1. Activity Modeling in Portland
January 22, 2006

2. Introduction
Fred Ducca

3. Questions asked of modelers
Credible forecasts of the impact and benefits of highway and transit improvements
Credible forecasts of air quality emissions
Support for important policy questions
Impact of travel demand management
Impact of traffic operations strategies
Environmental justice
Emergency preparedness
Toll roads and pricing
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4. Why we struggle Current models don’t represent: Examples:
Transportation system operations
Individual decision making process
Value components of travel
Behavior choice and response
Examples:
Congestion effects on travel
Peak Spreading / Trip Chaining
New Technologies / ITS
Toll Roads / Peak Hour Pricing
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5. What we need
Representation of the operations of specific streets and facilities
Disaggregate representation of space and time
Regional simulation of individual vehicles and persons to evaluate system performance
Representation of individual decisions in a household context
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6. How we get there
A transition pathway from current practice to advanced techniques
Practical
Cost-effective
Incremental
Feasible
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7. The Portland Test Case
Bill Davidson

8. Principles of the Portland Test Case
Disaggregate representation of individuals/vehicles
Simulate an entire metropolitan region to understand the transportation system interactions
Make maximum use of existing networks and trip tables to ease transition to microsimulation
Make maximum use of demand models to ease transition to activity-based analysis
Blend rules and stochastic techniques to represent travel choices
Incorporate individual feedback to find a feasible set of activity choices
Exceed the validation statistics of trip-based models
Provide more effective measures of travel behavior and system performance
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9. Existing to Advanced Practice
Transition Pathway
Activity Generation
through models
Microsimulation feedback
to activity-based models
Existing to Advanced Practice
Activity Generation using surveys
to define activity patterns
Microsimulation feedback to
traditional models by time of day
Regional traffic and transit Microsimulation
Route traditional trips by time of day / DTA
Assemble the data to support advanced models
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10. Presentation Topics Transition strategy from trip to activity models
Data
Router/Microsimulator
Simple Activity Models
Feedback to activity-based models
Assessment of Results
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11. Data Requirements
Cindy Pederson

12. Existing to Advanced Practice
Transition Pathway
Activity Generation
through models
Microsimulation feedback
to activity-based models
Existing to Advanced Practice
Activity Generation using surveys
to define activity patterns
Microsimulation feedback to
traditional models by time of day
Regional traffic and transit Microsimulation
Route traditional trips by time of day / DTA
Assemble the data to support advanced models
1/22/06
DRAFT DRAFT DRAFT

13. Key Points Good news – Bad news –
Work off of existing data – good foundation
Don’t need exhaustive data collection and data preparation
Bad news –
Need to be enhanced / refined to make it work
Lots of validation data
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14. Data Principles Categories of data Collect only what you need
Use what you have
Must be able to generate the data for the future
Categories of data
Transportation supply
Travel demand
Socio-economic characteristics – Census / marginals
Calibration / validation data
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15. Transportation Supply
Original Concept: reality in detail…
TIGER level street networks
Actual transit run-cut level schedules
Actual intersection control and timing plans
This level of data:
impossible to achieve
impossible to forecast
excessive run times
maintenance nightmare
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16. Transportation Supply
Simpler approaches seem to work well
MPO level networks are satisfactory, presuming collector level detail and ramp/freeway coding and detailed transit route data
Don’t need the full detail network
Easier to develop for future scenarios
Synthetic traffic control is a good starting point
Full detail is hard to obtain and harder to forecast
Microsimulator can help refine the data
Synthetic transit schedules can be created
Each bus, driver, and run generated from MPO transit data
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17. Network Detail All Streets MPO Level DRAFT DRAFT DRAFT 124,900 links
246,900 activity locations
30,400 lane miles
1,750 traffic signals
11,900 links
20,900 activity locations
8,100 lane miles
1,370 traffic signals
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18. Signal Locations Automatically Generated Refined Using Local Data
Green = Actuated, Blue = Timed
Automatically Generated
Refined Using Local Data
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19. Travel Demand Full home-interview survey is needed
Cleaning – very important and time consuming
Activity patterns internally consistent
Geographic coding
Activity or tour-based methods should be used if developing a full activity-based model
Must have good information about time of travel by trip purpose and geography
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20. Diurnal Distributions - Survey
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21. Smoothed Diurnal Distributions
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22. Socio-Economic Characteristics
Need all of the socio-economic information used in traditional models
Extra dimension – model activity locations rather than zone centroids – distribute traffic to links
Distribute by formula or with subzone data if available
Activity locations for major employers and shopping centers based on driveway locations or real access points
Parcel data and/or land-use designations
Census block group data
Building polygons
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23. Moving from Traditional Networks
443
2 loading points for all retail and residential activities
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24. With Help from Aerial Photos
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25. And Parcel-level Land Use Data
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26. To Activity Locations on Links
Residential Access
Larger number of loading points (by side of street) with land use information attached
Retail Activity Access Points
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27. Calibration / Validation Data
Standard data
Traffic counts, transit ridership, screen lines, trip length distributions, travel patterns (district level trip tables by trip purpose), etc.
Unique for microsimulation
Counts by time of day, speed information by time of day, major congestion points and duration (general insights about queues or real data)
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28. Router / Microsimulator
David Roden

