Using an Activity-Based Model to Explore the Potential Impacts of Automated Vehicles

Abstract

Automated vehicles (AVs) may enter the consumer market with various stages of automation in 10 years or even sooner. Meanwhile, regional planning agencies are envisioning plans for time horizons out to 2040 and beyond. To help decision makers understand the effect of AV technology on regional plans, modeling tools should anticipate its impact on transportation networks and traveler choices. This research uses the Seattle, Washington, region's activity-based travel model to test a range of travel behavior impacts from AV technology development. The existing model was not originally designed with AVs in mind, so some modifications to the model assumptions are described in areas of roadway capacity, user values of time, and parking costs. Larger structural model changes were not yet considered. Results of four scenario tests show that improvements in roadway capacity and in the quality of the driving trip may lead to large increases in vehicle miles traveled, while a shift to per mile usage charges may counteract that trend. Travel models will need to have major improvements in the coming years, especially with regard to shared ride, taxi modes, and the effect of multitasking opportunities, to better anticipate the arrival of this technology.

Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions

“…Changes to population size over time, automated vehicle price, and market penetration rates could also be incorporated, to better model transportation demand variations from population change and to reflect the influence that consumer demand could have on future VMT. As noted by Childress et al (2015), regions could conduct stated preference surveys to gain some additional understanding on how consumers might travel differently with automated vehicles. These types of surveys will be important to help understand the potential for disruptive changes in vehicle use, but their results will only be validated through the revealed preferences of actual users of automated vehicles.…”

“…Fully self-driving Level 4 automated vehicle technologies, as defined by the National Highway Traffic Safety Administration (NHTSA) (NHTSA, 2013), will likely promote an increase in per capita VMT within the elderly, disabled, and non-driving populations due to their potential latent demand and since they would rely less on walking, public transit, or family members and friends for daily travel. At high market penetration rates, automated vehicles could increase accessibility to jobs, leisure, and resources for both low and high-income groups (Childress et al, 2015). Higher accessibility to jobs for low-income groups would likely increase employment, provide better job opportunities, and increase disposable income along with travel (Ihlanfeldt and Sjoquist, 1990;Shen, 1998).…”

“…Another bounding study assumed autonomous cars are directed to pick up other household members for trips, resulting in a 75% increase in annual mileage per vehicle and a reduction in vehicle ownership of 43% (Schoettle and Sivak, 2015). Childress et al (2015) used Seattle region's activity model to estimate how changes in the value of travel time, road capacity, parking costs, and per mile driving costs could change VMT. One of the scenarios examined in this analysis assumed road capacity will increase by 30% while the value of travel times and parking costs will decrease by 65% and 50%, respectively, resulting in a 20% increase in VMT (Childress et al, 2015).…”

“…Childress et al (2015) used Seattle region's activity model to estimate how changes in the value of travel time, road capacity, parking costs, and per mile driving costs could change VMT. One of the scenarios examined in this analysis assumed road capacity will increase by 30% while the value of travel times and parking costs will decrease by 65% and 50%, respectively, resulting in a 20% increase in VMT (Childress et al, 2015). Brown et al (2014) estimated that new demand from underserved populations could increase VMT by as much as 40%, using the 2009 NHTS and the 2003 "Freedom of Travel" study (Brown et al, 2014).…”

Autonomous vehicles: The next jump in accessibilities?

“…In a second approach, Childress et al [28] used the Puget Sound activity-based transport model to study the impact of autonomous vehicles on the Seattle, WA, region, for four different scenarios. They assumed a 30% capacity increase on roads and 35% shorter perceived travel times when riding an autonomous 35 vehicle.…”