United States Average Vehicles per Household: 4 or More Licensed Drivers data was reported at 4.100 Unit in 2017. This records an increase from the previous number of 3.900 Unit for 2009. United States Average Vehicles per Household: 4 or More Licensed Drivers data is updated yearly, averaging 3.850 Unit from Dec 1991 (Median) to 2017, with 4 observations. The data reached an all-time high of 4.100 Unit in 2017 and a record low of 3.800 Unit in 2001. United States Average Vehicles per Household: 4 or More Licensed Drivers data remains active status in CEIC and is reported by Center for Transportation Analysis. The data is categorized under Global Database’s United States – Table US.TA003: Number of Vehicles per Household.

How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our mobility statistics program.

The "Trips by Distance" data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

These data are made available under a public domain license. Data should be attributed to the "Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland and the United States Bureau of Transportation Statistics."

Daily data for a given week will be uploaded to the BTS website within 9-10 days of the end of the week in question (e.g., data for Sunday September 17-Saturday September 23 would be updated on Tuesday, October 3). All BTS visualizations and tables that rely on these data will update at approximately 10am ET on days when new data are received, processed, and uploaded.

The methodology used to develop these data can be found at: https://rosap.ntl.bts.gov/view/dot/67520.

Key information about US Number of Registered Vehicles

• US Number of Registered Vehicles was reported at 283,400,986 Unit in Dec 2022.
• This records an increase from the previous number of 282,354,993 Unit for Dec 2021.
• US Number of Registered Vehicles data is updated yearly, averaging 93,949,852 Unit from Dec 1910 to 2022, with 113 observations.
• The data reached an all-time high of 283,400,986 Unit in 2022 and a record low of 468,500 Unit in 1910.
• US Number of Registered Vehicles data remains active status in CEIC and is reported by CEIC Data.
• The data is categorized under World Trend Plus’s Global Economic Monitor – Table: No of Registered Vehicles: Annual.

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.

Car Registrations in the United States increased to 219.90 Thousand in February from 192.40 Thousand in January of 2025. This dataset provides - United States Car Registrations - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Over the course of the 20th century, the number of operational motor vehicles in the United States grew significantly, from just 8,000 automobiles in the year 1900 to more than 183 million private and commercial vehicles in the late 1980s. Generally, the number of vehicles increased in each year, with the most notable exceptions during the Great Depression and Second World War.

United States Ave Vehicle Miles Traveled per Household: 3 Licensed Drivers data was reported at 37,700.000 Mile in 2009. This records a decrease from the previous number of 37,900.000 Mile for 2001. United States Ave Vehicle Miles Traveled per Household: 3 Licensed Drivers data is updated yearly, averaging 37,700.000 Mile from Dec 1991 (Median) to 2009, with 3 observations. The data reached an all-time high of 37,900.000 Mile in 2001 and a record low of 29,400.000 Mile in 1991. United States Ave Vehicle Miles Traveled per Household: 3 Licensed Drivers data remains active status in CEIC and is reported by Center for Transportation Analysis. The data is categorized under Global Database’s USA – Table US.TA005: Vehicles Miles Traveled per Household.

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.

United States Average Vehicles per Household: 2 Licensed Drivers data was reported at 2.200 Unit in 2009. This stayed constant from the previous number of 2.200 Unit for 2001. United States Average Vehicles per Household: 2 Licensed Drivers data is updated yearly, averaging 2.200 Unit from Dec 1991 (Median) to 2009, with 3 observations. The data reached an all-time high of 2.200 Unit in 2009 and a record low of 2.100 Unit in 1991. United States Average Vehicles per Household: 2 Licensed Drivers data remains active status in CEIC and is reported by Center for Transportation Analysis. The data is categorized under Global Database’s USA – Table US.TA003: Number of Vehicles per Household.

