These reports provides vehicle counts broken down by ZIP code, model year, fuel type, make and duty (light/heavy) of registered vehicles with specific as of dates.

This dataset contains information on all non-confidential assets within the California State Vehicle Fleet. The purpose of this data is to provide vehicle information including sustainability measurements such as fuel types. The data is extracted from the Fleet Asset Management System utilized by the Department of General Services’ Office of Fleet and Asset Management. The dataset covers the timeframe from the calendar year 2015 until the calendar year 2022 and includes vehicle information reported by all agencies under the executive branch and constitutional offices.

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.

Quarterly data on new motor vehicle registration by fuel type, vehicle type and number of vehicles, for Canada and provinces.

Number of vehicles authorized to drive in Quebec, both for road vehicles and for vehicles designed for off-road traffic. The data has been revised to comply with the new provisions of Bill 25 protecting the privacy of Quebecers.

Quarterly data on zero-emission vehicle registration by fuel type, vehicle type and number of vehicles, Canada, the provinces, census metropolitan areas and census sub-divisions.

The U.S. auto industry sold nearly three million cars in 2024. That year, total car and light truck sales were approximately 15.9 million in the United States. U.S. vehicle sales peaked in 2016 at roughly 17.5 million units. Pandemic impact The COVID-19 pandemic deeply impacted the U.S. automotive market, accelerating the global automotive semiconductor shortage and leading to a drop in demand during the first months of 2020. However, as demand rebounded, new vehicle supply could not keep up with the market. U.S. inventory-to-sales ratio dropped to its lowest point in February 2022, as Russia's war on Ukraine lead to gasoline price hikes. During that same period, inflation also impacted new and used car prices, pricing many U.S. consumers out of a market with increasingly lower car stocks. Focus on fuel economy The U.S. auto industry had one of its worst years in 1982 when customers were beginning to feel the effects of the 1973 oil crisis and the energy crisis of 1979. Since light trucks would often be considered less fuel-efficient, cars accounted for about 77 percent of light vehicle sales back then. Thanks to improved fuel economy for light trucks and cheaper gas prices, this picture had completely changed in 2020. That year, prices for Brent oil dropped to just over 40 U.S. dollars per barrel. The decline occurred in tandem with lower gasoline prices, which came to about 2.17 U.S. dollars per gallon in 2020 - and cars only accounted for less than one-fourth of light vehicle sales that year. Four years on, prices are dropping again, after being the highest on record since 1990 in 2022.

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.

Number of units and total sales of new motor vehicles by type of vehicle, annual.

DESCRIPTION This table contains data on the percent of residents aged 16 years and older mode of transportation to work for ...

SUMMARY This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

ind_id - Indicator ID ind_definition - Definition of indicator in plain language reportyear - Year that the indicator was reported race_eth_code - numeric code for a race/ethnicity group race_eth_name - Name of race/ethnic group geotype - Type of geographic unit geotypevalue - Value of geographic unit geoname - Name of a geographic unit county_name - Name of county that geotype is in county_fips - FIPS code of the county that geotype is in region_name - MPO-based region name; see MPO_County list tab region_code - MPO-based region code; see MPO_County list tab mode - Mode of transportation short name mode_name - Mode of transportation long name pop_total - denominator pop_mode - numerator percent - Percent of Residents Mode of Transportation to Work,
Population Aged 16 Years and Older LL_95CI_percent - The lower limit of 95% confidence interval UL_95CI_percent - The lower limit of 95% confidence interval percent_se - Standard error of the percent mode of transportation percent_rse - Relative standard error (se/value) expressed as a percent CA_decile - California decile CA_RR - Rate ratio to California rate version - Date/time stamp of a version of data

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.

