Annual State-reported licensed driver data from Highway Statistics for the 50 States and DC from Highway Statistics table DL-22.

OPT does not operate any buses nor does it directly employ any bus drivers or attendants. Drivers and attendants (sometimes referred to as ‘matrons’ or ‘escorts’) and employed by bus vendors themselves. OPT manages systems and processes to ensure that drivers and attendants have all requisite background checks and certifications. The Driver and Attendant summary data is available for each bus vendor and describes the number of active employees by job type.

PLEASE NOTE: This dataset, which includes all TLC Licensed Drivers who 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 If the list is outdated, please download the most recent list from the link below.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_drivers_active.csv

NYC TLC Licensed FHV drivers that are currently active and in good standing. 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.

USA driver license dataset

Dataset comprises 5,000+ photos of printed synthetic driver license images designed for training ML models in PII extraction. It features drivers licenses from various U.S. states, specifically engineered for data extraction and identification verification tasks. By utilizing this dataset, researchers and developers can advance their understanding and capabilities in OCR systems and information extraction technologies for motor vehicles documentation. -… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/synthetic-usa-driver-license.

Sample Data: https://cloud.drivertechnologies.com/shared?s=146&t=4:03&token=0f469c88-d578-4b4f-80b2-f53f195683b2

At Driver Technologies, we are dedicated to harnessing advanced technology to gather anonymized critical driving data through our innovative dash cam app, which operates seamlessly on end users' smartphones. Our Speed Over Limit Driver Behavior Data offering is a key resource for understanding driver behavior and improving safety on the roads, making it an essential tool for various industries.

What Makes Our Data Unique? Our Speed Over Limit Driver Behavior Data is distinguished by its real-time collection capabilities, utilizing our built-in computer vision technology to identify and capture instances where a driver nearly gets into an accident. This data reflects critical safety events that are indicative of potential risks and non-compliance with traffic regulations. By providing data on these significant events, our dataset empowers clients to perform in-depth analysis.

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. For our Speed Over Limit Driver Behavior Data, we leverage computer vision models to read speed limit signs as the driver drives past them, then compare that to speed data captured using the phone's sensor.

Primary Use-Cases and Verticals Driver Behavior Analysis: Organizations can leverage our dataset to analyze driving habits and identify trends in driver behavior. This analysis can help in understanding patterns related to rule compliance and potential risk factors.

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

Improving Risk Assessment: Insurers can utilize our dataset to refine their risk assessment models. By understanding the frequency and context of significant events, they can better evaluate driver risk profiles, leading to more accurate premium pricing and improved underwriting processes.

Integration with Our Broader Data Offering The Speed Over Limit Driver Behavior 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 smart city planning.

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

PLEASE NOTE: This dataset, which includes all TLC Licensed Drivers who 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_drivers_active.csv This is a list of drivers with a current TLC Driver License, which authorizes drivers to operate NYC TLC licensed yellow and green taxicabs and for-hire vehicles (FHVs). This list is accurate as of the date and time shown in the Last Date Updated and Last Time Updated fields. Questions about the contents of this dataset can be sent by email to: licensinginquiries@tlc.nyc.gov.

Introduction

Welcome to the US English Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Participant Diversity:

- Speakers: 50+ native English speakers from the FutureBeeAI Community.

- Regions: Ensures a balanced representation of United States of America1 accents, dialects, and demographics.

- Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.

Recording Nature: Scripted wake word and command type of audio recordings.

- Duration: Average duration of 5 to 20 seconds per audio recording.

- Formats: WAV format with mono channels, a bit depth of 16 bits. The dataset contains different data at 16kHz and 48kHz.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Different Automobile Related Wake Words: Hey Mercedes, Hey BMW, Hey Porsche, Hey Volvo, Hey Audi, Hi Genesis, Hey Mini, Hey Toyota, Ok Ford, Hey Hyundai, Ok Honda, Hello Kia, Hey Dodge.

Different Cars: Data collection was carried out in different types and models of cars.

