For Model Users Group
June 10, 2011
Kyeil Kim, Ph.D., PTP
Atlanta Regional Commission
Today’s Menu
 Overall features of ARC’s Activity-Based Model (ABM)
 ABM Visualization Software, ABMVIZ
 Quality Assurance/Quality Control of ABM
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)
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, Tourbased
 ABM reflects the scheduling of activities in time and
space
Structure of ARC Models:
TBM vs. ABM
Trip Generation
Trip Distribution
Mode Choice
Trip-Based Model
Route
Choice
Activity-Based Model
Long-Term
Choices
Daily Activity
Patterns
Tour Mode
Choice/Stop
Trip Mode
Choice
Synthetic
Population
Demand
Supply
Population Synthesizer
 Generates synthetic population to represent actual
households and population
 Base 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
Population Synthesizer (cont’d)
 Forecast year
 Input: ARC lane-use forecast, PUMS
 Control variables: HH size, HH income, householder’ age, number
of workers in HH
 Joint 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
Structure of ARC Models:
TBM vs. ABM
Trip Generation
Trip Distribution
Mode Choice
Trip-Based Model
Route
Choice
Activity-Based Model
Long-Term
Choices
Daily Activity
Patterns
Tour Mode
Choice/Stop
Trip Mode
Choice
Synthetic
Population
Demand
Supply
Long-Term Choices
 Mandatory 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 model
 Number of vehicles owned by each household
 Multinomial logit: [# cars]=[hh size, income, parking cost, mc
logsums, etc.]
Structure of ARC Models:
TBM vs. ABM
Trip Generation
Trip Distribution
Mode Choice
Trip-Based Model
Route
Choice
Activity-Based Model
Long-Term
Choices
Daily Activity
Patterns
Tour Mode
Choice/Stop
Trip Mode
Choice
Synthetic
Population
Demand
Supply
Coordinated Daily Activity Pattern
 Generates personal DAPs and individual tours by purpose
for all synthesized population
 DAPs
 Mandatory, Non-mandatory & At-home patterns
 Decision-making unit: Households
 Multinomial 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)
Tour Models
 Predicts the number and purpose of tours for each person,
destinations, and time-of-day choices
 Four different tours
Individual Mandatory
Residual Time
Joint Non-Mandatory
Individual Non-Mandatory
At-Work Sub-Tours
Individual Mandatory Tour
 Tour Frequency
 Number and purpose of tours for each person
 Multinomial logit: [# of work/school tour]=[hh composition,
income, car ownership, location of work/school activities,
accessibility, etc.]
 Tour Time-of-Day
 Select the combinations of tour departure/arrival time
 Multinomial 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
Joint Non-Mandatory Tour
 Joint tours by household members after mandatory tours
have been generated and scheduled
 Joint Tour Frequency
 Generates the number/purposes of joint tours
 Multinomial logit: [0, 1 or 2 tours by purpose]=[household
variables, accessibility, overlapping time windows, etc.]
 Joint Tour Composition
 Determines the person types participating in the tour
 Multinomial logit: [combination of adults & children]=[household
characteristics, purpose of joint tour, overlapping time windows]
Joint Non-Mandatory Tour (cont’d)
 Joint Tour Primary Destination Choice
 Location of the tour primary destination
 Multinomial logit: [subzones]=[household/person characteristics,
tour purpose, size variables, mc logsum, distance, etc.]
 Joint Tour Time-of-Day Choice
 Tour departure time from home and arrival time back at home
 Multinomial 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
Other Tours
 Individual Non-Mandatory Tour
 Tour Frequency
 Tour Primary Destination Choice
 Tour Time-of-Day Choice
 At-Work Sub-Tour
 Tour Frequency
 Tour Primary Destination Choice
 Tour Time-of-Day Choice
Structure of ARC Models:
TBM vs. ABM
Trip Generation
Trip Distribution
Mode Choice
Trip-Based Model
Route
Choice
Activity-Based Model
Long-Term
Choices
Daily Activity
Patterns
Tour Mode
Choice/Stop
Trip Mode
Choice
Synthetic
Population
Demand
Supply
Tour Mode Choice
 Tour mode choice: main tour mode used from origin to
primary destination and back
 Two-level mode choice in ARC ABM
 Tour mode level (upper-level choice)
 Trip mode level (lower-level choice conditional on the upper-level)
Tour Mode Choice (cont’d)
 Tour MC models
 Work, University, K-12, Non-mandatory, At-work
 12 Alternatives
 Nested 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
Intermediate Stop Models
 Stop 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 Model
 Location of stops along the tour other than the primary destination
 Multinomial logit: [Subzones]=[mc logsum, distance, size variables,
etc.]
Structure of ARC Models:
TBM vs. ABM
Trip Generation
Trip Distribution
Mode Choice
Trip-Based Model
Route
Choice
Activity-Based Model
Long-Term
Choices
Daily Activity
Patterns
Tour Mode
Choice/Stop
Trip Mode
Choice
Synthetic
Population
Demand
Supply
Trip Mode Choice
 Determines the mode for each trip along the tour
 Constrained by the main tour mode
 Correspondence rules to determine which trip modes are
available for which tour modes
 E.g., drive-alone pay trip is only available for drive-alone pay tour
 E.g., transit tours can include auto shared-ride trips for particular
legs
Structure of ARC Models:
TBM vs. ABM
Trip Generation
Trip Distribution
Mode Choice
Trip-Based Model
Route
Choice
Activity-Based Model
Long-Term
Choices
Daily Activity
Patterns
Tour Mode
Choice/Stop
Trip Mode
Choice
Synthetic
Population
Demand
Supply
Route Choice
 Same routine as the trip-based model
 Multimodal User Equilibrium Time-of-Day Assignment
 Bi-Conjugate Frank-Wolfe for both TBM and ABM,
departing from the traditional Frank-Wolfe
Run Environment
 Java-Package
 Cube/TP+
 Three 64-bit
Windows machines
 Each machine with
32GB of RAM
 Base year run:
approx. 30 hours
 2040 run:
approx. 50 hours
Visualization
 Model generates huge database
 Model visualization system, ABMVIZ
 Primary starting point for most model
analysis questions
 Interactive/dynamic visualization
of model estimates/results
 Some unique visualization types
 Tables, Bar Charts/Maps
 Time Use
 Tour Tracing
 Tree Map
 Radar Charts
Quality Assurance/Quality Control
 Quality Assurance (QA): a systematic review process by personnel
not directly involved in model development
 Quality 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
QA/QC
 Overall processes
 Reasonableness checking for EVERY modeling step
 Temporal validation between base and forecast years
 Comparability between ABM and TBM
 Components
 Modeling flows/Scripts
 Socioeconomic data
 Transportation network data
 External trips
 Trip generation, Trip distribution, Mode choice, & Traffic
assignment
QA/QC (cont’d)
 Tools
 SQL Express Management Studio
 STATA
 ABMVIZ
 Custom scripts


Voyager/TP+
R
 Our brain
Thank You!