SWUTC Research Project Description

Title of Project:  Vehicle Ownership, Retirement and Use Decisions: Response to Rising Fuel Prices and Implications for Carbon Emissions Forecasts

Project Number:  169202

Principal Investigator:
Kara Kockelman
(512) 471-0210
P.I. Affiliation:  University of Texas at Austin
kkockelm@mail.utexas.edu

Project Monitor:
Dr. David Greene
Oak Ridge National Laboratory
dlgreene@ornl.gov

Project Status:  Active

Date Started:  9/1/08

Estimation Completion Date:  8/31/09

Estimated Cost - Current Fiscal:  $39,000

Estimated Cost - Total Planned:  $39,000

Project Summary:
Project Abstract:
Rising gasoline prices, emerging engine technologies, and changes in fuel-economy policy will result in a variety of behavioral changes, including household holdings (number of vehicles, make and model), vehicle purchase and retirement timing decisions, mode choice and travel distances, speeds, and emissions.  This research will enable transportation planners and demand modelers to better anticipate the near- and long-term responses of U.S. households to a variety of vehicle design and pricing assumptions, in order to forecast the nation’s future vehicle fleet holdings and transport-based carbon emissions.  

Project Objectives:
Evaluate the near- and long-term behavioral changes of travelers and vehicle owners to a variety of gasoline and vehicle pricing scenarios, in order to anticipate the qualities of America’s evolving passenger vehicle fleet and carbon emissions, and anticipate the impacts of various energy-related policies.

Task Descriptions:
Task 1:  Synthesizing Relevant Literature and Experience
To begin this work, the research team will review relevant literature, through a combination of library holdings and on-line resources, computerized databases, and personal communications. Works will include those recognized in the vehicle ownership discussion of Kockelman et al.’s (2008) NAS-commissioned paper (on the topic of greenhouse gas emissions vis-à-vis human activities, emerging technologies, and potential policies) and Tirumalachetty and Kockelman’s (2009) recent paper. They also are likely to include works by Fisher (2007), Heffner et al. (2007), Chen and Niemeier (2005), Mannering and Winston (1985), Lemp and Kockelman (2008), Dumas et al. (2007), Kockelman and Zhao (2005), Parry et al. (forthcoming), Bomberg and Kockelman (2007), Turrentine and Kurani (2004), Greene et al. (2004, 2006, 2007), and Greene (1994).

Task 2: Initial Data Assembly
A variety of data sets will be examined and analyzed, to get a sense of trends in vehicle holdings, use and life times.  These have already been obtained by the PI and include the Toronto Area Car Ownership Study, National Household Travel Surveys, U.S. Panel Study of Income Dynamics, Vehicle Inventory and Use Survey, NHTSA’s General Estimates System, Texas vehicle registrations data base, and recent Austin and Dallas-Ft. Worth travel (and vehicle ownership) surveys.  Taken together, these surveys provide information on make and models owned recent vehicle purchases and retirements, vehicle use levels, and vehicle loss through crashes, as well as owner and driver attributes, among other details.  The PI has experience working with all of these data sets, and looks forward to having a focused look at vehicle ownership, retirement and use decisions.  By ascertaining what details are missing in existing data sets, the project team will fine-tune the project’s new survey instrument, in order to maximize information obtained from project expenditures on Task 3’s data collection.

Task 3: Data Collection
Depending on project funding, the team may design (and obtain human subject clearance for) a mail-based survey of roughly 500 Austin, Texas households for information on current, past and (likely) future vehicle holdings decisions, sensitivity to gasoline prices, strength of vehicle technology preferences, and the like. Strategic over-sampling of Austinites may be pursued, since the PI has immediate access to a wide variety of excellent Austin land use, transit, network, and other data sets (many at the parcel level, in fact) that will complement the analysis. Such variables (e.g., access to a local supermarket or bus route to one’s place of work) will supplement information obtained from the mailed surveys on household attributes and vehicle choices.  Access to such rich data sets will enhance our understanding of vehicle purchases, retirements and various travel choices – both stated and revealed/actual.

Note: As instructor of record for UT’s only Transportation Data Acquisition and Analysis (graduate) course and chair of the National Academy of Engineering’s Transportation Research Board’s Travel Survey Methods committee, the PI has a very strong track record with survey design and management, and subsequent data analysis.  She has managed design and implementation of five different surveys, including one as a series of 1200 phone interviews (for the Texas Department of Transportation on the subject of the public’s perceptions of tolling and other transportation policies). 

Task 4: Data Analysis and Model Calibration
Highly experienced in a variety of data analysis techniques (including classical and Bayesian methods, spatial and panel data sets, multiple equations systems, factor analysis, and econometric specifications), the PI looks forward to close analysis of the data from these experiments. Such analysis will include multinomial logit models (see, e.g., Zhao and Kockelman 2000) of vehicle choices, both stated and revealed.  The results of these will illuminate the value of gas savings (in the form of fuel economy, and as a function of gas prices) versus vehicle price, performance, age and other attributes.  It will be interesting to examine how heavily households discount future gas savings.
           
The analyses also will include hazard-based and other duration models (see, e.g., Gadda and Kockelman 2007) for vehicle holding period, till loss or resale.  Such details will provide insight on how long different types of vehicles are held by different individuals and households.  Models will include travel statistics for each vehicle, including miles traveled, number of trips taken and typical travel speeds – all of which impact a household’s carbon emissions.  Ordered probit models (see, e.g., Podgorski et al. 2004) of opinions about different vehicle technologies, energy policy, and climate change also will be calibrated, for additional results of interest (e.g., what types of individuals are most likely to adopt new technologies, and what policies may receive the most public support). Newer specifications allowing for modeling joint continuous-discrete responses (e.g., using Bhat’s [2006] multiple discrete-continuous extreme value specification to pivot off of Mannering and Winston’s [1985] work) will also be pursued.

Task 5: Model Applications
The great variety of data and models resulting from this work will allow examination of various transport scenarios and policies, both long-term and short-term.  These include investigations of the impact of rising fuel prices, the carbon emissions impacts of gas taxes, cap and trade policies across vehicle manufacturers (to attain certain fleet-level fuel economy standards), benefits of vehicle scrappage (“early retirement”) policies, use of feebates, and the like.

The models estimated above will be applied in order to anticipate the responsiveness of vehicle miles traveled and vehicle fleet composition to energy price fluctuations, the availability of new vehicle technologies and fuels, manufacturers’ vehicle-pricing and design decisions, and so forth.  The team will predict vehicle purchase, retirement, and use changes as well as greenhouse gas emissions from the personal-transportation sector as a result of a range of several gas pricing scenarios (e.g., $3, $5 and $7/gallon), over 5, 10, 20, and 30-year horizons.  Such vehicle holdings and retirement models are highly innovative, and will permit microscopic evolution of synthetic household (and vehicle) populations, in order to anticipate actual emissions.  The PI’s recent and on-going work microsimulating Austin’s land use and travel dynamics (e.g., Tirumalachetty and Kockelman [2008]) will prove helpful here.

Task 6:  Report Writing & Results Dissemination
Research papers will be generated, for presentation at major conferences (in the U.S. and abroad), and for publication (in well regarded peer-reviewed journals).  Modelers and policymakers at various MPOs, state and federal agencies may be approached, for in-house application of techniques developed under this research project.  The media also may be very interested in disseminating information about such research, given society’s interest in climate change policy and forecasting our future.

Index Terms:
Automobile ownership, Fuel prices, Pollutants, Exhaust gases, Travel behavior, Forecasting, Carbon, Vehicle design, Research projects