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.

A Chinese automobile company, Geely Auto, aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts.

They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting car pricing in the American market, as they may differ from the Chinese market.

The company wants to know the following things:

Which variables are significant in predicting the price of a car? How well do those variables describe the price of a car? Based on various market surveys, the consulting firm has gathered a large data set of different types of cars across the American market.

You are required to model the price of cars with the available independent variables. The management will use be using this model to understand exactly how the prices vary with the independent variables. Accordingly, they can change the design of the cars, the business strategy, etc., to meet certain price levels. Further, the model will allow the management to understand the pricing dynamics of a new market.

This dataset contains the file of vehicle, snowmobile and boat registrations in NYS. Registrations expired more than 2 years are excluded. Records that have a scofflaw, revocation and/or suspension are included with indicators specifying those kinds of records.

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for Leicester MSOAs and compare this with Leicester overall statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsCar availabilityThis dataset provides Census 2021 estimates on the number of cars or vans available to members of households for England and Wales. The estimates are as at Census Day, 21 March 2021.Definition: The number of cars or vans owned or available for use by household members.Vehicles included:pick-ups, camper vans and motor homesvehicles that are temporarily not working vehicles that have failed their MOTvehicles owned or used by a lodgercompany cars or vans if they're available for private useVehicles not included:motorbikes, trikes, quad bikes or mobility scootersvehicles that have a Statutory Off Road Notification (SORN)vehicles owned or used only by a visitor vehicles that are kept at another address or not easily accessedThe number of cars or vans in an area relates only to households. Cars or vans used by communal establishment residents are not counted.Households with 10 to 20 cars or vans are counted as having only 10.Households with more than 20 cars or vans were treated as invalid and a value imputed.This dataset includes data for Leicester city MSOAs.

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for Leicester and compare this with national statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsCar availabilityThis dataset provides Census 2021 estimates on the number of cars or vans available to members of households for England and Wales. The estimates are as at Census Day, 21 March 2021.Definition: The number of cars or vans owned or available for use by household members.Vehicles included:pick-ups, camper vans and motor homesvehicles that are temporarily not working vehicles that have failed their MOTvehicles owned or used by a lodgercompany cars or vans if they're available for private useVehicles not included:motorbikes, trikes, quad bikes or mobility scootersvehicles that have a Statutory Off Road Notification (SORN)vehicles owned or used only by a visitor vehicles that are kept at another address or not easily accessedThe number of cars or vans in an area relates only to households. Cars or vans used by communal establishment residents are not counted.Households with 10 to 20 cars or vans are counted as having only 10.Households with more than 20 cars or vans were treated as invalid and a value imputed.This dataset includes data for Leicester city and England overall.

Introduction

Welcome to the US Spanish 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 Spanish speakers from the FutureBeeAI Community.

- Regions: Ensures a balanced representation of USA1 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 Spanish voice assistant speech recognition models.

License

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

Total Vehicle Sales in the United States decreased to 15.30 Million in October from 16.40 Million in September of 2025. This dataset provides the latest reported value for - United States Total Vehicle Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Insurance rates for vehicles is a major market that is subject to a lot of variance. This simple and small dataset contains the insurance rate for all US states.

How to use

• Explore insurance rates per state, find optimal prices
• More datasets

Acknowledgements

Sources

• Howmuch
• ValuePenguin

License

License was not specified at the source

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Unlock insights into road scenes with our comprehensive Vehicle Image Captioning Dataset. This dataset comprises a diverse collection of images capturing vehicles in various settings. Each image is accompanied by detailed captions generated and verified by humans.

These captions, following a specific question format, describe every object on the road, including vehicle color, windshield presence, door and window status, vehicle type, visible wheels, number plate details, logos or brands, vehicle and people activity, and background description. With a 60-70 word description, this dataset offers rich contextual information for image understanding and captioning tasks.

Optimized for Generative AI, Visual Question Answering, Image Classification, and LMM development, this dataset provides a strong basis for achieving robust model performance.

Features:

• 1000+ high-resolution images captured across diverse Indian road scenes.
• Detailed captions describing each object on the road from left to right.
• Captions include vehicle color, windshield presence, door and window status, vehicle type, visible wheels, number plate details, logos or brands, vehicle and people activity, and background description.
• Images sourced from various cities and regions across India, covering day and night scenarios, varied distances, different backgrounds, viewpoints, and more.
• Ideal for image captioning, object detection, scene understanding, and AI research tasks.

Applications:

• Image captioning and description generation.
• Object detection and recognition.
• Autonomous vehicle navigation and scene understanding.
• Traffic analysis and management.
• Urban planning and infrastructure development.

Dataset with Bounding Boxes: The dataset also includes bounding box annotation for Indian Vehicles in 15+ classes. To access the dataset, please visit: https://www.kaggle.com/datasets/dataclusterlabs/indian-vehicle-dataset

The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. For more details contact us at sales@datacluster.ai or visit www.datacluster.ai

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.

The U.S. auto industry sold nearly ************* cars in 2024. That year, total car and light truck sales were approximately ************ in the United States. U.S. vehicle sales peaked in 2016 at roughly ************ units. Pandemic impact The COVID-19 pandemic deeply impacted the U.S. automotive market, accelerating the global automotive semiconductor shortage and leading to a drop in demand during the first months of 2020. However, as demand rebounded, new vehicle supply could not keep up with the market. U.S. inventory-to-sales ratio dropped to its lowest point in February 2022, as Russia's war on Ukraine lead to gasoline price hikes. During that same period, inflation also impacted new and used car prices, pricing many U.S. consumers out of a market with increasingly lower car stocks. Focus on fuel economy The U.S. auto industry had one of its worst years in 1982 when customers were beginning to feel the effects of the 1973 oil crisis and the energy crisis of 1979. Since light trucks would often be considered less fuel-efficient, cars accounted for about ** percent of light vehicle sales back then. Thanks to improved fuel economy for light trucks and cheaper gas prices, this picture had completely changed in 2020. That year, prices for Brent oil dropped to just over ** U.S. dollars per barrel. The decline occurred in tandem with lower gasoline prices, which came to about **** U.S. dollars per gallon in 2020 - and cars only accounted for less than one-fourth of light vehicle sales that year. Four years on, prices are dropping again, after being the highest on record since 1990 in 2022.

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

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.

Success.ai’s Email Address Data for Automotive Professionals Worldwide offers a comprehensive and verified dataset tailored for businesses looking to connect with key players in the global automotive industry. Covering roles such as engineers, designers, product managers, and executives, this dataset provides accurate contact details, professional histories, and actionable insights.

With access to over 700 million verified global profiles, Success.ai ensures your outreach, sales, and marketing strategies are powered by continuously updated, AI-validated data. Backed by our Best Price Guarantee, this solution is essential for succeeding in the fast-evolving automotive sector.

Why Choose Success.ai’s Email Address Data?

1. Verified Contact Data for Precision Engagement

   • Access verified work emails, phone numbers, and LinkedIn profiles of automotive professionals worldwide.
   • AI-driven validation ensures 99% accuracy, improving communication efficiency and engagement outcomes.

2. Comprehensive Global Coverage

   • Includes professionals from key automotive hubs in North America, Europe, Asia-Pacific, and beyond.
   • Gain insights into automotive trends, manufacturing innovations, and supply chain developments.

3. Continuously Updated Datasets

   • Real-time updates capture changes in roles, organizational structures, and industry advancements.
   • Stay aligned with the latest automotive trends and seize new opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Engage with automotive professionals across industries and regions worldwide.
• Verified Contact Details: Gain work emails, phone numbers, and LinkedIn profiles for precision targeting.
• Professional Histories: Access detailed career trajectories and expertise to guide outreach and engagement.
• Leadership Insights: Connect with engineers, product managers, and C-suite executives driving innovation in the automotive sector.

Key Features of the Dataset:

1. Comprehensive Automotive Professional Profiles

   • Identify and connect with individuals working in automotive design, manufacturing, supply chain management, and sales.
   • Target decision-makers leading sustainability efforts, electric vehicle innovation, and manufacturing optimization.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (electric vehicles, parts manufacturing, logistics), geographic location, or job role.
   • Tailor campaigns to align with specific business needs, such as technology adoption, supply chain partnerships, or market expansion.

3. Regional and Industry-specific Insights

   • Leverage data on global automotive trends, electrification efforts, and supply chain challenges.
   • Refine strategies to align with the unique demands of the automotive industry and its regions.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Lead Generation

   • Promote automotive solutions, manufacturing tools, or logistics services to professionals in the automotive industry.
   • Use verified contact data for multi-channel outreach, including email, phone, and digital campaigns.

2. Partnership Development and Collaboration

   • Build relationships with automotive manufacturers, parts suppliers, and logistics providers seeking strategic partnerships.
   • Foster collaborations that drive efficiency, sustainability, or innovation.

