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"Bicycle sharing programs."
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Bike share
\"There are now over 2,000 cities with a bike share program. Bike Share examines all the major developments in the 50-year history of bike share programs. The book provides a detailed focus on contemporary bike share programs, including many of the most prominent systems, such as those in Paris, London, and New York, as well as the rapidly emerging dockless bike share sector. This book also addresses how rapid technological innovation, particularly in terms of mobile internet devices and electric assist bicycles may change the face of not just cycling, but urban mobility more generally. By the end of 2018 it is estimated that there are more than 20 million bicycles in the global bike share fleet, with most of these dockless, coming online only in the last three years. Consequently, research examining bike share has not kept pace with the rapid deployment of this new form of urban mobility. Bike Share addresses a number of key themes such as: - The urban age, contextualising bike share within a wider urbanism movement and how it sits within the growing sharing economy, - The impact of bike share, looking at systems in China, Europe, North America and Australia to see how these programs have changed travel patterns and consequent impact on car use, emissions, congestion, public health and safety, - The bike share business model, including how ride sourcing services like Uber and Lyft are beginning to integrate their business with bike share service providers. - Public reaction to bike share, - Bike share gone wrong, looking at systems that have failed to achieve their ridership estimates - And the future of bike share including public transport smart card integration, mobile payments, and electric assist bicycles\"--Provided by publisher.
Book
Fleet Management for Vehicle Sharing Operations
Astochastic, mixed-integer program (MIP) involving joint chance constraints is developed that generates least-cost vehicle redistribution plans for shared-vehicle systems such that a proportion of all near-term demand scenarios are met. The model aims to correct short-term demand asymmetry in shared-vehicle systems, where flow from one station to another is seldom equal to the flow in the opposing direction. The model accounts for demand stochasticity and generates partial redistribution plans in circumstances when demand outstrips supply. This stochastic MIP has a nonconvex feasible region. A novel divide-and-conquer algorithm for generating
p
-efficient points, used to transform the problem into a set of disjunctive, convex MIPs and handle dual-bounded chance constraints, is proposed. Assuming independence of random demand across stations, a faster cone-generation method is also presented. In a real-world application for a system in Singapore, the potential of redistribution as a fleet management strategy and the value of accounting for inherent stochasticities are demonstrated.
Journal Article
Velo city : architecture for bikes
\"The world's major cities are making room for cyclists, helping them to ride, store, share, and buy their bicycles more easily than ever before. As a result, bike-related design has become one of the hottest fields in architecture. From racetracks to commuter paths and from bike sharing to bridges, this comprehensive survey details every aspect of this brave new cycling world. Drawing on the latest trends in bike design and fashion it places each project in context to provide an eclectic visual record of the world built around cycling. With an introductory essay that considers the history and future of cycling and packed with numerous color illustrations, this book is perfect for design enthusiasts and cyclists alike\"--Provided by publisher.
Book
Demand prediction for a public bike sharing program based on spatio-temporal graph convolutional networks
The operation of public bike sharing (PBS) programs has attracted attention again due to numerous problems encountered by free-floating bike sharing programs. These problems include malicious damage, theft, chaotic parking, large-scale deficit and bankruptcy. The short-time demand prediction is a key issue for the successful operation of PBS programs. In this study, we use a novel spatio-temporal graph convolutional network (STGCN) to predict the picking up/returning demand by exploring potential information from multi-view data. We apply graph convolutional neural networks (CNNs) to represent the spatial dependency based on the geographic information system data denoting the location of docks. Moreover, we use gated CNNs to denote the temporal dependency according to the time-series data representing the demand for picking up/returning public bikes. The STGCN and three recurrent neural network (RNN)-based competitors are trained and validated using the multi-view data from the Wenling PBS program for one month. The RNN-based competitors consist of the SimpleRNN, long short term memory and gated recurrent unit. Results show that the STGCN achieves higher prediction accuracy compared with its competitors. Although the STGCN consumes a longer training time compared with the SimpleRNN, it requires a minimal number of epochs to achieve convergence precision. The complete CNN structure in the STGCN can effectively address the spatial and temporal dependencies for PBS demand prediction.
Journal Article
On bicycles : a 200-year history of cycling in New York City
\"In its most recent ranking, Bicycling Magazine named New York the number one American city for cycling. But long before the Citi Bike era, New York has stood out as an important city in the history and development of cycling--as a pastime and a mirror of the city's shifting social, economic, and structural developments. In Bicycles and the Boroughs, Evan Friss traces the storied history of bicycling in the Big Apple, from the bicycle-like \"draisine\" in 1818 adopted by a small number of enthusiasts; to New York's ascent to the capital of the cycling world in the 1890s, which among other things triggered increased female mobility and corresponding conversations about the propriety of women cyclists; to Mayor Koch's bike ban of 1987 after the stock market's collapse, which shed light on the ways bankers, lawyers, and other professionals relied on this labor force and the immediacy of the information they delivered. Finally, the history shifts to Michael Bloomberg's Citi Bike initiative, the largest bike sharing system in the country, in an effort to make New York a \"greener\" city. But even in the wake of the program's mass adoption, Friss brings to light ongoing public debates over the location of bike lanes, the dangers of biking in certain areas, whether the program's financial model is sustainable, and the ways in which cycling will continue to shape and be shaped by the city\"-- Provided by publisher.
