Dynamic Discrete Choice Models: an application to vehicle holding decisions

1. Dynamic Discrete Choice Models:an application to vehicle holding decisions Cinzia Cirillo University of Maryland A. James Clark School of Engineering Department of Civil and Environmental Engineering Universita’ Roma tre July 2nd, 2012

3. Problem • What effect will the following factors have on the vehicle marketplace over the next five years: • New vehicle technology • Improvements in existing vehicle technology • Greater availability of different energy sources • Rising fuel prices • Transportation and energy policy

4. Objectives • Collect data on future household vehicle preferences in Maryland in relation to vehicle technology, fuel type, and public policy • Determine if respondent could make dynamic vehicle purchase decisions in a hypothetical short- to medium-term period • Determine if results from this hypothetical survey could be modeled using discrete choice methods

5. Definitions • BEV – battery electric vehicle, a vehicle which stores electricity in batteries as its only source of energy • HEV – hybrid electric vehicle, a vehicle which runs on gasoline but uses larger batteries to aid in the propulsion of the vehicle • PHEV – plug-in hybrid electric vehicle, a vehicle which stores electricity from the power grid in batteries and includes a gasoline engine • AFV – alternative fuel vehicle, a vehicle with an internal combustion engine that runs on a liquid fuel that is not gasoline or diesel (e.g. ethanol) • FFV – flex-fuel vehicle, a vehicle which can run on both gasoline and an alternative fuel • MPGe – miles per gallon gasoline equivalent, a measure of the average distance traveled per unit of energy in one US gallon of gasoline

6. Literature Review • Bunch et al. (1993) • Conducted Stated Preference (SP) survey in California • Vehicle Choice with two versions • New Gasoline, Alternative Fuel, Flex-fuel or Electric • Fuel Choice • Given a flex-fuel vehicle: choose a fuel • MNL and nested logit models • Kurani, Turrentine, Sperling (1996) • SP with reflexive designs • New Gasoline, CNG, HEV, 2 different highway-capable BEVs, and Neighborhood BEV • Hybrid Household Hypothesis (Multi-car households more likely to own BEVs) • Only analyzes with possible hybrid households

7. Literature Review • De Vlieger et al. (2005) • SP survey with MNL and nested logit models • Choice set: Gasoline, Diesel, AFV, BEV, Hydrogen Fuel Cell Vehicle • Musti and Kockelman (2011) • Choice set: 12 vehicle alternatives of varying size and technology (Conventional, HEV, PHEV) • SP survey with MNL Model • Included a simulation over a 25-year period

8. Literature Review • Brownstone and Train (1999) • Used mixed logit and probit models to estimate preference among gasoline, electric, methanol, and CNG vehicles • Able to create substitution patterns that more closely resemble real-life expectations • Bolduc et al. (2008) • Integrated Choice and Latent Variable Model (Hybrid Choice Model)

9. Survey Methodology

10. Survey Sections • Household Characteristics • Current Vehicles • Stated Preference Experiments • Vehicle Technology • Fuel Type • Taxation Policy

11. Experiment Directions • Make realistic decisions. Act as if you were actually buying a vehicle in a real life purchasing situation. • Take into account the situations presented during the scenarios. If you would not normally consider buying a vehicle, then do not. But if the situation presented would make you reconsider in real life, then take them into account. • Assume that you maintain your current living situation with moderate increases in income from year to year. • Each scenario is independent from one another. Do not take into account the decisions you made in former scenarios. For example, if you purchase a vehicle in 2011, then in the next scenario forget about the new vehicle and just assume you have your current real life vehicle.

12. Vehicle Technology Experiment

13. Fuel Type Experiment

14. Taxation Policy Experiment

15. Contributions • Dynamic Attributes • Attribute change from year to year • (e.g. EV price falls then raises, MPG increases annually) • Time of Purchase • Given two scenarios per year from 2010 -2015 • Choice Set • Includes “Keeping Current Vehicle” • If purchase new vehicle, can keep or sell current vehicle • Does not exclude models • Respondents • Includes respondents who don’t plan to purchase a vehicle in next five years

16. Results – Descriptive Statistics • Gender: 52% male • Age: 41 years (median), 43 years (mean) • Education: 76% with Bachelor degree or higher • Income: $50k – $75k (median), 22% with incomes above $150k • Vehicle Ownership: 1.9 (average), 2.0 (median) • Primary Vehicle Age: 6.4 years (average), 6.0 years (median) • Primary Vehicle Price: $23,763 (average, new), $11,367 (average, used) • Intend to Purchase Vehicle within Five Years: 62%

17. Results - Vehicle Technology

18. Results – Fuel Technology

19. Results – Taxation Policy

20. Model • Utility Function with Random Parameters and Error Components • Choice Probability for Mixed Logit with Panel Data

21. Results – Vehicle Technology

22. Results – Vehicle Technology • Gasoline and hybrid vehicles have a similar inherent preference • Families influenced by vehicle size • Fuel economy not significant for respondents who did not know their own vehicle’s fuel economy • Covariance between Vehicle Types • current vehicle + new gasoline vehicle (largest cov.) • new gasoline or current vehicle + new hybrid vehicle • new gasoline or current vehicle + new electric vehicle • new hybrid vehicle + new electric vehicle (smallest cov.) • About 65% of respondents preferred smaller vehicles

23. Results – Fuel Type

24. Results – Fuel Type • Respondents less sensitive to electricity price • Maybe lack of familiarity, no rule of thumb? • Charging time has influence on attractiveness of BEVs but not PHEVs • Error components shows that groups of respondents may have similar propensity towards electric vehicles (BEV and PHEV) and between liquid fuel vehicles

25. Results – Taxation Policy

26. Results – Taxation Policy • ASCs similar to Vehicle Technology Experiment • Toll discount only significant for residents near toll facilities • Higher VMT tax for gasoline vehicles dissuaded new gasoline vehicle purchases

27. Depreciation of new and old vehicles • Respondent’s vehicle depreciation was obtained by dividing the coefficient of vehicle age (new or used) by the coefficient of purchase price. • The models found that respondents depreciated their current vehicle at a rate between $1,950 and $1,310 per year for vehicles purchased new. • For respondents with used vehicles, depreciation was between $1,066 and $710 per year. • The MNL model placed greater depreciation on both new and used vehicles than the mixed models.