3. Market Research and Competitive Analysis

   • Analyze trends in the automotive industry to refine product offerings, marketing strategies, and business development plans.
   • Benchmark against competitors to identify growth opportunities and emerging demands.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers recruiting for roles in automotive engineering, supply chain management, and product development.
   • Provide workforce management platforms or training solutions tailored to the automotive sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality automotive email address data at competitive prices, ensuring strong ROI for your marketing, sales, and outreach efforts.

2. Seamless Integration

   • Integrate verified automotive data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, streamlining workflows and enhancing productivity.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide data-driven decisions, refine targeting, and boost conversion rates in campaigns.

4. Customizable and Scalable Solutions
   ...

Daily vehicle miles traveled (VMT) is a distance- and volume-based measure of driving on roadways for all motorized vehicle types—car, bus, motorcycle, and truck—on an average day. Per capita VMT is the same measure divided by the same area's population for the same year. Per vehicle VMT divides VMT by the number of household vehicles available by residents of that geography in the same year. These three value types can be selected in the dropdown in the first chart below. Use the legend items to explore various geographies. The second chart below shows per capita and total personal vehicles available to the region’s households from the American Community Survey.

Normalizing VMT by a county or region's population, or household vehicles, is helpful for context, but does not have complete parity with what is measured in VMT estimates. People and vehicles come into the region from other places, just as people and vehicles leave the region to visit other places. VMT per capita compares all miles traveled on the region's roads to the region's population (for all ages) from the U.S. Census Bureau's latest population estimates. Vehicle counts for VMT are classified by vehicle types, but not by vehicle ownership. In 2017, statewide estimates for VMT by motorcycles, passenger cars, and two-axle single-unit trucks with four wheels made up 88% of Pennsylvania's VMT, and 95% of New Jersey's. These vehicle types are highly likely to be personal vehicles, owned by households, but a small percent could be fleet vehicles of companies or governments. The remaining VMT is made up of vehicle types like school and commercial buses and trucks with more than two axles so they are highly likely to be commercial vehicles.

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.

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...

DESCRIPTION This table contains data on the percent of residents aged 16 years and older mode of transportation to work for ...

SUMMARY This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

ind_id - Indicator ID ind_definition - Definition of indicator in plain language reportyear - Year that the indicator was reported race_eth_code - numeric code for a race/ethnicity group race_eth_name - Name of race/ethnic group geotype - Type of geographic unit geotypevalue - Value of geographic unit geoname - Name of a geographic unit county_name - Name of county that geotype is in county_fips - FIPS code of the county that geotype is in region_name - MPO-based region name; see MPO_County list tab region_code - MPO-based region code; see MPO_County list tab mode - Mode of transportation short name mode_name - Mode of transportation long name pop_total - denominator pop_mode - numerator percent - Percent of Residents Mode of Transportation to Work,
Population Aged 16 Years and Older LL_95CI_percent - The lower limit of 95% confidence interval UL_95CI_percent - The lower limit of 95% confidence interval percent_se - Standard error of the percent mode of transportation percent_rse - Relative standard error (se/value) expressed as a percent CA_decile - California decile CA_RR - Rate ratio to California rate version - Date/time stamp of a version of data

