There are numerous car datasets available that provide information on various aspects of vehicles. Here is a general description of the common types of information you may find in car datasets:

Make and Model: The brand and model name of the car. Year: The manufacturing year of the vehicle. Price: The price at which the car was listed or sold. Mileage: The number of miles the car has been driven. Fuel Efficiency: The car's average fuel consumption or MPG (Miles Per Gallon) rating. Horsepower: The power output of the car's engine. Number of Cylinders: The number of cylinders in the car's engine. Transmission: The type of transmission system in the car (e.g., automatic, manual). Drivetrain: The configuration of the car's drivetrain (e.g., front-wheel drive, rear-wheel drive, all-wheel drive). Body Type: The category or style of the car (e.g., sedan, SUV, truck, coupe). Engine Displacement: The capacity or size of the car's engine. Dimensions: Information about the car's length, width, height, and weight. Safety Ratings: Data on the car's safety features and crash test ratings. Features: Additional features and specifications such as navigation system, infotainment system, sunroof, etc

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Description:

Welcome to the "Used Car Listings Dataset with Geographic Information" on Kaggle! This comprehensive dataset provides detailed information about a diverse collection of used cars available for sale in various regions across the United States. With a total of approximately 27,500 entries, this dataset offers a rich resource for analyzing and modeling the factors that influence used car prices and market trends.

Dataset Overview:

The dataset comprises three main files: train.csv, test.csv, and lat_long.csv. The primary data files, train.csv and test.csv, contain the following columns:

• id: A unique identifier for each car listing.
• region: The location where the car is listed for sale.
• year: The year the car was manufactured.
• manufacturer: The manufacturer of the car.
• condition: The condition of the car (e.g., excellent, good, fair).
• cylinders: The number of cylinders in the car's engine.
• fuel: The type of fuel the car uses (e.g., gas, diesel).
• odometer: The mileage on the car.
• title_status: The status of the car's title (e.g., clean, rebuilt).
• transmission: The type of transmission in the car (e.g., manual, automatic).
• drive: The type of drive in the car (e.g., fwd, rwd, 4wd).
• size: The size category of the car (e.g., compact, mid-size, full-size).
• type: The body type of the car (e.g., sedan, SUV, convertible).
• paint_color: The color of the car.
• state: The state where the car is listed for sale.
• **********price**: The listing price of the car.

These attributes collectively provide a comprehensive overview of each used car listing, making it an ideal dataset for exploratory analysis, feature engineering, and predictive modeling. While this dataset provides a comprehensive overview of each used car listing, it's important to be aware that some data cleaning, preprocessing, and advanced feature engineering might be necessary to ensure the most accurate and reliable analysis and modeling.

Geographic Information:

In addition to the main data files, the dataset includes lat_long.csv, which contains latitude and longitude information for the states mentioned in the primary dataset. This supplemental file facilitates geographical analysis and enables users to associate geographic coordinates with each car listing's location.

Potential Applications:

The "Used Car Listings Dataset with Geographic Information" is suitable for a variety of data science tasks, including but not limited to:

• Price Prediction: Build predictive models to estimate used car prices based on various attributes.
• Market Insights: Analyze market trends and consumer preferences in different regions.
• Geospatial Analysis: Combine geographic data to visualize and study spatial patterns.
• Feature Engineering: Create new features by combining or transforming existing attributes.

Acknowledgments:

We extend our gratitude to the data contributors for compiling this diverse and valuable dataset, which offers insights into the used car market across different regions in the United States.

Citation:

If you use this dataset for research or any other purpose, please provide appropriate credit and citation to the dataset and its contributors.

Explore, analyze, and innovate with the "Used Car Listings Dataset with Geographic Information" to uncover hidden insights and drive meaningful conclusions. Please note that this dataset's raw state might necessitate advanced preprocessing and feature engineering for optimal results. Happy analyzing!

Description

This is a countrywide car accident dataset that covers 49 states of the USA. The accident data were collected from February 2016 to March 2023, using multiple APIs that provide streaming traffic incident (or event) data. These APIs broadcast traffic data captured by various entities, including the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road networks. The dataset currently contains approximately 7.7 million accident records. For more information about this dataset, please visit here.

