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.

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.

Vehicle population is updated annually, each April, to reflect the number of vehicles “on the road” during the previous calendar year. Vehicle population counts vehicles whose registration is either current or less than 35 days expired. Sales are higher than population because of vehicle retirements, accidents, owners moving out of state, or other reasons.

Data as of: December 31, 2022

Regional Data Categories Map Filter. Data can be reflected at the county, metropolitan statistical area (MSA), or ZIP code level. Individual registrations were assigned to a region based on mailing address, with the following exceptions:

Out of State: vehicles registered in California with a mailing address in a different state

ZIP Code “99999”: indicates that the ZIP code in the Vehicle Registration database was invalid

Unassigned: remote areas not associated with an MSA

Unknown: Invalid ZIP codes categorized under MSA

This Data contains information from 2010 to 2022.

Please note, that columns 'Manufacturer' and 'Model' include Null values since it was less data in 2010-2012 and more data from 2013.

Examples of Questions you may answer with this Data:

• What is the trend in the number of Battery Electric Vehicles (BEVs) in California from 2010 to 2022?
• How has the adoption of Gasoline vehicles changed over the years in California, specifically for Flex Fuel?
• Which county in California had the highest number of Diesel vehicles in 2010?
• What is the distribution of Gasoline Hybrid vehicles across California counties in 2020?
• How many Natural Gas vehicles were registered in California in 2010, and did this number change significantly by 2020?
• Which car manufacturer produced the most Battery Electric Vehicles (BEVs) in California in 2015?
• What is the overall trend in the number of Zero Emission Vehicles (ZEVs) in California over the past decade?
• What is the average number of vehicles per make and model for all Fuel Types in 2019 in California?
• How has the market share of Battery Electric Vehicles (BEVs) compared to Gasoline vehicles evolved over the years in California?

Columns: Data Year
County
Dashboard Fuel Type Group
Fuel Type
Manufacturer
Model
Number of Vehicles

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.

Number of Registered Vehicles by Type, 1989 to 2021

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

The Car Prices dataset contains detailed information about various car models, including their manufacturing year, make, model, trim, body type, transmission, and state of condition. With over 550,000 entries, this dataset is an excellent resource for exploring trends in car prices, analyzing market value fluctuations, and developing predictive models for the automotive industry.

Features

1. Year: Manufacturing year of the vehicle (Range: 1982-2015).
2. Make: Brand of the car (Top makes: Ford - 17%, Chevrolet - 11%).
3. Model: Specific car models, including Altima (3%), F-150 (3%), and others.
4. Trim: Vehicle trim level or variant (Base - 10%, SE - 8%).
5. Body: Body type, such as Sedan (36%), SUV (21%), etc.
6. Transmission: Type of transmission (85% Automatic, 12% unknown).
7. VIN: Vehicle Identification Number, unique to each car.
8. State: Location where the vehicle is registered (e.g., CA - 13%, FL - 15%).
9. Condition: Condition score of the car (1–5 scale).
10. Odometer: Mileage covered by the car (Range: 1 to 1,000,000+ miles).

Use Cases

• Price Prediction: Build machine learning models to predict car prices based on features like make, model, year, and mileage.
• Market Analysis: Identify trends in car preferences and sales across different regions.
• Depreciation Study: Study how different brands and models lose value over time.

Statistics Overview

• Years Distribution: The dataset spans from 1982 to 2015, with most entries concentrated in the 2000s.
• Popular Brands: Ford and Chevrolet dominate the entries.
• Body Type Distribution: Sedans and SUVs make up over 50% of the dataset.
• Condition Scores: Vehicles with a condition score of 4-5 are most common.

Example Records

License

This dataset is available under the MIT License, making it suitable for both commercial and non-commercial use.

Tags

• Car Pricing
• Automotive Market
• Machine Learning
• Price Prediction
• Data Analysis

Download Now and explore the intricacies of car prices with this rich and diverse dataset!

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.

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

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.

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 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 ...).

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 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 table contains data on the annual miles traveled by place of occurrence and by mode of transportation (vehicle, pedestrian, bicycle), for California, its regions, counties, and cities/towns. The ratio uses data from the California Department of Transportation, the U.S. Department of Transportation, and the U.S. Census Bureau. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Miles traveled by individuals and their choice of mode – car, truck, public transit, walking or bicycling – have a major impact on mobility and population health. Miles traveled by automobile offers extraordinary personal mobility and independence, but it is also associated with air pollution, greenhouse gas emissions linked to global warming, road traffic injuries, and sedentary lifestyles. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which has many documented health benefits. More information about the data table and a data dictionary can be found in the About/Attachments section.

