A comparative study on sampling strategies for truck destination choice model: case of Seoul Metropolitan Area
Bibliographic record
Abstract
One of the major issues when applying truck destination choice models with a large number of alternatives is how to sample a set of non-chosen traffic analysis zones (TAZs) to construct a destination choice set. Despite the large number of studies applying various sampling strategies, the question remains as to what are optimal strategies in model development. This study examined how the sampling strategies affect the performances of truck destination choice models. Two sampling methods (simple random sampling and stratified importance sampling) and four different sample sizes were tested using the truck trip data of Korea. For stratified importance sampling, Moran's I statistics were used to divide the entire study area into multiple strata, and Neyman allocation was used to determine the appropriate number of samples for each stratum. The truck trip productions were distributed by a Monte Carlo simulation, and two measurements of effectiveness (MOEs), average trip length (ATL) and trip length distribution (TLD), were used to evaluate and compare the performance of the destination choice models with respect to the sampling strategy. The results showed that the models using stratified importance sampling with smaller sample sizes performed better than others.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".