Acknowledgements

If you use this dataset, please kindly cite the following papers:

• Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. “A Countrywide Traffic Accident Dataset.”, 2019.

• Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. "Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights." In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.

Content

This dataset was collected in real-time using multiple Traffic APIs. It contains accident data collected from February 2016 to March 2023 for the Contiguous United States. For more details about this dataset, please visit [here].

Inspiration

The US-Accidents dataset can be used for numerous applications, such as real-time car accident prediction, studying car accident hotspot locations, casualty analysis, extracting cause and effect rules to predict car accidents, and studying the impact of precipitation or other environmental stimuli on accident occurrence. The most recent release of the dataset can also be useful for studying the impact of COVID-19 on traffic behavior and accidents.

Sampled Data (New!)

For those requiring a smaller, more manageable dataset, a sampled version is available which includes 500,000 accidents. This sample is extracted from the original dataset for easier handling and analysis.

Other Details

Please note that the dataset may be missing data for certain days, which could be due to network connectivity issues during data collection. Regrettably, the dataset will no longer be updated, and this version should be considered the latest.

Usage Policy and Legal Disclaimer

This dataset is being distributed solely for research purposes under the Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By downloading the dataset, you agree to use it only for non-commercial, research, or academic applications. If you use this dataset, it is necessary to cite the papers mentioned above.

Inquiries or need help?

For any inquiries or assistance, please contact Sobhan Moosavi at sobhan.mehr84@gmail.com

This table contains data on the annual miles traveled by place of occurrence and by mode of transportation (vehicle, pedestrian, bicycle), for California, its regions, counties, and cities/towns. The ratio uses data from the California Department of Transportation, the U.S. Department of Transportation, and the U.S. Census Bureau. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Miles traveled by individuals and their choice of mode – car, truck, public transit, walking or bicycling – have a major impact on mobility and population health. Miles traveled by automobile offers extraordinary personal mobility and independence, but it is also associated with air pollution, greenhouse gas emissions linked to global warming, road traffic injuries, and sedentary lifestyles. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which has many documented health benefits. More information about the data table and a data dictionary can be found in the About/Attachments section.

Overview

This dataset consists of details on daily used Tesla cars sold in United States from Tesla. Data fields include vin, year, model, color, miles, trim, sold price, interior, wheels, features, country, location, metro, state, currency, sold date.

Fields

• vin
• year
• model
• color
• miles
• trim
• sold_price
• interior
• wheels
• features
• country
• location
• metro
• state
• currency
• sold_date

Sample data from May 2022

vin	year	model	color	miles	trim
5YJSA1E27KF308860	2019	ms	WHITE	20891	100D Long Range All-Wheel Drive

sold_price	interior	wheels	features
81900	WHITE	NINETEEN	Pearl White Multi-Coat Paint;19" Silver Slipstream Wheels;Black and White Premium Interior;Full Self-Driving Capability;Smart Air Suspension;Glass Roof;Ultra High Fidelity Sound;HEPA Air Filtration System;Subzero Weather Package;Keyless Entry;Power Liftgate;GPS Enabled Homelink;Dark Ash Wood Décor;Dark Headliner;Infotainment Upgrade;

country	location	metro	state	currency	sold_date
US	Pomona, CA		CA	USD	2022-05-30

Download Sample Dataset

Specifications

• Dataset Format: CSV (comma-separated)
• Update Frequency: Monthly
• Geographic coverage: US

Data Source

From tesla.com

Subscription

• Email

Support

You can reach us at support@saturndatacloud.com for any questions on the dataset.

About Saturn Data

Saturn Data provides data mining solutions from public sources to deliver insights for enterprises and the market. If you are interested in acquiring other datasets or customized data mining service, email us at info@saturndatacloud.com.

Number of vehicles travelling between Canada and the United States, by trip characteristics, length of stay and type of transportation. Data available monthly.

