AUTONOMOUS VEHICLES

There are few issues that bring everyone together in America, but our universal hatred of traffic congestion is among them. We tried for decades to build our way out of it by constructing new highways and adding more lanes until we finally learned that we couldn’t. Had Ben Franklin been born two centuries later he might have said “in this world nothing can be said to be certain but death and taxes and traffic congestion.”

As with so many prior transportation innovations, autonomous vehicles (AVs) have people proclaiming the end of congestion. Hopefully you view those proclamations with a healthy dose of skepticism. Congestion is the product of two variables: capacity (how many cars can a road handle) and volume (how many cars want to use that road). When the volume exceeds the capacity, congestion occurs. When “experts” claim that AVs will end congestion they are typically looking at the capacity side of the equation. Some research has shown that AVs will dramatically improve the capacity of our roads. Few have looked at the volume side of the equation, which is where Fehr & Peers (FP) comes in.

In there study, FP used regional travel models to understand the impact of AVs on travel patterns. Specifically, they looked at vehicle miles traveled (VMT), vehicle trips, and average vehicle trip length. They also looked at the impacts of AVs on transit. It is an interesting study, but the findings need to be heavily qualified. So before I dive into the findings of the study, I want to emphasize its limitations:

• As FP acknowledges, regional travel models were not designed to reflect the travel dynamics that autonomous vehicles will create. They have a tough time forecasting contemporary travel dynamics, much less those that will be created by AVs.
• Two scenarios were evaluated in each of the models it uses: 100% ownership and 50% shared. It isn’t clear how the shared scenario works. Is the an Uber-like company providing this shared service or is this carpooling? How are the costs of travel being experienced?
• A third, very possible scenarios, is that virtually all cars operate like taxis. Leaders in the AV field like Waymo and GM have no intention of selling their AVs directly to consumers. Instead, they plan on operating mobility service companies because they stand to make a lot more revenue. This might have a huge impact on travel patterns. It’s no guarantee that cars won’t be owned, but it is possible and it should be considered.
• The way we will experience the costs of travel will shift dramatically in any scenario with significant “shared” travel. The marginal costs of travel will increase even as the total costs of travel has the potential to significantly decrease. A lower total cost of travel could drive more people to car-sharing while a higher marginal cost of travel could encourage them to take fewer, shorter trips. The study is silent on this point.
• Transportation travel models assume land use is fixed. But that couldn’t be further from the truth. Transportation and land use are deeply intertwined. Changes to one, impacts the other, back and forth. Thus we can reasonably assume that AVs will have a big impact on land use. Additionally, AVs have the potential to virtually eliminate parking, opening more land to development. This will have an impact on travel patterns that regional travel models cannot account for in isolation. They need to be paired dynamically with a land use model, which wasn’t done in this case.
• The study acknowledges that parking may vanish. While I see this as far more likely in the “shared” scenario. It isn’t clear where the owned cars go when the aren’t being used. If they are driving back home, is this reflected in the VMT?
• AVs are likely to completely upset the existing system of funding for transportation, much of which comes from the gas tax, traffic citations and parking. If this happens, new funding will need to be identified. It is quite possible that funding will come in the form of a VMT tax and/or a congestion fee. These will have a direct impact on travel that would be helpful to consider.
• Does the model assume transit agencies are adopting and adapting to this technology or, instead, that they are static.

These are the primary concerns and questions that came to mind as I reviewed the study. Again, it was a worthwhile effort, but it would benefit from some additional scenarios, more clarity on the embedded assumptions, and more discussion on the relationship between the results and the assumptions.

Conclusions:

As with every forecast, the conclusions are based on the assumptions and scenarios considered within the study. Changes to the assumptions or the addition of new scenarios could translate to very different results. The assumptions are listed in the following section.

• It is immediately apparent from the charts that the results from the various travel models varied significantly. The models were most consistent on their forecasts for vehicle trips.

Excerpt from study:

• Vehicle trips increase by an average of 20% without any shared use regulation. That increase is virtually eliminated (on average) with 50% of the AVs required to be shared rides.
• VMT increases by an average of 31% without any shared-use regulation. That increase is halved (on average) with 50% of the AVs required to be shared rides.
• Transit trips decline by an average of 29% without any shared use regulation, which grows to 35% with 50% of the AVs required to be shared rides.
• On average, bus and transit trips less than 5 miles decrease more than rail and transit trips greater than 5 miles.

Key Assumptions:

Excerpt from study:

• Terminal Time – Travel models define the time needed to park your car and walk to a destination as “terminal time.” The higher a terminal time, the less likely a person will choose an auto for a particular trip. AVs are likely to reduce terminal times by eliminating the need to park.  The amount of reduction though will depend on the curb space management policies in cities and how they prioritize curb space use.
• Parking Cost – Most models include a variable for parking cost in areas where costs are imposed. AVs have the potential to lower or even eliminate these traditional parking costs.  However, cities in the future may impose pick-up and drop-off costs for AV use depending on location to help manage peak period traffic demands.
• Value of Time – Travel models also incorporate the value of time, but in different ways. We expect travelers using AVs will have lower values of time because the opportunity cost of driving will be reduced.
• Auto Availability – Models generally have variables tied to trip rates and auto availability.  AVs may increase trip rates due to their greater convenience and ready availability. Greater convenience could lead to more discretionary vehicle trips for shopping, social, leisure or recreational purposes.  Additionally, people not licensed to drive will be able to make vehicle trips.  Vehicle availability will increase for many households and at workplace locations – especially those in urban areas.
• Roadway Capacity – As vehicles become more automated and connected, they offer greater potential to increase roadway capacity especially on freeways.  The increase in capacity will come from shorter headways, less weaving, and more stable traffic flows. We expect that freeway capacity will increase first on freeways and expressways, then on major arterials.