New Generation Large Scale Activity
Microsimulation Models (SimAGENT)& Data Needs
Konstadinos (Kostas) G. GouliasUniversity of California Santa Barbara
goulias@geog.ucsb.edu+1-805-284-1597
http://www.geog.ucsb.edu/geotrans/publications.php
September 21st, 2013
Summer School

2

Policy Examples & Tools
 Policy Context -> DECREASE CO2 Emissions





Vehicle Technology
Fuel Technology
VMT Decrease Using Land Use & Pricing &……..
Equity analysis and allocation to small communities

 STATEWIDE & REGIONAL Land Use-Transportation Models
 PECAS development Travel Model Emissions

 SimAGENT for SCAG Dynamic Microsimulation
 Year to year Second by second

3

California Policies but will expand (we hope!)

4Southern California Association ofGovernments (SCAG)CSCAG Quick Facts

6§ US largest Metropolitan
Planning Organization (MPO)
San Bernardino
Ventura Los
AngelesOrangeRiverside§ 6 counties, 191 cities and38,000+ square miles.
§ 18 million people (5.8% of US
population; 48.5% of California
population)§ GRP in 2010: $910 Billion, 16thlargest economy in the world

Imperial§ 10,000 lane miles of freeway; 4major airports; Nation’s global
gateway for trade

SimAGENT(Simulator of Activities, Greenhouse
Emissions, Networks, and Travel)
Kostas Goulias& Yali Chen
+ others in GeoTrans laboratory
University of California
Santa Barbara

Chandra Bhat
& Rajesh Paleti+ others
The University of Texas
AustinGuoxiong	  Huang,	
  Hsi-­‐Hwa	
  Hu	
  
+	
  others	
  
SCAG	
  
Los	
  Angeles+	
  
	
  

7

Ram Pendyala &Karthik Konduri+ others
Arizona State University
Tempe

8

Typical Schema SimAGENT

9

Some Early Benefits of SimAGENT
 Synergy with SCAG and three Universities
 Motivated SCAG technical partners to develop their own new research applications
 Developed an inventory of the region’s characteristics and data availability = many
practical applications
 Updated and used older models including the 4-step model -> now 11K zones +synthetic population used Designed/Redesigned a large scale (50,000 household survey happening now) SCAG first of class pride = job satisfaction, more funding, leadership role in California
 Satisfied my professional curiosity and met challenge of building an ENORMOUS model Great education tool Students that worked on SimAGENT get very good job offers!

10

Team
Research Team:UCSB (prime): Dr. Kostas Goulias, Dr. Yali Chen, Dr.
Seo Youn Yoon, and…
UT Austin: Dr. Chandra Bhat, Dr. Rajesh Paleti, and…
ASU: Dr. Ram Pendyala, Dr. Karthik Konduri, and …
See papers for all the names

SCAG:
Program manager: Dr. Hsi-Hwa Hu
Model estimation and calibration:
Dr. Bayarmaa Aleksandr
Software and model operation: Hao Cheng
Model validation: Mana Sangkapichai andSung Su Yoon
Travel survey and data: Dr. Yongping Zhang

11

Model Conceptq The Activity-Based Travel Demand Modeling (ABM) is
an agent-based modeling in which individuals andtheir interaction with each other and theirenvironment are explicitly represented.
q Activity-based approaches view travel as a derived
demand to pursue activities.
Ø It considers link between activity participation
behavior and travel behavior,Ø It accommodates the interaction among different
activities pursued by an individual andØ It accommodates the interaction between thetemporal and spatial dimensions of activity
participation.

Model Purposeq SCAG ABM will be fully implemented for the 2016
Regional Transportation Plan/Sustainable
Community Strategies – major investment
strategies.
q The model will generate performance indicators,
conformity analysis, and environmental justiceanalysis.
q To analyze the impact of infrastructure investment,
land use development, pricing policy, active
transportation, high speed rail, and travel demand
management.

12

13

Stage 1Model Development

Motivation(of the funding agency)

RTP Guideline by California Transportation
Commission - the largest four MPOs in California
are encouraged to transition
to activity-based travel demand models.

14

15

Progress

April 2009
Oct. 2008
SCAG decided todevelop an activitybased model forthe region

SCAG launched
ABM development
project

Jan. 2013
End of Stage 1
development

Stage 1 Model
Development

A
B
C• Task 1: Modeling Framework Setup- Based on CEMDAP (UT Austin)• Task 2: Initial Estimation- Three Core Modules- Based on 2001 Travel Survey• Task 3: Initial Calibration- Base Year 2003

16

17

OVERVIEW OF
SimAGENT for SCAGSimAGENTq Creates synthetic household/person and their socioeconomic
characteristics.

q Simulates daily activities and travel patterns for each person(18 million +) of SCAG regionq Outcome = every person with a day timer attached to them –just like travel surveyq Model outputs are converted to OD matrix and input toassignment for SCAGq Model outputs also used as inputs for TRANSIMS and MATSIM
experiment

18

SimAGENTSCAG version Flowchart

19

Growth Forecasting
Networks &Attributes

Socioeconomic
Data in Zones

Attributes in
Zones

Accessibility(aggregate)

Population Synthesis
External Trips
Long Term Choices
Airports &Ports

Daily Allocation
Daily Scheduling

All Other(commercial)

PersonDaily
Tours &
trips

Passenger & Highway and Transit
Origin – Destination Trip Interchange Matrices
Traffic & Transit Assignment

Post
Processor
19	
  

20

Salient Features of SimAGENT SCAG
q

q

q

q

Can be applied to any metropolitan area
Comprehensively characterizes the activity-travel patterns of allhousehold members
Incorporates spatial-temporal dependencies and constraints inactivity-travel patterns between and within individuals of ahousehold.
Incorporates advanced vehicle type choice model, which
determines the mix of vehicles in a fleet and naturally impacts
mobile-source emissions estimation, energy consumption, andgreenhouse gas emissions.

21

Salient Features of SCAG ABMq It facilitates environmental justice (EJ) analyses by having theability to examine the effects of policies on any defined segment.
q Enables a holistic assessment of the effects of land-use, built
environment, and transportation policies on entire activity-travel
patterns.

q Accessibility that is sensitive to time of day, availability ofopportunities and variation of transportation level of service, offers
increased behavioral realism and behavioral sensitivity to the
combined impact of land use and level of service improvements.

22

Salient Features of SCAG ABM
Temporal ResolutionqContinuous time scaleqLevel-of-service data can be provided at any temporal resolution

Spatial Resolutionq

Allows for any number of zones

23

Salient Features of SCAG ABM
Softwareq

Involves a portable and flexible object-oriented softwarearchitecture design, ensuring data and data processing integrity,
parallel processing and multi-threading capability, extensibility andmodifiability in structure, and usability.

qStandard Window-based user interfaceq

Allows user to modify model parametersq

Provides a friendly diagrammatic interface to help the user
understand the logic of the system and the underlying models

24

SCAG ABM framework: SimAGENTq SimAGENT (Simulator of Activities, Greenhouse
Emissions, Networks, and Travel) is the base frameworkof SCAG ABM.

q SimAGENT is a model system that includes
Ø Synthetic population generator,
Ø Disaggregated socioeconomic module and work
location and vehicle ownership/type modules,Ø Daily activity and travel scheduling module

25

SimAGENT Model System
INPUTS

ModelsOUTPUTS

26

SimAGENT Sequence and Modules
Social-Economic Data

PopGenCEMSELTS
CEMDAP

PopGen generates eight basic
attributes that are not enough toreflect a rich variance in socioeconomic characteristics for theregion’s 18 million population.
CEMSELTS generates additional
person and householdsocioeconomic attributes that fed to
CEMDAP to simulate daily activitytravel patterns.
CEMDAP is the core module thatsimulates activity schedule andtravel characteristics for eachindividual of the region.

27

Summaryq PopGen and CEMSELTS modules create the entire population of theregion, provide locations for workplaces, and schools for each
person, estimates car ownership and type as well as main driverfor each vehicle, and provides other key personal and household
characteristics.

q CEMDAP module creates a complete description of the activities at
locations and movements among locations of each individual overspace and time that is congruent with the movements of the restof the household members.

q In this way, for each person, we have information about the typeof activity, when, where, how long, how to travel, with whom, inwhat sequence, and interrelationships with other persons andlocations in the engagement pattern.

