Synthesising the Transpose Times at Roadside Interview Sites Using Probability Functions Derived from Car Park Interview Data
Bibliographic record
Abstract
Roadside interviews are conducted to ascertain travel movements at the time they are made. The collection of roadside data is an expensive element in model development and there is often the pressure to minimizing the costs and traffic disruption. One consequence is that the data collected may be limited to responses that can be collected relatively quickly such as survey location, interview time, occupancy, vehicle type, origin address, origin purpose, destination address, and destination purpose. The paper will present recent work to synthesize the non-interview direction of travel using probability distributions derived from car park interviews for a number of towns in the UK including, Doncaster, Shrewsbury, Lancaster, Halifax, Colchester and Bury St Edmunds. Car park interview data from these towns provided a database with over 12,000 interviews. As part of the analysis the data collected at the towns were compared to assess similarities and differences in their characteristics with respect to trip purpose and duration of parking. Analysis of the data indicated that the duration of stay at the car parks was similar, which means that the probability functions derived could be applied to other towns.
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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 | 0.034 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".