This dataset contains data from a survey of new-car buying households in 13 US states conducted December 2014 to January 2015. The original study is described in these technical reports:

Kurani, K S., N. Caperello, J. TyreeHageman New Car Buyers' Valuation of Zero-Emission Vehicles: California, Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-16-05 (2016). https://escholarship.org/uc/item/28v320rq

Kurani, K.S., N. Caperello, J. TyreeHageman NCST Research Report: Are We Hardwiring Gender Differences into the Market for Plug-in Electric Vehicles? Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-18-05 (2018). https://itspubs.ucdavis.edu/publication_detail.php?id=2888

This dataset is associated specifically with a subsequent technical report:

Kurani, K.S. and K. Buch Across Early Policy and Market Contexts Women and Men Show Similar Interest in Electric Vehicles, National Center for Sustainable Transportation, University of California, Davis, Research Report. 2019. https://escholarship.org/uc/item/9zz8n5x5

Data are from households who had a acquired at least one household vehicle as new (rather than used) since January 2008. The questionnaire was administered on-line to households in the following US states: California, Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Oregon, Rhode, Island, Vermont, and Washington. Most of these states are so called "ZEV states," i.e., they had adopted California's Zero Emission Vehicle (ZEV) Mandate. Those states that were not ZEV states were included to facilitate regional analysis or because they were otherwise important to the initial launch of retail ZEV sales in 2011. The primary regional analysis was for the Northeast States for Coordinated Air Use Management (NESCAUM). The NESCAUM member states are Connecticut, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, and Vermont. The total sample size is 5,654 for all states; individual state samples sizes are available in the above referenced, Kurani et al (2016).

Analyses were conducted at the state and regional, i.e., NESCAUM, levels. Thus, there are individual data sets for each state for which there is a state-level analysis (California, Delaware, Maryland, Massachusetts, New Jersey, New York, Oregon, and Washington) and NESCAUM. Data for California are included in this release despite the fact its analysis was previously conducted under a separate study. California serves as the reference case because it has the most supportive policy and market context for ZEVs and its analysis is specifically referenced in the report associated with these data sets.

Since the goal was to produce the best possible analysis for each state or region, there are differences in their data sets. While variable names and codes follow consistent rules across all the data sets, which variables are in the data does vary across states and the NESCAUM region. The data released here are those required to replicate the analyses in the associated report.

For each state and region, data are available in two formats indicated by their file extensions: .jmp and .csv. Files with the .jmp extension are proprietary to the JMP© statistics program from SAS Institute. These files contain the data and as well as information about variable coding, variable values, value ordering, and other information in column notes. In effect, the .jmp files contain the data and the code book. The .csv files are generally accessible for import into a wide variety of analytical software but contain no explanatory notes.

Finally, an annotated version of the on-line questionnaire is available as Appendix F of the original report from California (Kurani et al 2016) cited above. The on-line instrument is customized to each respondent as they complete it. More than simple skip patterns, as respondents answer questions content of subsequent questions is populated with information participants provide. Some of this requires calls to data external to the survey instrument; some of these data are proprietary and some are no longer available. Therefore, no "live" version of the on-line questionnaire from 2014 is maintained. The annotated version and the description of the survey provided in the linked report are provided to assist data users.

While household ownership and purchase of all light-duty passenger cars and trucks approach gender parity, to date zero emission vehicles (ZEVs) are being purchased by far more men than women. Prior analysis of data from California finds no reason based in the prospective interest in ZEVs of female and male respondents why this difference should persist. The present report extends the California analysis to 12 other US states with varying ZEV policy and market contexts.

Among many other contextual, socio-economic, demographic, and attitudinal measures, the survey solicited participants' prospective interest in acquiring an ZEV, that is, their interest in their next new car. Participants then indicated why they were motivated to select a ZEV or what motivated them to not select one. Factor analysis was used to reduce the dimensionality of participants' prior awareness, experience, knowledge, and assessments of ZEVs. Via nominal logistic regression modeling, differences in prospective interest in ZEVs between female and male respondents are examined. Given their prospective interest, the motivations of female and male respondents are compared.

Overall, no difference between female and male participants in prospective interest in a ZEV rises to the level of the observed differences in real markets. Further, the multivariate modeling indicates no statistically significant effect of a sex indicator on prospective interest in ZEVS almost anywhere in these states. Where there is a difference, female participants are estimated to be more likely to choose a ZEV than their male counterparts.

