As discussed at the 2017 TRB Annual Meeting
Decision-makers need both answers they can use and full understanding of spectrum of likelihoods. How can policymakers organize themselves to maximize their impact in affecting technology development that is favorable for society? In many cases policymakers are already skeptical of the results of these models, so we should seek to minimize their uncertainty.
Most forecasters make educated guesses about uncertain futures and present ranges such as conservative, moderate, and liberal. But spectrums are hard to understand, hard to create, and in many cases not allowed by existing political and legal processes.
(i.e. Offer guidance? Document/categorize solutions? Undertake specific work or research? )
Document a different way of using existing models to forecast uncertain topics, emphasizing scenario planning while retaining the trust of decision-makers.
Present model results to decision-makers in a way that they can test different scenarios and alternative futures.
Make degrees of freedom easier to tweak in order to more easily do scenario analysis / reduce embedded assumptions.
There are considerable amount of people attempting to model CAV behavior and lots of research on CAV behavior being conducted as well. A synthesis and critique of research and forecasting approaches would be helpful.
Identify parts of existing models [ demand and network ] that are likely to require sensitivities and recommend elasticities to test.
Value of time and auto availability models are obvious examples. With auto availability models.
Engage decision-makers and modelers in developing new forecasting paradigm involving ‘futures’ rather than ‘forecasts’. Zephyr could bridge the discussion so that the two groups can identify weaknesses in models, and then brainstorm ways to make the models more extensive.
Zephyr should engage in advocacy for allowing models to appropriately tepresent uncertainty
Models are traditionally based on observed behavior. Do we need a change in the entire paradigm of modeling to forecast these uncertain topics?