Modeling Trip Generation with Data from Single and Two Independent Cross-Sectional Travel Surveys
Bibliographic record
Abstract
This paper investigates the possibility of developing trip generation models using data collected at two or more points in time in independent cross-sectional travel surveys conducted in the same urban area. Alternative methods for formulating a forecasting model based on the availability of cross-sectional data from two time periods are presented. Models are then estimated on the study data and the models assessed in terms of their ability to replicate the number of trips made at the disaggregate household level, and at the aggregate traffic zone level in the two model-estimation datasets, respectively. The performance of these jointly estimated models is compared to the predictive performance of a conventional single cross-section trip generation model estimated on each period’s data only. The formulated models are then applied to forecast trips at the disaggregate household level, and at the aggregate traffic zone level on a third independent cross-sectional dataset collected in the same urban area but at a different point in time. Again, forecast performance of the formulated jointly estimated models is compared to forecast performance of the conventional model estimated on this third independent dataset. The results show that well specified joint models estimated on data from two time periods yield superior disaggregate and aggregate forecasts to those obtained from conventional forecasting models, which are estimated with data from a single cross-sectional survey.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".