This workbook provides data and data dictionaries for the SFMTA 2017 Travel Decision Survey. On behalf of San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis (CC&G) undertook a Mode Share Survey within the City and County of San Francisco as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma and Solano. The primary goals of this study were to: • Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY2018 with a 95% confidence level and MOE +/- 5% or less. • Evaluate the above statement based on the following parameters: number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. • Provide additional trip details, including trip purpose for each trip in the mode share question series. • Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. • Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs. The survey was conducted as a telephone study among 804 Bay Area residents aged 18 and older. Interviewing was conducted in English, Spanish, Mandarin, Cantonese, and Tagalog. Surveying was conducted via random digit dial (RDD) and cell phone sample. All survey datasets incorporate respondent weighting based on age and home location; utilize the “weight” field when appropriate in your analysis. The survey period for this survey is as follows: 2017: February - April 2017 The margin of error is related to sample size (n). For the total sample, the margin of error is 3.4% for a confidence level of 95%. When looking at subsets of the data, such as just the SF population, just the female population, or just the population of people who bicycle, the sample size decreases and the margin of error increases. Below is a guide of the margin of error for different samples sizes. Be cautious in making conclusions based off of small sample sizes. At the 95% confidence level is: • n = 804(Total Sample). Margin of error = +/- 3.4% • n = 400. Margin of error = +/- 4.85% • n = 100. Margin of error = +/- 9.80%

**Please refer to the downloadable XLSX attachment (http://bit.ly/SFMTATravelSurvey2019) for the complete dataset, metadata, and instructions for use.** This workbook provides data and data dictionaries for the SFMTA 2019 Travel Decision Survey. On behalf of San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis (CC&G) undertook a Mode Share Survey within the City and County of San Francisco as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma and Solano. The primary goals of this study were to: • Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.2: Mode Share target of 80% sustainable travel by 2030. • Evaluate the above statement based on the following parameters: number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. • Provide additional trip details, including trip purpose for each trip in the mode share question series. • Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. • Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs. The survey was conducted as a telephone study among 801 Bay Area residents aged 18 and older. Interviewing was conducted in English, Spanish, Mandarin, Cantonese, and Tagalog. Surveying was conducted via random digit dial (RDD) and cell phone sample. All survey datasets incorporate respondent weighting based on age and home location; utilize the “weight” field when appropriate in your analysis. The survey period for this survey is as follows: 2019: May - August 2019 The margin of error is related to sample size (n). For the total sample, the margin of error is 3.3% for a confidence level of 95%. When looking at subsets of the data, such as just the SF population, just the female population, or just the population of people who bicycle, the sample size decreases and the margin of error increases. Below is a guide of the margin of error for different samples sizes. Be cautious in making conclusions based off of small sample sizes. At the 95% confidence level is: • n = 801(Total Sample). Margin of error = +/- 3.3% • n = 400. Margin of error = +/- 4.85% • n = 100. Margin of error = +/- 9.80%

This workbook provides data and data dictionaries for the SFMTA 2013 Travel Decision Survey. The 2013 Summary Report and Methodology, including the survey instrument, can be found online at https://www.sfmta.com/about-sfmta/reports/travel-decision-survey-2013. Data, methodologies, and summary report for other SFMTA travel decision surveys is available on sfmta.com. On behalf of San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis (CC&G) undertook a Mode Share Survey within the City and County of San Francisco as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma and Solano. The primary goals of this study were to: • Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY2018 with a 95% confidence level and MOE +/- 5% or less. • Evaluate the above statement based on the following parameters: number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. • Provide additional trip details, including trip purpose for each trip in the mode share question series. • Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. • Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs. The survey was conducted as a telephone study among with approximately 750 Bay Area residents aged 18 and older. Interviewing was conducted in English, Spanish, and Cantonese. Surveying was conducted via random digit dial (RDD) and cell phone sample. All three survey datasets incorporate respondent weighting based on age and home location; utilize the “weight” field when appropriate in your analysis. The survey period for this survey is as follows: 2013: April 2014 – May 2014 (survey name of 2013 reflects the originally planned start date of Fall 2013) The margin of error is related to sample size (n). For the total sample, the margin of error is 3.5% for a confidence level of 95%. When looking at subsets of the data, such as just the SF population, just the female population, or just the population of people who bicycle, the sample size decreases and the margin of error increases. Below is a guide of the margin of error for different samples sizes. Be cautious in making conclusions based off of small sample sizes. At the 95% confidence level is: • n = 767 (Total Sample). Margin of error = +/- 3.5% • n = 384. Margin of error = +/- 4.95% • n = 100. Margin of error = +/- 9.80%

**Please refer to the downloadable XLSX attachment for the complete dataset, metadata, and instructions for use.** On behalf of San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis (CC&G) conducted the 2021 Mode Share Survey within the City and County of San Francisco as well as the eight surrounding Bay Area counties (Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma and Solano). This study has been conducted periodically by CC&G since 2013. Primary goals of this study include: • Estimate mode share for trips into, within, and out of San Francisco, focusing on the percentage of trips made by walking, biking, and public transit compared to other modes (e.g. driving, TNC, etc.). This survey is the primary instrument for tracking the SFMTA Strategic Plan metric “transportation mode share” and provides information used by the agency across divisions. • Evaluation of the above statement based on the number of trips to, from, and within San Francisco by Bay Area residents. Note that trips by visitors from outside the Bay Area, and for commercial purposes whether originating in the Bay Area or not, are not included. • Provide additional trip details, including trip purpose for each trip in the mode share question series. • Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. The survey was conducted as a telephone study among 756 Bay Area residents aged 18 and older. Telephone surveying was conducted during May – September 2021. Interviewing was conducted in English, Spanish, and Chinese. Surveying was conducted via random digit dial (RDD) and cell phone sample. To enhance statistical accuracy, data was weighted by age and location. Not all totals will add to 100% due to rounding. In portions of this report, a dash (-) is used to indicate no respondents answered with the response code listed. The margin of error at the 95% confidence level is +/-3.56% for the total sample (n = 756). For other sample sizes, the margin of error is as follows: • San Francisco residents; n = 470. Margin of error = +/- 4.52% • Bay Area residents (outside San Francisco) n = 286. Margin of error = +/- 5.79%

This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

The Pedestrian and Bicycle Count Dataset documents non motorized traffic on state highways, originating from Caltrans district pilots initiated in 2023 to monitor active transportation. Data is vendor-collected using technologies like video analytics, then shared with HQ via APIs or files for statewide standardization in CSV format. Core variables include Loc_id (site identifier), Date/Hour (timestamp), Direction, Count (integer volumes), and geospatial (latitude/longitude). Limitations include potential undercounts in adverse weather and incomplete coverage. This supports equity-focused planning under Complete Streets policies, with updates as pilots expand.

The Pedestrian and Bicycle Count Dataset documents non motorized traffic on state highways, originating from Caltrans district pilots initiated in 2023 to monitor active transportation. Data is vendor-collected using technologies like video analytics, then shared with HQ via APIs or files for statewide standardization in CSV format. Core variables include Loc_id (site identifier), Date/Hour (timestamp), Direction, Count (integer volumes), and geospatial (latitude/longitude). Limitations include potential undercounts in adverse weather and incomplete coverage. This supports equity-focused planning under Complete Streets policies, with updates as pilots expand.

Counts by mode Travel characteristics (observed by data collector)