Concept: Bicycle sharing system
Bicycle sharing systems exist in hundreds of cities around the world, with the aim of providing a form of public transport with the associated health and environmental benefits of cycling without the burden of private ownership and maintenance. Five cities have provided research data on the journeys (start and end time and location) taking place in their bicycle sharing system. In this paper, we employ visualization, descriptive statistics and spatial and network analysis tools to explore system usage in these cities, using techniques to investigate features specific to the unique geographies of each, and uncovering similarities between different systems. Journey displacement analysis demonstrates similar journey distances across the cities sampled, and the (out)strength rank curve for the top 50 stands in each city displays a similar scaling law for each. Community detection in the derived network can identify local pockets of use, and spatial network corrections provide the opportunity for insight above and beyond proximity/popularity correlations predicted by simple spatial interaction models.
High prevalence of physical inactivity contributes to adverse health outcomes. Active transportation (cycling or walking) is associated with better health outcomes, and bike-sharing programs can help communities increase use of active transportation.
The emerging Bicycle Sharing System (BSS) provides a new social microscope that allows us to “photograph” the main aspects of the society and to create a comprehensive picture of human mobility behavior in this new medium. BSS has been deployed in many major cities around the world as a short-distance trip supplement for public transportations and private vehicles. A unique value of the bike flow data generated by these BSSs is to understand the human mobility in a short-distance trip. This understanding of the population on short-distance trip is lacking, limiting our capacity in management and operation of BSSs. Many existing operations research and management methods for BSS impose assumptions that emphasize statistical simplicity and homogeneity. Therefore, a deep understanding of the statistical patterns embedded in the bike flow data is an urgent and overriding issue to inform decision-makings for a variety of problems including traffic prediction, station placement, bike reallocation, and anomaly detection. In this paper, we aim to conduct a comprehensive analysis of the bike flow data using two large datasets collected in Chicago and Hangzhou over months. Our analysis reveals intrinsic structures of the bike flow data and regularities in both spatial and temporal scales such as a community structure and a taxonomy of the eigen-bike-flows.
Urban public bicycle sharing programs are on the rise in the United States. Launched in 2013, NYC’s public bicycle share program, Citi Bike™ is the fastest growing program of its kind in the nation, with nearly 100,000 members and more than 330 docking stations across Manhattan and Brooklyn. The purpose of this study was to assess helmet use behavior among Citi Bike™ riders at 25 of the busiest docking stations. The 25 Citi Bike™ Stations varied greatly in terms of usage: total number of cyclists (N = 96-342), commute versus recreation (22.9-79.5 % commute time riders), weekday versus weekend (6.0-49.0 % weekend riders). Helmet use ranged between 2.9 and 29.2 % across sites (median = 7.5 %). A total of 4,919 cyclists were observed, of whom 545 (11.1 %) were wearing helmets. Incoming cyclists were more likely to wear helmets than outgoing cyclists (11.0 vs 5.9 %, p = .000). NYC’s bike share program endorses helmet use, but relies on education to encourage it. Our data confirm that, to date, this strategy has not been successful.