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

2. Activity-Based ModelingSF-CHAMP is a tool which predicts activities, locations,and travel time for every individual traveler in SanFranciscoBased 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/SFMTASimulation of every Bay Area residents daily choices(and visitors too)Trucks and external trips borrowed from MTC ModelSF-CHAMP Model Basics2 3. Activities Grouped into Tours HOME BASEDTOUR7= TourA tour is an entire chain of DESTINATION SECONDARY = Trip HOME-BASEDtrips: from your primary origin,TOUR Number indicates trip orderto all of your destinations, 6and then back again.HOME 1Primary destinationsPRIMARY TOUR:INTERMEDIATE vs. 5 STOP ON Home-basedWAY TO WORKIntermediate stopsWork 2WORKConsequences of choices: Do you have a car available? Did you leave the car at home?34 WORK-BASED Do you have a complicated day?SUB-TOUR WORK-BASED DESTINATION SF-CHAMP Model Basics3 4. SF-CHained Activity Modeling ProcessPopulation Synthesis Land Use input Census Data Household+ demographics such as ages, income, hh sizes,workers SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 4 5. SF-CHained Activity Modeling Process Workplace Location Choice Land Use input Census (CTPP) Modes, costs, distancesWorkplaceDestination SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 5 6. SF-CHained Activity Modeling ProcessVehicle Availability Accessibility of home & work Accessibility between them Household Vehicles SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 6 7. SF-CHained Activity Modeling Process Tour Generation Accessibility of home & work Accessibility between them Demographics Tour pattern for the day SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 7 8. SF-CHained Activity Modeling Process Tour Destination Choice Initial tour schedule AccessibilityTour Destinations SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 8 9. SF-CHained Activity Modeling ProcessTour Mode Choice Accessibility to destinationsfor that time of day by modeTour Modes SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY9 10. SF-CHained Activity Modeling ProcessIntermediate Stop Choice Tour pattern requirements Accessibility of potential stopsgiven tour modeIntermediate Stops SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 10 11. SF-CHained Activity Modeling ProcessTrip Mode Choice Cost, Travel Time Demographics Tour ModeTrip Modes SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 11 12. Spatial Detail - Transit Every transit stopEvery transit line Every streetSF-CHAMP Model Basics12 13. Sample Auto Volume PlotSF-CHAMP Model Basics 13 14. Roadway Calibration DataCalibrated BPR functions using speed and volume sensors for base yearSF-CHAMP Model Basics 14 15. Validation: Volumes And Boardings Calibrated using 2000 base year data Validated to 2005 counts and boardings, and 2006 speedsE s tim a te d vs . O b s e rve d R o a d V o lu m e s160000 Traffic Volumes Muni Daily Boardings by RouteDaily Boardingsby Route Estimated vs. ObservedEstimated vs. Observed14000050,00045,00012000040,00010000035,000Estimated Daily Boardings30,000 8000025,000 6000020,00015,000 4000010,000 20000 5,000 00 0 2000040000 6000080000100000 120000 140000 1600000 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 eSF-CHAMP Model Basics 15 16. Van Ness BRT Transit Line ValidationValidated using 2007APC DataSF-CHAMP Model Basics 16 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 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 17 18. Land Use Inputs Households, Jobs, Households,Jobs, &ABAG Population & Population Countywide Totals SF Planning Dept. ABAGHouseholds & Jobs Households& JobsSF TAZs (Plan B) Non-SF TAZs ABAG/MTC All TAZsIncome & Age& Age TAZ Level Land Use for Bay Area SF-CHAMP Model Basics18 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 dependenciesSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 19 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 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 20 21. Spatial Detail Analysis Zones Trips areaggregated intozones 981 zones in SanFrancisco 1,275 in other BayArea 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 ChoiceAttribute Coef.SEt-stat.p-val.Length (mi)--1.05 0.09 --11.80 0.00Turns per mile --0.21 0.02 --12.15 0.00Prop. wrong way--13.300.67 --19.87 0.00Prop. bike paths 1.89 0.31 6.170.00Prop. bike lanes 2.15 0.12 17.69 0.00Cycling freq. < several per wk. 1.850.04 44.94 0.00Prop. bike routes0.35 0.11 3.140.00Avg. up-slope (ft/100ft) --0.50 0.08 --6.350.00Female --0.96 0.22 --4.340.00Commute--0.90 0.11 --8.210.00Log(path size) 1.07 0.04 26.38 0.00SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY23 24. New Component Bicycle Assignment Bikes / hour0 20180360SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 24 25. Bike Accessibility: From 4th and King SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 25 26. Bike Logsums: From 4th and KingEffect of Bike Plan Build SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 26 27. New Component Network-based Pedestrian LOSForecastable, continuouspedestrian utilityalong walk pathSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 27 28. New Pedestrian and Transit Environment Factors Attributes Empirically Estimated: Hills (rise) Indirectness Population and Employment Densities (logs) Street capacity 29. Example: Walking to SFCTAWork Purpose SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 29 30. Transit Walk Access Links: Perceived WeightWalk-Local-Walk, Destination Ferry BuildingSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 30 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 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 31 32. Transit Assignment with CrowdingIn 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 RouteC. Walk to another line (+walk, +ivt/wait)D. Switch modesE. Switch time periodsChange Travel Plans:Occurs in core activity modelF. Change destinationsG. Not make tripSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY32 33. Transit Assignment with CrowdingRoute changing algorithm implemented: Boarding availability is f(crowding level) If boarding is prevented, transit skim searches fornext 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 timesand in-vehicle timesSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 33 34. Transit Assignment with Crowding Dwell time on transit now a f(boardings, alightings) Estimated based on APC dataDwell Time Articulated (sec) = 7.35 + (3.01 boardings) + (2.04 alightings)Dwell Time Standard (sec)= 4.89 + (3.72 boardings) + (2.11 alightings)SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY34 35. Transit Assignment with CrowdingReflection 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 generationSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 35 36. SF Citywide Dynamic Traffic AssignmentWhy DTA? Its 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 geometrySAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 37. Why DTA? Because we shouldnt 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 0.12Income $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) SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 38. Dynamic Traffic Assignment AssumptionsDynamic User Equilibrium: No vehicle can unilaterally shift paths and improve theirgeneralized costGeneralized Cost: travel time + (left turn*left turn penalty)+ (right turns*right turn penalty) turn penalties decrease round-about pathsAdditional Network Inputs: Signal timings Stop signs Intersection Geometry Saturation Flow Rates/Jam densitySAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 39. Citywide DTA Network Calibration & Validation:Happening Now!SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 39 40. Thats it! Lisa.Zorn@sfcta.org www.sfcta.org/modelingSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY