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San Francisco County 
Transportation 
Authority: CycleTracks 
elizabeth@sfcta.org 
@e_lo
CycleTracks 
Amazon EC/2 Server running Apache 
mySQL 
JSON 
PHP PHP
CycleTracks
Broad User Base 
• 20% (500+) users infrequent cyclists (10% of 
trips) 
Trips and Users by Cycling Frequency 
2,036 
9,122 
Users Trips 
577 852 853 
187 411 
13,506 
Less than once a month Several times per month Several times per week Daily 
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 4
Combined Reach: ~44,000 trips 
Combined Reach 
SAN FRANCISCO COUNTY 
TRANSPORTATION AUTHORITY 
http://goo.gl/maps/DuqGh 5
Bias? 
M:PnP2013Memos01 Jan 15 
• Tradeoff between bias and quantity 
– But bias can be dealt with if quantity is high 
enough. 
• Which biases are acceptable and when? 
• i.e. does income affect how adverse to biking up hills 
you are (vs. biking around them) ? 
• What biases can we undo with technology?
Combined Reach: ~44,000 trips 
Where does it fit in Value 
Chain? 
http://goo.gl/maps/DuqGh 7
Planning for Network Connectivity
Identify Potential Barriers
Before and After Analysis 
 
Photo courtesy of Nathan Wilkes, City of Austin 
Before 
After 
After
Value of Bike Facilities 
Attribute Coef. T-Stat 
Length (mi.) - 1.05 -11.8 
Turns / Mile - 0.21 -12.15 
Prop. Wrong Way -13.3 - 19.87 
Prop. Bike Path 1.89 6.17 
Prop. Bike Lanes 2.15 7.69 
Cycle Freq < several 
per wk 
1.85 4.94 
Prop. Bike Routes 0.35 3.14 
Avg. up-slope (ft/100ft) - 0.50 - 6.35 
Female -0.96 - 4.34 
Commuter - 0.90 - 8.21 
Log(path size) 1.07 26.8
Represent in Regional Model
Represent in Regional Model
San Francisco County 
Transportation Authority 
elizabeth@sfcta.org 
@e_lo

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California Bike Summit - 2013

  • 1. San Francisco County Transportation Authority: CycleTracks elizabeth@sfcta.org @e_lo
  • 2. CycleTracks Amazon EC/2 Server running Apache mySQL JSON PHP PHP
  • 4. Broad User Base • 20% (500+) users infrequent cyclists (10% of trips) Trips and Users by Cycling Frequency 2,036 9,122 Users Trips 577 852 853 187 411 13,506 Less than once a month Several times per month Several times per week Daily SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 4
  • 5. Combined Reach: ~44,000 trips Combined Reach SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY http://goo.gl/maps/DuqGh 5
  • 6. Bias? M:PnP2013Memos01 Jan 15 • Tradeoff between bias and quantity – But bias can be dealt with if quantity is high enough. • Which biases are acceptable and when? • i.e. does income affect how adverse to biking up hills you are (vs. biking around them) ? • What biases can we undo with technology?
  • 7. Combined Reach: ~44,000 trips Where does it fit in Value Chain? http://goo.gl/maps/DuqGh 7
  • 8. Planning for Network Connectivity
  • 10. Before and After Analysis  Photo courtesy of Nathan Wilkes, City of Austin Before After After
  • 11. Value of Bike Facilities Attribute Coef. T-Stat Length (mi.) - 1.05 -11.8 Turns / Mile - 0.21 -12.15 Prop. Wrong Way -13.3 - 19.87 Prop. Bike Path 1.89 6.17 Prop. Bike Lanes 2.15 7.69 Cycle Freq < several per wk 1.85 4.94 Prop. Bike Routes 0.35 3.14 Avg. up-slope (ft/100ft) - 0.50 - 6.35 Female -0.96 - 4.34 Commuter - 0.90 - 8.21 Log(path size) 1.07 26.8
  • 14. San Francisco County Transportation Authority elizabeth@sfcta.org @e_lo

Editor's Notes

  1. Certainly the advent of webinars, email lists, and websites themselves have allowed us to be more constantly connected with constituents, but
  2. Certainly the advent of webinars, email lists, and websites themselves have allowed us to be more constantly connected with constituents, but
  3. 44,000 trips as of end of 2012
  4. Certainly the advent of webinars, email lists, and websites themselves have allowed us to be more constantly connected with constituents… I’m going to spend the rest of my time discussing three tools that we developed at SFCTA to help us improve outreach on all fronts.
  5. 44,000 trips as of end of 2012
  6. Important connection from UT campus into Downtown; Rio Grande bike Boulevard or “neighborhood greenway”.
  7. Currently there is a lack of data on how to identify barriers; planners might rely on citizens adivsory comittees, or even a city 3-1-1 program. Objectively identify barriers; assumption is that repeated devations from the shortest path signal that there may be a barrier. OR they are willing to go out of their way. Either way, these areas arrant close examination. Green- street segments that cyclists took despite the fact that it wasn’t their shortest path Red- areas that were their shortest path, but that they avoided for one reason or another.
  8. Powerfull visual to justify spending on infrastructure.
  9. Powerfull visual to justify spending on infrastructure.
  10. Powerfull visual to justify spending on infrastructure.
  11. Powerfull visual to justify spending on infrastructure.