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Future Connected and Automated Vehicle Adoption Will Likely Increase Car Dependence and Reduce Transit Use without Policy Intervention

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Listed:
  • Circella, Giovanni
  • Jaller, Miguel
  • Sun, Ran
  • Qian, Xiaodong
  • Alemi, Farzad

Abstract

California sits at the epicenter of self-driving vehicle technology development, with numerous companies testing connected and automated vehicles (CAVs) in the state. CAVs have the potential to improve safety and increase mobility for children, the elderly, and people with disabilities. These vehicles will operate more efficiently, use less space on the roadway, and cause fewer crashes, all of which are expected to relieve traffic congestion. However, CAVs will also likely bring about complex changes to travel demand, urban design, and land use. The degree to which these changes will affect vehicle miles traveled, energy use, and air pollution in California is unknown and could have wideranging implications for the state’s ability to meet its climate goals. 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. The researchers estimated the vehicle miles traveled and emissions of each scenario using a statewide travel demand model, emissions factors from California agencies, and assumptions derived from the scientific literature and expert input. This policy brief summarizes the findings from that research and provides policy implications. View the NCST Project Webpage

Suggested Citation

  • Circella, Giovanni & Jaller, Miguel & Sun, Ran & Qian, Xiaodong & Alemi, Farzad, 2022. "Future Connected and Automated Vehicle Adoption Will Likely Increase Car Dependence and Reduce Transit Use without Policy Intervention," Institute of Transportation Studies, Working Paper Series qt0rb439tv, Institute of Transportation Studies, UC Davis.
  • Handle: RePEc:cdl:itsdav:qt0rb439tv
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    Keywords

    Engineering; Social and Behavioral Sciences; Autonomous vehicles; Connected vehicles; Energy consumption; Forecasting; Impact; Modal split; Pollutants; Pricing; Simulation; Travel demand; Vehicle miles of travel; Zero emission vehicles;
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