This dataset contains comprehensive hourly data from January 1, 2021, to May 31, 2024, for the California region. The data focuses on electric vehicle (EV) charging demand and renewable energy production, including solar and wind energy. The dataset is structured to support analysis and optimization of EV charging using intelligent forecasting methods for solar and renewable energy in uncertain environments. The integrated solar and wind energy data are taken from https://catalog.data.gov/dataset/.

Dataset Description 1. Time Frame Start Date: January 1, 2021 End Date: May 31, 2024 Frequency: Hourly 2 Key Features Date: The specific date of the recorded data. Time: The hour of the day the data was recorded. EV Charging Demand (kW): The amount of electricity (in kilowatts) demanded by electric vehicles for charging during each hour. Solar Energy Production (kW): The amount of electricity (in kilowatts) produced from solar energy sources during each hour. Wind Energy Production (kW): The amount of electricity (in kilowatts) produced from wind energy sources during each hour. Electricity Price ($/kWh): The price of electricity per kilowatt-hour. Grid Availability: Indicates whether the grid was available ("Available") or not ("Unavailable") during each hour. Weather Conditions: Describes the weather during each hour, with possible values such as "Clear", "Cloudy", "Rainy", "Sunny", and "Partly Cloudy". Battery Storage (kWh): The amount of electricity stored in batteries during each hour. Charging Station Capacity (kW): The maximum capacity of the charging stations in kilowatts. EV Charging Efficiency (%): The efficiency of the EV charging process, expressed as a percentage. Number of EVs Charging: The number of electric vehicles charging during each hour. Peak Demand (kW): The peak electricity demand during each hour. Renewable Energy Usage (%): The percentage of energy used from renewable sources. Grid Stability Index: An index indicating the stability of the grid, with higher values indicating greater stability. Carbon Emissions (kgCO2/kWh): The amount of carbon emissions produced per kilowatt-hour of electricity. Power Outages (hours): The duration of power outages during each hour. Energy Savings ($): The amount of money saved through energy efficiencies during each hour. Derived Features Total Renewable Energy Production (kW): The sum of solar and wind energy production.

Total Renewable Energy Production (kW) = Solar Energy Production (kW) +Wind Energy Production (kW) Effective Charging Capacity (kW): The product of charging station capacity and EV charging efficiency.

\text{Effective Charging Capacity (kW)} = \text{Charging Station Capacity (kW)} \times \left( \frac{\text{EV Charging Efficiency (%)}}{100} \right)

Adjusted Charging Demand (kW): The EV charging demand adjusted by renewable energy usage. \text{Adjusted Charging Demand (kW)} = \text{EV Charging Demand (kW)} \times \left( \frac{\text{Renewable Energy Usage (%)}}{100} \right) Net Energy Cost ($): The product of EV charging demand and electricity price.

\text{Net Energy Cost ($)} = \text{EV Charging Demand (kW)} \times \text{Electricity Price ($/kWh)} Carbon Footprint Reduction (kgCO2): The reduction in carbon emissions due to renewable energy usage.

\text{Carbon Footprint Reduction (kgCO2)} = \text{EV Charging Demand (kW)} \times \text{Carbon Emissions (kgCO2/kWh)} \times \left( 1 - \frac{\text{Renewable Energy Usage (%)}}{100} \right) Renewable Energy Efficiency: The efficiency of utilizing renewable energy for charging electric vehicles.

Renewable Energy Efficiency = Total Renewable Energy Production (kW) / Effective Charging Capacity (kW)

Usage and Applications This dataset is valuable for research and development in the following areas:

• Optimizing EV Charging: By analyzing the data, strategies can be developed to optimize EV charging schedules to minimize costs and maximize the use of renewable energy. Renewable Energy Forecasting: The dataset supports the development of predictive models for solar and wind energy production. Grid Management: Insights from the data can help improve grid stability and manage peak demand periods. Carbon Emission Reduction: The dataset aids in calculating and strategizing ways to reduce carbon emissions from EV charging. Economic Analysis: The data allows for the assessment of energy savings and cost efficiencies in EV charging. This extensive dataset provides a robust foundation for advancing sustainable and intelligent EV charging solutions in the California region.

This dataset contains information on all non-confidential assets within the California State Vehicle Fleet. The purpose of this data is to provide vehicle information including sustainability measurements such as fuel types. The data is extracted from the Fleet Asset Management System utilized by the Department of General Services’ Office of Fleet and Asset Management. The dataset covers the timeframe from the calendar year 2015 until the calendar year 2024 and includes vehicle information reported by all agencies under the executive branch and constitutional offices.

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.