Using Odometer Readings as Panel Data to Estimate Historical Vehicle Kilometers Travelled for Light-Duty Vehicles in Metro Vancouver
Bibliographic record
Abstract
This paper discusses the development of a model to estimate historical vehicle kilometers traveled (VKT) for light-duty gasoline vehicles registered in the urban region of Metro Vancouver in British Columbia, Canada. The authors' model is based on regression analysis that uses observed odometer readings from a large sample of vehicles and a detailed set of socio-economic variables. Using a complete and anonymous database of registered vehicles in Metro Vancouver, the authors estimate quarterly VKT for each individual vehicle between the years 2000 and 2012. The authors' model results can be summarized on a range of geographic levels: from individual traffic analysis zones to the entire urban region. The authors' results show that total VKT by Metro Vancouver light-duty gasoline vehicles reached a plateau of close to zero growth between the years 2009 and 2012, but returned to a pre-2008 growth rate in 2013. The authors find as well that historical VKT trends appear to differ among parts of the Metro Vancouver region with different patterns of urban development. The authors' results suggest that total VKT is influenced much more by the level of vehicle ownership than by what the authors estimate to be small changes in individual vehicle use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".