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.

The percentage of households that do not have a personal vehicle available for use out of all households in an area.Source: American Community SurveyYears Available: 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023Please note: We do not recommend comparing overlapping years of data due to the nature of this dataset. For more information, please visit: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html

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.

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.

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

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.

Share prices of two very big American automobile manufacturer (From 2010 to 02.2024)

General Motors

General Motors (GM) is an American automobile company, one of the largest automakers in the world. It was founded in 1908 and is headquartered in Detroit, Michigan, USA.

Important facts about General Motors:

Car Brands: GM owns several well-known brands including Chevrolet, GMC, Cadillac and Buick. Each of these brands offers a wide range of models, from passenger cars to SUVs and trucks.

Innovation and Technology: GM is actively working to develop new technologies in the automotive industry. They invest in the research and development of electric and autonomous vehicles. For example, their Chevrolet Bolt EV electric model has gained recognition and popularity.

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Ford Motor

Ford Motor Company is an American automobile company, one of the leading automakers in the world. It was founded by Henry Ford in 1903 and is headquartered in Dearborn, Michigan, USA.

Important facts about Ford:

Car Brands: Ford owns several well-known brands, including Ford, Lincoln and Mustang. The Ford brand offers a wide range of vehicles, from passenger cars to trucks and SUVs.

Innovation and Technology: Ford is actively working to innovate and develop new technologies in the automotive industry. They invest in the research and development of electric and autonomous vehicles. For example, the Ford Mustang Mach-E, an all-electric crossover, is one of their new products.

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WFRC Community Focus Areas (2023)Geographic Representation Units WFRC’s Community Focus Areas (CFAs) are geographic areas for which additional consideration may be given within the planning and programming processes for future transportation, economic development, and other projects administered through WFRC. CFAs are used by WFRC in support of meeting the Council-established goal of promoting “inclusive engagement in transportation planning processes and equitable access to affordable and reliable transportation options.” CFAs are designated from Census block group geographic zones that meet the criteria described below. Census block groups are used as these are the smallest geographic areas for which more detailed household characteristics like employment, income, vehicle ownership, commute trip, and English language proficiency are available. WFRC recognizes the limitations of geography-based analysis, as proper planning work considers together the needs of individuals, groups and sectors, and geographic areas. However, geography-based analyses offer a useful starting point for the consideration and prioritization of projects that will serve specific community needs.2023 Community Focus Area Criteria UpdateFor the 2023 RTP planning cycle, WFRC will use two factors in designating geography-based CFAs: 1) concentration of low-income households and 2) concentration of persons identifying as members of racial and ethnic minority groups. The geography for these factors can be identified from consistent and regularly updated data sources maintained by the U.S. Census Bureau. WFRC will also make data available that conveys, while maintaining individual anonymity, the geographic distribution of additional measures including concentrations of persons with disabilities, households with limited English language proficiency, households that do not own a vehicle, older residents (65+ years of age), and younger residents (0-17 years of age). While the application of these factors within the planning process is less straightforward because of their higher statistical margins of error and comparatively even distribution within the region, these additional factors remain valuable as planning context. Low Income Focus Areas, Methodology for IdentificationThe block group-level data from the 2020 Census American Community Survey (ACS) 5-year dataset (Table C17002: Ratio of Income to Poverty Level), is used to determine the percentage of the population within each block group that are in households that have a ratio of income to federal poverty threshold of equal to or less than 1, i.e., their income is below the poverty level. The federal poverty threshold is set differently for households, considering their household size and age of household members.Census block groups in which more than 20% of the households whose income is less than or equal to the federal poverty threshold are included in the WFRC CFAs and designated as Low-Income focus areas. Racial and Ethnic Minority Focus AreasThe block group-level data from the 2020 ACS 5-year dataset (Table B03002: Hispanic or Latino Origin By Race) is used to determine the percentage of the population that did not self-identify their race and ethnicity as “White alone.” The average census block group area in the Wasatch Front urbanized areas has 24.2% of its population that identifies as Black or African American alone, American Indian, and Alaska Native alone, Asian alone, Native Hawaiian and other Pacific Islander alone, some other race alone, two or more races, or of Hispanic or Latino origin.Census blocks in which more than 40%2 of the population identifies as one or more of the racial or ethnic groups listed above are included in the WFRC CFAs and designated as Racial and Ethnic Minority focus areas.Excluding Predominantly Non-Residential Areas from CFAsSome census block groups that meet one or both of the CFA criteria described above contain large, non-residential areas or low density residential areas. Such census block areas may have small residential neighborhoods surrounded by predominantly commercial or industrial land uses, or large areas of public land or as-yet undeveloped lands. For this reason, WFRC staff may adjust the boundaries of an CFA whose census block group population density is less than 500 persons per square mile, to exclude areas of those block groups that have large, predominantly non-residential land uses.Community Focus Area Update FrequencyThe geography for WFRC CFAs will be updated not less than every four years, preceding the project phasing period of the Regional Transportation Planning update cycle. The update will use the most recent version of the 5 year ACS dataset. The next update is expected in the summer of 2026 (the beginning of the 4th year for the 2027 RTP development process) and is expected to use the 2024 5-year ACS results that average results across 2020-2024.Footnotes:1. The 2019 version of WFRC CFAs used ‘Zero Car Households’ as a third factor. This factor is no longer included because of its geographic and statistical fluctuation over time in data reported by the American Community Survey. Additionally, ‘Zero Car households’ was observed to have a strong relationship with the other two CFA designation factors.2. The percentage threshold specified here is approximately one standard deviation above the regional mean for this indicator. Assuming a statistically normal distribution, approximately 16% of the overall set (i.e. census blocks, in this case) would fall above a one standard deviation threshold.3. Table B03002 includes information from both 'Race' and 'Hispanic or Latino Origin' identification questions asked as part of the Census Bureau's American Community Survey.

