When asked about "Duration of daily commute", ** percent of U.S. respondents answer ******************. This online survey was conducted in 2025, among 13,687 consumers.

According to the Statista Consumer Insights, for the period between October 2023 and September 2024, around of U.S. American workers spent an average of **** an hour or less commuting to work. In the period between 2019 and 2024, the share of workers commuting less than ** minutes dropped by ***** percentage points to ** percent, while the share of workers commuting over **** an hour decreased from ** to ** percent. Rise of hybrid work models The transformation in commute times coincides with a surge in hybrid work arrangements. By the second quarter of 2024, ** percent of U.S. workers reported adopting a hybrid work model, blending remote and on-site work. This shift, initially sparked by the COVID-19 pandemic, has reshaped how Americans balance their professional and personal lives, offering increased flexibility and potentially reducing overall commute times for many. Driving remains most common form of commuting Among those workers who continue to travel to their place of work, driving remained the most popular mode. Over ********** of U.S. Americans drove to work by car, truck or van in 2022 and an additional nearly **** percent used a carpool to get to their job. Public transportation, meanwhile, was only used by *** percent of workers.

Average commute time in each U.S. county in minutes.This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

** percent of U.S. respondents answer our survey on "Most common modes of transportation for commuting" with *********************. The survey was conducted in 2025, among 10,697 consumers.

This EnviroAtlas dataset portrays the commute time of workers to their workplace for each Census Block Group (CBG) during 2008-2012. Data were compiled from the Census ACS (American Community Survey) 5-year Summary Data. The commute time is the amount of travel time in minutes for workers to get from home to work. This value includes private vehicle use, carpooling, public transit, bicycling, or walking. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Graph and download economic data for Mean Commuting Time for Workers (5-year estimate) in King County, WA (B080ACS053033) from 2009 to 2023 about King County, WA; commuting time; Seattle; WA; workers; average; 5-year; and USA.

VITAL SIGNS INDICATOR
Commute Time (T3)

FULL MEASURE NAME
Commute time by residential location

LAST UPDATED
January 2023

DESCRIPTION
Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence.

DATA SOURCE
U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census - http://www.bayareacensus.ca.gov/transportation.htm

U.S. Census Bureau: American Community Survey - https://data.census.gov/
2006-2021
Form C08136
Form C08536
Form B08301
Form B08301
Form B08301

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis.

For the American Community Survey (ACS) datasets, 1-year rolling average data was used for all metros, region and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies.

Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute times were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography.

Census tract data is not available for tracts with insufficient numbers of residents. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary metropolitan statistical areas (MSAs) for the nine other major metropolitan areas.

Among other questions related to the work commute, the American Community Survey (ACS) asks respondents about their primary workplace location. Workplace information is crucial for understanding the degree of interconnectedness among our nation's communities and it shapes the contours of metropolitan and micropolitan statistical areas. The U.S. Census Bureau's publicly available ACS tables present information about where people work by both residence-based and workplace-based data products, but information about the residence/workplace relationship is not provided as an origin-destination combination. A more complex story about commuting patterns emerges when residence location and workplace location are coupled, generating a "commuting flow." This page provides commuting flow data from the decennial Census and ACS, some of which has been produced for the purpose of redefining metropolitan and micropolitan statistical areas.From https://www.census.gov/hhes/commuting/data/commutingflows.html as of March 29, 2017.This archive contains the files derived by the Census Bureau from the 2009-2013 5-year American Community Survey. XLSX files are provided as downloaded from the Census Bureau on March 29, 2017.File format conversions (XLSX -> CSV) were performed by Lars Vilhuber, Labor Dynamics Institute, Cornell University.

In 2022, almost ** percent of workers in the U.S. commuted by personal vehicle on their own. During the same year, only *** percent of Americans workers traveled to work using public transportation. Meanwhile, around ** percent of U.S. Americans worked from home.

VITAL SIGNS INDICATOR Commute Time (T4)

FULL MEASURE NAME Commute time by employment location

LAST UPDATED April 2020

DESCRIPTION Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence.

DATA SOURCE U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation.htm

U.S. Census Bureau: American Community Survey Table B08536 (2018 only; by place of employment) Table B08601 (2018 only; by place of employment) www.api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis.

