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Dynamic Discrete Choice Models: an application to vehicle holding decisions Cinzia Cirillo University of Maryland A. James Clark School of Engineering.

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Presentation on theme: "Dynamic Discrete Choice Models: an application to vehicle holding decisions Cinzia Cirillo University of Maryland A. James Clark School of Engineering."— Presentation transcript:

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 2 nd, 2012

2 Vehicle Ownership Travel Mode Energy Consumption Air Pollution Emissions Greenhouse Gas Emissions Automobile Industry Battery Production and Waste Trade Balance National Infrastructure 2

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 3

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 4

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 5

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 6

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 7

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) 8

9 Survey Methodology Time FrameSummer – Fall 2010 Target PopulationSuburban and Urban Maryland Households Sampling FrameHouseholds with internet access in 5 Maryland counties Sample DesignMulti-stage cluster design by county and zipcode Use of InterviewerSelf-administered Mode of Administration Self-administered via the computer and internet for remaining respondents Computer Assistance Computer-assisted self interview (CASI) and web-based survey Reporting Unit One person age 18 or older per household reports for the entire household Time Dimension Cross-sectional survey with hypothetical longitudinal stated preference experiments FrequencyOne two-month phase of collecting responses Levels of ObservationHousehold, vehicle, person 9

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

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. 11

12 Vehicle Technology Experiment 12

13 Fuel Type Experiment 13

14 Taxation Policy Experiment 14

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 dont plan to purchase a vehicle in next five years 15

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% 16

17 Results - Vehicle Technology 17

18 Results – Fuel Technology 18

19 Results – Taxation Policy 19

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

21 Results – Vehicle Technology Coefficient Included in Utility ValueT-stat Current Gasoline HEV BEV ASC – New Gasoline Vehicle -1.320-3.28 ASC – New Hybrid Vehicle -1.760-2.93 ASC – New Electric Vehicle -3.450-5.70 Purchase Price [$10,000] -0.639-5.42 Fuel Economy Change [MPG] (current veh. MPG known) 0.0392.68 Fuel Economy Change [MPG] (current veh. MPG unknown) -0.002-0.21 Recharging Range [100 miles] 0.9094.37 Current Vehicle Age – Purchased New [yrs] -0.123-4.34 Current Vehicle Age – Purchased Used [yrs] -0.059-2.02 Minivan Dummy interacted with Family Households 1.4102.75 SUV Dummy interacted with Family Households 1.9004.77 Non-Electric Vehicle Error Component (standard deviation) 2.4006.00 Non-Hybrid Vehicle Error Component (standard deviation) 2.1506.71 Vehicle Size (mean) -0.435-2.42 Vehicle Size (standard deviation) 1.096.61 Likelihood with Zero Coefficients-1379.4"Rho-Squared" 0.406 Likelihood with Constants Only-1088.1Adjusted "Rho-Squared" 0.395 Final Value of Likelihood-819.6Number of Observations 995 (83) 21

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 vehicles 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 22

23 Results – Fuel Type 23 Coefficient Included in Utility ValueT-stat Current Gasoline AFV Diesel BEV PHEV ASC – New Gasoline Vehicle -8.810-6.81 ASC – New Alternative Fuel Vehicle -9.940-7.66 ASC – New Diesel Vehicle -10.300-7.84 ASC – New Battery Electric Vehicle -9.230-4.07 ASC – New Plug-in Hybrid Electric Vehicle -10.100-4.79 Fuel Price [$] -1.160-7.79 Gasoline Price – PHEV [$] -0.358-2.02 Electricity Price – BEV [$] -0.762-3.02 Electricity Price – PHEV [$] -0.569-2.79 Charge Time – BEV [hrs] -0.917-3.68 Charge Time – PHEV [hrs] -0.164-0.87 Average Fuel Economy [MPG, MPGe] 0.0393.91 Current Vehicle Age – Purchased New [yrs] -0.395-4.21 Current Vehicle Age – Purchased Used [yrs] -0.377-3.86 Current Vehicle Error Component (standard deviation) 2.2903.90 Electric Vehicle Error Component (standard deviation) 2.3003.92 Liquid Fuel Vehicle Error Component (standard deviation) 3.4604.91 Likelihood with Zero Coefficients-901.3"Rho-Squared" 0.508 Likelihood with Constants Only-667.7Adjusted "Rho-Squared" 0.489 Final Value of Likelihood-443.6Number of Observations 503 (42)

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 24

25 Results – Taxation Policy 25 Coefficient Included in Utility ValueT-stat Current Gasoline HEV BEV ASC – New Gasoline Vehicle -7.170-6.03 ASC – New Hybrid Vehicle -7.090-5.94 ASC – New Electric Vehicle -7.590-6.17 Hybrid Vehicle Deduction [$] divided by HH Income [$1000] 0.0932.71 Electric Vehicle Deduction [$] divided by HH Income [$1000] 0.2452.02 VMT Tax interacted with Annual Mileage [$100] -0.186-5.14 Toll Discount [%] (for HHs near toll facilities) 0.0652.76 Toll Discount [%] (for HHs not near toll facilities) 0.0050.75 Current Vehicle Age (new) interacted with Annual Mileage [years x 1000 miles] -0.049-5.24 Current Vehicle Age (used) interacted with Annual Mileage [years x 1000 miles] -0.026-2.47 New Vehicle Error Component (standard deviation) 3.7604.90 Current Vehicle Error Component (fixed to 0) 0.000Fixed Likelihood with Zero Coefficients-565.6"Rho-Squared" 0.455 Likelihood with Constants Only-456.7Adjusted "Rho-Squared" 0.436 Final Value of Likelihood-308.1Number of Observations 408 (34)