28

Summary cont.
q CEMDAP output is converted to OD
matrix as input for assignmentq Using the same assignment modulefrom SCAG Trip-based Model inTransCAD.

q Feedback skim and accessibility tillconverged.

29

Current Status
Three Core
Modules andSoftware
67 Sub-Models• PopGen, CEMSELTS+Household
evolution, CEMDAP• TRANSCAD 4 periods, TRANSIMS• Estimated Based on 2001 Travel
Survey• Research Papers and Conference Presentations

Research• Further Work and AnalysesNeeded to Enhance as a
Regional Activity-based Travel Demand Model

30

CORE MODULES

PopGenCEMSELTS

CEMDAP

Population Synthesizer

PopGen

CEMSELTS

CEMDAP
31

What is PopGen?
q A population synthesizer uses Iterative Proportional
Updating (IPU) method which can simultaneously
control household and person attributes.
q Generates complete synthetic population for baseyear by expanding the disaggregate sample data to
mirror known aggregate distributions of household
and person variables of interest.

PopGen

CEMSELTS

Why Synthetic Population
Data?

CEMDAP
32

q Need to disaggregate household and person socio-economic data forthe entire population of SCAG.
q Such data for the entire population is generally not available.
q This leads to the need to synthesize a regional population from known
statistical distributions on the population.
q What we have:v Disaggregate data for a sample of the population (PUMS, travel
survey)v Marginal distribution for the entire region (census summary files,
agency forecasts)

PopGen

CEMSELTS

CEMDAP
33

Features of PopGenq Controls for both household and person attributesq Automatically corrects for zero-marginal and zero-cell
problemsq Extendableq Scalableq Computationally tractable method implemented in userfriendly Windows and Linux operating systemsq Pr o v i d e s g o o d n e s s - o f - f i t m e a s u r e s t o a s s e s s
performance of population synthesis process

PopGen

CEMSELTS

CEMDAP
34

Procedure
Choose control variables

Obtain the marginal distribution of these variables from census
summary file (SF) or agency forecast

Create a seed matrix of the joint distribution from a microdata sample
data set (PUMS, travel survey)

Expand the seed matrix using an IPF procedure to match the given
marginal control totals while maintaining the joint distribution impliedby the seed matrix

PopGen

CEMSELTS

CEMDAP
35

SCAG Population Synthesizer
INPUTS

PopGenOUTPUTSHousehold andPerson Sample
Data

Household
Attributes at
TAZ Level

Household andperson MarginalDistributions

PersonAttributes at
TAZ Level

Geographic
correspondence
file

Summary
Statistics
Map

PopGen

CEMSELTS

CEMDAP
36

PopGen Inputs
Inputs

Sources:
Census 2000 - 5
percent PUMS; ACS
2005-2007 - 3 percent
PUMS

Household Sample

Household and Person
Sample Data

Group-quarter
SamplePerson Sample

Household and person
Marginal Distributions

Household MarginalGroup-quarter
Marginal
Person Marginal
Sources:
SCAG TAZ DataPopGen

CEMSELTS

CEMDAP
37

PopGen Outputs
Outputs
Children PresenceHousehold Size

Household TypeAge ofHouseholder

Household
Attributes

Family Type

Age
GenderRace/ Ethnicity

Person Attributes

PopGen

CEMSELTS

CEMDAP

Summary Statisticsq Marginal Totals (for 10,579 TAZs)Ø Household marginal total: 5,812,319
Ø Groupquarter marginal total: 336,451
Ø Person marginal total: 17,891,482q Sample SizeØ ACS 2005-2009 PUMS for State of CaliforniaØ Household sample size: 628,061 (10.81 percentof HH marginal total)Ø Groupquarter sample size: 34,358 (10.21
percent of GQ marginal total)Ø Person sample size: 1,727,790 (9.66 percent of
Person marginal total)PopGen

CEMSELTS

CEMDAP
39

Household Attributes

PopGen

CEMSELTS

CEMDAP
40

Person Attributes

PopGen

CEMSELTS

CEMDAP
41

Example of Outputs

Port of LA

PopGen

CEMSELTS

CEMDAP
42

Results – Example of Household
Comparison of Household Size Distribution

Percent

Scenario 1: Marginal

30%

Scenario 1:
Synthesized25%

Scenario 2 & 3:
Marginal

20%

Scenario 2:
Synthesized
Scenario 3:
Synthesized

15%

Scenario 4: Marginal

10%

Scenario 4:
Synthesized

5%0%

1 person

2 persons 3 persons 4 persons 5 persons 6 persons 7 or more
persons

Household Size

PopGen

CEMSELTS

CEMDAP
43

Results – Example ofPopulation
Comparison of Age Distribution
16%
14%
12%

Marginal
Scenario 1
Scenario 2
Scenario 3
Scenario 4

Percent

10%
8%
6%
4%
2%
0%
Under 5years

5 to 14years

15 to 24years

25 to 34years

35 to 44years

45 to 54
years

Age

55 to 64years

65 to 74years

75 to 84years

85 andmore

PopGen

CEMSELTS

CEMDAP
44

Summaryq Synthetic population data for SCAG ABM was created using PopGen(Population Generator) software developed at Arizona State University.

q For the 2003 simulation year:Ø Marginal distributions on control variables were furnished by the SCAG
at the level of the traffic analysis zone (TAZ) for a total of 4109 zones.
Ø Applied recently to 11,000 zone systemq Windows ServerØ 16 cores, 72GB of RAM
Ø Effective synthesis model run time = 23 hours

Richer set of inputs is neededq Synthesizing huge populations (18 million)
reduces variances in population
characteristics that would be desirable in thecontext of the ABM model implementation.
q Many key socio-economic attributes that may
explain people and household choices are
missing.

Social-Economic Data

PopGenCEMSELTS
CEMDAP

Next Step
45

CORE MODULES

PopGenCEMSELTS

CEMDAPCEMSELTS
Comprehensive Econometric Microsimulator of Socio-economics,
Land-use, and Transportation System

46

PopGen

CEMSELTS

CEMDAP47

CEMSELTSq Create additional variables
for each individual:o Education Attainmento Job Statuso Household Incomeo Housing Typeq Create Long-Term Choice
Variableso Vehicle Ownership & Typeo Job/School Location ChoicePopGen

CEMSELTS

CEMDAP48

CEMSELTS Sequence
PopGen(Base Year Outputs)

Individual Level
ModelsHousehold Level
Models

Inputs for CEMDAPPopGen

CEMSELTS

CEMSELTS Models
Individual Level
ModelsHousehold Income

Education
SchoolStatusHousehold Level
ModelsNo

Labor
Participation
Yes
Employment
Industry
Employment
Location

Residential Tenure
Housing Type
Annual Mileage
Vehicle Fleet

Work
DurationWork
Flexibility

Vehicle Make ChoicePrimary Driver Allocation

CEMDAPPopGen

CEMSELTS

CEMDAP
50

Vehicle type choice modelPopGen

CEMSELTS

CEMDAP51

CEMSELTS Individual level model output
Comparison with ACS 2003 and Census 2000
Values in Percent

Values in Percent

Individual Socio-demographics
Enrollment of Children (3 to 17 years)
Preschool - Grade 3
Grade 4 - Grade 8
Grade 9 - Grade 11
Educational Attainment (Adults)
Less than Grade 9
Grade 9 - Grade 12 (no diploma)
Completed High School
Associate or Bachelors
Graduate Degree (Masters or
Ph.D)
Labor Participation
Employed
Unemployed
Employment Industry
Construction and Manufacturing
Trade and Transportation
Personal, Professional and
Financial
Public and Military
Retail TradeOther