Different Types of Voice Commands:

- Navigational Voice Commands

- Mobile Control Voice Commands

- Car Control Voice Commands

- Multimedia & Entertainment Commands

- General, Question Answer, Search Commands

Recording Time: Participants recorded the given prompts at various times to make the dataset more diverse.

- Morning

- Afternoon

- Evening

Recording Environment: Various recording environments were captured to acquire more realistic data and to make the dataset inclusive of various types of noises. Some of the environment variables are as follows:

- Noise Level: Silent, Low Noise, Moderate Noise, High Noise

- Parking Location: Indoor, Outdoor

- Car Windows: Open, Closed

- Car AC: On, Off

- Car Engine: On, Off

- Car Movement: Stationary, Moving

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

Participant Metadata: Unique identifier, age, gender, country, state, district, accent, and dialect.

Other Metadata: Recording transcript, recording environment, device details, sample rate, bit depth, file format, recording time.

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of English voice assistant speech recognition models.

License

This US English In-car audio dataset is created by FutureBeeAI and is available for commercial use.

This study focuses on the drinking and driving habits of Americans. The questionnaire contained 51 questions. Respondents were interviewed over the telephone and asked about their frequency of consumption of alcoholic beverages, where they most often drank, their mode of transportation to and from this location, their driving and drinking experiences, and their age, sex, educational attainment, and socioeconomic status.

The ORNL Driver Identification Dataset was created to collect and analyze driving behavior data from 50 different drivers. Each driver operated a 2014 Kenworth T270 Class 6 truck around Fort Collins, Colorado while various data sources recorded their driving behavior and vehicle performance. The dataset includes CANbus (Controller Area Network) data, GPS data, inertial measurement data, and biometric data from a heart rate monitor. A cyberattack was executed during each drive, which caused multiple dashboard warning lights to illuminate and set the tachometer and speedometer to zero, regardless of actual speed. The attack was stopped either after one minute or if the driver pulled over.

This dataset features over 1,000,000 high-quality images of cars, sourced globally from photographers, enthusiasts, and automotive content creators. Optimized for AI and machine learning applications, it provides richly annotated and visually diverse automotive imagery suitable for a wide array of use cases in mobility, computer vision, and retail.

Key Features: 1. Comprehensive Metadata: each image includes full EXIF data and detailed annotations such as car make, model, year, body type, view angle (front, rear, side, interior), and condition (e.g., showroom, on-road, vintage, damaged). Ideal for training in classification, detection, OCR for license plates, and damage assessment.

1. Unique Sourcing Capabilities: the dataset is built from images submitted through a proprietary gamified photography platform with auto-themed competitions. Custom datasets can be delivered within 72 hours targeting specific brands, regions, lighting conditions, or functional contexts (e.g., race cars, commercial vehicles, taxis).

2. Global Diversity: contributors from over 100 countries ensure broad coverage of car types, manufacturing regions, driving orientations, and environmental settings—from luxury sedans in urban Europe to pickups in rural America and tuk-tuks in Southeast Asia.

3. High-Quality Imagery: images range from standard to ultra-HD and include professional-grade automotive photography, dealership shots, roadside captures, and street-level scenes. A mix of static and dynamic compositions supports diverse model training.

4. Popularity Scores: each image includes a popularity score derived from GuruShots competition performance, offering valuable signals for consumer appeal, aesthetic evaluation, and trend modeling.

5. AI-Ready Design: this dataset is structured for use in applications like vehicle detection, make/model recognition, automated insurance assessment, smart parking systems, and visual search. It’s compatible with all major ML frameworks and edge-device deployments.

6. Licensing & Compliance: fully compliant with privacy and automotive content use standards, offering transparent and flexible licensing for commercial and academic use.

Use Cases: 1. Training AI for vehicle recognition in smart city, surveillance, and autonomous driving systems. 2. Powering car search engines, automotive e-commerce platforms, and dealership inventory tools. 3. Supporting damage detection, condition grading, and automated insurance workflows. 4. Enhancing mobility research, traffic analytics, and vision-based safety systems.

