SF-CHAMP Basics: Version 4.3 AKA Fury

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

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  • Not shown:Initially Schedule Tours
  • Not shown:Tour scheduling based onAccessibility by time of day for chosen destination
  • We use a “Doubly Stochastic Route Search” to find other potential routes in the available choice setBovy, P. & Fiorenzo-Catalano, S. (2007), “Stochastic route choice set generation: behavioral and probabilistic foundations,” Transportmetrica 3, 173-189.
  • Talking points/or circle/or flip through
  • Yellow doesn’t stand out enough
  • What we’re doing now and why it’s not optimal (subjective which makes it hard to forecast, high-low, O/D-based)Goal: Represent effect of network projects and land use changes on pedestrian behavior. Better represent pedestrian level of service, the utility of the pedestrian path. Remove the subjectivity and use continuous variables.This presentation will show how we represent pedestrian utility for mode choice
  • Pretty consistent with previous findings (i.e. school purpose and indirectness)For transit, origin/dest refer to *tours*In general, the magnitude of all coefficients are bigger at the tour destination than the tour origin (including distance). Conditions of the walk at the tour destination matter more than at the tour origin (home).
  • These are based on Population Density and Indirectness (tour origin)- Talking point circles
  • SF-CHAMP Basics: Version 4.3 AKA Fury

    1. 1. SF-CHAMP Basics Version 4.3 AKA Fury Lisa ZornPresentation to the SFMTA Intern/New Staff Seminar August 3rd, 2012
    2. 2. Activity-Based ModelingSF-CHAMP is a tool which predicts activities, locations, and travel time for every individual traveler in San FranciscoBased 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 resident’s daily choices (and visitors too)Trucks and “external” trips borrowed from MTC Model SF-CHAMP Model Basics 2
    3. 3. Activities Grouped into “Tours” HOME BASED TOUR 7 = 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 destinations PRIMARY TOUR: INTERMEDIATE vs. 5 STOP ON Home-based WAY TO WORKIntermediate stops Work 2 WORKConsequences 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 SF-CHAMP Model Basics 3
    4. 4. SF-CHained Activity Modeling Process Population Synthesis • Land Use input • Census Data Household + demographics such as ages, income, hh sizes, workers SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 4
    5. 5. SF-CHained Activity Modeling Process Workplace Location Choice • Land Use input • Census (CTPP) • Modes, costs, distances Workplace Destination SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 5
    6. 6. SF-CHained Activity Modeling Process Vehicle Availability • Accessibility of home & work • Accessibility between them Household Vehicles SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 6
    7. 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. 8. SF-CHained Activity Modeling Process Tour Destination Choice • Initial tour schedule • Accessibility Tour Destinations SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 8
    9. 9. SF-CHained Activity Modeling Process Tour Mode Choice • Accessibility to destinations for that time of day by mode Tour Modes SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 9
    10. 10. SF-CHained Activity Modeling Process Intermediate Stop Choice • Tour pattern requirements • Accessibility of potential stops given tour mode Intermediate Stops SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 10
    11. 11. SF-CHained Activity Modeling Process Trip Mode Choice • Cost, Travel Time • Demographics • Tour Mode Trip Modes SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 11
    12. 12. Spatial Detail - Transit Every transit stop Every transit line Every street SF-CHAMP Model Basics 12
    13. 13. Sample Auto Volume Plot SF-CHAMP Model Basics 13
    14. 14. Roadway Calibration DataCalibrated BPR functions using speed and volume sensors for base year SF-CHAMP Model Basics 14
    15. 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 s160000 Traffic Volumes Muni Daily Boardings by Route Daily Boardings by Route Estimated vs. Observed Estimated vs. Observed140000 50,000 45,000120000 40,000100000 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. 16. Van Ness BRT – Transit Line ValidationValidated using 2007APC Data SF-CHAMP Model Basics 16
    17. 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. 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. 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 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 19
    20. 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. 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. 22. New Component – Bike Route Choice• Data: • CycleTracks smartphone app• Methodology: • Choice set generation: doubly stochastic method • Path size logit estimation
    23. 23. New Component – Bike Route ChoiceAttribute Coef. SE t-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 --13.30 0.67 --19.87 0.00Prop. bike paths 1.89 0.31 6.17 0.00Prop. bike lanes 2.15 0.12 17.69 0.00 Cycling freq. < several per wk. 1.85 0.04 44.94 0.00Prop. bike routes 0.35 0.11 3.14 0.00Avg. 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 0.00Log(path size) 1.07 0.04 26.38 0.00 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 23
    24. 24. New Component – Bicycle Assignment Bikes / hour 0 20 180 360 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 24
    25. 25. Bike Accessibility: From 4th and King SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 25
    26. 26. Bike Logsums: From 4th and KingEffect of Bike Plan Build SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 26
    27. 27. New Component – Network-based Pedestrian LOS Forecastable, continuous pedestrian utility along walk path SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 27
    28. 28. New Pedestrian and Transit Environment Factors • Attributes Empirically Estimated: • Hills (rise) • Indirectness • Population and Employment Densities (logs) • Street capacity
    29. 29. Example: Walking to SFCTAWork Purpose SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 29
    30. 30. Transit Walk Access Links: Perceived WeightWalk-Local-Walk, Destination Ferry Building SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 30
    31. 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. 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 periods Change Travel Plans: Occurs in core activity modelF. Change destinationsG. Not make trip SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 32
    33. 33. Transit Assignment with CrowdingRoute 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 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 33
    34. 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) SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 34
    35. 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 generation SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 35
    36. 36. SF Citywide Dynamic Traffic AssignmentWhy 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 SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
    37. 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 0.12 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) SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
    38. 38. Dynamic Traffic Assignment AssumptionsDynamic User Equilibrium:  No vehicle can unilaterally shift paths and improve their generalized 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 density SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
    39. 39. Citywide DTA Network Calibration & Validation:Happening Now! SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 39
    40. 40. That’s it! Lisa.Zorn@sfcta.org www.sfcta.org/modelingSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

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