Accessibility is an important characteristic of metropolitan areas and is often reflected in transportation and land-use planning goals. But the concept of accessibility has rarely been translated into performance measures by which policies are evaluated, despite a substantial literature on the concept. This paper is an attempt to bridge the gap between the academic literature and the practical application of such measures and provide a framework for the development of accessibility measures. Issues that planners must address in developing an accessibility measure are outlined, and two case studies suggestive of the range of possible approaches are presented.

Despite increasing regulatory attention and literature linking roadside air pollution to health outcomes, studies on near roadway air quality have not yet been well synthesized. We employ data collected from 1978 as reported in 41 roadside monitoring studies, encompassing more than 700 air pollutant concentration measurements, published as of June 2008. Two types of normalization, background and edge-of-road, were applied to the observed concentrations. Local regression models were specified to the concentration-distance relationship and analysis of variance was used to determine the statistical significance of trends. Using an edge-of-road normalization, almost all pollutants decay to background by 115-570 m from the edge of road; using the more standard background normalization, almost all pollutants decay to background by 160-570 m from the edge of road. Differences between the normalization methods arose due to the likely bias inherent in background normalization, since some reported background values tend to underpredict (be lower than) actual background. Changes in pollutant concentrations with increasing distance from the road fell into one of three groups: at least a 50% decrease in peak/edge-of-road concentration by 150 m, followed by consistent but gradual decay toward background (e.g., carbon monoxide, some ultrafine particulate matter number concentrations); consistent decay or change over the entire distance range (e.g., benzene, nitrogen dioxide); or no trend with distance (e.g., particulate matter mass concentrations).

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In response to the COVID-19 pandemic, a growing number of states, counties and cities in the United States issued mandatory stay-at-home orders as part of their efforts to slow down the spread of the virus. We argue that the consequences of this one-size-fits-all order will be differentially distributed among economic groups. In this paper, we examine social distance behavior changes for lower income populations. We conduct a comparative analysis of responses between lower-income and upper-income groups and assess their relative exposure to COVID-19 risks. Using a difference-in-difference-in-differences analysis of 3140 counties, we find social distance policy effect on the lower-income group is smaller than that of the upper-income group, by as much as 46% to 54%. Our explorations of the mechanisms behind the disparate effects suggest that for the work-related trips the stay-at-home orders do not significantly reduce low income work trips and this result is statistically significant. That is, the share of essential business defined by stay-at-home orders is significantly negatively correlated with income at county level. In the non-work-related trips, we find that both the lower-income and upper-income groups reduced visits to retail, recreation, grocery, and pharmacy visits after the stay-at-home order, with the upper-income group reducing trips more compared to lower-income group.