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SF-CHAMP Basics 
Version 5.0 AKA Frogger 
Elizabeth Sall 
Dan Tischler 
Drew Cooper 
Presentation to the City Family 
Sept...
WHAT IS SF-CHAMP? 
San Francisco’s Chained Activity Modeling Process 
A regional, activity-based travel demand model 
SF-C...
What’s SF-CHAMP? 
A tool that predicts activity schedules, trips, routes, and 
travel times for every individual in the Sa...
WHY DO WE HAVE A TRAVEL 
MODEL AT SFCTA? 
Because people have questions that it can help inform 
Because the current Bay A...
So what do we use it for? 
San Francisco Transportation Plan 
Fleet Plan 
Waterfront Transportation Analysis 
Transit Core...
HOW DOES IT WORK? 
SF-CHAMP Model Basics 6
Step 1 – Get the Land Use Inputs 
ABAG - SCS 
Countywide 
Totals 
SF Planning 
Dept. 
SF TAZs (Plan B) 
ABAG - SCS 
Non-SF...
Step 1 – Get the Land Use Inputs 
981 zones in San Francisco 
1,275 in other Bay Area 
counties 
# Households 
Population ...
Step 2 – Get the Network Inputs Coded 
Streetname 
Facility Type (i.e. Collector, Bikepath, Alleyway, etc) 
# Lanes (AM, P...
Network Version Control 
• Many projects might happen in the future 
• Many versions of projects being evaluated 
• Projec...
How do we keep track of this mess? 
• Code each project (back many years) individually in 
Python. 
• Plans are collection...
Behind the Curtain – Network Coding 
• Projects version controlled using Git 
• Grab projects via a tag for intra-project ...
Network Build Scripts 
• Scenarios built by project and “tag” 
• Limits errors from coding 
• Very simple to run a ton of ...
Network Coding QC 
• Can export coding in planner-digestable formats 
• Can review changes between scenarios so planners 
...
READY TO RUN? 
• Write the “client” a memo about the inputs to make 
sure everybody is on the same page. 
• Get another st...
Now we’re ready to roll… 
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 16
Population Synthesis: 
Make People & HHs 
Inputs 
• Land Use input by TAZ 
• Census Data by PUMA 
People x HH 
• Role (wor...
+ a Sim with a home 
HOME 
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 18
Workplace Location choice: 
Each worker chooses where to work 
Inputs 
• Jobs in each TAZ x type 
• Modes, costs, distance...
+ Workplace 
HOME 
WORK 
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 20
Vehicle Availability: 
How many cars does my home need? 
Inputs 
• Accessibility of home & work 
• Accessibility between t...
Day Pattern Model: 
What will I do today? 
Inputs 
• Accessibility of home & work 
• Accessibility between them 
• Demogra...
+ Day Pattern 
HOME 
PRIMARY TOUR: 
Home-based 
Work 
WORK 
= Tour 
INTERMEDIATE 
STOP ON 
WAY TO WORK 
WORK-BASED 
DESTIN...
Tour Destination Choice: 
What destination is making me go out? 
Inputs 
• Initial tour schedule 
• Accessibility 
• Demog...
+ Tour Destination 
HOME 
PRIMARY TOUR: 
Home-based 
Work 
WORK 
= Tour 
INTERMEDIATE 
STOP ON 
WAY TO WORK 
WORK-BASED 
D...
Tour Mode Choice: 
Is this a bike? Muni-ing? Take the car? 
Inputs 
• Accessibility to destinations 
for that time of day ...
+ Tour Mode 
HOME 
PRIMARY TOUR: 
Home-based 
Work 
WORK 
= Tour 
INTERMEDIATE 
STOP ON 
WAY TO WORK 
WORK-BASED 
DESTINAT...
Intermediate Stop Choice: 
So where am I stopping on the way? 
Inputs 
• Tour pattern requirements 
• Accessibility of pot...
+ intermediate stops/trips 
HOME 
Number indicates trip order 
PRIMARY TOUR: 
Home-based 
Work 
WORK 
= Tour 
= Trip 
INTE...
Trip Mode Choice: 
Exactly what mode between destinations 
Input 
• Cost, Travel Time, Access 
• Demographics 
• Tour Mode...
+ trip mode 
HOME 
Number indicates trip order 
PRIMARY TOUR: 
Home-based 
Work 
WORK 
= Tour 
= Trip 
INTERMEDIATE 
STOP ...
Route Choice: 
Exactly what route between destinations 
Inputs 
• Bike: hills, bike lanes, sharrows, turns, road capacity,...
