Copula-based analysis of benefit-to-cost ratios of public transit systems

Given the large investment in public transit systems worldwide as emphasized, it is of interest for policy makers to ask how the benefits of public transportation in urban areas compare with the associated costs. Using published data from eighty-one urbanized areas (UAZs) in the United States, we present a copula-based methodology for analyzing benefit-to-cost ratios of public transit systems under uncertainty. To describe the research motivation, we discuss the limitations of the existing probabilistic cost-benefit analysis (CBA) approaches, namely, Gaussian assumptions, linear correlation, and tail dependencies. This article provides a robust approach to evaluating how well transit systems compare across different urban areas and extends the probabilistic CBA models to incorporate nonlinear dependence.