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 26

27 Depreciation of new and old vehicles Respondents 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. 27

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 28

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 29

30 Primary Vehicle Purchase Reasons 30 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 31

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. 33

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. 34

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. 35

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 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. One period pay off

38 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. Terminal payoff

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 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 Industry evolution

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

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

43 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 Estimation methodology

44 Calculate ? Calculate Dynamic estimation process

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

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 46

47 Static Model- results

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

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

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

51 New gasoline vehicles, hybrid and electric vehicles occupy smaller market shares (around 10% each) at the end of the five year period; All new typologies become more popular after the fifth time period; Static models are incapable of recovering peaks in the demand function; MNL model underestimates the market share of the "not buy", and dramatically overestimate the share occupied by electric vehicles in the next five years; Dynamic model overestimates the market share of the "not buy", but is capable to reproduce the descending trend for this alternative. Conclusions

52 More than one dynamic attributes could be included in the utility specification; this is not trivial since multivariate random walks should be estimated and included in model estimation; Individuals perspective scenarios could be extended to more than two; Data collection techniques should be improved to capture the interdependency among successive observations over time, and to incorporate random walks into orthogonal design (for SP data). To compare the results from maximum likelihood method with those from the nested fixed point method; Apply dynamic framework to other case: dynamic pricing for revenue management, route choice behavior under dynamic tolling, activity scheduling for activity based analysis… Future work

53 Acknowledgments This is joint work with: Renting Xu; Michael Maness; Fabian Bastin. Thanks to all the students at UMD who participated to the data collection effort. 53

54 Q&A Thank you 54

55 References Bunch, D., M. Bradley, T. Golob, R. Kitamura, and G. Occhiuzzo. Demand for Clean-fuel Vehicles in California: A Discrete Choice Stated Preference Pilot Project, Transporation Research, Vol. 27A, 1993. pp. 237-253. Kurani, K.S., Turrentine, T., and Sperling, D. Testing Electric Vehicle Demand in `Hybrid Households Using a Reflexive Survey. Transportation Research, Vol. 1D, 1996. pp. 131-150. Ewing, G. and Sarigollu, E. Assessing Consumer Preferences for Clean-fuel Vehicles: a Discrete Choice Experiment. Journal of Public Policy and Marketing, Vol. 18, No. 1, 2000. pp. 106-118. Groves, R.M., Fowler, F.J., Jr., Couper, M.P., Lepkowski, J.M., Singer, E., and Tourangeau, R. Survey Methodology. John Wiley, New York, 2009. Ahn J, Jeong G, and Kim Y. A Forecast of Household Ownership and Use of Alternative Fuel Vehicles: a Multiple Discrete-continuous Choice Approach. Energy Economics, Vol. 30, No. 5, 2008. pp. 2091-2104. Bolduc, D., N. Boucher, and R. Alvarez-Daziano. Hybrid Choice Modeling of New Technologies for Car Choice in Canada. Transportation Research Record: Journal of the Transportation Research Board, No.2082, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 63-71. Mau, P., Eyzaguirre, J., Jaccard, M., Collins-Dodd, C., and Tiedemann, K. The Neighbor Effect: Simulating Dynamics in Consumer Preferences for New Vehicle Technologies. Ecological Economic, Vol. 68. No. 1–2, 2008. pp. 504-516. Axsen, J., Mountain, D. C., and Jaccard, M. Combining Stated and Revealed Choice Research to Simulate the Neighbor Effect: The Case of Hybrid-electric Vehicles. Resource and Energy Economics, Vol. 31, No. 3, 2009. pp. 221-238. 55

56 References Musti, S. and Kockelman, K. Evolution of the Household Vehicle Fleet: Anticipating Fleet Composition, PHEV Adoption and GHG Emissions in Austin, Texas. Transportation Research, Vol. 45A, No. 8, 2011. pp. 707-720. Eggers, F. and Eggers, F.. Where Have All the Flowers Gone? Forecasting Green Trends in the Automobile Industry with a Choice-based Conjoint Adoption Model, Technological Forecasting and Social Change, Vol. 78, No. 1, 2011. pp. 51-62. Axsen, J. and Kurani, K. S. Early U.S. Market for Plug-In Hybrid Electric Vehicles: Anticipating Consumer Recharge Potential and Design Priorities. Transportation Research Record: Journal of the Transportation Research Board, No. 2139, Transportation Research Board of the National Academies, Washington, D.C., 2009. pp. 64-72. Brownstone, D. and K. Train. Forecasting New Product Penetration with Flexible Substitution Patterns. Journal of Econometrics, Vol. 89, No. 1-2, 1998. pp. 109-129 Brownstone, D., D. S. Bunch, and K. Train. Joint Mixed Logit Models of Stated and Revealed references for Alternative-fuel Vehicles. Transportation Research, Vol 34B, No. 5, 2000. pp. 315-338. Bhat, C. Incorporating Observed and Unobserved Heterogeneity in Urban Work Travel Mode Choice Modeling. Transportation Science, Vol 34, No. 2, 2000. pp. 228-238. Bhat, C. Accomodating Variations in Responsiveness to Level-of-service Measures in Travel Mode Choice Modeling. Transportation Research, Vol. 32A, No. 7, 1998. pp. 495-507. Bierlaire, M. BIOGEME: A Free Package for the Estimation of Discrete Choice Models. Proc., 3 rd Swiss Transportation Research Conference, Ascona, Switzerland, 2003. Walker, J. Mixed Logit (or Logit Kernel) Model: Dispelling Misconceptions of Identification. Transportation Research Record: Journal of the Transportation Research Board, No. 1805, Transportation Research Board of the National Academies, Washington, D.C., 2002. pp. 86-98. 56


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