For the American Community Survey datasets, 1-year rolling average data was used for all metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies.

Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute time were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography. Census tract data is not available for tracts with insufficient numbers of residents.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.

VITAL SIGNS INDICATOR
Commute Mode Choice (T1)

FULL MEASURE NAME
Commute mode share by residential location

LAST UPDATED
January 2023

DESCRIPTION
Commute mode choice, also known as commute mode share, refers to the mode of transportation that a commuter usually uses to travel to work, such as driving alone, biking, carpooling or taking transit. The dataset includes metropolitan area, regional, county, city and census tract tables by place of residence.

DATA SOURCE
U.S. Census Bureau: Decennial Census (1960, 1970) - via MTC/ABAG Bay Area Census - http://www.bayareacensus.ca.gov/transportation/Means19602000.htm

U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census - http://www.bayareacensus.ca.gov/transportation/Means19802000.htm

U.S. Census Bureau: American Community Survey - https://data.census.gov/
2006-2021
Form B08301 (1-year and 5-year)

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Commute mode choice, also known as commute mode share, refers to the mode of transportation that a commuter usually uses to travel to work, such as driving alone, biking, carpooling or taking transit. For the decennial Census datasets, the breakdown of auto commuters between drive alone and carpool is not available before 1980. American Community Survey 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. This will result in discrepancies in cases like San Francisco where it is both a city and a county. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020. Additionally, for the County by place of residence breakdown, Napa was missing ACS 1-Year commute mode choice data for all modes for 2007, 2008, 2011 and 2021. 5-Year estimates were used to fill the missing data for 2011 and 2021, but not 2007 or 2008 since the 5-Year estimates start in 2009.

Regional mode shares are population-weighted averages of the nine counties' modal shares. "Auto" includes drive alone and carpool for the simple data tables and is broken out in the detailed data tables accordingly, as it was not available before 1980. "Transit" includes public operators (Muni, BART, etc.) and employer-provided shuttles (e.g., Google shuttle buses). "Other" includes motorcycle, taxi, and other modes of transportation; bicycle mode share was broken out separately for the first time in the 2006 data and is shown in the detailed data tables. Census tract data is not available for tracts with insufficient numbers of residents or workers.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary metropolitan statistical areas (MSAs) for other major metropolitan areas.

This layer shows workers' place of residence by commute length. 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 commuters whose commute is 90 minutes or more. 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): B08303Data 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.

Graph and download economic data for Mean Commuting Time for Workers (5-year estimate) in Denver County, CO (B080ACS008031) from 2009 to 2023 about Denver County, CO; commuting time; Denver; CO; workers; average; 5-year; and USA.

Graph and download economic data for Mean Commuting Time for Workers (5-year estimate) in Orange County, CA (B080ACS006059) from 2009 to 2023 about Orange County, CA; commuting time; Los Angeles; workers; average; CA; 5-year; and USA.

Graph and download economic data for Mean Commuting Time for Workers (5-year estimate) in Walker County, GA (B080ACS013295) from 2009 to 2023 about Walker County, GA; Chattanooga; commuting time; workers; average; GA; 5-year; and USA.

Our Demographics package in the USA offers data pertaining to the households, education, work, and commute of residents of the United States of America at Census Block Level. Each data variable is available as a sum, or as a percentage of the total population within each selected area.

Graph and download economic data for Mean Commuting Time for Workers (5-year estimate) in Houston County, TX (B080ACS048225) from 2009 to 2023 about Houston County, TX; commuting time; workers; average; TX; 5-year; and USA.

Percent of residents employed and working outside the home where commuting times to their place of work less is than 30 minutes.

Important Note: This item is in mature support as of June 2023 and will be retired in December 2025.This shows the market potential that an adult spends more than 6 hours commuting to and from work per week in the U.S. in 2022 in a multiscale map (by country, state, county, ZIP Code, tract, and block group).The pop-up is configured to include the following information for each geography level:Market Potential Index and count of adults expected to spend more than 6 hours commuting to and from work per weekMarket Potential Index and count of adults expected to spend various amounts of time commuting to workPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Graph and download economic data for Mean Commuting Time for Workers (5-year estimate) in New York County, NY (B080ACS036061) from 2009 to 2023 about New York County, NY; commuting time; New York; workers; average; NY; 5-year; and USA.