+ Route 
SF-CHAMP Model Basics 33
Roadway Calibration Data 
Calibrated BPR functions using speed and volume sensors for base year 
SF-CHAMP Model Basics 34
HOW DOES SHE DO? 
SF-CHAMP Model Basics 35
How are we looking? 
Daily Muni Boardings by Line 
800,000 
700,000 
600,000 
500,000 
400,000 
300,000 
200,000 
100,000 ...
Auto Validation 
Screenlines 
100,000 
80,000 
60,000 
40,000 
20,000 
0 
EA AM MD PM EV 
Flow 
Weekday Time of Day Observ...
Auto Validation 
Counts 
Intra-SF Count Volumes and Percent Estimation Error 
SF-CHAMP Model Basics 38 
100% 
80% 
60% 
40...
Now Speedier** 
Hours per Model Run 
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 39 
80 
70 
60 
50 
40 
30 
20 
10 
0 
...
WHAT DOES FROGGER 
PRODUCE? 
SF-CHAMP Model Basics 40
SF-CHAMP Model Basics 41 
Outputs 
Topsheet
SF-CHAMP Model Basics 42 
Outputs 
Modesum
SF-CHAMP Model Basics 43 
Outputs 
Quickboards
Outputs 
Highway Assignment 
SF-CHAMP Model Basics 44 
Link info by Time: 
• Vehicle Volume 
• Person Volume 
• Vehicle Mi...
Outputs 
Transit Assignment 
Link info by Time Period and Route: 
• Headway 
• Person Capacity 
• Vehicle type 
• Boarding...
SF-CHAMP Model Basics 46 
Outputs 
Trip Tables 
Trip flows by 
• Origin, 
• Destination, 
• Mode, 
• Time of day 
Formats ...
SF-CHAMP Model Basics 47 
Outputs 
Skims 
Trip Characteristics by 
• Origin, 
• Destination, 
• Mode, 
• Time of day 
Form...
SF-CHAMP Model Basics 48 
Outputs 
Trip List 
Trip Characteristics for each person: 
• Person ID / Household ID 
• # autos...
How do I get stuff? 
data@sfcta.org 
• Group inbox 
• Please let us know: 
• What project you are working on 
• What quest...
How do I get stuff? 
: Super standard example 
Howdy Modelers, 
We are doing a NegDec for streetscape project ABC. 
Would ...
How do I get stuff? 
You might have a big project… 
Howdy modelers, 
We are in the process of developing a scope and budge...
How do I get stuff? 
You might not know what you need… 
Howdy modelers, 
I’m trying to flush out a methodology to evaluate...
That’s it! 
data@sfcta.org 
www.sfcta.org/modeling 
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Activity-Based Travel Demand Model? 
A few principles 
• No cart before the horse / driving home if you 
walked to work / ...
Activity-Based Travel Demand Model? 
A few principles 
• No cart before the horse driving home if you walked 
to work / le...
Example: Walking to SFCTA 
Work Purpose 
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 56
Transit Walk Access Links: Perceived Weight 
Walk-Local-Walk, Destination Ferry Building 
SAN FRANCISCO COUNTY TRANSPORTAT...
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SF-CHAMP 5 - FROGGER - San Francisco's Newly-updated Travel Model

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SF-CHAMP 5 - FROGGER - San Francisco's Newly-updated Travel Model

  1. 1. SF-CHAMP Basics Version 5.0 AKA Frogger Elizabeth Sall Dan Tischler Drew Cooper Presentation to the City Family September 18th, 2014
  2. 2. WHAT IS SF-CHAMP? San Francisco’s Chained Activity Modeling Process A regional, activity-based travel demand model SF-CHAMP Model Basics 2
  3. 3. What’s SF-CHAMP? A tool that predicts activity schedules, trips, routes, and travel times for every individual in the San Francisco Bay Area based on land use, policy, and the built environment. SF-CHAMP Model Basics 3
  4. 4. WHY DO WE HAVE A TRAVEL MODEL AT SFCTA? Because people have questions that it can help inform Because the current Bay Area model maintained by MTC doesn’t meet our needs …and… Because the CMA legislation says the CMA is supposed to SF-CHAMP Model Basics 4
  5. 5. So what do we use it for? San Francisco Transportation Plan Fleet Plan Waterfront Transportation Analysis Transit Core Capacity Congestion Pricing (TI and Downtown) Climate Action Strategies and Inventories Feasibility Studies (i.e. Geneva BRT; Central Subway Phase III) Alternatives Analysis Environmental Analysis (EIS/EIR) Public Health Analysis SF-CHAMP Model Basics 5
  6. 6. HOW DOES IT WORK? SF-CHAMP Model Basics 6
  7. 7. Step 1 – Get the Land Use Inputs ABAG - SCS Countywide Totals SF Planning Dept. SF TAZs (Plan B) ABAG - SCS Non-SF TAZs Households, Jobs, & Population Households, Jobs, & Population Households & Jobs ABAG/MTC All TAZs Households & Jobs Income & Age TAZ Level Land Use for Bay Area Income & Age SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 7
