Joint Econometric Analysis of Temporal and Spatial Flexibility of Activities, Vehicle Type Choice, and Primary Driver Selection
Bibliographic record
Abstract
This study examined the relationship between four individual-level travel choice processes of daily activity: spatial flexibility of the activity, temporal flexibility of the activity, vehicle choice for the activity, and primary driver (for auto users). Activity flexibility (spatial and temporal) has been suggested as a precursor to the travel pattern observed for an activity. This study examined the impact of activity flexibility through unique data drawn from Quebec City, Quebec, Canada from 2003 to 2006. In traditional literature on travel behavior, vehicle fleet decisions have been examined as a long-term choice with annual usage metrics. However, the long-term vehicle usage observed (as studied in the literature) is an aggregation of the household's yearly vehicle type and usage behavior. Only recently have researchers begun to consider decisions about vehicle usage (type and mileage) as a short-term decision in travel behavior models. By examining short-term vehicle usage, this study explored, at a disaggregate level, the interaction of activity behavior (defined as flexibility) and vehicle type choice. A panel mixed multinomial logit model was applied to analyze the four choices within the decision process to account for the intrinsic unobserved taste preferences across individuals. The analysis results revealed that several individual and household sociodemographic characteristics, residential location, and activity attributes, as well as contextual variables, influenced the packaged choice of temporal flexibility, spatial flexibility, vehicle type choice, and primary driver selection. The presence of common unobserved correlation across various alternatives was also incorporated.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".