Total Vehicle Sales in the United States decreased to 15.65 Million in May from 17.27 Million in April of 2025. This dataset provides the latest reported value for - United States Total Vehicle Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The number of new and used vehicles and the sales dollars respectively sold by month. MDOT MVA’s Customer Connect modernization project, implemented in July 2020, has increased the amount of data that is collected and used to calculate car sales. This data is updated in real time and may fluctuate based on external factors, including electronic submissions from dealers and other vendors.

Explore the dynamic world of automobiles with this comprehensive car dataset. This dataset encompasses a wide range of information, including specifications, performance metrics, and market trends for various car models. Whether you're a data enthusiast, a researcher, or an industry professional, this dataset offers invaluable insights into the automotive landscape. Analyze trends, compare features, and uncover patterns to drive innovation and informed decision-making in the automotive industry. With detailed data on make, model, year, horsepower, fuel efficiency, and more, this dataset provides a rich resource for exploring the intricate nuances of the modern automobile market. Start your journey today and dive into the world of cars with this comprehensive dataset.

The U.S. auto industry sold nearly three million cars in 2024. That year, total car and light truck sales were approximately 15.9 million in the United States. U.S. vehicle sales peaked in 2016 at roughly 17.5 million 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 77 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 40 U.S. dollars per barrel. The decline occurred in tandem with lower gasoline prices, which came to about 2.17 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.

Autos include all passenger cars, including station wagons. The U.S. Bureau of Economic Analysis releases auto and truck sales data, which are used in the preparation of estimates of personal consumption expenditures.

Total Vehicle Sales in China decreased to 2686 Units in May from 2590000 Units in April of 2025. This dataset provides - China Total Vehicle Sales- actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2024, the auto industry in the United States sold approximately 15.9 million light vehicle units. This figure includes retail sales of about three million passenger cars and just under 12.9 million light trucks. Lower fuel consumption There are many kinds of light vehicles available in the United States. Light-duty vehicles are popular for their utility and improved fuel economy, making them an ideal choice for savvy consumers. As of Model Year 2023, the light vehicle manufacturer with the best overall miles per gallon was Kia, with one gallon of gas allowing for 30.4 miles on the road. Higher brand satisfaction When asked about light vehicle satisfaction, consumers in the United States were most satisfied with Toyota, Subaru, Tesla, and Mercedes-Benz models. Another survey conducted in 2018 and quizzing respondents on their stance regarding the leading car brands indicated that Lexus was among the most dependable brands based on the number of problems reported per 100 vehicles.

1. Title: 1985 Auto Imports Database

2. Source Information: -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) -- Date: 19 May 1987 -- Sources: 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. 2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037

3. Past Usage: -- Kibler,~D., Aha,~D.~W., & Albert,~M. (1989). Instance-based prediction of real-valued attributes. {\it Computational Intelligence}, {\it 5}, 51--57. -- Predicted price of car using all numeric and Boolean attributes -- Method: an instance-based learning (IBL) algorithm derived from a localized k-nearest neighbor algorithm. Compared with a linear regression prediction...so all instances with missing attribute values were discarded. This resulted with a training set of 159 instances, which was also used as a test set (minus the actual instance during testing). -- Results: Percent Average Deviation Error of Prediction from Actual -- 11.84% for the IBL algorithm -- 14.12% for the resulting linear regression equation

4. Relevant Information: -- Description This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.

   The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc...), and represents the average loss per car per year.

   -- Note: Several of the attributes in the database could be used as a "class" attribute.

