California Average Commute Time by County

Data Item State
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Mean travel time to work (minutes), workers age 16+, 2009-2013 - (Minutes)
County Value
Alameda 28.8
Alpine 23.7
Amador 30.0
Butte 20.3
Calaveras 35.5
Colusa 21.2
Contra Costa 33.3
Del Norte 14.2
El Dorado 28.8
Fresno 22.1
Glenn 21.8
Humboldt 17.5
Imperial 20.8
Inyo 14.8
Kern 23.6
Kings 21.4
Lake 27.5
Lassen 19.4
Los Angeles 29.3
Madera 25.5
Marin 28.6
Mariposa 32.0
Mendocino 18.6
Merced 26.3
Modoc 16.9
Mono 16.2
Monterey 22.5
Napa 23.6
Nevada 24.6
Orange 26.3
Placer 26.9
Plumas 22.9
Riverside 32.0
Sacramento 25.7
San Benito 30.2
San Bernardino 29.9
San Diego 24.4
San Francisco 30.5
San Joaquin 29.4
San Luis Obispo 21.1
San Mateo 25.7
Santa Barbara 19.4
Santa Clara 25.0
Santa Cruz 25.4
Shasta 20.1
Sierra 27.5
Siskiyou 18.8
Solano 28.9
Sonoma 25.3
Stanislaus 26.6
Sutter 27.4
Tehama 23.3
Trinity 18.3
Tulare 21.3
Tuolumne 25.1
Ventura 24.6
Yolo 21.6
Yuba 29.5

Value for California (Minutes): 27.2

Data item: Mean travel time to work (minutes), workers age 16+, 2009-2013

Source: U. S. Census Bureau, American Community Survey, 5-Year Estimates. Updated every year.


Travel time to work refers to the total number of minutes that it usually took the person to get from home to work each day during the reference week. The elapsed time includes time spent waiting for public transportation, picking up passengers in carpools, and time spent in other activities related to getting to work.

Data were tabulated for workers 16 years old and over--that is, members of the Armed Forces and civilians who were at work during the reference week--who reported that they worked outside their home.

Mean travel time to work is obtained by dividing the total number of minutes by the number of workers 16 years old and over who did not work at home. Mean travel time to work is rounded to the nearest tenth of a minute.

Scope and Methodology:

These data are collected in the American Community Survey (ACS). The data for each geographic area are presented together with margins of error at The data are period estimates, that is, they represent the characteristics of the population and housing over a specific 60-month data collection period.

Margins of Error (MOE). ACS estimates 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 MOE. The MOE used with ACS estimates can be interpreted as providing a 90 percent probability that the interval defined by the estimate plus the MOE and the estimate minus the MOE (the upper and lower confidence bounds) contains the full population value of the estimate.

For example, suppose the 5-year ACS reported the percentage of people 25 years and older in Birmingham, Alabama who had a bachelor's degree was 21.3 percent and that the MOE associated with this estimate is plus or minus (+/-) 0.9 percent. By adding and subtracting the MOE from the estimate, we can calculate the 90-percent confidence interval for this estimate at 21.3%, +/-0.9%:

21.3% - 0.9% = 20.4% = Lower-bound estimate
21.3% + 0.9% = 22.2% = Upper-bound estimate

Therefore, we can be 90 percent confident that the percent of the population in Birmingham, Alabama of age 25 years and older having a bachelor's degree in 2007-2011 falls somewhere between 20.4 percent and 22.2 percent.

For this Fact and other 5-year Economic Characteristic Facts (listed below), their estimates and margins of error or percents and percent margins of errors can be found on Data Profile - Economic Characteristics. This profile is displayed by geography. Click on the link for "Browse for Data sets (geography picked)" near the top of the Quick facts profile page, click on the link for People QuickLinks/American Community Survey - "Economic Characteristics" for the data profile.

Mean travel time to work (minutes), workers age 16 and over;
Per capita money income in the past 12 months,
Median household income,
Persons below poverty level, percent

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