Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 1: Executive Summary (2018)

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9 Planning Context The context of planning for CAV technology is one of deep uncertainty about the impacts, infrastructure needs, deployment timeline, market penetration, design, engineering, and many more aspects of automation. CAVs are expected to be disruptive and impactful, but that is the only certainty about which the planning community generally agrees. The framework presented in Figure 1 suggests a simple idea: the longer the planning horizon, the less certainty about predictive processes. The following subsections introduce methods for addressing uncertainty in the planning context. More information about each can be found in the NCHRP Research Report 896. Qualitative Methods for Managing Deep Uncertainty The most common qualitative method for addressing uncertainty is scenario planning. Scenario planning constructs diverse and often divergent narratives about the long-term future. A family of scenarios—often three or four—aims to span the range of plausible futures relevant to the decision at hand. The aim is for planners to use those scenarios to consider how near-term policies might shape and be shaped by those futures. Assumption-based planning hinges on identifying load-bearing assumptions (assumptions that, if broken, would require major revi- sion of the course of action) and, subsequently, the vulnerability of those load-bearing assumptions. For example, a plan to expand light rail to a planned development area hinges on the assumption that the proposed development will succeed. This assumption might be vul- nerable if the development hinges on an optimistic level of economic growth. Assumption-based planning guides organizations in determin- ing a course of action to deal with the vulnerability of load-bearing assumptions once they are identified. Quantitative Methods for Managing Deep Uncertainty In response to the difficulty of linking scenarios to policy choices when applying qualita- tive methods, many have turned to quantitative methods for decision making under deep uncertainty. Robust decision making (RDM) rests on a simple concept. Rather than using models and data to assess decision options under a single set of assumptions, RDM runs models over hundreds to thousands of different sets of assumptions to describe how plans perform in many plausible conditions. Unlike Monte Carlo analysis, which attaches probabilities to those assumptions to Qualitative methods include scenario planning and assumption-based planning. The former has limitations in linking multiple, diverse futures to near-term policy choices. Thus, the latter has evolved in response.

10 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles estimate expected outcomes, RDM uses simulations to stress-test strategies and helps decision makers identify robust strategies—those that perform well regardless of the assumptions or future conditions—and identify the key trade-offs among them. Info-gap theory also helps decision makers identify robust options, but it takes a somewhat different tack from RDM, which uses models to assess the performance of options in a wide range of potential future conditions and then identifies conditions that result in poor perfor- mance (i.e., conditions to which the system is vulnerable). In contrast, info-gap uses models to compute how options perform as a function of uncertainty. An info-gap analysis produces a graph showing the per- formance that planners can robustly achieve on one axis as a function of uncertainty on the other axis. Info-gap does not provide decision makers with the solution; rather, it informs decision makers on trade- offs, risks, and vulnerabilities. A third method is dynamic adaptive pathways planning (DAPP). With the DAPP approach, a plan is conceptualized as a series of actions over time (pathways), in response to how the future actually unfolds. The DAPP approach starts from the premise that policies or decisions have a design life and might fail as the operating conditions change. Once actions fail, other actions are needed to achieve objectives, and a series of pathways emerges; at predetermined trigger points, the course can change while the objectives are still achieved. By exploring different pathways and considering the path-dependency of actions, planners can design an adaptive plan that includes short-term actions and long-term options. The plan is monitored for signals that indicate when the next step of a pathway should be implemented or whether reassessment of the plan is needed. Quantitative methods for managing deep uncer- tainty include robust decision making, info-gap, and dynamic adaptive pathways. Rather than ask, “What will happen?” these methods ask, “What should we do today to most effectively manage the range of events that might happen?”