Interactions between Climate Policy and Technology-Influenced Travel Behavior: Mitigating Induced Demand from Cooperative Adaptive Cruise Control

Abstract

Advances in vehicle and computing technologies have influenced the development of automated vehicle systems, and vehicles that do not require human intervention are already deployed on roadway networks. While these advances are proving to increase roadway safety and highway capacity, more research is needed to understand the long-term and regional impacts on mobility, land use, energy consumption, and emissions. This study demonstrates a multimodel approach to analyze the effects of vehicle automation and the potential of deep decarbonization policies to mitigate associated increases in energy use and emissions, over a period from 2020 to 2040 in Austin, Texas. We use the global change analysis model with state-level resolution (GCAM-USA) to simulate the evolution of the US energy system under reference case and deep decarbonization scenarios. Fleet characterization and fuel prices projected by GCAM-USA are passed to the SMART Mobility modeling workflow. This large-scale simulation framework combines the POLARIS activity-based travel-demand model and mesoscopic traffic simulator, the Autonomie vehicle energy consumption model, and the UrbanSim land-use simulator, to jointly explore the mobility and energy use outcomes of the Level 4 automation with cooperative adaptive cruise control (L4-CACC) and a set of decarbonization policy responses. Results suggest that the introduction of L4-CACC vehicles could increase fuel consumption when no decarbonization policies are implemented, raising 2040 vehicle miles traveled (VMT) by approximately 9% and fuel use by about 13% relative to a no-automation reference case. Deep decarbonization policies, including energy pricing and vehicle electrification incentives, offset part of these increases by reducing fuel consumption by 22% relative to the automated case and 27% relative to the reference case, while also reducing overall well-to-wheel greenhouse gas emissions by shifting travel toward more efficient vehicle technologies. Energy pricing and vehicle electrification incentives could help reduce the impact of vehicle automation produced by the expected higher VMT. Finally, our analysis indicates the relevance of introducing land-use processes such as household and workplace choices in vehicle automation studies, because of the influence on the VMT that these decisions have in the long term.