This statistic shows the number of passenger cars and commercial vehicles in use worldwide from 2006 to 2015. In 2015, around 947 million passenger cars and 335 million commercial vehicles were in operation worldwide.

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.

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.

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

Croatia Number of Registered Vehicles: ow Passenger Cars data was reported at 1,910,131.000 Unit in 2023. This records an increase from the previous number of 1,840,767.000 Unit for 2022. Croatia Number of Registered Vehicles: ow Passenger Cars data is updated yearly, averaging 1,448,299.000 Unit from Dec 1993 (Median) to 2023, with 31 observations. The data reached an all-time high of 1,910,131.000 Unit in 2023 and a record low of 646,210.000 Unit in 1993. Croatia Number of Registered Vehicles: ow Passenger Cars data remains active status in CEIC and is reported by Croatian Bureau of Statistics. The data is categorized under Global Database’s Croatia – Table HR.TA003: Number of Vehicle Registrations.

United States Motor Vehicle Registration: Total data was reported at 283.401 Unit mn in 2022. This records an increase from the previous number of 282.355 Unit mn for 2021. United States Motor Vehicle Registration: Total data is updated yearly, averaging 93.950 Unit mn from Dec 1910 (Median) to 2022, with 113 observations. The data reached an all-time high of 283.401 Unit mn in 2022 and a record low of 0.469 Unit mn in 1910. United States Motor Vehicle Registration: Total data remains active status in CEIC and is reported by Federal Highway Administration. The data is categorized under Global Database’s United States – Table US.TA011: Motor Vehicle Registration. Data starting 2011 includes information on motorcycle registrations.

Key information about Saudi Arabia Registered Motor Vehicles

• Saudi Arabia Registered Motor Vehicles was reported at 6,600.000 Unit th in Dec 2015.
• This records an increase from the previous number of 6,240.000 Unit th for Dec 2014.
• Saudi Arabia Registered Motor Vehicles data is updated yearly, averaging 4,870.000 Unit th from Dec 2005 to 2015, with 11 observations.
• The data reached an all-time high of 6,600.000 Unit th in 2015 and a record low of 3,580.000 Unit th in 2005.
• Saudi Arabia Registered Motor Vehicles data remains active status in CEIC and is reported by International Organization of Motor Vehicle Manufacturers. The data is categorized under World Trend Plus’s Association: Automobile Sector – Table RA.OICA.VIU: Vehicle In Use: by Country (Discontinued).

This dataset shows the Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) that are currently registered through Washington State Department of Licensing (DOL).

Florida DOT (FDOT) installed Vehicle Awareness Devices (VADs) on a set of Lynx transit buses as part of a demonstration for the ITS World Congress held in Orlando in October 2011. These VADs recorded vehicle data during the World Congress and continue to operate after the World Congress. Periodically the VADs are removed from the vehicles and the data files are retrieved. FHWA Has confirmed that the data do not contain identification of individual transit operators or any other forms for Personally Identifiable Information (PII). This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.

Key information about United States Motor Vehicle Sales: Passenger Cars

• United States Motor Vehicle Sales: Passenger Cars was reported at 3,116,647.000 Unit in Dec 2023.
• This records an increase from the previous number of 2,858,575.000 Unit for Dec 2022.
• US Motor Vehicle Sales: Passenger Cars data is updated yearly, averaging 6,089,403.000 Unit from Dec 2005 to 2023, with 19 observations.
• The data reached an all-time high of 7,761,592.000 Unit in 2006 and a record low of 2,858,575.000 Unit in 2022.
• US Motor Vehicle Sales: Passenger Cars data remains active status in CEIC and is reported by International Organization of Motor Vehicle Manufacturers.
• The data is categorized under World Trend Plus’s Association: Automobile Sector – Table RA.OICA.MVS: Motor Vehicle Sales: by Country and Type: Passenger Car (PC).

This Dataset contains information about world's motor vehicle production for selected countries. 1961-2021. Data from US Department of Transportation. Notes:

Prior to 2000, the country of manufacture was recognized as the producing country. To conform with current OICA (International Organization of Motor Vehicle Manufacturers) practices, starting in 2000, the country of final assembly was recognized as the producing country. This explains the sudden change in trends across some countries from 1999 to 2000.

Numbers may not add to totals due to rounding. Also numbers may not add to totals due to the inclusion of small countries in the total.

