This page is open for editing because it is part of the Incubator. Have something to add? Please register so you can contribute. Have an option you would like to share? Please click on the 'Talk' button to enter the dialogue. The TF Resource Volunteers appreciate your feedback and interest.

This page is part of the Category autonomous vehicles.

Since much of the technology and adoption of autonomous vehicles is highly unknown, confidently forecasting future impacts will be extremely hard, if not impossible. There are some assumptions and extrapolations that can be explored for certain models, but much of the work of preparing for a CAV future likely requires a different approach to traditional forecasting that pivots from today’s reality, in reaction to slight and predictable changes in demand and network supply. Given the numerous unknown impacts of CAVs, in terms of vehicle adoption, pricing, efficiency, policies, and behavioral response, a simple pivoting framework may no longer be valid.

Modelers should be prepared to produce forecasts as CAVs appear on the market and unknowns drop away, but in early planning stages, modelers may need to focus on enumerating and understanding the impacts of individual unknowns. For instance, what are the implications of 20% of regional households owning CAVs, versus an 80% adoption rate? What are the VMT, emissions, and equity impacts at the upper and lower bounds of this single parameter change? Separately, what are the impacts of changing all these other assumptions at their upper and lower bounds? This method is not explicitly forecasting what the future will be, but seeking to understand the potential impacts of individual factors on outcomes. Testing a variety of factors may help define a likely scenario of combined assumptions that represent specific policies or potential futures. In the absence of forecasts, we still have a clear understanding of potential impacts on travel behaviors as a reaction to different future choice sets, so models are still worthwhile and necessary tools to explore CAVs at such an early phase in their development.

As CAVs become a more tangible concept in the future, it may be possible to refine assumptions and even to begin producing forecast ranges with increased confidence. Even today, drawing similarities between TNC/rideshare users and future CAV early adopters, for instance, might allow for preliminary forecasting.

Near Term Forecast

The assumption-driven sensitivity testing process above is useful for preliminary understanding of long-range impacts of CAVs, in line with a traditional long-range transportation plan at an MPO, for instance. However, there may be a need to understand more near-term impacts of CAVs, given that a transition period may look quite different from both today and a future 30 years away, and may exist for a relatively long period of time. Short-term forecasts, even as short out as 5 to 10 years may be necessary to describe early adoption of mixed CAV and conventional vehicle scenarios.

Risk and Uncertainty

The transportation planning profession is no stranger to uncertainty, but the range of uncertain outcomes has in the past been either a narrower range of potential outcomes (e.g. longer commutes) or an unforeseen outcome (e.g. women entering the workforce). The potential influence of autonomous vehicles and mobility as a service may produce positive or negative impacts to society depending on the range of policies governing the use of these technologies. The risk of not addressing this uncertainty in forecasting is significant, as future projects or investments may have substantial unforeseen consequences. Forecasting, therefore, must adapt to take uncertainty into consideration more directly.

The uncertainty surrounding the adoption of autonomous and connected vehicles can be addressed by selecting a set of key input assumptions in the travel demand model to vary and the levels that should be tested for each input assumption. Then an experimental design can be applied to design a set of model runs to test these assumption ranges. One method to quantify the risk analysis applies regression analysis to model the key outputs as a function of the input assumption levels and produce a probability distribution of the key model outputs.

A significant challenge to implementing these types of sound forecasting schemes is communicating the results with decision-makers, who often serve in many different capacities and may not have the time or the appetite to discuss dozens of potential hypothetical futures. More research and experimentation is needed into how to effectively engage busy decision makers in to this type of information consumption.

Scenario Planning

Scenario planning has been in the transportation planning toolbox for many decades, but is recently enjoying a resurgence due to the significant uncertainty in the deliver of new technologies. Scenario planning provides a structured forecasting process where multiple scenarios (ranging from a few to a few hundred) are evaluated to understand the range of possible outcomes.

Strategic modeling tools were developed to directly support scenario planning. These tools can be developed and applied quickly, focusing instead on the characteristics of the population and firms, the interaction between these agents and their responses to policies. They are limited in spatial and network detail, supporting the evaluation of the impacts to demand, rather than supply.


References

Content Charrette: Autonomous Vehicles