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.

Vehicle Make, Model, and Color Database Market Outlook

According to our latest research, the global Vehicle Make, Model, and Color Database market size reached USD 2.1 billion in 2024. The market is expected to grow at a CAGR of 7.3% during the forecast period, reaching a value of USD 3.9 billion by 2033. This robust growth is driven by the increasing digitization of automotive data, the proliferation of connected vehicles, and the heightened demand for real-time vehicle information across multiple industries. As per our latest research, the integration of advanced analytics and artificial intelligence within vehicle databases is further accelerating market expansion, enabling more precise and actionable insights for end-users globally.

The primary growth factor for the Vehicle Make, Model, and Color Database market is the escalating need for accurate and comprehensive vehicle information across diverse sectors. Automotive dealerships, insurance companies, and law enforcement agencies are increasingly relying on these databases to streamline operations, enhance customer experience, and improve decision-making processes. The rise in vehicle thefts, fraudulent insurance claims, and the need for efficient fleet management solutions have all contributed to a surge in demand for reliable vehicle data. Furthermore, the growing trend toward digital transformation within the automotive industry has led to the adoption of sophisticated database solutions, which offer seamless integration with existing IT infrastructures and ensure data accuracy and security.

Another significant growth driver is the rapid advancement in data collection technologies and the expanding sources of vehicle-related data. The proliferation of IoT-enabled vehicles, telematics, and connected car platforms has resulted in an exponential increase in the volume and variety of vehicle data available for analysis. This has enabled database providers to offer more granular and up-to-date information, catering to the specific requirements of end-users such as automotive manufacturers, government agencies, and transportation companies. The integration of machine learning and big data analytics further enhances the value proposition of these databases, enabling predictive insights and real-time data validation that support critical business functions and regulatory compliance.

The market is also witnessing increased collaboration between original equipment manufacturers (OEMs), aftermarket players, and technology providers to standardize and enrich vehicle data. These partnerships are essential for ensuring data consistency, interoperability, and scalability across different platforms and geographies. The adoption of cloud-based database solutions has further democratized access to vehicle data, allowing small and medium enterprises (SMEs) to leverage sophisticated analytics without significant upfront investments. Additionally, regulatory initiatives aimed at improving road safety and vehicle traceability are fueling the demand for comprehensive and up-to-date vehicle databases, particularly in emerging markets where vehicle ownership is on the rise.

From a regional perspective, North America continues to dominate the Vehicle Make, Model, and Color Database market, accounting for the largest share in 2024. This is attributed to the region's mature automotive ecosystem, high vehicle penetration, and early adoption of advanced data management technologies. Europe follows closely, driven by stringent regulatory requirements and a strong focus on vehicle safety and compliance. The Asia Pacific region is poised for the fastest growth during the forecast period, supported by rapid urbanization, increasing vehicle sales, and significant investments in digital infrastructure. Latin America and the Middle East & Africa are also emerging as promising markets, with growing awareness of the benefits of robust vehicle data management systems and the expansion of automotive and transportation sectors.

Database Type Analysis</h2

Context

Automobile data holds immense importance as it offers insights into the functioning and efficiency of the automotive industry. It provides valuable information about car models, specifications, sales trends, consumer demographics, and preferences, which car manufacturers and dealerships can leverage to optimize their operations and enhance customer experiences. By analyzing data on vehicle reliability, fuel efficiency, safety ratings, and resale values, the automotive industry can identify trends and implement strategies to produce more reliable and environmentally friendly vehicles, improve safety standards, and enhance the overall value of cars for consumers. Moreover, regulatory bodies and policymakers rely on this data to enforce regulations, set emissions standards, and make informed decisions regarding automotive policies and environmental impacts. Researchers and analysts use car purchase data to study market trends, assess the environmental impact of various vehicle types, and develop strategies for sustainable growth within the industry. In essence, car purchase data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the automotive sector.

Content

This dataset comprises diverse parameters relating to car purchases and ownership on a global scale. The dataset prominently incorporates fields such as 'First Name', 'Last Name', 'Country', 'Car Brand', 'Car Model', 'Car Color', 'Year of Manufacture', and 'Credit Card Type'. These columns collectively provide comprehensive insights into customer demographics, vehicle details, and payment information. Researchers and industry experts can leverage this dataset to analyze trends in car purchasing behavior, optimize the customer car-buying experience, evaluate the popularity of car brands and models, and understand payment preferences within the automotive industry.

