100+ datasets found
  1. Car Allowance Rebate System (CARS) - Trade-In Vehicles - Consumer Survey csv...

    • catalog.data.gov
    • data.transportation.gov
    Updated May 1, 2024
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    National Highway Traffic Safety Administration (2024). Car Allowance Rebate System (CARS) - Trade-In Vehicles - Consumer Survey csv file [Dataset]. https://catalog.data.gov/dataset/car-allowance-rebate-system-cars-trade-in-vehicles-consumer-survey-csv-file
    Explore at:
    Dataset updated
    May 1, 2024
    Description

    The Car Allowance Rebate System (CARS), otherwise known as Cash for Clunkers, was a program intended to provide economic incentives to United States residents to purchase a new and more fuel efficient vehicle when trading in a less full efficient vehicle. The program was promoted as providing stimulus to the economy by boosting auto sales, while putting safer, cleaner and more fuel efficient vehicles on the road.

  2. Vehicle Dataset

    • kaggle.com
    Updated Oct 17, 2022
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    Yue Cai (2022). Vehicle Dataset [Dataset]. https://www.kaggle.com/datasets/skyecai/vehicle-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 17, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Yue Cai
    Description

    This dataset contains features about the existing vehicle models. Before predict the new kind of car model, we want to determine which existing vehicles on the market are most like the new models, how vehicles can be grouped, which group is the most similar with the new models.

  3. Vehicle licensing statistics data files

    • gov.uk
    • s3.amazonaws.com
    Updated Jun 11, 2025
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    Department for Transport (2025). Vehicle licensing statistics data files [Dataset]. https://www.gov.uk/government/statistical-data-sets/vehicle-licensing-statistics-data-files
    Explore at:
    Dataset updated
    Jun 11, 2025
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Transport
    Description

    Recent changes

    A number of changes were introduced to these data files in the 2022 release to help meet the needs of our users and to provide more detail.

    Fuel type has been added to:

    • df_VEH0120_GB
    • df_VEH0120_UK
    • df_VEH0160_GB
    • df_VEH0160_UK

    Historic UK data has been added to:

    • df_VEH0124 (now split into 2 files)
    • df_VEH0220
    • df_VEH0270

    A new datafile has been added df_VEH0520.

    We welcome any feedback on the structure of our data files, their usability, or any suggestions for improvements; please contact vehicles statistics.

    How to use CSV files

    CSV files can be used either as a spreadsheet (using Microsoft Excel or similar spreadsheet packages) or digitally using software packages and languages (for example, R or Python).

    When using as a spreadsheet, there will be no formatting, but the file can still be explored like our publication tables. Due to their size, older software might not be able to open the entire file.

    Download data files

    Make and model by quarter

    df_VEH0120_GB: https://assets.publishing.service.gov.uk/media/68494aca74fe8fe0cbb4676c/df_VEH0120_GB.csv">Vehicles at the end of the quarter by licence status, body type, make, generic model and model: Great Britain (CSV, 58.1 MB)

    Scope: All registered vehicles in Great Britain; from 1994 Quarter 4 (end December)

    Schema: BodyType, Make, GenModel, Model, Fuel, LicenceStatus, [number of vehicles; 1 column per quarter]

    df_VEH0120_UK: https://assets.publishing.service.gov.uk/media/68494acb782e42a839d3a3ac/df_VEH0120_UK.csv">Vehicles at the end of the quarter by licence status, body type, make, generic model and model: United Kingdom (CSV, 34.1 MB)

    Scope: All registered vehicles in the United Kingdom; from 2014 Quarter 3 (end September)

    Schema: BodyType, Make, GenModel, Model, Fuel, LicenceStatus, [number of vehicles; 1 column per quarter]

    df_VEH0160_GB: https://assets.publishing.service.gov.uk/media/68494ad774fe8fe0cbb4676d/df_VEH0160_GB.csv">Vehicles registered for the first time by body type, make, generic model and model: Great Britain (CSV, 24.8 MB)

    Scope: All vehicles registered for the first time in Great Britain; from 2001 Quarter 1 (January to March)

