Indicator ST.1.e Traffic volume by miles of street

ST.1.e Traffic volume by miles of street

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Data Source

Traffic data estimates in 2005 conditions are at the street segment level and were obtained from the San Francisco County Transportation Authority's transportation forecasting model SF-CHAMP (the San Francisco County Chained Activity Modeling Process).  Additional information including SF-CHAMP model documentation can be downloaded from the SFCTA's website:  www.sfcta.org.

Street length in miles was calculated from the San Francisco of Public Works, Department of Technology's basemap street centerline network.

Map data is presented at the level of the census tract. The map also includes planning neighborhood names, in the vicinity of their corresponding census tracts.

Table data is presented by planning neighborhood. Planning neighborhoods are larger geographic areas then census tracts. SF DPH used ArcGIS software and a 'centroids within' methodology to convert census tracts to geographic mean center points. We then assigned census tracts to planning neighborhoods based on the spatial location of those geographic mean center points and calculated the planning neighborhood totals for the table.

Detailed information regarding census data, geographic units of analysis, their definitions, and their boundaries can be found in the HDMT at the following links:

http://www.thehdmt.org/etc/Geographic_Units_of_Analysis.September_2009.pdf

http://www.thehdmt.org/data_map_methods.php

Explanation and Limitations

Traffic volume estimates are at the street segment level, then aggregated and averaged over the length of street miles as an indicator of the relative volume of traffic on streets in the specified area.  Detailed information regarding SF-CHAMP traffic volume estimates and the SF CTA's model can be found on their website, sftca.org. 

This indicator reflects the volume of traffic on area roadways.  Standardizing by street length allows for a relative comparison of the traffic volume on streets in a specified area, adjusting for area differences in miles of street.  Notably, some areas would look different relative to other areas if absolute traffic volumes were assessed without adjusting for street length. 

Why is this a Community Health Indicator?

Children living next to busy roadways have more respiratory disease symptoms, asthma hospitalizations and doctor visits, and poorer lung function measures than children who live further away from high traffic areas.a  Air pollution associated with roadway proximity contributes to cancer, respiratory disease, and impaired lung development.b 

Moderate levels of traffic noise negatively impacts on stress, and is associated with higher risk for hypertension, blood pressure, and heart disease.c Children exposed to high levels of traffic noise are at increased risk of learning delays.d Traffic noise also contributes to sleep disturbance, annoyance, leading to decreased concentration, increased aggressive behavior, and decrease helping behavior.e

Research has shown that traffic volume is a significant, strong predictor of risk for people being injured or killed while walking.f Nationwide, pedestrians account for 11% of motor vehicle collision fatalities, with approximately 4,700 pedestrian deaths annually.g Pedestrians account for approximately 50% of traffic-related deaths in San Francisco – which has an injury rate approximately five times above the Healthy People 2010 national objective of no greater than 19 injuries/year/100,000 people.h

Higher traffic volumes on local streets in San Francisco were found to be associated with residents’ perceptions of the street as unsafe, complaints regarding noise, fewer local friends and acquaintances, smaller perceptions of personal territory and decreased attentiveness to the street environment.i Parental concerns about heavy traffic is also a deterrent for active commuting to school for children.j

  1. Brunekreef B, Janssen NA, Hartog J. 1997. Air pollution from truck traffic and lung function in children living near motorways. Epidemiology 8:298-303;  Kim JJ, Smorodinsky S, Lipsett M, Singer BC, Hodgson AT, Ostro B.  2004. Traffic-related air pollution and respiratory health: East Bay Children's Respiratory Health Study. American Journal of Respiratory and Critical Care Medicine 170:520-526; Lin S, Munsie JP, Hwang SA, Fitzgerald E, Cayo MR. 2002. Childhood asthma hospitalization and residential exposure to state route traffic.  Environmental Research 88(2):73-81; McConnell R, Berhane K, Yao L, Jerrett M, Lurmann F, Gilliland F, Künzli N, Gauderman J, Avol E, Thomas D, Peters J. 2006. Traffic,  susceptibility, and childhood asthma. Environmental Health Perspectives. 114(5):766-72.

  2. California Air Resources Board. 2005. Air Quality and Land Use Handbook: a Community Health Perspective. California Air Resources Board.

  3. Miedema HME, Vos H. 1998. Exposure response for transportation. Journal of the Acoustical Society of America. 104:3432-3445; Seto, EYW, Holt A, Rivard T, Bhatia R. 2007. Spatial distribution of traffic induced noise exposures in a US city: an analytic tool for assessing the health impacts of urban planning decisions. International Journal of Health Geographics. 6:24. Available at: http://www.ij-healthgeographics.com/content/6/1/24.

  4. Evans GW. 2006. Child development and the physical environment. Annual Review of Psychology 57:423-451.

  5. Berglund B, Lindvall T, Schwela DH. 1999. Extract from Guidelines for Community Noise: Sleep Disturbance. World Health Organization. Available at http://www.who.int/docstore/peh/noise/Comnoise-3.pdf; Morh D, Vedantham K, Neylan T, Metzler TJ, Best S, Marmar CR. 2003. The medicating effects of sleep in the relationship between traumatic stress and health symptoms in urban police officers. Psychosomatic Medicine 65:485-489; Stansfeld SA, Matheson MP. 2003. Noise pollution: non-auditory effects on health. British Medical Bulletin 68:243-257.

  6. Lee, C., Abdel-Aty, M., 2005. Comprehensive analysis of vehicle-pedestrian crashes at intersections in Florida. Accident Analysis and Prevention 37, 775-786;  Loukaitou-Sideris, A., Ligget, R., Sung, H.G., 2007.  Death on the Crosswalk: A Study of Pedestrian Automobile Collisions in Los Angeles. Journal of Planning Education and Research 26: 338-351; Wier M, Weintraub J, Humphreys E, Seto E, Bhatia R.  2008. An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning. Accident Analysis & Prevention 41:137-145.

  7. NHTSA (National Highway Traffic Safety Administration), 2006a. Fatality Analysis Reporting System, National Statistics. Available at: http://www-fars.nhtsa.dot.gov/.

  8. CCSF MTA (City and County of San Francisco Municipal Transportation Agency Traffic Engineering Division), 2006. San Francisco 2005 Collision Report. San Francisco, CA; USDHHS (U.S. Department of Health and Human Services), 2000. Healthy People 2010: Understanding and Improving Health. 2nd ed., U.S. Government Printing Office, Washington, DC.

  9. Appleyard D, Gerson MS, Lintell M. 1981. Livable Streets. University of California Press, Berkeley, California.