Used Car Prices YoY in the United States decreased to 0 percent in October from 2 percent in September of 2025. This dataset includes a chart with historical data for the United States Used Car Prices YoY.

Transportation to Work by Race/Ethnicity

Risk Factors and Inequalities in California

By Health [source]

About this dataset

This table contains important data on the mode of transportation used by California residents aged 16 years and older. This information is sourced from the U.S. Census Bureau Decennial Census and American Community Survey and given as part of a series of indicators as part of the Healthy Communities Data and Indicators Project created by the Office of Health Equity.

Commuting to work makes up a large portion - 19% -of overall travel miles in the United States, with automobiles being overwhelmingly preferred by commuters over other methods like walking or biking. Automobiles show an impressive level of personal mobility, however they are associated with certain hazards such as air pollution, car crashes, pedestrian injuries, sedentary lifestyles linked to stress-related health problems and more. Alternatives such as walking alone or combined with public transport offer physical activity which has been linked to lower rates for diseases like heart disease, stroke, diabetes colon cancer breast cancer dementia depression etc., however these forms do come with their own risks; urban areas especially feature higher collision risks seeking pedestrians due to increased vehicle density while bus/rail passengers face less risk than motorcyclists pedestrians or bicyclists.

But this isn't just any average statistic; certain disadvantaged minority communities bear a disproportionate share when it comes to pedestrian-car fatalities: Native American males have an astonishingly 4 times higher death rate compared to Whites or Asians whereas African-Americans & Latinos face double risk than their respective counterparts; factors like stereotypes regarding race based driving behavior can be partially responsible for this discrepancy further marching for more research into this area our part towards embracing greater equality for all races/ethnicities . As such this data acquired from HealthData & CHHS Open Data is presented in hopes that greater awareness can be generated on current situation leading ultimately towards improving safety & providing better mobility options uniformly across all communities

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How to use the dataset

This dataset contains information on the mode of transportation to work for California residents aged 16 and older by race/ethnicity. It provides an excellent opportunity to compare commute data across different regions, counties, geographies, and ethnicities. This dataset can be used in many ways and can give insights into how different communities utilize different modes of transportation.

To get started using this dataset, begin by filtering the data to narrow down the criteria you are looking for (e.g., region_code or county_fips). Once you have narrowed down your selection of data points, you can use a variety of visualizations to gain insights into population segments who use various means of transport. For example, you could create charts such as bar graphs, line graphs or pie charts that display population patterns across year groups within a given area or particular demographic groupings (race/ethnicity). Additionally, this information could be used for public policy related applications such as informing zones about allocating resources to increase accessibility or safety related concerns with certain modes etc.

By examining this dataset further it is also possible to make comparative analyses between several years which may shed light on social trends over time in regards to commuting behaviors which could potentially reveal potential opportunities when planning infrastructure projects or commuter-friendly services such as ridesharing groups etc., through identifying current commuting gaps in given areas relative two other nearby regions based on mode usage shifts throughout various timespans within the years included in this dataset's range (2000-2010).

In conclusion; whether studying historical trends or analyzing present activity –this Transportation To Work 2000-2006-2010 Dataset holds invaluable insight on travel trends among California’s populous providing great potential for expansive research endeavors as well as guiding decision makers from city councils toward more effective policies & projects delivering positive community impact & productivity benefits

Research Ideas

• Investigating the relationship between mode of transportation and health among different racial/ethnic groups in California and also comparisons across regions.
• ...

Louisiana had the most expensive annual car insurance premiums at ***** U.S. dollars for full coverage. Alaska ranked in first place, having the highest annual cost for minimum car insurance coverage at *** U.S. dollars.Why it varies state by state The huge variance in premiums between states is due to the difference in state laws, the percentage of uninsured drivers in the state, the frequency of natural disasters, and claim rates. For instance, Michigan has a no-fault car insurance system, which means that claims are more common. This drives up the cost of insurance for all drivers because insurers need to pay out more money in claims. Male drivers also pay more There is also a difference between premiums among different age groups. In 2025, 25-year-old male drivers paid more per month than 25-year-old female drivers did. This is due to the higher incidence of accidents among young male drivers. This means that young drivers in states that already have higher premiums must pay a lot for car insurance.