Bike sharing systems have been established in several cities across North America. An objective of all bike sharing programs is to maximize the number of trips to and from bike share stations. The purpose of this research is to identify correlates of bike station activity, with special emphases on the association of trips to and from bike stations with the number of nearby businesses and jobs. Using data on 2011 trips from Nice Ride stations in Minneapolis-St. Paul, we introduce three ordinary least square regression models to evaluate the marginal effects of the presence of businesses on annual total station trips, trip origins and trip destinations. Our models include 19 variables in four general categories, including, in addition to the presence of different types of businesses and jobs, sociodemographic, built environment, and transportation infrastructure variables that are used as controls. Our result shows the number of trips at Nice Ride stations is positively and significantly associated with food-related destinations near the station and job accessibility but not with general retail establishments. Use of bike share stations also is correlated with race, age, proximity to the central business district, proximity to water, accessibility to trails, and distance to other bike share stations. This research is important for planners, academics, and policymakers because the findings will facilitate the understanding of bike share operations, help planners locate new stations, evaluate the potential of implementing new bike share programs, assess economic activity associated with bike share trips, and minimize costs of operations.

Cities promote strong bicycle networks to support and encourage bicycle commuting. However, the application of network science to bicycle facilities is not very well studied. Previous work has found relationships between the amount of bicycle infrastructure in a city and aggregate bicycle ridership, and between microscopic network structure and individual tripmaking patterns. This study fills the missing link between these two bodies of literature by developing a standard methodology for measuring bicycle facility network quality at the macroscopic level and testing its association with bicycle commuting. Bicycle infrastructure maps were collected for 74 United States cities and systematically analyzed to evaluate their network structure. Linear regression models revealed that connectivity and directness are important factors in predicting bicycle commuting after controlling for demographic variables and the size of the city. These findings provide a framework for transportation planners and policymakers to evaluate their local bicycle facility networks and set regional priorities that support nonmotorized travel behavior, and for continued research on the structure and quality of bicycle infrastructure and behavior.

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a b s t r a c tWhat effects do bicycle infrastructure and the built environment have on people's decisions to commute by bicycle? While many studies have considered this question, commonly employed methodologies fail to address the unique statistical challenge of modeling modes with small mode shares. Additionally, personal characteristics that are not adequately accounted for may lead to overestimation of built environment impacts.This study addresses these two key issues by using an ordered probit Heckman selection model to jointly estimate participation in and frequency of commuting by bicycle, controlling for demographics, residential preferences, and travel attitudes. The findings suggest a strong influence of attitudinal factors, with modest contributions of bicycle accessibility. Bicycle lanes act as ''magnets'' to attract bicyclists to a neighborhood, rather than being the ''catalyst'' that encourages non-bikers to shift modes. The results have implications for planners and policymakers attempting to increase bicycling mode share via the strategic infrastructure development.