1. 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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2. 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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3. Distribution of Auto Ownership
Like worker status, the number of autos is one of the households attributes that is drawn from the PUMS data. If this distribution turns out to be unsatisfactory, an Auto Ownership model can be applied and the number of autos replaced.
The current thinking is that a separate auto ownership model is not necessary based on these results.
We avoid using the terms “auto availability” in TRANSIMS, even though that is in fact what the Census asks in the PUMS data. This is because in TRANSIMS there is one auto available to every person of driving age, parked near their household activity location. The number of household autos that is one of the PUMS attributes is used in the Mode Preference Model and could be used in the Location Choice model if using log sums that are stratified by auto ownership.
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4. Activity Pattern Generation
Activity patterns generated from surveys are a good first step to developing a full activity model
Easier and quicker to implement
Captures
Household interactions
Characteristics of the household and the geographic location
Ignores
Disconnect between activity schedules, locations, and modes
No ability for patterns to adjust over time to congestion
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5. Synthetic-to-Survey Household Matching
Classification and regression tree
Household attributes
Household size
Number of workers
Age of head of household
Income
Children less than age 5
Children ages 5 – 17
Adults ages 26 – 45
Urban area type
Very urban
Urban
Suburban
The tree used is essentially the same as that developed using the Classification and Regression Tree (CART) method by the National Institute of Statistical Sciences (NISS). To this, the Portland study added an eighth variable to each node (actually a trinary choice) corresponding to area type. The intent being to reflect different activity patterns based on density and access to transit.
(We have yet to verify statistically what difference this has made)
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6. Selecting Activity Patterns
Survey Household H S O W
Student, age 12
Worker, age 38
Low Income, Urban
Synthetic Household
Student, age 5-17
Worker, age 26-45
1 Car, Location Address
Activity Location Choices
Multiple survey cases
This illustrates how a surveyed household with a particular set of attributes and diary-derived activity patterns is classified using the binary tree. Surveyed households that match the attributes corresponding to a node in the tree are chosen at random and their activity patterns matched to one of the synthetic households generated in the Population synthesizer. The activity location choice model is a sub-model within the Activity Generator and assigns locations to each activity stop in each daily pattern.
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7. Location Choice
Tour activity location choice model derived from existing trip-based destination choice model
Two stage process
Choose zone of activity based on mode choice logsums
Choose activity location within the zone based on
Activity location weight
Airline distance to origin
Work location choice balanced to employment
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8. Approach Metro trip-based destination choice model
TRANSIMS tour-based location choice model O H W
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9. Location Choices on Complex Tours
LogSum
for Work
for Shop
for Recreate
Work
Attractor (1)
Recreate
Attractor (4)
Shop
Attractor (3)
Home
Other
for Other
Attractor (2)
1. Choose location of primary stop on anchor tour:
2. Choose other stops on anchor tour:
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10. Location Choice Calibration Factors
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11. Total Tour Distance by Tour Type
In analyzing tour patterns, we look at activity strings as shown here.
H = home
O = other activity type
W = work activity type
This illustrates the cumulative distances for entire tours. In general, the longer the tour, the greater the cumulative error. These look pretty good though.
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12. Mean Trip Length by Tour Leg Type
Here’s what happens when you break it down by trip legs on the tour.
The number of observations appears in parentheses. The WW segment is off, but it is a relatively infrequent pattern. We could have calibrated separate Betas for this and other trip types, but chose to focus on the most frequent patterns. We made no effort to calibrate “school” activity type trip lengths, because it was not subject to the location choice model—we used nearest school rules..
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13. HWH Total Tour Distance
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14. HWOH Total Tour Distance
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15. HWOWH Total Tour Distance
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16. HOH Total Tour Distance
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17. HOOH Total Tour Distance
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18. HWOWH Total Tour Distance
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19. HOH Total Tour Distance
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20. HOOH Total Tour Distance
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21. HOOOH Total Tour Distance
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22. Trip Length Comparison with Trip-Based Destination Choice Model
Converting to trip definitions, this looks pretty good except for schools.
