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Road Traffic Fatalities Exposure Rate

Indicator Description

Road traffic fatalities exposure rate measures the relative risk that 1) a driver or passenger, 2) a bicyclist, or 3) a pedestrian might die in a traffic collision. For a driver and passengers, the rate is calculated by dividing the vehicle road traffic fatality rate by the vehicle commute mode share (i.e., the percentage of workers who commute by private vehicle). The bicycle road traffic fatality rate uses the bicycle commute mode share and the pedestrian road traffic fatality rate uses the walking commute mode share. Although this indicator can be used to compare the safety of travel via different modes between states or metropolitan areas, it should not be used to compare the relative safety of one travel mode to another.

A limitation in using commute trips in the calculation is that they account for a small share of all trips. Trips for health care, dining, shopping, socializing, religious services, and volunteer work, for example, do not count as commute trips. Commute trips account for a small share of all bicycle and pedestrian trips, so commute mode share is more likely to underrepresent actual rates of bicycling and walking than for driving. This means that this indicator makes bicycling and walking appear less safe than driving, all other things being equal. Data on fatalities come from the 2008-2012 National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS). Population data come from the 2008-2012 American Community Survey (ACS) 5-year estimates. The THT includes the following person types, as entered in FARS, when measuring vehicle fatalities: passenger of a motor vehicle in-transport; occupant of a motor vehicle not in-transport; occupant of a non-motor vehicle transport device; unknown occupant type in a motor vehicle in-transport.  

Related Strategies

Transportation and Health Connection

Road traffic fatalities reflect a direct relationship between transportation and public health. However, road traffic fatalities by mode alone do not provide a full picture of the health risks associated with each mode of travel. This indicator takes into account the exposure to the risk for death by mode of transportation.

Motor vehicle crashes are one of the leading causes of death in the United States (CDC, 2013). In the United States, 32,719 people died in motor vehicle traffic crashes in 2013 (U.S. DOT, 2013). At $871 billion in economic loss and societal harm, the price tag for crashes comes at a heavy burden for U.S. residents. This includes $277 billion in economic costs and $594 billion in harm from the loss of life and the pain and decreased quality of life resulting from injuries (U.S. DOT, 2014).

Motorcyclists and pedestrians experience a disproportionately higher risk associated with fatal injuries (Nauman et al., 2010). Males, adolescents, and older adults are also at increased risk for injury, even though effective interventions (e.g., marked crosswalks, seat belt use) are available (Beck, Dellinger, O’Neil, 2007). Young people and minorities have a higher risk for pedestrian fatalities (American Academy of Pediatrics, 2009), but older adults are at most risk of dying if they are hit (U.S. DOT, 2012). This is mainly the result of older adults’ increased susceptibility to injury and medical complications, not an increased tendency to get into roadway crashes (Insurance Institute for Highway Safety, 2013). In addition, the perception and reality of disproportionate risk of injury is a barrier to walking and bicycling (Jacobsen, Racioppi, Rutter, (2009).

About the Data

Two different data sources are combined to measure the road traffic fatalities exposure rate: FARS for road traffic fatalities and the U.S. Census Bureau’s ACS to account for commute mode share. FARS produces an exact number of deaths; the ACS provides estimates for commute mode share. Additionally, ACS data on commute mode share only consider journey to work, not all trips made throughout the day (Pucher et al., 2011), whereas FARS includes all reported crashes and associated fatalities. As defined by the indicator, the fatalities exposure rate was calculated by dividing the fatalities per 100,000 population by the mode share. The data did not require additional processing because the values compiled for the individual indicators were used in this composite indicator.

One limitation of this indicator is that the data are affected by the rate of driving, rather than strictly the safety of driving. Many factors are related to pedestrian collisions, including the time of day and time of year (Griswold et al., 2011). Those factors might be important in designing prevention strategies but are not reflected in this data set. Another limitation to the data is that respondents to the ACS only select their primary mode of transportation for their commute. Other trips made throughout the day, such as walking to the grocery store or to the doctor’s office, are omitted from the data. Thus, the data only reflects a portion of daily travel behavior.

Moving Forward

Measuring the exposure risk for road traffic fatalities is an important link between public health and transportation. Transportation decision makers can use these data to identify areas for improvement, including road traffic safety measures for all modes. Additionally, these data can highlight disparities in safety between modes and provide context as to whether strategies for improving safety were effective or if any decreases are simply the result of a decline in the number of road users.

