Framework for development of the Scheduler for Activities, Locations, and Travel (SALT) model
Bibliographic record
Abstract
Understanding the travel behavior of individuals grouped by similar time-use activity patterns can contribute greatly to modeling regional spatial and temporal patterns of transport demand. In this paper, we present a comprehensive modeling framework to forecast and replicate individuals’ travel behavior, labeled as the Scheduler for Activities, Locations, and Travel (SALT). The prototype version of the SALT framework comprises a series of modules that employ behaviorally-based econometric, machine-learning, and data-mining techniques. The SALT model is cross-validated with 30% of the out-of-home sample survey data from the large Halifax Space Time Activity Research (STAR) household survey. Results show that the SALT scheduling model is able to assemble the travelers’ 24-hour schedules with an average 82% accuracy compared to the observed data. The proposed simulation modeling framework is useful for deeper understanding of individuals’ activity-travel decisions and may be utilized to examine sensitive policy issues such as transportation control measures and congestion-pricing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".