Dynamic Modeling of Household Automobile Transactions
Bibliographic record
Abstract
Automobiles play a pivotal role in daily life, which makes them a subject of interest in many academic fields. Transportation planners are interested in knowing how many and what types of automobiles are owned by households, how people adjust their fleet, and how they use their vehicles. The primary objective of this study was to develop a comprehensive dynamic model of household automobile transactions at a disaggregate level to be used in a dynamic microsimulation modeling framework that can provide a direct forecast of consumer demand for personal-use vehicles. A market-based decision-making process and a transaction approach were applied for this project because of their consistency with the actual processes followed by decision makers. In the proposed framework, each year a decision maker faces four choices: add a new vehicle to the fleet, dispose of one vehicle, trade one of the vehicles in the fleet, or do nothing. A mixed (random parameters) logit model was used to investigate the effects of heterogeneity in the dynamic transaction model and distinguish between heterogeneity- and state-dependence-based explanations for the observed persistence in choice behavior. In this study, the application of dynamic variables representing the occurrence of changes in household state and their impacts on the observed behavior were also investigated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".