ACS 2003

CEMSELTS
Predicted

Difference
inPercentage

Census2000

CEMSELTS Predicted

Difference
inPercentage

37.07
41.64
21.29

44.59
42.16
13.25

7.52
0.52
-8.04

41.17
38.76
20.07

44.59
42.16
13.25

3.42
3.40
-6.82

11.58
12.05
45.70
22.55

2.23
8.28
58.48
22.95

-9.35
-3.78
12.78
0.41

13.14
14.71
44.00
20.77

2.23
8.28
58.48
22.95

-10.91
-6.44
14.48
2.18

8.12

8.06

-0.06

7.37

8.06

0.69

59.47
40.53

59.07
40.93

-0.40
0.40

56.81
43.19

59.07
40.93

2.26
-2.26

19.92
4.94

14.46
7.32

-5.46
2.38

20.67
4.86

14.46
7.32

-6.21
2.46

50.63

49.42

-1.21

49.34

49.42

0.08

3.94
15.29
5.28

5.07
10.77
12.96

1.13
-4.51
7.68

4.04
15.60
5.49

5.07
10.77
12.96

1.03
-4.83
7.47

PopGen

CEMSELTS

CEMDAP52

CEMSELTS Household level model output
Comparison with ACS 2003 and Census 2000
Values in Percent

Household Socio-demographics
Number of Vehicles
Households with no vehicles
Households with 1 vehicle
Households with 2 vehicles
Households with 3 vehicles
Households with 4 or more vehicles
Number of Workers
Households with no workers
Households with 1 worker
Households with 2 or more worker
Household Income$0- $9999$10,000-$34,999$35,000-$49,999$50,000-$74,999$75,000 and moreHousehold Tenure
Owner
RenterHousehold Type for Owners
Single Unit (Attached/Detached)
Other
Household Type for Renters
Single Unit (Attached/Detached)
Apartment

Values in Percent
CEMSELTS
Predicted

Difference
inPercentage

ACS 2003

CEMSELTS
Predicted

Difference inPercentage

Census 2000

8.29
33.34
37.48
14.10

7.27
31.32
34.71
15.17

-1.02
-2.02
-2.77
1.07

10.07
34.85
37.16
12.59

7.27
31.32
34.72
15.17

-2.79
-3.55
-2.44
2.59

6.79

11.52

4.74

5.33

11.52

6.19

12.21
34.23
53.57

16.84
36.80
46.36

4.63
2.58
-7.21

11.31
32.98
55.71

16.84
36.80
46.36

5.53
3.82
-9.35

8.08
28.85
15.05
18.53
29.49

8.09
40.45
14.47
13.58
23.4

0.01
11.6
-0.58
-4.95
-6.09

8.98
29.56
15.24
18.89
27.32

8.09
40.45
14.48
13.58
23.40

-0.89
10.89
-0.76
-5.31
-3.93

55.74
44.26

61.05
38.95

5.30
-5.30

54.78
45.22

61.03
38.97

6.25
-6.25

88.15
11.85

93.42
6.58

5.27
-5.27

54.78
45.22

61.05
38.95

6.27
-6.27

27.87
72.13

50.49
49.51

22.62
-22.62

88.32
11.68

93.42
6.58

5.10
-5.10

PopGen

CEMSELTS

CEMDAP53

CEMSELTSWork Flow Distribution by Destination
Within Origin County

Outside Origin County

Total

OrigincountyLos
Angeles

ACS2003(%)

CEMSELTS
2003 (%)

Difference

ACS2003(%)

CEMSELTS
2003 (%)

Difference

ACS2003(%)

CEMSELTS
2003 (%)

Difference

52.79

52.63

-0.16

3.86

5.29

1.43

56.65

57.92

1.26

Orange

15.61

14.28

-1.32

3.11

3.45

0.35

18.71

17.74

-0.98

Riverside

6.57

7.65

1.09

3.19

1.85

-1.35

9.76

9.50

-0.26

San
Bernardino

6.88

7.58

0.70

3.18

2.60

-0.58

10.06

10.18

0.12

Ventura

3.73

3.67

-0.06

1.09

1.00

-0.09

4.82

4.67

-0.15

Total

85.57

85.81

0.24

14.43

14.19

-0.24

100

100

0.00

PopGen

CEMSELTS

CEMDAP54

CEMSELTS
Vehicle Type Choice Model Results

PopGen

CEMSELTS

CEMDAP
55

Summaryq CEMSELTS is - software/module that contains a series of choicemodels estimates for long-term choices & other attributes (14 submodels).

q Vehicle type choice determines vehicle fleet mix; critical to energy
and emission analysis.

q The resulting richer set of output is then fed to CEMDAP, the core
activity-based modeling engine within SimAGENT to simulate
complete daily activity-travel patterns for the population of theregion.

PopGen

CEMSELTS

CEMDAP
56

Household Evolution Modelq A model that progresses resident population year after
year using smooth transitions instead of abrupt
adjustments based on externally provided
demographic data.

q It enables to link demographic transition to behavioral
change and demonstrate the market penetration of
new technologies and adoption of new behavioral
patterns in a realistic and verifiable way.

q The project is almost complete (June, 2013).

CORE MODULES

PopGenCEMSELTS

CEMDAPCEMDAP
Comprehensive Econometric Microsimulator of Daily ActivityTravel Patterns

57

PopGen

CEMSELTS

CEMDAP58

CEMDAP Module

Comprehensive Econometric Microsimulator of Daily Activity-Travel Patterns

Social-Economic Data

PopGenCEMSELTS
CEMDAPq Simulates activity schedule and travel
characteristics for each individual of the regionqCore module of SimAgentq 52 sub-models.
q Developed by UT Austin with new functions
and acessibility.

PopGen

CEMSELTS

CEMDAP
59

Features of CEMDAPq A policy responsive toolq Continues time scale (1440 minutes in a day)q Allows any number of zonesq Level of service data can be provided at any temporal resolution ( 5time of-day periods for SCAG ABM)
q Explicitly considers time-space constraintsq Changes in the activity-travel pattern of one individual in ahousehold may bring about changes in activity-travel patterns ofother household membersq MDCEV approach facilitates modeling activity participation at ahousehold level with joint activity participation incorporated in a

PopGen

CEMSELTS

CEMDAP
60

Features of CEMDAP
Recognizing Fixities

Non-Workers

Workers§ No obvious activity with spatial
and temporal fixities§ Person more flexible inscheduling his/her activities§ The “work” activity has spatial
and temporal fixities§ Person schedules his/her
activities around the work
activityPopGen

CEMSELTS

CEMDAP61

CEMDAP System
Outputs of
CEMSELTSINPUTSHousehold table
Person table
Zone table
Zone to zone

Traffic and transit
assignment

CEMDAPOUTPUTS
AdultsChildrenWorkers
Students
Non-workers

Vehicle make

Tours

LOS

Stops
VehiclesNoGo

PopGen

CEMSELTS

CEMDAP
62

Tools for CEMDAPq CEMDAP includes 52 sub-modelsq The econometric structure for each sub-model falls under
one of the eight econometric model categories:
1.
2.
3.
4.
5.
6.
7.

Multiple Discrete Continuous Extreme Value (MDCEV),
Fractional split
Binary logit
Multinomial logit
Hazard-duration
Regression,
Ordered probit and8. Spatial location choice.

PopGen

CEMSELTS

CEMDAP
63

Person type
Population

Workers

Non-workers-Who goes to work
-Persons aged 16 or older-Who does not go to work
-Persons aged 16 or older-Who goes to school-Persons aged 15 oryounger-Who does not go to school-Persons aged 15 oryoungerPopGen

CEMSELTS

CEMDAP
64

Activity Types/Travel Purposesq Homeq MandatoryØ WorkØ Schoolq MaintenanceØ Drop-off at school
Ø
Ø
Ø
Ø
Ø

Pick-up from school
Other serve-passenger
Shopping
Work-related
Household/personal businessq Discretionaryq Joint discretionaryq Children discretionaryq Social recreationq Eating out

PopGen

CEMSELTS

CEMDAP
65

Representing Activity-Travel Patterns
Temporalfixity3 a.m. on
day dHome-Stay
Duration

BeforeWork
Tour

Home-Stay
Duration

HomeWork
CommuteWorkers

Work-Stay
Duration...

Leave homefor non-work
activities

S

S

1

2 Arrive backLeave for
work

home

Arriveat work

Leave work

TemporalfixityWorkBased
Tour

Work-Stay
Duration

WorkHome
Commute Home-Stay

After
Work
Tour

DurationHome-Stay
Duration

...

