Freedom from the Station: Spatial Equity in Access to Dockless Bike Share

Global comparison of urban bike-sharing accessibility across 40 cities

The global expansion of bike-sharing networks offers a cost-effective and environmentally friendly transportation alternative that complements public transit and promotes active, healthy lifestyles. Despite significant research, most studies focus on individual locations, specific environmental or health impacts, or infrastructure elements like bike lanes. Instead, this paper elaborates a comprehensive global comparison of bike-sharing systems by introducing a novel database that aggregates data from 40 cities worldwide. Our study integrates this data with population data and urban metrics to classify these networks topologically and assess their effective coverage concerning the population served and their relation with existing public transit systems. We introduce the "Bike-Share Service Accessibility Index" (BSAI), a new metric to evaluate and compare the performance of bike-sharing networks. Our findings provide valuable insights for urban planners and policymakers, offering data-driven strategies to enhance sustainable urban mobility through better-integrated and more spatially equitable bike-sharing systems.

Uncovering the spatio-temporal impact of the COVID-19 pandemic on shared e-scooter usage: A spatial panel model

This study examines the spatio-temporal effects of the COVID-19 pandemic on shared e-scooter usage by leveraging two years (2019 and 2020) of daily shared micromobility data from Austin, Texas. We employed a series of random effects spatial-autoregressive model with a spatially autocorrelated error (SAC) to examine the differences and similarities in determinants of e-scooter usage during regular and pandemic periods and to identify factors contributing to the changes in e-scooter use during the Pandemic. Model results provided strong evidence of spatial autocorrelation in the e-scooter trip data and found a spatial negative spillover effect in the 2020 model. The key findings are: i) while the daily e-scooter trips reduced, the average trip distance and the average trip duration increased during the Pandemic; ii) the central part of Austin city experienced a major decrease in e-scooter usage during the Pandemic compared to other parts of Austin; iii) areas with low median income and higher number of available e-scooter devices experienced a smaller decrease in daily total e-scooter trips, trip distance, and trip duration during the Pandemic while the opposite result was found in areas with higher public transportation services. The results of this study provide policymakers with a timely understanding of the changes in shared e-scooter usage during the Pandemic, which can help redesign and revive the shared micromobility market in the post-pandemic era.

Understanding cycling mobility: Bologna case study

Understanding human mobility in touristic and historical cities is of the utmost importance for managing traffic and deploying new resources and services. In recent years, the need to enhance mobility has been exacerbated due to rapid urbanisation and climate changes. The main objective of this work is to study cycling mobility within the city of Bologna, Italy. We used six months dataset that consists of 320,118 self-reported bike trips. First, we performed several descriptive analysis to understand the temporal and spatial patterns of bike users for understanding popular roads and most favourite points within the city. The findings show how bike users present regular daily and weekly temporal patterns and the characteristics of their trips (i.e. distance, time and speed) follow well-known distribution laws. We also identified several points of interest in the city that are particularly attractive for cycling. Moreover, using several other public datasets, we found that bike usage is more correlated to temperature and precipitation and has no correlation to wind speed and pollution. We also exploited machine learning approaches for predicting short-term trips in the near future (that is for the following 10, 30, and 60 minutes), which could help local governmental agencies with urban planning. The best model achieved an R square of 0.91 for the 30-minute time interval, and a Mean Absolute Error of 2.52 and a Root Mean Squared Error of 3.88 for the 10-minute time interval.

The effects of COVID-19 on female and male bike sharing users: Insights from Lisbon's GIRA

Women are among the groups most affected by the pandemic as they are more likely to be dependent on public transport (PT), which was heavily restricted during COVID-19. Thus, there is a need to consider transport alternatives such as bike sharing that can ensure their mobility needs. By conducting a survey to the bike sharing system (BSS) of Lisbon, we explored differences in travel behaviour and attitudes between female and male users before and during COVID-19. We found men to have higher bike ownership rates, a higher modal share of personal bicycle regarding commuting, and more likely to use their own bikes if BSS was unavailable. Conversely, women more frequently combined BSS with PT and were more likely to use PT if BSS was unavailable. Moreover, while men were using BSS more frequently than women pre-pandemic, during COVID-19 women are using BSS as frequently as men. Our research provides evidence on the potential role of BSS as a transport alternative during pandemics, inducing women to take up cycling who otherwise would not cycle, therefore, potentially decreasing the current cycling gender gap. Findings suggest that introducing family/friend discounts and promoting BSS for exercising may increase the share of female cyclists.