5. Number of Instances: 205

6. Number of Attributes: 26 total -- 15 continuous -- 1 integer -- 10 nominal

7. Attribute Information:
   Attribute: Attribute Range:

   1. symboling: -3, -2, -1, 0, 1, 2, 3.
   2. normalized-losses: continuous from 65 to 256.
   3. make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi, nissan, peugot, plymouth, porsche, renault, saab, subaru, toyota, volkswagen, volvo
   4. fuel-type: diesel, gas.
   5. aspiration: std, turbo.
   6. num-of-doors: four, two.
   7. body-style: hardtop, wagon, sedan, hatchback, convertible.
   8. drive-wheels: 4wd, fwd, rwd.
   9. engine-location: front, rear.
   10. wheel-base: continuous from 86.6 120.9.
   11. length: continuous from 141.1 to 208.1.
   12. width: continuous from 60.3 to 72.3.
   13. height: continuous from 47.8 to 59.8.
   14. curb-weight: continuous from 1488 to 4066.
   15. engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor.
   16. num-of-cylinders: eight, five, four, six, three, twelve, two.
   17. engine-size: continuous from 61 to 326.
   18. fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi.
   19. bore: continuous from 2.54 to 3.94.
   20. stroke: continuous from 2.07 to 4.17.
   21. compression-ratio: continuous from 7 to 23.
   22. horsepower: continuous from 48 to 288.
   23. peak-rpm: continuous from 4150 to 6600.
   24. city-mpg: continuous from 13 to 49.
   25. highway-mpg: continuous from 16 to 54.
   26. price: continuous from 5118 to 45400.

8. Missing Attribute Values: (denoted by "?") Attribute #: Number of instances missing a value:

   1. 41
   2. 2
   3. 4
   4. 4
   5. 2
   6. 2
   7. 4

Sales of used light vehicles in the United States came to around 39.2 million units in 2024. In the same period, approximately 15.8 million new light trucks and automobiles were sold here. Declining availability of vehicles In the fourth quarter of 2024, about 292.3 million vehicles were in operation in the United States, an increase of around 1.3 percent year-over-year. The rising demand for vehicles paired with an overall price inflation lead to a rise in new vehicle prices. In contrast, used vehicle prices slightly decreased. E-commerce: a solution for the bumpy road ahead? Financial reports have revealed how the outbreak of the coronavirus pandemic has triggered a shift in vehicle-buying behavior. With many consumer goods and services now bought online due to COVID-19, the automobile industry has also started to digitally integrate its services online to reach consumers with a preference for contactless test driving amid the global crisis. Several dealers and automobile companies had already begun to tap into online car sales before the pandemic, some of them being Carvana and Tesla.

Description:

This dataset contains the latest information on car prices in Australia for the year 2023. It covers various brands, models, types, and features of cars sold in the Australian market. It provides useful insights into the trends and factors influencing the car prices in Australia. The dataset includes information such as brand, year, model, car/suv, title, used/new, transmission, engine, drive type, fuel type, fuel consumption, kilometres, colour (exterior/interior), location, cylinders in engine, body type, doors, seats, and price. The dataset has over 16,000 records of car listings from various online platforms in Australia.

Key Features:

• Brand: Name of the car manufacturer
• Year: Year of manufacture or release
• Model: Name or code of the car model
• Car/Suv: Type of the car (car or suv)
• Title: Title or description of the car
• UsedOrNew: Condition of the car (used or new)
• Transmission: Type of transmission (manual or automatic)
• Engine: Engine capacity or power (in litres or kilowatts)
• DriveType: Type of drive (front-wheel, rear-wheel, or all-wheel)
• FuelType: Type of fuel (petrol, diesel, hybrid, or electric)
• FuelConsumption: Fuel consumption rate (in litres per 100 km)
• Kilometres: Distance travelled by the car (in kilometres)
• ColourExtInt: Colour of the car (exterior and interior)
• Location: Location of the car (city and state)
• CylindersinEngine: Number of cylinders in the engine
• BodyType: Shape or style of the car body (sedan, hatchback, coupe, etc.)
• Doors: Number of doors in the car
• Seats: Number of seats in the car
• Price: Price of the car (in Australian dollars)

Potential Use Cases:

• Price prediction: Predict the price of a car based on its features and location using machine learning models.
• Market analysis: Explore the market trends and demand for different types of cars in Australia using descriptive statistics and visualization techniques.
• Feature analysis: Identify the most important features that affect the car prices and how they vary across different brands, models, and locations using correlation and regression analysis.

If you find this dataset useful, your support through an upvote would be greatly appreciated ❤️🙂
Thank you

This dataset provides values for TOTAL VEHICLE SALES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Car Registrations in Indonesia decreased to 70895 Units in March from 72295 Units in February of 2025. This dataset provides - Indonesia Total Car Sales - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

Gain valuable insights into the automotive market with our comprehensive Car Prices Dataset. Designed for businesses, analysts, and researchers, this dataset provides real-time and historical car pricing data to support market analysis, pricing strategies, and trend forecasting.

