Access Across America: Auto 2022
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Access Across America: Auto 2022
Published Date
2024-09
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Center for Transportation Studies, University of Minnesota
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Report
Technical Report
Technical Report
Abstract
Accessibility is the ease and feasibility of reaching valued destinations. It can be measured for a wide array of transportation modes, to different types of destinations, and at different times of day. There are a variety of ways to define accessibility, but the number of destinations reachable within a given travel time is the most comprehensible and transparent as well as the most directly comparable across cities.
This study estimates the accessibility to jobs by auto for each of the 11 million U.S. census blocks and analyzes these data in the 50 largest (by population) metropolitan areas. Travel times are calculated using a detailed road network and speed data that reflect typical conditions for an 8 a.m. Wednesday morning departure. Additionally, the accessibility results for 8 a.m. are compared with accessibility results for 4 a.m. to estimate the impact of road and highway congestion on job accessibility.
Rankings are determined by a weighted average of accessibility, with a higher weight given to closer, easier-to-access jobs. Jobs reachable within 10 minutes are weighted most heavily, and jobs are given decreasing weights as travel time increases up to 60 minutes.
This report presents detailed accessibility values for each metropolitan area, as well as block-level maps which illustrate the spatial patterns of accessibility within each area. Year-over-year changes in accessibility, and in congestion impacts on accessibility, are provided for each area. The 2022 reporting year reflects the ongoing changes in local travel behavior after the onset of the COVID-19 pandemic, including some return of congestion and but still higher peak speeds due to reductions in office commutes due to telework.
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Owen, Andrew; Liu, Shirley Shiqin; Jain, Saumya; Lind, Eric. (2024). Access Across America: Auto 2022. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/266465.
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