Average weekly household expenditure on goods and services in the UK. Data are shown by region, age, income (including equivalised) group (deciles and quintiles), economic status, socio-economic class, housing tenure, output area classification, urban and rural areas (Great Britain only), place of purchase and household composition.

Over the course of the 20th century, the number of operational motor vehicles in the United States grew significantly, from just 8,000 automobiles in the year 1900 to more than 183 million private and commercial vehicles in the late 1980s. Generally, the number of vehicles increased in each year, with the most notable exceptions during the Great Depression and Second World War.

Number of Cars: Privately Owned: Per 1000 Person: CF: City of Moscow data was reported at 282.272 Unit in 2022. This records a decrease from the previous number of 297.353 Unit for 2021. Number of Cars: Privately Owned: Per 1000 Person: CF: City of Moscow data is updated yearly, averaging 232.100 Unit from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 306.351 Unit in 2017 and a record low of 69.800 Unit in 1990. Number of Cars: Privately Owned: Per 1000 Person: CF: City of Moscow data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.RAD005: Number of Cars Privately Owned per 1000 Persons.

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.

This map shows the percent of households where there are more cars than people in the household. This includes:1-person households with 2 cars1-person households with 3 cars1-person households with 4 or more cars2-person households with 3 cars2-person households with 4 or more cars3-person households with 4 or more carsThe pop-up shows the count of households by the various categories above. The pattern is shown by states, counties, and tracts. There are bookmarks in the map to help you jump to different cities. You can also search for any city in the Untied States to learn more about that area. Notice that cities tend to have less cars than people, whereas suburbs are more likely to have more cars than people.The data is from the American Community Survey (ACS), and is updated annually when the Census releases their newest estimates. To learn more about the ACS tables used in this layer and additional information about the layer, visit the layer metadata here.

Dataset

This dataset was created by Saurabh Sawhney

Contents

Dataset population: Households

Accommodation type

The type of accommodation used or available for use by an individual household. Examples include the whole of a terraced house, or a flat in a purpose-built block of flats.

Car or van availability

The number of cars or vans that are owned, or available for use, by one or more members of a household. This includes company cars and vans that are available for private use. It does not include motorbikes or scooters, or any cars or vans belonging to visitors.

Households with 10 to 20 cars or vans were counted as having only 1. Responses indicating a number of cars or vans greater than 20 were treated as invalid and a value was imputed.

The count of cars or vans in an area relates only to households. Cars or vans used by residents of communal establishments were not counted.

Number of usual residents aged 17 or over

This derived variable provides a count of the number of people aged 17 or over in the household.

A household is defined as:

• One person living alone
• A group of people (not necessarily related) living at the same address who share cooking facilities and share a living room, sitting room or dining area.

This includes:

• Sheltered accommodation units in an establishment where 50 per cent or more have their own kitchens (irrespective of whether there are other communal facilities)
• All people living in caravans on any type of site that is their usual residence (this will include anyone who has no other usual residence elsewhere in the UK)

A household must contain at least one person whose place of usual residence is at the address. A group of short-term residents living together is not classified as a household, and neither is a group of people at an address where only visitors are staying.

Context

Data science beginners start with curated set of data, but it's a well known fact that in a real Data Science Project, major time is spent on collecting, cleaning and organizing data . Also domain expertise is considered as important aspect of creating good ML models. Being an automobile enthusiast, I tool up this challenge to collect images of two of the popular car models from a used car website, where users upload the images of the car they want to sell and then train a Deep Neural Network to identify model of a car from car images. In my search for images I found that approximately 10 percent of the cars pictures did not represent the intended car correctly and those pictures have to be deleted from final data.

Content

There are 4000 images of two of the popular cars (Swift and Wagonr) in India of make Maruti Suzuki with 2000 pictures belonging to each model. The data is divided into training set with 2400 images , validation set with 800 images and test set with 800 images. The data was randomized before splitting into training, test and validation set.

The starter kernal is provided for keras with CNN. I have also created github project documenting advanced techniques in pytorch and keras for image classification like data augmentation, dropout, batch normalization and transfer learning

Inspiration

1. With small dataset like this, how much accuracy can we achieve and whether more data is always better. The baseline model trained in Keras achieves 88% accuracy on validation set, can we achieve even better performance and by how much.

