The transport needs of socially excluded populations– such as the low income and/or people of color – are often poorly described by prevailing modeling frameworks, leading to these populations being underserved by existing transport systems. The modeling, planning, and allocating of transport services for such users requires new methods that can complement predominant approaches like discrete choice models. In this dissertation, I link the empirical framework of quasi-experimental design with applied spatial methodologies to generate new inputs to equity-oriented transportation modeling. I employ the example of bikeshare use during the Covid-19 period to illustrate diverse ways of linking these analytic worldviews, by measuring the effect of the pandemic on bikeshare trip durations in Philadelphia, PA. First, using an interrupted time-series approach, I find that the effect of the pandemic on trip duration is substantial, positive, and similar across diverse geographic areas. Importantly, these findings are persistent and statistically significant for data from predominantly low income/high minority areas of Philadelphia. Second, using difference-in-difference models, I find that the impact of new and supportive bicycle infrastructure interventions made during the pandemic, through road closures to automobiles, yielded an even greater impact on trip durations than found in the main effect of the pandemic-period. Third, I build on the imperative from these studies to propose a framework for employing pandemic-period changes in bikeshare use as an input to an equity-oriented, multi-criteria decision making model to help proactively plan the spatial allocation of new bicycle infrastructure. I conclude by highlighting four key implications for bicycle planning policy specifically, and transportation modelling more generally: 1) planners should build on and support positive use patterns exhibited during the pandemic, 2) new infrastructure should link with the existing bike network to promote greater accessibility gains, 3) infrastructure interventions should actively work to serve underserved communities, and 4) planners and scholars should experiment with new empirical and spatial methodologies, which can generate new insights from existing, administrative data sources.