  8. 8. Step 1 – Get the Land Use Inputs 981 zones in San Francisco 1,275 in other Bay Area counties # Households Population Employment by 6 categories Income Quartiles Population by Age # Parking Spots Parking District* Percent Paying for Parking Parking Costs (commute/other) School Enrollment (Grade, High, College/Univ) Area Type Land Area SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 8
  9. 9. Step 2 – Get the Network Inputs Coded Streetname Facility Type (i.e. Collector, Bikepath, Alleyway, etc) # Lanes (AM, PM, Offpeak) Auto capacity Freeflow Auto Speed Bus Lanes (unpainted diamond, side, center) Transit Signal Priority (low/high benefit) Other transit priority treatments (seconds benefit) Bike facilities (bike class, paint) Slope Distance Transit operator (Muni, Caltrain, BART, etc) Mode (commuter rail, heavy rail, local bus, etc.) Frequency (by time of day) Vehicle Type (40’ motor, articulated trolley, 2 car LRT) Route (series nodes) Stops (permissions to board, exit vary) Delay by stop (based on riders getting on/off) Fare (case fare used as proxy)
  10. 10. Network Version Control • Many projects might happen in the future • Many versions of projects being evaluated • Projects evolve from analysis, public feedback, etc. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 10
  11. 11. How do we keep track of this mess? • Code each project (back many years) individually in Python. • Plans are collections of projects (i.e. SFTP, or 2030 Baseline) Network Wrangler • Code base to pull together transportation projects SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 11
  12. 12. Behind the Curtain – Network Coding • Projects version controlled using Git • Grab projects via a tag for intra-project consistency • Can always go back to a previous version • Model runs log which version they use so you can be consistent SF-CHAMP Model Basics 12
  13. 13. Network Build Scripts • Scenarios built by project and “tag” • Limits errors from coding • Very simple to run a ton of different scenarios Net Build Specifications in build_networks.py Network Coding – Network Build Script 13
  14. 14. Network Coding QC • Can export coding in planner-digestable formats • Can review changes between scenarios so planners can sign off Network Coding – Visualize and QC Coding 14
  15. 15. READY TO RUN? • Write the “client” a memo about the inputs to make sure everybody is on the same page. • Get another staff member to make sure you got it right on the technical side. Network Coding – Network Build Script 15
  16. 16. Now we’re ready to roll… SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 16
  17. 17. Population Synthesis: Make People & HHs Inputs • Land Use input by TAZ • Census Data by PUMA People x HH • Role (worker, student..) • Income • Age • Gender SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 17
  18. 18. + a Sim with a home HOME SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 18
  19. 19. Workplace Location choice: Each worker chooses where to work Inputs • Jobs in each TAZ x type • Modes, costs, distances Output • Workplace TAZ Calibration Data • Census Journey to Work Flows** • AM Peak bridge and transit volumes** SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 19
  20. 20. + Workplace HOME WORK SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 20
  21. 21. Vehicle Availability: How many cars does my home need? Inputs • Accessibility of home & work • Accessibility between them • Demographics • Residential parking restrictions** Outputs: • Household Vehicles Calibration Data: • American Community Survey ** SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 21
  22. 22. Day Pattern Model: What will I do today? Inputs • Accessibility of home & work • Accessibility between them • Demographics Outputs • Tour pattern for the day Calibration Data • California Household Travel Survey 2012/2013** SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 22
  23. 23. + Day Pattern HOME PRIMARY TOUR: Home-based Work WORK = Tour INTERMEDIATE STOP ON WAY TO WORK WORK-BASED DESTINATION HOME BASED TOUR DESTINATION SECONDARY HOME-BASED TOUR SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 23
  24. 24. Tour Destination Choice: What destination is making me go out? Inputs • Initial tour schedule • Accessibility • Demographics • Role Outputs • Tour Destinations Calibration Data • California Household Travel Survey 2012/2013** SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 24