United States Average Vehicles per Household: 3 Licensed Drivers data was reported at 3.000 Unit in 2009. This stayed constant from the previous number of 3.000 Unit for 2001. United States Average Vehicles per Household: 3 Licensed Drivers data is updated yearly, averaging 3.000 Unit from Dec 1991 (Median) to 2009, with 3 observations. The data reached an all-time high of 3.000 Unit in 2009 and a record low of 2.900 Unit in 1991. United States Average Vehicles per Household: 3 Licensed Drivers data remains active status in CEIC and is reported by Center for Transportation Analysis. The data is categorized under Global Database’s USA – Table US.TA003: Number of Vehicles per Household.

Graph and download economic data for Vehicle Miles Traveled (TRFVOLUSM227NFWA) from Jan 1970 to Jan 2025 about miles, travel, vehicles, and USA.

Analysis of ‘Parking Statistics in North America’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/terenceshin/searching-for-parking-statistics-in-north-america on 28 January 2022.

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

ABOUT

This dataset identifies areas within a city where drivers are experiencing difficulty searching for parking. Cities can use this data to identify problem areas, adjust signage, and more. Only cities with a population of more than 100,000 are included.

Data

Some variables to highlight:

• AvgTimeToPark: The average time taken to search for parking (in minutes)
• AvgTimeToParkRatio: The ratio between the average time taken to search for parking and of those not searching for parking in the current geohash
• TotalSearching: The number of drivers searching for parking
• PercentSearching: The percentage of drivers that were searching for parking
• AvgUniqueGeohashes: The average number of unique geohashes at the 7 character level (including neighbouring and parking geohashes) that were driven in among vehicles that searched for parking
• AvgTotalGeohashes: The average number of all geohashes at the 7 character level (including neighbouring and parking geohashes) that were driven in among vehicles that searched for parking
• CirclingDistribution: JSON object representing the neighbouring geohashes at the 7 character level whereby vehicles searching for parking tend to spend their time. Each geohash will have the average percentage of time spent in that geohash prior to parking.
• HourlyDistribution: JSON object representing the average prevalence of searching for parking by hour of day (% distribution based on number of vehicles experiencing parking problems)
• SearchingByHour: JSON object representing the average percentage of vehicles searching for parking within the hour
• PercentCar: Percentage of vehicles with parking issues that were cars
• PercentMPV: Percentage of vehicles with parking issues that were multi purpose vehicles
• PercentLDT: Percentage of vehicles with parking issues that were light duty trucks
• PercentMDT: Percentage of vehicles with parking issues that were medium duty trucks
• PercentHDT: Percentage of vehicles with parking issues that were heavy duty trucks
• PercentOther: Percentage of vehicles with parking issues that were unknown classification

Content

This dataset is aggregated over the previous 6 months and is updated monthly. This data is publicly available from Geotab (geotab.com).

Inspiration

As some inspiration, here are some questions:

• Which cities are the hardest to find parking?
• By joining population data externally, can you determine a relationship between a region's population and the time that it takes to find parking?
• Similarly, by finding external data, is there a correlation between GDP and parking times? What about average household income?

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

This is a list of authorized providers who offer the TLC Driver License 24 hour TLC Driver Education Course and exam. All TLC Driver License applicants must complete the course and pass an 80-question multiple choice exam on a computer with a grade of 70% or higher (you must answer 56 out of 80 questions correctly in order to pass). The course covers the following topics: TLC rules and regulations; geography; safe driving skills; traffic rules; and customer service.

When data and analytics leaders throughout Europe and the United States were asked what their key business drivers were for their company's data and analytics priorities, over half cite generating revenue as their number one reason as of 2021. Other popular business drivers include digital transformation, customer intimacy, plus regulatory and compliance to name a few.

