Application of Travel Activity Scheduler for Household Agents in a Chinese City
Bibliographic record
Abstract
The travel activity scheduler for household agents (TASHA) is an operational rule-based model that generates activity schedules and travel patterns for a 24-h typical weekday for all persons in a household. This paper reports the application and validation of the model in Changzhou, China. The data cleaning procedure of the traditional household survey and the verification results of the rules used in the TASHA are presented. After the results of the activity generation and scheduling models were analyzed, an iterative approach was applied to inflating the observed activity rates to calibrate the results. The model was shown to replicate observed activity frequency, tour frequency, and trip start time fairly accurately at the regional level. The activity duration of the model was underestimated by 13.1%, but this underestimate did not add much bias to the shape of the duration distribution by start time. The final model results show that the TASHA is an attractive alternative to conventional modeling systems currently used in Changzhou.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".