Meeting Climate, Mobility, and Equity Goals in Transportation Planning Under Wide-Ranging Scenarios

Abstract

This technical appendix describes the study's cohort model, developed by RAND to support a Robust Decision Making (RDM) analysis of SACOG's Metropolitan Transportation Plan and Sustainable Communities Strategy (MTP/SCS). It also provides additional results from this model and discusses some of its limitations. Cohort ModelThe cohort model draws on a modeling exercise SACOG conducted for the California Air Resources Board (CARB) to stress-test their plan's projected reduction of greenhouse gas (GHG) emissions for the year 2036 under the requirements of California Senate Bill 375 (Steinberg, 2008). To generate SB 375 emissions results for the exercise, SACOG used the 2016 version of SACSIM (Bradley, Bowman, & Griesenbeck, 2010) also known as SACSIM 16, a regional activity-based travel model that informs development and evaluates performance of the MTP/SCS. Construction of Cohort GroupsSACOG stratified the SACSIM model projections using categories of age (5), household income (5), residential density (6), and household proximity to transit (3) as shown in Table A1.A total of 450 cohorts were created from unique combinations of these four categories (5 × 5 × 6 × 3). These stratified cohorts and their associated SACSIM projections for 2036 provide a useful foundation for RAND's RDM analysis, allowing us to add elasticities representing external conditions (e.g., vehicle technology and fuel costs) and policy options (e.g., pricing), and then interpolating and extrapolating among different plausible futures.

How Might the COVID-19 Pandemic Affect 21st Century Urban Design, Planning, and Development?

“…Therefore, contingency planning and resilience strategies using new decision-making approaches based upon simulations and uncertainty models may become key tools for designing pandemic plans for a future outbreak. Transportation planners sometimes use simulations to engage in exploratory scenarios [116] and robust decision-making (RDM) planning [117] to model challenges to the urban transportation infrastructure. Similar approaches involving scenario planning, worst-case scenarios, and simulated urban health system stress-tests using data and computer simulations will likely become more important for urban health planning and for emergency managers developing pandemic plans and resilience strategies.…”

Speaking to the Future

“…In exploratory scenarios, different plausible futures provide a framework for assessing options. In this issue, Lempert et al (2020) outline another exercise, this time in the Sacramento (CA) region and using a specific This month's editorial examines the effects of COVID-19 on the Journal of the American Planning Association and highlights JAPA's ongoing engagement with topics related to change, equity, and the future. In addition to those reflections, I would like to thank the reviewers who have continued to contribute much-needed expertise to the journal through what has turned out to be a very busy time for faculty and researchers.…”

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

“…These DMDU approaches have been applied to decision making for many different types of infrastructure, including conservation and forest management (Kingsborough et al., 2017; Radke et al., 2010; Singh et al., 2015; Tingstad et al., 2017), renewable energy (François et al., 2017; Popper et al., 2009), agriculture (Maru et al., 2017; Tanaka et al., 2015), flood management (Kalra et al., 2015; Lempert, Kalra, et al., 2013; Reed & Kollat, 2013; Steinschneider et al., 2015), transportation planning (Lempert et al., 2020), and other applications (Halim et al., 2016; Sleptchenko et al., 2019). These approaches are useful for problems categorized as long‐term decisions, “shaping the next one hundred years”, where solutions or actions are large, permanent, or long‐lived investments with large capital costs (Lempert et al., 2003).…”