Abstract Manager

High levels of uncertainty have always been “baked into” transportation forecasts.  This was well-understood by travel modellers, though this may not always have been adequately conveyed to other planners and policy makers,  What has become apparent, even to a lay audience, is that predicting the future has become vastly more complicated due to extreme uncertainty.  Both climate change and the residual impacts of the COVID-19 virus are likely to impact medium and long-term transportation forecasts in ways that cannot be fully captured with traditional forecasting tools and techniques.

Fortunately, there are a number of useful tools and approaches that can explore uncertainty.  One emerging option is to embed a Monte Carlo approach into travel forecasts by varying key inputs such as population forecasts, the price of transit fares, tolls and parking charges, and even the value of time.  Another emerging technique is to develop a range of future scenarios (generally limited to between 4 and 6) that illustrate divergent future year conditions and then to work out what the key underlying differences are from the “baseline future” and then apply these in the forecasting process.  The downside to these techniques is that they can explode the number of results that must be examined and ultimately synthesized or reduced down to recommendations that can be understood by planners and policy makers.  While some decision makers are open to a somewhat open-ended process where technical outputs are given as ranges, others prefer or indeed insist on translating the answer back into what is essentially a point forecast.

This presentation will investigate three common planning exercises that rely on transportation forecasts: evaluating a regional plan, alternatives analysis for a transit corridor and a business case (or other go-no go decision) for a transportation project and demonstrate how extreme uncertainty about future conditions could be incorporated into each of them, along with recommendations on how to manage the process and bound the analysis to avoid overwhelming and exhausting stakeholders.