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

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.

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!

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.

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

This table contains 161 series, with data for years 1970 - 2010 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Vehicle type (161 items: Total, motor vehicles; Total passenger cars; American Motors, total cars; Hornet; ...).

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

The U.S. auto industry sold nearly ************* cars in 2024. That year, total car and light truck sales were approximately ************ in the United States. U.S. vehicle sales peaked in 2016 at roughly ************ units. Pandemic impact The COVID-19 pandemic deeply impacted the U.S. automotive market, accelerating the global automotive semiconductor shortage and leading to a drop in demand during the first months of 2020. However, as demand rebounded, new vehicle supply could not keep up with the market. U.S. inventory-to-sales ratio dropped to its lowest point in February 2022, as Russia's war on Ukraine lead to gasoline price hikes. During that same period, inflation also impacted new and used car prices, pricing many U.S. consumers out of a market with increasingly lower car stocks. Focus on fuel economy The U.S. auto industry had one of its worst years in 1982 when customers were beginning to feel the effects of the 1973 oil crisis and the energy crisis of 1979. Since light trucks would often be considered less fuel-efficient, cars accounted for about ** percent of light vehicle sales back then. Thanks to improved fuel economy for light trucks and cheaper gas prices, this picture had completely changed in 2020. That year, prices for Brent oil dropped to just over ** U.S. dollars per barrel. The decline occurred in tandem with lower gasoline prices, which came to about **** U.S. dollars per gallon in 2020 - and cars only accounted for less than one-fourth of light vehicle sales that year. Four years on, prices are dropping again, after being the highest on record since 1990 in 2022.

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

DESCRIPTION This table contains data on the percent of residents aged 16 years and older mode of transportation to work for ...

SUMMARY This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

ind_id - Indicator ID ind_definition - Definition of indicator in plain language reportyear - Year that the indicator was reported race_eth_code - numeric code for a race/ethnicity group race_eth_name - Name of race/ethnic group geotype - Type of geographic unit geotypevalue - Value of geographic unit geoname - Name of a geographic unit county_name - Name of county that geotype is in county_fips - FIPS code of the county that geotype is in region_name - MPO-based region name; see MPO_County list tab region_code - MPO-based region code; see MPO_County list tab mode - Mode of transportation short name mode_name - Mode of transportation long name pop_total - denominator pop_mode - numerator percent - Percent of Residents Mode of Transportation to Work,
Population Aged 16 Years and Older LL_95CI_percent - The lower limit of 95% confidence interval UL_95CI_percent - The lower limit of 95% confidence interval percent_se - Standard error of the percent mode of transportation percent_rse - Relative standard error (se/value) expressed as a percent CA_decile - California decile CA_RR - Rate ratio to California rate version - Date/time stamp of a version of data

Annual average daily traffic is the total volume for the year divided by 365 days. The traffic count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Traffic Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates. planning and designing highways and other purposes.Traffic Census Program Page

Number of vehicles authorized to drive in Quebec, both for road vehicles and for vehicles designed for off-road traffic. The data has been revised to comply with the new provisions of Bill 25 protecting the privacy of Quebecers.

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

Click “Export” on the right to download the vehicle trajectory data. The associated metadata and additional data can be downloaded below under "Attachments". Researchers for the Next Generation Simulation (NGSIM) program collected detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data was collected through a network of synchronized digital video cameras. NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provided the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Click the "Show More" button below to find additional contextual data and metadata for this dataset. For site-specific NGSIM video file datasets, please see the following: - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf

Since the launch of the iZEV Program on May 1, 2019, Transport Canada has been producing statistics on consumer uptake under the program for the following variables: - Province/territory or all of Canada - Province/territory and postal code of the dealership each vehicle was purchased/leased from - Make and/or model (including model year) - Engine type (i.e., 100% battery electric versus plug-in hybrids - both over and under 50 km of electric range.) - Recipient type (i.e., individual or organization and purchase or lease) - A time period, including: * A specific month * Ranges of months (e.g., June 2020 to January 2021) * Calendar year (January 1 to December 31) * The Government of Canada’s fiscal year (April 1 to March 31) The current data provides iZEV monthly statistics. Revisions of archived data will be updated quarterly, these revisions are generally minor and are mainly due to approval of incentive requests that were incomplete when first submitted to Transport Canada. Most revisions are typically from the most recent three-month period. If you have any questions, please contact us at iZEV-iVZE@tc.gc.ca

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.

Context

I curate a dataset of California traffic collisions, which you can find here: Kaggle: California Traffic Collision Data from SWITRS.

However, I made certain decisions about how to clean and process the data, which others might disagree with. This dataset contains the raw data, allowing you to make your own cleaning decisions!

Content

This data comes from the California Highway Patrol and covers collisions from January 1st, 2001 until mid-December, 2020. I have requested full database dumps from the CHP four times, once in 2016, 2017, 2018, 2020, 2021. These are the raw files as provided by the CHP with no post-processing

Acknowledgements

This data would not exist without the California Highway Patrol compiling it, thanks!

This table contains 35 series, with data for years 2000 - 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 (1 items: Canada ...), Type of vehicle (5 items: Total; all vehicles; Vehicles up to 4.5 tonnes; Trucks 4.5 tonnes to 14.9 tonnes; Trucks 15 tonnes and over ...), Type of vehicle body (10 items: Total; all vehicles body types; Car; Station wagon; Van ...).

The travel speeds were calculated with car GPS data collected by the smart phone application. The travel speeds and Buffer Time Index (BTI) were calculated with car GPS data collected by the smart phone traveller information application in 2011-2016.

This data is used by MTO's Systems Analysis and Forecasting Office to monitor speed and performance on major roadways within the province.

• [MTO]: Ministry of Transportation
• [GPS]: Global Positioning System