S

S

S

S

3

4

5

6

Arriveback
home

Arrive backhome

PopGen

CEMSELTS

CEMDAP
66

Representing Activity-Travel Patterns
Non-Workers
3 a.m.
on daydMorning HomeStay Duration

1st Tour

S S

Departure for
1
First Stop
(S1)

2

Home-Stay
Duration
before 2nd TourFirst ReturnHome
EpisodeDeparture for
Third Stop (S3)Home-Stay
Duration
before MthTour(M-1)thReturn-Home
Episode

3 a.m.
on dayd+1
Last HomeStay
Duration

Mth TourDeparture for(K-1)th Stop (SK-1)

SK-1 SK

Mth ReturnHome EpisodeCEMDAP Modeling
Framework

PopGenCEMSELTS

CEMDAP
67

Work and school activity participation and timing
decisionsChildren’s travel needs and allocation of escort
responsibilities to parents
Activity participation decisions (Independent and Joint)

Two major steps:
1.
2.

Generation
Allocation
Scheduling

Work-to-home and home-to-work commute characteristics
Drop-off tour of the nonworker escorting children toschoolPick-up tour of the nonworker escorting children fromschoolSchool-to-home and home-to-school commutes
Joint tour scheduling of activities determined by MDCEV
Independent home-based and work-based tours for eachworkerIndependent home-based tours for each non-worker
Independent discretionary activity tour for each child

PopGen

CEMSELTS

CEMDAPGA

Scheduling

68

Activity Generation & Allocationq Determine each person’s
decision on daily
activities:
 Workers: Commute
 Children: Go to School Non-workers: Non-work Activities
 Parents: Pick up/Drop off All Household Members: Joint
ActivityPopGen

CEMSELTS

CEMDAPGA

Scheduling

GA module: Generation of work and school
activity participationq Work and school activities
are the greatest space-time
constraints for most individualsq Participation in these
activities significantly
influences an individual’s
participation in all otheractivities during the day

PopGen

CEMSELTS

CEMDAPGA

Scheduling70

GA Module: Children’s Travel Needs & Allocation of
Escort Responsibilities

PopGen

CEMSELTS

CEMDAPGA

Scheduling

71

GA Module: Generation of Independent Activities
for Personal and Household Needs

PopGen

CEMSELTS

CEMDAPGA

Scheduling

72

Activity & Travel Schedulingq Produce:Ø sequence of activities,Ø with the departure and arrival
times,
Ø activity duration(s),Ø mode for each trip, andØ determination of the location ofeach activity.

PopGen

CEMSELTSGACEMDAP

Scheduling73

Example of scheduling process

For each worker
Predict characteristics of thework-to-home commute
Mode

Predict characteristics of thehome-to-work commute
Mode

Undertake nonwork activities
Number of
Stops

Undertake nonwork activities
Number of
Stops

PopGen

CEMSELTSGACEMDAP

Scheduling

74

Example of scheduling process
Known “available time” during
each of after-work, work-based
and before work periods

Predict characteristics of theafter-work (AW) tour (if any)
Mode &
Number of
Stops

Predict the decision toundertake tours ( for nonwork purposes) :Before work (BW),Work-based (WB), andAfter-work (AW)Modeling Non-work Activity
Scheduling of Workers:
Characterizing Tours

Home-stay
duration
before tour

Predict characteristics of thework-based (WB) tour (if any)
Mode &
Number of
Stops

Work-stay
duration
before tour

Predict characteristics of the
before-work (AW) tour (if any)
Mode &
Number of
Stops

Home-stay
duration
before tour

Proceedwithcharacterizing
the nonwork
activitystops

PopGen

CEMSELTSGACEMDAP

Example of scheduling process

Scheduling

75

Modeling Activity Scheduling of Non Workers
Charactering stops

Known duration and number of stops inthis tour
Predict characteristics of the first stop in tour
Activity type

Activity duration and travel time
to stop

Location

Known “available time” for all subsequent stops andtravel in this tour
Predict characteristics of the second stop in tour
Activity type

Activity duration and travel time
to stop

Location::
Known “available time” for last stop and travelin this tour
Predict characteristics of the last stop in tour
Activity type

Activity duration and travel time
to stop

Location

PopGen

CEMSELTSGACEMDAP

Scheduling76

Worker Scheduling Model System
Model Name

Econometric Structure

Choice Alternative

WSCH1

Commute mode

MNL

Solo driver, Driver with passenger,

WSCH2

Number of before-work tours

Probit0 or 1

WSCH3

Number of work-based tours

Ordered probit

0, 1 or 2

WSCH4

Number of after-work tours

Ordered probit

0, 1 or 2

WSCH5

Before-work tour mode

MNL

Solo driver, Driver with passenger,

WSCH6

Work-based tour mode

MNL

Solo driver, Driver with passenger,

WSCH7

After-work tour mode

MNL

Solo driver, Driver with passenger,

WSCH8

Number of stops in a tour

Ordered probit

1,2,3,4, or 5

WSCH9Home or work stay duration before the tour

Regression

Continuous time

WSCH10

Activity type at a stop

MNL

10 Activity purposes

WSCH11

Activity duration at stop

Regression

Continuous time

WSCH12

Travel time to a stop

Regression

Continuous time

WSCH13

Location of a stop

Spatial Location
ChoiceChoice alternatives based on estimatedtravel time

Model IDPopGen

CEMSELTSGACEMDAP

Scheduling

77

Non-Worker Scheduling Model System
Econometric
Structure
Ordered probit

Model ID

Model Name

NWSCH1

Number of independent tours
Decision to undertake an independent tour
Binary logit
before the pick-up or joint discretionary tour
Decision to undertake an independent tour afterBinary logit
the pick-up or joint discretionary tour

NWSCH2
NWSCH3
NWSCH4

Tour mode

NWSCH5
NWSCH7

Number of stops in a tour
Ordered probit
Number of stops following a pick-up/drop-off
Ordered probit
stop in a tour
Home stay duration before a tour
Regression

NWSCH8

Activity type at stop

NWSCH6

MNL

Choice Alternatives
1, 2, 3, or 4
Yes, No
Yes, No
Solo driver, Driver withpassenger, Passenger, andWalk/bike
1, 2, 3 4, or 5
0 or 1
Continuous time

MNL

10 Activity purposes

NWSCH9 Activity duration at stop
NWSCH10 Travel time to stop

Regression
Regression

Continuous timeContinuous time

NWSCH11 Stop location

Spatial Location
ChoiceChoice alternatives
based on estimated travel time

PopGen

CEMSELTSGACEMDAP

Scheduling

78

Children Scheduling Model System
CSCH1
CSCH2

School to home commute time
Home to school commute time

Econometric
Structure
Regression
Regression

CSCH3

Mode for independent discretionary tour

Binary logit

Model ID Model Name

CSCH5
CSCH6

Departure time from home for independent
Regression
discretionary tour (time from 3 a.m.)
Activity duration at independent discretionary stop Regression
Travel time to independent discretionary stop
Regression

CSCH7

Location of independent discretionary stop

CSCH4

Spatial Location
ChoiceChoice Alternatives
Continuous timeContinuous time
Drive by other,
Walk/bikeContinuous timeContinuous timeContinuous time

Predetermined subsetof the 4,109 zones

PopGen

CEMSELTS

CEMDAP
79

Joint Discretionary Tour Scheduling
Model System
Model ID

Model Name

Econometric
Structure

Choice Alternative

JASHCH01

Decision of Joint or Separate Travel

Binary Probit

Yes or No

JASHCH02

Joint Activity Start time

Regression

Continuous

JASHCH03

Joint Activity travel time to stop

Regression

Continuous

JASHCH04

Joint Activity location

Spatial Location
ChoicePredetermined subset of the 4,109
zones

JASHCH05

Vehicle Used For Joint Home-Based Tour

MDCEV

Vehicle types based on body type andvintageq Joint activities of workers scheduled in work-to-home commute or After-work
period§ Determined by the Joint Activity Start Timeq For non-workers participating in joint activities§ Decision to undertake independent tour before pick-up or joint tour§ Decision to undertake independent tour after pick-up or joint tour