Beginning in 1998, some smaller countries not listed in this table are included in the world totals.

a Does not include minivans, pickups, and sport utility vehicles.

b Formerly Czechoslovakia and Ward's does not report a number for Slovakia before 2005.

c Yugoslavia no longer exists and Ward's does not report numbers for countries that were previously a part of Yugoslavia.

d Includes all trucks and buses. Light trucks, such as pickups, sport utility vehicles, and minivans are included under Commercial vehicles.

e The 2000 and 2005-2009 figures for Total passenger cars and commercial vehicles are revised by the source. However, the detailed information for each component in 2000 is not available, thus the details are not revised in this table and will not add up to the total for this year.

Source: 1961-2016: Wards Intelligence, Motor Vehicle Facts & Figures (Southfield, MI: Annual Issues), p. 10 and similar pages in earlier editions. 2017-2019: Ibid., Wards Automotive Yearbook (Southfield, MI: Annual Issues), World Vehicle Production in Major Countries.

India Registered Motor Vehicles: Total data was reported at 354,018.000 Unit th in 2022. This records an increase from the previous number of 334,551.000 Unit th for 2021. India Registered Motor Vehicles: Total data is updated yearly, averaging 18,036.000 Unit th from Mar 1951 (Median) to 2022, with 66 observations. The data reached an all-time high of 354,018.000 Unit th in 2022 and a record low of 306.000 Unit th in 1951. India Registered Motor Vehicles: Total data remains active status in CEIC and is reported by Ministry of Road Transport and Highways. The data is categorized under Global Database’s India – Table IN.RAD002: Number of Registered Motor Vehicles.

Key information about Brazil Registered Motor Vehicles

• Brazil Registered Motor Vehicles was reported at 45,721.945 Unit th in Dec 2020.
• This records an increase from the previous number of 42,743.324 Unit th for Dec 2015.
• Brazil Registered Motor Vehicles data is updated yearly, averaging 33,409.975 Unit th from Dec 2005 to 2020, with 12 observations.
• The data reached an all-time high of 45,721.945 Unit th in 2020 and a record low of 23,038.596 Unit th in 2005.
• Brazil Registered Motor Vehicles data remains active status in CEIC and is reported by International Organization of Motor Vehicle Manufacturers. The data is categorized under World Trend Plus’s Association: Automobile Sector – Table RA.OICA.VIU: Vehicle In Use: by Country (Discontinued).

The VeRi dataset is a valuable resource for researchers working on vehicle re-identification (Re-ID) tasks. Here's a breakdown of its key features:

Purpose:

VeRi is designed to facilitate research in vehicle Re-ID, which aims to identify the same vehicle across different cameras or under varying conditions. This is crucial for applications like traffic monitoring, security surveillance, and autonomous vehicles. Data Characteristics:

Large Scale: VeRi boasts over 50,000 images of 776 vehicles captured by 20 cameras. This substantial size allows for training robust Re-ID models. Real-World Setting: Unlike some controlled datasets, VeRi captures images from a real urban traffic scenario. This means the data includes variations in: Viewpoint: Vehicles are seen from different angles due to camera placement. Illumination: Lighting conditions change throughout the day, affecting image appearance. Resolution: Cameras might have varying resolutions, impacting image quality. Occlusion: Other vehicles or objects might partially obscure the target vehicle. Rich Annotations: Beyond just images, VeRi provides additional information for each image, including: Bounding boxes: These define the location of each vehicle in the image. Vehicle attributes: Details like type (car, truck, etc.), color, and brand are included. License plates (optional): Some versions might include license plate information for more specific identification. Spatiotemporal data: This includes timestamps of vehicle capture and the distance between cameras, providing context for Re-ID tasks. Benefits of using VeRi:

The large-scale and diverse nature of VeRi enables researchers to develop Re-ID models that can handle the complexities of real-world scenarios. The rich annotations simplify data preparation and analysis for researchers. The dataset serves as a common benchmark for comparing the performance of different Re-ID algorithms.

