Autonomous vehicles (AV) are poised to induce disruptive changes, with significant implications for the economy, the environment, and society. This article reviews prior research on AVs and society, and articulates future needs. Research to assess future societal change induced by AVs has grown dramatically in recent years. The critical challenge in assessing the societal implications of AVs is forecasting how consumers and businesses will use them. Researchers are predicting the future use of AVs by consumers through stated preference surveys, finding analogs in current behaviors, utility optimization models, and/or staging empirical “AV-equivalent” experiments. While progress is being made, it is important to recognize that potential behavioral change induced by AVs is massive in scope and that forecasts are difficult to validate. For example, AVs could result in many consumers abandoning private vehicles for ride-share services, vastly increased travel by minors, the elderly and other groups unable to drive, and/or increased recreation and commute miles driven due to increased utility of in-vehicle time. We argue that significantly increased efforts are needed from the AVs and society research community to ensure 1) the important behavioral changes are analyzed and 2) models are explicitly evaluated to characterize and reduce uncertainty.

Among many changes potentially induced by the adoption of ridehailing, one key area of interest in transportation and urban planning research is how these services affect sustainable mobility choices, such as usage of public transit, walking, and biking modes and lower ownership of household vehicles. In this study, by using subsamples of the National Household Travel Survey (NHTS) 2017 data, propensity score matching technique is applied to generate matched samples of ridehailing adopters and non-adopters from ten different core-based statistical areas in the U.S. Results from multivariable count data regression models built on the matched samples indicate that, on average, the count of public transit trips is greater for adopters compared against identical non-adopters in all ten areas. Regarding average counts of walking and biking trips, adopters tend to make more trips in most of the places, although a few exceptions are also found. However, the relationship between ridehailing adoption and count of household vehicles appears to be more complicated as adopters, on average, seem to have a lower or higher number of vehicles than identical non-adopters, depending on the area. One major limitation of this study is that, in the statistical analyses, effects of attitudinal and detailed geographic variables are not directly controlled for, which complicates causal interpretations of findings.

No abstract

No abstract

This paper uses data from the Household Pulse Survey to examine whether and for how long the eligibility to receive state Earned Income Tax Credit (EITC) benefits reduced self-reported household food insufficiency among lower-income households with dependent children during the COVID-19 pandemic. The results of models estimated using difference-in-differences (DD) and difference-in-difference-in-differences (DDD) methods suggest that state EITC eligibility, on average, reduced food insufficiency by about 3 percentage points between March 2021 and early October 2021. However, the results of models estimated using an event study method show that the effect was not visible in all the post-March bimonthly periods. Overall, this paper finds some evidence to suggest that state EITC eligibility reduced food insufficiency over a short period.