Introduction Electric vehicles, marked by early innovations, periods of decline, and a remarkable resurgence in recent decades. From the pioneering efforts of the 19th century to the transformative breakthroughs of the 21st century, EVs have continually evolved, driven by technological advancements, environmental considerations, and shifting market dynamics.

I also examine the various types of electric vehicles currently available, including Battery Electric Vehicles (BEVs), Fuel Cell Electric Vehicles (FCEVs), and Plug-in Hybrid Electric Vehicles (PHEVs). Each of these powertrains offers unique advantages and challenges, reflecting the diverse needs and preferences of today’s drivers.

Through data visualisations and analysis, I present a snapshot of global EV trends, showcasing the growth of EV sales and the distribution of different powertrain types across regions. As we look towards the future, the Global EV Outlook underscores the potential of electric mobility to reshape the transportation landscape and drive us toward a more sustainable and innovative future.

History of Electric Vehicles The history of electric vehicles (EVs) is rich and varied, spanning well over a century of innovation, decline, and resurgence. Let's look at the evolution of EVs, focusing on their early history, the oil crisis of the 1970s, and notable vehicles like the Sinclair C5.

Early History of Electric Vehicles Late 19th Century - Early 20th Century:

Origins: The concept of electric vehicles dates back to the early 19th century. The first practical electric car was built by Scottish inventor Robert Anderson around 1832-1839. It was a crude electric carriage powered by non-rechargeable batteries. Early 20th Century Market Share: By the early 1900s, electric vehicles, petrol-powered cars, and steam cars each held significant shares of the market. In fact, during the turn of the 20th century, electric vehicles were quite popular. They were considered quieter and easier to drive compared to the noisy and cumbersome petrol cars of the time. In 1900, electric vehicles had about a third of the automotive market share. This was a time when EVs were favoured by many urban drivers due to their reliability and lack of the manual hand-cranking that petrol cars required. Notable early EVs included the Detroit Electric Car Company models, which were popular with wealthy individuals and celebrities like Thomas Edison and Henry Ford. Decline: The decline of electric vehicles began with the advent of more affordable and practical petrol-powered vehicles. Innovations like the electric starter, improved road infrastructure, and the mass production techniques of Henry Ford’s Model T made petrol cars more accessible and practical. By the 1920s, the market for electric vehicles had dwindled as internal combustion engines and the infrastructure to support them, such as petrol stations, became more widespread. The 1970s Oil Crisis and the Revival of Interest in EVs Oil Crisis: The 1970s oil crisis, triggered by the 1973 Arab Oil Embargo and the 1979 energy crisis, brought renewed interest in alternative energy sources, including electric vehicles. Rising oil prices and concerns about energy security highlighted the need for less oil-dependent transportation solutions. During this period, there was a push for the development of electric vehicles as a means to reduce reliance on fossil fuels and mitigate the impact of future oil shortages. Early 1970s Efforts: Various automotive manufacturers and research institutions experimented with electric vehicles during this time. Many of these early attempts were limited by the technology of the era, including the limitations of battery performance and range. Notable Vehicles and Innovations Sinclair C5 (1985):

Overview: The Sinclair C5, designed by Sir Clive Sinclair, was an electric vehicle launched in 1985. It was a small, three-wheeled electric vehicle intended for short trips and urban commuting. The C5 had a top speed of about 15 miles per hour and a range of around 20-30 miles on a single charge. It was designed to be affordable and practical for daily use. Reception: Despite its innovative concept, the Sinclair C5 faced criticism for its limited speed, range, and lack of weather protection. It was also considered unsafe by some due to its low profile and exposure to road hazards. The vehicle was not a commercial success and was discontinued after a short production run. However, it remains an important historical footnote in the evolution of electric vehicles. Other Notable Early EVs

General Motors EV1 (1996-1999): The GM EV1 was one of the first mass-produced electric cars of the modern era. Launched in the late 1990s, it was notable for its advanced technology and the fact that it was designed specifically as an electric vehicle. The EV1 was praised for its performance and efficiency but was limi...

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.

