On average, there are 1.88 vehicles per U.S. household. According to the U.S. Department of Transportation, the percentage of households without a car or light truck came to around nine percent in 2017, meaning that about 90 percent of households had at least one light vehicle at their disposal in that same year.

Most Americans drive daily

In a recent Gallup poll among U.S. adults, about 64 percent of respondents claimed to drive daily, while another 19 percent of respondents stated that they would use a motor vehicle multiple times in an average week. These figures are in line with the U.S. motorization rate, which stood at 821 vehicles per 1,000 inhabitants in 2015.

 These streets were made for driving  

The United States has the most extensive road network, compared to any other country in the world: its road network encompasses almost 6.6 million kilometers or about four million miles. In 2018, there were about 270 million vehicles roaming the streets of the country.

This statistic shows the percentage of households owning a passenger car in 2014, with a breakdown by major economy. In 2014, more than ** percent of Japanese households had registered at least one passenger vehicle.

Car ownership in households

Unsurprisingly, most countries with high car ownership rates in 2014 were regions with advanced economies. Americans were on the top of the list among surveyed countries, with ** percent reporting to own a car. More common places to find a car included Germany, South Korea, France, Malaysia, and Japan, each with more than an ** percent car ownership rate. By contrast, Vietnam and Bangladesh had the least passenger vehicles registered, with only two percent of the population reporting to own a car.

In the United States, a great share of people from affluent households reported owning or leasing a vehicle falling into the truck, SUV, and van category, followed by crossover vehicle. Toyota, Honda and Nissan were the best-selling passenger car manufacturers in the country, in terms of sales in 2015.

Two-wheelers, the more economical alternative to a car, were more often seen in South and Southeast Asia, as more than ** percent of households in Thailand, Vietnam, Indonesia, and Malaysia owned a motorcycle or scooter. Overall, bicycles were more common around the globe than cars. Countries with the most bike owners include Germany, Indonesia, China, and India.

Percentage of Canadian households that own various types of vehicles. That data are from the Households and the Environment Survey.

The East of England was the English region with the highest average number of cars or vans owned per household. All regions recorded an ownership of at least one car, with London being the only exception where the average numbered *** in 2017/18. According to a 2017 Statista survey, ** percent of respondents from the East of England reported owning a car. This was surprisingly lower than other English regions. The East Midlands had seen the highest share of car owners at ** percent, only outranked by Northern Ireland.

 East of England has most multiple car owners

The East of England also tied with the South East, South West, and East Midlands as having the highest percentage of households, owning more than one car. In 2017/18, it was estimated that ** percent of residents from the East were multiple car owners. By comparison, ** percent reported having no car or van within their household.

 ** percent of UK residents have car available  

A 2017 Statista survey found that roughly ** percent of UK residents had a car permanently available to them in their household. Of these, ** percent had their own car.

Statbel, het Belgische bureau voor de statistiek, onderzoekt voor het eerst het bezit van auto's door Belgische huishoudens, bepaald op basis van administratieve bronnen. Voorheen kon het aantal Belgische huishoudens met of zonder één of meerdere auto's alleen worden geschat op basis van enquêtes. Om dit te compenseren heeft Statbel een experimentele koppeling ontwikkeld die een nauwkeuriger maar nog steeds onvolledig beeld geeft van het aantal auto's van huishoudens. Hier worden vier indicatoren gepresenteerd: het aandeel particuliere huishoudens zonder auto, het aandeel particuliere huishoudens met precies één auto, het aandeel particuliere huishoudens met precies twee auto's en het aandeel particuliere huishoudens met drie of meer auto's.

This table provides information on vehicle ownership by Dutch households by household characteristics (urbanization degree of the municipality of residence, income class and household size). The ownership of means of transport in households is shown on an ordinal scale. The reason for this is that many households have more than one means of transport. To get a clearer picture of the way in which the means of transport ownership is arranged, it is advisable to select all subjects (the entire table). This is assumed in the following explanation. Example 1: the column under 'motorcycle' in 1999 states that 0.2% of two-person households own a motorcycle. However, it is possible that these two-person households also have one or more other means of transport in addition to a motorcycle. The table is arranged in such a way that means of transport can occur in two-person households that are listed to the right of the column 'motorcycle' (i.e. mopeds, bicycles, etc.), but no means of transport that are listed on the left (i.e. not, one or more cars). Example 2: To calculate how many two-person households have at least one car, the percentages under the columns '1 car', '2 cars' and 'More than two cars' may be added together. In 1999, 85.8% of two-person households owned at least one car, while 14.2% of these households were carless (total of the columns under 'motorcycle' up to and including 'None/other'). For number of households see statline table: Population history. The mobility data for the years 1985-2003 have been obtained from the annual survey Survey of Transport Behavior (OVG) conducted by Statistics Netherlands (CBS). Since 2004, the mobility data has come from the Netherlands Mobility Survey (MON) of the Traffic and Shipping Department (DVS), part of the Ministry of Transport, Public Works and Water Management. Data available from: 1985. Status of the figures Figures based on OVG/MON are always final. When will new numbers come out? This table has been discontinued as of 20-03-2012.

