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.

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.

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.

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 San Diego County, CA (B080ACS006073) from 2009 to 2023 about San Diego County, CA; commuting time; San Diego; workers; average; CA; 5-year; and USA.

In 2023, the average commute time from home to work on a weekday in Beijing was about 44 minutes, the highest among Chinese cities. This was followed by Shanghai, where the average one-way commute time was about 40 minutes.

Mean travel time to work (minutes) DP03_0025E County and State values are from the ACS 1 Year Survey.

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.

When asked about "Duration of daily commute", ** percent of U.S. respondents answer "15 to ** minutes". This online survey was conducted in 2025, among ****** consumers.

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.

Graph and download economic data for Mean Commuting Time for Workers (5-year estimate) in St. Louis County, MO (B080ACS029189) from 2009 to 2023 about St. Louis County, MO; commuting time; St. Louis; MO; workers; average; 5-year; and USA.

This statistic depicts the average time spent by U.S. workers to commute to work in 2019, by region. In that year, U.S. workers from the Northeast region spent on average 31 minutes to travel to work.

According to a report published by Baidu, the average one-way commuting time among workers in the Chinese city Beijing reached ** minutes in 2021, which was the highest average commuting time among cities in China. However, commuting times in other large Chinese cities did not tend to be considerably shorter.

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.

In 2022, the average commute time in Singapore amounted to about 47 minutes. The public transport system in Singapore is made up of buses and two different rail systems: the mass rapid transit (MRT) and light rail transit (LRT).

This statistic illustrates the average daily commute times in various countries of the European Union (EU) in 2014 in minutes and by gender. While the average daily commuting time of men in the EU increases if they have children, the average commuting time of women with children decreases.

In 2021, the weekly average time spent on commuting to and from school or work among participants living in Japan reached ** minutes per day for women and ** minutes per day for men. During the surveyed period, women spent less time on commuting on average.

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.

AGGREGATE TRAVEL TIME TO WORK (IN MINUTES) OF WORKERS BY TRAVEL TIME TO WORK Survey/Program: American Community Survey Universe: Workers 16 years and over who did not work at home TableID: B08135 Product: 2019: ACS 1-Year Estimates Detailed Tables

According to the Statista Consumer Insights, for the period between October 2022 and September 2023, just over half of Brazilian workers had an average daily commute time of less than half an hour. Between 2019 and 2023, the share of workers commuting 15 to 19 minutes increased by four percentage points, while the share of workers who commuted 60 to 119 minutes decreased by three percentage points.