Dataset Features

Vehicle Listings: Access detailed car listings, including make, model, year, trim, and specifications. Ideal for tracking market trends and pricing fluctuations. Pricing Data: Get real-time and historical car prices from multiple sources, including dealerships, marketplaces, and private sellers. Market Trends & Valuations: Analyze price changes over time, compare vehicle depreciation rates, and identify emerging pricing trends. Dealer & Seller Information: Extract seller details, including dealership names, locations, and contact information for lead generation and competitive analysis.

Customizable Subsets for Specific Needs Our Car Prices Dataset is fully customizable, allowing you to filter data based on vehicle type, location, price range, and other key attributes. Whether you need a broad dataset for market research or a focused subset for competitive analysis, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Pricing Strategy: Track vehicle price trends, compare competitor pricing, and optimize pricing strategies for dealerships and resellers. Automotive Valuation & Depreciation Studies: Analyze historical pricing data to assess vehicle depreciation rates and predict future values. Competitive Intelligence: Monitor competitor pricing, dealership inventory, and promotional offers to stay ahead in the market. Lead Generation & Sales Optimization: Identify potential buyers and sellers, track demand for specific vehicle models, and enhance sales strategies. AI & Predictive Analytics: Leverage structured car pricing data for AI-driven forecasting, automated pricing models, and trend prediction.

Whether you're tracking car prices, analyzing market trends, or optimizing sales strategies, our Car Prices Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

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  <h1>Similarity Computation with Extremely Randomized Clustering Forests (ERCF)</h1>

  <h2> Content </h2>

  This page briefly describes our work on similarity computation. <br>
  It provides:
  <ul>
    <li>the related <b>CVPR'07 paper</b></li>
    <li>the toy car <b>dataset</b></li>
    <li>a <b>binary</b> of the algorithm.</li>
  </ul>

  <h2> Objective </h2>

  <img src="sameordifferent.png" alt="same or different?">

  <p>
    Our purpose is to compute a similarity measure between two images.
    That measure should deal with never seen objects (never seen car
    models, never seen faces, ...) and should be robust to modifications in
    pose, background, light. It is trained with pairs of images labeld
    "Same" or "Different". This is less informative than fully labeled
    training images ("Car model 1", "Car model 2", ...) but much cheaper to
    obtain.
  </p>

  <h2> Algorithm, Paper</h2>

  The algorithm is fully described in the following paper, section 2 (quite self-contained).<br>
  Eric Nowak and Fr�d�ric Jurie, 
  <i>Learning Visual Similarity Measures for Comparing Never Seen Objects</i>,
  Computer Vision and Pattern Recognition 2007 (CVPR'07), <a href="../dwl/cvpr07.pdf">pdf</a>.
  <br>You can also download <a href="../dwl/nowak_jurie_cvpr07_slides.pdf">the slides of the talk</a>.

  <h2> Datasets </h2>

  <p>
    Our algorithm is evaluated on three public datasets, and also on our own dataset of toy cars.
    It can be downloaded <a href="../dwl/toycarlear.tar.gz">here (23Mb)</a>. 
    The archive contains the images and a metadata file (pairs.txt).
  </p>

  <p>
    The pairs of images of the toycar dataset are made from these
    vehicles:<br>
    <img src="allcars_nb.jpg" alt="cars from toycar dataset">
  </p>

  <h2> Binary </h2>

  <p>
    You can <a href="../dwl/pRazSimiERCF.gz">download a binary (~1Mb)</a> of our algorithm for linux machines.
    It should work on many distributions, but we have only tested
    Mandrakelinux 10.1 for i586, kernel 2.6. 
    The binary requires the following standard libraries: linux-gate.so.1,
    libpng.so.3, libjpeg.so.62, libpthread.so.0, libstdc++.so.6, libm.so.6,
    libgcc_s.so.1, libc.so.6, libz.so.1, /lib/ld-linux.so.2.
  </p>

  <p>
     This binary is a reimplementation of
    our CVPR07 algorithm, for simplicity reasons it does NOT contain
    geometry based split conditions, which usually increase the overall
    EER-PR of 1%.
  </p>

  <p>
    <b>Help</b> about command line options is obtained by: 
    <br>
    <b>The best way to understand the behavior of the algorithm is to try it on our toy car dataset.</b> 
    <br>
    Use randseed=1 to reprocude the following result, or randseed=0 to
    initizalize the random number generator with the current time.
  </p>


