1. SF-CHAMP BasicsVersion 5.0 AKA FroggerElizabeth SallDan TischlerDrew CooperPresentation to the City FamilySeptember 18th, 2014

2. WHAT IS SF-CHAMP?San Franciscos Chained Activity Modeling ProcessA regional, activity-based travel demand modelSF-CHAMP Model Basics 2 3. Whats SF-CHAMP?A tool that predicts activity schedules, trips, routes, andtravel times for every individual in the San FranciscoBay Area based on land use, policy, and the builtenvironment.SF-CHAMP Model Basics 3 4. WHY DO WE HAVE A TRAVELMODEL AT SFCTA?Because people have questions that it can help informBecause the current Bay Area model maintained by MTC doesntmeet our needsandBecause the CMA legislation says the CMA is supposed toSF-CHAMP Model Basics 4 5. So what do we use it for?San Francisco Transportation PlanFleet PlanWaterfront Transportation AnalysisTransit Core CapacityCongestion Pricing (TI and Downtown)Climate Action Strategies and InventoriesFeasibility Studies (i.e. Geneva BRT; Central SubwayPhase III)Alternatives AnalysisEnvironmental Analysis (EIS/EIR)Public Health AnalysisSF-CHAMP Model Basics 5 6. HOW DOES IT WORK?SF-CHAMP Model Basics 6 7. Step 1 Get the Land Use InputsABAG - SCSCountywideTotalsSF PlanningDept.SF TAZs (Plan B)ABAG - SCSNon-SFTAZsHouseholds, Jobs,& PopulationHouseholds, Jobs,& PopulationHouseholds & JobsABAG/MTCAll TAZsHouseholds & JobsIncome & AgeTAZ Level Land Use for Bay AreaIncome & AgeSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 7 8. Step 1 Get the Land Use Inputs981 zones in San Francisco1,275 in other Bay Areacounties# HouseholdsPopulationEmployment by 6 categoriesIncome QuartilesPopulation by Age# Parking SpotsParking District*Percent Paying for ParkingParking Costs (commute/other)School Enrollment (Grade, High, College/Univ)Area TypeLand AreaSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 8 9. Step 2 Get the Network Inputs CodedStreetnameFacility Type (i.e. Collector, Bikepath, Alleyway, etc)# Lanes (AM, PM, Offpeak)Auto capacityFreeflow Auto SpeedBus Lanes (unpainted diamond, side, center)Transit Signal Priority (low/high benefit)Other transit priority treatments (seconds benefit)Bike facilities (bike class, paint)SlopeDistanceTransit 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. 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. How do we keep track of this mess? Code each project (back many years) individually inPython. Plans are collections of projects (i.e. SFTP, or 2030Baseline)Network Wrangler Code base to pull together transportation projectsSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 11 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 consistentSF-CHAMP Model Basics 12 13. Network Build Scripts Scenarios built by project and tag Limits errors from coding Very simple to run a ton of different scenariosNet Build Specifications in build_networks.pyNetwork Coding Network Build Script 13 14. Network Coding QC Can export coding in planner-digestable formats Can review changes between scenarios so plannerscan sign offNetwork Coding Visualize and QC Coding 14 15. READY TO RUN? Write the client a memo about the inputs to makesure everybody is on the same page. Get another staff member to make sure you got it righton the technical side.Network Coding Network Build Script 15 16. Now were ready to rollSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 16 17. Population Synthesis:Make People & HHsInputs Land Use input by TAZ Census Data by PUMAPeople x HH Role (worker,student..) Income Age GenderSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 17 18. + a Sim with a homeHOMESAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 18 19. Workplace Location choice:Each worker chooses where to workInputs Jobs in each TAZ x type Modes, costs, distancesOutput Workplace TAZCalibration Data Census Journey to Work Flows** AM Peak bridge and transit volumes**SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 19 20. + WorkplaceHOMEWORKSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 20 21. Vehicle Availability:How many cars does my home need?Inputs Accessibility of home & work Accessibility between them Demographics Residential parkingrestrictions**Outputs: Household VehiclesCalibration Data: American Community Survey **SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 21 22. Day Pattern Model:What will I do today?Inputs Accessibility of home & work Accessibility between them DemographicsOutputs Tour pattern for the dayCalibration Data California Household Travel Survey2012/2013**SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 22 23. + Day PatternHOMEPRIMARY TOUR:Home-basedWorkWORK= TourINTERMEDIATESTOP ONWAY TO WORKWORK-BASEDDESTINATIONHOME BASEDTOURDESTINATIONSECONDARYHOME-BASEDTOURSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 23 24. Tour Destination Choice:What destination is making me go out?Inputs Initial tour schedule Accessibility Demographics RoleOutputs Tour DestinationsCalibration Data California Household Travel Survey2012/2013**SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 24 25. + Tour DestinationHOMEPRIMARY TOUR:Home-basedWorkWORK= TourINTERMEDIATESTOP ONWAY TO WORKWORK-BASEDDESTINATIONHOME BASEDTOURDESTINATIONWORK-BASEDSUB-TOURSECONDARYHOME-BASEDTOURSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 25 26. Tour Mode Choice:Is this a bike? Muni-ing? Take the car?Inputs Accessibility to destinationsfor that time of day by mode DemographicsOutput Tour modeCalibration Data California Household Travel Survey2012/2013**SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 26 27. + Tour ModeHOMEPRIMARY TOUR:Home-basedWorkWORK= TourINTERMEDIATESTOP ONWAY TO WORKWORK-BASEDDESTINATIONHOME BASEDTOURDESTINATIONWORK-BASEDSUB-TOURSECONDARYHOME-BASEDTOURSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 27 28. Intermediate Stop Choice:So where am I stopping on the way?Inputs Tour pattern requirements Accessibility of potential stopsgiven tour modeOutput Stop locationsCalibration Data California Household Travel Survey2012/2013**SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 28 29. + intermediate stops/tripsHOMENumber indicates trip orderPRIMARY TOUR:Home-basedWorkWORK= Tour= TripINTERMEDIATESTOP ONWAY TO WORK123WORK-BASEDDESTINATIONHOME BASEDTOURDESTINATIONWORK-BASEDSUB-TOUR7SECONDARYHOME-BASEDTOUR546SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 29 30. Trip Mode Choice:Exactly what mode between destinationsInput Cost, Travel Time, Access Demographics Tour ModeOutput 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. + trip modeHOMENumber indicates trip orderPRIMARY TOUR:Home-basedWorkWORK= Tour= TripINTERMEDIATESTOP ONWAY TO WORK123WORK-BASEDDESTINATIONHOME BASEDTOURDESTINATIONWORK-BASEDSUB-TOUR7SECONDARYHOME-BASEDTOUR546SF-CHAMP Model Basics 31 32. Route Choice:Exactly what route between destinationsInputs 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, traveltime, crowding/available spotsCalibration Data CycleTracks bike route data 2013 Transit Ridership Data and Traffic Counts**SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 32 33. + RouteSF-CHAMP Model Basics 33 34. Roadway Calibration DataCalibrated BPR functions using speed and volume sensors for base yearSF-CHAMP Model Basics 34 35. HOW DOES SHE DO?SF-CHAMP Model Basics 35 36. How are we looking?Daily Muni Boardings by Line800,000700,000600,000500,000400,000300,000200,000100,000150,000100,00050,000-(50,000)(100,000)Daily Screenlines in/out of SFObserved ModeledSF-CHAMP Model Basics 3650,00045,00040,00035,00030,00025,00020,00015,00010,0005,00000 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000Modeled BoardingsObserved BoardingsMuni BusesMuni CableCarsMuni LRTy=x0Muni BART GoldenGateAC Transit Caltrain SamTransDaily BoardingsDaily Boardings by OperatorObserved Modeled(150,000)Golden Gate Peninsula Bay Bridge 37. Auto ValidationScreenlines100,00080,00060,00040,00020,0000EA AM MD PM EVFlowWeekday Time of Day Observed - EB Estimated - EB100,00080,00060,00040,00020,0000Observed - NB Estimated - NBEA AM MD PM EVFlow100,00080,00060,00040,00020,0000EA AM MD PM EVFlowSF-CHAMP Model Basics Time of Day37Bay BridgeGolden Gate BridgeSouthern County Line 38. Auto ValidationCountsIntra-SF Count Volumes and Percent Estimation ErrorSF-CHAMP Model Basics 38100%80%60%40%20%0%-20%-40%-60%-80%-100%0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000Percent Difference from ObservedObserved Volume 39. Now Speedier**Hours per Model RunSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 3980706050403020100CHAMP 3 Harold Fury Frogger 40. WHAT DOES FROGGERPRODUCE?SF-CHAMP Model Basics 40 41. SF-CHAMP Model Basics 41OutputsTopsheet 42. SF-CHAMP Model Basics 42OutputsModesum 43. SF-CHAMP Model Basics 43OutputsQuickboards 44. OutputsHighway AssignmentSF-CHAMP Model Basics 44Link info by Time: Vehicle Volume Person Volume Vehicle Miles Travelled Person Miles Travelled Vehicle Hours Delay Person Hours Delay Travel Time Distance Speed V/CFormats Cube Network Shapefile 45. OutputsTransit AssignmentLink info by Time Period and Route: Headway Person Capacity Vehicle type Boardings/Exits Volume Impossible boardingsSF-CHAMP Model Basics 45Formats Shapefile 46. SF-CHAMP Model Basics 46OutputsTrip TablesTrip flows by Origin, Destination, Mode, Time of dayFormats Cube Matrix OMX HDF5 47. SF-CHAMP Model Basics 47OutputsSkimsTrip Characteristics by Origin, Destination, Mode, Time of dayFormats Cube Matrix OMX HDF5Characteristics Access/Egress Distance Access/Egress Node Transfers In Vehicle Time Initial and Transfer Wait Transfer walk Cost 48. SF-CHAMP Model Basics 48OutputsTrip ListTrip 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 dayFormats HDF5 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 makesure we are as consistent as possible we have aLOT of model runsSF-CHAMP Model Basics 49 50. How do I get stuff?: Super standard exampleHowdy Modelers,We are doing a NegDec for streetscape project ABC.Would you please send us the latest official current andfuture baseline (2040) traffic volumes for the PM Peakfor streets A and B in the vicinity of C. I am enclosingour latest traffic counts in the area. We are on a tightdeadline, so getting something before next Tuesday theX would be awesome.When possible, you should always use the modeleddifferences between scenarios layered on existing dataAppropriate methods documented in NCHRP 765SF-CHAMP Model Basics 50 51. How do I get stuff?You might have a big projectHowdy modelers,We are in the process of developing a scope and budgetfor a big study of the transit systems core capacityneeds over the next 30 years. Well be needing you allto do some SF-CHAMP analysis. Lets sit down anddiscuss what we think an appropriate scope is for youand our consultants.o The sooner the bettero We can probably help you save consultant money.o Even just putting it on our radar for the medium futurehelps (so we dont accept other large projects)SF-CHAMP Model Basics 51 52. How do I get stuff?You might not know what you needHowdy modelers,Im trying to flush out a methodology to evaluate theeconomic impacts of the Muni system. Im pretty sure itinvolves some model outputs, but Im not quite surewhat would be useful just yet. Can we sit down anddiscuss 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 bigo But plenty of times we have something on the shelfSF-CHAMP Model Basics 52 53. Thats it!data@sfcta.orgwww.sfcta.org/modelingSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 54. Activity-Based Travel Demand Model?A few principles No cart before the horse / driving home if youwalked to work / leaving work before you got there interdependence explicitly recognized.If it looks likethis outsideevery morningthen youllprobablydecide toBut you donthave a car atwork nowSo even ifthe evening autocommute is cake,youll need toSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 54 55. Activity-Based Travel Demand Model?A few principles No cart before the horse driving home if you walkedto work / leaving work before you got there interdependence explicitly recognized.If this area where youwork has a congestionfee from 4 to 6 pmAnd you live hereYou realize that ifyou drive like thisIn the AMThat it will costyou like thisIn the PM$SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 55 56. Example: Walking to SFCTAWork PurposeSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 56 57. Transit Walk Access Links: Perceived WeightWalk-Local-Walk, Destination Ferry BuildingSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 57