This dataset delivers a large-scale, high-fidelity foundation for AI innovation in transportation, automotive tech, and intelligent infrastructure. Custom dataset curation and region-specific filters are available. Contact us to learn more!

The size of the US Data Center Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 6.00% during the forecast period.A data center is a facility that keeps computer systems and networking equipment housed, processing, and transmitting data. It represents the infrastructure on which organizations carry out their IT operations and host websites, email servers, and database servers. Data centers, therefore, are imperative to any size business: small start-ups or large enterprise since they enable digital transformation, thus making business applications available.The US data center industry is one of the largest and most developed in the world. The country boasts robust digital infrastructure, abundant energy resources, and a highly skilled workforce, making it an attractive destination for data center operators. Some of the drivers of the US data center market are the growing trend of cloud computing, internet of things (IoT), and high-performance computing requirements.Top-of-the-line technology companies along with cloud service providers set up major data center footprints in the US, mostly in key regions such as Silicon Valley and Northern Virginia, Dallas, for example. These data centers support applications such as e-commerce-a manner of accessing streaming services-whose development depends on its artificial intelligence financial service type. As demand increases concerning data center capacity, therefore, the US data centre industry will continue to prosper as the world's hub for reliable and scalable solutions. Recent developments include: February 2023: The expansion of Souther Telecom to its data center in Atlanta, Georgia, at 345 Courtland Street, was announced by H5 Data Centers, a colocation and wholesale data center operator. One of the top communication service providers in the southeast is Southern Telecom. Customers in Alabama, Georgia, Florida, and Mississippi will receive better service due to the expansion of this low-latency fiber optic network.December 2022: DigitalBridge Group, Inc. and IFM Investors announced completing their previously announced transaction in which funds affiliated with the investment management platform of DigitalBridge and an affiliate of IFM Investors acquired all outstanding common shares of Switch, Inc. for USD approximately USD 11 billion, including the repayment of outstanding debt.October 2022: Three additional data centers in Charlotte, Nashville, and Louisville have been made available to Flexential's cloud customers, according to the supplier of data center colocation, cloud computing, and connectivity. By the end of the year, clients will have access to more than 220MW of hybrid IT capacity spread across 40 data centers in 19 markets, which is well aligned with Flexential's 2022 ambition to add 33MW of new, sustainable data center development projects.. Key drivers for this market are: , High Mobile penetration, Low Tariff, and Mature Regulatory Authority; Successful Privatization and Liberalization Initiatives. Potential restraints include: , Difficulties in Customization According to Business Needs. Notable trends are: OTHER KEY INDUSTRY TRENDS COVERED IN THE REPORT.

USA ID CARD FRONT

## Overview

USA ID CARD FRONT is a dataset for object detection tasks - it contains TEXT annotations for 29 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

United States US: Road Passenger Transport: Passenger Cars data was reported at 5,286,161.874 Person-km mn in 2022. This records a decrease from the previous number of 5,586,348.601 Person-km mn for 2021. United States US: Road Passenger Transport: Passenger Cars data is updated yearly, averaging 4,298,629.006 Person-km mn from Dec 1970 (Median) to 2022, with 37 observations. The data reached an all-time high of 6,060,622.152 Person-km mn in 2019 and a record low of 2,817,796.000 Person-km mn in 1970. United States US: Road Passenger Transport: Passenger Cars data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.ITF: Passenger Transport by Mode of Transport: OECD Member: Annual. [STAT_CONC_DEF] Road passenger transport: any movement of passengers using a road vehicle on a given road network. National road passenger transport: road passenger transport between two places (a place of loading/embarkation and a place of unloading/disembarkation) located in the same country irrespective of the country in which the road motor vehicle is registered. It may involve transit through a second country. International road passenger transport: road passenger transport between a place of loading/embarkation or unloading/disembarkation in the declaring country and a place of loading/embarkation or unloading/disembarkation in another country. Such transport may involve transit through one or more additional countries. Road passenger: any person who makes a journey by a road vehicle. Drivers of passenger cars, excluding taxi drivers, are counted as passengers. Road passenger-kilometre: unit of measurement representing the transport of one passenger by road over one kilometre. [STAT_CONC_DEF] Since 2000, the definition of passenger car is determined by the size of the wheel base. In 2009, there was a change in passenger car occupancy factor, that creates a break in the series. Transport by buses and coaches by the American Public Transportation Association (APTA). [COVERAGE] Data should include urban transport.

