Neighborhood Characteristics that Support Bicycle Commuting

Abstract

This study examined American Community Survey journey-to-work data from 2008 to 2012 to identify the characteristics of neighborhoods with the highest levels of bicycle commuting in the United States. The 100 census tracts with the highest bicycle commute mode shares (top 100 census tracts) were identified and paired with 100 other randomly selected census tracts from the same county (100 comparison census tracts). As a whole, the top 100 census tracts had a bicycle commute mode share of 21%. Seventy of the top 100 census tracts were in locations that had fewer than 10 days per year with high temperatures below 32°F (0°C), and 68 were within 2 mi (3.2 km) of a college or university campus. Seventeen had relatively low college populations and were in high-density neighborhoods close to large city central business districts. Conditional logistic regression was used to estimate the likelihood of a paired tract being in the top 100 rather than the comparison tract. After climate and topography were controlled for, being a top 100 census tract was associated with several socioeconomic and local-environment characteristics, including being located closer to a university and having more households without automobiles, more people born in other states and countries, higher population density, more housing constructed before 1940, and greater bicycle facility density. The results suggest that policies to model employment centers after university campuses; design neighborhoods that support routine, multimodal travel; and reduce barriers to bicycling in bad weather may help create more local areas with high rates of bicycle commuting.

Equity implications of electric bikesharing in Philadelphia

“…Findings from both RF and regression analyses show that Indego usage has some correlation with a high rate of student population. A high correlation of shared micromobility usage with students is a recurring finding in micromobility research (Caspi et al, 2020 ; Schneider & Stefanich, 2015 ) and was used in this study to control for that effect. This correlation raises the concern that low-income users are students rather than disadvantaged people.…”

“…The common tool across micromobility studies is income (Caspi & Noland, 2019 ; Goodman & Cheshire, 2014 ; Heinen et al, 2010 ; Ogilvie & Goodman, 2012 ; Smith et al, 2015 ). However, low income can misleadingly classify students, who tend to use micromobility more than other populations, as disadvantaged (Caspi et al, 2020 ; Schneider & Stefanich, 2015 ). Another indicator can be the rate of the population under the poverty line, which may reduce the false indication of students.…”

“…They have had great usage in developed countries, such as Denmark and the Netherlands (Garrard et al, 2012 ) and developing economies such as China and India (Zuidgeest & Brussel, 2012 ). In the U.S., census tracts with the highest bicycle commute share have lower median income and higher poverty levels than other census tracts (Schneider & Stefanich, 2015 ).…”