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Mean Commuting Time for Workers (5-year estimate) in Garrett County, MD (B080ACS024023)

Observations

2024: 23.67989
Updated: Jan 29, 2026 9:51 AM CST
2024:  23.67989  
2023:  24.13993  
2022:  25.02131  
2021:  24.17464  
2020:  23.66098  

Units:

Minutes,
Not Seasonally Adjusted

Frequency:

Annual

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Notes

Title Release Dates
Mean Commuting Time for Workers in Garrett County, MD 2017-02-03 2018-12-05
Mean Commuting Time for Workers (5-year estimate) in Garrett County, MD 2018-12-06 2026-01-29
 
Source    
U.S. Census Bureau 2017-02-03 2026-01-29
 
Release    
Mean Commute Time 2017-02-03 2026-01-29
 
Units    
Minutes 2017-02-03 2026-01-29
 
Frequency    
Annual 2017-02-03 2026-01-29
 
Seasonal Adjustment    
Not Seasonally Adjusted 2017-02-03 2026-01-29
 
Notes    
This series is calculated by dividing the aggregate travel time to work for all workers by the total number of workers, 16-years old and older, who commute. The data is comprised of estimates found in tables B08013 and B08012 of the American Community Survey, respectively.

Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period.

Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010–2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011–2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates.

Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook for a more thorough clarification. https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf

2017-02-03 2018-12-05
Mean commuting time is calculated by dividing the aggregate travel time to work for all workers (in minutes) by the total number of workers, 16-years old and older, who commute (ACS 5-year variables B08013_001E from table B08013 and B08012_001E from table B08012, respectively).

Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period.

Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010–2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011–2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates.

Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

2018-12-06 2026-01-29

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Mean Commute Time

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Garrett County, MD Commuting Time Maryland Workers Average American Community Survey 5-Year Census County or County Equivalent Annual Public Domain: Citation Requested United States of America Not Seasonally Adjusted