The household and commercial vehicle survey data are used to update the light-duty vehicle choice models. These models are used to generate light duty vehicle demand forecasts and provide understanding about such questions as:
- How many light-duty cars and trucks will be on California roads?
- How much and what kind of fuels will they use?
- What will be the transportation energy infrastructure needs of California in the future?
In addition to the main survey of California vehicle owners, the 2017 survey included an add-on survey of Plug-in Electric Vehicle (PEV) owners. The 2019 survey included an add-on survey of Zero Emission Vehicle (ZEV) owners in both household and commercial light duty vehicle market segments.
Resources Systems Group (RSG) is conducting Energy Commission’s 2024 vehicle survey, currently in progress. RSG will send invitations to a random sample of household and commercial fleet owners to participate in our 2024 survey. Your responses to survey questions are much appreciated, as they help Energy Commission update our understanding of California vehicle owners in 2024 and their energy infrastructure needs. The information will help planners, energy suppliers, and policy makers anticipate the changing needs of California vehicle owners to better meet future infrastructure needs of the state.
Accessing the Complete Survey Data
The 2013, 2017 and 2019 survey data are at the National Renewable Energy Laboratory (NREL) at Transportation Secure Data Center (TSDC). Create a free account to access the data.
The data include information on the participants’ current household and commercial light-duty vehicles, as well as their preferences for a hypothetical set of vehicles. The data also includes some information on travel behavior.
Accessing Survey Reports
Resources Systems Group prepared the technical reports for the 2017 California Vehicle Survey for the Energy Commission. The first report is on survey design and light duty vehicle choice models. The second report includes design details and additional models.