Bike-sharing Programs Research Articles

A Study on Bike Share Analysis

This study explores the role of bike-sharing programs within urban mobility and the potential impacts of autonomous vehicles (AVs) on cyclists' safety perceptions and behaviour. The research focuses on analysing factors that influence the adoption of bike-sharing services, including safety perceptions, AV technology familiarity, and demographic characteristics. Using tools like MS Excel and statistical models such as multiple regression and logistic regression, the study examines patterns in bike-sharing usage, public attitudes toward AVs, and the implications of AV testing on public streets. Findings suggest that factors like age, perceived safety, and AV familiarity significantly influence bike-sharing behaviour. The study recommends enhanced infrastructure, integration with public transit, and data-driven insights to improve bike-sharing systems and promote safer interactions with AVs

Partnering with Communities to Understand Social Determinants of Health (SDoH) Impacts on Access to Shared Micromobility

This study explored the facilitators and barriers of community bike share use in a mid-sized city with high incidence of poverty and racial diversity using a community-based participatory action research (CBPAR) photovoice framework with the Stanford Our Voice (OV) Discovery Tool digital application. Community members participated in one of three community citizen science walks with follow up focus groups facilitated by osteopathic medical student researcher to address “What makes it easy or hard to ride a bike using the bike share?” Twenty-seven diverse community members partnered with four osteopathic medical students exploring vulnerable individuals’ lived experiences, beliefs/understanding of the Social Determinants of Health (SDoH) and access to the bike share program. A total of 322 photos and narrative comments from citizen science walk audits developed deductive themes and follow up focus groups informed inductive themes. Themes addressed challenges to access, maintenance, safety in bike transit, comfort, and environment that create barriers to use and increase inequities for lower income and historically underrepresented communities. The use of OV provided photograph, narrative, and geocoded photo location. This novel approach served as an effective tool for community action with city decision makers. The narrative research identified the impact of the barriers, and the photographs and geocoding provided clear descriptions for locations to prioritize change by adding street signs for access and safety, fixing road safety issues or bike maintenance concerns. It actively engaged the community with the city to drive discussions and plans for change in repair systems and infrastructure that also addressed equity and acknowledged the SDoH supporting residents in lower income or historically underrepresented communities. Citizen science engaged community voices, supporting change in city policies and transportation initiatives to support the sustainability of the bike share program.

The effects of built environments on bicycle accidents around bike-sharing program stations using street view images and deep learning techniques: The moderating role of streetscape features

With the global rise of bike-sharing programs (BSP), planners and traffic experts have raised concerns as to whether the rapid growth of BSP ensures cycling safety. Despite numerous studies on built environments encouraging bike usage, there is limited knowledge whether streetscape environments around BSP stations affect bicycle accidents. We address this gap by investigating the relationships between various built environments and bicycle accidents around BSP stations, with a particular focus on the moderating effects of comprehensive streetscapes. Streetscape environments, estimated through semantic segmentation and k-means clustering, were used in two-level negative binomial regression models to clarify how street- and station-level environments affect different types of bicycle accidents. The findings indicate increased crash likelihoods on higher-speed roads, streets with traffic facilities, in proximity to public transportation infrastructure, and specific streetscape types. In particular, streetscape features like green oasis streets and open-sky roadways positively contribute to mitigating the negative effects of traffic facilities, such as bike networks and crosswalks, on cycling safety. This study aids in developing comprehensive strategies and guidelines to retrofit built environments for better traffic safety and thereby promote urban cycling.

Prediction of bike-sharing station demand using explainable artificial intelligence

Bike-sharing systems have grown in popularity in metropolitan areas, providing a handy and environmentally friendly transportation choice for commuters and visitors alike. As demand for bike-sharing programs grows, efficient capacity planning becomes critical to ensuring good user experience and system sustainability in terms of demand. The random forest model was used in this study to predict bike-sharing station demand and is considered a strong ensemble learning approach that can successfully capture complicated nonlinear correlations and interactions between input variables. This study employed data from the Smart Location Database (SLD) to test the model accuracy in estimating station demand and used a form of explainable artificial intelligence (XAI) function to further understand machine learning (ML) prediction outcomes owing to the blackbox tendencies of ML models. Vehicle Miles of Travel (VMT) and Greenhouse Gas (GHG) emissions were the most important features in predicting docking station demand individually but not holistically based on the datasets. The percentage of zero-car households, gross residential density, road network density, aggregate frequency of transit service, and gross activity density were found to have a moderate influence on the prediction model. Further, there may be a better prediction model generating sensible results for every type of explanatory variable, but their contributions are minimum to the prediction outcome. By measuring each feature's contribution to demand prediction in feature engineering, bike-sharing operators can acquire a better understanding of the bike-sharing station capacity and forecast future demands during planning. At the same time, ML models will need further assessment before a holistic conclusion.