Correlates of bike share use and its association with weight status at an urban university

Background

Research on the influences on bike share use and potential favorable relationships between use and obesity is limited, particularly in the U.S. context. Therefore, the aims of this exploratory study were to examine correlates of awareness and use of Boston’s Bluebikes bike share system and assess the association between use and weight status.

Methods

Students, faculty, and staff (n = 256) at a public urban university completed an online survey that assessed sociodemographic, behavioral, and physical activity characteristics, Bluebikes awareness, and use of Bluebikes and personal bikes. Multivariable logistic regression models were estimated to examine associations between sociodemographic and behavioral factors and bike share awareness and use; and between use and overweight/obesity status.

Results

Respondents were mostly students (72.2%), female (69.1%), White (62.1%), and the mean age was 32.4±13.8 years. The percentage of respondents classified as aware of Bluebikes was 33.6% with only 12.9% reporting any use of the system. Living in a community where bike share stations were located (odds ratio (OR) = 2.01, 95% confidence interval (CI): 1.10, 3.67), personal bike ownership (OR = 2.27, 95% CI:1.27, 4.45), and not exclusively commuting to campus via car (OR = 3.19, 95% CI:1.63, 6.22) had significant positive associations with awareness. Living in a bike share community (OR = 2.34; 95% CI:1.04, 5.27) and personal bike ownership (OR = 3.09; 95% CI:1.27, 7.52) were positively associated with bike share use. Any reported use of Bluebikes was associated with 60% lower odds of being overweight/obese (OR = 0.40; 95% CI:0.17, 0.93).

Conclusions

Several environmental and behavioral variables, including access to stations and personal bicycle ownership, were significantly associated with Bluebikes awareness and use. Findings also suggest a potential benefit to bike share users in terms of maintaining a healthy weight, though further longitudinal studies are needed to rule out the possibility that more active and leaner individuals tend to use bike share more frequently.

Approaching Sustainable Bike-Sharing Development: A Systematic Review of the Influence of Built Environment Features on Bike-Sharing Ridership

Bike-sharing is known as a sustainable form of transportation. This travel mode is able to tackle the “last mile” transit issue and deliver financial, well-being, and low-carbon lifestyle advantages to users. To date, many studies have analysed the influence of various factors, including built environments, on bike-sharing ridership. However, no study has exclusively synthesised these findings regarding the association between built-environment attributes and bike-sharing ridership. Thus, in this study, a systematic literature review was conducted on 39 eligible studies. These studies were assessed with respect to (1) bike-sharing usage, (2) studies’ geographical distribution, (3) data collection and analysis method, and (4) built environment factor type. Most studies were carried out in the US and Chinese cities. Variables associated with diversity, density, and distance to public transport stations and public transport infrastructure were frequently employed by the studies reviewed. It was found that BS stations with an average capacity of 24.63 docks and street network systems with an average length of 12.57 km of cycling lanes had a significant impact on the bike-sharing ridership. The findings of these studies were combined, and a series of recommendations were proposed based on them for bike-sharing service providers and researchers in academia. The findings of this evaluation can help practitioners and scholars understand the important built environment elements that influence bike-sharing ridership. Knowledge in this field will enable bike-sharing service providers to direct their resources sufficiently to enhance the more essential aspects of bike-sharing users’ satisfaction.

Integrative urban AI to expand coverage, access, and equity of urban data

We consider the use of AI techniques to expand the coverage, access, and equity of urban data. We aim to enable holistic research on city dynamics, steering AI research attention away from profit-oriented, societally harmful applications (e.g., facial recognition) and toward foundational questions in mobility, participatory governance, and justice. By making available high-quality, multi-variate, cross-scale data for research, we aim to link the macrostudy of cities as complex systems with the reductionist view of cities as an assembly of independent prediction tasks. We identify four research areas in AI for cities as key enablers: interpolation and extrapolation of spatiotemporal data, using NLP techniques to model speech- and text-intensive governance activities, exploiting ontology modeling in learning tasks, and understanding the interaction of fairness and interpretability in sensitive contexts.

