the city, at different times, and in evaluating other proposed improvements. This paper was accepted by Vishal Gaur, operations management.

Factors Influencing Electric Bike Share Ridership: Analysis of Park City, Utah

In recent years, bike share programs have become more popular as they contribute to the move toward sustainable mobility in cities. Electric bike sharing, however, remains in the early stages of development. In contrast to traditional bikes, electric bicycles (e-bikes) provide an extra boost via an electric pedal-assist motor, thereby making it much easier to travel around a city with a hilly terrain, such as Park City, Utah. The Summit Bike Share system in Park City is the nation’s first fully electric bike share system (e-BSS). Based on an analysis of historical trip data of Summit Bike Share, this paper presents the system’s performance experience and evaluates user characteristics and travel behavior. A Poisson regression analysis is performed to investigate the factors that influence the e-bike share usage of this e-BSS. The regression results reveal that weather factors, including temperature and wind speed, significantly affect e-bike share usage. It was also found that weekends, summer months, high population density, proximity to public transit centers, recreational centers, and bike trails positively affect the demand for e-bikes. The findings of this paper can help the operators of Summit Bike Share to better understand their users and their e-BSS, while also providing a guide for other e-bike projects currently in the planning stages.

What factors influence bike share ridership? An investigation of Hamilton, Ontario’s bike share hubs

Hamilton, Ontario’s bike share system was launched officially on March 22, 2015. This study analyzes the effects of weather conditions, temporal variables, hub attributes (most of which are derived for 200 m buffers around hubs), and a one-day lag on daily ridership at the bike share’s hubs during its first year of operation. Two random intercept multilevel models are estimated – one for daily trip departures, the other for daily trip arrivals. All weather (temperature, precipitation) and temporal variables (daylight hours, university terms, weekdays, holidays) are statistically significant in both models. Conversely, variables measuring transportation infrastructure in the vicinity of hubs, including the amount of bike lanes, are largely insignificant, suggesting that these features of the built environment have little to no influence on ridership. Proximity to important locations in the city (McMaster University, Hamilton’s downtown) has a strong impact on ridership. Although population density was an important consideration when locating the hubs, population does not influence daily departures or arrivals. Employment in the vicinity of hubs, which serves as a surrogate for an area’s activities or its attractiveness, does influence ridership, as is the case for the one-day lag effect. While all of these variables are able to explain some of the differences in daily ridership activity between hubs, the random intercept models confirm that they do not explain all of it. In other words, there remain intrinsic differences between hubs that are not captured by the independent variables – differences that influence ridership.

How transit service closures influence bikesharing demand; lessons learned from SafeTrack project in Washington, D.C. metropolitan area

Transportation disruptions offer opportunities to study how people adapt to using new modes of transportation and have important implications for transportation policy and planning. Bikeshare has emerged as a new popular mode of transportation in recent years as it offers a fast, easy, and reliable way to travel short distances, and for its convenience as a first- and last-mile mode to complement transit. It also offers many social, environmental, and health-related benefits and has the potential to promote low-carbon mobility. This study examines changes in bikeshare ridership due to rail transit closures in the Washington, D.C. area and investigates how promoting bikeshare systems in large metropolitan areas could be beneficial in cases of transit disruptions – regardless of the type, cause, and duration. We use disaggregate trip history data to analyze the impact of three different transit closures in 2016 lasting 7 to 25 days. The objective of this paper is to provide insight on how transit disruptions affect bikeshare use. An autoregressive Poisson time series model is used to estimate effects of transit closures on bikeshare activity. Kernel density estimation is applied to understand spatial changes in ridership from a week before, one year before, and after each closure. Results are compared both temporally and spatially and confirm that transit disruptions were associated with increased bikeshare ridership at the local level. Once the affected Metro stations reopened, bikeshare ridership returned to original levels. We conclude that when within 0.25 mile of a rail station and with a rail station spacing of <3 miles, bikeshare can be used as a mechanism for low-carbon mobility to complement transit.

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

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.