Dataset Glossary (Column-wise)

• First Name - The first name of the car purchaser.
• Last Name - The last name of the car purchaser.
• Country - The country of residence of the car purchaser.
• Car Brand - The brand or manufacturer of the purchased car.
• Car Model - The specific model of the purchased car.
• Car Color - The color of the purchased car.
• Year of Manufacture - The year the car was manufactured.
• Credit Card Type - The type of credit card used for the car purchase.

Structure of the Dataset

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Acknowledgement

The dataset provided here is a simulated example and was generated using the online platform found at Mockaroo. This web-based tool offers a service that enables the creation of customizable mock datasets that closely resemble real data. It is primarily intended for use by developers, testers, and data experts who require sample data for a range of uses, including testing databases, filling applications with demonstration data, and crafting lifelike illustrations for presentations and tutorials. To explore further details, you can visit their website.

Cover Photo by: Freepik

Thumbnail by: Car icons created by Freepik - Flaticon

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).

In addition, we append our Persistent ID, telephone numbers, and demographics for a complete file that can support your direct mail and email marketing, 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 -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

About this Dataset

This list includes all makes, models, years, and trim specifications for the fifty one makes listed below. Includes makes that were released after the year 2000 up until now. Trim data includes unique specifications such as engine data, AWD/FWD/2WD, and additional technology packages just to name a few. Useful data for populating databases, programming that requires accurately identifying/defining a car, and many other purposes. Other websites would typically sell data like this for >100$ (go check for yourself!!). Car Makes Included: Acura, Alfa Romeo, Am General, Audi, BMW, Buick, Cadillac, Chevrolet, Chrysler, Daewoo, Dodge, FIAT, Fisker, Ford, GMC, Genesis, Honda, Hummer, Hyundai, INFINITI, Isuzu, Jaguar, Jeep, Kia, Land Rover, Lexus, Lincoln, Lotus, Maserati, Maybach, Mazda, Mercedes-Benz, Mercury, MINI, Mitsubishi, Nissan, Oldsmobile, Panoz, Plymouth, Pontiac, Porsche, RAM, Saab, Saturn, Scion, smart, Subaru, Suzuki, Toyota, Volkswagen, Volvo

Category

Product Lists

Keywords

cars,automotive,auto,vehicle

Row Count

45888

Price

$56.00

This dataset consists of various types of cars. The dataset is organized into 2 folders (train, test) and contains subfolders for each car category. There are 4,165 images (JPG) and 7 classes of cars.

Please give credit to this dataset if you download it.

Vehicle Product Information Catalog with factory specifications, build-plant data and technical details for vehicles manufactured since 1981.

Used Car Price Prediction Dataset is a comprehensive collection of automotive information extracted from the popular automotive marketplace website, https://www.cars.com. This dataset comprises 4,009 data points, each representing a unique vehicle listing, and includes nine distinct features providing valuable insights into the world of automobiles.

• Brand & Model: Identify the brand or company name along with the specific model of each vehicle.
• Model Year: Discover the manufacturing year of the vehicles, crucial for assessing depreciation and technology advancements.
• Mileage: Obtain the mileage of each vehicle, a key indicator of wear and tear and potential maintenance requirements.
• Fuel Type: Learn about the type of fuel the vehicles run on, whether it's gasoline, diesel, electric, or hybrid.
• Engine Type: Understand the engine specifications, shedding light on performance and efficiency.
• Transmission: Determine the transmission type, whether automatic, manual, or another variant.
• Exterior & Interior Colors: Explore the aesthetic aspects of the vehicles, including exterior and interior color options.
• Accident History: Discover whether a vehicle has a prior history of accidents or damage, crucial for informed decision-making.
• Clean Title: Evaluate the availability of a clean title, which can impact the vehicle's resale value and legal status.
• Price: Access the listed prices for each vehicle, aiding in price comparison and budgeting.

This dataset is a valuable resource for automotive enthusiasts, buyers, and researchers interested in analyzing trends, making informed purchasing decisions or conducting studies related to the automotive industry and consumer preferences. Whether you are a data analyst, car buyer, or researcher, this dataset offers a wealth of information to explore and analyze.

The fields available include make, model, year, trim, style, fuel type, MSRP, and many more.

We have developed this file to be tied to our Consumer Demographics Database so additional demographics can be applied as needed. Each record is ranked by confidence and only the highest quality data is used. This file contains over 180 million records in addition to over 1 million+ fresh automotive intender records per day.

Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

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.

