Overview of SF-CHAMP, the San Francisco CHained Activity Modeling Process.

1. SF-CHAMP Basics
Version 4.3 AKA Fury
Lisa Zorn
Presentation to the SFMTA Intern/New Staff Seminar
August 3rd, 2012

2. Activity-Based Modeling
SF-CHAMP is a tool which predicts activities, locations,
and travel time for every individual traveler in San
Francisco
Based on Census Data and local surveys
 MTC 1996 and 2000 Home Interview Activity Diaries
 Census 2000, 2010, and ACS
 Muni 2004/2005 Onboard passenger survey
 2007 Stated Preference Survey: Pricing
 Muni “APC” count data
 Traffic Counts (MTC/Caltrans/SFMTA
Simulation of every Bay Area resident’s daily choices
(and visitors too)
Trucks and “external” trips borrowed from MTC Model
SF-CHAMP Model Basics 2

3. Activities Grouped into “Tours”
HOME BASED
TOUR
7 = Tour
A tour is an entire chain of
DESTINATION SECONDARY = Trip
HOME-BASED
trips: from your primary origin, TOUR Number indicates trip order
to all of your destinations, 6
and then back again. HOME
1
Primary destinations
PRIMARY TOUR: INTERMEDIATE
vs. 5 STOP ON
Home-based WAY TO WORK
Intermediate stops
Work
2
WORK
Consequences of choices:
Do you have a car available?
Did you leave the car at home? 3
4 WORK-BASED
Do you have a complicated day? SUB-TOUR
WORK-BASED
DESTINATION
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4. SF-CHained Activity Modeling Process
Population Synthesis
• Land Use input
• Census Data
Household
+ demographics
such as ages,
income, hh sizes,
workers
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5. SF-CHained Activity Modeling Process
Workplace Location Choice
• Land Use input
• Census (CTPP)
• Modes, costs, distances
Workplace
Destination
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6. SF-CHained Activity Modeling Process
Vehicle Availability
• Accessibility of home & work
• Accessibility between them
Household Vehicles
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7. SF-CHained Activity Modeling Process
Tour Generation
• Accessibility of home & work
• Accessibility between them
• Demographics
Tour pattern for the
day
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8. SF-CHained Activity Modeling Process
Tour Destination Choice
• Initial tour schedule
• Accessibility
Tour Destinations
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9. SF-CHained Activity Modeling Process
Tour Mode Choice
• Accessibility to destinations
for that time of day by mode
Tour Modes
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10. SF-CHained Activity Modeling Process
Intermediate Stop Choice
• Tour pattern requirements
• Accessibility of potential stops
given tour mode
Intermediate Stops
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11. SF-CHained Activity Modeling Process
Trip Mode Choice
• Cost, Travel Time
• Demographics
• Tour Mode
Trip Modes
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12. Spatial Detail - Transit
Every transit stop
Every transit line
Every street
SF-CHAMP Model Basics 12

13. Sample Auto Volume Plot
SF-CHAMP Model Basics 13

14. Roadway Calibration Data
Calibrated BPR functions using speed and volume sensors for base year
SF-CHAMP Model Basics 14

15. Validation: Volumes And Boardings
Calibrated using 2000 base year data
Validated to 2005 counts and boardings, and 2006 speeds
E s tim a te d vs . O b s e rve d R o a d V o lu m e s
160000 Traffic Volumes Muni Daily Boardings by Route
Daily Boardings
by Route
Estimated vs. Observed Estimated vs. Observed
140000 50,000
45,000
120000
40,000
100000 35,000
Estimated Daily Boardings
30,000
80000
25,000
60000
20,000
15,000
40000
10,000
20000
5,000
0 0
0 20000 40000 60000 80000 100000 120000 140000 160000 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000
Observed Daily Boardings
D a ily O b s e rv e d V o lu m e
SF-CHAMP Model Basics 15

16. Van Ness BRT – Transit Line Validation
Validated using 2007APC Data
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17. Newly Added Data
• New estimations (BATS 2000):
• Mode choice
• Auto Availability
• New calibration (ACS)
• Workplace Location Choice
• Auto Availability
• Tour mode choice
• New validation
• 2010 APCs and Ridership
• Recent Traffic Counts
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18. Land Use Inputs
Households, Jobs,
Households,Jobs, &
ABAG Population
& Population
Countywide Totals
SF Planning Dept. ABAG Households & Jobs
Households& Jobs
SF TAZs (Plan B) Non-SF TAZs
ABAG/MTC
All TAZs Income & Age
& Age
TAZ Level Land Use for Bay Area
SF-CHAMP Model Basics 18