    Schema: BodyType, Make, GenModel, Model, Fuel, [number of vehicles; 1 column per quarter]

    df_VEH0160_UK: https://assets.publishing.service.gov.uk/media/68494ad7aae47e0d6c06e078/df_VEH0160_UK.csv">Vehicles registered for the first time by body type, make, generic model and model: United Kingdom (CSV, 8.26 MB)

    Scope: All vehicles registered for the first time in the United Kingdom; from 2014 Quarter 3 (July to September)

    Schema: BodyType, Make, GenModel, Model, Fuel, [number of vehicles; 1 column per quarter]

    Make and model by age

    In order to keep the datafile df_VEH0124 to a reasonable size, it has been split into 2 halves; 1 covering makes starting with A to M, and the other covering makes starting with N to Z.

    df_VEH0124_AM: <a class="govuk-link" href="https://assets.

  4. Car Price Prediction Challenge

    • kaggle.com
    Updated Jul 6, 2022
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    Deep Contractor (2022). Car Price Prediction Challenge [Dataset]. https://www.kaggle.com/datasets/deepcontractor/car-price-prediction-challenge
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 6, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Deep Contractor
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Assignment

    Your notebooks must contain the following steps:

    • Perform data cleaning and pre-processing.
      • What steps did you use in this process and how did you clean your data.
    • Perform exploratory data analysis on the given dataset.
      • Explain each and every graphs that you make.
    • Train a ml-model and evaluate it using different metrics.
      • Why did you choose that particular model? What was the accuracy?
    • Hyperparameter optimization and feature selection is a plus.
    • Model deployment and use of ml-flow is a plus.
    • Perform model interpretation and show feature importance for your model.
      • Provide some explanation for the above point.
    • Future steps. Note: try to have your notebooks as presentable as possible.

    Dataset Description

    CSV file - 19237 rows x 18 columns (Includes Price Columns as Target)

    Attributes

    ID Price: price of the care(Target Column) Levy Manufacturer Model Prod. year Category Leather interior Fuel type Engine volume Mileage Cylinders Gear box type Drive wheels Doors Wheel Color Airbags

    Confused or have any doubts in the data column values? Check the dataset discussion tab!

  5. A

    ‘Car Prices Dataset’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Sep 30, 2021
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2021). ‘Car Prices Dataset’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-car-prices-dataset-b8f6/032ec7ac/?iid=054-797&v=presentation
    Explore at:
    Dataset updated
    Sep 30, 2021
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Car Prices Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sidharth178/car-prices-dataset on 29 August 2021.

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

    Context

    With the rise in the variety of cars with differentiated capabilities and features such as model, production year, category, brand, fuel type, engine volume, mileage, cylinders, colour, airbags and many more, we are bringing a car price prediction challenge for all. We all aspire to own a car within budget with the best features available. To solve the price problem we have created a dataset of 19237 for the training dataset and 8245 for the test dataset.

    Data Description

    Train.csv - 19237 rows x 18 columns (Includes Price Columns as Target) - Attributes - ID - Price: price of the care(Target Column) - Levy - Manufacturer - Model - Prod. year - Category - Leather interior - Fuel type - Engine volume - Mileage - Cylinders - Gear box type - Drive wheels - Doors - Wheel - Color - Airbags Test.csv - 8245 rows x 17 columns

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

  6. NHTSA Product Information Catalog and Vehicle Listing (vPIC) - Vehicle API...

    • catalog.data.gov
    • data.transportation.gov
    • +1more
    Updated May 1, 2024
    + more versions
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    National Highway Traffic Safety Administration (2024). NHTSA Product Information Catalog and Vehicle Listing (vPIC) - Vehicle API CSV [Dataset]. https://catalog.data.gov/dataset/nhtsa-product-information-catalog-and-vehicle-listing-vpic-vehicle-api-csv
    Explore at:
    Dataset updated
    May 1, 2024
    Description

    The NHTSA Product Information Catalog and Vehicle Listing (vPIC) is a consolidated platform that presents data collected within the manufacturer reported data from CFR 49 Parts 551 - 574 for use in a variety of modern tools. NHTSA's vPIC platform is intended to serve as a centralized source for basic Vehicle Identification Number (VIN) decoding, Manufacturer Information Database (MID), Manufacturer Equipment Plant Identification and associated data. vPIC is intended to support the Open Data and Transparency initiatives of the agency by allowing the data to be freely used by the public without the burden of manual retrieval from a library of electronic documents (PDFs). While these documents will still be available online for viewing within the Manufacturer Information Database (MID) module of vPIC one can view and use the actual data through the VIN Decoder and Application Programming Interface (API) modules.

  7. A

    ‘cars.csv’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 28, 2022
    + more versions
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘cars.csv’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-cars-csv-93c7/3ce90662/?iid=003-646&v=presentation
    Explore at:
    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘cars.csv’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/huseyinrakun/carscsv on 28 January 2022.

    --- No further description of dataset provided by original source ---

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

  8. Old car price prediction

    • kaggle.com
    Updated Dec 24, 2022
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    Milan Vaddoriya (2022). Old car price prediction [Dataset]. https://www.kaggle.com/datasets/milanvaddoriya/old-car-price-prediction
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 24, 2022
    Dataset provided by
    Kaggle
    Authors
    Milan Vaddoriya
    Description

    The steps listed below must be included in your notebooks:

    1. Understand the problem statement.
    2. Import required libraries and Data.
    3. Check the Data
    4. Pre-processing and data cleansing should be done.
    5. Utilize the provided dataset to conduct exploratory data analysis. Each and every graph you create should be explained.
    6. Feature Selection
    7. Data splitting
    8. Create an ML model, then test it using various metrics.

    Data source - https://www.cardekho.com/used-car-details Cover image source - https://cdni.autocarindia.com/Utils/ImageResizer.ashx?n=https://cdni.autocarindia.com/Galleries/20200206032922_Tata-Harrier-BS6-5.jpg&w=872&h=578&q=75&c=1

  9. A

    ‘car_sales.csv’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Feb 14, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘car_sales.csv’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-car-sales-csv-2cb7/6526765f/?iid=004-967&v=presentation
    Explore at:
    Dataset updated
    Feb 14, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘car_sales.csv’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/smritisingh1997/car-salescsv on 14 February 2022.

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

    Context

    This data contains data related to Car Sales

    Content

    The data is required for the basic Linear Regression model. It can be used to explore all the basic Linear Regression assumptions, which are required if one wants to apply Linear Regression on the given data

    Acknowledgements

    We wouldn't be here without the help of others. I would especially like to thanks @Udemy, @Coursera, and @KhanAcademy

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

  10. g

    Car Damage Dataset

    • gts.ai
    • kaggle.com
    json
    Updated Apr 12, 2024
    + more versions
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    GTS (2024). Car Damage Dataset [Dataset]. https://gts.ai/dataset-download/car-damage-dataset/
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Apr 12, 2024
    Dataset provided by
    GLOBOSE TECHNOLOGY SOLUTIONS PRIVATE LIMITED
    Authors
    GTS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This dataset contains a set of damaged car images, each labeled with information about being fraudulent or non fraudulent with respect to damage claims in the csv file.

  11. A

    ‘cars.csv’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 28, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘cars.csv’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-cars-csv-49f0/60f96776/?iid=019-704&v=presentation
    Explore at:
    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘cars.csv’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/karimkeraani/carscsv on 28 January 2022.

    --- No further description of dataset provided by original source ---

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

  12. f

    CAR INS DATASET MODIFIED 1.csv

    • figshare.com
    csv
    Updated Jan 13, 2025
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    Prakash M C (2025). CAR INS DATASET MODIFIED 1.csv [Dataset]. http://doi.org/10.6084/m9.figshare.28188854.v1
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jan 13, 2025
    Dataset provided by
    figshare
    Authors
    Prakash M C
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The dataset consists of information about 50,815 policyholders who are car owners.

  13. Z

    Open synthetic data on travel and charging demand of battery electric cars:...

    • data.niaid.nih.gov
    Updated Feb 9, 2023
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    Tozluoglu, Caglar (2023). Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7549846
    Explore at:
    Dataset updated
    Feb 9, 2023
    Dataset provided by
    Yeh, Sonia
    Tozluoglu, Caglar
    Dhamal, Swapnil
    Sprei, Frances
    Liao, Yuan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Background

    Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc.

    A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation).

    https://doi.org/10.1016/j.trd.2023.103645

    Methodology

    This data product is centered on the 1.7 million inhabitants of the Västra Götaland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden.