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 registration counts per month by county

Live-updated database of open safety recalls and technical service bulletins issued by OEMs and NHTSA.

Labeled Car images suitable for training and evaluating computer vision and deep learning models.

Automotive Map Database Market Outlook

As per our latest research, the global automotive map database market size in 2024 stands at USD 5.7 billion, reflecting robust demand driven by advancements in connected vehicle technologies and the proliferation of autonomous driving systems. The market is expected to grow at a CAGR of 12.3% from 2025 to 2033, reaching a projected value of USD 16.1 billion by the end of the forecast period. Growth is primarily fueled by the increasing integration of advanced driver assistance systems (ADAS), the rapid adoption of electric and autonomous vehicles, and the need for real-time, high-definition mapping solutions across global automotive fleets.

One of the key growth factors propelling the automotive map database market is the surge in demand for sophisticated navigation and safety features in modern vehicles. The automotive industry is undergoing a paradigm shift, with manufacturers and consumers alike prioritizing enhanced safety, real-time traffic updates, and seamless route optimization. As vehicles become more connected, the need for highly detailed and frequently updated map databases has become critical. These databases underpin the functionality of navigation systems, ADAS, and autonomous driving platforms, ensuring accurate positioning, route planning, and hazard detection. The increasing reliance on digital mapping for vehicle-to-everything (V2X) communications and smart mobility solutions further amplifies the demand for comprehensive and reliable automotive map databases.

Another significant driver is the evolution of autonomous driving technologies, which require high-definition (HD) maps capable of providing centimeter-level accuracy. Autonomous vehicles depend on detailed mapping data to interpret their environment, recognize road features, and make real-time driving decisions. The automotive map database market is witnessing substantial investments from both traditional automotive players and technology giants, aiming to develop next-generation mapping solutions that support Level 3 and above autonomous driving capabilities. The proliferation of electric vehicles (EVs) also adds to the momentum, as EV manufacturers integrate advanced mapping solutions for efficient route planning, charging station location, and range optimization.

Furthermore, the growing adoption of cloud-based deployment models and advancements in artificial intelligence (AI) are transforming the automotive map database landscape. Cloud computing enables the storage and processing of vast amounts of mapping data, facilitating real-time updates and seamless integration with vehicle infotainment and telematics systems. AI-driven analytics enhance the accuracy and predictive capabilities of map databases, supporting dynamic rerouting, hazard identification, and personalized navigation experiences. These technological advancements are not only improving user experience but are also driving market expansion by enabling scalable and cost-effective mapping solutions for automotive OEMs and fleet operators.

From a regional perspective, Asia Pacific is emerging as the fastest-growing market for automotive map databases, driven by rapid urbanization, increasing vehicle production, and government initiatives promoting smart transportation infrastructure. North America and Europe continue to dominate the market in terms of revenue share, owing to the early adoption of connected and autonomous vehicles, robust automotive ecosystems, and strong presence of leading map database providers. Meanwhile, regions such as Latin America and the Middle East & Africa are witnessing steady growth as automotive digitization gains traction and investments in intelligent transportation systems accelerate.

Type Analysis

The automotive map database market is segmented by type into in-house databases and third-party databases. In-house databases are developed and maintained by automotive manufacturers or their technology partners, offering enhanced control over data quality, customization, and

Vehicle Maintenance Dataset

Overview

This dataset provides synthetic data related to vehicle maintenance to help predict whether a vehicle requires maintenance or not based on various features.

Features

1. Vehicle_Model: Type of the vehicle (Car, SUV, Van, Truck, Bus, Motorcycle)
2. Mileage: Total mileage of the vehicle
3. Maintenance_History: Maintenance history of the vehicle (Good, Average, Poor)
4. Reported_Issues: Number of reported issues
5. Vehicle_Age: Age of the vehicle in years
6. Fuel_Type: Type of fuel used (Diesel, Petrol, Electric)
7. Transmission_Type: Transmission type (Automatic, Manual)
8. Engine_Size: Size of the engine in cc (Cubic Centimeters)
9. Odometer_Reading: Current odometer reading of the vehicle
10. Last_Service_Date: Date of the last service
11. Warranty_Expiry_Date: Date when the warranty expires
12. Owner_Type: Type of vehicle owner (First, Second, Third)
13. Insurance_Premium: Insurance premium amount
14. Service_History: Number of services done
15. Accident_History: Number of accidents the vehicle has been involved in
16. Fuel_Efficiency: Fuel efficiency of the vehicle in km/l (Kilometers per liter)
17. Tire_Condition: Condition of the tires (New, Good, Worn Out)
18. Brake_Condition: Condition of the brakes (New, Good, Worn Out)
19. Battery_Status: Status of the battery (New, Good, Weak)
20. Need_Maintenance: Target variable indicating whether the vehicle needs maintenance (1 = Yes, 0 = No)

Target Variable

• Need_Maintenance: Indicates whether the vehicle requires maintenance or not based on specified conditions.