Unlocking Trends: Socio-Demographic Insights into Bike Sharing from the 2017 National Household Travel Survey

The implementation of bike-sharing systems represents a novel strategy aimed at enhancing urban mobility by offering shared bicycle services to improve accessibility and user convenience. Nevertheless, there has been a limited exploration into the user demographics of bike-sharing programs on a national scale. This study investigates the socio-demographic characteristics of bike-sharing system users, leveraging the 2017 National Household Travel Survey and negative binomial regression models. It finds that bike-sharing usage is more common among users than non-users, with African American and Hispanic individuals using the system more than other racial groups. Furthermore, low-income individuals (<USD 15,000) and residents of high-density areas (≥25,000 per square mile) show a higher tendency towards bike sharing. The research highlights the lack of national-level studies on bike-sharing user demographics and suggests that future policy and planning should address the needs of specific populations, aiming to enhance urban mobility and accessibility through bike-sharing services.

Investigating the influence of spatial characteristics on cycling volume: A multi-scale geographic weighted regression approach

Cycling has seen a remarkable rise, signifying a paradigmatic move towards sustainable, eco-friendly, and efficient commuting alternatives in the contemporary urban setting. Cities also encourage this trend by establishing cycle lanes, bike-sharing programs, and incentives for frequent riders. To enhance these motivations from an urbanistic perspective, it is essential to comprehend the influence of urban characteristics on cycling volume and to incorporate this understanding into data-driven decision-making processes.This research examines the Bicification project data from Istanbul with a spatial perspective. Utilising a comprehensive array of spatial big data, the study explores the impact of urban land use, transport services, land morphology, and sociodemographic factors on cycling volume through a Multi-scale Geographically Weighted Regression (MGWR). With an Adj R2 value of 0.68, the model demonstrates a strong relation between cycling volume and several factors, including biking park stations, park and ride points, pier stops, rail stops, transfer points, main roads, elevation, population, industrial facilities, health facilities, sports areas, and residential areas. The findings will serve to develop a data-driven strategic approach to promote cycling in Istanbul.

Spatial Analysis of Bike-Sharing Ridership for Sustainable Transportation in Houston, Texas

This study aims to analyze bike-sharing information and related urban factors to promote bike-sharing utilization in Houston, Texas. The research was initiated with a descriptive analysis, where the hourly and daily variations in bike demand are investigated, thereby revealing the time-related patterns of bike tours. The models included data on socio-demographics, public transportation availability, land use patterns, tree canopy coverage, bike routes, and job density within 0.25-mile and 0.5-mile buffer zones around each bike-sharing station. Stepwise regression was utilized to examine the effects of urban factors on bike-sharing ridership, and the explanatory power of the model was enhanced by selecting meaningful variables. The analysis found that tree canopy coverage was a significant factor in influencing bike-sharing ridership. Expansion of tree coverage can help make biking a sustainable mode of transportation. These findings have the potential to guide the development of practical policies that aim to promote sustainable urban mobility through bike-sharing programs.

Geographic Information System-Based Comprehensive Shared Micromobility Station Siting Optimization for Small Urban Areas

Shared micromobility travel modes such as dockless e-scooters and bikeshare programs have become increasingly popular in the U.S. in the last decade because of their potential to improve multi-modal accessibility within communities. Smaller urbanized areas with lower population densities and fewer resources for system planning, operation, and maintenance present unique challenges with siting optimal station/service area locations. This research develops a comprehensive geographic information system (GIS)-based methodology for optimizing micromobility stations/service area locations using available and rasterized geospatial data to capture bikeshare demand indicators. Inputs are prioritized by overall importance according to the results of a survey of transportation professionals, with weights calculated by an analytic hierarchy process. These different factors are combined to create a new spatial index value to identify hotspots for candidate station/service area locations, which can be further analyzed to choose optimal locations based on the budgeted quantity of station/service area locations and ideal spacing between stations/service areas. The case study of the methodology is presented on a bikeshare station siting study in Iowa City, Iowa, U.S., using data from the Metropolitan Planning Organization of Johnson County. This research seeks to improve shared micromobility station/service area planning to better orient service to a variety of transportation goals including regular/commuting use, recreational use, equity, first/last mile connection to transit, and operational partnership opportunities. Multimodal travel times and job accessibility in the study area are evaluated both before and after the introduction of bikeshare, and both greatly improve with the introduction of optimal stations. Public agencies could expect to benefit from this comprehensive methodology because it uses easily obtainable data sources and provides the flexibility to weight the importance of factors differently to fit their communities’ specific transportation goals.

