Transportation Planning for Uncertain Times
A Practical Guide to Decision Making Under Deep Uncertainty for MPOs
Published in: National Transportation Library website (2022)
Posted on RAND.org on July 11, 2023
Transportation agencies must pursue ambitious goals in the face of intense, large-scale, and increasingly fast-paced change. They are also mandated to produce regular planning documents. But those documents have diminishing credence in the face of rapid and uncertain change, and the means used to produce them have difficulty in supporting more far-reaching strategic deliberation. Traditional planning methods, designed to use simulation models to provide future reliable demand forecasts, may often prove less sufficient than in the past. Such "predict-then-act" analyses can foster over-confidence and so limit consideration of strategic alternatives or the range of plausible future conditions. Planners and modelers might arrive at myopic decisions because they underestimate the uncertainty. Predict-then-act analyses can lead to gridlock when stakeholders contest the assumptions used to justify a proposed plan rather than work together to identify a plan that performs well over a wide range of scenarios.
MPOs now have available emerging methods and tools for decision making under conditions of deep uncertainty (DMDU). DMDU approaches can help planners and modelers augment their current modeling capabilities to identify and evaluate strategies that can help their agencies meet goals in the face of today's fast-paced change. DMDU methods use simulation models not as prediction engines but as exploratory tools. DMDU methods may therefore run an MPO's simulation models or their surrogates over a range of plausible futures to stress-test proposed plans and then use the results of those stress-tests to identify strategies that are low-regret, flexible in their ability to adjust over time, and shape the future along pathways consistent with desirable policy outcomes. DMDU use in MPOs has to date been limited, in part owing to both analytic and organizational challenges. Based on experiences interacting with MPO planning and modeling staffs, this report summarizes DMDU analytics, stakeholder processes supported by DMDU, and offers guidance for agencies interested in bringing such methods into their organization.
The report first introduces the DMDU concept which embraces a range of quantitative and qualitative methods. In particular, the report discusses in detail robust decision making (RDM), a specific model-based DMDU method that recommends itself as being potentially consonant with model-based analysis currently practiced in MPOs and other transportation agencies. The report reviews the methods and quantitative tools available to support MPOs in using RDM including the participatory processes with stakeholders supported by the analyses and the software packages available to facilitate RDM analyses. The report describes case studies of RDM and other quantitative and qualitative DMDU applications which have been employed by MPOs.
The report concludes with an organizational perspective of MPOs and their transportation planning functions. It lays out a value proposition for enhanced strategic foresight capabilities - the goal that DMDU methods seek to achieve, lays out several of the obstacles that might retard or prevent movement in that direction, suggests incremental steps toward implementation, and finally provides a guide that may be used by agencies as a whole or their modeling or planning staffs to gauge progress toward achieving a greater facility for accounting for and managing future uncertainties as part of their regular process.