While participants from both sexes tend to give high scores to the same ZEV (de)motivations, differences in their rank orders repeat generalizations from other research. On average, female respondents score environmental motivations higher than do male respondents. On average, interest in "new technology" is more motivating to male than female participants. Conversely, on average female respondents who do not select a ZEV score "unfamiliar technology" more highly than their male counterparts.

Within the variation in policy and market contexts represented by the states in this study, no finding here explains why similar prospective interest among female and male participants in ZEVs from the beginning of 2015 has yet to be turned toward equal participation in ZEV markets. Explanations may lie in factors not modeled here.

This table contains data on the annual miles traveled by place of occurrence and by mode of transportation (vehicle, pedestrian, bicycle), for California, its regions, counties, and cities/towns. The ratio uses data from the California Department of Transportation, the U.S. Department of Transportation, and the U.S. Census Bureau. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Miles traveled by individuals and their choice of mode – car, truck, public transit, walking or bicycling – have a major impact on mobility and population health. Miles traveled by automobile offers extraordinary personal mobility and independence, but it is also associated with air pollution, greenhouse gas emissions linked to global warming, road traffic injuries, and sedentary lifestyles. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which has many documented health benefits. More information about the data table and a data dictionary can be found in the About/Attachments section.

Annual vehicle registrations, by type of vehicle (road motor vehicles, trailers, off-road, construction and farm vehicles).

Number of vehicles travelling between Canada and the United States, by trip characteristics, length of stay and type of transportation. Data available monthly.

Click “Export” on the right to download the vehicle trajectory data. The associated metadata and additional data can be downloaded below under "Attachments". Researchers for the Next Generation Simulation (NGSIM) program collected detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data was collected through a network of synchronized digital video cameras. NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provided the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Click the "Show More" button below to find additional contextual data and metadata for this dataset. For site-specific NGSIM video file datasets, please see the following: - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf

This table contains 161 series, with data for years 1970 - 2010 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Vehicle type (161 items: Total, motor vehicles; Total passenger cars; American Motors, total cars; Hornet; ...).

Dataset population: Households

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.

Religion of HRP

This is a person's current religion, or if the person does not have a religion, 'No religion'. No determination is made about whether a person was a practicing member of a religion. Unlike other census questions where missing answers are imputed, this question was voluntary, and where no answer was provided the response is categorised as 'Not stated'.

The concept of a Household Reference Person (HRP) was introduced in the 2001 Census (in common with other government surveys in 2001/2) to replace the traditional concept of the 'head of the household'. HRPs provide an individual person within a household to act as a reference point for producing further derived statistics and for characterising a whole household according to characteristics of the chosen reference person.

Tenure

Tenure provides information about whether a household rents or owns the accommodation that it occupies and, if rented, combines this with information about the type of landlord who owns or manages the accommodation.

Data set containing the number of electric and hybrid vehicles by vehicle type and by administrative unit.

This dataset contains Average Daily Traffic (ADT) counts collected for the City of San Jose for the previous 15 years and is updated on a yearly basis. This dataset can be read as follows: The count location is given as “Collected on ‘Street One’, ‘Direction’, ‘Street Two’, in a ‘Travel Direction.’” ADT values are then given as: ‘ADT One’ and ‘ADT Two’ which correspond to the ADT collected in the recorded travel directions. If the street is a one-way street, a travel direction of ‘one-way’ is recorded and ‘ADT One’ and ‘ADT Two’ are left blank. ‘ADT’ corresponds to the total ADT which is a sum of ‘ADT One’ and ‘ADT Two.’ Putting it all together gets the following: “A total ADT of 39, 057 was recorded on 9/26/2018 along Murphy Rd. east of Oakland Road. Travel flows along Murphy Rd. in an East/West direction with a corresponding ADT One of 21,444 and ADT Two of 17,613.” Note that only counts collected after January 2018 will have a travel direction and corresponding ADT One and ADT Two values listed.Data is published on Mondays on a weekly basis.