The Comprehensive Vehicle Make and Model Dataset provides a detailed list of automotive manufacturers and their corresponding models. This dataset includes data on various car makes (manufacturers) and models (specific car names under each make), making it ideal for use in automotive research, machine learning projects, or data enrichment tasks related to the automotive industry.

Dataset Features: Make: The name of the car manufacturer (e.g., Toyota, Ford, BMW). Model: The specific car model associated with each manufacturer (e.g., Camry, F-150, X5).

This dataset is structured to be easily accessible for relational databases, making it suitable for building relational models where car makes are linked to their models. It is especially useful for tasks like recommendation systems, market analysis, trend analysis, or training machine learning models that require automotive industry data.

Use Cases: Recommendation Engines: Develop systems that recommend car models based on user preferences. Market Research: Analyze the popularity or trends in specific car makes and models. Data Enrichment: Enrich datasets with car make and model information for enhanced data quality.

Data Structure: Each entry in the dataset consists of: Make: Manufacturer name. Models: List of car models associated with that make.

Here are a few use cases for this project:

1. Traffic Management Systems: The model can be used for automated vehicle identification in traffic control systems – tracking and managing the flow of different car makes and models on the roads to create more effective traffic strategies.

2. Security and Law Enforcement: The AI model can be used in security cameras at checkpoints or toll booths to automatically detect and log car makes and models, assisting in investigations or vehicle tracking.

3. Automotive Industry Research: Market researchers can use this model to analyze street-level data on car ownership, helping auto manufacturers understand consumer preferences per region.

4. Insurance Services: Insurance companies could use the model to verify the car make and model claimed by their clients during insurance purchase or claim process.

5. Car Dealership Services: Dealers of these car makes and models could use these models to identify potential customers based on the cars they currently own and drive, targeting their marketing and sales efforts accordingly.

Alesco Data's Automotive records are updated monthly from millions of proprietary sourced vehicle transactions. These incoming transactions are processed through compilation rules and are either added as new, incremental records to our file, or contribute to validating existing records.

Our recent focus is on compiling new vehicle ownership, and the file includes over 14.2 million late model vehicle owners (2020-2025).

We also append our Persistent ID, telephone numbers, and demographics for a complete file that can support your direct mail and email marketing campaigns, lead validation, and identity verification needs. A Persistent ID is assigned to each vehicle record and tracks consumers as they change addresses or phone numbers, and vehicles as they change owners.

The database is not derived from state motor vehicle databases and therefore not subject to the Shelby Act also known as the Driver's Privacy Protection Act (DPPA) of 2000. The data is deterministic and sources include sales and service data, warranty data and notifications, aftermarket repair and maintenance facilities, and scheduled maintenance records.

Fields Included: Make Model Year VIN Data Vehicle Class Code (crossover, SUV, full-size, mid-size, small) Vehicle Fuel Code (gas, flex, hybrid) Vehicle Style Code (sport, pickup, utility, sedan) Mileage Number of Vehicles per Household First seen date Last seen date Email

Fuel economy data are the result of vehicle testing done at the Environmental Protection Agency's National Vehicle and Fuel Emissions Laboratory in Ann Arbor, Michigan, and by vehicle manufacturers with oversight by EPA.In 2016, the Department of Justice alleged violations of the Clean Air Act by Volkswagen (including Audi and Porsche) covering all of Volkswagen's 2.0L and 3.0L diesel vehicles sold in the United States since model year 2009. All relevant data from the affected vehicles have been removed from this website until there is an EPA-approved emissions.EPA has issued a Notice of Violation to Fiat Chrysler Automobiles N.V. and FCA US LLC for Model Year 2014-2016 light-duty diesel vehicles (Ram 1500 and Jeep Grand Cherokee). All relevant data from the affected vehicles has been removed from this website until further information is available.

Electric Vehicle Sales: Fisker data was reported at 1,660.000 Unit in Mar 2024. This records a decrease from the previous number of 1,672.000 Unit for Dec 2023. Electric Vehicle Sales: Fisker data is updated quarterly, averaging 1,660.000 Unit from Sep 2023 (Median) to Mar 2024, with 3 observations. The data reached an all-time high of 1,672.000 Unit in Dec 2023 and a record low of 997.000 Unit in Sep 2023. Electric Vehicle Sales: Fisker data remains active status in CEIC and is reported by Cox Automotive. The data is categorized under Global Database’s United States – Table US.RA007: Electric Vehicle Sales: by Brand and Model.

The attached dataset contains over 17.5 hours of experimental sensor data, including measurements from the following sensors:

• Front axle steering angle [°]
• Longitudinal acceleration [g]
• Lateral acceleration [g]
• Yaw rate [deg/s]
• Wheel speed (front left) [km/h]
• Wheel speed (front right) [km/h]
• Wheel speed (rear left) [km/h]
• Wheel speed (rear right) [km/h]

Data was sampled at a rate of 0.01 seconds and includes three distinct driving scenarios: calm driving, aggressive driving, and city driving. The dataset also captures variations such as reduced tire pressure (one tire at a time), different passenger loads, and measurements from three different vehicles.

The data was collected at the Continental Test Track in Veszprém, Hungary, as well as within the city of Veszprém.

The data is stored in Apache Parquet format that can be processed via Pandas library in Python.

For more information please check our article: TBD (citation from Mechanical Systems and Signal Processing (https://www.sciencedirect.com/journal/mechanical-systems-and-signal-processing).

Electric Vehicle Sales: VW data was reported at 9,564.000 Unit in Mar 2025. This records an increase from the previous number of 1,808.000 Unit for Dec 2024. Electric Vehicle Sales: VW data is updated quarterly, averaging 6,049.000 Unit from Mar 2021 (Median) to Mar 2025, with 17 observations. The data reached an all-time high of 10,707.000 Unit in Sep 2023 and a record low of 474.000 Unit in Mar 2021. Electric Vehicle Sales: VW data remains active status in CEIC and is reported by Cox Automotive. The data is categorized under Global Database’s United States – Table US.RA007: Electric Vehicle Sales: by Brand and Model.

Electric Vehicle Sales: Volvo data was reported at 1,817.000 Unit in Dec 2024. This records an increase from the previous number of 1,535.000 Unit for Sep 2024. Electric Vehicle Sales: Volvo data is updated quarterly, averaging 1,762.500 Unit from Mar 2021 (Median) to Dec 2024, with 16 observations. The data reached an all-time high of 4,722.000 Unit in Jun 2023 and a record low of 320.000 Unit in Mar 2021. Electric Vehicle Sales: Volvo data remains active status in CEIC and is reported by Cox Automotive. The data is categorized under Global Database’s United States – Table US.RA007: Electric Vehicle Sales: by Brand and Model.

This light-duty vehicle inventory dataset provides information on vehicle registrations by vehicle type (car vs. truck), fuel type, and model year showing the changes in adoption trends over time and average fuel economies. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset.

This table contains the instantaneous data processed from radar and GPS. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/b3k6-qwyh) and runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr).

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

Analysis of ‘Cars Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/abineshkumark/carsdata on 28 January 2022.

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

Cars Data has Information about 3 brands/make of cars. Namely US, Japan, Europe. Target of the data set to find the brand of a car using the parameters such as horsepower, Cubic inches, Make year, etc.

A decision tree can be used create a predictive data model to predict the car brand.

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

Automotive Simulation Market size was valued at USD 1.99 Billion in 2024 and is projected to reach USD 4.28 Billion By 2032, growing at a CAGR of 10.11% during the forecast period 2026 to 2032.

Global Automotive Simulation Market Drivers

The market drivers for the Automotive Simulation Market can be influenced by various factors. These may include:

Increase in Vehicle Complexity: With cutting-edge technologies like connectivity, electrification, and autonomous driving, modern cars are getting more and more complicated. Through the use of simulation, automakers can test these intricate systems virtually before putting them into production, which lowers development costs and accelerates time to market. Stricter Regulations: Governments all around the world are enforcing more stringent laws pertaining to fuel economy, pollution, and vehicle safety. Automakers can optimize vehicle designs by simulating different scenarios and using simulation tools to help assure compliance with these criteria. Cost and Time Efficiency: It might take a lot of time and money to develop and test new automobile technology. Rapid virtual prototyping and testing of several design iterations is made possible by simulation, which shortens the development period and eliminates the need for expensive physical prototypes. Growing Need for Electric automobiles (EVs): Government incentives and environmental concerns are driving the transition towards electric automobiles. In order to help the development and uptake of EVs, automotive modeling is essential for improving battery performance, range, and charging infrastructure. Developments in Simulation Technology: The accuracy and fidelity of virtual testing are improved by ongoing developments in simulation software and hardware, such as computational fluid dynamics (CFD), finite element analysis (FEA), and real-time simulation. This increases the dependability of simulations for automotive applications. Implementation of Digital Twins: The automotive sector is beginning to adopt the idea of digital twins, which are virtual equivalents of physical assets. With the use of simulation data, digital twins allow for the real-time monitoring, analysis, and optimization of a vehicle's performance over its whole lifecycle, enhancing predictive analytics, design, and maintenance. Industry 4.0 and IoT Integration: The automotive industry can benefit from connected and intelligent manufacturing processes through the integration of simulation with Industry 4.0 technology and the Internet of Things. Production optimization, quality control, and predictive maintenance are made easier by the combination of simulation models and real-time data from Internet of Things sensors. Demand for Improved Driver Experience and Safety: Modern cars with cutting-edge driver assistance technologies, safety features, and engaging user interfaces are in high demand from customers. With the use of automotive simulation, manufacturers can thoroughly test and create these features to make sure they live up to customer expectations for performance, safety, and dependability. Partnerships and Collaborations: In the field of automotive simulation, cooperation among research institutes, simulation software suppliers, and OEMs promotes innovation and knowledge exchange. Strategic alliances and joint ventures hasten the advancement and industry-wide uptake of simulation technologies. Impact of the COVID-19 Pandemic: The automobile industry's digital transition has accelerated due to the COVID-19 pandemic, with remote work and virtual collaboration becoming increasingly common. The use of automotive simulation has become essential for preserving testing and product development continuity in the face of disruptions to conventional supply chains and processes.