Impact of pricing and transit disruptions on bikeshare ridership and revenue

Bikeshare operators routinely explore options to improve ridership and revenue by studying interaction among pricing, service and operations. The objective of this research is to study the impact of introducing a new $2 fare for single-trip on revenue and ridership at Capital Bikeshare (CaBi) in the metro Washington DC region. The single-trip fare (STF) at CaBi is one of the three fare products aimed at casual users—the other two being 24-h pass and 3-day pass. STF was introduced inconjuction with SafeTrack, which is a major Metrorail track maintenance program intiated by the Washington Metropolitan Area Transit Authority. The impact analysis of STF includes studying the influence of SafeTrack on CaBi ridership. The analysis was based on revenue and ridership data before and after the implementation of STF and weather data for the region. The results showed that the first-time casual members increased by as much as 79% immediately after the introduction of STF, along with an overall increase in ridership. Jurisdiction-level analysis indicated a statistically significant increase in casual user ridership for identical 12-month periods before and after the introduction of STF. However, the analysis indicated that the impact of STF on revenue from casual users before and after STF at jurisdiction-level was inconclusive. As would be expected, the launch of STF, which is a casual fare product, did not impact ridership and revenue of monthly and annual registered members. Sensitivity analysis of ridership to rail transit disruptions due to SafeTrack indicated that there was a statistically significant increase in ridership by registered members and casual users at the CaBi stations affected by SafeTrack. The concurrency of STF launch with SafeTrack may have played a role in this increase. For, the new fare product created an opportunity for commuters to try CaBi as an alternative travel mode at an affordable price that is compatible with transit fare during transit service disruptions. However, the analysis did not present any evidence on the sustained nature of CaBi ridership increase attributable to SafeTrack. The methods used in this research are helpful for bikeshare operators to model changes in ridership and revenue from attributable to pricing structure.

Providing quantified evidence to policy makers for promoting bike-sharing in heavily air-polluted cities: A mode choice model and policy simulation for Taiyuan-China

Developing countries are facing increasing challenges to make urban mobility sustainable and to tackle the continuously growing air pollution and congestion caused by the rapid increase in car ownership. As part of a broad strategy to achieve sustainable urban mobility, bike-sharing services could contribute to car usage decrease, especially for short-distance trips. However, most of the developing countries have limited quantified evidence regarding the factors affecting bike-sharing choice and this hinders policy makers from effectively promoting bike-sharing usage. The case study city is Taiyuan, which operates one of the most in demand bike-sharing schemes in China. This research investigates the factors affecting mode choice behavior with a focus on bike-sharing, and explores the effectiveness of different policy options aiming at increasing bike-sharing ridership. Nested logit and mixed nested logit models are developed using both stated preference and revealed preference data. Policy effectiveness is studied by examining modal split changes. The results reveal the significant negative impact of air pollution on bike-sharing choice. Nevertheless, improving air quality is found to be less effective in promoting bike-sharing ridership than improving bike-sharing service itself (e.g. through access time saving, travel cost saving); although it is more effective in suppressing private car usage.

Spatial Analysis of Bikeshare Ridership With Smart Card and POI Data Using Geographically Weighted Regression Method

The primary objective of this paper is to explore the spatial analysis of bikeshare ridership with a consideration of the diversity across different station categories using smart card data and points of interests (POIs) data. The bikeshare trip records were obtained from the Citi Bike system of New York City. The POI data in the vicinity of each station were collected through the Google Places API. K-means clustering method was employed to classify the bikeshare stations into five categories. Then, the geographically weighted regression (GWR) method was applied to establish the relationship between bikeshare ridership and various kinds of influencing factors. To account for the diversity across different station categories, five separate GWR models for each station category were developed and compared with the joint model of all station categories. The results of likelihood ratio test confirmed the superiority and importance of building separate models for each bikeshare station category instead of a joint model. In addition, all the developed bikeshare ridership models were applied to predict the ridership of the newly opened stations in the next year. The results were indicated that the prediction performance of separate bikeshare ridership models was generally better than that of the joint model. The findings of this paper could help transportation agency to develop specific planning and management strategies for each station category of the entire bikesharing system.

Impacts of Level of Traffic Stress on Bikeshare Ridership in the Case of Capital Bikeshare in Montgomery County, Maryland

This research explored the relationship between level of traffic stress (LTS) and bikeshare ridership within the Capital Bikeshare (CB) network in Montgomery County, Maryland. Linear regression was used to build a model that could predict the relationship between bikeshare ridership and low-stress bicycle connections between stations, enhanced by various demographic and built environment variables that helped to define the regional context. Linear regression models were used to estimate total trips per year and to inform further studies that sought to increase ridership through improved bicycle facilities and environments. On the basis of the varying land uses within Montgomery County and the geography of CB stations in the county, two separate data sets were created to measure the ridership propensity within the northern part of the county around Rockville and Shady Grove and the southern part of the county around Bethesda and Silver Spring. The models illustrated the utility of LTS measures in identifying the effect of low-stress bicycle connections on bikeshare ridership. The models indicated that bikeshare station pairs that were connected by links with a higher percentage of low-stress facilities were correlated with higher bikeshare ridership. Station pairs that required a longer detour to achieve a low-stress route relative to a shortest-path route were correlated with lower bikeshare ridership. Application of the linear regression tool to estimate ridership on the basis of LTS and other context variables is transferable to other similar networks to draw conclusions about the relationship between quality bicycle connections and the level of bikeshare ridership.

Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto

Bike Share Toronto is Canada’s second largest public bike share system. It provides a unique case study as it is one of the few bike share programs located in a relatively cold North American setting, yet operates throughout the entire year. Using year-round historical trip data, this study analyzes the factors affecting Toronto’s bike share ridership. A comprehensive spatial analysis provides meaningful insights on the influences of socio-demographic attributes, land use and built environment, as well as different weather measures on bike share ridership. Empirical models also reveal significant effects of road network configuration (intersection density and spatial dispersion of stations) on bike sharing demands. The effect of bike infrastructure (bike lane, paths etc.) is also found to be crucial in increasing bike sharing demand. Temporal changes in bike share trip making behavior were also investigated using a multilevel framework. The study reveals a significant correlation between temperature, land use and bike share trip activity. The findings of the paper can be translated to guidelines with the aim of increasing bike share activity in urban centers.

Bike-Share Systems: Accessibility and Availability

The cities of Paris, London, Chicago, and New York (among many others) have set up largescale bike-share systems to facilitate the use of bicycles for urban commuting. This paper estimates the impact on bike-share ridership of two facets of system performance: accessibility (how far the user must walk to reach stations) and bike-availability (the likelihood of finding a bicycle). Our analysis is based on a structural demand model for spatially differentiated products that includes distinct mechanisms for the short and long-term effects of bike-availability (via lost sales and increased user-interest, respectively). The bike-share context, and the distinct mechanisms require us to go beyond past work in incorporating real time changes in product (bike)-availability information, and including much finer data on potential demand sources. These enhancements render traditional estimation methods computationally infeasible; we transform our estimation from the time domain to the “local-stockout- state” domain to address this. Our estimates for the Velib’ bike-share system in Paris suggest that a 10% increase in station density would increase ridership by 5.09% (±0.45%), while a 10% increase in bike-availability would increase ridership by 12.29% (±0.39%), three-fourths of which comes from fewer lost-sales, and the rest from increased user interest. We illustrate the use of our estimates in identifying neighborhoods and times to target for improvements, and in comparing alternate operational improvements and station networks.

Ridership and effectiveness of bikesharing: The effects of urban features and system characteristics on daily use and turnover rate of public bikes in China

As a pinnacle of green transportation with transit attributes, bikesharing has become particularly popular since the mid-2000s. Two crucial questions for the success of bikesharing adoption are how many riders can bikesharing attract, and what influences its effectiveness. To shed light on answers to these questions, this paper models the impacts of urban features and system characteristics on bikesharing daily use and turnover rate, using data constructed on 69 bikesharing systems in China. Prior to modeling, we provide an overview of bikesharing adoption in China, describing why they have been adopted, how they have matured, and how they have expanded. Results from data regression and comparison indicate that bikesharing ridership and turnover rate tend to increase with urban population, government expenditure, the number of bikesharing members and docking stations, whilst the number of public bikes shows significant but adverse signs in impacting bikesharing ridership and turnover rate. Data comparison shows that, to pursue an ideal bikesharing turnover rate in most Chinese cities, the bike-member (supply-demand) ratio should be better controlled within 0.2. Moreover, this study suggests that personal credit cards (allowing bikesharing members to pay “personal credit” rather than money if they do not return public bikes within the free use hours) and universal cards (integrating bikesharing systems into other urban transit systems through the use of a rechargeable smart card that can cover a range of payments and trips) can significantly raise bikesharing daily use and turnover rate. We recommend that bikesharing operators and transit agencies take the supply-demand thresholds and the adoption of personal credit cards and universal cards into consideration in the future bikesharing operation and development policy.

Station-Level Forecasting of Bikesharing Ridership

This study investigated the effects on bikesharing ridership levels of demographic and built environment characteristics near bikesharing stations in three operational U.S. systems. Although earlier studies focused on the analysis of a single system, the increasing availability of station-level ridership data has created the opportunity to compare experiences across systems. In this study, particular attention was paid to data quality and consistency issues raised by a multicity analysis. This project also expanded on earlier studies with the inclusion of the network effects of the size and spatial distribution of the bikesharing station network, which contributed to a more robust regression model for the prediction of station ridership. The regression analysis identified a number of variables that had statistically significant correlations with station-level bikesharing ridership: population density; retail job density; bike, walk, and transit commuters; median income; education; presence of bikeways; nonwhite population (negative association); days of precipitation (negative association); and proximity to a network of other bikesharing stations. Proximity to a greater number of other bike-sharing stations exhibited a strong positive correlation with ridership in a variety of model specifications. This finding suggested that, with the other demographic and built environment variables controlled for, access to a comprehensive network of stations was a critical factor to support ridership. Compared with earlier models, this model is more widely applicable to a diverse range of communities and can help those interested in the adoption of bikesharing systems to predict potential levels of ridership and to identify station locations that serve the greatest number of riders.