behavioral modeling for design, planning, and policy analysis
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Behavioral Modeling for Design, Planning, and Policy Analysis. Joan Walker Behavior Measurement and Change Seminar October 2013 @ UC Berkeley. Outline. Motivation Discrete Choice Modeling Increasing Behavioral Realism Values and Attitudes Continuous e xample 1: power and hedonism - PowerPoint PPT PresentationTRANSCRIPT
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Behavioral Modeling for Design, Planning, and Policy Analysis
Joan WalkerBehavior Measurement and Change Seminar
October 2013 @ UC Berkeley
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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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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
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Travelers are faced with a set of alternatives,which make up a choice set.
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Travelers are able to assign preferences that rank these alternatives in terms of attractiveness
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Uauto Utransit Ubike
The utility function is a mathematical representation of these preferences
> >
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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)
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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
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Increasing behavioral realism
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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
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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)
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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
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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)
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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)
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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)
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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