A comprehensive data analysis of e-bike mobility and greenhouse gas emissions in a higher education community: IPBike study of case

Sustainable mobility is a goal for several countries. This kind of mobility depends not only on personal motivation but also on government actions. Encouraging people, particularly children and younger, to raise awareness of the importance and benefits of using sustainable transport, like bicycles, is crucial to developing a sustainable society. This work presents the IPBike project, a Portuguese project applied at the Polytechnic Institute of Bragança. This project aims to promote the use of sustainable transport in the academic community through a rental bike program. This paper presents the results of the over three years of the IPBike project, as well as the user’s impressions and suggestions to improve the project. Moreover, a greenhouse gas emission reduction is estimated, comparing the displacement using bikes or only by cars. In general, the results achieved are promising to promote sustainable cities and plan the future since the bikes of the project are constantly rented and the user’s positive feedback, which makes the IPBikes a popular community asset. Moreover, the results demonstrate a bike-sharing program’s capacity to impact people’s lives, improve their health, reduce travel expenses, and impact society and the environment. Besides, according to the estimates, the IPBikes provided a reduction of 7% per year in the greenhouse gases emitted by the users, which means a significant reduction all over the project implantation.

Planning bikeway network for urban commute based on mobile phone data: A case study of Beijing

Driven by the increasing bike-sharing programs, demand for urban cycling has increased rapidly in recent years. As one of the effective ways to encourage cycling, bikeway has been recognized to support the cycling commutes. Therefore, the rational planning of the bikeway routes and the construction of bikeway network is crucial for improving cycling commutes in urban areas. Taking Beijing as the case, this study proposes an analytical framework for selecting and optimizing bikeway routes based on mobile phone data. The cycling demand is analyzed by mining the mobile phone geographic information, followed by evaluating the available road spaces for bikeway by applying the TOPSIS approach. Finally, the study applies the least-cost path method to select the bikeway routes, which are then classified by cycling potentials and traffic volumes for two scenairos of bikeway construction, respectively. The results show that (1) In city center, the bikeway are arranged to serve for cycling commutes within the central urban area, while the bike lanes in the suburbs (e.g., Haidian, Chaoyang, and Yizhuang) primarily serve for inter-district commute travels; (2) Bikeways that meet for the demand of inter-district commutes should be given priority to construct and is suggested to be as bicycle expressways; (3) A total length of 450 km bikeways is planned to form a city-wide network, which covers more than 70% commute population and primarily connects the high-density suburbs with central areas. The results are expected to provide references for urban planners and local governments to reasonably plan the bikeway network.

Notching for free: Do cyclists reveal the opportunity cost of time?

We estimate time-for-money tradeoffs for cyclists by exploiting revealed behavior in response to a bikesharing program’s discontinuous, “notched” pricing structure. The cost of a bicycle trip increases from $0 to $1 after 30 min. We observe cyclists adding time to their trip by checking-in at an intermediate station along their route to avoid the price, thus revealing their value of saving time (VST) cycling. Estimates of the VST for cycling are close to the minimum wage or roughly 20% of hourly wages implied by median incomes. These estimates are smaller than comparable estimates for automobiles, likely driven by leisure benefits of cycling. Although bicycle commuters exhibit somewhat larger VSTs, our results across user type, time, and route-specific characteristics are relatively homogeneous, suggesting a more fundamental time–money tradeoff. Additionally, we estimate small changes along extensive margins in response to a large price increase, but changes along intensive margins are virtually nonexistent.