The Weigh-in-Motion (WIM) Stations dataset was updated on December 31, 2024 from the Federal Highway Administration (FHWA), with attribute data from the end of calendar year 2024 and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data included in this database is provided by the Federal Highway Administration (FHWA) for Weigh-in-Motion (WIM), and can be found via the FHWA Weight Station Map (https://doi.org/10.21949/7e9e-gw75). WIM is a primary technology used for monitoring vehicle weights and axle loads on roadways. Weighing vehicles in motion is done by capturing data as vehicles drive over sensors installed in a roadway or under a bridge. Aggregated summary statistics including annual truck (class 4-14) counts and weights (in tenth of a metric ton) are also included. Summary statistics were derived from WIM data for all sites in the US that are in the FHWA Travel Monitoring Analysis System (TMAS).Traffic loading data is collected for each vehicle that passes over a WIM sensor and includes wheel (single or dual tires) loads, axle loads, and gross vehicle weights (GVW). In addition, WIM sensors collect traffic volume, axle spacings, vehicle classification, and speed data. For more information and guides about WIM stations, please navigate to https://www.fhwa.dot.gov/policyinformation/knowledgecenter/wim_guide/. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/180Q-FC93

This dataset contains motor vehicle collision reports from the New York City Police Department (NYPD), covering the period January–August 2020. Each record represents an individual collision, including detailed information on the date, time, and location of the accident (borough, ZIP code, street name, latitude/longitude), as well as vehicles, victims, and contributing factors.

Key Features

• Date & Time – Exact timestamp of the reported collision.
• Location – Borough, ZIP code, street(s), and geographical coordinates.
• Vehicles Involved – Type of vehicles and their count per accident.
• Victims – Number of injuries and fatalities, broken down by category (pedestrians, cyclists, motorists).
• Contributing Factors – Primary cause(s) of the accident (e.g., distracted driving, speeding).

Potential Analyses

• Monthly trends – Compare the percentage of total accidents by month to identify seasonal patterns.
• Day & time patterns – Analyze accident frequency by day of the week and hour of the day.
• Street-level analysis – Determine which street recorded the most accidents and what share of total accidents that represents.
• Cause analysis – Identify the most common contributing factors for all accidents and for fatal accidents specifically.

Source

Data originally obtained from NYC Open Data. Licensed under Public Domain.

US State Transportation & Health

Modes, Fatalities, and Physical Activity

By Department of Transportation [source]

About this dataset

This dataset provides a comprehensive look at the transportation and health of each US state. Included are important indicators such as commute mode share (auto, transit, bicycle and walk), complete streets policies, person miles of travel by private vehicle and walking, physical activity from transportation sources, road traffic fatalities exposure rates (auto, bicycle and pedestrian), seat belt use, transit trips per capita, use of federal funds for bicycle/pedestrian efforts, vehicle miles traveled per capita and proximity to major roadways. All these parameters allow for a comprehensive evaluation of the health state in regards to transportation. Thus allowing users to gain insights into the way different states go about their fundamental transport practices that may have implications on their overall health. This tool will allow you to compare different states across these variables in order to make correlations between policy choices and public health outcomes over time – equipping decision makers with crucial information that could help make data-driven decisions in the future

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains transportation and health information for every state in the US. This data can be used to gain a better understanding of how transportation affects our health and quality of life.

To use this dataset, you first need to understand what each column means. The columns are: State, Commute Mode Share - Auto, Commute Mode Share - Transit, Commute Mode Share - Bicycle, Commute Mode Share - Walk , Complete Streets Policies, Person Miles of Travel by Private Vehicle , Person Miles of Travel by Walking , Physical Activity from Transportation , Road Traffic Fatalities Exposure Rate- Auto , Road Traffic Fatalities Exposure Rate- Bicycle , Road Traffic Fatalities Exposure Rate-Pedestrian , Seat Belt Use Transit Trips per Capita Use of Federal Funds for Bicycle and Pedestrian Efforts Vehicle Miles Traveled per Capita Proximity to Major Roadways . Each column describes a different aspect related to transportation and health in the US states such as the number commuters who drive their own car or those who use the public transit system.