    This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging & discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7).

    Data description

    The current data product covers seven data files.

    (1) Agents' experienced activity plans

    File name: 1_activity_plans.csv

    Column

    Description

    Data type

    Unit

    person

    Agent ID

    Integer

    -

    act_id

    Activity index of each agent

    Integer

    -

    deso

    Zone code of Demographic statistical areas (DeSO)1

    String

    -

    POINT_X

    Coordinate X of activity location (SWEREF99TM)

    Float

    meter

    POINT_Y

    Coordinate Y of activity location (SWEREF99TM)

    Float

    meter

    act_purpose

    Activity purpose (work, home, other)

    String

    -

    mode

    Transport mode to reach the activity location (car)

    String

    -

    dep_time

    Departure time in decimal hour (0-23.99)

    Float

    hour

    trav_time

    Travel time to reach the activity location

    String

    hour:minute:second

    trav_time_min

    Travel time in decimal minute

    Float

    minute

    speed

    Travel speed to reach the activity location

    Float

    km/h

    distance

    Travel distance between the origin and the destination

    Float

    km

    act_start

    Start time of activity in minute (0-1439)

    Integer

    minute

    act_time

    Activity duration in decimal minute

    Float

    minute

    act_end

    End time of activity in decimal hour (0-23.99)

    Float

    hour

    score

    Utility score of the simulation day given by MATSim

    Float

    -

    1 https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/

    (2) Travel trajectories

    File name: 2_input_zip

    Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days.

    Column

    Description

    Data type

    Unit

    time

    Time in second in a simulation day (0-86399)

    Integer

    Second

    type

    Event type defined by MATSim simulation2

    String

    -

    person

    Agent ID

    Integer

    -

    link

    Nearest road link consistent with (5)

    String

    -

    vehicle

    Vehicle ID identical to person

    Integer

    -

    2 One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)

    (3) Overnight charger access

    File name: 3_home_charger_access.csv

    Column

    Description

    Data type

    Unit

    person

    Agent ID

    Integer

    -

    home_charger

    Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)

    Integer

    -

    (4) Car fleet composition

    File name: 4_car_fleet.csv

    Column

    Description

    Data type

    Unit

    person

    Agent ID

    Integer

    -

    income_class

    Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K)

    Integer

    -

    car

    Car model class (B=40 kWh, C=60 kWh, D=100 kWh)

    String

    -

    (5) Road network with slope information

    File name: 5_road_network_with_slope.shp (5 files in total)

    Column

    Description

    Data type

    Unit

    length

    The length of road link

    Float

    meter

    freespeed

    Free speed

    Float

    km/h

    capacity

    Number of vehicles

    Integer

    -

    permlanes

    Number of lanes

    Integer

    -

    oneway

    Whether the segment is one-way (0=no, 1=yes)

    Integer

    -

    modes

    Transport mode (car)

    String

    -

    link_id

    Link ID

    String

    -

    from_node

    Start node of the link

    String

    -

    to_node

    End node of the link

    String

    -

    count

    Aggregated traffic (number of cars travelled per day)

    Integer

    -

    slope

    Slope in percent from -6% to 6%

    Float

    -

    geometry

    LINESTRING (SWEREF99TM)

    geometry

    meter

    (6) Simulation scenarios specifying the parameter sets

    File name: 6_scenarios.txt

    Parameter set

    (paraset)

    Strategy 1

    Strategy 2

    Strategy 3

    Fast charging power (kW)

    Minimum parking time for charging (min)

    Intermediate charging power (kW)

    0

    0.2

    0.2

    0.9

    150

    5

    22

    1

    0.2

    0.2

    0.9

    50

    5

    22

    2

    0.3

    0.3

    0.9

    150

    5

    22

    3

    0.3

    0.3

    0.9

    50

    5

    22

    (7) Time history of travel trajectories and charging of the simulated BEVs

    File name: 7_output.zip

    Produced by the BEV simulation, the zip folder contains four files (parasetX.csv.gz, X=1, 2, 3, 4) corresponding to the four parameter sets specified in (6). They are the moving trajectories of the car agents with simulated energy and charging time history in their simulation days.

