Factors and policies affecting demand for light vehicle transportation in the lower mainland of British Columbia
Bibliographic record
Abstract
As transportation is a key component of economic success, it is crucial that the transportation systems in the Lower Mainland accommodate, and shape the projected increases in population. This paper has two main objectives. The first is to explore the factors and variables influencing demand for automobile transportation that are unique to the Lower Mainland of BC. General trends and statistics are explored for peak a.m. period automobile demand. The second part of this paper looks at the policies affecting demand for automobile transportation. Economic theory is introduced to two prominent traffic demand management (TDM) policies: road pricing and high occupancy vehicle (HOV) lanes. Conceptual models are proposed for both policies. In 1993 the GVRD completed the Transport 2021 study. Using data that was generated by the EMME2 model, empirical estimates of consumer surplus changes (resulting from various T DM policies being implemented) are considered under a range of elasticities. Empirical estimates of consumer surplus changes are also calculated for the conceptual models.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".