29. Existing to Advanced Practice
Transition Pathway
Activity Generation
through models
Microsimulation feedback
to activity-based models
Existing to Advanced Practice
Activity Generation using surveys
to define activity patterns
Microsimulation feedback to
traditional models by time of day
Regional traffic and transit Microsimulation
Route traditional trips by time of day / DTA
Assemble the data to support advanced models
1/22/06
DRAFT DRAFT DRAFT

30. Research Objectives Implement regional simulation practically
Use traditional regional model data as input to simulation methods
Can regional networks and trip tables be converted?
Is the level of detail adequate for regional simulation?
Develop synthetic data generation methods to simplify the conversion process
Do they reduce the time and cost of developing a simulation model?
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31. Network Data Conversion
Convert EMME/2  generic format
Highway and/or transit
Convert generic format  TRANSIMS
Synthesize traffic controls and other data
use simple rules and traffic engineering principles
Review and refine incrementally or as needed
Start working with the software ASAP
Focus resources on simulation problems
(list synthetic elements)
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32. Portland Highway Network
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33. Pocket Lanes
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34. Activity Locations and Parking
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35. Lane Connectivity
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36. Signal and Sign Warrants
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37. Signal Inventory
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38. Signal Warrant Areas
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39. Signal Warrant Validation
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40. Portland Transit Network
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41. TRANSIMS Coding Details
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42. Trip Table Conversion
Distribute zone-to-zone trips by trip purpose to activity locations by second of the day
EMME/2 daily P-A trip fractions  O-D trips and time-of-day distributions
Home Interview Survey used for diurnal distributions by trip purpose and orientation
23 trip tables  5 million trips
Unique household, person, vehicle, and activity pattern for each trip
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43. Trip Start Time Distribution
Home Based Work Diurnal Distribution
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44. Trip Start Time Distribution
Home Based Other Diurnal Distribution
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45. Zone  Activity Location
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46. Primary Challenges Transit service by time of day
Route schedule coordination
Transit vehicle components
Drivers, parking lots, and end of line maneuvers
Spatial accuracy of the network
Assigning activity locations to zones
Network changes by time of day
HOV, parking and turn restrictions, signals
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47. Track 1 Objectives Router-Microsimulator-based process
Time dependent highway and transit assignments
Apply to MPO-level or All-Streets networks
Using trip tables or activity-based travel
Develop TRANSIMS-based methods
Theoretically sound
Computationally efficient
Reproducible/consistent results
Clear/robust process with defined stopping criteria
Applicable for long-range regional forecasts
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48. TRANSIMS Assignment Iterative process of:
Routing -- building paths through the network
Microsimulation -- simulating vehicle interactions on those paths to calculate the resulting travel times
Each traveler can only have one path
Incrementally address problems or opportunities for a subset of travelers
Behavior emerges through feedback
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49. TRANSIMS Routing Start with Activity Patterns for each household
Location and time range
Build travel plans for specified households
Specific activity times
Time and path of each travel leg between activities
Walk to vehicle, drive path to parking, walk to next activity location
Walk and wait for transit route, ride to alighting stop, walk to next route, etc.
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50. Router Multi-modal, time-dependent, vine-based minimum impedance paths
Travel times in 15-minute increments
Impedance based on in-vehicle time, walk time, first wait, transfer wait, distance, cost/fare, and transfer penalties
Optional features
V/C-based volume-delay updates (BPR)
Time of day network changes (HOV, turns, etc.)
Maximum walk distance, maximum wait, number of transfers, and travel schedule
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51. V/C-Based Travel Times
Quick way of generating reasonable demand on links prior to simulation
Update 15-minute link travel times based on V/C ratios and BPR equation
During each Router application
After all Routers have completed
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52. Freeway V/C Ratios (lane-miles)
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53. Arterial V/C Ratios (lane-miles)
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54. Microsimulation Issues
Network – connections and timing
“modeled” vs. literal
Lost vehicles (10-15%  1.3%)
Short links and lane changing
Software bug fixes and enhancements
Screenlines (7-10% low  -0.3%)
Arterials lower than freeways
Fidelity – time step and cell size
1 second, 7.5 meters  0.5 sec., 3.75 m.
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55. Lost Vehicle Locations
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56. “Assignment” Approach
16 Incremental Loading Runs
10, 20, 30, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100
10 Router-only Stabilization Runs
11%  4% qualified (V/C ratios over 1.0)
10 Stabilization by Time-of-Day Runs
30%  15% qualified, 2% lost vehicles, -5% validation
15 Automated Stabilization Runs
20%  6% qualified, 1.4% lost vehicles, -0.8% validation
20 Equilibrium/Convergence Runs
30%  2% qualified, 1.3% lost vehicles, -0.2% validation
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57. US-26 Vista Ridge Tunnel -EB
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58. I-5 Interstate Bridge - NB
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59. US-26 Vista Ridge Speeds
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60. I-84 Hoyt Speed
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61. Simulated Traffic
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62. Conclusions Traditional regional model data  TRANSIMS
Microsimulator fidelity affects results
MPO-level networks work well
Synthetic generation of network characteristics is useful starting point
Time-based user-equilibrium assignment
Assigned volumes match the observed counts
Time-of-day analysis provides useful insights
Location and duration of congestion
Likelihood of peak spreading
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63. Simple Activity Models
Keith Lawton