2. Is the data collected for the two car models representative of all possible car from all over country or there is sample bias .

3. I would also like someone to extend the concept to build a use case so that if user uploads an incorrect car picture of car , the ML model could automatically flag it. For example user uploading incorrect model or an image which is not a car

This dataset provides a comprehensive, state-level view of the key factors influencing electric vehicle (EV) adoption across the United States. Compiled from authoritative sources such as the US Census Bureau, Department of Energy, National Renewable Energy Laboratory (NREL), and others, it includes annual data on EV registrations, socioeconomic indicators, infrastructure availability, policy incentives, and energy prices from multiple years.

The dataset is designed to support research and analysis on the drivers of EV adoption, enabling users to explore questions around policy effectiveness, infrastructure planning, and market dynamics.

Context & Motivation The transition to electric vehicles is a cornerstone of US climate and energy policy, yet EV adoption rates remain highly uneven across states. While states like California lead with robust infrastructure and incentives, other regions-particularly in the Midwest and South-lag behind. Understanding what drives these differences is crucial for policymakers, automakers, and energy providers.

This dataset was created as part of a research project investigating the determinants of EV adoption. By making this data publicly available, I hope to empower further research, foster data-driven policy decisions, and encourage innovation in sustainable transportation.

Data Sources EV Registrations: National Renewable Energy Laboratory (NREL)

Socioeconomic Indicators: US Census Bureau (population, income, education, labor force, unemployment)

Charging Infrastructure & Incentives: Alternative Fuels Data Center (AFDC)

Fuel Economy & Vehicle Registrations: Bureau of Transportation Statistics

Gasoline Prices: American Automobile Association (AAA)

Electricity Prices: Energy Information Administration (EIA)

CO2 Emissions: Bureau of Transportation Statistics Variables Included

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.

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.

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

These data were developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. .
For a deep dive into the data model including every specific metric, see the ACS 2018-2022 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2018-2022). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

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

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

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

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

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

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

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

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

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

Kenya Road Transport: Number of Motor Vehicles: Registered data was reported at 4,973,017.000 Unit in 2024. This records an increase from the previous number of 4,784,156.000 Unit for 2023. Kenya Road Transport: Number of Motor Vehicles: Registered data is updated yearly, averaging 2,210,907.000 Unit from Dec 2004 (Median) to 2024, with 21 observations. The data reached an all-time high of 4,973,017.000 Unit in 2024 and a record low of 711,142.000 Unit in 2004. Kenya Road Transport: Number of Motor Vehicles: Registered data remains active status in CEIC and is reported by Kenya National Bureau of Statistics. The data is categorized under Global Database’s Kenya – Table KE.TA: Road Transport: Number of Motor Vehicles: Registered.

When asked about "Car ownership", ** percent of Emirati respondents answer "Yes, a company car". This online survey was conducted in 2024, among ***** consumers.As an element of Statista Consumer Insights, our Consumer Insights Global survey offers you up-to-date market research data from over ** countries and territories worldwide.

Key information about Nigeria Registered Motor Vehicles

• Nigeria Registered Motor Vehicles was reported at 11,605.207 Unit th in Dec 2020.
• This records an increase from the previous number of 3,750.000 Unit th for Dec 2015.
• Nigeria Registered Motor Vehicles data is updated yearly, averaging 3,090.000 Unit th from Dec 2005 to 2020, with 12 observations.
• The data reached an all-time high of 11,605.207 Unit th in 2020 and a record low of 1,747.000 Unit th in 2005.
• Nigeria Registered Motor Vehicles data remains active status in CEIC and is reported by International Organization of Motor Vehicle Manufacturers. The data is categorized under World Trend Plus’s Association: Automobile Sector – Table RA.OICA.VIU: Vehicle In Use: by Country (Discontinued).

This annual release provides a snapshot of the number of active vehicle registration counts of light-duty vehicles and medium-duty vehicles by type of vehicle and fuel type, heavy-duty vehicles, buses, and motorcycles and mopeds. Data are obtained from the administrative files from provincial and territorial governments.