Cities can enhance their sustainability planning by prioritizing accessibility as a performance metric for transportation planning. In this context, accessibility measures the ease of reaching opportunities and captures more characteristics of sustainable cities than the more conventionally used mobility metrics, focusing on the overall ability to move and generally recommending faster speeds to overcome distance. However, how cities can prioritize accessibility in planning is understudied. This research presents a case of Tempe, Arizona, putting accessibility into practice through a 20-min lens. Using a 20-min threshold for all modes, the city aims to promote travel for daily activities that is less reliant on private autos. The analysis presented here documents the challenges that planners face when planning for accessibility. The challenges range from technical points to decisions that need to be made about the quality of the built environment. The analysis also shows that Tempe, which is a classically suburban city of wide roads and single-family homes that was built around the automobile, is highly accessible by a 20-min metric by bicycling, walking and transit. These results suggest that planners focus on street network improvements that prioritize accessibility as part of a sustainability strategy.

The advent of ridehailing services such as Uber and Lyft has expanded for-hire vehicle travel. We use data from the 2017 National Household Travel Survey (NHTS) to investigate the extent of this expansion in the United States. We report changes in the for-hire vehicle market since ridehailing services became available and statistically estimate the determinants of ridehailing use. From 2009–2017, the for-hire vehicle market share doubled. While for-hire vehicles still only account for 0.5% of all trips, the percent of all Americans who use ridehailing in any given month is nearly 10%. Within the for-hire vehicle market, this trend of growth has not been uniformly distributed across demographic groups or geographies; it has been greater in mid-sized and large cities, and among younger individuals and wealthier households. This suggests that understanding the equity implications of ridehailing is an important avenue for research. Multivariate analysis provides evidence that both transit and nonmotorized transport use are correlated with ridehailing use, that ridehailing has a negative relationship with vehicle ownership, and that residents of denser areas have higher ridehailing use. Given the rapid growth of ridehailing, it has become important for cities to include for-hire vehicles in their planning going forward. These NHTS data provide a starting point, but more detailed and frequent data collection is needed to fully understand this many-faceted, rapidly-changing market.

We document the falling socioeconomic status of American households without private vehicles and the continuing financial burden that cars present for low-income households that own them. We tie both these trends to the auto-orientation of America’s built environment, which forces people to either spend heavily on cars or risk being locked out of the economy. We first show that vehicle access remains difficult for low-income households and vehicle operating costs remain high and volatile. Using data from the Panel Study of Income Dynamics, Survey of Consumer Finances, and Census Public Use Microdata, we then show that in the last fifty years households without vehicles have lost income, both in absolute terms and relative to households with vehicles. We link these trends to the built environment by examining the fortunes of carless households in New York City, and particularly in Manhattan. Most of New York’s built environment did not change to accommodate cars, and in New York the fortunes of the carless did not fall. Our results suggest that planners should see vehicles, in most of the United States, as essential infrastructure, and work to close gaps in vehicle access.

To reduce the spread of COVID-19, governments across the world enacted various levels of “shelter-in-place” policies, leading to a notable reduction in urban mobility. To understand the relationship between policy implementation and mobility effects, we use Apple COVID-19 Mobility Trends Reports to assess how urban travel, by mode, changed in response to public policy. The data were used to statistically evaluate and visualize the changes in urban mobility patterns across four regions: Sydney, London, Phoenix, and Pune, which reflect the global nature of the pandemic and the local nature of policy responses. The results provide insights into how policies can receive a starkly varied response from communities across global regions.