How Travel Attributes Affect Planning Time Horizon of Activities
Bibliographic record
Abstract
This study focuses on incorporating the travel attributes (time, cost, and availability of modes) in the activity planning time horizon analysis and presents mixed logit models estimated separately for different activity groups. Although the effects of many other variables are estimated, the focus is on the effects of location (in-home versus out-of-home) and log-sum (measure of travel characteristics) variables. The inclusion of several other variables (household and individual characteristics and activity attributes) allows controlling for many factors affecting this choice and increases the explanatory power of the models. The results of the models reveal that the effects of location and travel characteristics on planning time horizon choice vary among different activity types. Toward a better understanding and quantification of the effects of location and travel attributes, the probabilities of planning the activities in different time horizons are estimated separately for in-home and out-of-home, and with an incremental increase in the logsum. Results reveal that the household obligations and active activities are very sensitive to travel characteristics; therefore, significant changes in the planning and execution of these activities may be expected with changes in the transportation system characteristics. The results of this study enhance the understanding of relationships between travel time, cost, availability of transportation modes, and the activity planning process.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".