Prediction for Origin-Destination Distribution of Dockless Shared Bicycles: A Case Study in Nanjing City

Shared bicycles are currently widely welcomed by the public due to their flexibility and convenience; they also help reduce chemical emissions and improve public health by encouraging people to engage in physical activities. However, during their development process, the imbalance between the supply and demand of shared bicycles has restricted the public's willingness to use them. Thus, it is necessary to forecast the demand for shared bicycles in different urban regions. This article presents a prediction model called QPSO-LSTM for the origin and destination (OD) distribution of shared bicycles by combining long short-term memory (LSTM) and quantum particle swarm optimization (QPSO). LSTM is a special type of recurrent neural network (RNN) that solves the long-term dependence problem existing in the general RNN, and is suitable for processing and predicting important events with very long intervals and delays in time series. QPSO is an important swarm intelligence algorithm that solves the optimization problem by simulating the process of birds searching for food. In the QPSO-LSTM model, LSTM is applied to predict the OD numbers. QPSO is used to optimize the LSTM for a problem involving a large number of hyperparameters, and the optimal combination of hyperparameters is quickly determined. Taking Nanjing as an example, the prediction model is applied to two typical areas, and the number of bicycles needed per hour in a future day is predicted. QPSO-LSTM can effectively learn the cycle regularity of the change in bicycle OD quantity. Finally, the QPSO-LSTM model is compared with the autoregressive integrated moving average model (ARIMA), back propagation (BP), and recurrent neural networks (RNNs). This shows that the QPSO-LSTM prediction result is more accurate.

A Multi-Perspective Assessment of the Introduction of E-Scooter Sharing in Germany

Electric scooter sharing (e-scooter sharing) is a new urban micro-mobility service that is expected to shape individual urban mobility. The introduction of e-scooter sharing systems poses challenging questions for cities and transportation planners regarding their effects on their transportation system. This study addresses the question concerning the strategies which are applied for the introduction of e-scooter sharing systems in different operation areas in Germany. An interview study with 21 stakeholders with different backgrounds (local transport authorities, public transport providers, e-scooter sharing operators, municipalities, associations, planning offices and consulting companies, and other mobility providers) was conducted to reflect upon the introduction of e-scooter sharing systems in Germany and stakeholders’ involvement in planning. The qualitative content analysis provides insights into the stakeholders’ assessment of the introduction process and thus contributes to a multi-perspective understanding on the topic. Derived hypotheses and recommendations further contribute to knowledge sharing and learning from experience. The paper concludes with a description of three introduction styles: protective, pro-active, and laissez-faire.

Towards Equity in Micromobility: Spatial Analysis of Access to Bikes and Scooters amongst Disadvantaged Populations

In recent years, cities around the world have launched various micromobility programs to offer more convenient and efficient mobility options that make transit networks more accessible. However, the question of whether micromobility services are accessible to and equitably distributed amongst all populations still remains unanswered. In this study, we investigate the spatial accessibility of disadvantaged communities, such as racial and ethnic minorities, low-income populations, and transit-dependent populations, to scooter and bike services. The ultimate goal of this study is to examine associations between the level of access to bikes and scooters and the racial and social characteristics of communities throughout the City of Austin, Texas. To achieve this goal, first, equity analysis with a Lorenz curve was performed to understand how bike and scooter accessibility is distributed among the population. Then, both Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models were generated to explore factors associated with bike and scooter accessibility. The analysis of the residuals showed more consistent results in the GWR models than in the OLS models. The equity analysis with the Lorenz curve conducted herein reveals extreme inequity in access to micromobility services. Almost 80 percent of residents have no access to bikes and scooters. Access is even worse for transit-dependent people when compared to the general population. The regression models further revealed that areas with a higher proportion of Black residents were less likely to have access to both bikes and scooters, yet positive associations were found for both bike and scooter accessibility and low-income populations. Increased understanding of spatial access to bikes and scooters can support ongoing efforts to deliver equitable transportation systems, improve transportation alternatives for disadvantaged populations, and support future policy actions related to bike and scooter services.

