1. Implementing VMT as the LOS Replacement Metric in San Francisco
Drew Cooper, SFCTA
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
DATE

2. A brief history Level of Service Measures vehicle delay
Under CEQA intersection delay is an environmental impact, requires study, mitigation

3. A brief history
Can’t cause delay at intersections, might as well build suburban

4. A brief history
And maybe some extra wide roads just to be sure…

5. A brief history
But spread out development + lots of road space + probably not well-served by transit = lots of driving
Lots of driving = lots of emissions = bad for the environment
SB375 – set GHG reduction targets
SB743 – says LOS can’t be used as sole measure of impact under CEQA
OPR rulemaking process, not yet complete put out preliminary guidance for VMT
SF decides to not wait for state

6. Legislative Direction
SB 375
SB 743
Office of Planning and Research
SB375 – set GHG reduction targets
SB743 – says LOS can’t be used as sole measure of impact under CEQA
OPR rulemaking process, not yet complete put out preliminary guidance for VMT
SF likes to be first to things, so we decided not to wait
Although Pasedena beat us to it

7. Goals Align CEQA impacts with City policies
Encourage projects with better environmental outcomes
Consistent and fair methodology
Predictable outcomes
So we formed a working group and laid out some goals for ourselves. First we want to make sure that CEQA transportation analysis is consistent with City goals
City Hall by Roberto Arias

8. Some Things Considered
VMT
Per person… household… project… person-trip… etc.
Maxwell
We considered VMT in combination with any and all denominators you could think of. We considered inverting it so VMT is the denominator and called it a Maxwell. Ultimately we decided to go with VMT per person

9. Why VMT? Measure of how much driving Already use VMT for GHG
accounts for both # trips and distance
Already use VMT for GHG
Measure of how much driving
accounts for both # trips and distance
Already use VMT for GHG
By Daniel Schwen - Own work, CC BY-SA 2.5,

10. Methodology
We started off by dividing up land use projects into three broad categories: residential, office, and retail. Then for each of these categories we developed a unique VMT-based metric, and a threshold of significance. Then, using outputs from our travel demand model, we calculated the metric for each TAZ in the Bay Area.

11. Methodology Residential and office: fairly straightforward
Residential is average daily VMT per person. Relatively simple because with model output we can trace their movements through the day, determine which trips happened in cars, add up the VMT, and tag that VMT to their home location
Office is similar: miiples, if workers, have a primary work location, and we can add up the VMT from all the work-related trips and assign it to the work location
With VMT calculated, we then simply divide by number of people for res or jobs for office

12. Everything gets weird when you talk about retail
Trip 1
Trip 4
Trip 2
Residential and office are easy because they are anchor points of daily travel. Retail isn’t like that, though, so we needed a different methodology. We think that people choose these destinations more based on their location and accessibility relative to the anchor locations. It’s not appropriate to look at daily VMT for these locations so we used a trip-based measure instead
Start with all trips where at least one trip end is not home, work, or school
Add up VMT for all drive trips to a TAZ
If the other end of the trip is home, work, or school then 100% goes to this TAZ
Otherwise, split VMT between both TAZs
Trip 3

13. Everything gets weird when you talk about retail
VMT = Trip 1 + ½ Trip 2
Trip 1
Trip 4
Trip 2
Residential and office are easy because they are anchor points of daily travel. Retail isn’t like that, though, so we needed a different methodology. We think that people choose these destinations more based on their location and accessibility relative to the anchor locations. It’s not appropriate to look at daily VMT for these locations so we used a trip-based measure instead
Start with all trips where at least one trip end is not home, work, or school
Add up VMT for all drive trips to a TAZ
If the other end of the trip is home, work, or school then 100% goes to this TAZ
Otherwise, split VMT between both TAZs
VMT = Trip 1 + ½ Trip 2
Trip 3

14. Everything gets weird when you talk about retail
What do we divide by?
“Retail size” measure
Base on how model chooses “other” destinations
Residential and office are easy because they are anchor points of daily travel. Retail isn’t like that, though, so we needed a different methodology. We think that people choose these destinations more based on their location and accessibility relative to the anchor locations. It’s not appropriate to look at daily VMT for these locations so we used a trip-based measure instead
Start with all trips where at least one trip end is not home, work, or school
Add up VMT for all drive trips to a TAZ
If the other end of the trip is home, work, or school then 100% goes to this TAZ
Otherwise, split VMT between both TAZs
What do we divide by?
“Retail size” measure
Current “retail size” based on the way the model chooses “other” destinations
Accounts for # retail + households + CIE + school enrollment, and a few other land use categories

15. Thresholds and Results
OPR suggested a threshold of 15% below the regional average, so rather than try to pick our own arbitrary number, we went with that. These maps show, on the left, TAZs that exceed 85% of the regional daily residential VMT, and on the left, TAZs that exceed 85% of the regional daily work-related VMT

16. Thresholds and Results
And finally, TAZs that exceed the retail threshold. The upshot of all this is that, if a project is located in one of these grey zones and they don’t propose something egregious, they’re project is going to be presumed to have less than significant impacts on VMT and wouldn’t require an EIR for transportation-related issues.

17. Takeaways Analysis tools may put restrictions on the methodology
Similar methodologies may have profoundly different implications for different regions and jurisdictions
One size does not fit all
Simple is best

18. Thanks! drew.cooper@sfcta.org