Project Summary
The ultimate goal of our research is to specify and estimate vehicle choice and usage models that operate a much higher level of detail, so that they can be used to produce improved tools for evaluating alternative policy options, particularly in the area of transportation energy usage and greenhouse gas reduction. A particular concern is to fill a gap that clearly exists in relation to other policy analysis models. For example, the current California State Travel Demand Model (CSTDM) system addresses many important transport-related effects at a very high level of detail, but limits personal vehicle behavior to a matter of car ownership levels and mode choice. Although intended for analysis of long-term policies to combat climate change, it has essentially no capability to address emerging issues regarding alternative fuels and new vehicle technologies. Similarly, the EMFAC model used by a variety of agencies to translate projections of future VMT into greenhouse gases bases its results on projecting trends tied to current vehicle technology sales distributions, efficiencies and usage patterns (with some correction terms based on current CAFÉ/greenhouse regulations). We have performed a detailed review of these and other models for the California Energy Commission, and have identified a variety of options for how these models could be modified or extended in conjunction with improved vehicle choice and usage models. One particular outcome would be an extended version of the current CSTDM that could functionally replace current models used by the California Energy Commission (CEC), Caltrans, and the California Air Resources Board (ARB). This research will yield two key pieces of a proposed extended CSTDM: a detailed household vehicle choice model and a model that predicts the annual miles driven for each household vehicle. Our previous research has uncovered serious biases in existing models that work at the level of vehicle classes (e.g. small compact, SUV, etc.), and we have demonstrated the feasibility of fitting models at the make/model/year level. This year we will extend these models to include used vehicles, and we will also include network accessibility measures and stated preference data on new technology vehicles recently obtained from the CEC. We will also develop a comprehensive method for imputing annual vehicle miles traveled from data collected in surveys similar to the National Household Transportation Survey (NHTS) and California Household Travel Survey (CHTS).