    Column

    Description

    Data type

    Unit

    person

    Agent ID

    Integer

    -

    home_charger

    Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)

    Integer

    -

    car

    Car model class (B=40 kWh, C=60 kWh, D=100 kWh)

    String

    -

    seq

    Sequence ID of time history by agent

    Integer

    -

    time

    Time (0-86399)

    Integer

    Second

    purpose

    Valid for activities (home, work, school,

  14. Automotive Datasets

    • brightdata.com
    .json, .csv, .xlsx
    Updated Nov 25, 2024
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    Bright Data (2024). Automotive Datasets [Dataset]. https://brightdata.com/products/datasets/automotive
    Explore at:
    .json, .csv, .xlsxAvailable download formats
    Dataset updated
    Nov 25, 2024
    Dataset authored and provided by
    Bright Datahttps://brightdata.com/
    License

    https://brightdata.com/licensehttps://brightdata.com/license

    Area covered
    Worldwide
    Description

    Our automotive datasets provide comprehensive insights into the global vehicle market, covering a wide range of data points related to car listings, pricing trends, vehicle specifications, and market demand. These datasets are ideal for businesses, analysts, and developers looking to enhance automotive research, optimize pricing strategies, or improve vehicle inventory management.

    Key Features:
    
      Vehicle Listings & Specifications: Access detailed information on cars, trucks, SUVs, motorcycles, and electric vehicles, 
        including make, model, year, trim, mileage, fuel type, and transmission.
      Pricing & Market Trends: Analyze historical and real-time pricing data to track market fluctuations, assess vehicle depreciation, 
        and optimize pricing strategies.
      Dealer & Private Seller Insights: Gain visibility into vehicle listings from dealerships and private sellers, including contact details, 
        location, and availability.
      Vehicle Condition & Features: Identify key attributes such as accident history, service records, safety features, and additional specifications.
      Regional & Global Coverage: Access datasets segmented by country, state, or city to analyze local and international automotive markets.
    
    
    Use Cases:
    
      Market Research & Competitive Analysis: Monitor automotive trends, track competitor pricing, and assess consumer demand.
      Pricing Optimization: Adjust vehicle pricing based on real-time market data to maximize profitability and sales efficiency.
      Inventory & Fleet Management: Improve vehicle sourcing, inventory tracking, and fleet management for dealerships and rental companies.
      Automotive AI & Machine Learning: Train predictive models for vehicle valuation, demand forecasting, and fraud detection.
      Consumer Insights & Lead Generation: Identify potential buyers, analyze purchasing behavior, and enhance targeted marketing efforts.
    
    
    
      Our automotive datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
      API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
      Gain valuable insights into the automotive industry with high-quality, structured data tailored to your needs.
    
  15. d

    Electric Vehicle Population Data

    • catalog.data.gov
    • data.wa.gov
    • +3more
    Updated Jun 14, 2025
    + more versions
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    data.wa.gov (2025). Electric Vehicle Population Data [Dataset]. https://catalog.data.gov/dataset/electric-vehicle-population-data
    Explore at:
    Dataset updated
    Jun 14, 2025
    Dataset provided by
    data.wa.gov
    Description

    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).

  16. used_cars_csv

    • kaggle.com
    Updated Jun 15, 2024
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    Oyekoya Temidayo (2024). used_cars_csv [Dataset]. https://www.kaggle.com/datasets/oyekoyatemidayo/clean-df-csv
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 15, 2024
    Dataset provided by
    Kaggle
    Authors
    Oyekoya Temidayo
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    This dataset was hosted on IBM Cloud object

    You can find the "Automobile Dataset" from the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data.

    I cleaned the data myself, you can check notebook "Used Car Pricing Data Wrangling" for details.

  17. Z

    Regression analysis in Galaxy with car purchase price prediction dataset

    • data.niaid.nih.gov
    Updated Aug 4, 2022
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    Kaivan Kamali (2022). Regression analysis in Galaxy with car purchase price prediction dataset [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_4660496
    Explore at:
    Dataset updated
    Aug 4, 2022
    Dataset authored and provided by
    Kaivan Kamali
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Source/Credit: Michael Grogan https://github.com/MGCodesandStats https://github.com/MGCodesandStats/datasets/blob/master/cars.csv

    Sample dataset for regression analysis. Given 5 attributes (age, gender, miles driven per day, debt, and income) predict how much someone will spend on purchasing a car. All 5 of the input attributes have been scaled to be in 0 to 1 range. Training set has 723 training examples. Test set has 242 test examples.

