Neighborhood socio-demographic characteristics and bike share member patterns of use

J. Wang, Greg Lindsey

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Information about factors that affect patterns of use by bike share members is needed to manage systems effectively and equitably. Recent research into the patterns of use by members across heterogeneous neighborhoods indicates that neighborhood characteristics as well as built environment characteristics may be associated with use. We use 2017 data on trips taken by 30-day and annual members of the Nice Ride Bike Share System in Minneapolis-St. Paul and examine the associations between user behavior and socio-demographic characteristics at the Census block group (CBG) level where members live. We estimate linear mixed-effects models and multinomial logistic models to analyze the associations of neighborhood socio-demographic characteristics with, respectively, (1) the frequency and duration of weekday and weekend trips by members and (2) the temporal and spatial patterns of their trips. Our results show that, after controlling for station accessibility, nearby bike infrastructure, the built environment, gender, and age, members who reside in minority-concentrated and lower socio-economic status (SES) neighborhoods use bike share more frequently, take trips at more varied times-of-day and across days-of-week, and have more frequently-used origin-destination pairs of stations. A limitation of our analysis is that patterns of use of casual users are not investigated. Our findings have implications for efforts to serve members in neighborhoods with higher concentrations of minorities and residents of lower SES and illustrate the need for more detailed surveys of members to obtain additional information about individual characteristics associated with behaviors of bike share users.

Original languageEnglish (US)
Article number102475
JournalJournal of Transport Geography
Volume79
DOIs
StatePublished - Jul 2019

Keywords

  • Bike share use behavior
  • Neighborhood characteristics
  • Spatial and temporal patterns
  • Transportation equity

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