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