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.

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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The U.S. auto industry sold nearly three million cars in 2024. That year, total car and light truck sales were approximately 15.9 million in the United States. U.S. vehicle sales peaked in 2016 at roughly 17.5 million 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 77 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 40 U.S. dollars per barrel. The decline occurred in tandem with lower gasoline prices, which came to about 2.17 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.

The graph illustrates the average number of cars per household in the United States from 1969 to 2022. The x-axis represents the years, labeled from '69 to '22, while the y-axis displays the average number of cars per household. Over this period, the average increased from 1.16 cars per household in 1969 to a peak of 1.89 in 2001. The lowest recorded average was 1.16 in 1969, and the highest was 1.89 in 2001. After 2001, the average slightly decreased to 1.83 in 2022. The data indicates an overall upward trend in the average number of cars per household over the decades, with a slight decline in recent years.

Sales of used light vehicles in the United States came to around 39.2 million units in 2024. In the same period, approximately 15.8 million new light trucks and automobiles were sold here. Declining availability of vehicles In the fourth quarter of 2024, about 292.3 million vehicles were in operation in the United States, an increase of around 1.3 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.

**This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **

Overview

Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.

This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.

Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.

This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.

Findings

• Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
• Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
• New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least. ## About This Data The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.

Included Data

01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

Additional Data Queries

* Filter for specific state - filters 02_vmt_state.csv daily data for specific state.

* Filter counties by state - filters 03_vmt_county.csv daily data for counties in specific state.

* Filter for specific county - filters 03_vmt_county.csv daily data for specific county.

Interactive

The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:

@(https://interactives.ap.org/vmt-map/)

Interactive Embed Code

Using the Data

This data can help put your county's mobility in context with your state and over time. The data set contains different measures of change - daily comparisons and seven day rolling averages. The rolling average allows for a smoother trend line for comparison across counties and states. To get the full picture, there are also two available baselines - vehicle miles traveled in January 2020 (pre-pandemic) and vehicle miles traveled at each geography's low point during the pandemic.

Caveats

• The data from StreetLight Data, Inc does not include data for some low-population counties with low VMT (<5,000 miles/day in their baseline month of January 2020). In our analyses, we only include the 2,779 counties that have daily data for the entire period (March 1, 2020 to current).
• In some cases, a lack of decline in mobility from March to April can indicate that movement in the county is essential to keeping the larger economy going or that residents need to drive further to reach essentials businesses like grocery stores compared to other counties.
• The VMT includes both passenger and commercial miles, so truck traffic is included. However, the proxy is based on the "total number of trip starts and ends for all devices whose most frequent location is in this county". It does not count the VMT of trucks cutting through a county.
• For those instances where travel begins in one county and ends in another, the county where the miles are recorded is always the vehicle’s home county. ###### Contact reporter Angeliki Kastanis at akastanis@ap.org.

Snapshot img

US Used Car Market Size 2025-2029

The us used car market size is forecast to increase by USD 40.2 billion at a CAGR of 4.3% between 2024 and 2029.

The used car market in the US exhibits robust growth, driven by the excellent value proposition that pre-owned vehicles offer to consumers. This market trend is further bolstered by the increasing penetration of online platforms dedicated to selling used cars, providing greater convenience and accessibility for buyers. However, the market faces regulatory challenges as stricter emission regulations limit the sale of non-compliant used cars, necessitating investments in upgrading inventory and adhering to regulatory frameworks. These hurdles, while significant, can be navigated through strategic partnerships with emission testing centers and ongoing investment in fleet modernization. Companies that effectively address these challenges and leverage the opportunities presented by the growing demand for used cars and the digital shift in sales channels will thrive in this dynamic market.

What will be the size of the US Used Car Market during the forecast period?

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In the dynamic used car market, consumers face various challenges such as car scams and fraudulent activities. To mitigate risks, car buyers turn to comprehensive car buying guides and car detailing services. A VIN number check is essential for vehicle identification and history assessment, while emissions testing ensures environmental compliance. Car sharing and subscription services offer flexible mobility solutions. Vehicle registration and title transfer processes can be streamlined through digital means, and car refurbishment and connected car technology enhance safety and convenience. Blind spot monitoring and adaptive cruise control are popular safety features, while collision avoidance systems and lane departure warning systems provide added protection. Used car logistics and online financing applications simplify the purchasing process, and extended warranties offer peace of mind. Wireless charging, smartphone integration, and vehicle diagnostics are essential features for modern cars. Sustainable mobility and car comparison tools cater to eco-conscious consumers, while car maintenance schedules and roadside assistance ensure long-term vehicle care. Remote vehicle inspection and car care tips help maintain a car's resale value, and car subscription services offer flexible ownership alternatives. Used car fraud prevention and vehicle identification technologies protect buyers from potential risks. Car safety ratings and vehicle identification numbers are crucial tools for informed decision-making.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Distribution Channel3P channel salesOEM channel salesProductMid sizeFull sizeCompact sizeVendor TypeOrganizedUnorganizedFuel TypeDieselPetrolGeographyNorth AmericaUS

By Distribution Channel Insights

The 3p channel sales segment is estimated to witness significant growth during the forecast period.