Equity Analysis of Bikeshare Access: A Case Study of New York City

This study investigates the association between bikeshare program usage and neighborhoods with different socio-demographic information. This study investigated 2023 Citi Bike bikeshare data in New York City. For the analysis, Generalized Linear Regression (GLR) in ArcGIS Pro was used. The results revealed that Citi Bike bikeshare ridership increases in neighborhoods with higher income, more workers, more males, higher density, and lower African American and Hispanic population in New York City.

Exploring the potential role of bikeshare to complement public transit: The case of San Francisco amid the coronavirus crisis

The recent worldwide SARS-CoV-2 (COVID-19) pandemic has reshaped the way people live, how they access goods and services, and how they perform various activities. For public transit, there have been health concerns over the potential spread to transit users and transit service staff, which prompted transportation agencies to make decisions about the service, e.g., whether to reduce or temporarily shut down services. These decisions had substantial negative consequences, especially for transit-dependent travelers, and prompted transit users to explore alternative transportation modes, e.g., bikeshare. However, local governments and the public in general have limited information about whether and to what extent bikeshare provides adequate accessibility and mobility to those transit-dependent residents. To fill this gap, this study implemented spatial and visual analytics to identify how micro-mobility in the form of bikesharing has addressed travel needs and improved the resilience of transportation systems. The study analyzed the case of San Francisco in California, USA, focusing on three phases of the pandemic, i.e., initial confirmed cases, shelter-in-place, and initial changes in transit service. First, the authors implemented unsupervised machine learning clustering methods to identify different bikesharing trip types. Moreover, through spatiotemporally matching bikeshare ridership data with transit service information (i.e., General Transit Feed Specification, GTFS) using the tool called OpenTripPlanner (OTP), the authors studied the travel behavior changes (e.g., the proportion of bikeshare trips that could be finished by transit) for different bikeshare trip types over the three specified phases. This study revealed that during the pandemic, more casual users joined bikeshare programs; the proportion of recreation-related bikeshare trips increased; and routine trips became more prevalent considering that docking-station-based bikeshare trips increased. More importantly, the analyses also provided insights about mode substitution, because the analyses identified an increase in dockless bikeshare trips in areas with no or limited transit coverage.

Power relations are central to shaping collaborative governance of the urban sharing economy

Since its rise in the early 2000s, the sharing economy has expanded and developed rapidly worldwide. While the sharing economy can boost resource-use efficiency and encourage sustainable urban living, it also challenges urban governance. Recently, a collaborative governance (CG) approach involving public and private partnerships has been adopted in various global cities to address these governance dilemmas. However, the influence of stakeholder power relations on the CG of the sharing economy remains inadequately explored in the literature. This article argues that multi-actor collaboration can be enhanced by clarifying how power relations shape effective governance, actor engagement, shared motivation, and capacity for joint actions. This article draws on practical insights by discussing examples of the governance practices of urban bike-sharing programs to demonstrate how the nature of public-private power relations can result in specific (and quite different) forms of CG. This article will help CG researchers, policymakers, urban planners, and communities understand CG practices in the new era of shared cities and global cities.

Demand Prediction on Bike Sharing Data Using Regression Analysis Approach

In order to forecast the need for bike-sharing services, this paper suggests a rule-based regression model. Commuters and tourists alike are taking advantage of public bike sharing programs because of the convenience and low carbon footprint they provide. Used information from the UCI Machine Learning Repository. Repeated cross-validation was used to fine-tune the hyper-parameters of five statistical models. Conditional Inference Tree, K-Nearest Neighbor Analysis, Regularized Random Forest, Classification and Regression Trees, and CUBIST. The predictive accuracy of the regression models was measured by calculating the Root Mean Squared Error, R-Squared, Mean Absolute Error, and Coefficient. For both the Seoul Bike and Capital Bikeshare programs, the rule-based model CUBIST was able to account for 95 and 89% of the Variance (R2), respectively. All models built from the two datasets using WEKA v3.8.6, and are used a variable significance analysis to establish which variables were most crucial. The most important factors in determining the hourly demand for bike rentals are the weather and the time of day.

