In 2023, California had the most automobile registrations: almost 13.2 million such vehicles were registered in the most populous U.S. federal state. California also had the highest number of registered motor vehicles overall: nearly 30.4 million registrations.

Some 283.4 million vehicles were registered in the United States in 2022. The figures include passenger cars, motorcycles, trucks, buses, and other vehicles. The number of light trucks sold in the U.S. stood at 10.9 million units in 2022. U.S. vehicle registrations The United States is one of the world’s largest automobile markets based on the number of new light vehicle registrations, with more than 13.8 million new light vehicle registrations in 2021. However, domestic production of automobiles fell to around 1.6 million units in 2021 and has struggled to increase in 2022. At the same time, the United States imports a significant number of vehicles from various countries, such as Japan, Mexico, and Canada. Leading car manufacturers in the United States The leading car manufacturers overall in the United States include the domestic heavyweights General Motors and Ford. With respect to car brands, the Ford brand clocked in at number one in 2022, selling around 1.8 million vehicles in the United States alone. The brand's holding company is the Ford Motor Company; it was founded by Henry Ford in 1903 in Dearborn, Michigan. The company pioneered in large-scale car manufacturing and introduced production methods such as the assembly line.

The Indian state of Maharashtra had the highest number of registered motor vehicles, at over 37 million, at the end of fiscal year 2020. India’s automotive market was dominated by two-wheelers and passenger vehicles. Two-wheeler sales have been constantly over 80 percent.

In 2023, Toyota was the best-selling Asian car brand in the United States, followed by Honda and Nissan. Hyundai sold roughly 796,500 vehicles under its namesake brand that year.

Leading Asian car brands in the U.S., based on sales

In 2023, Toyota continues to be the leading Asian car brand in the United States: Japan’s largest carmaker sold approximately 1.89 million units under its namesake brand in 2023. This was around 63.3 percent more than Honda, the second best-selling Asian brand in the country. Asian brands continue to be the most successful group among the non-domestic manufacturers. This trend is further underpinned by the fact that Asian motor vehicle manufacturers are also the producers of some of the most popular car models in the United States. The best-selling Asian car models in the U.S. include the Toyota Camry, the Honda CR-V, and the Toyota Corolla. Furthermore, the Hyundai Ioniq 5 and Kia's EV6 are among the best-selling all-electric car models in the United States. The United States is the world’s second-largest market for passenger vehicles. Mid-size cars are proving increasingly popular among U.S. customers, although cross utility vehicles (CUVs) remain in high demand.

In 2023, California was the state with the most motor vehicle thefts, with 208,668 motor vehicle thefts. Texas had the second most motor vehicle thefts, at 115,013.

US: First Registrations of Brand New Road Vehicles: %: Passenger Cars data was reported at 92.930 % in 2013. This records an increase from the previous number of 92.926 % for 2012. US: First Registrations of Brand New Road Vehicles: %: Passenger Cars data is updated yearly, averaging 92.930 % from Dec 2009 (Median) to 2013, with 5 observations. The data reached an all-time high of 94.285 % in 2009 and a record low of 92.817 % in 2011. US: First Registrations of Brand New Road Vehicles: %: Passenger Cars data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.ITF: Motor Vehicles Statistics: OECD Member: Annual. A passenger car is a road motor vehicle, other than a motorcycle, intended for the carriage of passengers and designed to seat no more than nine persons (including the driver). The term 'passenger car' therefore covers microcars (need no permit to be driven), taxis and hired passenger cars, provided that they have fewer than ten seats. This category may also include pick-ups. A good road motor vehicle is any single road motor vehicle designed to carry goods (lorry), or any coupled combination of road vehicles designed to carry goods, (i.e. lorry with trailer(s), or road tractor with semi-trailer and with or without trailer).; First registrations of brand new passenger cars do not include light trucks. First registrations of brand new goods vehicles refer to commercial vehicles over 10,000 pounds gross vehicle weight.

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.