Book
Rethinking the Utility of Public Bicycles: The Development and Challenges of Station-Less Bike Sharing in China
Cycling is known to be environmentally friendly and beneficial to public health and sustainable urban development. Cycling has recently increased in Chinese cities as a result of the emergence of station-less bike-sharing systems. This study examines the emergence, rapid growth and consolidation of station-less bike-sharing systems and the role of suppliers, users and government regulators. It shows that these systems developed unevenly, growing most in large cities in eastern and south-eastern China, and explores the relationship between this spatial distribution and the nature of the service and the socio-economic characteristics of cities. To investigate patterns of, and reasons for, the use of these systems, this research also reports the results of a survey of users and non-users, identifying their gender, age, income characteristics and attitudes to station-less systems.
Journal Article
Who is in the near market for bicycle sharing? Identifying current, potential, and unlikely users of a public bicycle share program in Vancouver, Canada
Background
Public bicycle share programs in many cities are used by a small segment of the population. To better understand the market for public bicycle share, this study examined the socio-demographic and transportation characteristics of current, potential, and unlikely users of a public bicycle share program and identified specific motivators and deterrents to public bicycle share use.
Methods
We used cross-sectional data from a 2017 Vancouver public bicycle share (Mobi by Shaw Go) member survey (
n
= 1272) and a 2017 population-based survey of Vancouver residents (
n
= 792). We categorized non-users from the population survey as either potential or unlikely users based on their stated interest in using public bicycle share within the next year. We used descriptive statistics to compare the demographic and transportation characteristics of current users to non-users, and multiple logistic regression to compare the profiles of potential and unlikely users.
Results
Public bicycle share users in Vancouver tended to be male, employed, and have higher educations and incomes as compared to non-users, and were more likely to use active modes of transportation. The vast majority of non-users (74%) thought the public bicycle share program was a good idea for Vancouver. Of the non-users, 23% were identified as potential users. Potential users tended to be younger, have lower incomes, and were more likely to use public transit for their main mode of transportation, as compared to current and unlikely users. The most common motivators among potential users related to health benefits, not owning a bicycle, and stations near their home or destination. The deterrents among unlikely users were a preference for riding their own bicycle, perceived inconvenience compared to other modes, bad weather, and traffic. Cost was a deterrent to one-fifth of unlikely users, notable given they tended to have lower incomes than current users.
Conclusion
Findings can help inform targeted marketing and outreach to increase public bicycle share uptake in the population.
Journal Article
Gamification and service quality in bike sharing: an empirical study in Italy
PurposeBike sharing (BS) is a phenomenon of growing interest in the sustainable mobility field. In recent years, many governments have implemented concrete actions to diffuse the services in cities, trying to encourage citizens' sustainable behavior. Several mobile applications (apps) related to the mobility sector have embedded gamification mechanics applied in non-gaming contexts, able to create and increase user engagement and to manage users' behavior (Deterding et al., 2011). The main purpose of this study is to understand whether app perception influences gamification, and how gamification improves service quality and user loyalty in BS systems.Design/methodology/approachTo examine the impact of gamification on service quality and loyalty, the study performed secondary data collection and qualitative analysis with in-depth interviews. Thereafter, a quantitative analysis was conducted, and the theoretical model was analyzed through structural equation modeling (SEM).Findingsfindings showed that the use of gamification mechanics in BS services improves users' loyalty and directly influences service quality. The gamification tool improves users' engagement, transferring rules, facilitating the achievement of goals and quality standards and enhancing the BS usage.Originality/valueThis study uniquely contributes an understanding of the effect of gamification on service quality and loyalty in BS usage. It also provides some insight for companies and policymakers into implementing gamification mechanics in order to address new challenges for quality management.
Journal Article
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.
Journal Article
Addressing the joint occurrence of self-selection and simultaneity biases in the estimation of program effects based on cross-sectional observational surveys: case study of travel behavior effects in carsharing
We estimate the effect of carsharing on travel behavior (specifically, household vehicle holdings, frequency of transit usage, and frequency of biking and walking) using data from the 2011–2012 California Household Travel Survey (CHTS). The effect of carsharing on vehicle ownership is a dynamic process that plays out over a period of time—past ownership influences enrollment decisions, which in turn influence holdings in a later period. Representing this process using cross-sectional data conflates causal effects with simultaneity bias. Further, members and non-members differ in various observed and unobserved ways—demographics, built environment of residential and workplace location, and attitudes—raising the potential for self-selection bias in comparing travel behavior between the two groups given the observational nature of the data. Drawing on established methods for dealing with each bias individually, we develop a method to help control for this joint occurrence of self-selection and simultaneity biases. Restricting the analysis to employed respondents residing in the San Francisco Bay Area, we find that 80% of the observed difference of 0.9 units in average vehicle holdings between carsharing non-members and members may be explained by the biases listed above. The remaining difference of 0.17 units reflects the estimated effect of carsharing, which is the equivalent of shedding one vehicle by about one out of every six households whose member(s) are enrolled in carsharing. The effect on transit usage and walking and biking frequency is positive, albeit small and statistically non-significant. We identify factors that may affect the internal and external validity of our results. Our methods cannot completely replace randomized experiments or panel data. However, the methods used here provide a way to help control for the joint occurrence of self-selection and simultaneity biases, and provide a ballpark estimate of causal effects, for large-scale, general-purpose, cross-sectional datasets such as the CHTS.
Journal Article