How will automated vehicles influence the future of travel?
Agencies dedicate a great deal of time and effort developing and using software tools to estimate future travel behavior. It’s not clear how people’s travel choices will change as automated vehicles (AVs) become more prevalent, nor is it clear how to best predict those choices.
Although little is clear at this point, it is clear that our existing models need to evolve. So our FP Think Initiative tested how AVs might change the predicted outcomes of seven regional travel models from around the U.S. The results are shown for scenarios where AVs are privately owned and where half of trips are made as shared rides.
How did we approximate an AV future?
Each model is structured a bit differently, so our approach varied somewhat in each case. The following lists the most common variables used:
- 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.
What were the key findings?
The following highlight the key findings of our results of the seven models we’ve tested thus far. Please note that all results presented are comparisons to the future condition without any AVs.
- Vehicle Miles Traveled (VMT) increased in all seven models (range of +12% to +68%) when assuming no regulatory requirement for ridesharing.
- Regulations requiring 50% of AV trips to be shared would help mitigate the VMT increase, but would not fully offset it.
- The regulated ridesharing scenario would also substantially reduce vehicle delay, but would increase the average trip length.
- Total transit trips declined in five of the seven models tested by a range of -8% to -43%. The other two showed increased transit trips of 5% to 16%.
- Of the four models for which we were able to test specific types of transit trips, three showed declines ranging from -26% to -47% for bus trips and -13% to -40% for rail trips.
Photo credit: madan.org.il/en/node/9552
What’s next for travel demand models?
Current modeling platforms are capable of roughly estimating the effects of AVs on travel demand and behavior, as long as automated travel can be approximated via a small number of scenarios. However, it is noteworthy that the range of predicted outcomes for the same scenario varies appreciably between certain models. While it is certainly possible that the future effects of AVs could vary by geographic region, we feel it would be much more useful to directly model these variables in a context-specific manner rather than approximating them. Future travel forecasting models will need to evolve so they can more directly capture AV-related variables to more reliably predict future outcomes associated with automated technology.