PopGen

CEMSELTS

CEMDAP80

CEMDAP Simulation Outputq CEMDAP produces complete activity-travel patterns for a day for every
individual in the population of interestq There are nine output files:§ Adults: decisions to undertake activities of different types for adults§Children: decisions to undertake activities of different types for children§Workers: pattern-level attributes of the workers’ (including adult students)§Students: pattern-level attributes of the child students§

NoGo: list of people who stayed at-home the whole day§Non-workers: pattern-level attributes of non-workers§Tours: tour-level attributes§Stops: stop-level attributes§Activities: activity episode attributes

PopGen

CEMSELTS

CEMDAP81

CEMDAP

Initial validation results

PopGen

CEMSELTS

CEMDAP
82

Average Number of Trips
per Household
Type of Trips

SimAGENT

SurveySimAGENT(85% WorkScenario)

Home Based Work

1.27

1.33

1.68

Home Based Non-work

5.13

4.90

4.94

Non-home based

2.31

2.59

2.69

Total

8.71

8.82

9.30

PopGen

CEMSELTS

CEMDAP
83

Distribution of Number of Tours (Workers)
Before Work
Number of
Tours

0
1
2

Work Based

After Work

SurveySimAGENT

SurveySimAGENT

SurveySimAGENT

94.26

96.69

81.03

76.67

79.48

81.36

5.74

3.31

16.59

18.01

17.86

17.17

2.38

5.32

2.66

1.47

--

--

PopGen

CEMSELTS

CEMDAP

Distribution of Number of Tours(Non-Workers)
Number of Tours

SurveySimAGENT

1

58.81

55.51

2

27.53

24.79

3

9.49

12.55

4

4.17

7.15

84

PopGen

CEMSELTS

CEMDAP
85

Average Number of Stops
by Tour Type
Average number of stops

SurveySimAGENTWork Based tours

1.37

1.36

Before work tours

1.41

1.34

After work tours

1.40

1.36

Work-to-home commute

0.40

0.35

Home-to-work commute

0.26

0.18

Non-worker tour

1.78

1.66

PopGen

CEMSELTS

CEMDAP
86

Chaining Propensity
SurveySimAGENT

Worker
Chaining Propensity

0.85

0.86

Non Worker
Chaining Propensity

0.71

0.76

PopGen

CEMSELTS

CEMDAP
87

Tour Mode Shares
Work-to-homeWork based

Before work

After work

Non-Worker

ABM

SurveyABM

SurveyABM

SurveyABM

SurveyABM

Survey

77.7

78.2

64.2

69.3

56.5

44.0

55.0

56.2

51.9

39.8

8.9

9.8

15.9

13.8

26.2

39.1

35.3

31.7

28.8

36.7

8.1

6.6

6.0

6.3

4.0

2.5

3.9

5.1

12.2

14.1

2.7

2.9

13.7

10.1

12.7

13.9

4.9

6.3

5.7

7.5

2.6

2.5

0.2

0.5

0.6

0.5

0.9

0.7

1.4

1.9

Drive alone
Drive aspassenger

Shared ride
Walk orbike
TransitSequence of SimAGENT Models

88Year 1
Initialization

Year 2 & later

PopGenSpatial Distribution
People andActivities

GrowthForecast
CEMSELTS

CEMDAP

DTA/TRANSIMS&CEMSELTS

POPGEN: For each spatial unit recreates resident
Householdpopulation person by person and household by
Evolution
household using externally provided data
CEMDAP

Uses data at the person and household level jointly
withLandseedUsetables- of relationships among control
DTA/TRANSIMSPECAS variables of our choice&

Static Assignment

Static Assignment

MicroEmissions&

MicroEmissions
&

EMFAC

EMFAC

Sequence of SimAGENT Models

89Year 1
Initialization

Year 2 & later

PopGenSpatial Distribution
People andActivities

GrowthForecast
CEMSELTS

CEMDAP

DTA/TRANSIMS&
Static Assignment

CEMSELTS: For each person andCEMSELTS
householdgenerated in PopGen, additional attributes are
createdhere using econometric models, lookup
Household
Evolution
tables, and consistency rules
CEMDAP

Attributes added to each household and person:
education, employment attributes (employed or
Landduration,
Use not, work
work flexibility,
work location,
DTA/TRANSIMSPECAS&
industry), driver’s license holding, student
statusand school location, numberStaticof Assignment
cars, etc.

MicroEmissions&

MicroEmissions
&

EMFAC

EMFAC

Sequence of SimAGENT Models

90Year 1
Initialization

Year 2 & later

PopGenSpatial Distribution
People andActivities

GrowthForecast
CEMSELTS

CEMDAP

DTA/TRANSIMS&CEMSELTS

CEMDAP: Creates a complete day for each person
Householdwith activities, locations, tours, trips, mode used
Evolutionetc.
CEMDAP

It also ensures consistent schedules within ahouseholdcars to each person/tour/
Land and
Use allocates
DTA/TRANSIMS
trip following a set of models
PECAS&

Static Assignment

Static Assignment

MicroEmissions&

MicroEmissions
&

EMFAC

EMFAC

Sequence of SimAGENT Models

91

Year 1
Initialization

Year 2 & later

PopGenSpatial Distribution
People andActivities

GrowthForecast
CEMSELTS

CEMSELTS

CEMDAPSTATICASSIGNMENT: Builds OD matrices that are
Household
Evolutionused inthe four-step SCAG model to assign traffic
on the network in the four time
periods
CEMDAP

DTA/TRANSIMS&DTA/TRANSIMS: Early stages and eventually will
Land every
Use - car during the simulated day
track
DTA/TRANSIMSPECAS&Static Assignment

Static Assignment

MicroEmissions&

MicroEmissions
&

EMFAC

EMFAC

Sequence of SimAGENT Models

92

Year 1
Initialization

Year 2 & later

PopGenSpatial Distribution
People andActivities

GrowthForecast
CEMSELTS

CEMDAP

DTA/TRANSIMS&CEMSELTSEMFAC: Uses vehicle group activity data by highway
Householdclass and vehicle types to estimate pollutants
Evolution
including GHG emissions
CEMDAP

MicroEmissions: Profiles of emissions are used toestimateemissionsfor each car simulated – notLand Useavailable
research)
PECAS yet (MOVES & CADTA/TRANSIMS&

Static Assignment

Static Assignment

MicroEmissions&

MicroEmissions
&

EMFAC

EMFAC

Policy Sensitivity Illustration
Generation
Allocation Model
SystemIncrease inretail andservice landuse

Change in work
location

Household’s	
  Decision	
  
to	
  do	
  Grocery	
  Shopping	  1) Commute mode
2) Number of commute
stops
Decision toparticipate in otherindependent
activities

Change in decision to
go to work and thework timing
Allocation of Grocery
shopping among
adultsChange	  in	
  Child’s	
  school	  travel	
  mode	  Change in allocationof parent to dropoff/ pick-up

Child’s	
  decision	  to	
  par:cipate	
  in	
  independent	
  and	
  joint	  discre:onary	
  ac:vity	  3) Presence and number
of after-work, workbased, and before-worktours and stops4) All aspects of eachstop in each tour
(including timing,
location, and activity
duration)

Scheduling Model
System94	
  Vehicle Type Choice Simulation
Component•	
   Vehicle	
  type	  choice	
  determines	
  vehicle	
  ﬂeet	  mix;	
  cri:cal	
  to	
  energy	
  and	
  emissions	
  analysis	
  
	
  
•	
   SimAGENT	  incorporates	
  joint	
  vehicle	
  type	  choice	
  and	
  primary	  driver	
  alloca:on	
  model	  which	  determines:	  	
   • Mul:ple	
  vehicle	
  holdings	
  • Body	  type	  (Sub-­‐compact,	
  Compact	
  car,	
  Mid-­‐sized	
  car,	
  Large	
  car,	
  Small	
  SUV,	
  Mid-­‐sized	
  SUV,	
  Large	  SUV,	
  Van,	
  and	  Pickup)	
  • Age	  (Less	
  than	
  2	
  years	
  old,	
  2	
  to	
  3	
  years	
  old,	
  4	
  to	
  5	
  years	
  old,	
  6	
  to	
  9	
  
years	
  old,	
  10	
  to	
  12	
  years	
  old,	
  Older	
  than	  12	
  years)	
  • Use	  (miles)	
  	
  • Primary	  driver	  of	
  each	  vehicle,	
  simultaneously	
  	
  

95	
  

Multimodal Capabilities	  • SimAGENT	  incorporates	
  full	  mul:modal	
  capabili:es	
  
	
  

• 	
   Explicit	
  considera:on	
  of	
  non-­‐motorized	
  transporta:on	
  mode	  use	
  for	
  both	  u:litarian	
  and	  recrea:onal	
  purposes	  	
  • Focus	
  on	
  both	  the	  person	
  and	  the	
  vehicle	
  

95	
  

96	
  

Other	  Salient	
  Features	
  of	
  SimAGENT	  Temporal	  Resolu:on	
  
•
•
•
•

Con:nuous	
  :me	
  scale	  Level-­‐of-­‐service	
  data	  can	  be	
  provided	
  at	
  any	
  temporal	
  resolu:on	  Explicitly	
  considers	
  :me-­‐space	
  interac:ons/constraints	  Enables	  considera:on	
  of	
  :me-­‐varying	
  and	  dynamic	
  pricing	
  policies	
  Spa:al	  Resolu:on	
  •Allows	  for	
  any	
  spa:al	
  resolu:on,	
  and	
  mul:-­‐scale	
  spa:al	
  resolu:on.	
  

	
  

Ac:vity	
  Resolu:on	
  •Allows	  for	
  any	
  purpose	
  resolu:on,	
  and	
  mul:ple	  resolu:on	
  

	
  

Graphical	
  User	  Interface	
  	
  
•
•
•

Standard	  Windows-­‐based	
  user	  interface	  Allows	  user	  to	
  modify	
  model	  parameters	  	
  Provides	  a	
  friendly	
  diagramma:c	
  interface	
  to	
  help	  the	
  user	  understand	
  the	
  logic	
  of	
  the	
  system	  and	
  the	
  underlying	
  models	
  

96	
  

97

Sample SimAGENT Output: Daily activity-travel
undertaken by a household
School

12:33 pm

12:27 pm
10:48 am

8:01 am

Work

drive

12:35 pmbus

7:59 pm

1:48 pm

drive
10:29 am

7:28 am

8:16 pm

drive

Work

10:13 amHome
8:35 pm

1:42 pm

drivebus
1:08 pm

Restaurantdrive

6:32 pm

10:32 am

5:44 pm

Drive/shared
6:46 pm

drive
5:23 pm

Drive/shared
8:23 pm

Family @Restaurant

GEOLOCATED ACTIVITIES

98

99

Time-Space Prims

Seo Youn Yoon artwork!

100

Dynamic Opportunity-based
Accessibility is Key Input

Santa Monica

AM Peak Max
Finance

MiddayMax Finance

PM Peak Max
Finance

Night TimeMax Finance101Santa Monica Retail102Transit Accessibility

60 meters / min walking speed

6 min penalty for switching transit lines

Max time set at 3 hours



Sparse distance matrix in output
 Do not report if > 3 hours

Access Points to Public Transportation

103

Routes of Public Transportation

Walk Time Isochrones Example

Transit Travel Time Isochrones Example

Manufacturing: maximum accessible employees within 10 mins

AM Peak

MiddayPM PeakNighttime

Retail: maximum accessible employees within 10 mins

AM Peak

MiddayPM PeakNighttimeFinance: maximum accessible employees within 10 mins

AM Peak

MiddayPM PeakNighttime

109

110

11 pm to 12
Midnight

111

7:00 to 8:00 am

11:00 am 12:00 Noon

Presence of Persons at
Places by Type of Activity

11 pm to 12
Midnight

7:00 to 8:00 am

11:00 am 12:00 Noon

Presence of Persons at
Places by Type of Activity

112

Comparison with 2008 4-step model
Emissions and Fuel Consumption
Organic Gases (g/mile)
CO (g/mile)NOx (g/mile)
CO2 (g/mile)
Gasoline (gallons/mile)
Gasoline (mile/ gallons)
Diesel (gallons/mile)
Diesel (mile /gallons)
Organic Gases (g/person-day)
CO (g/person-day)NOx (g/person-day)
CO2 (g/person-day)
Vehicle Miles Travel/person-day

4-StepModel*

SimAGENT
Baseline*

0.943
9.498
1.929
561.340
0.051
19.3770.102
9.833
22.291224.553
45.60613271.790
23.643

0.926
9.348
1.955
543.5450.050
20.203
0.101
9.893
21.333215.388
45.05012524.452
23.042113

Screenline Counts Comparison

114

3000000
Observed Count
Trip Based Model

2500000

Phase I
SimAGENT (Dec 2010)
SimAGENT (March 2011)

2000000

1500000

1000000

500000

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Sample of Scenarios Tested
Difference between GC Baseline and Drive Alone Cost Increase 100%

Difference between GC Baseline and Auto Cost Increase 100%

Difference between GC Baseline and DA IVTT increase 25%

Difference between GC Baseline and Auto IVTT Increase 25%

115

116

Example output from TRANSIMS

117

118

Router & Router Feedback
Can use activity
records and/or trips
that are converted intoplans of a day
OR
RouterSynthetic surveys!
PlanSelectLinkDelay

Plan Merge/
Sort

PlanSum

TRANSIMS Router & Feedback Main Processing
FlowVolume on Network

1:00 AM

Volume on Network

2:00 AM

3:00 AM

4:00 AM

5:00 AM

6:00 AM

7:00 AM

8:00 AM

9:00 AM

10:00 AM

11:00 AM

12:00 PM

1:00 PM

2:00 PM

3:00 PM

4:00 PM

5:00 PM

6:00 PM

7:00 PM

8:00 PM

9:00 PM

10:00 PM

11:00 PM

12:00 AM

144Second by second emissions
and fuel consumption

145

146

X-axis is second by second of a vehicle

147

Grams pervehicle in
one second

148

Activities to Business
Locations

149

150

Activity	
  Record
Household	
  ID
Person	
  ID
Activity	
  TypeStart	  TimeEnd	
  TimeDuration
OriginDestination…

Start	  /	  End	  TimeOrigin	  /	
  Destination	
  ZoneActivity	
  TypeNAICS-­‐Activity	
  TypeCross	  Matrices

New	
  Business	
  Locations
IDX	
  CoordY	
  CoordSIC	
  CodeNAICS	
  CodeEmployee	
  by	
  TimeLocation	
  ID	
  in	
  TRANSIMS
Zone	
  ID
…

NAICS	
  CodeEmployee	
  by	
  TimeLocation	
  list	
  by	
  
Zone

Available	  Locations

Assign	  Activity	  to	
  one	
  location

151

152

153

VMT of households inparcels of land
VMT=vehicle miles travelled

Easily by time of day and person ofhousehold

154

155

156

157

158

159

Illustration of the Modifiable Area Unit Problem

160

In Closing
 This is still not enough!
 Business Establishments – spatial distribution ofopportunities, time of day availability, commodity flows
and trucks/commercial vehicles
 Firmographics to parallel Demographics

 Deliveries to households (UPS/FEDEX, DHL, Postal,
Gardeners, Maintenance) See recent supply chain simulation models