64. Existing to Advanced Practice
Transition Pathway
Activity Generation
through models
Microsimulation feedback
to activity-based models
Existing to Advanced Practice
Activity Generation using surveys
to define activity patterns
Microsimulation feedback to
traditional models by time of day
Regional traffic and transit Microsimulation
Route traditional trips by time of day / DTA
Assemble the data to support advanced models
1/22/06
DRAFT DRAFT DRAFT

65. Conceptual Design DRAFT DRAFT DRAFT 1/22/06 Population Synthesis
Activity
Patterns
Mode
Preference
Router
Microsimulator
Stabilization
Refine Modes
Change Activity Times or Patterns
Route Attributes
Attraction Balancing
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66. Theme
Existing models and data can be the foundations for an activity-based implementation
The building blocks
Population Synthesizer
Activity Generator
Activity patterns assigned to households
Location choice: From Metro Trip-Based Destination Choice
Mode preference: From Metro Trip-Based Mode Choice with Application Rules
TRANSIMS Modules:
Need scripts to apply models
Parallel Processing - Very Fast
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67. Population Synthesizer
Controlled joint distribution of households by block group
Draw synthetic households matching joint distribution
Assign synthetic households to activity location within block group
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68. Synthetic Household Attributes
IPF Procedure
Draw matching synthetic household from PUMS
Uncontrolled attributes
Worker status
Auto ownership
Number of block group households with controlled attributes
Household size
Income
Age of head
These are the attributes used in the Portland Study. Race is not used in the Portland study but may be of interest elsewhere.
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69. Households by Number of Workers
We are showing distribution of households by workers, because it is an important variable that is not controlled by the IPF procedure. The working status of individuals is simply drawn from the PUMS records, so it is important to see whether the resulting distribution is reasonably accurate.
The resulting TRANSIMS distribution slightly underestimates 0 and 1 worker households and over-estimates 2+ worker households.
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