28. Survey Redesign • Eliminate the taxation policy experiment • Incorporate VMT tax into fuel type experiment • Incorporate Rebates into vehicle technology experiment • Added open-ended questions for purchase reason of current vehicles • Able to elicit some opinions about vehicle preferences, attitudes, and concerns • All respondents participate in both choice experiments

29. Survey Redesign • Vehicle Technology Experiment • Incorporate MPGe into vehicle technology experiment • Respondents able to compare mpge and mpg in fuel technology experiment well • Added fees and rebates for different vehicle types • Added Plug-in Hybrid Vehicle (PHEV) alternative • Fuel Technology Experiment • Removed diesel vehicle option, added flex-fuel vehicle option • Added VMT tax depending on fuel type

30. Primary Vehicle Purchase Reasons • Preference for: • Fuel Economy • Family Vehicle / Transporting Passengers • Low Maintenance, High Reliability • Personal Appeal • Comfort and Safety

31. Secondary Vehicle Purchase Reason • Preference for: • Fuel Economy • Vehicle Cost or Value • Family Vehicle • Cargo Capacity • Low Maintenance, High Reliability

32. Dynamic Discrete Choice Models for Transportation Part II

33. Background • Discrete choice models are commonly used in transportation planning and modeling, but their theoretical basis and applications have been mainly developed in a static context. • With the continuous and rapid changes in modern societies (i.e. introduction of advanced technologies, aggressive marketing strategies and innovative policies) it is more and more recognized by researchers in various disciplines that choice situations take place in a dynamic environment and that strong interdependencies exist among decisions made at different points in time.

34. Dynamics models in economics • Dynamic discrete choice models have been firstly developed in economics and related fields. • In dynamic discrete choice structural models, agents are forward looking and maximize expected inter-temporal payoffs. • The consumers get to know the rapidly evolving nature of product attributes within a given period of time and different products are supposed to be available on the market. • As a result, a consumer can either decide to buy the product or to postpone the purchase at each time period. This dynamic choice behavior has been treated in a series of different research studies.

35. Review of economics literature • John Rust (1987) --- bus engine replacement, single agent, two options, one purchase, homogenous attributes of the products, infinite-horizon. Nested Fixed Point method to estimate. • Oleg Melnikov (2000) --- printer machine demand one purchase, differentiated durable products, homogenous consumers. • Szabolcs LŐrincz (2005) --- computer servers demand, persistency effects, choice between using the original product and upgrading its format (operating systems). Dynamic nested logit model. • Juan Esteban Carranza (2006) --- digital camera demand, heterogeneity over consumers’ preferences and dynamics of quality. • Gowrisankaran and Rysman (2007) --- digital camcorder, repeat purchases, heterogeneous consumers and differentiated products.

36. Model formulation Dynamic, regenerative, optimal stopping problem Consumer i state at time t In each time period consumer i in status has two options: (a) to buy one of the products or (b) to postpone If (a) the consumer i obtains a terminal payoff If (b) is chosen the consumer obtains a one period payoff .

37. One period pay off a vector of attributes for i at t, e.g. gender, education, professional status, income. , a vector of characteristics of current vehicle owned by i, e.g. age, mileage, purchase price, etc. , are parameters for and .

38. Terminal payoff is a vector of individual attributes (e.g. age, income, education) and is the related parameter; is a vector of vehicle static attributes (e.g. vehicle size) and is the related parameter; is a vector of dynamic attributes (e.g. energy cost per mile, purchase cost, environment incentives) , is the related parameter ; is a random utility component (i.i.d. GEV) is the mean utility.

39. Each time period, the consumer decides to buy or postpone where: Hypothesis is the payoff when postponing is time period when consumer decides to buy (set 1) expected utility (Based on Bellman equation): where: is time period when consumer decides to buy

40. Industry evolution The evolution of the industry is represented by a so called random walk; dynamic variable is supposed to follow a normal diffusion process, specified as a random walk with drift (j=1,…,J, t = 1,…,T) are i.i.d. multivariate standard normal random vectors. is the Cholesky factor of the variance-covariance matrix

41. Utility formulation This is standard optimal stopping problem. The stopping set is given when: Reservation utility Here, Equation (1) becomes:

42. Demand structure Probability of postponing until next period: Product adoption rate:

43. Estimation methodology The parameters estimation can therefore be formulated as a traditional maximum likelihood problem: Decisions include: buy a car of type j, not buy a car

44. Dynamic estimation process Calculate ? Calculate Calculate Calculate

45. Scenario tree At t=0 t=1 buy Not buy buy Not buy t=2 buy Not buy buy Not buy t=3

46. DDCM applied to carownership • What effect will the following factors have on the vehicle marketplace over the next five years: • New vehicle technology • Improvements in existing vehicle technology • Greater availability of different energy sources • Rising fuel prices • Transportation and energy policy

47. Static Model- results

48. Dynamic model -results Choose electric car price as the dynamic variable

49. UNIVERSITY OF MARYLAND DEPARTMENT OF CIVIL & ENVIRONMENTAL ENGINEERING Application – market share forecasting

50. Market shares - comparison Gas car Hybrid car Electric car Current car