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Practical Application of Activity-Based Models
Aggregate
4-step
Tour-basedActivity-based
Micro-simulation
4-Step is a “top down” approach
Divide population by zone / income / hh size
(maybe also number of workers, car ownership, age group)
More segments would be better, but there is a practical problem…
4-Step is a “top down” approach
Divide population by zone / income / hh size
TRIP GENERATIONAdds trip purpose dimension
For example,Home-based work,Home-based school,Home based shoppingHome-based otherWork-basedOther Non-home-based (NHB)
4-Step is a “top down” approach
Divide population by zone / income / hh size
TRIP GENERATIONAdds purpose dimension
TRIP DISTRIBUTIONAdds origin-destination dimension
Output is many trip matrices
4-Step is a “top down” approach
Divide population by zone / income / hh size
TRIP GENERATIONAdds purpose dimension
TRIP DISTRIBUTIONAdds origin-destination dimension
MODE CHOICEAdds mode dimension
Output is even more trip matrices
4-Step is a “top down” approach
Divide population by zone / income / hh size
TRIP GENERATIONAdds purpose dimension
TRIP DISTRIBUTIONAdds origin-destination dimension
MODE CHOICEAdds mode dimension
NETWORK ASSIGNMENTAdds time of day and route dimensions
DIMENSIONALITY CRISIS!
ACTIVITY SCHEDULEHOME
1 Eat, 6 Eat, 8 Sleep
2 & 4SCHOOL
3 LUNCH
5 SHOP
1
32
4
5
7 MOVIE
67
30 min
25 min
240 min
240 min
10 min
40 min
10 min
20 min
25 min
10 min
90 min
120 min
15 min
15 min
550 min
START 7:00AM
Major problems with aggregate trip-based
approachesNon-home-based trips!
Mode choice not consistent with adjacent tripsDestination choice not consistent with next tripTime of day not constrained by adjacent trips
No substitution between toursNo interactions between household membersAggregation errors/biases
Trip-Based to Tour-Based
Trip generation Tour generation(fixed rates?)
Trip time period Tour time periods(fixed factors?)
Trip distribution Tour destination choice(gravity model?)
Trip mode choice Tour mode choice
Intermediate stops
Tour-based to person-day-based
Tour generation Day-pattern choiceActivity generationTrip chaining
Tour time periods Tour sequencing and time periods
Tour destinations Tour destinationsTour mode choice Tour mode choiceIntermediate stops Intermediate stops
Person-day to Household-day
Day pattern choice Day patterns linked across HH members
Activity generation Joint HH activitiesLinked HH activities (escorting)Allocated HH activities (maintenance
tasks)Individual activities
All tours individual Some tours joint/linked
Geography of New Generation
Developed & UsedPortland (METRO)San Francisco County (SFCTA) New York (NYMTC)Columbus (MORPC)
Started:Atlanta (ARC)Denver (DRCOG)Dallas (NCTCOG)Tampa Bay (FDOT)
Considering:Houston (HGCOG)Raleigh-Durham (CAMPO)Sacramento (SACOG)Kansas City (MARC)Seattle (PSRC)San Diego (SANDAG)
Geography of New Generation
NY
SF
Portland
ColumbusDenver
Atlanta
Houston
Raleigh
KansasSacramento
Dallas
Tampa
San Diego
Seattle
Main Features• Already in earlier designs (Portland, San Francisco, New
York):– Tour as unit of modeling– Consistent generation of all tours made during a person-day– Stochastic micro-simulation application framework
• Added in later designs (Columbus, Atlanta, Denver):– Explicit modeling of intra-household interactions– Greater temporal detail (1 hour or less) and consistency in time use
and activity / travel scheduling – Greater spatial detail (10,000-20,000 grid cells) for LU and walk /
bike / transit accessibility
Microsimulation is a bottom-up approach
POPULATION SYNTHESIZERCreate a synthetic population by sampling
from actual households to matches control statistics or forecasts by zone
Output is a full list of households/persons(like census data)
Microsimulation is a bottom-up approach
ACTIVITY AND TRAVEL SIMULATORUses similar models to 4-step (activity generation,
destination choice, mode choice) but uses the Monte Carlo method to simulate discrete
choices from probabilitiesAlso considers trip-chaining (tours)
and scheduling (time-of-day)
Output is a list of trips and activities (like household travel survey data)
POPULATION SYNTHESIZER
Microsimulation is a bottom-up approach
AGGREGATORCompile trip matrices for network assignment
or simulation. Can also produce reports to look at travel by specific population
segments.
