THE FUTURE OF ACTIVITY-BASEDMODELLING AND BEHAVIORAL DATACOLLECTIONOCTHARRY TIMMERMANS&SOORA RASOULI

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Eindhoven University of Technology
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Activity-based models
1. Scope

5. Behavioral data
Collection

2. History

4. Research agenda

3. Progressin Eindhoven

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Activity-based models
Scope

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4 step versus activity-based models
4 stepPurpose.
Destination.
Mode.
Route.

ABM
Activity type.
Destination.
Mode.
Route.

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Reasons for change
Reason 1: Integrity

FROM
4 stepmodels

Reason 2: Interdependencies

TOABMmodels

Reason 3: Behavioral basis
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Activity-based models

Models which predict how households organize theiractivities and implied travel, in time and space, subject toa set of constraints

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Activity-based models

Are much more sensitive to a wide set of alternative waysof responding to exogenous policies
Focus on primary and secondary effects

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Urban PlanningGroupLOGOUrban PlanningGroupLOGO

4 step vs. activity-based models: resolution
4 stepABM

Peak vs offpeak.

Minutes.

Traffic zones.

Parcel or zip
codes.

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4 step vs. activity-based models: representation
4 step

ABM

Trips.
Individuals.
Traffic zones.
No constraints.

Activity episodes
Household.
People.
Variousconstraints.

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Activity-based models
History

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History

1967
1978
2000

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CONSTRAINTS-BASED MODELS

TheorySpecification

OutputBehavior is primarily
influenced by various
kinds of constraints.

Set of constraints is
applied to observed
input activity-travel
schedules.

Check of feasibility ofscheduleSocial exclusion
Potential action space

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Constraints-based models

Your Title Here

2012

2010
Extensions

1994
Kwan GISICASPROGRESS1986-1995

Huigen BSP – Dijst MASTIC

1978Lentrop PESASPLOGODISCRETE CHOICE MODELS

TheorySpecification

OutputProbabilistic choicetheoryAlgebraic typicallylinear function

Predicted choices forset of travel choicefacetsRandom utility theory

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Discrete choice models

Non IIA models:
1978-1985

MNL:
1972

Nested logit:
1976

Hierarchicalchoice model:
2005
Mixed logit – LCM
1996 - 2005
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TheorySpecification

OutputContext-dependent
choice heuristics

If THEN ELSE
Boolean expressions

Simulated individual
space-time trajectories

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Computational process models

4 Stage Filter

2000 - …

Albatross(Arentze Timmermans)

2007

Tasha(Roorda Miller)

2010

Feathers – Belgium

2012

South Korea.
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Constraints-based models

Incorporation of constraints
Integral choice sets

No adaptation
No activity generation
No notion of value
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Discrete choice models

Simple representation
Easy to estimate
Easy interpretation

No constraints
Focus on outcomes
Strict behavioral
assumptions
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Computational process models

Preference and constraints
Flexible specification

Complex
Difficult to interpret

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Comparison

Easy to develop
Data requirements
Easy of application
Behavioral richness

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Activity-based modelsProgress in Eindhoven

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TIME WINDOWLongLife Trajectory Induced
Activity Agendas

Multi-Day Multi-PersonMid

Task Allocation

Daily Activity-Travel
Short

Rescheduling
PROCESS

Long term dynamics
Plans (t)Resources
Constraints

Desires
Aspirations
Nonactivat
ed plans
Planning.............

Commitments (t)Activated, prioritizedcommitments (t)Nonactivated,
postponedplans
UpdatedReplanning/
Cognitive
Environment

Consideration
Set
Choice SettCommitments(t+1)

Execution

Cognitive
EnvironmenttPlans (t+1)
Resources
Constraints

Consideration
Set
Choice Sett................
Learning

t+1

t+1t+1........
Rasouli & Timmermans

Short term dynamics
Activity repertoire (t)Resources
Constraints

Needs
Activity
Commitme
calendar (t)ntsAppointme
Activity
Schedulingnts
schedule (t)Postponed
Cognitive
activities Environment.............

t

Unexecuted,postponed
activities

Rescheduling/
Execution

Consideration
Set
Choice Sett

Activity repertoire (t+1)
Resources
Constraints

Updatedcalendar (t+1)

Cognitive
Environment
Consideration
Set
Choice Sett................
Learning

t+1

t+1

t+1

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DYNAMIC ACTIVITY-BASED MODEL SYSTEM

Execution
Activityparticipation
Time use

Learning

Needs

Education

History

Job-house

Traffic flows

SynchronisationSocial network

Scripts

TimeNetwork
Cognitive
environmentLifetrajectories

AgendaIndividual(Cognitive)
HouseholdEnvironment
Synchronisatio
Social networkn
Scheduling

Synthetic
population

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Activity-based models
Research agenda

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FROM SINGLE DAY TO MULTIPLE DAY TO VARYING TIME HORIZON

Week 1

Week 1Work
Grocery
shopping

Week 2

Week 2
SchoolvacationSportsevent

Week 3

Week 4

Week 5

Week 3
ClothsWeek 4Work

Week 5Work

Theatreshow

Grocery
shopping

Grocery
shoppingFamilyreunion

City trip

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FROM INDIVIDUAL TO HOUSEHOLD TO SOCIAL NETWORKIndividual

ParentsSiblings

HouseholdFriends

FamilyCo-workers

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Household decision making
Shift from individual level models to householdlevel models• Resource allocation• Task and time allocation• Joint activity participation• Joint travel arrangements

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FROM OUTPUT MODELLING TO PROCESS MODELLING

Your Title HereSCHEDULINGPROCESS

Needs, Preferences, Constraints
Activity-travel schedule
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FROM OUTPUT MODELLING TO PROCESS MODELLING

Your Title Here

Process is assumed more robust as outcomes as these dependon spatial structure and variance-covariances in the data.
Current interest in modelling errror terms may be
counterproductive

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FROM PHYSICAL ENVIRONMENTS TO COGNITIVE-AFFECTIVE
REPRESENTATIONS OF THE ENVIRONMENT

Uniqueness

Structure

Landmarks

Network

Location

Travel times

Location

Meaning

Destinations

Destinations

Attributes

Affections

ArousalPhysiology

Quality of life

Happiness

Excitement

EmotionLOGOFROM CONTEXT INVARIANCE TO CONTEXT DEPENDENCY
Political
situation

TRAVELPARTY

ACTIVITYTRAVELSCHEDULE

Partnerrecognition

TIME
PRESSURE

TIME

Economicconditions

Weather

LOGOFROM HOMOGENEITY TO BEHAVIORAL MIXINGYour Title HereLOGO

FROM HOMOGENEITY TO BEHAVIORAL MIXINGYour Title HereMNL: specification and parameters the samefor ALL agents
ML: specification the same for ALL agents, only
parameters differ
LCM: specification and parameters the samefor ALL agents within a class
Behavioral mixing: also different specification

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FROM CERTAINTY TO UNCERTAINTYYour Title Here

Expected utility theory
Bayesian models: e.g. Arentze & Timmermans
Prospect theory: e.g. Aveneri
Regret theory: Chorus, Arentze &Timmermans,
2008

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Activity-based modelsBehavioral datacollection

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SHIFTS IN DATA COLLECTION

GPS
enabledmulti-daysurveys

Smartphones andsocial media

DATA

Clever fusionof existingdata sets

Seriousgames

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Questions?
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Thank You

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