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Problem, research strategy, and findings: Prediction-based approaches, the heart of current transportation planning practice, are inadequate for informing transportation decisions in today’s rapidly changing conditions. In this study we offer an initial demonstration of how robust decision making (RDM) might enhance current long-range planning by applying the approach to selected components of Sacramento Area Council of Government’s (SACOG’s) 2016 regional transportation plan. RDM, a quantitative, exploratory, scenario-based method, informs decisions under deep uncertainty by stress-testing proposed plans over thousands of plausible futures, identifying scenarios that best distinguish futures in which plans meet and miss planning goals, and using these scenarios to identify more robust plans. Our analysis suggests that SACOG’s ability to meet critical mobility and climate goals depends on socioeconomic growth, fuel price, and fuel efficiency assumptions. This study explores potential responses to these vulnerabilities and suggests a path toward wider RDM usage in transportation planning. Our study is limited by the use of a simple cohort model, calibrated to a single predictive scenario run of SACOG’s Sacramento Regional Activity-Based Simulation Model (SACSIM) travel demand model. A more complete RDM analysis would require multiple runs of a model with more explicit treatments of feedbacks and spatial representations.Takeaway for practice: RDM offers a promising complement and possibly future alternative to current prediction-based and scenario approaches that could help planners better manage in today’s conditions of fast-paced change.
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