1. Estimating the Traffic Flow Impact of Pedestrians With Limited Data
Daniel Tischler, Elizabeth Sall,
Lisa Zorn & Jennifer Ziebarth
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
14th TRB Planning Applications Conference
May 6th, 2013

2. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Making the Case
I have problems.
I’m trying to model San Francisco traffic conditions, but
Pedestrians affect vehicle capacity
My dynamic traffic assignment model is needy
Data is scarce
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3. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Network capacity
Road capacity is a key input in traffic assignment
We assign capacities to roads by facility classification schemes
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4. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Network capacity
In our DTA model, signal timing becomes the primary determinant of capacity
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5. Pedestrians interact with vehicles
Lots of pedestrians crossing the street prevent cars from turning
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6. Pedestrian volumes vary
Our meso-level, dynamic assignment model does not simulate pedestrians.
It does not know that right turns at “A” are more restricted than at “B”
Intersection A
Intersection B
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7. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Methodologies
Analytical methods
Simulation methods
Local observations
Techniques for estimating impacts of pedestrians on automotive capacity
Analytical methods
Simulation methods
Local observations
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8. Analytical approaches
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9. Analytical approaches
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10. Pedestrian volume – vehicle flow relationship
Assumes 2 sec veh-veh headway, 50% green time
Doesn’t allow for ped volumes > 5,000
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11. Comparing different methods
HCM2010
Rouphail and Eads, 1997
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12. Applying pedestrian friction in the model
Saturation flow rate
Green time
Follow-up time
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13. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Scale of Application
Area Type Method
Neighborhood Type
Method
Uniform Parameters Method
Unique Intersection
Method
55%
Existing approach
Finding the right balance
Need enough detail and accuracy to reflect approximate capacity of system
Necessary to simplify physical environment to develop useful models
74%
Under consideration
Do nothing approach
87%
Under consideration
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14. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Resources
SFMTA pedestrian count program
SFMTA pedestrian model
Project-related pedestrian counts
Pedestrian-vehicle observations
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15. SFMTA pedestrian count program
2-hour pedestrian counts at 50 locations (2009/2010)
Six automatic pedestrian counters (24/7 observations)
Actual pedestrian count binder
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

16. Automated pedestrian counters
Time and day profiles
Tenderloin
Union Square
Castro
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17. Pedestrian count locations
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18. HCM 2010 flow rate adjustment factors
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19. SFMTA pedestrian model
Used count data and log-linear regression model to estimate pedestrian volumes throughout San Francisco
lnYi = β0+ β1 X1i+ β2 X2i+ … + βj Xji
Combined with auto traffic and collision data to develop pedestrian crossing accident risk factors
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

20. Pedestrian volume estimates
We can also use the model to forecast pedestrian model, but for various reasons, a model based on observed characteristics is more appropriate for estimating vehicle capacity constraints caused by pedestrians
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21. Local validation of pedestrian estimation
Ped model doesn’t validate adequately at key locations due to known weaknesses
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22. Pedestrian-vehicle interaction observations
SFCTA staff observed downtown intersections of varying pedestrian volumes
Collected information on quantity of pedestrians and relative restriction of vehicle turning movement capacity
The urban environment is complex:
Groupings of peds, directions of travel bicycles, unique timing plans, variations in geometry, etc.
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

23. Observed pedestrian-vehicle interactions
Incremental pedestrians have the greatest impact at low pedestrian volumes
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

24. Observed pedestrian-vehicle interactions
HCM2010
Rouphail and Eads, 1997
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25. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Model Tests
Uniform parameters method
Area type method
Neighborhood type method – sat flow rate
Neighborhood type method – green time reduction
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

26. Focus on pedestrian count-rich area
SoMa pedestrian count locations
4th / Market
6th / Market
8th / Market
6th / Mission
3rd / Howard
7th / Folsom 1 5 2 4 3 6
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

27. Hourly pedestrian counts
7th St
5th St
3rd St
Market St
3,300
3,000
10,000
Mission St
1,100
Howard St
1,200
Folsom St
600
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28. Network parameters Uniform parameter method
All Signals
Sat flow
1,800
Spacing
2.0s
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29. Network parameters Area type method
Hi Peds
Sat flow
1,620
Spacing
2.22s
Med Peds
Sat flow
1,710
Spacing
2.11s
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30. Network parameters Neighborhood type method
Adjust saturation flow rates
Sat Flow
810
Spacing
4.4s
Sat Flow
180
Spacing
20s
Sat Flow
1,260
Spacing
2.9s
Sat Flow
1,530
Spacing
2.4s
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

31. Network parameters Neighborhood type method
Reduce green time for turning movements
55% less green
90% less green
30% less green
15% less green
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32. Testing pedestrian friction methods
Area type method
Evaluation not yet complete!
Numbers
At select subarea nodes, turning movement volume validation improves with neighborhood type method
Queuing
Neighborhood method seems to produce more realistic queue formation than area method
Neighborhood type method
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33. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Conclusions
Pedestrian-vehicle friction is very important at some locations!
Numerous options to make assignment models sensitive to pedestrians
The most realistic approaches are very difficult
We still have work to do:
Run more tests
More local validation
Develop better neighborhood system
Incorporate time of day factors
Develop strategy for scenarios that change pedestrian volumes
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

34. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
The End.
Questions?
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