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

TL;DR

This study models bike share station activity in Minneapolis, identifying key socio-economic and geographic factors influencing trip counts, with implications for optimizing station placement and operations.

Contribution

It introduces a comprehensive regression analysis linking neighborhood characteristics and location factors to bike share usage, enhancing understanding of station activity determinants.

Findings

Trips are influenced by neighborhood demographics and proximity to water and trails.

Economic activity and distance to other stations significantly affect trip counts.

Models show high goodness of fit and significance of variables.

Abstract

The purpose of this research is to identify correlates of bike station activity for Nice Ride Minnesota, a bike share system in Minneapolis - St. Paul Metropolitan Area in Minnesota. We obtained the number of trips to and from each of the 116 bike share stations operating in 2011 from Nice Ride Minnesota. Data for independent variables included in models come from a variety of sources; including the 2010 US Census, the Metropolitan Council, a regional planning agency, and the cities of Minneapolis and St. Paul. We use log-linear and negative binomial regression models to evaluate the marginal effects of these factors on average daily station trips. Our models have high goodness of fit, and each of 13 independent variables is significant at the 10% level or higher. The number of trips at Nice Ride stations is associated with neighborhood socio demographics (i.e., age and race), proximity to the central business district, proximity to water, accessibility to trails, distance to other bike share stations, and measures of economic activity. Analysts can use these results to optimize bike share operations, locate new stations, and evaluate the potential of new bike share programs.