    This dataset will be used in an upcoming Galaxy Training Network tutorial (https://training.galaxyproject.org/training-material/topics/statistics/) on use of feedforward neural networks for regression analysis.

  18. h

    MeshFleet

    • huggingface.co
    Updated Mar 18, 2025
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    Damian Boborzi (2025). MeshFleet [Dataset]. https://huggingface.co/datasets/DamianBoborzi/MeshFleet
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    Dataset updated
    Mar 18, 2025
    Authors
    Damian Boborzi
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    This is a curated collection of 3D car models derived from Objaverse-XL described in MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific Generative Modeling. The MeshFleet dataset provides metadata for 3D car models, including their SHA256 from Objaverse-XL, vehicle category, and size. The core dataset is available as a CSV file: meshfleet_with_vehicle_categories_df.csv. You can easily load it using pandas: import pandas as pd

    meshfleet_df =… See the full description on the dataset page: https://huggingface.co/datasets/DamianBoborzi/MeshFleet.

  19. z

    Electric Vehicle Usage and Charging Analysis Dataset Across Seven Major...

    • zenodo.org
    bin, csv
    Updated Nov 6, 2024
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    Weipeng Zhan; Yuan Liao; Yuan Liao; Junjun Deng; Zhenpo Wang; Sonia Yeh; Sonia Yeh; Weipeng Zhan; Junjun Deng; Zhenpo Wang (2024). Electric Vehicle Usage and Charging Analysis Dataset Across Seven Major Cities in China [Dataset]. http://doi.org/10.5281/zenodo.13852045
    Explore at:
    bin, csvAvailable download formats
    Dataset updated
    Nov 6, 2024
    Dataset provided by
    Zenodo
    Authors
    Weipeng Zhan; Yuan Liao; Yuan Liao; Junjun Deng; Zhenpo Wang; Sonia Yeh; Sonia Yeh; Weipeng Zhan; Junjun Deng; Zhenpo Wang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    China
    Description

    Background

    This dataset provides supporting data for the figures presented in our study on electric vehicle (EV) usage and charging behavior across major Chinese cities. The detailed analysis and raw data are thoroughly described in Zhan et al (2025). The study examines 1.69 million EVs, representing 42% of China's total EV fleet, from November 2020 to October 2021. The study provides insights into operational demands, infrastructure requirements, and energy consumption patterns by analyzing diverse vehicle types—including private cars, taxis, buses, and special purpose vehicles (SPVs).

    The purpose of this dataset is to enable researchers who do not have access to the same raw data to replicate, calibrate, or extend our findings using the processed data that underpins each figure. This resource is valuable for further research on EV infrastructure planning, energy consumption, and vehicle performance. This dataset is made available to help the research community leverage our findings and facilitate advancements in electric vehicle research and infrastructure planning. Please refer to Zhan et al (2025) for full details on the methodology and analysis.

    Data description

    This dataset includes the processed data underlying each figure in Zhan et al (2025), covering various aspects of EV usage, battery capacity, and charging behavior across seven major Chinese cities: Beijing, Shanghai, Guangzhou, Shenzhen, Nanjing, Chengdu, and Chongqing. The dataset is organized to correspond directly with the figures in the paper, facilitating its use for further analysis and model calibration. Each dataset is aligned with specific figures, providing essential data to help researchers without access to the original raw data.

    1. EV Type and Battery Energy Distribution Across Cities

    Fig1a.Distribution of EV types across selected Chinese cities

    File: Fig1a.Distribution of EV types across selected Chinese cities.csv

    Description: Distribution of EV types across seven cities, detailing the share of different vehicle types.

    Column

    Description

    Data type

    Unit

    Beijing

    Distribution of EV types in Beijing

    Float

    %

    Shenzhen

    Distribution of EV types in Shenzhen

    Float

    %

    Shanghai

    Distribution of EV types in Shanghai

    Float

    %

    Guangzhou

    Distribution of EV types in Guangzhou

    Float

    %

    Chengdu

    Distribution of EV types in Chengdu

    Float

    %

    Chongqing

    Distribution of EV types in Chongqing

    Float

    %

    Nanjing

    Distribution of EV types in Nanjing

    Float

    %

    Fig1b.Distribution of battery energy by vehicle types

    File: Fig1b.Distribution of battery energy by vehicle types.csv

    Description: Distribution of battery energy across different vehicle types, represented as box plot statistics.