US: First Registrations of Brand New Road Vehicles: Per One Million Units of Current USD GDP data was reported at 0.486 Ratio in 2013. This records an increase from the previous number of 0.481 Ratio for 2012. US: First Registrations of Brand New Road Vehicles: Per One Million Units of Current USD GDP data is updated yearly, averaging 0.422 Ratio from Dec 2009 (Median) to 2013, with 5 observations. The data reached an all-time high of 0.486 Ratio in 2013 and a record low of 0.396 Ratio in 2009. US: First Registrations of Brand New Road Vehicles: Per One Million Units of Current USD GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.ITF: Motor Vehicles Statistics: OECD Member: Annual. FIRST REGISTRATIONS A passenger car is a road motor vehicle, other than a motorcycle, intended for the carriage of passengers and designed to seat no more than nine persons (including the driver). The term 'passenger car' therefore covers microcars (need no permit to be driven), taxis and hired passenger cars, provided that they have fewer than ten seats. This category may also include pick-ups. A good road motor vehicle is any single road motor vehicle designed to carry goods (lorry), or any coupled combination of road vehicles designed to carry goods, (i.e. lorry with trailer(s), or road tractor with semi-trailer and with or without trailer).; FIRST REGISTRATIONS First registrations of brand new passenger cars do not include light trucks. First registrations of brand new goods vehicles refer to commercial vehicles over 10,000 pounds gross vehicle weight.

In 2021, there were almost 232.8 million licensed drivers in the United States. At around 27 million, California issued the highest number of licenses in the country that year. Not only is California the U.S. state with the highest number of licensed drivers, but it is also the most populous state in the U.S. overall, representing close to 12 percent of the country’s total population.

Young people are most likely to be involved in car accidents
When it comes to accidents, people aged 21 to 24 are most at risk. While there are more female license holders in the U.S., men are more likely to drive at least occasionally. Across all age groups, the male population has substantially higher death rates than the female population.

About licenses in the U.S. The driver’s license became mandatory in the United States in the early 20th century, with Missouri and Massachusetts being the first states to require an official license for operating certain types of motor vehicles. Such vehicles include motorcycles, passenger vehicles, trucks, trailers, or buses. New Jersey became the first state to require all drivers to pass a mandatory test before being granted an official driver’s license.

This layer shows household size by number of vehicles available. This is shown by tract, county, and state boundaries. 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 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.

This layer shows household size by number of vehicles available. This is shown by tract, county, and state boundaries. 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 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: 2018-2022ACS Table(s): B08201 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 7, 2023The 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 2022 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.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.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..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Workers include members of the Armed Forces and civilians who were at work last week..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..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.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..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.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). The effect of nonsampling error is not represented in these tables..Workers include members of the Armed Forces and civilians who were at work last week..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

In 2024, the auto industry in the United States sold approximately 15.9 million light vehicle units. This figure includes retail sales of about three million passenger cars and just under 12.9 million light trucks. Lower fuel consumption There are many kinds of light vehicles available in the United States. Light-duty vehicles are popular for their utility and improved fuel economy, making them an ideal choice for savvy consumers. As of Model Year 2023, the light vehicle manufacturer with the best overall miles per gallon was Kia, with one gallon of gas allowing for 30.4 miles on the road. Higher brand satisfaction When asked about light vehicle satisfaction, consumers in the United States were most satisfied with Toyota, Subaru, Tesla, and Mercedes-Benz models. Another survey conducted in 2018 and quizzing respondents on their stance regarding the leading car brands indicated that Lexus was among the most dependable brands based on the number of problems reported per 100 vehicles.

Automobile towing services have seen profound changes driven by increased road congestion. Surging car ownership and sales led to road congestion and, according to data from the US Department of Transportation, a 3.1% annual uptick in vehicle accidents since 2020. Strong demand for towing services brought solid revenue growth in 2021 and 2022, attracting new entrants to the market and intensifying competition. Allegations of price gouging have further complicated automobile towing, bringing federal regulatory scrutiny and emphasizing towing services' need to navigate complex pricing and fee structures while keeping clients' trust. Largely a result of two years of pandemic-related fuel surcharges, automobile towing services' revenue has been climbing at a CAGR of 5.4% to an estimated $14.5 billion over the five years through 2025. Revenue is set to swell 0.8% in 2025 alone. Automobile manufacturers' innovations are slowing towing providers' revenue growth. Vehicle advancements like active steering and ADAS, expected to be standard by 2030, are poised to reduce accident volumes, negatively affecting demand for towing services. Technologies like ZenDrive's predictive analytics preempt potential crashes, while autonomous developments, exemplified by Waymo's trials, show reduced accident rates compared to human-driven vehicles. Also, newer vehicles equipped with predictive maintenance features anticipate and prevent mechanical failures, reducing roadside assistance calls. Some towing companies are pivoting by focusing on business from law enforcement agencies, towing cars violating parking ordinances regardless of their safety features. Car sales are set to rise alongside disposable incomes and sinking interest rates, bolstering road congestion and incidences of collisions. Urbanization trends may limit car ownership rates in developed transit areas, but cities' continued efforts to crack down on parking infractions will help buoy demand for towing. Embracing electric tow trucks, like the Lion5, will help towing companies differentiate themselves by offering sustainable towing at more stable rates than their competitors exposed to diesel prices' volatility. Revenue is set to climb 1.1% to an estimated $15.3 billion through the end of 2030.

