No abstract

As federal spending and planning for air transportation infrastructure looks to prioritize access for disadvantaged populations, aviation systems planning metrics that measure accessibility at the individual level are necessary. Existing metrics, from the mobility-driven metrics focused on efficiency and on-time performance to geographic accessibility metrics focused on connectivity, lack the detail of the multiple, interlocking constraints that limit potential travelers (especially lower-income travelers) from executing their agency and accessing the aviation system. We seek to develop a methodology, resulting in new analysis metrics, to quantify accessibility on an origin–destination basis based on individual constraints, time- and cost-based impedance, and aviation travel supply. We develop and apply our Aviation-accessibility Integrated Mobility (AIM) metric to empirically model relative accessibility based on traveler-specific constraints, accounting for individual-level sensitivity to travel costs and propensity to travel by ground access modes. We illustrate how equity-focused variables can change the calculus and geographic distribution of accessibility by applying the AIM to our case study region: Philadelphia, Baltimore, and Newark metropolitan areas, a region with significant socioeconomic disparities, to diverse markets (Austin, Atlanta, Nashville). Our findings indicate that incorporating individual constraints greatly influences the calculation of accessibility; additionally, we find that transportation supply and service characteristics alter the distribution of accessibility. Our model supports a national map of accessibility and potential policy recommendations to expand traditional federal airport infrastructure projects, such as targeted air service enhancement.

From “pop-up” road closures to decreased transit frequencies, the COVID-19 pandemic has changed the overall supply of transport options. Even in the absence of a change in bikeshare supply, the pandemic provides a “natural experiment” under which we can assess changes in bikeshare use across diverse communities in response to transportation system changes. The pandemic offers a unique moment to particularly measure changes in use for low socioeconomic status (SES) populations as historically limited deployments of bikeshare in low-income neighborhoods limit evaluation of key metrics for this population. For low SES users to realize greater accessibility through bikeshare, they may need to take relatively longer trips, given the sparse nature of the network in low-income areas and the existing inequitable geography of opportunities in urban environments in the United States. As such, we measure the effect of the COVID-19 pandemic on average daily bikeshare trip durations in Philadelphia, PA—the major city with the highest poverty rate in the United States. Through an interrupted time series approach, we find that the effect of the pandemic on trip duration for all bikeshare users is substantial (approximately 7–12 min increase), positive, and similar across diverse geographic areas. Importantly, these findings are persistent and statistically significant even when fitting models only on data from predominantly low SES areas of Philadelphia. This change pattern suggests first that low SES users exhibit roughly equal propensity as the general population to take longer trips, and second that bikeshare can provide a resilient, equitable travel mode.