In 2023, California had the most automobile registrations: almost 13.2 million such vehicles were registered in the most populous U.S. federal state. California also had the highest number of registered motor vehicles overall: nearly 30.4 million registrations.

These datasets provide vehicle counts broken down by ZIP code, model year, fuel type, make and duty (light/heavy) of registered vehicles with specific as of dates.

This annual release provides a snapshot of the number of active vehicle registration counts of light-duty vehicles and medium-duty vehicles by type of vehicle and fuel type, heavy-duty vehicles, buses, and motorcycles and mopeds. Data are obtained from the administrative files from provincial and territorial governments.

Quarterly data on vehicle registration by fuel type, vehicle type and number of vehicles, Canada, the provinces, census metropolitan areas and census sub-divisions.

Quarterly data on new motor vehicle registration by fuel type, vehicle type and number of vehicles, for Canada and provinces.

The number of Electric Vehicles registered in Nova Scotia with an active licence plate attached. This data breaks down the number of electric vehicles by vehicle model year, vehicle make and County of Nova Scotia in which the licence plate owner resides.

This data set applies to vehicles with valid licence plates only and are recorded as of April 1.All blank fields – data not available.

This dataset represents valid driver licenses and identification cards that have not expired within the past 90 days. The identification card count includes individuals who hold only an identification card as well as those who possess both an identification card and a driver's license. While licenses are not issued by county, the dataset reflects the number of valid licenses with a 'county of residence' indicator, which is typically derived from the residence address on record, or the mailing address if a residence address is unavailable.

Annual vehicle registrations, by type of vehicle (road motor vehicles, trailers, off-road, construction and farm vehicles).

This dataset presents results from a survey of 906 FCV and 12,910 BEV households in California. The researcher investigated the sociodemographic profile of FCV buyers and compare them to BEV households. The publicly available dataset file includes the following information: response ID, date survey submitted, information on vehicle owned, ownership of previous PHEVs, BEVs, HEVs, CNGs, household income, home ownership, home type, highest level of education, longest trip in the last 12 months, number of trips over 200 miles in the last 12 months, one-way commute distance, number of people in the household, age, gender, number of vehicles in the household, and annual VMT estimate.

Data on registration of motor vehicles by type including passenger automobiles, trucks, motorcycles, buses, trailers and others are presented in this publication. A historical table of total registrations is provided. Motor vehicle registrations are shown by census divisions and municipalities where available. Data definitions, analysis, the methodology employed, an explanation of data quality and a bibliography are included.

Quarterly data on zero-emission vehicle registration by fuel type, vehicle type and number of vehicles, Canada, the provinces, census metropolitan areas and census sub-divisions.

This table contains 70 series, with data for years 1999 - 2009 (not all combinations necessarily have data for all years), and was last released on 2014-06-19. This table contains data described by the following dimensions (Not all combinations are available): Geography (14 items: Prince Edward Island; Nova Scotia; Newfoundland and Labrador; Canada ...), Type of vehicle (5 items: Total; all vehicles; Trucks 15 tonnes and over; Trucks 4.5 tonnes to 14.9 tonnes; Vehicles up to 4.5 tonnes ...).

Green Vehicle Guide Datafile Model – vehicle make and model Displ – engine displacement in liters Cyl – number of engine cylinders Trans – transmission type plus number of gears  Auto - Automatic  Man - Manual  SemiAuto - Semi-Automatic  SCV - Selectable Continuously Variable (e.g. CVT with paddles)  AutoMan - Automated Manual  AMS - Automated Manual-Selectable (e.g. Automated Manual with paddles)  Other - Other  CVT - Continuously Variable  CM3 - Creeper/Manual 3-Speed  CM4 - Creeper/Manual 4-Speed  C4 - Creeper/Manual 4-Speed  C5 - Creeper/Manual 5-Speed  Auto-S2 - Semi-Automatic 2-Speed  Auto-S3 - Semi-Automatic 3-Speed  Auto-S4 - Semi-Automatic 4-Speed  Auto-S5 - Semi-Automatic 5-Speed  Auto-S6 - Semi-Automatic 6-Speed  Auto-S7 - Semi-Automatic 7-Speed Drive – 2-wheel Drive, 4-wheel drive/all-wheel drive Fuel – fuel(s) Cert Region –  CA - California  CE - Calif. + NLEV (Northeast trading area)  CF - Clean Fuel Vehicle  CL - Calif. + NLEV (All states)  FA - Federal All Altitude  FC - Tier 2 Federal and Calif.  NF - CFV + NLEV(ASTR) + Calif.  NL - NLEV (All states) Stnd – vehicle emissions standard code. See Stnd Description. Stnd Description – vehicle emissions standard description. See https://www.epa.gov/greenvehicles/federal-and-california-light-duty-vehicle-emissions-standards-airpollutants Underhood ID – engine family or test group ID. See http://www.fueleconomy.gov/feg/findacarhelp.shtml#airPollutionScore Veh Class – EPA vehicle class. See http://www.fueleconomy.gov/feg/findacarhelp.shtml#epaSizeClass Air Pollution Score (Smog Rating) – see http://www.fueleconomy.gov/feg/findacarhelp.shtml#airPollutionScore and https://www.epa.gov/greenvehicles/smog-rating City MPG – city fuel economy in miles per gallon Hwy MPG – highway fuel economy in miles per gallon Cmb MPG – combined city/highway fuel economy in miles per gallon Greenhouse Gas Score (Greenhouse Gas Rating) – see https://www.epa.gov/greenvehicles/greenhouse-gas-rating SmartWay – Yes, No, or Elite. See https://www.epa.gov/greenvehicles/consider-smartwayvehicle Comb CO2 – combined city/highway CO2 tailpipe emissions in grams per mile

Number of units and total sales of new motor vehicles by vehicle type and origin of manufacture, monthly.