  <table>
    <tbody><tr>
      <td>Algorithm</td>
      <td>
        Eric Nowak and Fr�d�ric Jurie, 
        <i>Learning Visual Similarity Measures for Comparing Never Seen Objects</i>,
        Computer Vision and Pattern Recognition 2007 (CVPR'07). 
        <a href="../dwl/cvpr07.pdf">pdf, algorithm in section 2</a>.
        <br>You can also download <a href="../dwl/nowak_jurie_cvpr07_slides.pdf">the slides of the talk</a>.
      </td>
    </tr>
    <tr>
      <td>Binary</td>
      <td><a href="../dwl/pRazSimiERCF.gz">for linux (~1Mb)</a></td>
    </tr>
    <tr>
      <td>Dataset</td>
      <td><a href="../dwl/toycarlear.tar.gz">toycars dataset (~23Mb)</a></td>
    </tr>
    <tr>
      <td>Command line</td>
      <td>

      </td>
    </tr>
    <tr>
      <td> Output files</td>
      <td> 
        <a href="../dwl/run_5_trees.tar.gz">outputs of the previous command line (~6Mb)</a>,
        shows the trees, mem usage, detailed performance information, etc.
      </td>
    </tr>
    <tr>
      <td> Performance<br> (SVM C=1) </td>
      <td>
        <ul>
          <li>Precision Recall Equal Error Rate (EER-PR): 84.4%</li>
          <li>Computation time (learn+test): 17 hours on a P4-3.4GHz</li>
          <li>Maximum memory usage: 465Mb </li>
        </ul>
      </td>
    </tr>
  </tbody></table>

  <p>
    The binary allows to visualize the patch pairs used to learn the trees.
    The following patch pairs have been produced with:<br>

  </p>

  <table>
    <tbody><tr><th>Pair label</th><th>Random patch in first image</th><th>Corresponding patch in second image</th></tr>
    <tr><td>Different</td><td><img src="pairs/res_treepatches_neg_0_0_I0.jpg" alt="im0"></td><td><img src="pairs/res_treepatches_neg_0_0_I1.jpg" alt="im1"></td></tr>
    <tr><td>Different</td><td><img src="pairs/res_treepatches_neg_1_0_I0.jpg" alt="im0"></td><td><img src="pairs/res_treepatches_neg_1_0_I1.jpg" alt="im1"></td></tr>
    <tr><td>Different</td><td><img src="pairs/res_treepatches_neg_2_0_I0.jpg" alt="im0"></td><td><img src="pairs/res_treepatches_neg_2_0_I1.jpg" alt="im1"></td></tr>
    <tr><td>Different</td><td><img src="pairs/res_tr...

Analysis of ‘CANADA CARS SALES FIGURES (2019-2021)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mohamedhanyyy/canada-cars-sales-figures-20192021 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Intro:

I love cars so i was curious about knowing how many cars of different models is getting sold in some country for no reason i chose Canada

My resource is an online resource which is goodcarbadcar website

Data Content

We have here only one spreadsheet contain 237 Rows and 16 Columns

Columns:

1. model: which is the car model name
2. Jan :January
3. Feb:February
4. Mar:March
5. Apr:April
6. May:May
7. Jun:June
8. Jul:July
9. Aug:August
10. Sep:September
11. Oct:October
12. Nov:November
13. Dec:December
14. Sumofsales : is the sum of sale of each car model
15. Category : Contain 5 categories of cars MLS :MIDSIZE LUXURY SUV SALES FIGURES SLS:SMALL LUXURY SUV SALES FIGURES LLS: LARGE LUXURY SUV SALES FIGURES LS:LARGE SUV SALES FIGURES MS:MIDSIZE SUV SALES FIGURES
16. Year : the year of sales

Thanks for all people who support me !

--- Original source retains full ownership of the source dataset ---

This dataset contains information about India's Sales of Motor Vehicles for2007-2019.Data from Ministry of Road Transport and Highways.

This dataset was collected by me from a homework for study and practice. Dataset contains 50618 rows and 5 variables: Name: Name of car Production Year: Year of production Price: Seller's price Color: Color of the car Type: Type of the car

I hope this dataset can be useful for you!

Total Vehicle Sales in Australia increased to 105285 Units in May from 90614 Units in April of 2025. This dataset provides the latest reported value for - Australia New Car Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.