The percentage of households that do not have a personal vehicle available for use out of all households in an area. Source: American Community Survey Years 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, 2019-2023

In 2021, approximately **** million people in Great Britain lived in a household that owned one car. In the same year, nearly nine million people lived in a household with *** cars, an increase compared to the previous year. Ovez ** million people lived in a household with no car at all.

The Seattle Household Travel Survey Wave 1, 1989, is the first wave in a ten-part longitudinal panel survey initiated by the Puget Sound Council of Governments (now known as the Puget Sound Regional Council) to assess the travel patterns of households in the Puget Sound region of Washington State. This collection contains the first set of panel data for 1,687 households in King, Kitsap, Pierce, and Snohomish counties. Due to various sources of attrition, approximately 20 percent of households needed to be replaced for each subsequent survey wave. The survey relied on the willingness of study area residents to (1) provide demographic information about the household, its members, and its vehicles, (2) document all travel for each household member, aged 15 years or older, for an assigned 2-day period, and (3) agree to participate in additional survey waves. After an initial telephone screening, survey participants received mailed travel diaries to aid in documenting travel information for the 2-day assessment period. Respondents were instructed to record their mode of transportation, trip purpose, number of vehicle passengers, departure and arrival times, ride fare, and parking costs. Demographic information for this study includes age, gender, education, employment status, and household income.

The 1988/1989 Maricopa Household Travel Study was intended to document how residents use the streets, highways, and transit services in the Phoenix Metropolitan area. Respondents were asked to record their travel and activities for a 24-hour period. They were also asked for detailed information regarding their trips, including mode of transportation, trip purpose, departure and arrival times, and number of passengers. Demographic variables include gender, age, employment status, household size, number of children over five years old in the household, household income, and whether respondents had a valid drivers license at the time of the survey. The primary objectives of this study were to update the trip generation rates used in the Maricopa Association of Governments (MAG) travel demand forecasting process, and to provide data to validate the MAG trip distribution model. The data contain two weight variables that users may wish to use in analysis: HHWGT (Household Weighting Factor) and PERWGT (Person Weighting Factor). ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: The response rate for this survey was 65 percent. The universe for this study includes households within the Phoenix Metropolitan Area. Smallest Geographic Unit: Traffic Analysis Zone A disproportionate stratified sample was utilized on this project. Using this procedure, the sample was stratified on the basis of household size and vehicle ownership and quotas were assigned to each strata. The base for the quota assignments was the trip making variability within each strata. During the pre-test segment of this project it was determined that with repeated call backs and travel day re-scheduling approximately 65 percent of those households which agreed to participate in this study would actually do so. With this in mind, screening quotas were assigned that took into account a non-participation rate. mail questionnaire, telephone interviewThe original data deposit for this collection contained several discrepancies that could not be resolved due to a lack of documentation. Therefore, this collection has been minimally processed by ICPSR. For additional information regarding this study, please refer to the Metropolitan Travel Survey Archive Web site.

Survey on Households and the Environment: Percentage of dwellings whose residents have a vehicle for personal use, by net monthly household income and the number of available vehicles. National.

The Seattle Household Travel Survey Wave 6, 1996 is the sixth wave in a ten-part longitudinal panel survey of the travel patterns of households in the Puget Sound region of Washington State. The survey series was initiated in 1989 by the Puget Sound Council of Governments (now known as the Puget Sound Regional Council); wave 6 was conducted during the second and third quarters of 1996. This collection contains the sixth set of panel data for approximately 2,000 households in King, Kitsap, Pierce, and Snohomish counties. Due to various sources of attrition, approximately 20 percent of households needed to be replaced for each survey wave. The survey relied on the willingness of study area residents to (1) provide demographic information about the household, its members, and its vehicles, (2) document all travel for each household member, aged 15 years or older, for an assigned 2-day period, and (3) agree to participate in additional survey waves. After an initial telephone screening, survey participants received mailed travel diaries to aid in documenting travel information for the 2-day assessment period. Respondents were instructed to record their mode of transportation, trip purpose, number of vehicle passengers, departure and arrival times, ride fare, and parking costs. Demographic information for this study includes age, gender, education, employment status, and household income.