Context

The concept of searching and localizing vehicles from live traffic videos based on descriptive textual input has yet to be explored in the scholarly literature. Endowing Intelligent Transportation Systems (ITS) with such a capability could help solve crimes on roadways. While artificial intelligence (AI) can be a powerful tool for this data intensive application, existing state-of-the-art AI models struggle with fine-grain vehicle recognition. Typically, only reporting model performance on still input image data, often captured at high resolution and at pristine quality. These settings are not reflective of real-world operating conditions and thus, recognition accuracies typically cannot be replicated on video data. One major impediment to the advancement of fine-grain vehicle recognition models is the lack of video testbench datasets with annotated ground-truth data. Additionally, to the best of our knowledge, no metrics currently exist for evaluating the robustness and performance efficiency of a vehicle recognition model on live videos, and even less so for vehicle search and localization models. In this paper, we address these challenges by proposing V-Localize, a novel artificial intelligence framework for vehicle search and continuous localization captured from live traffic videos based on input textual descriptions. An efficient hashgraph algorithm is introduced to process input text (such as a sentence, paragraph, or report) to extract detailed target information used to query the recognition and localization model. This work further introduces two novel datasets that will help advance AI research in these challenging areas. These datasets include: a) the most diverse and large-scale Vehicle Color Recognition (VCoR) dataset with 15 colors classes -- twice as many as the number of color classes in the largest existing such dataset -- to facilitate finer-grain recognition with color information; and b) a Vehicle Recognition in Video (VRiV) dataset, which is a first of its kind video test-bench dataset for evaluating the performance of vehicle recognition models in live videos rather than still image data. The VRiV dataset will open new avenues for AI researchers to investigate innovative approaches that were previously intractable due to the lack of a traffic vehicle recognition annotated test-bench video dataset. Finally, to address the gap in the field, 5 novel metrics are introduced in this paper for adequately accessing the performance of vehicle recognition models in live videos. Ultimately, the proposed metrics could also prove intuitively effective at quantitative model evaluation in other video recognition applications. The novel metrics and VRiV test-bench dataset introduced in this paper are specifically aimed at advancing state-of-the-art research for vehicle recognition in videos. Likewise, the proposed novel vehicle search and continuous localization framework could prove assistive in cases such as of amber alerts or hit-and-run incidents. One major advantage of the proposed system is that it can be integrated into intelligent transportation system software to help aid law-enforcement.

Image Acquisition

The proposed Vehicle Recognition in Video (VRiV) dataset is the first of its kind and is aimed at developing, improving, and analyzing performance of vehicle search and recognition models on live videos. The lack of such a dataset has limited performance analysis of modern fine-grain vehicle recognition systems to only still image input data, making them less suitable for video applications. The VRiV dataset is introduced to help bridge this gap and foster research in this direction. The proposed VRiV dataset consists of up to 47 video sequences averaging about 38.5 seconds per video. The videos are recorded in a traffic setting focusing on vehicles of volunteer candidates whose ground truth make, model, year and color information are known. For security reasons and safety of participants, experiments are conducted on streets/road with low traffic density. For each video, there is a target vehicle with known ground truth information, and there are other vehicles either moving in traffic or parked on side streets, to simulate real-world traffic scenario. The goal is for the algorithm to be able to search, recognize and continuously localize just the specific target vehicle of interest for the corresponding video based on the search query. It is worth noting that the ground truth information about other vehicles in the videos are not known. The 47 videos in the testbench dataset are distributed across 7 distinct makes and 17 model designs as shown in Figure 10. The videos are also annotated to include ground truth bounding boxes for the specific target vehicles in corresponding videos. The dataset includes more than 46k annotated frames averaging about 920 frames per video. This dataset will be made available on Kaggle, and new videos will be added as they become available.

Content

There is one main zip file available for download. The zip file contains 94 files. 1) 47 video files 2) 47 ground-truth annotated files which identifies locations where the vehicle of interest is in the frame. Each video file is labelled with the corresponding vehicle brand name, model, year, and color information.

Terms and Conditions

• Videos provided in this dataset are freely available for research and education purposes only. Please be sure to properly credit the authors by citing the article below.
• Be sure to upvote this dataset if you find it useful by scrolling up and clicking the ^ sign at the top-right corner of the cover image of this page.
• Be sure to blur out all plate numbers before publishing any of the contents available in this dataset.

Acknowledgements

Any publication using this database must reference to the following journal manuscript:

• Panetta, Karen, Landry Kezebou, Victor Oludare, James Intriligator, and Sos Agaian. 2021. "Artificial Intelligence for Text-Based Vehicle Search, Recognition, and Continuous Localization in Traffic Videos" AI 2, no. 4: 684-704. https://doi.org/10.3390/ai2040041
• open access : https://www.mdpi.com/2673-2688/2/4/41

Note: if the link is broken, please use http instead of https.

