SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Estimating the Traffic Flow Impact of Pedestrians With
Limited Data
Daniel Tischler, Elizabeth Sall,Lisa Zorn & Jennifer Ziebarth
14th TRB Planning Applications ConferenceMay 6th, 2013
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Making the Case
I have problems.
I’m trying to model San Francisco traffic conditions, but
Pedestrians affect vehicle capacityMy dynamic traffic assignment model is needyData is scarce
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Network capacity
Road capacity is a key input in traffic assignmentWe assign capacities to roads by facility classification schemes
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Network capacity
In our DTA model, signal timing becomes the primary determinant of capacity
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Pedestrians interact with vehicles
Lots of pedestrians crossing the street prevent cars from turning
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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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Methodologies
Analytical methodsSimulation methodsLocal observations
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Analytical approaches
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Analytical approaches
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Pedestrian volume – vehicle flow relationship
• Assumes 2 sec veh-veh headway, 50% green time
• Doesn’t allow for ped volumes > 5,000
0 500 1000 1500 2000 25000
500
1000
1500
2000
Right Turn Saturation Flow Rate (HCM 2010)
Pedestrians crossing Street (peds / hr)
Sa
tura
tio
n F
low
Ra
te(v
eh
/ h
r)
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Comparing different methods
HCM2010
Rouphail and Eads, 1997
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Applying pedestrian friction in the model
Saturation flow rate
Follow-up time
Green time
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Scale of Application
55%
74%
87%
Existing approach
Under consideration
Area Type Method
Neighborhood TypeMethod
Under consideration
Unique IntersectionMethod
Do nothing approach
Uniform Parameters Method
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Resources
SFMTA pedestrian count programSFMTA pedestrian modelProject-related pedestrian countsPedestrian-vehicle observations
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SFMTA pedestrian count program
• 2-hour pedestrian counts at 50 locations (2009/2010)
• Six automatic pedestrian counters (24/7 observations)
Actual pedestrian count binder
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Automated pedestrian counters
• Time and day profiles
Union Square
Castro
Tenderloin
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Pedestrian count locations
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HCM 2010 flow rate adjustment factors
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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
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Pedestrian volume estimates
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Local validation of pedestrian estimation
8th7t
h6t
h5t
h4t
h3r
d
0
2,000
4,000
6,000
8,000
10,000
12,000
Market
Howard
Ped Model Estimates
PM
Peak
Hour
Ped C
ross
ings
8th
7th
6th
5th
4th
3rd
0
2,000
4,000
6,000
8,000
10,000
12,000
Market
Howard
Ped Counts
PM
Peak
Hour
Ped C
ross
ings
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Pedestrian-vehicle interaction observations
SFCTA staff observed downtown intersections of varying pedestrian volumesCollected information on quantity of pedestrians and relative restriction of vehicle turning movement capacityThe urban environment is complex:
• Groupings of peds, directions of travel bicycles, unique timing plans, variations in geometry, etc.
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0 500 1,000 1,500 2,0000%
20%
40%
60%
80%
100%
120%
Pedestrian Impedance of Turning Vehicles
Pedestrian Flow in Crosswalk (peds/hr during walk phase)
Perc
en
t re
du
cti
on
in
Veh
icle
C
ap
acit
y
Observed pedestrian-vehicle interactions
Incremental pedestrians have the greatest impact at low pedestrian volumes
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HCM2010
Rouphail and Eads, 1997
Observed pedestrian-vehicle interactions
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Model Tests
Uniform parameters methodArea type methodNeighborhood type method – sat flow rateNeighborhood type method – green time reduction
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Focus on pedestrian count-rich area
SoMa pedestrian count locations1. 4th / Market2. 6th / Market3. 8th / Market4. 6th /
Mission5. 3rd /
Howard6. 7th / Folsom
1
2
6
4
5
3
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Hourly pedestrian counts
3,300 3,000 10,000
1,100
600
1,200
Market St
Mission St
Howard St
Folsom St
3rd St
5th St
7th St
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Network parametersUniform parameter method
All Signals
Sat flow
1,800
Spacing
2.0s
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Network parametersArea type method
Hi PedsSat flow
1,620
Spacing
2.22s
Sat flow
1,710
Spacing
2.11s
Med Peds
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Network parametersNeighborhood type method
Sat Flow810
Spacing
4.4s
Sat Flow180
Spacing
20s
Spacing
2.9s
Spacing
2.4s
Sat Flow1,260
Sat Flow1,530
Adjust saturation flow rates
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Network parametersNeighborhood type method
55% less
green
90% less
green
30% less
green
15% less
green
Reduce green time for turning movements
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Testing pedestrian friction methods
• Evaluation not yet complete!
• NumbersAt select subarea nodes, turning movement volume validation improves with neighborhood type method
• QueuingNeighborhood method seems to produce more realistic queue formation than area method
Area type method
Neighborhood type method
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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
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The End.
Questions?