The Center for Disease Control and Prevention (CDC) has developed the Social Vulnerability Index to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, or after a hazardous event. SVI indicates the relative vulnerability of every U.S. Census tract, which are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts of 15 social factors and further groups them into four related themes. Tracts in the top 10% or at the 90th percentile of values are given a value of 1 to indicate high vulnerability. Tracts below the 90th percentile are given a value of zero. For each tract, we have calculated the number of flags for the fifteen individual variables, the flags for the themes, and the overall estimated percentage of the fifteen individual factors.See complete documentation here: https://gis.cdc.gov/grasp/svi/SVI2018Documentation.pdf. For additional questions, contact the SVI Lead at SVI_Coordinator@cdc.gov.

The Seattle Household Travel Survey Wave 3, 1992, is the third wave in a ten-part longitudinal panel survey initiated by the Puget Sound Council of Governments (now known as the Puget Sound Regional Council) to assess the travel patterns of households in the Puget Sound region of Washington State. This collection contains the third set of panel data for approximately 1,700 households in King, Kitsap, Pierce, and Snohomish counties. Due to various sources of attrition, approximately 20 percent of households needed to be replaced for each survey wave. The survey relied on the willingness of study area residents to (1) provide demographic information about the household, its members, and its vehicles, (2) document all travel for each household member, aged 15 years or older, for an assigned 2-day period, and (3) agree to participate in additional survey waves. After an initial telephone screening, survey participants received mailed travel diaries to aid in documenting travel information for the 2-day assessment period. Respondents were instructed to record their mode of transportation, trip purpose, number of vehicle passengers, departure and arrival times, ride fare, and parking costs. Demographic information for this study includes age, gender, education, employment status, and household income.

Key Table Information.Table Title.Number of Workers in Household by Vehicles Available.Table ID.ACSDT1Y2024.B08203.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.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.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..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.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.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..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.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...

Abstract (en): The Atlanta Household Travel Survey sampled 8,069 households in the thirteen-county metropolitan Atlanta region. The survey relied on the willingness of area residents to complete diary records of all travel for a 48-hour period. Household recruitment for the study was conducted through the use of a recruitment interview, in which respondents were informed of the survey, its purpose, and the obligation of all household members to complete the survey. The 8,069 participating households, when weighted, represent 21,323 persons, 14,449 vehicles, and 126,127 places visited during the 48-hour travel period. Data were collected on trip generation, trip distribution, modal choice, transit use, neighborhood preferences, and trip activities. Household data includes demographic information such as household size, household vehicles, dwelling type, home ownership status, tenure, and computer ownership. Also included are summary statistics regarding the number of workers, students, and trips made during the 48-hour travel period. Person data includes demographic information about the household members, student data, employment data for first and second jobs, and health related information. The objective of the household travel survey was to collect information on work and non-work travel behavior. This includes trip generation, trip distribution, and modal choice data as well as data on transit use, neighborhood preferences, health and activity. The Atlanta Household Travel Survey utilizes both weighting and expansion factors to (1) adjust the sample data to match population parameters, and (2) expand trip information to all households in the survey area. This includes the counties of Cherokee, Clayton, Cobb, Coweta, Dekalb, Douglas, Fayette, Forsyth, Fulton, Gwinnett, Henry, Paulding, and Rockdale. For additional information on weights, please see the "Weighting and Expansion" section of the Final Report. Response Rates: The response rate was calculated for recruitment, and for retrieval. The overall response rate was determined by multiplying the two resultant rates. The recruitment rate for this study was 44.8 percent, the retrieval rate was 67.8 percent, and the overall response rate was 30.4 percent. All households within the 13-county region of metropolitan Atlanta, Georgia. Smallest Geographic Unit: county The sample for the Atlanta Household Travel Survey is intended to optimize the production of data with sufficient observations for all relevant levels in three variables: net residential density level (NRDL), household size, and household income. The survey employed a list assisted random digit dial design with a probability sample selection process that selected households for inclusion in the study. The major requirement for probability samples was that the relative probability (or chance) of any given household in the universe being included in the sample was known. Once the sampling procedure was determined, the selection of specific households for inclusion in the sample was left entirely to chance. The sample included both listed and unlisted households. The definition of a completed household was one in which travel and activity data were collected from all household members age five and older. A total of 8,069 households met this criterion. The type of probability sampling employed was stratified sampling in which the sample goal was to have 20 percent of the sample in each of the five preset NRDL categories. Current income data was unknown at the time of the study and the relationship between NRDL and household size was unknown, hence these two variables were not used to stratify the sample. Overall sample goals for the Atlanta Household Travel Survey include the following: (1) Inclusion of high density areas; (2) County level representation; (3) Low income representation; and (4) Minority representation. computer-assisted telephone interview (CATI), mail questionnaireThis collection has not been processed by ICPSR. The deposited files are being released as they were received from the Principal Investigator. For additional information regarding the Atlanta Household Travel Survey, please visit the Metropolitan Travel Survey Archive Web site.

