for model users group june 10, 2011 kyeil kim, ph.d., ptp atlanta regional commission

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For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

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Page 1: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

For Model Users GroupJune 10, 2011

Kyeil Kim, Ph.D., PTP

Atlanta Regional Commission

Page 2: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Today’s MenuOverall features of ARC’s Activity-Based Model

(ABM)ABM Visualization Software, ABMVIZQuality Assurance/Quality Control of ABM

Page 3: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Daily Travel

• Trip-Based Model - Home-Work: 2 trips - Work-Eat: 2 trips - Home-Gym: 2 trips

• Activity-Based Model - Follows daily activity patterns (departure time, duration, location, frequency, mode)

Page 4: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

What is Activity-Based Model?ABM aims at predicting which activities are

conducted where, when, for how long, with whom, the transportation mode involved and ideally also the implied route decisions

Disaggregate, Micro-simulation, Behavioral, Tour-based

ABM reflects the scheduling of activities in time and space

Page 5: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Structure of ARC Models: TBM vs. ABM

Trip GenerationTrip

DistributionMode Choice

Route Choice

Long-Term

Choices

Daily Activity Patterns

Tour Mode

Choice/Stop

Trip Mode Choice

Synthetic Populatio

n

Trip-Based Model

Activity-Based Model

Demand

Supply

Page 6: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Population SynthesizerGenerates synthetic population to represent

actual households and populationBase year

Input: Census data (marginal distributions of various household control variables), PUMS

Control variables: householder age, HH size, HH income, presence of children in HH, number of workers in HH

Joint distribution through Frata PUMS 5% sample as seed matrix, control totals from Census

Draws from PUMS households from the joint distribution 1 record/hh and 1 record/person

Page 7: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Population Synthesizer (cont’d)Forecast year

Input: ARC lane-use forecast, PUMSControl variables: HH size, HH income, householder’

age, number of workers in HHJoint distribution through Frata

Base year distribution as seed matrix, control totals from land-use forecasts

Draws from PUMS households from the joint distribution 1 record/hh and 1 record/person

Page 8: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Structure of ARC Models: TBM vs. ABM

Trip GenerationTrip

DistributionMode Choice

Route Choice

Long-Term

Choices

Daily Activity Patterns

Tour Mode

Choice/Stop

Trip Mode Choice

Synthetic Populatio

n

Trip-Based Model

Activity-Based Model

Demand

Supply

Page 9: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Long-Term ChoicesMandatory activity location choice

Work/school/university locations for the synthesized population Work location choice for workers Grade school for persons age 5-12 University for university students

Multinomial logit: [subzones]=[person characteristics, size terms, mc logsums, distance, etc.]

Car ownership modelNumber of vehicles owned by each householdMultinomial logit: [# cars]=[hh size, income, parking

cost, mc logsums, etc.]

Page 10: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Structure of ARC Models: TBM vs. ABM

Trip GenerationTrip

DistributionMode Choice

Route Choice

Long-Term

Choices

Daily Activity Patterns

Tour Mode

Choice/Stop

Trip Mode Choice

Synthetic Populatio

n

Trip-Based Model

Activity-Based Model

Demand

Supply

Page 11: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Coordinated Daily Activity PatternGenerates personal DAPs and individual tours by

purpose for all synthesized populationDAPs

Mandatory, Non-mandatory & At-home patternsDecision-making unit: HouseholdsMultinomial logit: [# DAPs]=[person/hh characteristics,

accessibility measures, intra-household interaction terms, etc.]

363 alternatives: 3 (1-p hh), 9 (2-p hh), 27 (3-p hh), 81 (4-p hh), 243 (5-p hh)

Page 12: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Tour ModelsPredicts the number and purpose of tours for

each person, destinations, and time-of-day choices

Four different toursIndividual Mandatory

Joint Non-Mandatory

Individual Non-Mandatory

At-Work Sub-Tours

Residual Time

Page 13: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Individual Mandatory TourTour Frequency

Number and purpose of tours for each personMultinomial logit: [# of work/school tour]=[hh

composition, income, car ownership, location of work/school activities, accessibility, etc.]

Tour Time-of-DaySelect the combinations of tour departure/arrival timeMultinomial logit: [combination of tour departure/arrival

hours]=[household and personal characteristics, network LOS variables, etc.]

Alternatives: 190 combinations of tour departure hour and arrival hour back at home

Page 14: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Joint Non-Mandatory TourJoint tours by household members after mandatory

tours have been generated and scheduledJoint Tour Frequency

Generates the number/purposes of joint toursMultinomial logit: [0, 1 or 2 tours by purpose]=[household

variables, accessibility, overlapping time windows, etc.]