This dataset shows the Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) that are currently registered through Washington State Department of Licensing (DOL).

A Battery Electric Vehicle (BEV) is an all-electric vehicle using one or more batteries to store the electrical energy that powers the motor and is charged by plugging the vehicle in to an electric power source. A Plug-in Hybrid Electric Vehicle (PHEV) is a vehicle that uses one or more batteries to power an electric motor; uses another fuel, such as gasoline or diesel, to power an internal combustion engine or other propulsion source; and is charged by plugging the vehicle in to an electric power source.

Clean Alternative Fuel Vehicle (CAFV) Eligibility is based on the fuel requirement and electric-only range requirement as outlined in RCW 82.08.809 and RCW 82.12.809 to be eligible for Alternative Fuel Vehicles retail sales and Washington State use tax exemptions. Sales or leases of these vehicles must occur on or after 8/1/2019 and meet the purchase price requirements to be eligible for Alternative Fuel Vehicles retail sales and Washington State use tax exemptions.

Monthly count of vehicles for a county may change from this report and prior reports. Processes were implemented to more accurately assign county at the time of registration.

Updated: March 12, 2024

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.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..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Workers include members of the Armed Forces and civilians who were at work last week..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Number of Businesses statistics on the Car Wash & Auto Detailing industry in United States

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows household size by number of vehicles available. This is shown by tract, county, and state boundaries. 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 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. Vintage: 2010-2014ACS Table(s): B08201 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 11, 2020National 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 has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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.

Traffic Accidents in Arizona

Demographics, Injury Severity, Location, and Other Factors

By Sarah Cohen [source]

About this dataset

The Arizona fatal accidents dataset contains detailed information on 3,904 fatal car accidents that occurred in the state of Arizona between. The dataset has been parsed to include only the essential data points, such as location, time, weather, contributing factors and demographic information of the motorists involved in the accident. Using this data, researchers can gain valuable insights into automobile safety trends in the Grand Canyon state.

The dataset consists of three files - AZ_ACCIDENT (79 columns with 3,904 accidents), AZ_VEHICLE (131 columns with 5,889 vehicles) and AZ_PERSON (96 columns with 10,611 people). It includes translated fields for easy interpretation alongside numerical codes for greater detail about each variable. For example there is WEATHER as a numeric code and WEATHER_LIT as its literal translation. Additionally relevant indicators from auxiliary files are included such as whether a police pursuit was involved or whether a pedestrian was involved or killed. However it does not include every piece of information – some more arcane fields were not translated nor included in the set such as road use classifications from 2015 NHTSA changes– so advanced queries may be difficult to perform depending on what is needed out of this data set.

Overall this set is great for those looking to gain an understanding of fatal car accident trends in Arizona during its most recent 5 year window but be aware it might require extra effort when performing complex query operations due to exclusions made by NHTSA due either technical difficulty or intentional removal

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How to use the dataset

This dataset provides detailed information on fatal traffic accidents in Arizona from 2012-2016. It contains three files that provide information about the state of Arizona, the people involved in fatal accidents and the vehicles involved in those accidents. This guide will help you utilize this dataset to better understand and explore the data so that you can draw meaningful insights from it.

Research Ideas

• Using the geographic data to map out locations of fatal accidents in Arizona, and creating an interactive heat map to help identify the highest risk areas for such events.
• Analyzing the demographics (age, sex, race) of people involved in fatal accidents and using this information to develop campaigns geared towards high-risk demographic populations.
• Investigating relationships between contributing factors to accidents (such as drugs/alcohol use, speed limits, etc.) and fatality rates or types of vehicles involved in accidents to better inform public safety policy or vehicle safety regulations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: person.csv | Column name | Description | |:---------------|:----------------------------------------------------------------------------------------------------| | STATE | The state in which the accident occurred. (String) | | ST_CASE | The unique case number assigned to the accident. (Integer) | | VE_FORMS | The number of vehicles involved in the accident. (Integer) | | VEH_NO | The number of the vehicle involved in the accident. (Integer) | | PER_NO | The number of people involved in the accident. (Integer) | | STR_VEH | The type of vehicle involved in the accident. (String) | | COUNTY | The county in which the accident occurred. (String) | | DAY | The day of the month on which the accident occurred. (Integer) | | MONTH | The month in which the accident occurred. (Integer) | | HOUR | The hour of the day on which the accident occurred. (Integer) | | MINUTE | The minute of the hour on which the accident occurred. (Integer) ...