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Behavioral Modeling for Design, Planning, and Policy Analysis
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Behavioral Modeling for Design, Planning, and Policy Analysis

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  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

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

  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

  5. 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) Behavioral Model Traveler Choices (yn) 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

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

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

  9. > > Uauto Utransit Ubike The utility function is a mathematical representation of these preferences

  10. Utility is a function of • Attributesof the alternative • E.g., price, travel time, reliability, emissions, … • Parameters that represent tastesof 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 errorlead to the choice probabilities: Probabilityn(auto) = f (attributes, characteristics, tastes)

  11. MODELProbabilityn(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?

  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

  13. Increasing behavioral realism Explanatory Variables (Xn) Behavioral Model Traveler Choices (yn) 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

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

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

  17. 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)

  18. Paulssenet 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 (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

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

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

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

  24. Latent Modality Styles Individual Characteristics Modality Style Mode attributes for work trip 1 Mode attributes for non-work trip 1 2 2 … … Utilities for work trip 1 Utilities for non-work trip 1 2 2 Errors wt1 … … Errors nwt1 2 2 … … Mode choice for non-work trip 1 Mode choice for work trip 1 2 2 … … Vij (2013)

  25. 1. Inveterate Drivers 2. Car Commuters 3. Moms in Cars Vij (2013) 4. Transit Takers 5. Multimodals 6. Empty Nesters

  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

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

  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 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

  29. Static Model Explanatory Variables Xt Explanatory Variables Xt-1 Error et-1 Error et Preferences Ut Preferences Ut-1 Choice yt Choice yt-1

  30. + Agent Effect Explanatory Variables Xt Explanatory Variables Xt-1 Error et-1 Error et Preferences Ut Preferences Ut-1 Choice yt Choice yt-1

  31. + Manifest Markov Explanatory Variables Xt Explanatory Variables Xt-1 Error et-1 Error et Preferences Ut Preferences Ut-1 Choice yt Choice yt-1

  32. + Hidden Markov (HMM) Explanatory Variables Xt Explanatory Variables Xt-1 Inertia AttitudesX*t Attitudes X*t-1 Error et-1 Error et Experience Preferences Ut Preferences Ut-1 Choice yt Choice yt-1

  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

  34. Panel dataset

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

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

  37. Shift in modality styles 120 100 80 TRANSANTIAGO INTRODUCED 60 NUMBER OF PEOPLE 40 20 0 Dec 06 Feb 07 May 07 Dec 07 Oct 08 TIMELINE OF EVENTS Unimodal Transit Multimodal All Unimodal Auto 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