Once you understand what each column represents you can start exploring different states’ data on that particular feature with statistics such as mean value or maximum/minimum value or visualize it in charts/graphs. Additionally, you can look at correlations between different features across multiple states and try to see if they have any relationship or not. You may also want to combine multiple columns together in order create new metrics (or score) that can be compared across all the states (e.g., calculate a “Commuting Score” based on commute mode share for private vehicle/transit/bicycle). Once your analysis is complete you should have an idea about which state has better (or worse) conditions concerning transportation & health indicators and draw conclusions from there!

Research Ideas

• Creating an interactive map of the US illustrating transportation and health data from each state.
• Developing predictive models to forecast the impact of different transportation policies on health outcomes in various states.
• Identifying correlations between changes in transit mode share and road traffic fatalities/injuries based on locations/states within the US over a particular period of time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: THT_Data_508.csv | Column name | Description | |:----------------------------------------------|:------------------------------------------------------------------------------| | State | The name of the US state. (String) | | Commute Mode Share - Auto | The score assigned to the commute mode share for auto. (Number) | | **Commute Mode Share ** | Score | | Commute Mode Share - Transit | The score assigned to the commute mode share for transit. (Number) ...

PLEASE NOTE: This dataset, which includes all TLC licensed medallion 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_medallion_vehicles_authorized.csv This list contains information on the status of current medallion vehicles authorized to operate in New York City. 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. For a historical data up to and including 2016, please refer to https://data.cityofnewyork.us/Transportation/Historical-Medallion-Vehicles-Authorized/pvkv-25ck/

At Driver Technologies, we specialize in collecting high-quality, highly-anonymized driving data crowdsourced through our dash cam app. Our Car Object Detection Video Data is built from millions of miles of driving data captured by our users and is optimized for training object detection models and enhancing various applications in transportation and safety.

What Makes Our Data Unique? What sets our Car Object Detection Video Data apart is its comprehensive approach to road object detection. By leveraging advanced computer vision models, we analyze the captured video to identify and classify various road objects encountered during an end user's trip. This includes vehicles, pedestrians, traffic signs, road conditions, and more, resulting in rich, annotated datasets that can be applied across a range of industries.

How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios

Primary Use-Cases and Verticals The Car Object Detection Video Data is tailored for various sectors, particularly those involved in transportation, urban planning, and autonomous vehicle development. Key use cases include:

Training Object Detection Models: Clients can utilize our annotated data to develop and refine their own object detection models for applications in autonomous vehicles, ensuring better decision-making capabilities in complex driving environments.

Urban Planning and Infrastructure Development: Our data helps municipalities understand road usage patterns, enabling them to make informed decisions regarding infrastructure improvements, safety measures, and traffic management.

Insurance Analytics: Insurance companies can leverage insights from our data to assess risk in various driving environments, aiding in the development of tailored insurance products and improving claims processing.

Integration with Our Broader Data Offering The Car Object Detection Video Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and object detection models.

In summary, Driver Technologies' Car Object Detection Video Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Car Object Detection Video Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.