This layer provides high utilization results where:A Public Level 2 charger that is located within an eighth of a mile of households, defined as walking distance, can adequately serve 5 EVs that likely lack home charging (3 EVs overnight on separate days and 2 EVs during the day)A Public DC fast charger that is located within two miles of households, defined as in the neighborhood, can adequately serve 30 EVs that likely lack home charging during the day (DC fast chargers would not be used for long-duration overnight charging) The quarter-mile hexagons show results that are based off model estimates: The "ev_no_walk" field can be used to identify areas for public Level 2 charging deployment that would capture charging demand within walking distance (1/8th mile) of households with EVs that likely lack home charging and also lack sufficient existing public Level 2 charging within walking distance. The "ev_no_2mi" field can be used to identify areas for public Level 2 and DC fast charging deployment that would capture charging demand within the neighborhood (2 miles) of households with EVs that likely lack home charging and also lack sufficient existing public Level 2 or DC fast charging within the neighborhood.Data dictionary:ObjectID: Unique IDsfh: Number of single-family homes (SFHs) estimated within the selected areamfh: Number of multifamily homes (MFHs) estimated within the selected areaall_veh: Number of vehicles estimated within the selected areavf_home: Number of electric vehicles (EVs) in a 100% EV future estimated to have home charging in the selected areavf_nohome: Number of electric vehicles (EVs) in a 100% EV future estimated to not have home charging in the selected areaev_home: Number of electric vehicles (EVs) estimated to have home charging within the selected areaev_no_home: Number of electric vehicles (EVs) estimated to not have home charging within the selected areaev_no_walk: Among electric vehicles (EVs) estimated to not have home charging within the selected area, number of EVs that also do not have sufficient public Level 2 charging within an 1/8th of a mile (or walking distance) from home within the selected areasfh_2mi: Number of single-family homes (SFHs) estimated that are 2 miles from the center of the selected areamfh_2mi: Number of multifamily homes (MFHs) estimated that are 2 miles from the center of the selected areaall_veh2mi: Number of 2024 vehicles estimated that are 2 miles from the center of the selected areavf_home2mi: Number of electric vehicles (EVs) in a 100% EV future estimated to have home charging that are 2 miles from the center of the selected areavf_nohome2: Number of electric vehicles (EVs) in a 100% EV future estimated to not have home charging that are 2 miles from the center of the selected areaev_home2mi: Number of electric vehicles (EVs) estimated to have home charging that are 2 miles from the center of the selected areaev_nohome2: Number of electric vehicles (EVs) estimated to not have home charging that are 2 miles from the center of the selected areaev_no_2mi: Number of electric vehicles (EVs) estimated to not have home charging and also do not have sufficient public Level 2 or direct-current (DC) fast charging within 2 miles from home that are 2 miles from the center of the selected areaShape_Length: Census tract shape area (square meters)Shape_Area: Census tract shape length (square meters)Data sources: Results are based off model estimates. See the 2025 SB 1000 Staff Report for a full description of data sources and methodology.

The increasing diversity of vehicle type holdings and growing demand for BEVs and PHEVs have serious policy implications for travel demand and air pollution. Consequently, it is important to accurately predict or estimate the preference for vehicle holdings of households as well as the vehicle miles traveled by vehicle body and fuel type to project future VMT changes and mobile source emission levels. The current report presents the application of a utility-based model for multiple discreteness that combines multiple vehicle types with usage in an integrated model, specifically the MDCEV model. We use the 2019 California Vehicle Survey data here that allows us to analyze the driving behavior associated with more recent EV models (with potentially longer ranges). Important findings from the model include:

Household characteristics like size or having children have an expected impact on vehicle preference: larger vehicles are preferred. College education, rooftop solar ownership, and the number of employed workers in a household affect the preference for BEVs and PHEVs in the small car segment dominated by the Leaf, Bolt, Prius-Plug-in and the Volt often used as a commuter car. Among built environment factors, population density and the walkability index of a neighborhood have a statistically significant impact on the type of vehicle choice and VMT. It is observed that a 10% increase in population density reduces the preference for ICEV pickup trucks by 0.34% and VMT by 0.4%. However, if the increase in population density is 25%, the reduction in preference for pickup trucks is 8.4% and VMT is 8.6%. The other built environment factor we consider is the walkability index. If the walkability index of a neighborhood increases by 25%, it reduces the preference for ICEV pickup trucks by 15% and their VMT by 16%. Overall, these results suggest that if policies encourage mixed development of neighborhoods and increase density, it can have an important impact on ownership and usage of gas guzzlers like pickup trucks and help in the process of electrification of the transportation sector.

Methods The dataset used in this report was created using the following public data sources:

2019 California Vehicle Survey: "Transportation Secure Data Center." ([2019]). National Renewable Energy Laboratory. Accessed [04/26/2023]: www.nrel.gov/tsdc. The Smart Mapping Tool by EPA: https://www.epa.gov/smartgrowth/smart-location-mapping

American Community Survey: https://www.census.gov/programs-surveys/acs

Quarterly data on new motor vehicle registration by fuel type, vehicle type and number of vehicles, for Canada and provinces.

The data set contains registered vehicle population count by various criteria such as vehicle class, vehicle status, vechicle make, vehicle model, vehicle year, plate class, plate declaration, county, weight related class and other vehicle decriptors.

This annual release provides a snapshot of the number of active vehicle registration counts of light-duty vehicles and medium-duty vehicles by type of vehicle and fuel type, heavy-duty vehicles, buses, and motorcycles and mopeds. Data are obtained from the administrative files from provincial and territorial governments.