  25. 25. + Tour Destination HOME PRIMARY TOUR: Home-based Work WORK = Tour INTERMEDIATE STOP ON WAY TO WORK WORK-BASED DESTINATION HOME BASED TOUR DESTINATION WORK-BASED SUB-TOUR SECONDARY HOME-BASED TOUR SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 25
  26. 26. Tour Mode Choice: Is this a bike? Muni-ing? Take the car? Inputs • Accessibility to destinations for that time of day by mode • Demographics Output • Tour mode Calibration Data • California Household Travel Survey 2012/2013** SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 26
  27. 27. + Tour Mode HOME PRIMARY TOUR: Home-based Work WORK = Tour INTERMEDIATE STOP ON WAY TO WORK WORK-BASED DESTINATION HOME BASED TOUR DESTINATION WORK-BASED SUB-TOUR SECONDARY HOME-BASED TOUR SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 27
  28. 28. Intermediate Stop Choice: So where am I stopping on the way? Inputs • Tour pattern requirements • Accessibility of potential stops given tour mode Output • Stop locations Calibration Data • California Household Travel Survey 2012/2013** SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 28
  29. 29. + intermediate stops/trips HOME Number indicates trip order PRIMARY TOUR: Home-based Work WORK = Tour = Trip INTERMEDIATE STOP ON WAY TO WORK 1 2 3 WORK-BASED DESTINATION HOME BASED TOUR DESTINATION WORK-BASED SUB-TOUR 7 SECONDARY HOME-BASED TOUR 5 4 6 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 29
  30. 30. Trip Mode Choice: Exactly what mode between destinations Input • Cost, Travel Time, Access • Demographics • Tour Mode Output • Detailed mode for all trips • LRT vs Bus vs Walk etc. Calibration Data • California Household Travel Survey 2012/2013** • 2013 Transit Ridership Data and Traffic Counts** SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 30
  31. 31. + trip mode HOME Number indicates trip order PRIMARY TOUR: Home-based Work WORK = Tour = Trip INTERMEDIATE STOP ON WAY TO WORK 1 2 3 WORK-BASED DESTINATION HOME BASED TOUR DESTINATION WORK-BASED SUB-TOUR 7 SECONDARY HOME-BASED TOUR 5 4 6 SF-CHAMP Model Basics 31
  32. 32. Route Choice: Exactly what route between destinations Inputs • Bike: hills, bike lanes, sharrows, turns, road capacity, distance, demographics • Walk: employment density, road capacity, hills, distance, indirectness • Car: travel time, cost, distance • Transit: walk distance, wait times, transfer distances, travel time, crowding/available spots Calibration Data • CycleTracks bike route data • 2013 Transit Ridership Data and Traffic Counts** SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 32
  33. 33. + Route SF-CHAMP Model Basics 33
  34. 34. Roadway Calibration Data Calibrated BPR functions using speed and volume sensors for base year SF-CHAMP Model Basics 34
  35. 35. HOW DOES SHE DO? SF-CHAMP Model Basics 35
  36. 36. How are we looking? Daily Muni Boardings by Line 800,000 700,000 600,000 500,000 400,000 300,000 200,000 100,000 150,000 100,000 50,000 - (50,000) (100,000) Daily Screenlines in/out of SF Observed Modeled SF-CHAMP Model Basics 36 50,000 45,000 40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000 Modeled Boardings Observed Boardings Muni Buses Muni Cable Cars Muni LRT y=x 0 Muni BART Golden Gate AC Transit Caltrain SamTrans Daily Boardings Daily Boardings by Operator Observed Modeled (150,000) Golden Gate Peninsula Bay Bridge
  37. 37. Auto Validation Screenlines 100,000 80,000 60,000 40,000 20,000 0 EA AM MD PM EV Flow Weekday Time of Day Observed - EB Estimated - EB 100,000 80,000 60,000 40,000 20,000 0 Observed - NB Estimated - NB EA AM MD PM EV Flow 100,000 80,000 60,000 40,000 20,000 0 EA AM MD PM EV Flow SF-CHAMP Model Basics Time of Day 37 Bay Bridge Golden Gate Bridge Southern County Line
  38. 38. Auto Validation Counts Intra-SF Count Volumes and Percent Estimation Error SF-CHAMP Model Basics 38 100% 80% 60% 40% 20% 0% -20% -40% -60% -80% -100% 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000 Percent Difference from Observed Observed Volume
  39. 39. Now Speedier** Hours per Model Run SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 39 80 70 60 50 40 30 20 10 0 CHAMP 3 Harold Fury Frogger
  40. 40. WHAT DOES FROGGER PRODUCE? SF-CHAMP Model Basics 40
  41. 41. SF-CHAMP Model Basics 41 Outputs Topsheet
  42. 42. SF-CHAMP Model Basics 42 Outputs Modesum
  43. 43. SF-CHAMP Model Basics 43 Outputs Quickboards
  44. 44. Outputs Highway Assignment SF-CHAMP Model Basics 44 Link info by Time: • Vehicle Volume • Person Volume • Vehicle Miles Travelled • Person Miles Travelled • Vehicle Hours Delay • Person Hours Delay • Travel Time • Distance • Speed • V/C Formats • Cube Network • Shapefile
