Descriptive Title: Proportion of trips made by walking, biking, or public transit
Geographic Unit of Analysis:
Transportation Analysis Zone - TAZ (San Francisco County Transportation Authority)
|Proportion of trips made by non-auto mode (2015)|
|Neighborhood||% Walk||% Bike||% Transit||% Non-auto||% Auto|
|Financial District/South Beach||43%||3%||30%||76%||24%|
|Golden Gate Park||9%||3%||12%||24%||76%|
|South of Market||44%||4%||25%||73%||27%|
|West of Twin Peaks||20%||3%||16%||38%||62%|
Environments that support walking, biking and transit trips as an alternative to driving have multiple potential positive health impacts. Quality, safe pedestrian and bicycle environments support a decreased risk of motor vehicle collisions and an increase in physical activity and social cohesion with benefits including the prevention of obesity, diabetes, and heart disease as well as stress reduction and mental health improvements that promote individual and community health. Environments that encourage walking and biking while discouraging driving can further reduce traffic-related noise and air pollution – associated with cardiovascular and respiratory diseases, premature death, and lung function changes especially in children and people with lung diseases such as asthma.a
Public transportation links people to jobs, goods and services, and also helps communities achieve public health benefits such as increased physical activity via walking to transit, reduced pollution, and reduced fatalities and injuries. Walk-to-transit trips account for 16% of all recorded walking trips based on an analysis of U.S. travel survey data; these trips tend to be longer than average walking trips.b The lifetime odds of dying as a car driver or passenger are 1 in 303 – approximately 300 times the odds of dying as a bus occupant (1 in 89,945) and 750 times the odds of dying as a train occupant (1 in 227,509).c
Public transit can also provide an equitable transportation option and improve mobility, independent of age, ability, income or race.d In California, transportation costs are the third largest expense behind housing and food among low-income households—roughly the poorest 25%.e Walking and biking are relatively inexpensive forms of transportation when they are able to provide access for residents to nearby goods, services, or jobs. Ensuring adequate non-auto trip opportunities – including via public transit, walking or cycling - with matching infrastructure, particularly in lower income communities, could increase non-auto trips and support multiple health benefits in communities often disproportionately burdened with poorer health outcomes.
Nearly three-quarters of all trips that originate in Chinatown, Financial District/South Beach, Nob Hill, and the Tenderloin are made by non-auto modes. This is consistent with increased residential density, decreased parking availability and lower household car ownership, greater transit access and greater household proximity to goods and services in the northeast quadrant relative to the other districts – all factors supportive of increased walking, biking and transit use. Conversely, less than 40% of all trips originating in Treasure Island, Presidio, West of Twin Peaks, and Twin Peaks are by walking, biking or transit - which is consistent with the lack of infrastructure that supports transit and active transportation in these areas.
Using the San Francisco County Transportation Authority's CHAMP model data for 2015, the percent trips made by each mode choice was calculated for each Transportation Analysis Zone (TAZ). TAZ data was aggregated to analysis neighborhoods for the table.
Data is from the SFCTA’s travel forecasting model, SF CHAMP. While the model is internationally regarded as a sophisticated travel forecasting approach which provides the best available estimates, its outputs are not precise predictions estimates presented in the above map and table have not been validated. Additionally, the data presented is for all trips that originate in each TAZ (residents and visitors) and is not a reflection of only the behaviors of residents that live in that area.
Transportation mode choice is influenced by multiple factors including cost, distance, accessibility, perceived and actual safety, weather, pedestrian safety, traffic patterns, availability of bicycle lanes, availability of transit routes, hours of operation, availability of parking, and availability of travel stipends/incentives provided by work or to low income families.
Transportation District-level data provided by the San Francisco County Transportation Authority from their travel forecasting model, SF CHAMP.
Map and table prepared by City and County of San Francisco, Department of Public Health, Environmental Health Section using ArcGIS software.
Map data presented at the level of the transportation analysis zone.
Table data presented at the level of of the analysis neighborhood.
Detailed information regarding census data, geographic units of analysis, their definitions, and their boundaries can be found at the following links:
PolicyLink, Prevention Institute, the Convergence Partnership. 2009. Healthy, Equitable Transportation Policy, Recommendations and Research. Available at: http://www.convergencepartnership.org/atf/cf/%7B245a9b44-6ded-4abd-a392-ae583809e350%7D/HEALTHTRANS_FULLBOOK_FINAL.PDF
Weinstein A, Schimek P. 2005. How much do Americans walk? an analysis of the 2001 NHTS. Presented at: Transportation Research Board Annual Meeting; January 9-13, 2005, Washington, DC.
National Safety Council. 2011. Injury Facts 2011 Edition. Available at: http://www.nsc.org/Documents/Injury_Facts/Injury_Facts_2011_w.pdf
American Public Health Association. 2012. Public Transportation: A Link to Better Health and Equity. Washington, DC. (Accessed March 19, 2012: http://www.apha.org/NR/rdonlyres/BA1F8FA3-71DC-4100-B58D-A26C21D7A2A4/0/APHAPublicTransportationFactSheetFebruary2012.pdf)
Public Policy Institute of California. 2004. Transportation Spending by Low-Income California Households: Lessons for the San Francisco Bay Area. Available at: http://www.ppic.org/content/pubs/report/R_704LRR.pdf.