How Is Urban Greenness Spatially Associated with Dockless Bike Sharing Usage on Weekdays, Weekends, and Holidays?

Dockless bike sharing plays an important role in residents’ daily travel, traffic congestion, and air pollution. Recently, urban greenness has been proven to be associated with bike sharing usage around metro stations using a global model. However, their spatial associations and bike sharing usage on public holidays have seldom been explored in previous studies. In this study, urban greenness was obtained objectively using eye-level greenness with street-view images by deep learning segmentation and overhead view greenness from the normalized difference vegetation index (NDVI). Geographically weighted regression (GWR) was applied to fill the research gap by exploring the spatially varying association between dockless bike sharing usage on weekdays, weekends, and holidays, and urban greenness indicators as well as other built environment factors. The results showed that eye-level greenness was positively associated with bike sharing usage on weekdays, weekends, and holidays. Overhead-view greenness was found to be negatively related to bike usage on weekends and holidays, and insignificant on weekdays. Therefore, to promote bike sharing usage and build a cycling-friendly environment, the study suggests that the relevant urban planner should pay more attention to eye-level greenness exposure along secondary roads rather than the NDVI. Most importantly, planning implications varying across the study area during different days were proposed based on GWR results. For example, the improvement of eye-level greenness might effectively promote bike usage in northeastern and southern Futian districts and western Nanshan on weekdays. It also helps promote bike usage in Futian and Luohu districts on weekends, and in southern Futian and southeastern Nanshan districts on holidays.

Parking Places to Moped-Style Scooter Sharing Services Using GIS Location-Allocation Models and GPS Data

Moped-style scooters are one of the most popular systems of micro-mobility. They are undoubtedly good for the city, as they promote forms of environmentally-friendly mobility, in which flexibility helps prevent traffic build-up in the urban centers where they operate. However, their increasing numbers are also generating conflicts as a result of the bad behavior of users, their unwarranted use in public spaces, and above all their parking. This paper proposes a methodology for finding parking spaces for shared motorcycle services using Geographic information system (GIS) location-allocation models and Global Positioning System (GPS) data. We used the center of Madrid and data from the company Muving (one of the city’s main operators) for our case study. As well as finding the location of parking spaces for motorbikes, our analysis examines how the varying distribution of demand over the course of the day affects the demand allocated to parking spaces. The results demonstrate how reserving a relatively small number of parking spaces for scooters makes it possible to capture over 70% of journeys in the catchment area. The daily variations in the distribution of demand slightly reduce the efficiency of the network of parking spaces in the morning and increase it at night, when demand is strongly focused on the most central areas.

Who is Biking for? Urban Bikeshare Networks' Responses to the COVID-19 Pandemic, Disparities in Bikeshare Access, and a Way Forward

Black, Latinx, and Indigenous people have contracted the SARS-CoV-2 virus and died of COVID-19 at higher rates than White people. Individuals rated public transit, taxis, and ride-hailing as the modes of transportation putting them at greatest risk of COVID-19 infection. Cycling may thus be an attractive alternative for commuting. Amid the increase in bikeshare usage during the early months of the pandemic, bikeshare companies made changes to membership requirements to increase accessibility, targeting especially essential workers. Essential workers in the United States are disproportionately Black and Latinx, underpaid, and reliant on public transit to commute to work. We document changes made by bikeshare companies, including benefits to various groups of essential workers, and we discuss such changes in the context of longstanding racial disparities in bikeshare access. While well intended, the arbitrary delineation in eligibility for such benefits by class of essential workers unwittingly curtailed access for many who may have benefited most. Given that equity in bikeshare is an important tool to improve access to safe transportation, critical changes in the distribution, accessibility, and usability of bikeshare networks is essential. Bikeshare companies, city planners, and policy makers should collaborate with community-based bike advocates to implement changes, as vocalized by those most in need of alternative forms of transportation.