  45. 45. Outputs Transit Assignment Link info by Time Period and Route: • Headway • Person Capacity • Vehicle type • Boardings/Exits • Volume • Impossible boardings SF-CHAMP Model Basics 45 Formats • Shapefile
  46. 46. SF-CHAMP Model Basics 46 Outputs Trip Tables Trip flows by • Origin, • Destination, • Mode, • Time of day Formats • Cube Matrix • OMX HDF5
  47. 47. SF-CHAMP Model Basics 47 Outputs Skims Trip Characteristics by • Origin, • Destination, • Mode, • Time of day Formats • Cube Matrix • OMX HDF5 Characteristics • Access/Egress Distance • Access/Egress Node • Transfers • In Vehicle Time • Initial and Transfer Wait • Transfer walk • Cost
  48. 48. SF-CHAMP Model Basics 48 Outputs Trip List Trip Characteristics for each person: • Person ID / Household ID • # autos • Gender • Age • Income • Household size • Role (worker, student, etc) • Job/School TAZ • Value of time • Tour purpose • Origin TAZ / Destination TAZ • Mode • Time of day Formats • HDF5
  49. 49. How do I get stuff? data@sfcta.org • Group inbox • Please let us know: • What project you are working on • What question you are trying to answer • Depending on applicability: • Time of day, analysis years, geographic realm • We might ask more questions – just trying to make sure we are as consistent as possible – we have a LOT of model runs SF-CHAMP Model Basics 49
  50. 50. How do I get stuff? : Super standard example Howdy Modelers, We are doing a NegDec for streetscape project ABC. Would you please send us the latest official current and future baseline (2040) traffic volumes for the PM Peak for streets A and B in the vicinity of C. I am enclosing our latest traffic counts in the area. We are on a tight deadline, so getting something before next Tuesday the X would be awesome. When possible, you should always use the modeled differences between scenarios layered on existing data Appropriate methods documented in NCHRP 765 SF-CHAMP Model Basics 50
  51. 51. How do I get stuff? You might have a big project… Howdy modelers, We are in the process of developing a scope and budget for a big study of the transit system’s core capacity needs over the next 30 years. We’ll be needing you all to do some SF-CHAMP analysis. Let’s sit down and discuss what we think an appropriate scope is for you and our consultants. o The sooner the better… o We can probably help you save consultant money. o Even just putting it on our radar for the medium future helps (so we don’t accept other large projects) SF-CHAMP Model Basics 51
  52. 52. How do I get stuff? You might not know what you need… Howdy modelers, I’m trying to flush out a methodology to evaluate the economic impacts of the Muni system. I’m pretty sure it involves some model outputs, but I’m not quite sure what would be useful just yet. Can we sit down and discuss sometime in the next week? WE WANT TO HELP! LET US HELP YOU HELP US HELP YOU! o Get us involved sooner rather than later. o Sometimes we might need an MOA/$ if things get big… o But plenty of times we have something “on the shelf” SF-CHAMP Model Basics 52
  53. 53. That’s it! data@sfcta.org www.sfcta.org/modeling SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
  54. 54. Activity-Based Travel Demand Model? A few principles • No cart before the horse / driving home if you walked to work / leaving work before you got there  interdependence explicitly recognized. If it looks like this outside every morning… then you’ll probably decide to… But you don’t have a car at work now… So even if the evening auto commute is cake, you’ll need to… SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 54
  55. 55. Activity-Based Travel Demand Model? A few principles • No cart before the horse driving home if you walked to work / leaving work before you got there  interdependence explicitly recognized. If this area where you work has a congestion fee from 4 to 6 pm… And you live here… You realize that if you drive like this In the AM… That it will cost you like this In the PM… $ SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 55
  56. 56. Example: Walking to SFCTA Work Purpose SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 56
  57. 57. Transit Walk Access Links: Perceived Weight Walk-Local-Walk, Destination Ferry Building SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 57

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