Joint Tour CompositionDetermines the person types participating in the tourMultinomial logit: [combination of adults &

children]=[household characteristics, purpose of joint tour, overlapping time windows]

Page 15: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Joint Non-Mandatory Tour (cont’d)Joint Tour Primary Destination Choice

Location of the tour primary destinationMultinomial logit: [subzones]=[household/person

characteristics, tour purpose, size variables, mc logsum, distance, etc.]

Joint Tour Time-of-Day ChoiceTour departure time from home and arrival time back at

homeMultinomial logit: [combination of tour departure/arrival

hours]=[household and personal characteristics, network LOS variables, etc.]

Alternatives: 190 combinations of tour departure hour and arrival hour back at home

Page 16: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Other ToursIndividual Non-Mandatory Tour

Tour FrequencyTour Primary Destination ChoiceTour Time-of-Day Choice

At-Work Sub-TourTour FrequencyTour Primary Destination ChoiceTour Time-of-Day Choice

Page 17: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Structure of ARC Models: TBM vs. ABM

Trip GenerationTrip

DistributionMode Choice

Route Choice

Long-Term

Choices

Daily Activity Patterns

Tour Mode

Choice/Stop

Trip Mode Choice

Synthetic Populatio

n

Trip-Based Model

Activity-Based Model

Demand

Supply

Page 18: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Tour Mode ChoiceTour mode choice: main tour mode used from

origin to primary destination and backTwo-level mode choice in ARC ABM

Tour mode level (upper-level choice)Trip mode level (lower-level choice conditional on the

upper-level)

Page 19: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Tour Mode Choice (cont’d)Tour MC models

Work, University, K-12, Non-mandatory, At-work12 AlternativesNested logit: [tour mode]=[household and personal

characteristics, urban form variables, network LOS variables, etc.]

Use the round-trip LOS between the tour anchor location and the primary destination

Page 20: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Intermediate Stop ModelsStop Frequency Model

Number of intermediate stops on the way to/from the primary destination by tour purpose

Multinomial logit: [# of stops]=[household and personal characteristics, tour duration, tour distance, accessibility, etc.]

Stop Location Choice ModelLocation of stops along the tour other than the primary

destinationMultinomial logit: [Subzones]=[mc logsum, distance,

size variables, etc.]

Page 21: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Structure of ARC Models: TBM vs. ABM

Trip GenerationTrip

DistributionMode Choice

Route Choice

Long-Term

Choices

Daily Activity Patterns

Tour Mode

Choice/Stop

Trip Mode Choice

Synthetic Populatio

n

Trip-Based Model

Activity-Based Model

Demand

Supply

Page 22: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Trip Mode ChoiceDetermines the mode for each trip along the tourConstrained by the main tour modeCorrespondence rules to determine which trip

modes are available for which tour modesE.g., drive-alone pay trip is only available for drive-alone

pay tourE.g., transit tours can include auto shared-ride trips for

particular legs

Page 23: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Structure of ARC Models: TBM vs. ABM

Trip GenerationTrip

DistributionMode Choice

Route Choice

Long-Term

Choices

Daily Activity Patterns

Tour Mode

Choice/Stop

Trip Mode Choice

Synthetic Populatio

n

Trip-Based Model

Activity-Based Model

Demand

Supply

Page 24: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Route ChoiceSame routine as the trip-based modelMultimodal User Equilibrium Time-of-Day

AssignmentBi-Conjugate Frank-Wolfe for both TBM and

ABM, departing from the traditional Frank-Wolfe

Page 25: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Run Environment Java-PackageCube/TP+Three 64-bit Windows machinesEach machine with 32GB of RAMBase year run: approx. 30 hours2040 run:

approx. 50 hours

Page 26: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

VisualizationModel generates huge databaseModel visualization system, ABMVIZPrimary starting point for most model

analysis questions Interactive/dynamic visualization

of model estimates/resultsSome unique visualization types

Tables, Bar Charts/Maps Time Use Tour Tracing Tree Map Radar Charts

Page 27: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Quality Assurance/Quality ControlQuality Assurance (QA): a systematic review process

by personnel not directly involved in model developmentQuality Control (QC): a technical routine to control

quality of the model performed in model development

Full understanding of the models’ capabilities/limits

ARC initiated internal a year-long QA/QC process for both ABM and TBM

New QA/QC guidelines

Page 28: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

QA/QCOverall processes

Reasonableness checking for EVERY modeling stepTemporal validation between base and forecast yearsComparability between ABM and TBM

ComponentsModeling flows/ScriptsSocioeconomic dataTransportation network dataExternal tripsTrip generation, Trip distribution, Mode choice, &

Traffic assignment

Page 29: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

QA/QC (cont’d)Tools

SQL Express Management StudioSTATAABMVIZCustom scripts

Voyager/TP+ R

Our brain

Page 30: For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Thank You!