Socio-Ecological Predictors of Frequent Bike Share Trips: Do Purposes Matter?

Using bike share could increase physical activity and improve health. This study used the social-ecological model to identify predictors of frequent bike share trips for different purposes. Participants residing in the U.S. were recruited via Amazon Mechanical Turk (MTurk). Self-report trip purposes were used to group participants into using bike share for commuting only (n = 260), social/entertainment only (n = 313), exercise only (n = 358), dual or triple-purpose (n = 501), and purposes other than commuting, social/entertainment, and exercise (n = 279). Results showed that at the intrapersonal level, perceived use of bike share to be helpful for increasing physical activity was a significant predictor for all groups, except for the other purpose group. Adjusting outdoor activity based on air quality was a significant predictor for the dual or triple-purpose group. At the interpersonal level, having four or more friends/family using bike share was a significant predictor for the other purpose group. At the community level, distance to the nearest bike share within acceptable range was a significant predictor for social/entertainment and dual or triple-purpose groups. The findings suggest that it is important to consider factors at multiple levels for predicting bike share usage. Moreover, health educators and policy makers should adopt different strategies for promoting bike share usage based on trip purposes.

Exploring Dockless Bikeshare Usage: A Case Study of Beijing, China

The rapid emergence of dockless bikeshare systems has had a considerable influence on individuals’ daily mobility patterns. However, information is still limited regarding the role that sociodemographics, social environments, travel attitudes and the built environment play on the adoption and usage of dockless bikeshare systems. To gain insight into what influences individuals to start and continue to use dockless bikeshare systems, this study sets out to assess the influential factors that are related to individuals’ initial adoption and frequency of usage of this transportation mode. A survey was conducted among the residents of Beijing to assess their usage of dockless bikeshare systems. A binary logistic regression is employed to assess travel mode adoption, and a set of hurdle negative binominal regressions is used to assess the travel frequency for four trip purposes. The results reveal that dockless bikeshare systems are more popular among younger, higher educated, or median-income groups and appear to be gender-independent. The total number of kilometers of roads within an individual’s neighborhood was reported to be positively associated with having higher odds of dockless bikeshare adoption, while the total length of bicycle paths does not show a significant relationship. Having a pro-bicycle attitude was found to play a strong positive role in deciding whether to use the dockless bikeshare system initially, but it became less important in determining bikeshare users’ frequency of usage. Finally, this study confirms that it is relevant to consider various trip purposes when exploring individuals’ travel behavior and dockless bikeshare usage.

Understanding the Shared E-scooter Travels in Austin, TX

This paper investigated the travel patterns of 1.7 million shared E-scooter trips from April 2018 to February 2019 in Austin, TX. There were more than 6000 active E-scooters in operation each month, generating over 150,000 trips and covered approximately 117,000 miles. During this period, the average travel distance and operation time of E-scooter trips were 0.77 miles and 7.55 min, respectively. We further identified two E-scooter usage hotspots in the city (Downtown Austin and the University of Texas campus). The spatial analysis showed that more trips originated from Downtown Austin than were completed, while the opposite was true for the UT campus. We also investigated the relationship between the number of E-scooter trips and the surrounding environments. The results show that areas with higher population density and more residents with higher education were correlated with more E-scooter trips. A shorter distance to the city center, the presence of transit stations, better street connectivity, and more compact land use were also associated with increased E scooter usage in Austin, TX. Surprisingly, the proportion of young residents within a neighbourhood was negatively correlated with E-scooter usage.

Evaluation of free-floating bike-share on a university campus using a multi-method approach

