Anticipating the Regional Impacts of Connected and Automated Vehicle Travel in Austin, Texas

Abstract

Automated vehicles are undergoing very rapid development and have potential to revolutionize the existing transportation system. This paper investigates the impacts of connected automated vehicles (CAVs) and shared automated vehicles (SAVs) using a conventional travel-demand model for the Austin, Texas, region. A series of eight test scenarios run in the year 22 setting suggests that the introduction of CAVs and SAVs will add 2% or greater demand for new vehicle-kilometers traveled (VKT) to the six-county region’s roadway network. Relatively low values of travel time for passengers of automated vehicles and competitive pricing assumptions of SAV use result in greater demand for longer distance travel and reduced transit system use. Empty-vehicle travel for self-parking vehicles and SAVs will add to the network’s VKT, presumably increasing roadway congestion further, unless rides can be shared, traffic flows smoothed, and intervehicle headways tightened. The scenario simulations are sensitive to parking cost and vehicle operating cost assumptions. Policymakers, transportation planners, system operators, and designers may do well to simulate additional scenarios.