Kidmose CANid Dataset (KCID)The Kidmose CANid Dataset (KCID) contains CAN bus data collected by Brooke and Andreas Kidmose from 16 different drivers across 4 different vehicles. This dataset is designed to support driver identification and authentication research.The term "CANid" reflects the dataset's dual purpose: data collected from the CAN bus for driver identification research.VEHICLESThe dataset includes data from four different vehicles across various manufacturers and model years:2011 Chevrolet Traverse - 5-door full-size SUV crossover, AWD, 8 drivers (8 unique drivers in single-driver traces; 1 additional driver in a mixed trace)2017 Ford Focus - 5-door compact station wagon, FWD, 4 drivers2017 Subaru Forester - 5-door compact SUV crossover, AWD, 6 drivers (6 unique drivers in single-driver traces; 3 additional drivers in mixed traces)2022 Honda CR-V Touring - 5-door compact SUV crossover, AWD, 1 driverNote: The number of drivers includes volunteer drivers whose data was captured in single-driver traces, where we know who was driving at all times. We exclude volunteer drivers whose data is only available in mixed traces because we do not know when each specific driver was actually operating the vehicle.DRIVERSThe dataset includes 16 drivers across different demographic categories:Male Drivers:Under 30 years: 4 drivers ("male-under30-1" through "male-under30-4")30-55 years: 4 drivers ("male-30-55-1" through "male-30-55-4")Over 55 years: 3 drivers ("male-over55-1" through "male-over55-3")Female Drivers:All ages: 5 drivers ("female-all-ages-1" through "female-all-ages-5")Driver Directory Structure: Driver identifiers are used as directory/folder names. Within each directory, you will find traces collected from that particular driver, with additional information (location, data collection method, etc.) specified in the filename.Note: We use "unknown driver(s)" in directory names when we know that one or more volunteer drivers was operating the vehicle, but we cannot identify who was driving or when. We used a standalone data logger for some data collection sessions. If we failed to download the data and clear the logger's memory before switching drivers, this resulted in mixed traces and, occasionally, "unknown driver(s)" entries. Unfortunately, some of our volunteer drivers were short-term visitors, so we did not have the opportunity to redo their traces as single-driver traces.LOCATIONSData collection took place across multiple locations:DK - DenmarkUSA - United States of AmericaFL - FloridaNE - NebraskaNE-to-FL - Trip from Nebraska to FloridaTN - TennesseeTN-to-NE - Trip from Tennessee to NebraskaLocation codes appear in filenames (e.g., USA-FL-CANEdge-00000001.mf4 indicates data collected in Florida, USA).DATA COLLECTION METHODSThree different data collection methods were employed:CANEdge - CSS Electronics CANEdge2: Standalone data logger that connects to the OBD-II port and logs to an SD cardKorlan - Korlan USB2CAN: CAN-to-USB cable connecting the vehicle's OBD-II port to a laptopKvaser - Kvaser Hybrid CAN-LIN: CAN-to-USB cable connecting the vehicle's OBD-II port to a laptopThe data collection method is indicated in filenames (e.g., USA-FL-CANEdge-00000001.mf4).FILE TYPESThe dataset provides data in three formats to support different use cases:.mf4 (MDF4) Format: Measurement Data Format version 4 (MDF4)Binary format standardized by the Association for Standardization of Automation (ASAM)Advantages: Compact size, popular with automotive/CAN toolsUse case: Native format from CSS Electronics CANEdge2Reference: https://www.csselectronics.com/pages/mf4-mdf4-measurement-data-format.log Format: Text-based log formatCompatibility: Linux SocketCAN can-utilsAdvantages: Compatibility with SocketCAN can-utils; if a .log file is replayed, then data can be captured and monitored using Python's python-can libraryReferences: https://github.com/linux-can/can-utils, https://packages.debian.org/sid/can-utils, https://python-can.readthedocs.io/en/stable/.csv Format: Text-based comma-separated values (CSV) formatAdvantages: Easy to load with Python using the pandas library; easy to use with Python-based machine learning frameworks (e.g., scikit-learn, Keras, TensorFlow, PyTorch)Usage: Load with Python pandas: pd.read_csv()Reference: https://pandas.pydata.org/SPECIALIZED EXPERIMENTSThe KCID Dataset includes five specialized experiments:Fixed Routes ExperimentVehicles: 2011 Chevrolet Traverse, 2017 Subaru ForesterDrivers: male-30-55-3, male-30-55-4, male-over55-1, female-all-ages-1, female-all-ages-2, female-all-ages-5Location: Florida, USA (specific routes)Data Collection Methods: CSS Electronics CANEdge2, Kvaser Hybrid CAN-LINPurpose: Capture CAN traces for specific, mappable routes; eliminate route-based variations in driver authentication data (e.g., low-speed local routes vs. high-speed long-distance routes)OBD Requests and Responses ExperimentVehicle: 2011 Chevrolet TraverseDriver: female-all-ages-5Location: Florida, USAData Collection Method: CSS Electronics CANEdge2Purpose: Capture OBD requests and responses Arbitration IDs: Requests: 0x7DF, Responses: 0x7E8Tire Pressure ExperimentVehicle: 2011 Chevrolet TraverseDriver: female-all-ages-5Location: Florida, USAData Collection Method: Kvaser Hybrid CAN-LINPurpose: Capture normal and low tire pressure scenariosApplications: Detect tire pressure issues via CAN bus analysis; develop predictive maintenance strategiesDriving Modes and Features ExperimentVehicle: 2017 Ford FocusDriver: male-30-55-1Location: DenmarkData Collection Method: Korlan USB2CANPurpose: Capture different driving (and non-driving) modes and featuresExamples: gear (park, reverse, neutral, drive, sport); headlights on/offStationary Vehicles ExperimentVehicles: 2024 Chevrolet Malibu, 2025 Toyota CorollaDriver: N/A (vehicles remained stationary)Location: Florida, USAData Collection Method: Kvaser Hybrid CAN-LINPurpose: Capture CAN bus traffic from very new, very modern vehicles; identify differences between an older vehicle's CAN bus (e.g., 2011 Chevrolet Traverse), and a newer vehicle's CAN bus (e.g., 2024 Chevrolet Malibu)ADDITIONAL DOCUMENTATIONEach "specialized experiment" directory contains a detailed README.md file with specific information about the experiment and the data collected.RESEARCH APPLICATIONSThis dataset supports various research areas:Driver authentication, driver fingerprintingBehavioral biometrics in the automotive domainVehicle diagnostics and predictive maintenanceMachine learning in the automotive domainCAN bus analysis and reverse engineeringCITATIONIf you use the Kidmose CANid Dataset in your research, please cite appropriately. Citation information will be updated when our paper is published in a peer-reviewed venue.Article Citation:APA Style: Kidmose, B. E., Kidmose, A. B., and Zou, C. C. (2025). A critical roadmap to driver authentication via CAN bus: Dataset review, introduction of the Kidmose CANid Dataset (KCID), and proof of concept. arXiv. https://arxiv.org/pdf/2510.25856MLA Style: Kidmose, Brooke Elizabeth, Andreas Brasen Kidmose, and Cliff C. Zou. "A Critical Roadmap to Driver Authentication via CAN Bus: Dataset Review, Introduction of the Kidmose CANid Dataset (KCID), and Proof of Concept." arXiv, 2025. doi:10.48550/arXiv.2510.25856Chicago Style: Kidmose, Brooke Elizabeth, Andreas Brasen Kidmose, and Cliff C. Zou. "A Critical Roadmap to Driver Authentication via CAN Bus: Dataset Review, Introduction of the Kidmose CANid Dataset (KCID), and Proof of Concept." arXiv (2025). doi:10.48550/arXiv.2510.25856Dataset Citation:APA Style: Kidmose, B. E. and Kidmose, A. B. (2025). Kidmose CANid Dataset (KCID) v1. [Data set]. Technical University of Denmark. https://doi.org/10.11583/DTU.30483005.v1MLA Style: Kidmose, Brooke Elizabeth, and Andreas Brasen Kidmose. "Kidmose CANid Dataset (KCID) v1." Technical University of Denmark, 30 Oct. 2025. Web. {Date accessed in dd mmm yyyy format}. doi:10.11583/DTU.30483005.v1Chicago Style: Kidmose, Brooke Elizabeth, and Andreas Brasen Kidmose. 2025. "Kidmose CANid Dataset (KCID) v1." Technical University of Denmark. doi:10.11583/DTU.30483005.v1

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.

Used Car Prices MoM in the United States decreased to -2 percent in October from -0.20 percent in September of 2025. This dataset includes a chart with historical data for the United States Used Car Prices MoM.

A digital record of all Tesla fires - including cars and other products, e.g. Tesla MegaPacks - that are corroborated by news articles or confirmed primary sources. Latest version hosted at https://www.tesla-fire.com.

The Motor Vehicle Collisions crash table contains details on the crash event. Each row represents a crash event. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details.For the most accurate, up to date statistics on traffic fatalities, please refer to the NYPD Motor Vehicle Collisions page (updated weekly) or Vision Zero View (updated monthly).

Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable.