Jointly Estimated Cross-Sectional Mode Choice Models: Specification and Forecast Performance
Bibliographic record
Abstract
This paper investigates a number of issues associated with jointly estimating disaggregate logit mode choice models for two periods using data collected at two points in time in independent cross-sectional travel surveys in a given urban area. These include: (1) the effect socio-economic characteristics of travelers have on the predictive and forecast performance of jointly estimated models; and (2) the effect of allowing the variance of the random utilities in the different time periods to differ and, more broadly, the impact transfer-bias scale parameters could have on joint-model predictive performance. The results show that well-specified jointly estimated models using data from two time periods yield comparable disaggregate and aggregate forecasts to those obtained from conventional forecasting models, estimated with data from a single cross-sectional survey. Socio-economic variables and transfer-bias scale parameters are found to enhance model fit to estimation data as well as precision of predictions. The shorter the intervening period between when the two cross-sectional data sets used in joint estimation are collected, the better the jointly estimated models are able to predict the travel choices in each of the survey years.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".