Examining the Nature and Extent of the Activity-Travel Preplanning Decision Process
Bibliographic record
Abstract
This paper presents results from an in-depth survey method for capturing the content and attributes of peoples’ preplanned schedules. The focus is on preplanned daily activity and travel events, their typically observable attributes (event type, start and end time, location, and involved person), and the extent to which these attributes are specified and elaborated on. The survey started with an interviewer simply asking subjects to write down or discuss their schedule for the next 2 days in as much or as little detail as they knew. The interviewer subsequently probed for further details as needed. This method elicited considerable detail on decision hierarchies in the subjects’ own words. Overall, it was found that activity type is the most often preplanned activity attribute, followed by location, start time, involved persons, and end time. For trips, the mode type and start time are most often planned, followed by involved persons and end time. Additional analysis further confirms that the preplanning is an ongoing decision process, wherein tentative decisions on each attribute are often made and then revisited at some point closer to execution. The implications of these findings for model development and future survey design are discussed. In particular, the results imply that activity scheduling models should adopt a nested or continuous planning loop, wherein certain activity attribute decisions are made first but are followed by subsequent stages of refinement and elaboration.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".