This United States Environmental Protection Agency (US EPA) feature layer represents monitoring site data, updated hourly concentrations and Air Quality Index (AQI) values for the latest hour received from monitoring sites that report to AirNow.Map and forecast data are collected using federal reference or equivalent monitoring techniques or techniques approved by the state, local or tribal monitoring agencies. To maintain "real-time" maps, the data are displayed after the end of each hour. Although preliminary data quality assessments are performed, the data in AirNow are not fully verified and validated through the quality assurance procedures monitoring organizations used to officially submit and certify data on the EPA Air Quality System (AQS).This data sharing, and centralization creates a one-stop source for real-time and forecast air quality data. The benefits include quality control, national reporting consistency, access to automated mapping methods, and data distribution to the public and other data systems. The U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service, tribal, state, and local agencies developed the AirNow system to provide the public with easy access to national air quality information. State and local agencies report the Air Quality Index (AQI) for cities across the US and parts of Canada and Mexico. AirNow data are used only to report the AQI, not to formulate or support regulation, guidance or any other EPA decision or position.About the AQIThe Air Quality Index (AQI) is an index for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you. The AQI focuses on health effects you may experience within a few hours or days after breathing polluted air. EPA calculates the AQI for five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate matter), carbon monoxide, sulfur dioxide, and nitrogen dioxide. For each of these pollutants, EPA has established national air quality standards to protect public health. Ground-level ozone and airborne particles (often referred to as "particulate matter") are the two pollutants that pose the greatest threat to human health in this country.A number of factors influence ozone formation, including emissions from cars, trucks, buses, power plants, and industries, along with weather conditions. Weather is especially favorable for ozone formation when it’s hot, dry and sunny, and winds are calm and light. Federal and state regulations, including regulations for power plants, vehicles and fuels, are helping reduce ozone pollution nationwide.Fine particle pollution (or "particulate matter") can be emitted directly from cars, trucks, buses, power plants and industries, along with wildfires and woodstoves. But it also forms from chemical reactions of other pollutants in the air. Particle pollution can be high at different times of year, depending on where you live. In some areas, for example, colder winters can lead to increased particle pollution emissions from woodstove use, and stagnant weather conditions with calm and light winds can trap PM2.5 pollution near emission sources. Federal and state rules are helping reduce fine particle pollution, including clean diesel rules for vehicles and fuels, and rules to reduce pollution from power plants, industries, locomotives, and marine vessels, among others.How Does the AQI Work?Think of the AQI as a yardstick that runs from 0 to 500. The higher the AQI value, the greater the level of air pollution and the greater the health concern. For example, an AQI value of 50 represents good air quality with little potential to affect public health, while an AQI value over 300 represents hazardous air quality.An AQI value of 100 generally corresponds to the national air quality standard for the pollutant, which is the level EPA has set to protect public health. AQI values below 100 are generally thought of as satisfactory. When AQI values are above 100, air quality is considered to be unhealthy-at first for certain sensitive groups of people, then for everyone as AQI values get higher.Understanding the AQIThe purpose of the AQI is to help you understand what local air quality means to your health. To make it easier to understand, the AQI is divided into six categories:Air Quality Index(AQI) ValuesLevels of Health ConcernColorsWhen the AQI is in this range:..air quality conditions are:...as symbolized by this color:0 to 50GoodGreen51 to 100ModerateYellow101 to 150Unhealthy for Sensitive GroupsOrange151 to 200UnhealthyRed201 to 300Very UnhealthyPurple301 to 500HazardousMaroonNote: Values above 500 are considered Beyond the AQI. Follow recommendations for the Hazardous category. Additional information on reducing exposure to extremely high levels of particle pollution is available here.Each category corresponds to a different level of health concern. The six levels of health concern and what they mean are:"Good" AQI is 0 to 50. Air quality is considered satisfactory, and air pollution poses little or no risk."Moderate" AQI is 51 to 100. Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people. For example, people who are unusually sensitive to ozone may experience respiratory symptoms."Unhealthy for Sensitive Groups" AQI is 101 to 150. Although general public is not likely to be affected at this AQI range, people with lung disease, older adults and children are at a greater risk from exposure to ozone, whereas persons with heart and lung disease, older adults and children are at greater risk from the presence of particles in the air."Unhealthy" AQI is 151 to 200. Everyone may begin to experience some adverse health effects, and members of the sensitive groups may experience more serious effects."Very Unhealthy" AQI is 201 to 300. This would trigger a health alert signifying that everyone may experience more serious health effects."Hazardous" AQI greater than 300. This would trigger a health warnings of emergency conditions. The entire population is more likely to be affected.AQI colorsEPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.Air Quality Index Levels of Health ConcernNumericalValueMeaningGood0 to 50Air quality is considered satisfactory, and air pollution poses little or no risk.Moderate51 to 100Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups101 to 150Members of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy151 to 200Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy201 to 300Health alert: everyone may experience more serious health effects.Hazardous301 to 500Health warnings of emergency conditions. The entire population is more likely to be affected.Note: Values above 500 are considered Beyond the AQI. Follow recommendations for the "Hazardous category." Additional information on reducing exposure to extremely high levels of particle pollution is available here.

Context

The attached files are a sample of used car values from Honda (CRV), Toyota (Corolla) and Ford (Fiesta) scrapped from the used car website CarGuru in April 2015 as part of a team project for the "Designing Data Products" course at Cornell University.

We sourced this data to create a predictive model of used car. The goal was a MVP that could tell people where they could buy the best valued used car near them (or anywhere in the US). The scrapped data was used to create a model that estimated used car's value. We also create a search algorithm that would evaluate the value (relative to the predicted) of a specific car listing based on its attributes. Our MVP worked very well and could be implemented to scale. If interested please reach out to me and I will be happy to discuss further.

Content

Acknowledgements

This work was done in collaboration with Ope Akanji, Nick Paterson, Francisco De Carvalho Neto, Ankit Dhawan.