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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".