This dataset provides information on motor vehicle operators (drivers) involved in traffic collisions occurring on county and local roadways. The dataset reports details of all traffic collisions occurring on county and local roadways within Montgomery County, as collected via the Automated Crash Reporting System (ACRS) of the Maryland State Police, and reported by the Montgomery County Police, Gaithersburg Police, Rockville Police, or the Maryland-National Capital Park Police. This dataset shows each collision data recorded and the drivers involved. Please note that these collision reports are based on preliminary information supplied to the Police Department by the reporting parties. Therefore, the collision data available on this web page may reflect: -Information not yet verified by further investigation -Information that may include verified and unverified collision data -Preliminary collision classifications may be changed at a later date based upon further investigation -Information may include mechanical or human error This dataset can be joined with the other 2 Crash Reporting datasets (see URLs below) by the State Report Number. * Crash Reporting - Incidents Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Incidents-Data/bhju-22kf * Crash Reporting - Non-Motorists Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Non-Motorists-Data/n7fk-dce5 Update Frequency : Weekly

Graph and download economic data for Moving 12-Month Total Vehicle Miles Traveled (M12MTVUSM227NFWA) from Dec 1970 to Jan 2025 about miles, travel, vehicles, and USA.

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.

Opah labs 3227 specializes in providing third-party software solutions for last-mile delivery services in North America, excluding the United States. Established in 2015, their primary focus is on business-to-business (B2B) services. Their platform streamlines delivery operations, overseeing orders, drivers, and historical data, including Ecommerce data. They have a database containing 5,632,203 records spanning from August 5, 2021, to March 23, 2022, with no duplicates. They serve 1,134,167 unique users, with an 82.30% similarity rate. Their mission is to enhance last-mile delivery efficiency through software and data-driven solutions.

The data is sourced from an application that facilitates various deliveries, acting as a backend for providers. The company offers ETL services and can adjust delivery frequency, including daily collection. It has data coverage for Ozempic, Saxenda, Orlistat, and Hydrogel.

The dataset includes prescription delivery data and consumer goods data for users. This information offers insights into geographical transactions at a consumer level, with implications for consumer behavior and publicly traded companies.

Restaurant & Food Delivery Transaction Data is part of Opah 3227's comprehensive dataset, providing valuable insights into the dynamics of the food delivery industry. | Volume and Stats | Industry records undergo an unmatched refresh every two weeks. Many prominent sales and marketing platforms rely on curating firsthand data.

Delivery formats: JSON, XLS, CSV

| Data Points | With an impressive average of over 1,255,918 unique users. Key fields include Location, Payment Method, transit times, branch locations, and products.

| Use Cases | Pharma, Restaurant & Food Delivery Transaction, Ecommerce Pharma Data pertaining to the pharmaceutical industry. Restaurant & Food Delivery Transaction Details of transactions in the restaurant and food delivery sector. Ecommerce Online transactions, including products, purchases, and customer behavior, vital for optimizing online retail operations.

| Data Use Cases | Understand consumer purchasing and delivery behavior within the Mexico market

Data provides insights into pharmaceutical usage within the Mexico Market Insights into food consumption and food delivery usage

Insights into delivery applications transactions within the Mexico market, Rappi, Uber Eats, Ivoy, etc

Verified delivery consumer, email address, address information, etc

Geospatial, latitude, longitude, ping data coordinate values for consumers with timestamps

Geospatial, latitude, longitude, ping data coordinate values for delivery drivers

Insights into Mexico Farmacia brands delivery transactions

Insights to build targeted marketing campaigns for Mexico market consumers

| Delivery Options | Choose from various delivery options such as flat files, databases, APIs, and more, tailored to your needs.

| Other key features | Free data samples

Tags: Third-party software, On-Demand delivery, Last-mile, Drivers, Transit times, Branch locations, Products, Payments, Customer data, Food delivery, E-commerce, Pharmacy, Prescription action, Order processing, Efficiency, Customer behavior, Demand forecasting, Route optimization.

Car Production in the United States increased to 10.35 Million Units in February from 9.28 Million Units in January of 2025. This dataset provides - United States Car Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Dataset Diversity

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

Metadata

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