19. Data Storage
• HDF5:
• easily viewable in ViTables, HDFview
• not as heavy a relational database
• easily scriptable in Python, R
• compressed
• Free
• Use for:
• Trip diaries :
• easy to write scripts at the end to mine the data
• Skim matrices:
• removes our Cube matrix 32-bit dependencies
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20. Code Base
• Primarily C++ with Boost library
• Secondary is Python with 64-bit numpy
• 64-bit operations
• Can bring more skims into mode choice
• More distributed processes
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21. Spatial Detail – Analysis Zones
• Trips are
aggregated into
“zones”
• 981 zones in San
Francisco
• 1,275 in other Bay
Area counties
SF-CHAMP Model Basics 21

22. New Component – Bike Route Choice
• Data:
• CycleTracks smartphone app
• Methodology:
• Choice set generation: doubly stochastic method
• Path size logit estimation

23. New Component – Bike Route Choice
Attribute Coef. SE t-stat. p-val.
Length (mi) --1.05 0.09 --11.80 0.00
Turns per mile --0.21 0.02 --12.15 0.00
Prop. wrong way --13.30 0.67 --19.87 0.00
Prop. bike paths 1.89 0.31 6.17 0.00
Prop. bike lanes 2.15 0.12 17.69 0.00
Cycling freq. < several per wk. 1.85 0.04 44.94 0.00
Prop. bike routes 0.35 0.11 3.14 0.00
Avg. up-slope (ft/100ft) --0.50 0.08 --6.35 0.00
Female --0.96 0.22 --4.34 0.00
Commute --0.90 0.11 --8.21 0.00
Log(path size) 1.07 0.04 26.38 0.00
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24. New Component – Bicycle Assignment
Bikes / hour
0
20
180
360
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25. Bike Accessibility: From 4th and King
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26. Bike Logsums: From 4th and King
Effect of Bike Plan Build
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27. New Component – Network-based Pedestrian LOS
Forecastable, continuous
pedestrian utility
along walk path
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28. New Pedestrian and Transit Environment Factors
• Attributes Empirically Estimated:
• Hills (rise)
• Indirectness
• Population and Employment Densities (logs)
• Street capacity

29. Example: Walking to SFCTA
Work Purpose
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30. Transit Walk Access Links: Perceived Weight
Walk-Local-Walk, Destination Ferry Building
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31. Transit Assignment with Crowding
• Motivation:
• Transit is crowded today and expected to get
worse in SF
• Failure to represent transit capacity leads to:
• Unrealistically high forecasts
• Poor line-level validation
• No relationship between capacity projects and
effectiveness measures such as: mode share,
emissions, travel time
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32. Transit Assignment with Crowding
In real-life, if a line is crowded one can:
A. Wait for a vehicle with room (+ wait time)
Same Trip:
B. Walk to an earlier stop (+walk time, +ivt) Change
Route
C. Walk to another line (+walk, +ivt/wait)
D. Switch modes
E. Switch time periods Change Travel Plans:
Occurs in core activity model
F. Change destinations
G. Not make trip
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33. Transit Assignment with Crowding
Route changing algorithm implemented:
• Boarding availability is f(crowding level)
• If boarding is prevented, transit skim searches for
next best route by:
• Walking to an earlier stop
• Taking a slower line that arrives at that stop
• Walking to a different line
• Iterations averaged until reach a stable solution:
• Skim is representation of average of walk times
and in-vehicle times
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34. Transit Assignment with Crowding
• Dwell time on transit now a f(boardings, alightings)
• Estimated based on APC data
Dwell Time Articulated (sec) = 7.35 + (3.01 × boardings) + (2.04 × alightings)
Dwell Time Standard (sec) = 4.89 + (3.72 × boardings) + (2.11 × alightings)
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35. Transit Assignment with Crowding
Reflection of crowding in change of travel plans:
• OD pairs with crowding will result in sub-optimal
skims
• Travel time skims flow up through the model chain
via logsums to affect mode choice, time of day
choice, destination choice, and tour generation
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36. SF Citywide Dynamic Traffic Assignment
Why DTA? It’s a Better Representation of Reality!
• Queues exist and are considered
• Finer network detail that includes:
• Transit vehicles that interact with cars
• Intersection control
• Signal timings
• Intersection geometry
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37. Why DTA?
Because we shouldn't just discard relevant
information that we gain from our activity model.
0.18
0.16
Income $0-30k
0.14
Income $30-60k
Probability Density
0.12 Income $60-100k
Income $100k+
0.1
0.08
0.06
0.04
0.02
0
$- $5 $10 $15 $20 $25 $30
Value of Time ($/Hour)
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38. Dynamic Traffic Assignment Assumptions
Dynamic User Equilibrium:
 No vehicle can unilaterally shift paths and improve their
generalized cost
Generalized Cost:
 travel time + (left turn*left turn penalty)
+ (right turns*right turn penalty)
 turn penalties decrease round-about paths
Additional Network Inputs:
 Signal timings
 Stop signs
 Intersection Geometry
 Saturation Flow Rates/Jam density
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39. Citywide DTA Network Calibration & Validation:
Happening Now!
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40. That’s it!
Lisa.Zorn@sfcta.org
www.sfcta.org/modeling
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