ACTIVITY AND TRAVEL SIMULATOR
POPULATION SYNTHESIZER
Microsimulation is a bottom-up approach
NETWORK ASSIGNMENT/SIMULATION
AGGREGATOR
ACTIVITY AND TRAVEL SIMULATOR
POPULATION SYNTHESIZER
“Continuous” spaceUse very small units – GIS parcels or grid cells (e.g. 200 meter squares)
Very good for modeling transit accessibility and activity attractions.
Density variables used to capture surrounding land uses.
Matrix-based measures such as in-vehicle times remain at zonal level.
Benefits of using grid cell data
Walk access time to transit based on grid cell GIS measures – much better resultsIntra-zonal walk times based on distance between O and D grid cells - intrazonal dummy variable becomes insignificant
Grid cell-based measure of percent of streets with sidewalks gives better explanation of walk/bike share than CBD dummy or other zone-based measures.
“Continuous” timeUse small time periods- 1 hour or half-hour
Model activity or tour start and end times simultaneously, conditional on time remaining after higher priority activities.
Better to capture interactions between tours and activities.
Better for modeling peak-spreading
More accurate input to traffic simulation
Important Policy Areas
Congestion pricing / time-of-day incentivesPolicies affecting work or business hoursParking policiesRidesharing policiesDemographic shifts (aging, household composition)
How should models be judged?
Ability to predict future changes
Sensitivity to a wide range of policies
Ability to match current data
How are models typically judged?
Ability to match data on current situation
Simplicity of models, data, and forecasts
Predictability of forecasts
Replicability of forecasts
Issues in simulation errorStochastic models do not necessarily converge
Need to separate real variability from simulation error.
Simulation error decreases with square root of iterations.
Stability of results depends on level of resolution (TAZ, county, etc.)
Simulation errors do not multiply – compensation is more likely.
Tests of Random Simulation Error
Ran the model system (except for assignment) 100 timesChanged the random seed for each model for each run.Analyzed the variability in results obtained from each model in the system.Main questions:
What is the range of results obtained?How fast do the results converge toward the mean?How is the variability related to the level of aggregation?
Trips per Person % Difference from Final Mean
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97
COUNTYMEAN
NEIGHMEAN
TAZMEAN
Tours by Mode from a Single Origin TAZ % Difference from Final Mean
-8.0%
-6.0%
-4.0%
-2.0%
0.0%
2.0%
4.0%
6.0%
8.0%
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
%DIFF-AUTO
%DIFF-TRANSIT
%DIFF-NONMOTOR
Conclusions Regarding Simulation Error
For region-wide results, a single run is adequate.For corridor-level or neighborhood-level results, 5 to 10 runs should be adequate.Looking at very small areas (TAZ’s), rare sub-populations (e.g. single parents) or rare behavior (e.g. transit use in some regions) requires more runs to reach stable results.We have not yet looked at results with full equilibration with assignment. The feedback from level-of-service should dampen the variation even further.
Further Conceptual Evolution
Intra-person integrityActivity & travel pattern configurationTime use & activity generationTime-space constraints on activity locationFeedback through individual time budgets
Inter-person intra-household integrityCoordinated daily patternsEpisodic joint activity & travelMaintenance task allocationCar allocation
Simultaneous vs. sequential choices
At the tour or trip level – sequence ofMode choicesDestination choicesScheduling/sequencing choicesTrip chaining decisions
Empirical question, may vary by purpose.More data on constraints and flexibility would be usefulUse different sequences for different types of situations or individuals? Need a more flexible modeling framework.
Need dynamic models to deal with …
Advance vs. real-time planningSimultaneous vs. sequential processesLearning and information acquisitionFeedback processes over time
Direction of causalityLocation vs. travel (induced demand)Supply vs. demand (peak spreading)
Dynamic models will require …
Different types of dataPanels (?)Before and after surveysRetrospective surveysHypothetical choice contexts
Different types of models (?)Strict adherence to econometric choice theory has prevented the use of non-static models