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23. Mode Preference Work in progress
Started with the existing mode choice model for tour-mode choice at the zone level
Market segmentation – constrained choice sets
Activity location and time-of-day based routing to refine the tour leg (trip) mode choice
Transit tour feasibility – location adjustments
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24. Tour-Trip Mode Relationships
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25. Purpose and Age Segmentation
Work Tours
Non-work Tours
Age < 16
Age 16+
School Tours
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26. Market Segmentation by Distance
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27. Tour Mode Calibration by Trip Modes
Iterative adjustment of mode-specific constants using observed and estimated shares:
Drive Trips = Drive Trips on Drive Tours
+ Drive Trips on Park-n-Ride Tours
Transit Trips = Transit Trips on Transit Tours
+ Transit Trips on Park-n-Ride Tours
+ Transit Trips on Passenger Tours
Walk Trips = Walk Trips on Walk Tours
+ Walk Trips on Transit Tours
+ Walk Trips on Park-n-Ride Tours
+ Walk Trips on Passenger Tours
+ Walk Trips on Drive Tours
Park-n-Ride Trips = 2 * Park-n-Ride Tours
Bike Trips = Bike Trips on Bike Tours
Passenger Trips = Pass. Trips on Passenger Tours
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28. Initial Approach – Red Flags
Mode specific constants too high
Routing issues
Procedures didn’t map to a logical decision process
Made feedback extremely complex
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29. Mode Choice Challenges in Microsimulation Environment
Tracking people, vehicles and constraints
Tour path needs to be physically feasible
Time dependent path information
Travel time is function of the exact departure time
Transit schedule and service period limitations
Router reveals illogical and infeasible paths
Person-vehicle inconsistency
Schedule infeasibility
Improbable activity patterns
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30. Feedback to Activity Models
John Gliebe

31. 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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32. Microsimulation Complications
When decisions are applied to individual tours
Feedback from microsimulation and point to point routing result in infeasible activity patterns
Probability clashes with reality!
When Time of Day (TOD) is modeled discretely (needed)
We need to replace infeasible patterns with feasible patterns of similar probability
OR -- leave probability theory out, and concentrate on process and rules
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33. Recap Intended Modeling Process
Choose an activity pattern
Includes timed itinerary from survey
Choose activity locations
Choose tour modes
Route the tour
Feedback corrects inconsistencies with original itinerary
Simulated arrival and departure times exceed maximum deviation from itinerary
Some paths infeasible
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34. Planned Feedback Design
Hierarchy of solutions:
Choose new starting times
Choose new travel modes
Choose new activity locations
Choose whole new activity pattern
Requires successive iterations and complex mode swapping routines to maintain calibration
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35. Some things worked… Work tour starting time regeneration
Reselected intermediate stop locations for transit tours
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36. Regenerating Location Choices for Secondary Stops for Transit Routing
Rationale
Stage 1 Tour Mode choice based on utility of trip from home to primary activity
Log-sum impedance values are dominated by auto modes
Difficult to Route
Solution: regenerate location choices of secondary stops using Transit-Walk log-sums
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37. Regenerating Location Choices on Transit ToursW O
If Tour Mode is Auto… O
If Tour Mode is Transit…
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38. But we had some problems…
Generic description of path options from zone-to-zone skims led to infeasible choices
Activity pattern complexity infeasible for many transit tours
Response:
Provide more specific information up front? or
Use feedback to resolve the problems?
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39. Information from time-dependent transit paths reveals…
Two key problems:
Individual transit run information and arrival time information inconsistent with the assumptions from the skims
Don’t use zone aggregate skims
Use specific tour/trip skims
Path routing isn’t very realistic
Requires fundamental reconsideration of transit path-building weights
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40. Example graphic
Proximate transit stops that don’t service destination… or get you home… at least not at the right time of day… without many transfers… and long walks and waits.
Reliance on zonal log-sums masks real service direction of travel and temporal availability
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41. Example graphic
Need to consider both ends of the tour for transit availability
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42. Example graphic Corridor-oriented vs. Looping
Graphic example
Current solution is reselection of intermediate stops on transit tours
Suffers from problems noted above
Mode-specific location choice?
Shapes of Transit Tours vs. Auto Tours
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43. Are there better ways to intra-tour consistency?
Begin with skims generated by router rather than generic zone-to-zone skims
Time of day choice model?
Consider activity schedule in location choice
May need to develop more analytical methods of feedback rather than relying on iterating out of problems
e.g., variant on Evans algorithm (combined model)
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44. Assessment of Initial Feedback Results
Feed forward created many infeasible travel plans
With generic skims, feedback had no information to improve choices
Feedback won’t solve mode choice problems until mode choice is better informed about travel options
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45. Assessment of Results
Keith Lawton

46. Key Lessons Learned
Initial principles (use current models) let us down in mode choice
Retrospective should have…
Why this model is different from trip models
Reengineer mode choice to be consistent with what we discovered
Auto availability-specific location choice?
Time-period-specific location choice?
Priority: skim methods development
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47. Theme – a constructive critique
Is this a good approach for transitioning from traditional to advanced practice models?
Is this worth doing? Do we get worthwhile benefits?
How would this help MPOs support decision makers? Does it address the technical and policy issues?
Next steps…, current areas of research, lessons learned, recommendations for further study/research/evaluation
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