Behavioral Modeling for Design, Planning, and Policy Analysis

Joan WalkerBehavior Measurement and Change Seminar

October 2013 @ UC Berkeley

2

Outline• Motivation• Discrete Choice Modeling• Increasing Behavioral Realism

– Values and Attitudes• Continuous example 1: power and hedonism• Discrete example 2: modality styles

– Dynamics example 3: Transantiago

• Conclusion

3

London Congestion Pricing

• 2003 £5 ($8)• Impact?

- 34% VKT by private car+ 38% enter zone by bus+ 28% VKT by bike

• today £10

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Transantiago

• 2007• Complete overhaul of transit• New vehicles, new payment• Hierarchical trunk & feeder – Increased transfers– Longer access/egress

• Big bang implementation• Impact?– Large drop off in transit riders– Significantly lowered government’s approval ratings

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The Problem

• What are decisions that cities have to make?• Need to understand and predict how travelers react.• Develop practical, empirical, behavioral models

Explanatory Variables (Xn)

Traveler Choices (yn)

BehavioralModel

McFadden (2001)

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Outline• Motivation• Discrete Choice Modeling• Increasing Behavioral Realism

– Values and Attitudes• Continuous example 1: power and hedonism• Discrete example 2: modality styles

– Dynamics example 3: Transantiago

• Conclusion

Travelers are faced with a set of alternatives,which make up a choice set.

Travelers are able to assign preferences that rank these alternatives in terms of attractiveness

> >

Uauto Utransit Ubike

The utility function is a mathematical representation of these preferences

> >

Utility is a function of– Attributes of the alternative

• E.g., price, travel time, reliability, emissions, …– Parameters that represent tastes of the attributes

• Estimated from data– Characteristics of the decision-maker and context

• E.g., income, education, purpose, attitudes, beliefs, peers, …– Random error

Assumptions on (1) Decision protocol(2) Distribution of the random error

lead to the choice probabilities:Probabilityn(auto) = f (attributes, characteristics, tastes)

What will be impact of new infrastructure or transport policy?

How do you get me to change my travel habits?

MODELProbabilityn(auto) = f (attributes, characteristics, tastes)

> >

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Outline• Motivation• Discrete Choice Modeling• Increasing Behavioral Realism

– Values and Attitudes• Continuous example 1: power and hedonism• Discrete example 2: modality styles

– Dynamics example 3: Transantiago

• Conclusion

Increasing behavioral realism

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Explanatory Variables (Xn)

Traveler Choices (yn)

BehavioralModel

McFadden (2001)

14

Outline• Motivation• Discrete Choice Modeling• Increasing Behavioral Realism

– Values and Attitudes• Continuous example 1: power and hedonism• Discrete example 2: modality styles

– Dynamics example 3: Transantiago

• Conclusion

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Choice and Continuous Latent Variable Model

ExplanatoryVariables

Utilities

LatentVariables

Choice

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Choice and Continuous Latent Variable Model

( , | )n n nf y I X

Choice Kernel Latent Variable Measurement Model

Latent VariableStructural Model

ExplanatoryVariables

Utilities

LatentVariables Indicators

Latent VariableModel

Choice Model

(McFadden, 1986; Ben-Akiva et al., 2002)

Choice

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Value-attitude-behavior hierarchical model

• In moving from left to right, the constructs become more numerous and context-specific, and less stable

Homer and Kahle (1988)

18Paulssen et al. (2013)

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Examples of indicators

• Attitudes (based on Johansson et al., 2006)

– Flexibility: That a means of transport is available right away is… – Convenience and Comfort: That a means of transport is

exceedingly convenient and comfortable is… – Ownership: That you own the means of transport is…

• Values (based on Schwartz et al., 2001)

– Power: She wants to be the one who makes decisions – Hedonism: She seeks every chance she can to have fun – Security: It is very important to her that her country be safe

(Paulssen et al., 2013)

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Outline• Motivation• Discrete Choice Modeling• Increasing Behavioral Realism

– Values and Attitudes• Continuous example 1: power and hedonism• Discrete example 2: modality styles

– Dynamics example 3: Transantiago

• Conclusion

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Latent Modality Styles

Modality StylesDefined as: lifestyles built around particular travel modes

Latent modal preferences- Choice set- Taste heterogeneity

Vij (2013)

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Hybrid Choice ModelChoice Probability

( | )nP i X

1

* * *( |( | , ( | () )), , )S

ns

n ns P s X fP i X f X dX dX X

LatentClasses Latent Variables such

as Attitudes and Perceptions

Flexible Substitution Patterns &

Taste Heterogeneity

Basic Choice ModelKernel

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Latent Modality Styles

Mode choice for work trip 1

Utilities for work trip 1

Individual Characteristics

Modality StyleMode attributes for work trip 1

Errors

wt1

Vij (2013)