Several studies have indicated that pedestrian countdown signals and traffic calming strategies such as lane reduction (road diets) are effective in increasing pedestrian safety (Pulugurtha, Desai, Pulugurtha, 2010; Chen et al., 2013). They have also been identified as policies to reduce children’s exposure to dangerous traffic (American Academy of Pediatrics, 2009). Planners can also use these data when developing pedestrian and bicycle master plans, which use multiple strategies for improving safety for pedestrians and cyclists (Jones et al., 2010).

References

American Academy of Pediatrics, Committee on Injury, Violence, and Poison Prevention. Policy statement-Pedestrian safety. Pediatrics; 2009:124:802-12. http://www.ncbi.nlm.nih.gov/pubmed/19651595 *

Beck L, Dellinger A, O’Neil M. Motor Vehicle Crash Injury Rates by Mode of Travel, United States: Using Exposure-Based Methods to Quantify Differences. American Journal of Epidemiology; 2007:166(2):212-8. http://aje.oxfordjournals.org/content/166/2/212

Centers for Disease Control and Prevention. Motor Vehicle Traffic-Related Pedestrian Deaths — United States, 2001–2010. Morbidity and Mortality Weekly Report (MMWR); 2013:62(15):277-282.

Chen L, Chen C, Ewing R, McKnight CE, Srinivasan R, Roe M. Safety countermeasures and crash reduction in New York City – Experience and lessons learned. Accident; Analysis and Prevention; 2013:50:312-22. http://www.ncbi.nlm.nih.gov/pubmed/22658461 *

Griswold J, Fishbain B, Washington S, Ragland DR. Visual assessment of pedestrian crashes. Accident; Analysis and Prevention; 2011:43:301-6. http://eprints.qut.edu.au/38537/1/38537.pdf

Insurance Institute for Highway Safety (IIHS). Fatality facts 2013, Older people; 2013. http://www.iihs.org/iihs/topics/t/older-drivers/fatalityfacts/older-people/2013. †

Jacobsen PL, Racioppi F, Rutter H. Who owns the roads? How motorized traffic discourages walking and bicycling. Injury Prevention; 2009:15:369-73. http://injuryprevention.bmj.com/content/15/6/369.short

Jones DK, Evenson KR, Rodriguez DA, Aytur SA. Addressing pedestrian safety: a content analysis of pedestrian master plans in North Carolina. Traffic Injury Prevention; 2010:11:57-65. http://www.ncbi.nlm.nih.gov/pubmed/20146144

Nauman RB, Dellinger AM, Zaloshnja E, Lawrence BA, Miller TR. Incidence and total lifetime costs of motor vehicle-related fatal and nonfatal injury by road user type. Traffic Injury Prevention; 2010:11:353-60. http://www.ncbi.nlm.nih.gov/pubmed/20730682

Pucher J, Buehler R, Merom D, Bauman A. Walking and Cycling in the United States, 2001-2009: Evidence from the National Household Travel Survey. American Journal of Public Health; 2011:101:S:310-7. http://ajph.aphapublications.org/doi/abs/10.2105/AJPH.2010.300067

Pulugurtha SS, Desai A, Pulugurtha NM. Are pedestrian countdown signals effective in reducing crashes? Traffic Injury Prevention; 2010:11:632-41. http://www.ncbi.nlm.nih.gov/pubmed/21128194 *

U.S. Department of Transportation National Highway Traffic Safety Administration. Motor Vehicle Crashes; 2013. http://www-nrd.nhtsa.dot.gov/Pubs/812101.pdf

U.S. Department of Transportation National Highway Traffic Safety Administration. The Economic and Societal Impact of Motor Vehicle Crashes, 2010; 2015. http://www-nrd.nhtsa.dot.gov/pubs/812013.pdf †

U.S. Department of Transportation National Highway Traffic Safety Administration. Traffic Safety Facts: 2010 Data – Pedestrians; 2012. http://www-nrd.nhtsa.dot.gov/Pubs/811625.PDF

* Indicates research that supports policies analyzed

† Indicates research that supports equity or vulnerable populations studied

Updated: Tuesday, February 2, 2016
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