    Column

    Description

    Data type

    Unit

    type_2

    vehicle types

    String

    -

    Lower Whisker

    The battery energy corresponding to the Lower Whisker of the box plot.

    Float

    kWh

    Q1 (25%)

    The 25th percentile value of battery energy.

    Float

    kWh

    Median (50%)

    The median value of battery energy.

    Float

    kWh

    Q3 (75%)

    The 75th percentile value of battery energy.

    Float

    kWh

    Upper Whisker

    The battery energy corresponding to the Upper Whisker of the box plot.

    Float

    kWh

    2. Variations in Battery Energy

    Fig1c.Variations of battery energy of buses

    File: Fig1c.Variations of battery energy of buses across studied cities.csv

    Description: Battery energy variations for buses across the studied cities.

    Column

    Description

    Data type

    Unit

    city_En

    English name of 7 Chinese city

    String

    -

    Lower Whisker

    The battery energy of buses corresponding to the Lower Whisker of the box plot.

    Float

    kWh

    Q1 (25%)

    The 25th percentile value of battery energy of buses.

    Float

    kWh

    Median (50%)

    The median value of battery energy of buses.

    Float

    kWh

    Q3 (75%)

    The 75th percentile value of battery energy of buses.

    Float

    kWh

    Upper Whisker

    The battery energy of buses corresponding to the Upper Whisker of the box plot.

    Float

    kWh

    Fig1d.Variations of battery energy of SPVs

    File: Fig1c.Variations of battery energy of SPVs across studied cities.csv

    Description: Battery energy variations for special purpose vehicles (SPVs) across cities.

    Column

    Description

    Data type

    Unit

    city_En

    English name of 7 Chinese city

    String

    -

    Lower Whisker

    The battery energy of SPVs corresponding to the Lower Whisker of the box plot.

    Float

    kWh

    Q1 (25%)

    The 25th

  20. d

    Monthly Traffic and Transport Digest (CSV) - Section 4 : Vehicle...

    • data.gov.hk
    csv
    Updated Mar 17, 2020
    + more versions
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    Transport Department (2020). Monthly Traffic and Transport Digest (CSV) - Section 4 : Vehicle Registration, Licensing and Inspection Statistics - Table 4.1 (f) - First Registration of Light Goods Vehicles by Make, First Registration Vehicle Status, Fuel Type and Body Type(Simplified Chinese) [Dataset]. https://data.gov.hk/en-data/dataset/hk-td-tis_17-monthly-traffic-and-transport-digest-csv/resource/e95b5211-a33a-43c0-b601-8a162dea876d
    Explore at:
    csv(4055479)Available download formats
    Dataset updated
    Mar 17, 2020
    Dataset provided by
    Transport Department
    License

    http://data.gov.hk/en/terms-and-conditionshttp://data.gov.hk/en/terms-and-conditions

    Description

    Table 4.1 (f) - First Registration of Light Goods Vehicles by Make, First Registration Vehicle Status, Fuel Type and Body Type(Simplified Chinese)

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Email
Click to copy link
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Close
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National Highway Traffic Safety Administration (2024). Car Allowance Rebate System (CARS) - Trade-In Vehicles - Consumer Survey csv file [Dataset]. https://catalog.data.gov/dataset/car-allowance-rebate-system-cars-trade-in-vehicles-consumer-survey-csv-file
Organization logo

Car Allowance Rebate System (CARS) - Trade-In Vehicles - Consumer Survey csv file

Explore at:
Dataset updated
May 1, 2024
Description

The Car Allowance Rebate System (CARS), otherwise known as Cash for Clunkers, was a program intended to provide economic incentives to United States residents to purchase a new and more fuel efficient vehicle when trading in a less full efficient vehicle. The program was promoted as providing stimulus to the economy by boosting auto sales, while putting safer, cleaner and more fuel efficient vehicles on the road.

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