The Synset Boulevard dataset contains a total of 259,200 synthetically generated images of cars from a frontal traffic camera perspective, annotated by vehicle makes, models and years of construction for machine learning methods (ML) in the scope (task) of vehicle make and model recognition (VMMR). The data set contains 162 vehicle models from 43 brands with 200 images each, as well as 8 sub-data sets each to be able to investigate different imaging qualities. In addition to the classification annotations, the data set also contains label images for semantic segmentation, as well as information on image and scene properties, as well as vehicle color. The dataset was presented in May 2024 by Anne Sielemann, Stefan Wolf, Masoud Roschani, Jens Ziehn and Jürgen Beyerer in the publication: Sielemann, A., Wolf, S., Roschani, M., Ziehn, J. and Beyerer, J. (2024). Synset Boulevard: A Synthetic Image Dataset for VMMR. In 2024 IEEE International Conference on Robotics and Automation (ICRA). The model information is based on information from the ADAC online database (www.adac.de/rund-ums-fahrzeug/autokatalog/marken-modelle). The data was generated using the simulation environment OCTANE (www.octane.org), which uses the Cycles ray tracer of the Blender project. The dataset's website provides detailed information on the generation process and model assumptions. The dataset is therefore also intended to be used for the suitability analysis of simulated, synthetic datasets. The data set was developed as part of the Fraunhofer PREPARE program in the "ML4Safety" project with the funding code PREPARE 40-02702, as well as funded by the "Invest BW" funding program of the Ministry of Economic Affairs, Labour and Tourism as part of the "FeinSyn" research project.

Analysis of ‘Enhancing Microsimulation Models for Improved Work Zone Planning: Car-Following Data from Western Massachusetts (Instances)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/be1af838-7026-421d-93f6-50161d5e8300 on 27 January 2022.

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

The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset.

This table contains the car-following instances recorded by Volpe staff. See also the runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/b3k6-qwyh) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).

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

Electric Vehicle Sales: Lucid: Lucid Air data was reported at 1,944.000 Unit in Sep 2024. This records an increase from the previous number of 1,855.000 Unit for Jun 2024. Electric Vehicle Sales: Lucid: Lucid Air data is updated quarterly, averaging 1,405.000 Unit from Dec 2021 (Median) to Sep 2024, with 12 observations. The data reached an all-time high of 1,967.000 Unit in Mar 2024 and a record low of 460.000 Unit in Mar 2022. Electric Vehicle Sales: Lucid: Lucid Air data remains active status in CEIC and is reported by Cox Automotive. The data is categorized under Global Database’s United States – Table US.RA007: Electric Vehicle Sales: by Brand and Model.

Analysis of ‘Enhancing Microsimulation Models for Improved Work Zone Planning: Car-Following Data from Western Massachusetts (Runs)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7c1a1080-415e-46ff-a8a9-e4b51cb05eb8 on 27 January 2022.

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

The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset.

This table contains metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).

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

The CVS Database provides a catalogue of original vehicle dimensions, for use in vehicle safety research and collision investigation. The purpose of this database is to provide users with a comprehensive listing of vehicle dimensions commonly used in the field of collision investigation and reconstruction, for the North American fleet of passenger cars, light trucks, vans and SUV’s. The database includes model years dating back to 2011 and is comprised of both commonly available dimensions such as overall length, wheelbase and track widths, and also several dimensions which are not typically readily available from the manufacturers, nor from automotive publications. Note – To obtain database of model years dating back to 1971, please contact Transport Canada.