Uber And Lyft Drivers Carjackings

Dataset from: "Uber And Lyft Drivers Are Being Carjacked at Alarming Rates"

About this dataset

Original Author: The dataset was first posted here By The Markup

This dataset contains information on carjackings of gig economy workers in the United States. columns include: state, city, date, company, carjacking_happened_via_the_app, driver_deceased, source, additional_sources

How to use the dataset

This dataset can be used to research gig worker carjackings in the United States. The data includes information on the date, location, and circumstances of each carjacking, as well as the name of the gig economy company involved. Researchers can use this dataset to study patterns in carjackings, identify risk factors for drivers, and develop strategies for prevention and response

Research Ideas

• Finding trends in gig worker carjackings across the United States
• Determining which cities are most at risk for gig worker carjackings
• Analyzing which companies' gig workers are most at risk for carjackings

Acknowledgements

We would like to thank the researchers at the University of Michigan for their work in compiling this dataset

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: carjackings.csv | Column name | Description | |:------------------------------------|:---------------------------------------------------------------------------------------| | state | The state in which the carjacking occurred. (String) | | city | The city in which the carjacking occurred. (String) | | date | The date on which the carjacking occurred. (Date) | | company | The company for which the driver was working at the time of the carjacking. (String) | | carjacking_happened_via_the_app | Whether or not the carjacking occurred through the use of the company's app. (Boolean) | | driver_deceased | Whether or not the driver was killed as a result of the carjacking. (Boolean) | | source | The source of the information on the carjacking. (String) | | additional_sources | Any additional sources of information on the carjacking. (String) |

Historical licensee status data. Data dictionary available here: https://data.cityofnewyork.us/api/views/p32s-yqxq/files/20bca1de-74f0-462e-8574-f25ca66b8346?download=true&filename=NewDriverAppStatusLookupLegend.pdf For questions, please email newdriverapp@tlc.nyc.gov.

PLEASE NOTE: This dataset, which includes all TLC Licensed Drivers who 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/tlcshldrivers_active.csv

NYC TLC licensed drivers that are currently active, in good standing and authorized to operate Street Hail Livery (SHL) vehicles. 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.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Percentage of Drivers and Front Seat Passengers Wearing Seat Belts, 2012 & 2014, Region 9 - San Francisco

  Description

Source for 2012 national data: National Occupant Protection Use Survey (NOPUS), 2012. Source for 2014 national data: National Occupant Protection Use Survey (NOPUS), 2014. Source for 2012 state data: State Observational Survey of Seat Belt Use, 2012. Source for 2014 state data: Seat Belt Use in 2014- Use Rates in the States and Territories

  Dataset… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/percentage-of-drivers-and-front-seat-passengers-we.

First Registrations of Brand New Passenger Cars data was reported at 746,788.000 Number in Dec 2024. This records an increase from the previous number of 738,592.000 Number for Sep 2024. First Registrations of Brand New Passenger Cars data is updated quarterly, averaging 747,191.500 Number from Mar 2020 (Median) to Dec 2024, with 20 observations. The data reached an all-time high of 1,044,835.000 Number in Jun 2021 and a record low of 652,191.000 Number in Dec 2021. First Registrations of Brand New Passenger Cars data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.ITF: Motor Vehicles Statistics: OECD Member: Quarterly. [COVERAGE] A passenger car is a road motor vehicle, other than a motorcycle, intended for the carriage of passengers and designed to seat no more than nine persons (including the driver). The term 'passenger car' therefore covers microcars (need no permit to be driven), taxis and hired passenger cars, provided that they have fewer than ten seats. This category may also include pick-ups. [COVERAGE] Data do not include light trucks. [STAT_CONC_DEF] Until 2008, data are not available. Quarterly and monthly data are not available. Since 2022, data are not yet available.