As the U.S. looks to a greener future, cities need to better understand whether car dependence grows out of need or desire. This study challenges the assumption that all car ownership is strictly a necessity. It turns out that many cities with relatively robust infrastructure for walking, biking, and public transit can exhibit excessive reliance on cars, while others with limited options manage to get by with fewer vehicles. The greatest offenders tend to be in the Western half of the country.
It is clearly possible for urban areas to function with fewer cars. The question is whether cities are willing to follow the example set by others across the state—or across the country.
Methodology
This study looks at which cities in the U.S. have the most car access relative to the alternative mobility options available. The research team focused on the largest cities in the U.S.—those with at least 250,000 people (a total of 91 cities).
The team calculated the average number of vehicles per household in each city. The data for this calculation comes from the American Community Survey's 2023 estimates of "vehicles available" by household (Table B08201, available via the Census Bureau). This is a conservative average, as any households with "four or more" vehicles are assumed to have four.
Data for non-car mobility comes from Walkscore.com, a site owned by the real estate brokerage Redfin that scores neighborhoods and cities by a) walkability, b) bike riding access, and c) public transportation options. Each of these three metrics is ranked from 0 to 100, where the higher the score, the easier it is to get around. To get an overall sense of what non-car mobility looks like in each city, the team averaged the three metrics. (Greensboro, NC, Winston-Salem, NC, Toledo, OH, and Laredo, TX are not included, as they do not have public transit scores on Walkscore.com.)
Using the average cars per household and the average walk-bike-transit score, DataPulse ran a regression analysis to determine the expected number of vehicles per household for each score. The team then compared the actual number of vehicles per household to the expected number. This revealed whether each city had relatively more cars or relatively fewer cars compared to cities with similar walk-bike-transit scores.