Bibliographic record
Abstract
The development of simulation models of activity-scheduling behavior has gained momentum over the past decade as a means to forecast travel demands. Of fundamental concern in these models is the process or timing of scheduling decisions–-or planning time horizon. Conceptually, it is understood that activities are planned over varying time horizons, but little empirical evidence exists. One way to explore these issues is to ask people to self-report when they planned their activities. However, this is a difficult question for researchers to formulate and for people to comprehend and recall, because people often plan (and replan) activity attributes over an extended period of time, some without much conscious thought. The objective of this paper is to describe the development of a planning time horizon query that was part of a larger activity scheduling process survey and to provide one of the first empirical analyses based on a random sample of 373 respondents. Included is a detailed examination of activity addition, modification, and trip-planning time horizons as well as analysis of “routine” and “unrecalled” decisions. Results indicate that people have the ability to recall a high level of detail on a planning time horizon, ranging from decisions made long ago that establish an initial skeleton schedule to continued preplanning in the days leading up to the event day and impulsive decisions made the day of the event. The implications of these results for future survey design and development of an activity-scheduling process simulation model are discussed.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".