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Commute Mode Share

Indicator Description

Commute mode share measures the percentage of workers aged 16 years and over who commute either

  1. by bicycle
  2. by private vehicle, including car, truck, van, taxicab, and motorcycle
  3. by public transportation, including bus, rail, and ferry
  4. by foot.

Data on commute mode share come from the 2012 one-year estimates from the American Community Survey (ACS).

Related Strategies

Transportation and Health Connection

Commute mode share reflects how well infrastructure, policies, investments, and land-use patterns support different types of travel to work. Commute patterns are directly tied to the economy (where jobs are located within a region relative to housing). Commute mode share is linked to environmental conditions and contributing factors that affect health outcomes, such as air pollutant emissions, which vary by transportation mode. Motor vehicle emissions contribute nearly a quarter of world energy-related greenhouse gases. Reducing motor vehicle use and increasing active transportation are ways to mitigate harmful environmental impacts caused by a large amount of vehicle use (Xia et al., 2013).

Traveler safety is also an issue related to commuting, and long commutes in motor vehicles (i.e., cars and trucks) are linked to physical inactivity and associated health problems (Ewing, Schieber, Zegeer, 2003). Conversely, active commute modes are a potential source of health-enhancing physical activity.  Additionally, pedestrian and motor vehicle traffic fatalities decrease in more compact communities, suggesting that shorter commutes are safer for commuters in all modes.

It is important to also consider other influences when connecting various health outcomes to modes of travel. These factors include food choices, sedentary hobbies, stress, unemployment rates, and regional culture, and may have impacts on obesity and diabetes (Price and Godwin, 2012).

About the Data

Commute mode share, collected continuously by the ACS, examines what mode workers choose for travel. It is a comprehensive data source on commuting patterns associated with work travel (Pisarski, 2006). The data are provided based on 1-, 3-, and 5-year estimates. The 1-year estimates are available for geographic areas with 65,000 residents or more, 5-year estimates are available for all geographic areas down to the census block group. State data are collected from 1-year estimates and metropolitan-scale data are from 5-year estimates. Shifts in commute modes over time are reflective of changes in the population and the built environment. Regional differences can also be derived from the ACS data (Ewing and Kreutzer, 2006), so that transportation decision makers can use this data to compare states and regions similar to theirs.
 
The ACS provides estimates of the numbers of commuters traveling by each mode. The data for this indicator were downloaded by state and by metropolitan statistical area (MSA).
 
The greatest limitation to commute mode share data is that only travel to work is considered, and not all trips made throughout the day, nor trips by unemployed individuals. Commutes account for less than 20%of all trips taken, but commutes have a unique role when considering the balance of overall trips by determining peak travel demand across transportation systems (Federal Highway Administration, 2011). Nonetheless, the data showing commute mode share miss important information on other trips made throughout the day, which might include more walking and bicycling.

Moving Forward

The information gleaned from commute mode share data can inform decisions about Complete Streets policies and infrastructure investments for street connectivity, bicycle lanes, and sidewalks. Commute mode share can be a useful way for transportation decision makers to measure the success of such investments or policies over time. Changes in commute mode choice, for example, have occurred in areas where bicycling facilities have been added, suggesting that changes to the built environment might lead to changes in travel behavior (Pucher, Dill, Handy, 2010).

References

Ewing R, Kreutzer R. Understanding the Relationship Between Public Health and the Built Environment: A Report Prepared for the LEED-ND Core Committee 2006. http://www.usgbc.org/resources/understanding-relationship-between-public....

Ewing R, Schieber RA, Zegeer CV . Urban sprawl as a risk factor in motor vehicle occupant and pedestrian fatalities. American Journal of Public Health 2003;93:1541-5. http://ajph.aphapublications.org/doi/pdf/10.2105/AJPH.93.9.1541.

Federal Highway Administration. Summary of Travel Trends: 2009 National Household Travel Survey. FHWA-PL-11-022. http://nhts.ornl.gov/2009/pub/stt.pdf​ 

Pisarski A. Commuting in America III: The Third National Report on Commuting Patterns and Trends; 2006. http://onlinepubs.trb.org/onlinepubs/nchrp/CIAIII.pdf

Price A. Godwin A. Mapping Transportation and Health in the United States; 2012. http://www.planetizen.com/node/53728.

Pucher J, Dill J, Handy S. Infrastructure, programs, and policies to increase bicycling: an international review. Preventive medicine 2010;50:S106-S125. http://www.ncbi.nlm.nih.gov/pubmed/19765610.

Xia T, Zhang Y, Crabb S, Shah P. Cobenefits of Replacing Car Trips with Alternative Transportation: A Review of Evidence and Methodological Issues. Journal of Environmental and Public Health 2013(Article ID 797312). http://www.hindawi.com/journals/jeph/2013/797312/.

* Indicates research that supports policies analyzed

† Indicates research that supports equity or vulnerable populations studied

Updated: Tuesday, February 2, 2016
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