1. Behavioral Modeling for Design, Planning, and Policy Analysis Joan Walker Behavior 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 2

3. London Congestion Pricing 2003 £5 ($8) Impact? - 34%VKT by private car + 38%enter zone by bus + 28%VKT by bike today £10 3

4. 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 4

5. The Problem What are decisions that cities have to make? Need to understand and predict how travelers react. Develop practical, empirical, behavioral models 5 Explanatory Variables ( X n ) Traveler Choices ( y n ) Traveler Choices ( y n ) Behavioral Model Behavioral Model McFadden (2001)

6. 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 6

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

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

9. U auto U transit U bike The utility function is a mathematical representation of these preferences >>

10. 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: Probability n (auto) = f (attributes, characteristics, tastes)

11. What will be impact of new infrastructure or transport policy? How do you get me to change my travel habits? MODEL Probability n (auto) = f (attributes, characteristics, tastes) > >

12. 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 12

13. Increasing behavioral realism 13 Explanatory Variables ( X n ) Traveler Choices ( y n ) Traveler Choices ( y n ) Behavioral Model Behavioral Model 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 14

15. Choice and Continuous Latent Variable Model 15 Explanatory Variables Utilities Latent Variables Choice

16. Choice and Continuous Latent Variable Model 16 Choice Kernel Latent Variable Measurement Model Latent Variable Structural Model Explanatory Variables Utilities Latent Variables Indicators Latent Variable Model Choice Model (McFadden, 1986; Ben-Akiva et al., 2002) Choice

17. Value-attitude-behavior hierarchical model In moving from left to right, the constructs become more numerous and context-specific, and less stable 17 Homer and Kahle (1988)

18. 18 Paulssen et al. (2013)

19. 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 19 (Paulssen et al., 2013)

20. 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 20

21. Latent Modality Styles 21 Modality Styles Defined as: lifestyles built around particular travel modes Latent modal preferences -Choice set -Taste heterogeneity Vij (2013)

22. Hybrid Choice Model Choice Probability 22 Latent Classes Latent Variables such as Attitudes and Perceptions Flexible Substitution Patterns & Taste Heterogeneity Basic Choice Model Kernel

23. Latent Modality Styles 23 Mode choice for work trip 1 Utilities for work trip 1 Individual Characteristics Modality Style Mode attributes for work trip 1 Errors wt1 Vij (2013)

24. Latent Modality Styles 24 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 Style Mode 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)

25. 1. Inveterate Drivers 2. Car Commuters3. Moms in Cars 4. Transit Takers5. Multimodals 6. Empty Nesters 25 Vij (2013)

26. 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 26

27. Temporal Dependencies Choice may depend on past experience Learning Memory Attitudes Familiarity Habit Inertia Addiction (and future expectations) 27

28. 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 j t = 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 28

29. Static Model 29 Explanatory Variables X t-1 Explanatory Variables X t-1 Choice y t-1 Preferences U t-1 Error  t-1 Explanatory Variables X t Explanatory Variables X t Choice y t Preferences U t Error  t

30. + Agent Effect 30 Explanatory Variables X t-1 Explanatory Variables X t-1 Choice y t-1 Preferences U t-1 Error  t-1 Explanatory Variables X t Explanatory Variables X t Choice y t Preferences U t Error  t

31. + Manifest Markov 31 Explanatory Variables X t-1 Explanatory Variables X t-1 Choice y t-1 Preferences U t-1 Error  t-1 Explanatory Variables X t Explanatory Variables X t Choice y t Preferences U t Error  t

32. + Hidden Markov (HMM) 32 Explanatory Variables X t-1 Explanatory Variables X t-1 Choice y t-1 Attitudes X * t- 1 Preferences U t-1 Error  t-1 Explanatory Variables X t Explanatory Variables X t Choice y t Attitudes X * t Preferences U t Error  t Inertia Experience

33. 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 33

34. Panel dataset 34 WaveDateDataRespondents 1Dec 065-day pseudo diary + socioeconomic data303 -Feb 07Transantiago Introduced- 2May 075-day pseudo diary + socioeconomic data + subjective perception 286 3Dec 075-day pseudo diary + socioeconomic data + subjective perception + additional activities 279 4Oct 085-day pseudo diary + socioeconomic data + additional activities + likert-scale indicators towards modal comfort, reliability and safety 258

35. 35 disturbances travel time choice for work trips waiting time number of transfers utility for work trips modality style incomegender 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 incomegender number of cars owned disturbances travel costs Characteristics of the Individual Level-of-Service Attributes Time period t Time period t + 1 Vij (2013)

36. Unimodal transit 0.49 cars per household Men more likely Low income Low value of travel time (0.4$/hr) Unimodal auto 1.46 cars per household Women more likely High income Multimodal all 0.61 cars per household Men more likely Median income High value of travel time (30$/hr) Vij (2013)

37. Dec 06Feb 07 TRANSANTIAGO INTRODUCED May 07Dec 07Oct 08 0 20 40 60 80 100 120 NUMBER OF PEOPLE TIMELINE OF EVENTS Unimodal Auto Unimodal TransitMultimodal 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 38