Some Publications

161

1.
Goulias K.G., C. R.Bhat, R.M. Pendyala, Y. Chen, R. Paleti, K. Konduri, G. Huang, and H. Hu (2012) Simulator of Activities,
Greenhouse Emissions, Networks, and Travel (SimAGENT) in Southern California. Paper 12-0845 presented at the January 2012 91st
Annual Meeting of the Transportation Research Board, Washington, D.C., January 22-26, 2012.
2.
Lei T., Y. Chen, and K. G. Goulias (2012) Opportunity-Based Dynamic Transit Accessibility in Southern California: Measurement,
Findings, and Comparison with Automobile Accessibility. Paper 12-3813 presented at the January 2012 91st Annual Meeting of theTransportation Research Board, Washington, D.C., January 22-26, 2012. (in press)
3.
Bhat C. R., K.G. Goulias, R.M. Pendyala, R. Paleti, R. Sidhartan, L. Schmitt, and H. Hu (2012) A Household-Level Activity Pattern
Generation Model: Simulator of Activities, Greenhouse Emissions, Networks, and Travel (SimAGENT) System in Southern California.
Paper 12-4226 presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., January 22-26, 2012.
4.
Vyas G., R. Paleti, C.R. Bhat, K.G. Goulias, R. M. Pendyala, H. Hu, T. J. Adler, A. Bahreinian (2012) A Joint Vehicle Holdings (Typeand Vintage) and Primary Driver Assignment Model with an Application for California. Paper 12-3701 presented at the 91st Annual
Meeting of the Transportation Research Board, Washington, D.C., January 22-26, 2012. (in press)
5.
Seraj S., R. Sifhartan, C.R. Bhat, R.M. Pendyala, and K.G. Goulias (2012) Parental Attitudes Toward Children Walking andBicycling to School: Multivariate Ordered Response Analysis. Paper 12-3675 presented at the 91st Annual Meeting of the Transportation
Research Board, Washington, D.C., January 22-26, 2012. (in press)
6.
Pendyala R.M., C.R. Bhat, K.G. Goulias, R. Paleti, K.C. Konduri, E. Sidhartan, H. Hu, G. Huang, and K. P. Christian (2012) The
Application of Socioeconomic Model System for Activity-Based Modeling: Experience from Southern California Paper 12-2186 presented
at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., January 22-26, 2012.
7.
Goulias K.G., C. R. Bhat, R. M. Pendyala, Y. Chen, R. Paleti, K. C. Konduri, G. Huang, and H. Hu (2011) Simulator of Activities,
Greenhouse Emissions, Networks, and Travel (SimAGENT) in Southern California: Design, Implementation, Preliminary Findings, andIntegration Plans. Paper in proceedings of the Institute of Electrical and Electronics Engineers (IEEE) Forum on Integrated andSustainable Transportation System, Vienna, Austria, June 29 to July 1, 2011, pp. 164-169.
8.
Goulias K.G., R.M. Pendyala, and C.R. Bhat (2011) Total Design Data Needs for the New Generation Large Scale Activity
Microsimulation Models. Keynote paper presented at the plenary of the 9th International Conference on Travel Survey Methods, Chile,
Nov. 14-18, 20119.
Chen, Y., S. Ravulaparthy, K. Deutsch, P. Dalal, S.Y. Yoon, T. Lei, K.G. Goulias, R.M. Pendyala, C.R. Bhat, and H-H. Hu (2011)
Development of Indicators of Opportunity-based Accessibility. Transportation Research Record: Journal of the Transportation Research
Board, No. 2255, Transportation Research Board of the National Academies, Washington D.C., 2011, pp.58-68.
10.
Paleti, R., N. Eluru, C.R. Bhat, R.M. Pendyala, T.J. Adler, and K.G. Goulias (2011) The Design of a Comprehensive Microsimulatorof Household Vehicle Fleet Composition, Utilization, and Evolution. Transportation Research Record: Journal of the Transportation
Research Board, No. 2254, Transportation Research Board of the National Academies, Washington D.C., 2011, pp.44-57.
11.
Sidharthan, R., C.R. Bhat, R.M. Pendyala, and K.G. Goulias (2011) Model of Children's School Travel Mode Choice Behavior
Accounting for Effects of Spatial and Social Interaction. Transportation Research Record: Journal of the Transportation Research Board,
No. 2213, Transportation Research Board of the National Academies, Washington D.C., 2011, pp.78-86.

162

Reports (http://www.scag.ca.gov/modeling/)

Goulias, K.G., C. R. Bhat, R. M. Pendyala, Y. Chen, R. Paleti, K. Konduri, S. Y. Yoon, and
D. Tang (2012). Simulator of Activities, Greenhouse Emissions, Networks, and Travel(SimAGENT) in Southern California. SimAGENT Overview. Phase 2 Final Report 1
Submitted to SCAG, March 31, 2012, Santa Barbara, CA.



Goulias, K.G., C. R. Bhat, R.M. Pendyala, Y. Chen, T. Lei, S. Ravulaparthy, K. Deutsch, P.
Dalal, and S. Y. Yoon (2012). Opportunity-Based Dynamic Accessibility Indicators in
SimAGENT Phase 2 Final Report 2 Submitted to SCAG, March 31, 2012, Santa Barbara,CA.



Pendyala, R.M. , C. R. Bhat, K. G. Goulias, R. Paleti, K. Konduri, R. Sidharthan , and K.
P. Christian. (2012) SimAGENT Population Synthesis. Phase 2 Final Report 3 Submitted to
SCAG, March 31, 2012, Santa Barbara, CA.



Bhat, C. R., R. Paleti, R. M. Pendyala, and K. G. Goulias.(2012) SimAGENT ActivityBased Travel Demand Analysis: Framework, Behavioral Models, and Application Results.
SimAGENT Core Models. Phase 2 Final Report 4 Submitted to SCAG, March 31, 2012,
Santa Barbara, CA.



Goulias, K. G., N. A. Isbell, D. Tang, M. Balmer, Y. Chen, C. R. Bhat, and R. M. Pendyala(2012) TRANSIMS and MATSIM Experiments in SimAGENT. Phase 2 Final Report 5
Submitted to SCAG, March 31, 2012, Santa Barbara, CA.

Thank you!
Domo Arigatou Gozaimasu
どうも	
 有難う	
 御座います163

164

Data Needs
 Core Behavior and Household Characteristics
 Other aspects – policy dependent (cars and costs, long term
choices and lifestyle, attitudes) Other agents (firms, institutions, plans, and so forth) Landscape/Environment/Context
 Activity locations
 Homes/Jobs/Schools
 Availability over time

165

What to expect

One Week Activity and Travel
Diary Account for day-to-day variation in activity schedulingand travel and attempt to Identify shifting of tasks and activities from one day of
the week to the next.
 Design to capture the behavioral processes of schedulingactivities, and planning and subsequent re-schedulingmodifications (see the Toronto tradition).


166

167

Toll Willingness to Pay
 Attitudes and willingness to pay for tolls on highways
 Develop behavioral equations of the willingness to pay Large scale regional simulation models to develop
pricing strategies(Bhat and Castellar, 2002; Bhat and Sardesai, 2006).

168

GPS and GPS OBD
 Develop a database to correlate destinations to routes and identify atypology of different types of routes and stop making patterns;
 Develop a route choice model;
 Estimate the level and nature of misreported trips by different modesof the main two-day activity diary; Verify day-to-day behavioral change in other survey components andday of the week effects; and Provide detailed operating characteristics of the household vehicles.
 NOTE: This component for persons carrying GPS devices (wearableGPS) can also be supplemented with an online diary and vehiclemounted GPS (week long to capture day to day variation) and On-Board Diagnostics devices (to identify driving patterns andcorrelate/link them with emissions models).

169

Mode Supplement Reasons for not using specific modes, including non-motorizedmodes for active living studies.
 The survey objective is to identify situational constraints, attitudes,
and predispositions in favor or against modes such as walk, bike andpublic transportation.
 Create models to study policy actions that go beyond the time-costcomfort analysis.
 Add a stated choice, intentions, and preference component to thismodule.
 Emphasis on collecting data about walking and biking either as amain mode for each trip or as an access mode to another mainmode (e.g., walking from a parking lot to an office, biking to a bus
stop and then taking the bus).

Residence, Workplace, and School
Location Choice: Critical survey component for behaviorally integrated land use
travel demand models!
 In-depth survey to identify the determinants for each of theresidential, workplace, and school choices (see Kortum et al.,2012).
 Both primary locations and secondary locations should be
examined in more detail than typical household surveys and datacollected to estimate choice models for each facet.
 Examine behavior retrospectively and prospectively.
 Possibly add questions about personal biography of eachhousehold member using techniques that are not used by typicalhousehold surveys (e.g., ethnography).

170

In Depth Car Ownership Change and
Car Assignment
 Identify the determinants for each of the car ownership,
car type (e.g., new/used, model, make, and fuel type),
and car assignment decisions.
 In the car assignment data collection, both the primaryand secondary drivers should be identified.
 Identify determinants of changes in car ownership,
type, and assignment of cars to household members.
 Particular emphasis should be given to policy controlled
determinants (e.g., taxation, incentives). One
approach to study this latter part is using combinationsof revealed and stated preference surveys.