The used car market in the US is a dynamic and significant sector, with numerous entities shaping its activity. Used car buyers continuously seek value, leading to a high demand for pre-owned vehicles. Search engine optimization and online advertising play crucial roles in connecting buyers with sellers, whether they're private parties or car dealerships. Wholesale car lots and auctions provide inventory for dealerships, ensuring a steady supply of used cars. Fleet vehicles, often traded in for newer models, contribute to the used car inventory. Maintenance records and vehicle history reports are essential for buyers, influencing their purchasing decisions. Safety features, infotainment systems, and driver assistance are increasingly desired in used cars, especially among budget-conscious consumers and luxury car buyers. Electric and hybrid vehicles are gaining popularity, driving the demand for used models in these categories. Car negotiation, fuel economy, and vehicle valuation are essential factors in used car selling. Digital marketing, including social media, mobile apps, and data analytics, helps sellers reach a wider audience. Certified pre-owned vehicles, reconditioned cars, and consignment sales offer buyers additional options and peace of mind. Car financing, vehicle inspections, and warranties are essential components of the used car buying process. Autonomous driving technology and car pricing trends continue to evolve, impacting the used car market. As the average ownership cycle shortens, the market will see an increase in the availability of used cars, making it an exciting and ever-changing landscape for both buyers and sellers.

D

This layer shows household size by number of vehicles available. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percentage of households with no vehicle available. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08201 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Analysis of ‘Vehicle Miles Traveled During Covid-19 Lock-Downs ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/vehicle-miles-travelede on 13 February 2022.

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

About this dataset

**This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **

Overview

Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.

This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.

Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.

This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.

Findings

• Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
• Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
• New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least.

About This Data

The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.

Included Data

01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

Additional Data Queries

* Filter for specific state - filters 02_vmt_state.csv daily data for specific state.

* Filter counties by state - filters 03_vmt_county.csv daily data for counties in specific state.

* Filter for specific county - filters 03_vmt_county.csv daily data for specific county.

Interactive

The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:

This dataset was created by Angeliki Kastanis and contains around 0 samples along with Date At Low, Mean7 County Vmt At Low, technical information and other features such as: - County Name - County Fips - and more.

How to use this dataset

• Analyze State Name in relation to Baseline Jan Vmt
• Study the influence of Date At Low on Mean7 County Vmt At Low
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Angeliki Kastanis

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

2013-2023 Virginia Non-Single Occupancy Vehicle (SOV) Travel Percent by Census Urban Area. Contains estimates. Workers 16 years and over, commuting to work, who are NOT using a car, truck, or van when driving alone.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table DP03, Column DP03_0019PE Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

Documentation of the method to calculate Non-SOV is provided by the Federal Highway Administration (https://www.fhwa.dot.gov/tpm/guidance/hif18024.pdf) page 38 explains the calculation of the Non-SOV Travel measure.

Urban areas with values of -666,666,666 or 0 have blanks calculated for Non-SOV values.

The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html)

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html)

Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/)

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties.

Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

Annual average net outlays for vehicle purchases came to above 5,500 U.S. dollars for all U.S. consumers in 2023, ranging between around 1,900 U.S. dollars for those in the lowest income bracket to nearly 14,100 U.S. dollars for consumers in the highest income group.

The data is based upon traffic volume trends data collected by the United States Department of Transportation data from January 1971 to February 2013.Since June 2005, vehicle miles driven have fallen 8.75 percent. This decline has remained steady for the past 92 months. There are several reasons that may be causing this steady downward trend. It has been suggested that due to rising gas prices, the Great Recession, an aging population led by the Baby Boom generation which is comprised of Americans over the age of 55 who tend to drive less, and quite possibly younger Americans choosing to drive less. Between 2001 and 2009, the average yearly number of miles driven by 16- to 34-year-olds has dropped 23 percent.Researchers indicate that this trend may be linked to five principal factors:The cost of Driving has increasedThe recent recessionIt is harder to get a license in many statesMore younger people are choosing to live in transit-oriented areas andTechnology is making it easier to go car-freeData Source Information: Traffic Volume Trends is a monthly report based on hourly traffic count data reported by the States. These data are collected at approximately 4,000 continuous traffic counting locations nationwide and are used to estimate the percent change in traffic for the current month compared with the same month in the previous year. Estimates are re-adjusted annually to match the vehicle miles of travel from the Highway Performance Monitoring System and are continually updated with additional data.

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.

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.