Survey on Households and the Environment: Percentage of dwellings whose residents have a vehicle for personal use, size of the dwelling (no. of persons occupying it) and number of available vehicles. National.

The Seattle Household Travel Survey Wave 7, 1997, is the seventh wave in a ten-part longitudinal panel survey initiated by the Puget Sound Council of Governments (now known as the Puget Sound Regional Council) to assess the travel patterns of households in the Puget Sound region of Washington State. This collection contains the seventh set of panel data for approximately 1,700 households in King, Kitsap, Pierce, and Snohomish counties. Due to various sources of attrition, approximately 20 percent of households needed to be replaced for each survey wave. The survey relied on the willingness of study area residents to (1) provide demographic information about the household, its members, and its vehicles, (2) document all travel for each household member, aged 15 years or older, for an assigned 2-day period, and (3) agree to participate in additional survey waves. After an initial telephone screening, survey participants received mailed travel diaries to aid in documenting travel information for the 2-day assessment period. Respondents were instructed to record their mode of transportation, trip purpose, number of vehicle passengers, departure and arrival times, ride fare, and parking costs. Demographic information for this study includes age, gender, education, employment status, and household income.

Survey on Households and the Environment: Percentage of dwellings whose residents have acquired a new vehicle for personal use in the last 12 months, by Autonomous Community of residence and factors that have influenced the purchase. Autonomous Community of residence.

Percentage of housing units in a county without a car and more than 1 mile from a supermarket or large grocery store.", "availableYears": "2015", "name": "Households, no car & low access to store (%), 2015", "units": "Percent", "shortName": "PCT_LACCESS_HHNV15", "geographicLevel": "County", "dataSources": "Data are from the 2017 report, Low-Income and Low-Supermarket-Access Census Tracts, 2010-2015 and the 2012 report, Access to Affordable and Nutritious Food: Updated Estimates of Distances to Supermarkets Using 2010 Data. In each of these reports, a directory of supermarkets and large grocery stores authorized to accept SNAP benefits was merged with Trade Dimensions' TDLinx directory of stores within the United States, including Alaska and Hawaii, for the years 2010 and 2015. Stores met the definition of a supermarket or large grocery store if they reported at least $2 million in annual sales and contained all the major food departments found in a traditional supermarket, including fresh meat and poultry, dairy, dry and packaged foods, and frozen foods. The combined list of supermarkets and large grocery stores was converted into a GIS-usable format by geocoding the street address into store-point locations. Data on 2010 households are drawn at the block group-level from the 2006-10 American Community Survey, and data on 2015 households are drawn at the block group-level from the 2010-14 American Community Survey. These data were first allocated to blocks and then aerially allocated down to 1/2-kilometer-square grids across the United States. For each 1/2-kilometer-square grid cell, the distance was calculated from its geographic center to the center of the grid cell with the nearest supermarket. Vehicle access was measured based on an American Community Survey question that asks respondents whether the household has access to a car, truck or van, of 1-ton capacity or less. Once distance to the nearest supermarket or large grocery store was calculated for each grid cell, the number of housing units more than 1 mile from the nearest supermarket or large grocery store in urban areas and more than 10 miles from a supermarket or large grocery store in rural areas was aggregated to the county level and divided by the total number of housing units in the county to obtain the percentage of housing units in the county that were more than 1 or 10 miles from a supermarket and without a vehicle.