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Latent Modality Styles

Mode choice for non-work trip 1

Mode choice for work trip 1

Utilities for non-work trip 1

Utilities for work trip 1

Individual Characteristics

Modality StyleMode attributes for work trip 1

Mode attributes for non-work trip 1

Errors

nwt1

Errors

wt1

2…

2…

2… 2

…

2…

2…

2…

2…Vij (2013)

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1. Inveterate Drivers 2. Car Commuters 3. Moms in Cars

4. Transit Takers 5. Multimodals 6. Empty NestersVij (2013)

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Outline• Motivation• Discrete Choice Modeling• Increasing Behavioral Realism

– Values and Attitudes• Continuous example 1: power and hedonism• Discrete example 2: modality styles

– Dynamics example 3: Transantiago

• Conclusion

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Temporal Dependencies

• Choice may depend on past experience• Learning• Memory• Attitudes• Familiarity • Habit• Inertia• Addiction

(and future expectations)

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Simplifying Markov Assumption

• All influence of history and experience is summarized by state from previous 1 period.– Choice in period t is only influenced only by

state in period t-1

where jt = choice in time t

– Can relax by treating longer lags as if first order• The state– Can reflect choice, realized attributes, perceptions,

attitudes, choice environment, budget, … – Can be observed or latent

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Static ModelExplanatory

Variables Xt-1

Choice yt-1

Preferences Ut-1

Error et-1

ExplanatoryVariables Xt

Choice yt

Preferences Ut

Error et

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+ Agent EffectExplanatory

Variables Xt-1

Choice yt-1

Preferences Ut-1

Error et-1

ExplanatoryVariables Xt

Choice yt

Preferences Ut

Error et

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+ Manifest MarkovExplanatory

Variables Xt-1

Choice yt-1

Preferences Ut-1

Error et-1

ExplanatoryVariables Xt

Choice yt

Preferences Ut

Error et

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+ Hidden Markov (HMM)Explanatory

Variables Xt-1

Choice yt-1

Attitudes X*t-

1

Preferences Ut-1

Error et-1

ExplanatoryVariables Xt

Choice yt

Attitudes X*

t

Preferences Ut

Error et

Inertia

Expe

rienc

e

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Transantiago

• 2007• Complete overhaul of transit• New vehicles, new payment• Hierarchical trunk & feeder – Increased transfers– Longer access/egress

• Big bang implementation• Impact?– Large drop off in transit riders– Significantly lowered government’s approval ratings

34

Panel dataset

Wave Date Data Respondents

1 Dec 06 5-day pseudo diary + socioeconomic data 303

- Feb 07 Transantiago Introduced -

2 May 07 5-day pseudo diary + socioeconomic data + subjective perception

286

3 Dec 07 5-day pseudo diary + socioeconomic data + subjective perception + additional activities

279

4 Oct 08 5-day pseudo diary + socioeconomic data + additional activities + likert-scale indicators towards modal comfort, reliability and safety

258

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disturbances

travel time

choice for work trips

waiting time

number of transfers

utility for work trips

modality style

income gender number of cars owned

disturbances

travel costs

disturbances

travel time

choice for work trips

waiting time

number of transfers

utility for work trips

modality style

income gender number of cars owned

disturbances

travel costs

Characteristics of the Individual Characteristics of the Individual

Leve

l-of-

Serv

ice A

ttrib

utes

Leve

l-of-

Serv

ice

Attr

ibut

es

Time period t Time period t + 1Vij (2013)

Unimodal transit0.49 cars per household

Men more likelyLow income

Low value of travel time (0.4$/hr)

Unimodal auto1.46 cars per household

Women more likelyHigh income

Multimodal all0.61 cars per household

Men more likelyMedian income

High value of travel time (30$/hr)

Vij (2013)

Dec 06 Feb 07

TRA

NSA

NTI

AG

O

INTR

OD

UC

ED

May 07 Dec 07 Oct 08

0

20

40

60

80

100

120

NU

MB

ER

OF

PEO

PLE

TIMELINE OF EVENTS

Unimodal Auto Unimodal Transit Multimodal All

Shift in modality styles

Vij (2013)

38

Outline• Motivation• Discrete Choice Modeling• Increasing Behavioral Realism

– Values and Attitudes• Continuous example 1: power and hedonism• Discrete example 2: modality styles

– Dynamics example 3: Transantiago

• Conclusion