Exploring commonalities and disparities of seattle residents' perceptions on dockless bike-sharing across gender

In recent years, dockless bike-sharing programs have been introduced to either substitute or complement docked bike-sharing programs. Riders of these devices always have perceived differences of one system over the other, which could vary across gender. This study applied a text network approach to explore the residents' perceptions of the dockless bike-sharing program across gender. The study used over 700 responses collected between February and March 2018 in Seattle, Washington. The results revealed that ease of use, convenience, safety, pricing, and quality areas make a tremendous difference in the perception of dockless over docked bike-sharing systems. The perception of ease of use and convenience does not vary significantly across genders. On the other hand, male respondents were more aligned on the better pricing scheme and the bikes' quality than female respondents. Conversely, female respondents did care more about safety in terms of helmet use. Moreover, female respondents were more explicit in explaining the negative characteristics of the dockless bike-sharing system over docked ones. Study findings can help policymakers and operators of dockless bikes to provide equity in service for both genders.

Factors associated with bicycle helmet use and proper fit: a cross-sectional survey of Montreal cyclists during the COVID-19 pandemic.

With increased bicycle use during the COVID-19 pandemic and growing availability of bicycle-sharing programs in Montreal, we hypothesize helmet use has decreased. The aim of this study was to evaluate helmet use and proper fit among Montreal cyclists during the pandemic relative to historical data. Nine observers collected data on bike type, gender, helmet use, and ethnicity using the iHelmet© app at 18 locations across the island of Montreal from June to September 2021. Proper helmet wear was assessed at one busy location. Multiple logistic regression was used to identify factors associated with helmet wear and results were compared to a historical study. Of the 2200 cyclists observed, 1109 (50.4%) wore a helmet. Males (OR = 0.78, 95%CI = 0.65-0.95), young adults (OR = 0.65, 95%CI = 0.51-0.84), visible minorities (OR = 0.38, 95%CI = 0.28-0.53), and bike-share users (OR = 0.21, 95%CI = 0.15-0.28) were less likely to be wearing a helmet, whereas children (OR = 3.92, 95%CI = 2.17-7.08) and cyclists using racing bicycles (OR = 3.84, 95%CI = 2.62-5.62) were more likely to be wearing a helmet. The majority (139/213; 65.3%) of assessed cyclists wore properly fitting helmets. Children had the lowest odds of having a properly fitted helmet (OR = 0.13, 95%CI = 0.04-0.41). Compared to 2011, helmet use during the pandemic increased significantly (1109/2200 (50.4%) vs. 2192/4789 (45.8%); p = 0.032). Helmet use among Montreal cyclists was associated with age, gender, ethnicity, and type of bicycle. Children were least likely to have a properly fitted helmet. The recent increase in popularity of cycling and expansion of bicycle-sharing programs reinforce the need for bicycle helmet awareness initiatives, legislation, and funding prioritization.

Does bike sharing improve public health? A case study in New York City

Bike sharing contributes to public health by providing physical exercise opportunities. Meanwhile, the shift of vehicular travel demand to bike sharing may cut vehicular emissions and severe crashes. However, according to each city’s different composition of travel modes, the net health benefits are not clear and worth studying case by case. In this paper, we focus on the Citi Bike bike-sharing program in New York City. We modify the Integrated Transport Health Impact Model and assess the net health effects by comparing a baseline scenario (with Citi Bike) against a hypothetical scenario (without Citi Bike). For each scenario, we investigate the overall health effects of the Citi Bike program in the unit of Disability Adjusted Life Years (DALYs) in three categories: physical activity, fine inhalable particles (i.e., PM 2.5) exposure, and road traffic injuries. The results indicate that the implementation of Citi Bike plays a positive role in improving public health. It is worth noting that the emission reduction due to the shift from vehicular transport to Citi Bike contributes the most health benefit to the public. Hence we further propose a sensitivity analysis of the Citi Bike market share illustrating the potential benefits of promoting Citi Bike among non-users.

Will Smart Bikes Succeed as Public Transportation in the United States?

Bicycle-sharing programs have received increasing attention in recent years with initiatives to increase bike usage, better meet the demand of a more mobile public, and lessen the environmental impacts of our transportation activities. In 1996, the smart bike, or automated bike rental system, was first implemented in the United Kingdom, leading to a growing number of programs throughout Europe and Asia. However, there are presently no such programs in the United States. This article examines the potential success of smart bike programs in the United States.

Bike-sharing: History, Impacts, Models of Provision, and Future

This paper discusses the history of bike-sharing from the early 1st generation program to present day 3rd generation programs. Included are a detailed examination of models of provision, with benefits and detriments of each, and a description of capital and operating costs. The paper concludes with a look into the future through discussion about what a 4th generation bike-sharing program could be.