Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations

Abstract

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.

Modeling Bike Sharing System using Built Environment Factors

“…[6] [16] [20]. These studies were conducted using daily, monthly or yearly aggregated data which can hide the variety of daily bike sharing usage [16] [20].…”

Expanding Bicycle-Sharing Systems: Lessons Learnt from an Analysis of Usage

“…In general, some studies found that population and job density, proximity to transit stations (metro and public bus stations) and bike lanes, and points of interests (retail shops, parks, restaurants, etc.) within the service area are positively associated with ridership at stations [23–32]. Moreover, station size and number of bike stations within the catchment area also have an impact on the bike-sharing demand at stations [25,26,28].…”

“…Some studies have employed data mining techniques [11–14] and visualization techniques [15–17] to uncover the spatial and temporal patterns of cycle trips. Other studies have explored bike-sharing use, in terms of its impact on other transport [18–20], user demographics [21,22], and the influence of built environment factors [23–32], weather and calendar events [33,34] on shared bike demand. Most of the aforementioned studies, except one from Goodman and Cheshire [21], did not address the changes in usage (i.e.…”

Built environment determinants of bicycle volume: A longitudinal analysis

“…Bicycle use is associated with many socio-demographic factors including gender, age, physical condition, family composition, and occupation (Akar & Clifton, 2009;Ma & Dill, 2015;Wang, Lindsey, Schoner, & Harrison, 2015). Females, senior citizens, and businessmen are less likely to ride bicycles.…”