This study helps understand how the anticipated emergence of autonomous vehicles will affect various aspects of society and transportation, including travel demand, vehicle miles traveled, energy consumption, and emissions of greenhouse gases and other pollutants.
This project examines individual attitudes to a new autonomous shuttle at UC Davis. Survey results may inform future autonomous vehicle adoption and policy.
This project focuses on safety, mobility, and environmental sustainability Measures of Effectiveness (MOEs) of Connected and Automated Vehicle (CAV) applications.
This project focuses on Connected and/or Automated Vehicle (CAV) applications. The researchers studied different types of CAV applications in terms of measures of effectiveness.
Researchers at the University of California, Davis surveyed residents and employees of the West Village area of the UC Davis campus during the three-month pilot deployment of a self-driving, electric shuttle to understand attitudes toward self-driving technology. This policy brief summarizes findings from that research and provides policy implications of self-driving shuttles.
This study aims to explore these research questions based on data collected from people who live or work in the West Village area of the University of California, Davis, campus after a self-driving electric shuttle was piloted in this area.
Researchers at the University of California, Davis investigated the range of potential impacts that rapid adoption of CAVs in California might have on vehicle miles traveled and emissions.
This study helps understand how the anticipated emergence of CAVs will affect various aspects of society and transportation, including travel demand, vehicle miles traveled, energy consumption, and emissions of greenhouse gases and other pollutants. The research team designed a set of future system configurations under the California Statewide Travel Demand Model framework to simulate scenarios for the deployment of passenger CAVs in California by 2050.
Reliable, lane-level, absolute position determination for connected and automated vehicles (CAV’s) is near at hand due to advances in sensor and computing technology. This project investigated, analyzed, and demonstrated these related technologies.