Bike-sharing, especially free-floating bike-share, has tremendous potential for increasing active transport on a college campus. Increased bike use improves public health, reduces pollution, and solves traffic congestion problems. Like other innovations, free-floating bikeshare proceeds through various stages while disseminated and before being widely adopted and accepted. A multi-method study using quantitative bike usage data, a cross-sectional survey, and focus group discussions was used to evaluate the Spring 2018 launch of a free-floating bike-share program at a large public university. Three months after implementation, there were 19,504 registered users, 24,371 different riders, 165,854 rides, and 85,778 miles traveled. The average trip length was 0.52 miles and lasted 8.3 min. Survey data from 2845 students, faculty, and staff revealed that 33.6% had used the bikes. Bike users were more likely to be students, freshmen, living on campus, be a current biker, and have confidence in their biking ability. Focus groups revealed that safety was a concern, knowledge about how the program worked was low among non-users and faculty and staff, cost was a barrier, and that adherence to bike-share rules needed to be improved. A large segment of the university population quickly adopted free-floating bike-share. However, continued work needs to be done to enhance safety, provide clear guidelines on bike-share rules (e.g., bike parking), and increase knowledge of the program with a specific focus on use by faculty and staff to ensure continued success and ultimately improve health.

Roadmap for free-floating bikeshare research and practice in North America

The deployment of smartphone-operated, non-station-based bicycle fleets ("dockless" or "free-floating" bikeshare) represents a new generation of bikesharing. Users locate bikes in these free-floating systems using Global Positioning Systems (GPS) and lock bikes in place at their destinations. In this paper, we review current free-floating bikesharing systems in North America and discuss priorities for future research and practice. Since launching in 2017, free-floating bikeshare has expanded rapidly to encompass 200+ systems operating 40,000+ bikes within 150+ cities. In contrast with previous systems, free-floating systems operate almost exclusively using commercial "for-profit" models, amidst concerns of financial sustainability. Governance for these systems is in early stages and can include operating fees, fleet size caps, safety requirements, parking restrictions, data sharing, and equity obligations. We identify research and practice gaps within the themes of usage, equity, sharing resources, business model, and context. While some existing bikesharing literature translates to free-floating systems, novel topics arise due to the ubiquity, fluidity, and business models of these new systems. Systems have numerous obstacles to overcome for long-term sustainability, including barriers common to station-based systems: limited supportive infrastructure, equity, theft or vandalism, and funding. Other unique obstacles arise in free-floating bikeshare around parking, sidewalk right of ways, varied bicycle types, and data sharing. This review offers background in and critical reflection on the rapidly evolving free-floating bikeshare landscape, including priorities for future research and practice. If concerns can be overcome, free-floating bikeshare may provide unprecedented opportunities to bypass congested streets, encourage physical activity, and support urban sustainability.

Free-Floating Bikeshare and Helmet Use in Seattle, WA

Wearing a helmet when bicycling prevents traumatic brain injury in the event of a crash. Most cyclists nationwide use helmets when riding. However, the growth of free-floating bike sharing systems, which offer short-term access to bicycles but not helmets, may erode helmet-wearing norms among cyclists. We counted cyclists over several hours at four locations in Seattle, WA. We categorized each rider according to whether he or she was wearing a helmet and to whether or not he or she was riding a bike share bike. Whereas 91% of riders of private bikes wore helmets, only 20% of bike share riders wore helmets. Moreover, in locations where a greater proportion of riders were on bikes hare bikes, fewer riders of private bicycles wore helmets (r = - 0.96, p = 0.04). The impact of bike sharing programs on helmet wearing norms among private bike riders warrants further exploration.

System Dynamics Modeling of Dockless Bike-Sharing Program Operations: A Case Study of Mobike in Beijing, China

In China, dockless bike-sharing programs (DBSPs) have changed people’s travel modes, alleviated urban traffic congestion, and reduced carbon emissions. However, a number of DBSPs have experienced financial crises since 2018. This means that research on DBSPs must be considered not only in terms of the environment and technology, but also in the operation of the program. In this paper, we modeled the DBSP operations in a certain area using a system dynamics simulation approach. The main purpose was to explore the dynamics of the program’s operation process and evaluate possible improvement strategies for maximizing the revenue of the overall DBSP. Specifically, the analysis focused on the economic profits of DBSPs in an environment of competition and government regulation. The research findings revealed that the dockless bike-sharing industry has great economic profits, but in the current environment, the market needs to be regulated by the local government. If a DBSP does not introduce new technologies or find new profit channels, it will be difficult to develop sustainably by only relying on riding profits. In addition, we provide a case study of Mobike’s operations in Beijing to support these findings and validate the developed model. Finally, we discuss Mobike’s possible improvement strategies.