This collection focuses on how changes in the legal drinking age affect the number of fatal motor vehicle accidents and crime rates. The principal investigators identified three areas of study. First, they looked at blood alcohol content of drivers involved in fatal accidents in relation to changes in the drinking age. Second, they looked at how arrest rates correlated with changes in the drinking age. Finally, they looked at the relationship between blood alcohol content and arrest rates. In this context, the investigators used the percentage of drivers killed in fatal automobile accidents who had positive blood alcohol content as an indicator of drinking in the population. Arrests were used as a measure of crime, and arrest rates per capita were used to create comparability across states and over time. Arrests for certain crimes as a proportion of all arrests were used for other analyses to compensate for trends that affect the probability of arrests in general. This collection contains three parts. Variables in the Federal Bureau of Investigation Crime Data file (Part 1) include the state and year to which the data apply, the type of crime, and the sex and age category of those arrested for crimes. A single arrest is the unit of analysis for this file. Information in the Population Data file (Part 2) includes population counts for the number of individuals within each of seven age categories, as well as the number in the total population. There is also a figure for the number of individuals covered by the reporting police agencies from which data were gathered. The individual is the unit of analysis. The Fatal Accident Data file (Part 3) includes six variables: the FIPS code for the state, year of accident, and the sex, age group, and blood alcohol content of the individual killed. The final variable in each record is a count of the number of drivers killed in fatal motor vehicle accidents for that state and year who fit into the given sex, age, and blood alcohol content grouping. A driver killed in a fatal accident is the unit of analysis.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

The Vulnerable Population Index (VPI) is intended to guide location selection and stakeholder identification for public involvement and inform Title VI and Environmental Justice (EJ) performance measurement. The Baltimore Regional Transportation Board uses data from the US Census Bureau to determine the concentrations of seven sensitive populations for the region and for each census tract. A tract with a concentration of a sensitive population greater than the concentration of the Baltimore region as a whole is considered to be “vulnerable” for the sensitive population. The Vulnerable Population Index (VPI) indicated the number of vulnerable populations for each tract, and thus provides a general indication of the extent to which each tract is vulnerable. The VPI looks at the following variables:Population in Poverty (American Community Survey 2006-2010 5-Year Estimates)Age 75 and up (Census 2010) Non-Hispanic Minority (people who are non-White and non-Hispanic) (Census 2010) Hispanic or Latino Heritage (Census 2010)Limited English Proficiency (population who speaks English “not well” or “not at all.”) (American Community Survey 2006-2010 5-Year Estimates)Households with No Car (American Community Survey 2006-2010 5-Year Estimates)Disabled Population (Census 2000) This data was used in the interactive mapping application found at http://gis.baltometro.org/Application/VPI/index.html. For more information on Transportation Equity work and studies at BMC, go to http://www.baltometro.org/about-the-brtb/transportation-equity. Note that for ACS and Census 2000 data margins of error are not provided. This data has been modified by the Baltimore Metropolitan Council and should not replace data directly loaded from the Census.Source: Variables are American Community Survey 2006-2010 5-Year Estimates, the 2000 Census (SF3), and the 2010 Census. Census tracts are the 2010 Census. Main Index is calculated by BMC.Date: Index published in May 2015. Date of raw data is either 2000, 2010, or 2006-2010 depending on the variable. See the above list for more information.Update: The VPI is updated approximately every 5 years. Data will be added as a separate layer.Data fields:PCT_NotWhite_NotHisp - Percent of the population in each tract that is a non-Hispanic minority. PCT_Hispanic - Percent of the population in each tract that is Hispanic or Latino. Pct75up - Percent of the population in each tract that is age 75 or higher. PCT_LEP - Percent of the Limited English Proficiency population in each tract.PCT_People_in_Poverty - Percent of the population in each tract that is living below the Federal poverty level.PCT_NOCAR - Percent of households in each tract that do not have a car.PCT_Disabl - Percent of the population in each tract that is disabled. Reg_NotWhite_NotHisp - Regional average for the population that is a non-Hispanic minority. This is for the same time period as the tract data. Reg_Hispanic - Regional average for the population that is Hispanic or Latino. This is for the same time period as the tract data. Reg_75up - Regional average for the population that is age 75 or higher. This is for the same time period as the tract data. Reg_LEP - Regional average for the Limited English Proficiency population. This is for the same time period as the tract data. Reg_Poverty - Regional average for the population that is living below the Federal poverty level. This is for the same time period as the tract data. Reg_NOCAR - Regional average for percent of households that do not have a car. This is for the same time period as the tract data. Reg_Disabl - Regional average for the population that is disabled. This is for the same time period as the tract data. FLAG_NotWhite_NotHisp - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_Hispanic - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_75up - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_LEP - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_Poverty - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_NOCAR - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_Disabl - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". INDEX - The sum of all the FLAG fields.