In Chrome, use the steps recommended in the following website to view the webpage if it appears to be broken https://www.technipages.com/chrome-enabledisable-not-secure-warning

VCoR dataset: https://www.kaggle.com/landrykezebou/vcor-vehicle-color-recognition-dataset VRiV dataset: https://www.kaggle.com/landrykezebou/vriv-vehicle-recognition-in-videos-dataset

For any enquires regarding the VCoR dataset, contact: landrykezebou@gmail.com

Context

3D object representations are valuable resources for multi-view object class detection and scene understanding. Fine-grained recognition is a growing subfield of computer vision that has many real-world applications on distinguishing subtle appearances differences. This cars dataset contains great training and testing sets for forming models that can tell cars from one another. Data originated from Stanford University AI Lab (specific reference below in Acknowledgment section).

Content

The Cars dataset contains 16,185 images of 196 classes of cars. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. Classes are typically at the level of Make, Model, Year, ex. 2012 Tesla Model S or 2012 BMW M3 coupe.

Acknowledgements

Data source and banner image: http://ai.stanford.edu/~jkrause/cars/car_dataset.html contains all bounding boxes and labels for both training and tests.

If you use this dataset, please cite the following paper:

3D Object Representations for Fine-Grained Categorization

Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei

4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.

Inspiration

• Can you form a model that can tell the difference between cars by type or colour?
• Which cars are manufactured by Tesla vs BMW?

**This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **

Overview

Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.

This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.

Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.

This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.

Findings

• Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
• Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
• New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least. ## About This Data The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.

Included Data

01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

Additional Data Queries

* Filter for specific state - filters 02_vmt_state.csv daily data for specific state.

* Filter counties by state - filters 03_vmt_county.csv daily data for counties in specific state.

* Filter for specific county - filters 03_vmt_county.csv daily data for specific county.

Interactive

The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:

@(https://interactives.ap.org/vmt-map/)

Interactive Embed Code

Using the Data

This data can help put your county's mobility in context with your state and over time. The data set contains different measures of change - daily comparisons and seven day rolling averages. The rolling average allows for a smoother trend line for comparison across counties and states. To get the full picture, there are also two available baselines - vehicle miles traveled in January 2020 (pre-pandemic) and vehicle miles traveled at each geography's low point during the pandemic.

Caveats

• The data from StreetLight Data, Inc does not include data for some low-population counties with low VMT (<5,000 miles/day in their baseline month of January 2020). In our analyses, we only include the 2,779 counties that have daily data for the entire period (March 1, 2020 to current).
• In some cases, a lack of decline in mobility from March to April can indicate that movement in the county is essential to keeping the larger economy going or that residents need to drive further to reach essentials businesses like grocery stores compared to other counties.
• The VMT includes both passenger and commercial miles, so truck traffic is included. However, the proxy is based on the "total number of trip starts and ends for all devices whose most frequent location is in this county". It does not count the VMT of trucks cutting through a county.
• For those instances where travel begins in one county and ends in another, the county where the miles are recorded is always the vehicle’s home county. ###### Contact reporter Angeliki Kastanis at akastanis@ap.org.

Statistical analysis of the vehicle fleet for passenger cars by province and type of vehicle owner. Vehicle fleet statistics are prepared from data in open format made available on the portal of the Automobile Club of Italy (ACI - https://www.aci.it/laci/studies-and-research/data-e-statistics/self-portrait.html). The data were cut out for the Piedmont Region only and completed with the provincial ISTAT codes, for easy reading and possible comparison with other datasets. The data, taken from the Ente’s archives (primary source of the statistics concerning the vehicle fleet is the Pra, Pubblico vehicle register) and from the analysis of the vehicle fleet, in this case in Piedmont, may be of interest both to the world of the economy and the environment, both for land management and for surveys of a social nature. The publication shall take place by the ACI by October of each year with data relating to 31 December of the previous year.

Interactive plot of registered cars and motorized two- and three-wheelers in worldwide countries based the data set from the "WHO Global Status Report on Road Safety 2018" of the World Health Organization. To view the interactive plot, download and open the HTML file in a browser of your choice.

Sales of used light vehicles in the United States came to around **** million units in 2024. In the same period, approximately **** million new light trucks and automobiles were sold here. Declining availability of vehicles In the fourth quarter of 2024, about ***** million vehicles were in operation in the United States, an increase of around *** 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.