Data Range

• Total number of records: 50,000

Source

This dataset is synthetic and was generated using Python. It is intended for educational and research purposes.

Acknowledgements

• The dataset was generated using Python and the data is synthetic.

PLEASE NOTE: This dataset, which includes all TLC licensed for-hire vehicles which are in good standing and able to drive, is updated every day in the evening between 4-7pm. Please check the 'Last Update Date' field to make sure the list has updated successfully. 'Last Update Date' should show either today or yesterday's date, depending on the time of day. If the list is outdated, please download the most recent list from the link below. http://www1.nyc.gov/assets/tlc/downloads/datasets/tlc_for_hire_vehicle_active_and_inactive.csv

TLC authorized For-Hire vehicles that are active. This list is accurate to the date and time represented in the Last Date Updated and Last Time Updated fields. For inquiries about the contents of this dataset, please email licensinginquiries@tlc.nyc.gov.

Change Data Capture for Vehicle Databases Market Outlook

According to our latest research, the global market size for Change Data Capture (CDC) for Vehicle Databases reached USD 1.37 billion in 2024, reflecting robust adoption across the automotive industry. The market is projected to grow at a CAGR of 13.2% from 2025 to 2033, reaching an estimated USD 4.12 billion by 2033. Key growth factors include the accelerated digital transformation of the automotive sector, the proliferation of connected vehicles, and the increasing demand for real-time data analytics and regulatory compliance. As per our latest research, these trends are fundamentally reshaping data management strategies for automotive stakeholders worldwide.

The growth of the Change Data Capture for Vehicle Databases market is being propelled by the exponential rise in connected vehicle technologies and the integration of advanced telematics systems. Modern vehicles are equipped with a myriad of sensors and IoT devices that continuously generate vast amounts of data. CDC solutions enable seamless real-time synchronization and transfer of this data across distributed databases, ensuring that automotive OEMs, fleet operators, and service providers can leverage up-to-date information for predictive maintenance, enhanced safety, and improved customer experiences. This surge in demand for real-time and accurate data management is a primary driver behind the substantial market expansion observed in recent years.

Another significant growth factor is the increasing regulatory requirements for vehicle data transparency and compliance. Governments across the globe are instituting stringent mandates for data retention, emissions monitoring, and safety reporting, necessitating robust database management solutions. CDC technologies facilitate the efficient capture and tracking of data modifications, enabling stakeholders to maintain comprehensive audit trails and demonstrate compliance with evolving standards. Moreover, the automotive insurance sector is leveraging CDC-enabled data pipelines to refine risk assessment models and offer usage-based insurance products, further broadening the market's application scope and fueling its upward trajectory.

The market is also benefiting from the rapid adoption of cloud-based deployment models and the integration of CDC solutions with AI-driven analytics platforms. Cloud deployment not only reduces infrastructure costs but also enhances scalability and accessibility, making advanced data management feasible for both large enterprises and small-to-medium-sized fleet operators. The synergy between CDC and AI technologies is unlocking new opportunities for real-time diagnostics, automated decision-making, and personalized vehicle services. As industry players continue to invest in digital transformation initiatives, the CDC for Vehicle Databases market is poised to witness sustained growth and innovation throughout the forecast period.

From a regional perspective, North America currently leads the market due to its early adoption of connected vehicle technologies and a mature automotive ecosystem. However, Asia Pacific is expected to witness the fastest growth, driven by the rapid expansion of the automotive sector, increasing vehicle electrification, and supportive government policies promoting digital infrastructure. Europe remains a critical market, characterized by stringent data privacy regulations and a strong focus on sustainability. Each region presents unique opportunities and challenges, with localization of CDC solutions emerging as a key strategy for market penetration and compliance with diverse regulatory frameworks.

Component Analysis

The Component segment of the Change Data Capture for Vehicle Databases market is typically categorized into software, hardware, and services. The software component dominates the market, accounting for the largest revenue share in 2024. This dominance is attributed to the critical role of CD