171

172

Activity Satisfaction Survey
 Provide a benchmark for the diary instrument; and Create an assessment of activities (including trips) andsubjective experiences that is able to capture
preferences, satisfaction, and perceived quality of life.
 This second set of objectives will enable estimation of
choice models with latent variables and classes that are
by far richer and more informative than theircounterpart observed variable discrete choice models(see the “happiness literature”).

173

Destinations & Perceptions
 We know that places have symbolic and other meanings
that travel behavior models neglect.
 This component identifies how destinations areperceived and what role these perceptions play in theirselection.
 Major aspects = mental maps and sense of place

Panel of Households and Persons
and Multi-Day Activity
 Undecided: would like a Mobidrive (6 weeks) Would also like year to year evolutionary measurement
 Most likely a rotating panel of longer than one week
duration

174

175

Energy Use and Expenditures
 Link housing to transportation demand.
 Develop more complete household Greenhouse footprints
 Develop models of comprehensive accounting of energy
demand.
 Annual, monthly, or even weekly expenditures for activity
participation, travel, and vehicles and housing units
maintenance ownership and energy consumption are notcollected in typical travel surveys.
 This component will provide the data needed to enable a
direct association between travel and at home energy
consumption to eventually create models of the type inFissore et al. (2011).

Carbon, Nitrogen, Phosphorous byHouseholds/Housing Unit

176

177

Long Distance Travel Travel models in Mega regions and statewide
applications also need models that are able to capture
what is called interregional travel and long-distancetravel.
 Many of the trips in this class are business related,leisure related, or simply long commutes.
 Maybe also study trade-offs people make when they
engage in travel that, for example, requires anovernight stay outside the home base.

178	
  

CEMSELTS EVOLUTION

179	
  

Overview of Evolution Framework

180	
  

Individual Level Evolution Models

181	
  

Household Formation Models

182	
  

Vehicle Evolution Framework

CEMSELTS Evolution Module: Individual Models
Sl. Num.

Model Description

Model Type

Current Data Source

Comment

Individual Models: Demographics & Mobility
California Department of
Public Health
California Department of
Public Health1

Mortality Model

Binary Logit

2

Birth Model

Binary Logit

3

Base Year License Model

Binary Logit

FHWA

4

Obtain License Model

Binary Logit

Highway Statistics 2010
FHWA (California)

5

Maintain License Model

Binary Logit

Rule Based

Individual Models: Schooling Models
6

School location modelMultinomial Logit

SCAG Survey 2003

7

College location modelMultinomial Logit

SCAG Survey 2003

Binary Logit

SCAG Survey 2003

Multinomial Logit

SCAG Survey 2003

Base Year Dropout Rate
Model
Base Year Education
9
Attainment Model
Individual Models: Employment Models
8

10

Labor Participation Model

Binary Logit

SCAG Survey 2003

11

Start Employment Model

Binary Logit

SCAG Survey 2003

12

Employment Industry
Model

Multinomial Logit

SCAG Survey 2003

13

Work Flexibility Model

Ordered Probit

SCAG Survey 2003

14

Work Duration Model

Multinomial Logit

SCAG Survey 2003

15

Continue Employment
Model

Multinomial Logit

Bureau of Labor Statistics
2012

16

Household Income Model

Ordered Probit

SCAG Survey 2003

17

Work location modelMultinomial Logit

SCAG Survey 2003

Currently a very simple
constants only model isused. It is preferable tohave a morecomprehensive model
based on survey data.

CEMSELTS Evolution Module: Household
Formation ModelsSl.	
  Num.	
  Model	  Descrip>on	  Model	
  Type	  Current	  Data	  Source	
  Comment	
  

18	
  

Marriage	  Model	  Binary	  Logit	  Na:onal	  Survey	
  of	
  Family	
  Growth	  -­‐	
  CDC	  (2006-­‐2010)	
  

	
  	
  

19	
  

Divorce	  Model	  Binary	  Logit	  Na:onal	  Survey	
  of	
  Family	
  Growth	  -­‐	
  CDC	  (2006-­‐2010)	
  

	
  	
  

20	
  

Child	
  Custody	  Model	
  

	
  	
  

No	
  Data	  Need	
  data	  to	
  es:mate	
  model	
  

21	
  

Resource	
  alloca:on	
  Model	
  

	
  	
  

No	
  Data	  Need	
  data	  to	
  es:mate	
  model	  Binary	  Logit	  No	
  Data	  Need	
  data	  to	
  es:mate	
  model	  -­‐	
  ignoring	
  the	  move-­‐in	
  process	
  can	
  poten:ally	
  lead	  to	
  over-­‐es:ma:on	
  of	
  single	
  person	
  households.	
  Mul:nomial	  Logit	  	
  Na:onal	  Survey	
  of	
  Family	
  Growth	  -­‐	
  CDC	  (2006-­‐2010)	
  

	
  	
  

Mul:nomial	  Logit	
  	
  

No	
  Data	
  

Same	
  as	
  Move-­‐in	
  model	  Pew	
  Research	
  Center	
  2011	  Currently	
  a	
  very	  simple	  constants	
  only	  model	  is	
  used.	
  
It	
  is	
  preferable	
  to	
  have	
  a	
  
more	
  comprehensive	  model	  based	  on	
  survey	
  data.	
  Binary	  Logit	  No	
  Data	
  

Same	
  as	
  Move-­‐in	
  model	  Mul:nomial	  Logit	  	
  Na:onal	  Survey	
  of	
  Family	
  Growth	  -­‐	
  CDC	  (2006-­‐2010)	
  

	
  	
  

Mul:nomial	  Logit	  	
  Na:onal	  Survey	
  of	
  Family	
  Growth	  -­‐	
  CDC	  (2006-­‐2010)	
  

	
  	
  

22	
  

Move	
  In	
  Model	
  

23	
  

Husbands	
  Age	  Model	
  

24	
  

Move	
  In	
  Age	  Model	
  

25	
  

Move	
  out	  Model	  Mul:nomial	  Logit	
  	
  

26	
  

Move	
  In	
  Gender	  Model	
  

27	
  

Husbands	
  Race	  Model	
  

28	
  

Husbands	
  Educ	  Adainment	  Model	
  

CEMSELTS Evolution Module: Householdlevel long term choice models29	
  Residen:al	
  Mobility	  Model	  Binary	  Logit	  Rate	
  based	  model	  (using	
  data	
  from	  London,	  UK)	  We	
  need	  data	  speciﬁc	
  to	
  LA	
  
(or	
  California)	
  

30	
  

Residen:al	
  Tenure	  Model	  Housing	
  Type	  for	
  Owners	  Model	
  Housing	
  Type	  for	
  Renters	  Model	
  Vehicle	
  Type	  Choice	
  and	
  Vehicle	
  Transac:on	  Models	  Binary	  Logit	  SCAG	  Survey	
  2003	
  

	
  	
  

Mul:nomial	  Logit	
  	
  

SCAG	
  Survey	
  2003	
  

	
  	
  

Mul:nomial	  Logit	
  	
  

SCAG	
  Survey	
  2003	
  

	
  	
  

MDCEV,	
  Binary	
  logit	
  models	  CEC	
  Data	
  

	
  	
  

34	
  

Residen:al	
  loca:on	
  model	  Mul:nomial	  Logit	
  	
  

SCAG	
  Survey	
  2003	
  

	
  	
  

35	
  

Emigra:on	
  Model	  Binary	  Logit	
  US	
  Census	
  Bureau	
  2009	
  36	
  Immigra:on	
  Model	  for	
  non-­‐
single	  households	  Binary	  Logit	
  US	
  Census	
  Bureau	
  2009	  Immigra:on	
  Model	  for	  single	  households	  Binary	  Logit	
  US	
  Census	
  Bureau	
  2009	
  

31	
  
32	
  
33	
  

37	
  

We	
  currently	
  have	  a	
  simple	  rate	
  based	  model	  to	  predict	  net	
  immigra:on	
  rate	  (Immigra:on	
  rate-­‐Emigra:on	
  rate).	
  It	
  is	
  
preferable	
  to	
  have	  a	
  more	  comprehensive	  model	  based	  on	
  household	
  demographics	
  for	
  both	  emigra:on	
  and	
  immigra:on.